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multi_class_classification_dataset

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Title: Rotation Invariance Neural Network, Abstract: Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using those invariance.

Title: A finite element approximation for the stochastic Maxwell--Landau--Lifshitz--Gilbert system, Abstract: The stochastic Landau--Lifshitz--Gilbert (LLG) equation coupled with the Maxwell equations (the so called stochastic MLLG system) describes the creation of domain walls and vortices (fundamental objects for the novel nanostructured magnetic memories). We first reformulate the stochastic LLG equation into an equation with time-differentiable solutions. We then propose a convergent $\theta$-linear scheme to approximate the solutions of the reformulated system. As a consequence, we prove convergence of the approximate solutions, with no or minor conditions on time and space steps (depending on the value of $\theta$). Hence, we prove the existence of weak martingale solutions of the stochastic MLLG system. Numerical results are presented to show applicability of the method.

Title: Comparative study of Discrete Wavelet Transforms and Wavelet Tensor Train decomposition to feature extraction of FTIR data of medicinal plants, Abstract: Fourier-transform infra-red (FTIR) spectra of samples from 7 plant species were used to explore the influence of preprocessing and feature extraction on efficiency of machine learning algorithms. Wavelet Tensor Train (WTT) and Discrete Wavelet Transforms (DWT) were compared as feature extraction techniques for FTIR data of medicinal plants. Various combinations of signal processing steps showed different behavior when applied to classification and clustering tasks. Best results for WTT and DWT found through grid search were similar, significantly improving quality of clustering as well as classification accuracy for tuned logistic regression in comparison to original spectra. Unlike DWT, WTT has only one parameter to be tuned (rank), making it a more versatile and easier to use as a data processing tool in various signal processing applications.

Title: Adverse effects of polymer coating on heat transport at solid-liquid interface, Abstract: The ability of metallic nanoparticles to supply heat to a liquid environment under exposure to an external optical field has attracted growing interest for biomedical applications. Controlling the thermal transport properties at a solid-liquid interface then appears to be particularly relevant. In this work, we address the thermal transport between water and a gold surface coated by a polymer layer. Using molecular dynamics simulations, we demonstrate that increasing the polymer density displaces the domain resisting to the heat flow, while it doesn't affect the final amount of thermal energy released in the liquid. This unexpected behavior results from a trade-off established by the increasing polymer density which couples more efficiently with the solid but initiates a counterbalancing resistance with the liquid.

Title: $\mathcal{R}_{0}$ fails to predict the outbreak potential in the presence of natural-boosting immunity, Abstract: Time varying susceptibility of host at individual level due to waning and boosting immunity is known to induce rich long-term behavior of disease transmission dynamics. Meanwhile, the impact of the time varying heterogeneity of host susceptibility on the shot-term behavior of epidemics is not well-studied, even though the large amount of the available epidemiological data are the short-term epidemics. Here we constructed a parsimonious mathematical model describing the short-term transmission dynamics taking into account natural-boosting immunity by reinfection, and obtained the explicit solution for our model. We found that our system show "the delayed epidemic", the epidemic takes off after negative slope of the epidemic curve at the initial phase of epidemic, in addition to the common classification in the standard SIR model, i.e., "no epidemic" as $\mathcal{R}_{0}\leq1$ or normal epidemic as $\mathcal{R}_{0}>1$. Employing the explicit solution we derived the condition for each classification.

Title: Role-separating ordering in social dilemmas controlled by topological frustration, Abstract: "Three is a crowd" is an old proverb that applies as much to social interactions, as it does to frustrated configurations in statistical physics models. Accordingly, social relations within a triangle deserve special attention. With this motivation, we explore the impact of topological frustration on the evolutionary dynamics of the snowdrift game on a triangular lattice. This topology provides an irreconcilable frustration, which prevents anti-coordination of competing strategies that would be needed for an optimal outcome of the game. By using different strategy updating protocols, we observe complex spatial patterns in dependence on payoff values that are reminiscent to a honeycomb-like organization, which helps to minimize the negative consequence of the topological frustration. We relate the emergence of these patterns to the microscopic dynamics of the evolutionary process, both by means of mean-field approximations and Monte Carlo simulations. For comparison, we also consider the same evolutionary dynamics on the square lattice, where of course the topological frustration is absent. However, with the deletion of diagonal links of the triangular lattice, we can gradually bridge the gap to the square lattice. Interestingly, in this case the level of cooperation in the system is a direct indicator of the level of topological frustration, thus providing a method to determine frustration levels in an arbitrary interaction network.

Title: On Varieties of Ordered Automata, Abstract: The classical Eilenberg correspondence, based on the concept of the syntactic monoid, relates varieties of regular languages with pseudovarieties of finite monoids. Various modifications of this correspondence appeared, with more general classes of regular languages on one hand and classes of more complex algebraic structures on the other hand. For example, classes of languages need not be closed under complementation or all preimages under homomorphisms, while monoids can be equipped with a compatible order or they can have a distinguished set of generators. Such generalized varieties and pseudovarieties also have natural counterparts formed by classes of finite (ordered) automata. In this paper the previous approaches are combined. The notion of positive $\mