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Remote sensing scene classification deals with the problem of classifying land use/cover of a region from images. To predict the development and socioeconomic structures of cities, the status of land use in regions are tracked by the national mapping agencies of countries. Many of these agencies use land use types that are arranged in multiple levels. In this paper, we examined the efficiency of a hierarchically designed CNN based framework that is suitable for such arrangements. We use NWPU-RESISC45 dataset for our experiments and arranged this data set in a two level nested hierarchy. We have two cascaded deep CNN models initiated using DenseNet-121 architectures. We provide detailed empirical analysis to compare the performances of this hierarchical scheme and its non hierarchical counterpart, together with the individual model performances. We also evaluated the performance of the hierarchical structure statistically to validate the presented empirical results. The results of our experiments show that although individual classifiers for different sub-categories in the hierarchical scheme perform well, the accumulation of classification errors in the cascaded structure prevents its classification performance from exceeding that of the non hierarchical deep model.

The purpose of this paper is to define statistically convergent sequences with respect to the metrics on generalized metric spaces (g-metric spaces) and investigate basic properties of this statistical form of convergence.

Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trusted intermediaries and arbitration. However, the deployment of smart contracts introduces new attack vectors into the cryptocurrency systems. In particular, programming flaws in smart contracts can be and have already been exploited to gain enormous financial profits. It is thus an emerging yet crucial issue to detect vulnerabilities of different classes in contracts in an efficient manner. Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable, or train individual classifiers for each specific vulnerability, or demonstrate multi-class vulnerability detection without extensibility consideration. To overcome the scalability and generalization limitations of existing works, we propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for Ethereum smart contracts that support lightweight transfer learning on unseen security vulnerabilities, thus is extensible and generalizable. ESCORT leverages a multi-output NN architecture that consists of two parts: (i) A common feature extractor that learns the semantics of the input contract; (ii) Multiple branch structures where each branch learns a specific vulnerability type based on features obtained from the feature extractor. Experimental results show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract. When extended to new vulnerability types, ESCORT yields an average F1-score of 93%. To the best of our knowledge, ESCORT is the first framework that enables transfer learning on new vulnerability types with minimal modification of the DNN model architecture and re-training overhead.

Kinetic simulations and theory demonstrate that whistler waves can excite oblique, short-wavelength fluctuations through secondary drift instabilities if a population of sufficiently cold plasma is present. The excited modes lead to heating of the cold populations and damping of the primary whistler waves. The instability threshold depends on the density and temperature of the cold population and can be relatively small if the temperature of the cold population is sufficiently low. This mechanism may thus play a significant role in controlling amplitude of whistlers in the regions of the Earth's magnetosphere where cold background plasma of sufficient density is present.

Knowledge graph embedding has been an active research topic for knowledge base completion (KGC), with progressive improvement from the initial TransE, TransH, RotatE et al to the current state-of-the-art QuatE. However, QuatE ignores the multi-faceted nature of the entity and the complexity of the relation, only using rigorous operation on quaternion space to capture the interaction between entitiy pair and relation, leaving opportunities for better knowledge representation which will finally help KGC. In this paper, we propose a novel model, QuatDE, with a dynamic mapping strategy to explicitly capture the variety of relational patterns and separate different semantic information of the entity, using transition vectors to adjust the point position of the entity embedding vectors in the quaternion space via Hamilton product, enhancing the feature interaction capability between elements of the triplet. Experiment results show QuatDE achieves state-of-the-art performance on three well-established knowledge graph completion benchmarks. In particular, the MR evaluation has relatively increased by 26% on WN18 and 15% on WN18RR, which proves the generalization of QuatDE.

One of the features of the unconventional $s_\pm$ state in iron-based superconductors is possibility to transform to the $s_{++}$ state with the increase of the nonmagnetic disorder. Detection of such a transition would prove the existence of the $s_\pm$ state. Here we study the temperature dependence of the London magnetic penetration depth within the two-band model for the $s_\pm$ and $s_{++}$ superconductors. By solving Eliashberg equations accounting for the spin-fluctuation mediated pairing and nonmagnetic impurities in the $T$-matrix approximation, we have derived a set of specific signatures of the $s_\pm \to s_{++}$ transition: (1) sharp change in the behavior of the penetration depth $\lambda_{L}$ as a function of the impurity scattering rate at low temperatures; (2) before the transition, the slope of $\Delta \lambda_{L}(T) = \lambda_{L}(T)-\lambda_{L}(0)$ increases as a function of temperature, and after the transition this value decreases; (3) the sharp jump in the inverse square of the penetration depth as a function of the impurity scattering rate, $\lambda_{L}^{-2}(\Gamma_a)$, at the transition; (4) change from the single-gap behavior in the vicinity of the transition to the two-gap behavior upon increase of the impurity scattering rate in the superfluid density $\rho_{s}(T)$.

We extend applications of Furstenberg boundary theory to the study of $C^*$-algebras associated to minimal actions $\Gamma\!\curvearrowright\! X$ of discrete groups $\Gamma$ on locally compact spaces $X$. We introduce boundary maps on $(\Gamma,X)$-$C^*$-algebras and investigate their applications in this context. Among other results, we completely determine when $C^*$-algebras generated by covariant representations arising from stabilizer subgroups are simple. We also characterize the intersection property of locally compact $\Gamma$-spaces and simplicity of their associated crossed products.

Deep Reinforcement Learning (RL) techniques can benefit greatly from leveraging prior experience, which can be either self-generated or acquired from other entities. Action advising is a framework that provides a flexible way to transfer such knowledge in the form of actions between teacher-student peers. However, due to the realistic concerns, the number of these interactions is limited with a budget; therefore, it is crucial to perform these in the most appropriate moments. There have been several promising studies recently that address this problem setting especially from the student's perspective. Despite their success, they have some shortcomings when it comes to the practical applicability and integrity as an overall solution to the learning from advice challenge. In this paper, we extend the idea of advice reusing via teacher imitation to construct a unified approach that addresses both advice collection and advice utilisation problems. We also propose a method to automatically tune the relevant hyperparameters of these components on-the-fly to make it able to adapt to any task with minimal human intervention. The experiments we performed in 5 different Atari games verify that our algorithm either surpasses or performs on-par with its top competitors while being far simpler to be employed. Furthermore, its individual components are also found to be providing significant advantages alone.

A new approach to deal with the scattering amplitudes in Glauber theory is proposed. It relies on the use of generating function, that has been explicitly found. The method is applied to the analytical calculation of the nucleus-nucleus elastic scattering amplitudes in the all interaction orders of the Glauber theory.

Neutral hydrogen (HI) intensity mapping is a promising technique to probe the large-scale structure of the Universe, improving our understanding on the late-time accelerated expansion. In this work, we first scrutinize how an alternative cosmology, interacting Dark Energy, can affect the 21-cm angular power spectrum relative to the concordance $\Lambda$CDM model. We re-derive the 21-cm brightness temperature fluctuation in the context of such interaction and uncover an extra new contribution. Then we estimate the noise level of three upcoming HI intensity mapping surveys, BINGO, SKA1-MID Band$\,$1 and Band$\,$2, respectively, and employ a Fisher matrix approach to forecast their constraints on the interacting Dark Energy model. We find that while $\textit{Planck}\,$ 2018 maintains its dominion over early-Universe parameter constraints, BINGO and SKA1-MID Band$\,$2 put complementary bounding to the latest CMB measurements on dark energy equation of state $w$, the interacting strength $\lambda_i$ and the reduced Hubble constant $h$, and SKA1-MID Band$\,$1 even outperforms $\textit{Planck}\,$ 2018 in these late-Universe parameter constraints. The expected minimum uncertainties are given by SKA1-MID Band$\,$1+$\textit{Planck}\,$: $\sim 0.35\%$ on $w$, $\sim 0.27\%$ on $h$, $\sim 0.61\%$ on HI bias $b_{\rm HI}$, and an absolute uncertainty of about $3\times10^{-4}$ ($7\times10^{-4}$) on $\lambda_{1}$ ($\lambda_{2}$). Moreover, we quantify the effect of increasing redshift bins and inclusion of redshift-space distortions in updating the constraints. Our results indicate a bright prospect for HI intensity mapping surveys in constraining interacting Dark Energy, whether on their own or further by a joint analysis with other measurements.

It is still an open and challenging problem for mobile robots navigating along time-efficient and collision-free paths in a crowd. The main challenge comes from the complex and sophisticated interaction mechanism, which requires the robot to understand the crowd and perform proactive and foresighted behaviors. Deep reinforcement learning is a promising solution to this problem. However, most previous learning methods incur a tremendous computational burden. To address these problems, we propose a graph-based deep reinforcement learning method, SG-DQN, that (i) introduces a social attention mechanism to extract an efficient graph representation for the crowd-robot state; (ii) directly evaluates the coarse q-values of the raw state with a learned dueling deep Q network(DQN); and then (iii) refines the coarse q-values via online planning on possible future trajectories. The experimental results indicate that our model can help the robot better understand the crowd and achieve a high success rate of more than 0.99 in the crowd navigation task. Compared against previous state-of-the-art algorithms, our algorithm achieves an equivalent, if not better, performance while requiring less than half of the computational cost.

Epidemic processes on random graphs or networks are marked by localization of activity that can trap the dynamics into a metastable state, confined to a subextensive part of the network, before visiting an absorbing configuration. Quasistationary (QS) method is a technique to deal with absorbing states for finite sizes and has played a central role in the investigation of epidemic processes on heterogeneous networks where localization is a hallmark. The standard QS method possesses high computer and algorithmic complexity for large systems besides parameters whose choice are not systematic. However, simpler approaches, such as a reflecting boundary condition (RBC), are not able to capture the localization effects as the standard QS method does. In the present work, we propose a QS method that consists of reactivating nodes proportionally to the time they were active along the preceding simulation. The method is compared with the standard QS and RBC methods for the susceptible-infected-susceptible model on complex networks, which is a prototype of a dynamic process with strong and localization effects. We verified that the method performs as well the as standard QS in all investigated simulations, providing the same scaling exponents, epidemic thresholds, and localized phases, thus overcoming the limitations of other simpler approaches. We also report that the present method has significant reduction of the computer and algorithmic complexity than the standard QS methods. So, this method arises as a simpler and efficient tool to analyze localization on heterogeneous structures through QS simulations.

As 5th Generation research reaches the twilight, the research community must go beyond 5G and look towards the 2030 connectivity landscape, namely 6G. In this context, this work takes a step towards the 6G vision by proposing a next generation communication platform, which aims to extend the rigid coverage area of fixed deployment networks by considering virtual mobile small cells (MSC) that are created on demand. Relying on emerging computing paradigms such as NFV (Network Function Virtualization) and SDN (Software Defined Networking), these cells can harness radio and networking capability locally reducing protocol signaling latency and overhead. These MSCs constitute an intelligent pool of networking resources that can collaborate to form a wireless network of MSCs providing a communication platform for localized, ubiquitous and reliable connectivity. The technology enablers for implementing the MSC concept are also addressed in terms of virtualization, lightweight wireless security, and energy efficient RF. The benefits of the MSC architecture towards reliable and efficient cell offloading are demonstrated as a use-case.

In this study, 18F-FDG PET/CT brain scans of 50 patients with head and neck malignant lesions were employed to systematically assess the relationship between the amount of injected dose (10%, 8%, 6%, 5%, 4%, 3%, 2%, and 1% of standard dose) and the image quality through measuring standard image quality metrics (peak-signal-to-noise-ration (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and standard uptake value (SUV) bias) for the whole head region as well as within the malignant lesions, considering the standard-dose PET images as reference. Furthermore, we evaluated the impact of post-reconstruction Gaussian filtering on the PET images in order to reduce noise and improve the signal-to-noise ratio at different low-dose levels. Significant degradation of PET image quality and tumor detectability was observed with a decrease in the injected dose by more than 5%, leading to a remarkable increase in RMSE from 0.173 SUV (at 5%) to 1.454 SUV (at 1%). The quantitative investigation of the malignant lesions demonstrated that SUVmax bias greatly increased in low-dose PET images (in particular at 1%, 2%, 3% levels) before applying the post-reconstruction filter, while applying the Gaussian filter on low-dose PET images led to a significant reduction in SUVmax bias. The SUVmean bias within the malignant lesions was negligible (less than 1%) in low-dose PET images; however, this bias increased significantly after applying the post-reconstruction filter. In conclusion, it is strongly recommended that the SUVmax bias and SUVmean bias in low-dose PET images should be considered prior to and following the application of the post-reconstruction Guassain filter, respectively.

As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade. As methods have been proposed, a comprehensive survey of these topics is of great importance. In this work, we review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets and standard evaluation metrics that are widely used in this field. We also discuss the key challenges in this field, development history of this field, advantages/disadvantages of the methods in each category, the relationships between methods in different categories, applications of the weakly supervised object localization and detection methods, and potential future directions to further promote the development of this research field.

We prove an inequality bounding the renormalized area of a complete minimal surface in hyperbolic space in terms of the conformal length of its ideal boundary.

Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As such, recent research has proposed a number of novel, dialog-specific metrics that correlate better with human judgements. Due to the fast pace of research, many of these metrics have been assessed on different datasets and there has as yet been no time for a systematic comparison between them. To this end, this paper provides a comprehensive assessment of recently proposed dialog evaluation metrics on a number of datasets. In this paper, 23 different automatic evaluation metrics are evaluated on 10 different datasets. Furthermore, the metrics are assessed in different settings, to better qualify their respective strengths and weaknesses. Metrics are assessed (1) on both the turn level and the dialog level, (2) for different dialog lengths, (3) for different dialog qualities (e.g., coherence, engaging), (4) for different types of response generation models (i.e., generative, retrieval, simple models and state-of-the-art models), (5) taking into account the similarity of different metrics and (6) exploring combinations of different metrics. This comprehensive assessment offers several takeaways pertaining to dialog evaluation metrics in general. It also suggests how to best assess evaluation metrics and indicates promising directions for future work.

We present best practices and tools for professionals who support computational and data intensive (CDI) research projects. The practices resulted from an initiative that brings together national projects and university teams that include individual or groups of such professionals. We focus particularly on practices that differ from those in a general software engineering context. The paper also describes the initiative , the Xpert Network , where participants exchange successes, challenges, and general information about their activities, leading to increased productivity, efficiency, and coordination in the ever growing community of scientists that use computational and data-intensive research methods.

Neural architecture search (NAS) has been successfully applied to tasks like image classification and language modeling for finding efficient high-performance network architectures. In ASR field especially end-to-end ASR, the related research is still in its infancy. In this work, we focus on applying NAS on the most popular manually designed model: Conformer, and then propose an efficient ASR model searching method that benefits from the natural advantage of differentiable architecture search (Darts) in reducing computational overheads. We fuse Darts mutator and Conformer blocks to form a complete search space, within which a modified architecture called Darts-Conformer cell is found automatically. The entire searching process on AISHELL-1 dataset costs only 0.7 GPU days. Replacing the Conformer encoder by stacking searched cell, we get an end-to-end ASR model (named as Darts-Conformner) that outperforms the Conformer baseline by 4.7\% on the open-source AISHELL-1 dataset. Besides, we verify the transferability of the architecture searched on a small dataset to a larger 2k-hour dataset. To the best of our knowledge, this is the first successful attempt to apply gradient-based architecture search in the attention-based encoder-decoder ASR model.

We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability. We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems.

Zeroth-order (ZO, also known as derivative-free) methods, which estimate the gradient only by two function evaluations, have attracted much attention recently because of its broad applications in machine learning community. The two function evaluations are normally generated with random perturbations from standard Gaussian distribution. To speed up ZO methods, many methods, such as variance reduced stochastic ZO gradients and learning an adaptive Gaussian distribution, have recently been proposed to reduce the variances of ZO gradients. However, it is still an open problem whether there is a space to further improve the convergence of ZO methods. To explore this problem, in this paper, we propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. To find the optimal policy, an actor-critic RL algorithm called deep deterministic policy gradient (DDPG) with two neural network function approximators is adopted. The learned sampling policy guides the perturbed points in the parameter space to estimate a more accurate ZO gradient. To the best of our knowledge, our ZO-RL is the first algorithm to learn the sampling policy using reinforcement learning for ZO optimization which is parallel to the existing methods. Especially, our ZO-RL can be combined with existing ZO algorithms that could further accelerate the algorithms. Experimental results for different ZO optimization problems show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios.

In this work, we investigate the role of multi-nucleon (MN) effects (mainly 2p-2h and RPA) on the sensitivity measurement of various neutrino oscillation parameters, in the disappearance channel of NO$\nu$A (USA) experiment. Short-range correlations and detector effects have also been included in the analysis. We use the kinematical method of reconstruction of the incoming neutrino energy, both at the near and far detectors. The extrapolation technique has been used to estimate oscillated events at the far detector. The latest global best fit values of various light neutrino oscillation parameters have been used in the analysis. We find that MN effects increase uncertainty in the measurement of neutrino oscillation parameters, while lower detector efficiency is reflected in more uncertainty. This study can give useful insight into precision studies at long-baseline neutrino experiments in future measurements.

Predicting future frames of video sequences is challenging due to the complex and stochastic nature of the problem. Video prediction methods based on variational auto-encoders (VAEs) have been a great success, but they require the training data to contain multiple possible futures for an observed video sequence. This is hard to be fulfilled when videos are captured in the wild where any given observation only has a determinate future. As a result, training a vanilla VAE model with these videos inevitably causes posterior collapse. To alleviate this problem, we propose a novel VAE structure, dabbed VAE-in-VAE or VAE$^2$. The key idea is to explicitly introduce stochasticity into the VAE. We treat part of the observed video sequence as a random transition state that bridges its past and future, and maximize the likelihood of a Markov Chain over the video sequence under all possible transition states. A tractable lower bound is proposed for this intractable objective function and an end-to-end optimization algorithm is designed accordingly. VAE$^2$ can mitigate the posterior collapse problem to a large extent, as it breaks the direct dependence between future and observation and does not directly regress the determinate future provided by the training data. We carry out experiments on a large-scale dataset called Cityscapes, which contains videos collected from a number of urban cities. Results show that VAE$^2$ is capable of predicting diverse futures and is more resistant to posterior collapse than the other state-of-the-art VAE-based approaches. We believe that VAE$^2$ is also applicable to other stochastic sequence prediction problems where training data are lack of stochasticity.

Bone is mineralized tissue constituting the skeletal system, supporting and protecting body organs and tissues. At the molecular level, mineralized collagen fibril is the basic building block of bone tissue, and hence, understanding bone properties down to fundamental tissue structures enables to better identify the mechanisms of structural failures and damages. While efforts have focused on the study of the micro- and macro-scale viscoelasticity related to bone damage and healing based on creep, mineralized collagen has not been explored on a molecular level. We report a study that aims at systematically exploring the viscoelasticity of collagenous fibrils with different mineralization levels. We investigate the dynamic mechanical response upon cyclic and impulsive loads to observe the viscoelastic phenomena from either shear or extensional strains via molecular dynamics. We perform a sensitivity analysis with several key benchmarks: intrafibrillar mineralization percentage, hydration state, and external load amplitude. Our results show a growth of the dynamic moduli with an increase of mineral percentage, pronounced at low strains. When intrafibrillar water is present, the material softens the elastic component but considerably increases its viscosity, especially at high frequencies. This behaviour is confirmed from the material response upon impulsive loads, in which water drastically reduces the relaxation times throughout the input velocity range by one order of magnitude, with respect to the dehydrated counterparts. We find that upon transient loads, water has a major impact on the mechanics of mineralized fibrillar collagen, being able to improve the capability of the tissue to passively and effectively dissipate energy, especially after fast and high-amplitude external loads.

Determining whether two particle systems are similar is a common problem in particle simulations. When the comparison should be invariant under permutations, orthogonal transformations, and translations of the systems, special techniques are needed. We present an algorithm that can test particle systems of finite size for similarity and, if they are similar, can find the optimal alignment between them. Our approach is based on an invariant version of the root mean square deviation (RMSD) measure and is capable of finding the globally optimal solution in $O(n^3)$ operations where $n$ is the number of three-dimensional particles.

As observations of the Hubble parameter from both early and late sources have improved, the tension between these has increased to be well above the 5$\sigma$ threshold. Given this, the need for an explanation of such a tension has grown. In this paper, we explore a set of 7 assumptions, and show that, in order to alleviate the Hubble tension, a model needs to break at least one of these 7, providing a quick and easy to apply check for new model proposals. We also use this framework to make a rough categorisation of current proposed models, and show the existence of at least one under-explored avenue of alleviating the Hubble tension.

Humans express feelings or emotions via different channels. Take language as an example, it entails different sentiments under different visual-acoustic contexts. To precisely understand human intentions as well as reduce the misunderstandings caused by ambiguity and sarcasm, we should consider multimodal signals including textual, visual and acoustic signals. The crucial challenge is to fuse different modalities of features for sentiment analysis. To effectively fuse the information carried by different modalities and better predict the sentiments, we design a novel multi-head attention based fusion network, which is inspired by the observations that the interactions between any two pair-wise modalities are different and they do not equally contribute to the final sentiment prediction. By assigning the acoustic-visual, acoustic-textual and visual-textual features with reasonable attention and exploiting a residual structure, we attend to attain the significant features. We conduct extensive experiments on four public multimodal datasets including one in Chinese and three in English. The results show that our approach outperforms the existing methods and can explain the contributions of bimodal interaction in multiple modalities.

The article considers a two-level open quantum system, whose evolution is governed by the Gorini--Kossakowski--Lindblad--Sudarshan master equation with Hamiltonian and dissipation superoperator depending, correspondingly, on piecewise constant coherent and incoherent controls with constrained magnitudes. Additional constraints on controls' variations are also considered. The system is analyzed using Bloch parametrization of the system's density matrix. We adapt the section method for obtaining outer parallelepipedal and pointwise estimations of reachable and controllability sets in the Bloch ball via solving a number of problems for optimizing coherent and incoherent controls with respect to some objective criteria. The differential evolution and dual annealing optimization methods are used. The numerical results show how the reachable sets' estimations depend on distances between the system's initial states and the Bloch ball's center point, final times, constraints on controls' magnitudes and variations.

The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired remarkable breakthroughs in development of vaccines against the virus and the launch of several phase 3 vaccine trials in Summer 2020 to evaluate vaccine efficacy (VE). Trials of vaccine candidates using mRNA delivery systems developed by Pfizer-BioNTech and Moderna have shown substantial VEs of 94-95%, leading the US Food and Drug Administration to issue Emergency Use Authorizations and subsequent widespread administration of the vaccines. As the trials continue, a key issue is the possibility that VE may wane over time. Ethical considerations dictate that all trial participants be unblinded and those randomized to placebo be offered vaccine, leading to trial protocol amendments specifying unblinding strategies. Crossover of placebo subjects to vaccine complicates inference on waning of VE. We focus on the particular features of the Moderna trial and propose a statistical framework based on a potential outcomes formulation within which we develop methods for inference on whether or not VE wanes over time and estimation of VE at any post-vaccination time. The framework clarifies assumptions made regarding individual- and population-level phenomena and acknowledges the possibility that subjects who are more or less likely to become infected may be crossed over to vaccine differentially over time. The principles of the framework can be adapted straightforwardly to other trials.

This Report provides an extensive review of the experimental programme of direct detection searches of particle dark matter. It focuses mostly on European efforts, both current and planned, but does it within a broader context of a worldwide activity in the field. It aims at identifying the virtues, opportunities and challenges associated with the different experimental approaches and search techniques. It presents scientific and technological synergies, both existing and emerging, with some other areas of particle physics, notably collider and neutrino programmes, and beyond. It addresses the issue of infrastructure in light of the growing needs and challenges of the different experimental searches. Finally, the Report makes a number of recommendations from the perspective of a long-term future of the field. They are introduced, along with some justification, in the opening Overview and Recommendations section and are next summarised at the end of the Report. Overall, we recommend that the direct search for dark matter particle interactions with a detector target should be given top priority in astroparticle physics, and in all particle physics, and beyond, as a positive measurement will provide the most unambiguous confirmation of the particle nature of dark matter in the Universe.

We present an analysis of the spatial clustering of 695 Ly$\alpha$-emitting galaxies (LAE) in the MUSE-Wide survey. All objects have spectroscopically confirmed redshifts in the range $3.3<z<6$. We employ the K-estimator of Adelberger et al. (2005), adapted and optimized for our sample. We also explore the standard two-point correlation function approach, which is however less suited for a pencil-beam survey such as ours. The results from both approaches are consistent. We parametrize the clustering properties by, (i) modelling the clustering signal with a power law (PL), and (ii) adopting a Halo Occupation Distribution (HOD) model. Applying HOD modeling, we infer a large-scale bias of $b_{\rm{HOD}}=2.80^{+0.38}_{-0.38}$ at a median redshift of the number of galaxy pairs $\langle z_{\rm pair}\rangle\simeq3.82$, while the PL analysis results in $b_{\rm{PL}}=3.03^{+1.51}_{-0.52}$ ($r_0=3.60^{+3.10}_{-0.90}\;h^{-1}$Mpc and $\gamma=1.30^{+0.36}_{-0.45}$). The implied typical dark matter halo (DMH) mass is $\log(M_{\rm{DMH}}/[h^{-1}\rm{M}_\odot])=11.34^{+0.23}_{-0.27}$. We study possible dependencies of the clustering signal on object properties by bisecting the sample into disjoint subsets, considering Ly$\alpha$ luminosity, UV absolute magnitude, Ly$\alpha$ equivalent width, and redshift as variables. We find a suggestive trend of more luminous Ly$\alpha$ emitters residing in more massive DMHs than their lower Ly$\alpha$ luminosity counterparts. We also compare our results to mock LAE catalogs based on a semi-analytic model of galaxy formation and find a stronger clustering signal than in our observed sample. By adopting a galaxy-conserving model we estimate that the LAEs in the MUSE-Wide survey will typically evolve into galaxies hosted by halos of $\log(M_{\rm{DMH}}/[h^{-1}\rm{M}_\odot])\approx13.5$ at redshift zero, suggesting that we observe the ancestors of present-day galaxy groups.

We consider the reflection seismology problem of recovering the locations of interfaces and the amplitudes of reflection coefficients from seismic data, which are vital for estimating the subsurface structure. The reflectivity inversion problem is typically solved using greedy algorithms and iterative techniques. Sparse Bayesian learning framework, and more recently, deep learning techniques have shown the potential of data-driven approaches to solve the problem. In this paper, we propose a weighted minimax-concave penalty-regularized reflectivity inversion formulation and solve it through a model-based neural network. The network is referred to as deep-unfolded reflectivity inversion network (DuRIN). We demonstrate the efficacy of the proposed approach over the benchmark techniques by testing on synthetic 1-D seismic traces and 2-D wedge models and validation with the simulated 2-D Marmousi2 model and real data from the Penobscot 3D survey off the coast of Nova Scotia, Canada.

We show that all the semi-smooth stable complex Godeaux surfaces, classified in [FPR18a], are smoothable, and that the moduli stack is smooth of the expected dimension 8 at the corresponding points.

Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers. Fully Homomorphic Encryption (FHE) is a promising technique to enable machine learning and inferencing while providing strict guarantees against information leakage. Since deep convolutional neural networks (CNNs) have become the machine learning tool of choice in several applications, several attempts have been made to harness CNNs to extract insights from encrypted data. However, existing works focus only on ensuring data security and ignore security of model parameters. They also report high level implementations without providing rigorous analysis of the accuracy, security, and speed trade-offs involved in the FHE implementation of generic primitive operators of a CNN such as convolution, non-linear activation, and pooling. In this work, we consider a Machine Learning as a Service (MLaaS) scenario where both input data and model parameters are secured using FHE. Using the CKKS scheme available in the open-source HElib library, we show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters have a major impact on the design of the machine learning model (especially, the choice of the activation function and pooling method). Our empirical study shows that choice of aforementioned design parameters result in significant trade-offs between accuracy, security level, and computational time. Encrypted inference experiments on the MNIST dataset indicate that other design choices such as ciphertext packing strategy and parallelization using multithreading are also critical in determining the throughput and latency of the inference process.

The Spin Physics Detector, a universal facility for studying the nucleon spin structure and other spin-related phenomena with polarized proton and deuteron beams, is proposed to be placed in one of the two interaction points of the NICA collider that is under construction at the Joint Institute for Nuclear Research (Dubna, Russia). At the heart of the project there is huge experience with polarized beams at JINR. The main objective of the proposed experiment is the comprehensive study of the unpolarized and polarized gluon content of the nucleon. Spin measurements at the Spin Physics Detector at the NICA collider have bright perspectives to make a unique contribution and challenge our understanding of the spin structure of the nucleon. In this document the Conceptual Design of the Spin Physics Detector is presented.

We study the measurement of Higgs boson self-couplings through $2\rightarrow 3$ vector boson scattering (VBS) processes in the framework of Standard Model effective field theory (SMEFT) at both proton and lepton colliders. The SMEFT contribution to the amplitude of the $2\to3$ VBS processes, taking $W_L W_L\rightarrow W_L W_L h$ and $W_L W_L\rightarrow h h h$ as examples, exhibits enhancement with the energy $\frac{\mathcal{A}^{\text{BSM}}}{\mathcal{A}^{\text{SM}}} \sim \frac{E^2}{\Lambda^2}$, which indicates the sensitivity of these processes to the related dimension-six operators in SMEFT. Simulation of the full processes at both hadron and lepton colliders with a variety of collision energies are performed to estimate the allowed region on $c_6$ and $c_{\Phi_1}$. Especially we find that, with the help of exclusively choosing longitudinal polarizations in the final states and suitable $p_T$ cuts, $WWh$ process is as important as the more widely studied triple Higgs production ($hhh$) in the measurement of Higgs self-couplings. Our analysis indicates that these processes can play important roles in the measurement of Higgs self-couplings at future 100 TeV pp colliders and muon colliders. However, their cross sections are generally tiny at low energy machines, which makes them much more challenging to explore.

The proposition that life can spread from one planetary system to another (interstellar panspermia) has a long history, but this hypothesis is difficult to test through observations. We develop a mathematical model that takes parameters such as the microbial survival lifetime, the stellar velocity dispersion, and the dispersion of ejecta into account in order to assess the prospects for detecting interstellar panspermia. We show that the correlations between pairs of life-bearing planetary systems (embodied in the pair-distribution function from statistics) may serve as an effective diagnostic of interstellar panspermia, provided that the velocity dispersion of ejecta is greater than the stellar dispersion. We provide heuristic estimates of the model parameters for various astrophysical environments, and conclude that open clusters and globular clusters appear to represent the best targets for assessing the viability of interstellar panspermia.

In this work, we present a number of generator matrices of the form $[I_{2n} \ | \ \tau_k(v)],$ where $I_{kn}$ is the $kn \times kn$ identity matrix, $v$ is an element in the group matrix ring $M_2(R)G$ and where $R$ is a finite commutative Frobenius ring and $G$ is a finite group of order 18. We employ these generator matrices and search for binary $[72,36,12]$ self-dual codes directly over the finite field $\mathbb{F}_2.$ As a result, we find 134 Type I and 1 Type II codes of this length, with parameters in their weight enumerators that were not known in the literature before. We tabulate all of our findings.

Multiexcitons in monolayer WSe2 exhibit a suite of optoelectronic phenomena that are unique to those of their single exciton constituents. Here, photoluminescence action spectroscopy shows that multiexciton formation is enhanced with increasing optical excitation energy. This enhancement is attributed to the multiexciton formation processes from an electron-hole plasma and results in over 300% more multiexciton emission than at lower excitation energies at 4 K. The energetic onset of the enhancement coincides with the quasiparticle bandgap, corroborating the role of the electron-hole plasma, and the enhancement diminishes with increasing temperature. The results reveal that the strong interactions responsible for ultrafast exciton formation also affect multiexciton phenomena, and both multiexciton and single exciton states play significant roles in plasma thermalization in 2D semiconductors.

The recently discovered non-Hermitian skin effect (NHSE) manifests the breakdown of current classification of topological phases in energy-nonconservative systems, and necessitates the introduction of non-Hermitian band topology. So far, all NHSE observations are based on one type of non-Hermitian band topology, in which the complex energy spectrum winds along a closed loop. As recently characterized along a synthetic dimension on a photonic platform, non-Hermitian band topology can exhibit almost arbitrary windings in momentum space, but their actual phenomena in real physical systems remain unclear. Here, we report the experimental realization of NHSE in a one-dimensional (1D) non-reciprocal acoustic crystal. With direct acoustic measurement, we demonstrate that a twisted winding, whose topology consists of two oppositely oriented loops in contact rather than a single loop, will dramatically change the NHSE, following previous predictions of unique features such as the bipolar localization and the Bloch point for a Bloch-wave-like extended state. This work reveals previously unnoticed features of NHSE, and provides the observation of physical phenomena originating from complex non-Hermitian winding topology.

Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures. In spite of this, due to the great performance provided by the architectures which are found, these methods are widely applied. The final outcome of neuroevolutionary processes is the best structure found during the search, and the rest of the procedure is commonly omitted in the literature. However, a good amount of residual information consisting of valuable knowledge that can be extracted is also produced during these searches. In this paper, we propose an approach that extracts this information from neuroevolutionary runs, and use it to build a metamodel that could positively impact future neural architecture searches. More specifically, by inspecting the best structures found during neuroevolutionary searches of generative adversarial networks with varying characteristics (e.g., based on dense or convolutional layers), we propose a Bayesian network-based model which can be used to either find strong neural structures right away, conveniently initialize different structural searches for different problems, or help future optimization of structures of any type to keep finding increasingly better structures where uninformed methods get stuck into local optima.

This study analyzes the case of Romanian births, jointly distributed by age-groups of mother and father covering 1958-2019 under the potential influence of significant disruptors. Significant events such as anti-abortion laws application or abrogation, communism fall, and migration and their impact are analyzed. While in practice we may find pro and contra examples, a general controversy arises regarding whether births should or should not obey the Benford Law (BL). Moreover, the significant disruptors' impacts are not detailed discussed in such analysis. I find the distribution of births is First Digit Benford Law (BL1) conformant on the entire sample, but mixed results regarding the BL obedience in the dynamic analysis and by main sub-periods. Even though many disruptors are analyzed, only the 1967 Anti-abortion Decree has a significant impact. I capture an average lag of 15 years between the event, the Anti-abortion Decree, and the start of distortion of the births distribution. The distortion persists around 25 years, almost the entire fertility life (15 to 39) for the majority of the people from the cohorts born in 1967-1968.

We implement an algorithm for the computation of Schouten bracket of weakly nonlocal Hamiltonian operators in three different computer algebra systems: Maple, Reduce and Mathematica. This class of Hamiltonian operators encompass almost all the examples coming from the theory of (1+1)-integrable evolutionary PDEs

We provide a rigorous analysis of the quantum optimal control problem in the setting of a linear combination $s(t)B+(1-s(t))C$ of two noncommuting Hamiltonians $B$ and $C$. This includes both quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). The target is to minimize the energy of the final ``problem'' Hamiltonian $C$, for a time-dependent and bounded control schedule $s(t)\in [0,1]$ and $t\in \mc{I}:= [0,t_f]$. It was recently shown, in a purely closed system setting, that the optimal solution to this problem is a ``bang-anneal-bang'' schedule, with the bangs characterized by $s(t)= 0$ and $s(t)= 1$ in finite subintervals of $\mc{I}$, in particular $s(0)=0$ and $s(t_f)=1$, in contrast to the standard prescription $s(0)=1$ and $s(t_f)=0$ of quantum annealing. Here we extend this result to the open system setting, where the system is described by a density matrix rather than a pure state. This is the natural setting for experimental realizations of QA and QAOA. For finite-dimensional environments and without any approximations we identify sufficient conditions ensuring that either the bang-anneal, anneal-bang, or bang-anneal-bang schedules are optimal, and recover the optimality of $s(0)=0$ and $s(t_f)=1$. However, for infinite-dimensional environments and a system described by an adiabatic Redfield master equation we do not recover the bang-type optimal solution. In fact we can only identify conditions under which $s(t_f)=1$, and even this result is not recovered in the fully Markovian limit. The analysis, which we carry out entirely within the geometric framework of Pontryagin Maximum Principle, simplifies using the density matrix formulation compared to the state vector formulation.

We identify the exact microscopic structure of the G photoluminescence center in silicon by first principles calculations with including a self-consistent many-body perturbation method, which is a telecommunication wavelength single photon source. The defect constitutes of $\text{C}_\text{s}\text{C}_\text{i}$ carbon impurities in its $\text{C}_\text{s}-\text{Si}_\text{i}-\text{C}_\text{s}$ configuration in the neutral charge state, where $s$ and $i$ stand for the respective substitutional and interstitial positions in the Si lattice. We reveal that the observed fine structure of its optical signals originates from the athermal rotational reorientation of the defect. We attribute the monoclinic symmetry reported in optically detected magnetic resonance measurements to the reduced tunneling rate at very low temperatures. We discuss the thermally activated motional averaging of the defect properties and the nature of the qubit state.

Transition metal dichalcogenides (TMDs) have been a core constituent of 2D material research throughout the last decade. Over this time, research focus has progressively shifted from synthesis and fundamental investigations, to exploring their properties for applied research such as electrochemical applications and integration in electrical devices. Due to the rapid pace of development, priority is often given to application-oriented aspects while careful characterisation and analysis of the TMD materials themselves is occasionally neglected. This can be particularly evident for characterisations involving X-ray photoelectron spectroscopy (XPS), where measurement, peak-fitting, and analysis can be complex and nuanced endeavours requiring specific expertise. To improve the availability and accessibility of reference information, here we present a detailed peak-fitted XPS analysis of ten transition metal chalcogenides. The materials were synthesised as large-area thin-films on SiO2 using direct chalcogenisation of pre-deposited metal films. Alongside XPS, the Raman spectra with several excitation wavelengths for each material are also provided. These complementary characterisation methods can provide a more complete understanding of the composition and quality of the material. As material stability is a crucial factor when considering applications, the in-air thermal stability of the TMDs was investigated after several annealing points up to 400 {\deg}C. This delivers a trend of evolving XPS and Raman spectra for each material which improves interpretation of their spectra while also indicating their ambient thermal limits. This provides an accessible library and set of guidelines to characterise, compare, and discuss TMD XPS and Raman spectra.

The properties of ideal tri-functional dendrimers with forty-five, ninety-three and one hundred and eighty-nine branches are investigated. Three methods are employed to calculate the mean-square radius of gyration, $g$-ratios, asphericity, shape parameters and form factor. These methods include a Kirchhoff matrix eigenvalue technique, the graph theory approach of Benhamou et al. (2004), and Monte Carlo simulations using a growth algorithm. A novel technique for counting paths in the graph representation of the dendrimers is presented. All the methods are in excellent agreement with each other and with available theoretical predictions. Dendrimers become more symmetrical as the generation and the number of branches increase.

The colonisation of a soft passive material by motile cells such as bacteria is common in biology. The resulting colonies of the invading cells are often observed to exhibit intricate patterns whose morphology and dynamics can depend on a number of factors, particularly the mechanical properties of the substrate and the motility of the individual cells. We use simulations of a minimal 2D model of self-propelled rods moving through with a passive compliant medium consisting of particles that offer elastic resistance before being plastically displaced from their equilibrium positions. It is observed that the motility-induced clustering of active (self-propelled) particles is crucial for understanding the morphodynamics of colonisation. Clustering enables motile colonies to spread faster than they would have as isolated particles. The colonisation rate depends non-monotonically on substrate stiffness with a distinct maximum at a non-zero value of substrate stiffness. This is observed to be due to a change in the morphology of clusters. Furrow networks created by the active particles have a fractal-like structure whose dimension varies systematically with substrate stiffness but is less sensitive to particle activity. The power-law growth exponent of the furrowed area is smaller than unity, suggesting that, to sustain such extensive furrow networks, colonies must regulate their overall growth rate.

We use exponent pairs to establish the existence of many $x^a$-smooth numbers in short intervals $[x-x^b,x]$, when $a>1/2$. In particular, $b=1-a-a(1-a)^3$ is admissible. Assuming the exponent-pairs conjecture, one can take $b=(1-a)/2+\epsilon$. As an application, we show that $[x-x^{0.4872},x]$ contains many practical numbers when $x$ is large.

Dust attenuation of an inclined galaxy can cause additional asymmetries in observations, even if the galaxy has a perfectly symmetric structure. {Taking advantage of the integral field spectroscopic data observed by the SDSS-IV MaNGA survey, we investigate the asymmetries of the emission-line and continuum maps of star-forming disk galaxies.} We define new parameters, $A_a$ and $A_b$, to estimate the asymmetries of a galaxy about its major and minor axes, respectively. Comparing $A_a$ and $A_b$ in different inclination bins, we attempt to detect the asymmetries caused by dust. For the continuum images, we find that $A_a$ increases with the inclination, while the $A_b$ is a constant as inclination changes. Similar trends are found for $g-r$, $g-i$ and $r-i$ color images. The dependence of the asymmetry on inclination suggests a thin dust layer with a scale height smaller than the stellar populations. For the H$\alpha$ and H$\beta$ images, neither $A_a$ nor $A_b$ shows a significant correlation with inclination. Also, we do not find any significant dependence of the asymmetry of $E(B-V)_g$ on inclination, implying that the dust in the thick disk component is not significant. Compared to the SKIRT simulation, the results suggest that the thin dust disk has an optical depth $\tau_V\sim0.2$. This is the first time that the asymmetries caused by the dust attenuation and the inclination are probed statistically with a large sample. Our results indicate that the combination of the dust attenuation and the inclination effects is a potential indicator of the 3D disk orientation.

We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalising flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the sampler's settings.

Recently, the LHCb Collaboration reported on the evidence for a hidden charm pentaquark state with strangeness, i.e., $P_{cs}(4459)$, in the $J/\psi\Lambda$ invariant mass distribution of the $\Xi_b^-\to J/\psi \Lambda K^-$ decay. In this work, assuming that $P_{cs}(4459)$ is a $\bar{D}^*\Xi_c$ molecular state, we study this decay via triangle diagrams $\Xi_b\rightarrow \bar{D}_s^{(*)}\Xi_c\to (\bar{D}^{(*)}\bar{K})\Xi_c\to P_{cs} \bar{K}\to (J/\psi\Lambda) \bar{K}$. Our study shows that the production yield of a spin 3/2 $\bar{D}^*\Xi_c$ state is approximately one order of magnitude larger than that of a spin $1/2$ state due to the interference of $\bar{D}_s\Xi_c$ and $\bar{D}_s^*\Xi_c$ intermediate states. We obtain a model independent constraint on the product of couplings $g_{P_{cs}\bar{D}^*\Xi_c}$ and $g_{P_{cs}J/\psi\Lambda}$. With the predictions of two particular molecular models as inputs, we calculate the branching ratio of $\Xi_b^-\to (P_{cs}\to)J/\psi\Lambda K^- $ and compare it with the experimental measurement. We further predict the lineshape of this decay which could be useful to future experimental studies.

A graph $G$ is called a $2K_2$-free graph if it does not contain $2K_2$ as an induced subgraph. In 2014, Broersma, Patel and Pyatkin showed that every 25-tough $2K_2$-free graph on at least three vertices is Hamiltonian. Recently, Shan improved this result by showing that 3-tough is sufficient instead of 25-tough. In this paper, we show that every 2-tough $2K_2$-free graph on at least three vertices is Hamiltonian, which was conjectured by Gao and Pasechnik.

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration is the variations in image appearance. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have computational efficiency that is several orders of magnitude greater than traditional optimization-based registration methods (ORs). A major drawback, however, of DLRs is a disregard for the target-pair-specific optimization that is inherent in ORs and instead they rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. Thus, DLRs inherently have degraded ability to adapt to appearance variations and perform poorly, compared to ORs, when image pairs (fixed/moving images) have large differences in appearance. Hence, we propose an Appearance Adjustment Network (AAN) where we leverage anatomy edges, through an anatomy-constrained loss function, to generate an anatomy-preserving appearance transformation. We designed the AAN so that it can be readily inserted into a wide range of DLRs, to reduce the appearance differences between the fixed and moving images. Our AAN and DLR's network can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with two widely used DLRs - Voxelmorph (VM) and FAst IMage registration (FAIM) - on three public 3D brain magnetic resonance (MR) image datasets - IBSR18, Mindboggle101, and LPBA40. The results show that DLRs, using the AAN, improved performance and achieved higher results than state-of-the-art ORs.

Angle-resolved photoemission spectroscopy (ARPES) is one of the most powerful experimental techniques in condensed matter physics. Synchrotron ARPES, which uses photons with high flux and continuously tunable energy, has become particularly important. However, an excellent synchrotron ARPES system must have features such as a small beam spot, super-high energy resolution, and a user-friendly operation interface. A synchrotron beamline and an endstation (BL03U) were designed and constructed at the Shanghai Synchrotron Radiation Facility. The beam spot size at the sample position is 7.5 (V) $\mu$m $\times$ 67 (H) $\mu$m, and the fundamental photon range is 7-165 eV; the ARPES system enables photoemission with an energy resolution of 2.67 meV@21.2 eV. In addition, the ARPES system of this endstation is equipped with a six-axis cryogenic sample manipulator (the lowest temperature is 7 K) and is integrated with an oxide molecular beam epitaxy system and a scanning tunneling microscope, which can provide an advanced platform for in-situ characterization of the fine electronic structure of condensed matter.

Run-and-tumble particles, frequently considered today for modeling bacterial locomotion, naturally appear outside a biological context as well, e.g. for producing waves in the telegraph process. Here, we use a wave function to drive their propulsion and tumbling. Such quantum-active motion realizes a jittery motion of Dirac electrons (as in the famous Zitterbewegung): the Dirac electron is a run-and-tumble particle, where the tumbling is between chiralities. We visualize the trajectories in diffraction and double slit experiments for electrons. In particular, that yields the time-of-arrival statistics of the electrons at the screen. Finally, we observe that away from pure quantum guidance, run-and-tumble particles with suitable spacetime-dependent parameters produce an interference pattern as well.

We introduce a basis set consisting of three-dimensional Deslauriers--Dubuc wavelets and numerically solve the Schr\"odinger equations of hydrogen atom, helium atom, hydrogen molecule ion, hydrogen molecule, and lithium hydride molecule with Hartree-Fock and DFT methods. We also compute the 2s and 2p excited states of hydrogen. The Coulomb singularity at the nucleus is handled by using a pseudopotential. Results are compared with those of CCCBDB and BigDFT. The eigenvalue problem is solved with Arnoldi and Lanczos methods, and the Poisson equation with GMRES and CGNR methods. The various matrix elements are computed using the biorthogonality relations of the interpolating wavelets.

A ubiquitous challenge in design space exploration or uncertainty quantification of complex engineering problems is the minimization of computational cost. A useful tool to ease the burden of solving such systems is model reduction. This work considers a stochastic model reduction method (SMR), in the context of polynomial chaos (PC) expansions, where low-fidelity (LF) samples are leveraged to form a stochastic reduced basis. The reduced basis enables the construction of a bi-fidelity (BF) estimate of a quantity of interest from a small number of high-fidelity (HF) samples. A successful BF estimate approximates the quantity of interest with accuracy comparable to the HF model and computational expense close to the LF model. We develop new error bounds for the SMR approach and present a procedure to practically utilize these bounds in order to assess the appropriateness of a given pair of LF and HF models for BF estimation. The effectiveness of the SMR approach, and the utility of the error bound are presented in three numerical examples.

Membranes derived from ultrathin polymeric films are promising to meet fast separations, but currently available approaches to produce polymer films with greatly reduced thicknesses on porous supports still faces challenges. Here, defect-free ultrathin polymer covering films (UPCFs) are realized by a facile general approach of rapid solvent evaporation. By fast evaporating dilute polymer solutions, we realize ultrathin coating (~30 nm) of porous substrates exclusively on the top surface, forming UPCFs with a block copolymer of polystyrene-block-poly(2-vinyl pyridine) at room temperature or a homopolymer of poly(vinyl alcohol) (PVA) at elevated temperatures. With subsequent selective swelling to the block copolymer and crosslinking to PVA, the resulting bi-layered composite structures serve as highly permeable membranes delivering ~2-10 times higher permeability in ultrafiltration and pervaporation applications than state-of-the-art separation membranes with similar rejections and selectivities. This work opens up a new, facile avenue for the controllable fabrication of ultrathin coatings on porous substrates, which shows great potentials in membrane-based separations and other areas.

Semantic Scene Completion aims at reconstructing a complete 3D scene with precise voxel-wise semantics from a single-view depth or RGBD image. It is a crucial but challenging problem for indoor scene understanding. In this work, we present a novel framework named Scene-Instance-Scene Network (\textit{SISNet}), which takes advantages of both instance and scene level semantic information. Our method is capable of inferring fine-grained shape details as well as nearby objects whose semantic categories are easily mixed-up. The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene. SISNet conducts iterative scene-to-instance (SI) and instance-to-scene (IS) semantic completion. Specifically, the SI is able to encode objects' surrounding context for effectively decoupling instances from the scene and each instance could be voxelized into higher resolution to capture finer details. With IS, fine-grained instance information can be integrated back into the 3D scene and thus leads to more accurate semantic scene completion. Utilizing such an iterative mechanism, the scene and instance completion benefits each other to achieve higher completion accuracy. Extensively experiments show that our proposed method consistently outperforms state-of-the-art methods on both real NYU, NYUCAD and synthetic SUNCG-RGBD datasets. The code and the supplementary material will be available at \url{https://github.com/yjcaimeow/SISNet}.

We prove that a system of equations introduced by Demailly (to attack a conjecture of Griffiths) has a smooth solution for a direct sum of ample line bundles on a Riemann surface. We also reduce the problem for general vector bundles to an a priori estimate using Leray-Schauder degree theory.

We use $Gaia$ eDR3 data and legacy spectroscopic surveys to map the Milky Way disc substructure towards the Galactic Anticenter at heliocentric distances $d\geq10\,\rm{kpc}$. We report the discovery of multiple previously undetected new filaments embedded in the outer disc in highly extincted regions. Stars in these over-densities have distance gradients expected for disc material and move on disc-like orbits with $v_{\phi}\sim170-230\,\rm{km\,s^{-1}}$, showing small spreads in energy. Such a morphology argues against a quiescently growing Galactic thin disc. Some of these structures are interpreted as excited outer disc material, kicked up by satellite impacts and currently undergoing phase-mixing ("feathers"). Due to the long timescale in the outer disc regions, these structures can stay coherent in configuration space over several Gyrs. We nevertheless note that some of these structures could also be folds in the perturbed disc seen in projection from the Sun's location. A full 6D phase-space characterization and age dating of these structure should help distinguish between the two possible morphologies.

Scoring rules aggregate individual rankings by assigning some points to each position in each ranking such that the total sum of points provides the overall ranking of the alternatives. They are widely used in sports competitions consisting of multiple contests. We study the tradeoff between two risks in this setting: (1) the threat of early clinch when the title has been clinched before the last contest(s) of the competition take place; (2) the danger of winning the competition without finishing first in any contest. In particular, four historical points scoring systems of the Formula One World Championship are compared with the family of geometric scoring rules, recently proposed by an axiomatic approach. The schemes used in practice are found to be competitive with respect to these goals, and the current rule seems to be a reasonable compromise close to the Pareto frontier. Our results shed more light on the evolution of the Formula One points scoring systems and contribute to the issue of choosing the set of point values.

We consider a stochastic model describing the spiking activity of a countable set of neurons spatially organized into a homogeneous tree of degree $d$, $d \geq 2$; the degree of a neuron is just the number of connections it has. Roughly, the model is as follows. Each neuron is represented by its membrane potential, which takes non-negative integer values. Neurons spike at Poisson rate 1, provided they have strictly positive membrane potential. When a spike occurs, the potential of the spiking neuron changes to 0, and all neurons connected to it receive a positive amount of potential. Moreover, between successive spikes and without receiving any spiking inputs from other neurons, each neuron's potential behaves independently as a pure death process with death rate $\gamma \geq 0$. In this article, we show that if the number $d$ of connections is large enough, then the process exhibits at least two phase transitions depending on the choice of rate $\gamma$: For large values of $\gamma$, the neural spiking activity almost surely goes extinct; For small values of $\gamma$, a fixed neuron spikes infinitely many times with a positive probability, and for "intermediate" values of $\gamma$, the system has a positive probability of always presenting spiking activity, but, individually, each neuron eventually stops spiking and remains at rest forever.

For a complex projective manifold, Walker has defined a regular homomorphism lifting Griffiths' Abel-Jacobi map on algebraically trivial cycle classes to a complex abelian variety, which admits a finite homomorphism to the Griffiths intermediate Jacobian. Recently Suzuki gave an alternate, Hodge-theoretic, construction of this Walker Abel-Jacobi map. We provide a third construction based on a general lifting property for surjective regular homomorphisms, and prove that the Walker Abel-Jacobi map descends canonically to any field of definition of the complex projective manifold. In addition, we determine the image of the l-adic Bloch map restricted to algebraically trivial cycle classes in terms of the coniveau filtration.

For every $p\in(0,\infty)$, a new metric invariant called umbel $p$-convexity is introduced. The asymptotic notion of umbel convexity captures the geometry of countably branching trees, much in the same way as Markov convexity, the local invariant which inspired it, captures the geometry of bounded degree trees. Umbel convexity is used to provide a "Poincar\'e-type" metric characterization of the class of Banach spaces that admit an equivalent norm with Rolewicz's property $(\beta)$. We explain how a relaxation of umbel $p$-convexity, called umbel cotype $p$, plays a role in obtaining compression rate bounds for coarse embeddings of countably branching trees. Local analogs of these invariants, fork $q$-convexity and fork cotype $q$, are introduced and their relationship to Markov $q$-convexity and relaxations of the $q$-tripod inequality is discussed. The metric invariants are estimated for a large class of Heisenberg groups. Finally, a new characterization of non-negative curvature is given.

Distance metrics and their nonlinear variant play a crucial role in machine learning based real-world problem solving. We demonstrated how Euclidean and cosine distance measures differ not only theoretically but also in real-world medical application, namely, outcome prediction of drug prescription. Euclidean distance exhibits favorable properties in the local geometry problem. To this regard, Euclidean distance can be applied under short-term disease with low-variation outcome observation. Moreover, when presenting to highly variant chronic disease, it is preferable to use cosine distance. These different geometric properties lead to different submanifolds in the original embedded space, and hence, to different optimizing nonlinear kernel embedding frameworks. We first established the geometric properties that we needed in these frameworks. From these properties interpreted their differences in certain perspectives. Our evaluation on real-world, large-scale electronic health records and embedding space visualization empirically validated our approach.

Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL preserves the user's privacy, reduces the communication costs, and exploits the unprecedented capabilities of edge devices to train a shared global model by leveraging a massive amount of data generated at the network edge. However, FEEL might significantly shorten energy-constrained participating devices' lifetime due to the power consumed during the model training round. This paper proposes a novel approach that endeavors to minimize computation and communication energy consumption during FEEL rounds to address this issue. First, we introduce a modified local training algorithm that intelligently selects only the samples that enhance the model's quality based on a predetermined threshold probability. Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker's energy budget, available bandwidth, channel states, beamforming, and local CPU speed. After that, we introduce a tractable solution to the formulated problem that ensures the robustness of FEEL. Our simulation results show that our solution substantially outperforms the baseline FEEL algorithm as it reduces the local consumed energy by up to 79%.

A class of bivariate infinite series solutions of the elliptic and hyperbolic Kepler equations is described, adding to the handful of 1-D series that have been found throughout the centuries. This result is based on an iterative procedure for the analytical computation of all the higher-order partial derivatives of the eccentric anomaly with respect to the eccentricity $e$ and mean anomaly $M$ in a given base point $(e_c,M_c)$ of the $(e,M)$ plane. Explicit examples of such bivariate infinite series are provided, corresponding to different choices of $(e_c,M_c)$, and their convergence is studied numerically. In particular, the polynomials that are obtained by truncating the infinite series up to the fifth degree reach high levels of accuracy in significantly large regions of the parameter space $(e,M)$. Besides their theoretical interest, these series can be used for designing 2-D spline numerical algorithms for efficiently solving Kepler's equations for all values of the eccentricity and mean anomaly.

Galaxy internal structure growth has long been accused of inhibiting star formation in disc galaxies. We investigate the potential physical connection between the growth of dispersion-supported stellar structures (e.g. classical bulges) and the position of galaxies on the star-forming main sequence at $z\sim0$. Combining the might of the SAMI and MaNGA galaxy surveys, we measure the $\lambda_{Re}$ spin parameter for 3781 galaxies over $9.5 < \log M_{\star} [\rm{M}_{\odot}] < 12$. At all stellar masses, galaxies at the locus of the main sequence possess $\lambda_{Re}$ values indicative of intrinsically flattened discs. However, above $\log M_{\star}[\rm{M}_{\odot}]\sim10.5$ where the main sequence starts bending, we find tantalising evidence for an increase in the number of galaxies with dispersion-supported structures, perhaps suggesting a connection between bulges and the bending of the main sequence. Moving above the main sequence, we see no evidence of any change in the typical spin parameter in galaxies once gravitationally-interacting systems are excluded from the sample. Similarly, up to 1 dex below the main sequence, $\lambda_{Re}$ remains roughly constant and only at very high stellar masses ($\log M_{\star}[\rm{M}_{\odot}]>11$), do we see a rapid decrease in $\lambda_{Re}$ once galaxies decline in star formation activity. If this trend is confirmed, it would be indicative of different quenching mechanisms acting on high- and low-mass galaxies. The results suggest that while a population of galaxies possessing some dispersion-supported structure is already present on the star-forming main sequence, further growth would be required after the galaxy has quenched to match the kinematic properties observed in passive galaxies at $z\sim0$.

We propose two deep neural network-based methods for solving semi-martingale optimal transport problems. The first method is based on a relaxation/penalization of the terminal constraint, and is solved using deep neural networks. The second method is based on the dual formulation of the problem, which we express as a saddle point problem, and is solved using adversarial networks. Both methods are mesh-free and therefore mitigate the curse of dimensionality. We test the performance and accuracy of our methods on several examples up to dimension 10. We also apply the first algorithm to a portfolio optimization problem where the goal is, given an initial wealth distribution, to find an investment strategy leading to a prescribed terminal wealth distribution.

We present Meta Learning for Knowledge Distillation (MetaDistil), a simple yet effective alternative to traditional knowledge distillation (KD) methods where the teacher model is fixed during training. We show the teacher network can learn to better transfer knowledge to the student network (i.e., learning to teach) with the feedback from the performance of the distilled student network in a meta learning framework. Moreover, we introduce a pilot update mechanism to improve the alignment between the inner-learner and meta-learner in meta learning algorithms that focus on an improved inner-learner. Experiments on various benchmarks show that MetaDistil can yield significant improvements compared with traditional KD algorithms and is less sensitive to the choice of different student capacity and hyperparameters, facilitating the use of KD on different tasks and models. The code is available at https://github.com/JetRunner/MetaDistil

The origin and composition of ultra-high energy cosmic rays (UHECRs) remain a mystery. The common lore is that UHECRs are deflected from their primary directions by the Galactic and extragalactic magnetic fields. Here we describe an extragalactic contribution to the deflection of UHECRs that does not depend on the strength and orientation of the initial seed field. Using the IllustrisTNG simulations, we show that outflow-driven magnetic bubbles created by feedback processes during galaxy formation deflect approximately half of all $10^{20}$ eV protons by $1^{\circ}$ or more, and up to $20$-$30^{\circ}$. This implies that the deflection in the intergalactic medium must be taken into account in order to identify the sources of UHECRs.

To understand the true nature of black holes, fundamental theoretical developments should be linked all the way to observational features of black holes in their natural astrophysical environments. Here, we take several steps to establish such a link. We construct a family of spinning, regular black-hole spacetimes based on a locality principle for new physics and analyze their shadow images. We identify characteristic image features associated to regularity (increased compactness and relative stretching) and to the locality principle (cusps and asymmetry) that persist in the presence of a simple analytical disk model. We conjecture that these occur as universal features of distinct classes of regular black holes based on different sets of construction principles for the corresponding spacetimes.

We develop a convergence-rate analysis of momentum with cyclical step-sizes. We show that under some assumption on the spectral gap of Hessians in machine learning, cyclical step-sizes are provably faster than constant step-sizes. More precisely, we develop a convergence rate analysis for quadratic objectives that provides optimal parameters and shows that cyclical learning rates can improve upon traditional lower complexity bounds. We further propose a systematic approach to design optimal first order methods for quadratic minimization with a given spectral structure. Finally, we provide a local convergence rate analysis beyond quadratic minimization for the proposed methods and illustrate our findings through benchmarks on least squares and logistic regression problems.

We study the constraints imposed by perturbative unitarity on the new physics interpretation of the muon $g-2$ anomaly. Within a Standard Model Effective Field Theory (SMEFT) approach, we find that scattering amplitudes sourced by effective operators saturate perturbative unitarity at about 1 PeV. This corresponds to the highest energy scale that needs to be probed in order to resolve the new physics origin of the muon $g-2$ anomaly. On the other hand, simplified models (e.g.~scalar-fermion Yukawa theories) in which renormalizable couplings are pushed to the boundary of perturbativity still imply new on-shell states below 200 TeV. We finally suggest that the highest new physics scale responsible for the anomalous effect can be reached in non-renormalizable models at the PeV scale.

Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the self-documented addition of technical debt to a software project in the form of source code comments. SATD can linger in projects and degrade source-code quality, but it can also be more visible than unintentionally added or undocumented technical debt. Understanding the implications of adding SATD to a software project is important because developers can benefit from a better understanding of the quality trade-offs they are making. However, empirical studies, analyzing the survivability and removal of SATD comments, are challenged by potential code changes or SATD comment updates that may interfere with properly tracking their appearance, existence, and removal. In this paper, we propose SATDBailiff, a tool that uses an existing state-of-the-art SATD detection tool, to identify SATD in method comments, then properly track their lifespan. SATDBailiff is given as input links to open source projects, and its output is a list of all identified SATDs, and for each detected SATD, SATDBailiff reports all its associated changes, including any updates to its text, all the way to reporting its removal. The goal of SATDBailiff is to aid researchers and practitioners in better tracking SATDs instances and providing them with a reliable tool that can be easily extended. SATDBailiff was validated using a dataset of previously detected and manually validated SATD instances. SATDBailiff is publicly available as an open-source, along with the manual analysis of SATD instances associated with its validation, on the project website

Requirements engineering (RE) is a key area to address sustainability concerns in system development. Approaches have been proposed to elicit sustainability requirements from interested stakeholders before system design. However, existing strategies lack the proper high-level view to deal with the societal and long-term impacts of the transformation entailed by the introduction of a new technological solution. This paper proposes to go beyond the concept of system requirements and stakeholders' goals, and raise the degree of abstraction by focusing on the notions of drivers, barriers and impacts that a system can have on the environment in which it is deployed. Furthermore, we suggest to narrow the perspective to a single domain, as the effect of a technology is context-dependent. To put this vision into practice, we interview 30 cross-disciplinary experts in the representative domain of rural areas, and we analyse the transcripts to identify common themes. As a result, we provide drivers, barriers and positive or negative impacts associated to the introduction of novel technical solutions in rural areas. This RE-relevant information could hardly be identified if interested stakeholders were interviewed before the development of a single specific system. This paper contributes to the literature with a fresh perspective on sustainability requirements, and with a domain-specific framework grounded on experts' opinions. The conceptual framework resulting from our analysis can be used as a reference baseline for requirements elicitation endeavours in rural areas that need to account for sustainability concerns.

In the Standard Model of particle physics, charged leptons of different flavour couple to the electroweak force carriers with the same interaction strength. This property, known as lepton flavour universality, was found to be consistent with experimental evidence in a wide range of particle decays. Lepton flavour universality can be tested by comparing branching fractions in ratios such as $R_K = \mathcal{B}(B^+ \rightarrow K^+ \mu^+ \mu^-)/\mathcal{B}(B^+ \rightarrow K^+ e^+ e^-)$. This observable is measured using proton-proton collision data recorded with the LHCb detector at CERN's Large Hadron Collider corresponding to an integrated luminosity of $9 \rm{~fb}^{-1}$. For a dilepton invariant mass range of $q^{2} \in [1.1,6.0]~\rm{Ge}\kern -0.1em \rm{V}^{2}$, the measured value of $R_{K}=0.846\,^{+\,0.042}_{-\,0.039}\,^{+\,0.013}_{-\,0.012}$, where the first uncertainty is statistic and the second systematic, is in tension with the Standard Model predicted value at the $3.1\sigma$ level raising evidence for lepton flavour universality violation in $B^+ \rightarrow K^+ \ell^+ \ell^-$ decays.

In a modern distributed storage system, storage nodes are organized in racks, and the cross-rack communication dominates the system bandwidth. In this paper, we focus on the rack-aware storage system. The initial setting was immediately repairing every single node failure. However, multiple node failures are frequent, and some systems may even wait for multiple nodes failures to occur before repairing them in order to keep costs down. For the purpose of still being able to repair them properly when multiple failures occur, we relax the repair model of the rack-aware storage system. In the repair process, the cross-rack connections (i.e., the number of helper racks connected for repair which is called repair degree) and the intra-rack connections (i.e., the number of helper nodes in the rack contains the failed node) are all reduced. We focus on minimizing the cross-rack bandwidth in the rack-aware storage system with multiple erasure tolerances. First, the fundamental tradeoff between the repair bandwidth and the storage size for functional repair is established. Then, the two extreme points corresponding to the minimum storage and minimum cross-rack repair bandwidth are obtained. Second, the explicitly construct corresponding to the two points are given. Both of them have minimum sub-packetization level (i.e., the number of symbols stored in each node) and small repair degree. Besides, the size of underlying finite field is approximately the block length of the code. Finally, for the convenience of practical use, we also establish a transformation to convert our codes into systematic codes.

The evolution of electronic media is a mixed blessing. Due to the easy access, low cost, and faster reach of the information, people search out and devour news from online social networks. In contrast, the increasing acceptance of social media reporting leads to the spread of fake news. This is a minacious problem that causes disputes and endangers societal stability and harmony. Fake news spread has gained attention from researchers due to its vicious nature. proliferation of misinformation in all media, from the internet to cable news, paid advertising and local news outlets, has made it essential for people to identify the misinformation and sort through the facts. Researchers are trying to analyze the credibility of information and curtail false information on such platforms. Credibility is the believability of the piece of information at hand. Analyzing the credibility of fake news is challenging due to the intent of its creation and the polychromatic nature of the news. In this work, we propose a model for detecting fake news. Our method investigates the content of the news at the early stage i.e. when the news is published but is yet to be disseminated through social media. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. In summary, we introduce stance as one of the features along with the content of the article and employ the pre-trained contextualized word embeddings BERT to obtain the state-of-art results for fake news detection. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.

Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene--graphene dynamics induced by nuclear quantum effects and allow to rationalize the Arrhenius behavior of hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.

We study the effects of elastic anisotropy on the Landau-de Gennes critical points for nematic liquid crystals, in a square domain. The elastic anisotropy is captured by a parameter, $L_2$, and the critical points are described by three degrees of freedom. We analytically construct a symmetric critical point for all admissible values of $L_2$, which is necessarily globally stable for small domains i.e., when the square edge length, $\lambda$, is small enough. We perform asymptotic analyses and numerical studies to discover at least $5$ classes of these symmetric critical points - the $WORS$, $Ring^{\pm}$, $Constant$ and $pWORS$ solutions, of which the $WORS$, $Ring^+$ and $Constant$ solutions can be stable. Furthermore, we demonstrate that the novel $Constant$ solution is energetically preferable for large $\lambda$ and large $L_2$, and prove associated stability results that corroborate the stabilising effects of $L_2$ for reduced Landau-de Gennes critical points. We complement our analysis with numerically computed bifurcation diagrams for different values of $L_2$, which illustrate the interplay of elastic anisotropy and geometry for nematic solution landscapes, at low temperatures.

Conversational agents (CAs) represent an emerging research field in health information systems, where there are great potentials in empowering patients with timely information and natural language interfaces. Nevertheless, there have been limited attempts in establishing prescriptive knowledge on designing CAs in the healthcare domain in general, and diabetes care specifically. In this paper, we conducted a Design Science Research project and proposed three design principles for designing health-related CAs that embark on artificial intelligence (AI) to address the limitations of existing solutions. Further, we instantiated the proposed design and developed AMANDA - an AI-based multilingual CA in diabetes care with state-of-the-art technologies for natural-sounding localised accent. We employed mean opinion scores and system usability scale to evaluate AMANDA's speech quality and usability, respectively. This paper provides practitioners with a blueprint for designing CAs in diabetes care with concrete design guidelines that can be extended into other healthcare domains.

In this work, we propose three pilot assignment schemes to reduce the effect of pilot contamination in cell-free massive multiple-input-multiple-output (MIMO) systems. Our first algorithm, which is based on the idea of random sequential adsorption (RSA) process from the statistical physics literature, can be implemented in a distributed and scalable manner while ensuring a minimum distance among the co-pilot users. Further, leveraging the rich literature of the RSA process, we present an approximate analytical approach to accurately determine the density of the co-pilot users as well as the pilot assignment probability for the typical user in this network. We also develop two optimization-based centralized pilot allocation schemes with the primary goal of benchmarking the RSA-based scheme. The first centralized scheme is based only on the user locations (just like the RSA-based scheme) and partitions the users into sets of co-pilot users such that the minimum distance between two users in a partition is maximized. The second centralized scheme takes both user and remote radio head (RRH) locations into account and provides a near-optimal solution in terms of sum-user spectral efficiency (SE). The general idea is to first cluster the users with similar propagation conditions with respect to the RRHs using spectral graph theory and then ensure that the users in each cluster are assigned different pilots using the branch and price (BnP) algorithm. Our simulation results demonstrate that despite admitting distributed implementation, the RSA-based scheme has a competitive performance with respect to the first centralized scheme in all regimes as well as to the near-optimal second scheme when the density of RRHs is high.

For those deformations that satisfy a certain non-degeneracy condition, we describe the structure of certain simple modules of the deformations of the subcharacter algebra of a finite group. For finite abelian groups, we prove that the deformation given by the inclusion of the natural numbers, which corresponds to the algebra generated by the fibred bisets over a field of characteristic zero, is not semisimple. In the cyclic group of prime order case, we provide a complete description of the semisimple deformations.

We present a quantitative, near-term experimental blueprint for the quantum simulation of topological insulators using lattice-trapped ultracold polar molecules. In particular, we focus on the so-called Hopf insulator, which represents a three-dimensional topological state of matter existing outside the conventional tenfold way and crystalline-symmetry-based classifications of topological insulators. Its topology is protected by a \emph{linking number} invariant, which necessitates long-range spin-orbit coupled hoppings for its realization. While these ingredients have so far precluded its realization in solid state systems and other quantum simulation architectures, in a companion manuscript [1901.08597] we predict that Hopf insulators can in fact arise naturally in dipolar interacting systems. Here, we investigate a specific such architecture in lattices of polar molecules, where the effective `spin' is formed from sublattice degrees of freedom. We introduce two techniques that allow one to optimize dipolar Hopf insulators with large band gaps, and which should also be readily applicable to the simulation of other exotic bandstructures. First, we describe the use of Floquet engineering to control the range and functional form of dipolar hoppings and second, we demonstrate that molecular AC polarizabilities (under circularly polarized light) can be used to precisely tune the resonance condition between different rotational states. To verify that this latter technique is amenable to current generation experiments, we calculate from first principles the AC polarizability for $\sigma^+$ light for ${}^{40}$K$^{87}$Rb. Finally, we show that experiments are capable of detecting the unconventional topology of the Hopf insulator by varying the termination of the lattice at its edges, which gives rise to three distinct classes of edge mode spectra.

This paper investigates the relationship between the spread of the COVID-19 pandemic, the state of community activity, and the financial index performance across 20 countries. First, we analyze which countries behaved similarly in 2020 with respect to one of three multivariate time series: daily COVID-19 cases, Apple mobility data and national equity index price. Next, we study the trajectories of all three of these attributes in conjunction to determine which exhibited greater similarity. Finally, we investigate whether country financial indices or mobility data responded quicker to surges in COVID-19 cases. Our results indicate that mobility data and national financial indices exhibited the most similarity in their trajectories, with financial indices responding quicker. This suggests that financial market participants may have interpreted and responded to COVID-19 data more efficiently than governments. Further, results imply that efforts to study community mobility data as a leading indicator for financial market performance during the pandemic were misguided.

We introduce two methods for improving the performance of agents meeting for the first time to accomplish a communicative task. The methods are: (1) `message mutation' during the generation of the communication protocol; and (2) random permutations of the communication channel. These proposals are tested using a simple two-player game involving a `teacher' who generates a communication protocol and sends a message, and a `student' who interprets the message. After training multiple agents via self-play we analyse the performance of these agents when they are matched with a stranger, i.e. their zero-shot communication performance. We find that both message mutation and channel permutation positively influence performance, and we discuss their effects.

This position paper draws from the complexity of dark patterns to develop arguments for differentiated interventions. We propose a matrix of interventions with a \textit{measure axis} (from user-directed to environment-directed) and a \textit{scope axis} (from general to specific). We furthermore discuss a set of interventions situated in different fields of the intervention spaces. The discussions at the 2021 CHI workshop "What can CHI do about dark patterns?" should help hone the matrix structure and fill its fields with specific intervention proposals.

Cyclic codes are among the most important families of codes in coding theory for both theoretical and practical reasons. Despite their prominence and intensive research on cyclic codes for over a half century, there are still open problems related to cyclic codes. In this work, we use recent results on the equivalence of cyclic codes to create a more efficient algorithm to partition cyclic codes by equivalence based on cyclotomic cosets. This algorithm is then implemented to carry out computer searches for both cyclic codes and quasi-cyclic (QC) codes with good parameters. We also generalize these results to repeated-root cases. We have found several new linear codes that are cyclic or QC as an application of the new approach, as well as more desirable constructions for linear codes with best known parameters. With the additional new codes obtained through standard constructions, we have found a total of 14 new linear codes.

Nonlocal quantum correlations among the quantum subsystems play essential roles in quantum science. The violation of the Svetlichny inequality provides sufficient conditions of genuine tripartite nonlocality. We provide tight upper bounds on the maximal quantum value of the Svetlichny operators under local filtering operations, and present a qualitative analytical analysis on the hidden genuine nonlocality for three-qubit systems. We investigate in detail two classes of three-qubit states whose hidden genuine nonlocalities can be revealed by local filtering.

We present a novel method named truncated hierarchical unstructured splines (THU-splines) that supports both local $h$-refinement and unstructured quadrilateral meshes. In a THU-spline construction, an unstructured quadrilateral mesh is taken as the input control mesh, where the degenerated-patch method [18] is adopted in irregular regions to define $C^1$-continuous bicubic splines, whereas regular regions only involve $C^2$ B-splines. Irregular regions are then smoothly joined with regular regions through the truncation mechanism [29], leading to a globally smooth spline construction. Subsequently, local refinement is performed following the truncated hierarchical B-spline construction [10] to achieve a flexible refinement without propagating to unanticipated regions. Challenges lie in refining transition regions where a mixed types of splines play a role. THU-spline basis functions are globally $C^1$-continuous and are non-negative everywhere except near extraordinary vertices, where slight negativity is inevitable to retain refinability of the spline functions defined using the degenerated-patch method. Such functions also have a finite representation that can be easily integrated with existing finite element or isogeometric codes through B\'{e}zier extraction.

A new technical method is developed for soft x-ray spectroscopy of near-edge x-ray absorption fine structure (NEXAFS). The measurement is performed with continuously rotating linearly polarized light over 2$\pi$, generated by a segmented undulator. A demonstration of the rotational NEXAFS experiment was successfully made with a 2D film, showing detailed polarization-dependence in intensity of the molecular orbitals. The present approach provides varieties of technical opportunities that are compatible with the state-of-the-art experiments in nano-space and under the $operando$ condition.

This article concerns the predictive modeling for spatio-temporal data as well as model interpretation using data information in space and time. We develop a novel approach based on supervised dimension reduction for such data in order to capture nonlinear mean structures without requiring a prespecified parametric model. In addition to prediction as a common interest, this approach emphasizes the exploration of geometric information from the data. The method of Pairwise Directions Estimation (PDE; Lue, 2019) is implemented in our approach as a data-driven function searching for spatial patterns and temporal trends. The benefit of using geometric information from the method of PDE is highlighted, which aids effectively in exploring data structures. We further enhance PDE, referring to it as PDE+, by incorporating kriging to estimate the random effects not explained in the mean functions. Our proposal can not only increase prediction accuracy, but also improve the interpretation for modeling. Two simulation examples are conducted and comparisons are made with four existing methods. The results demonstrate that the proposed PDE+ method is very useful for exploring and interpreting the patterns and trends for spatio-temporal data. Illustrative applications to two real datasets are also presented.

This paper describes a recently initiated research project aiming at supporting development of computerised dialogue systems that handle breaches of conversational norms such as the Gricean maxims, which describe how dialogue participants ideally form their utterances in order to be informative, relevant, brief, etc. Our approach is to model dialogue and norms with co-operating distributed grammar systems (CDGSs), and to develop methods to detect breaches and to handle them in dialogue systems for verbal human-robot interaction.

As more people flock to social media to connect with others and form virtual communities, it is important to research how members of these groups interact to understand human behavior on the Web. In response to an increase in hate speech, harassment and other antisocial behaviors, many social media companies have implemented different content and user moderation policies. On Reddit, for example, communities, i.e, subreddits, are occasionally banned for violating these policies. We study the effect of these regulatory actions as well as when a community experiences a significant external event like a political election or a market crash. Overall, we find that most subreddit bans prompt a small, but statistically significant, number of active users to leave the platform; the effect of external events varies with the type of event. We conclude with a discussion on the effectiveness of the bans and wider implications for the online content moderation.

We isolate a new preservation class of Suslin forcings and prove several associated consistency results in the choiceless theory ZF+DC regarding countable chromatic numbers of various Borel hypergraphs.

Extremely compact objects trap gravitational waves or neutrinos, assumed to move along null geodesics in the trapping regions. The trapping of neutrinos was extensively studied for spherically symmetric extremely compact objects constructed under the simplest approximation of the uniform energy density distribution, with radius located under the photosphere of the external spacetime; in addition, uniform emissivity distribution of neutrinos was assumed in these studies. Here we extend the studies of the neutrino trapping for the case of the extremely compact Tolman VII objects representing the simplest generalization of the internal Schwarzschild solution with uniform distribution of the energy density, and the correspondingly related distribution of the neutrino emissivity that is thus again proportional to the energy density; radius of such extremely compact objects can overcome the photosphere of the external Schwarzschild spacetime. In dependence on the parameters of the Tolman VII spacetimes, we determine the "local" and "global" coefficients of efficiency of the trapping and demonstrate that the role of the trapping is significantly stronger than in the internal Schwarzschild spacetimes. Our results indicate possible influence of the neutrino trapping in cooling of neutron stars.

Quantum computing provides a new way for approaching problem solving, enabling efficient solutions for problems that are hard on classical computers. It is based on leveraging how quantum particles behave. With researchers around the world showing quantum supremacy and the availability of cloud-based quantum computers with free accounts for researchers, quantum computing is becoming a reality. In this paper, we explore both the opportunities and challenges that quantum computing has for location determination research. Specifically, we introduce an example for the expected gain of using quantum algorithms by providing an efficient quantum implementation of the well-known RF fingerprinting algorithm and run it on an instance of the IBM Quantum Experience computer. The proposed quantum algorithm has a complexity that is exponentially better than its classical algorithm version, both in space and running time. We further discuss both software and hardware research challenges and opportunities that researchers can build on to explore this exciting new domain.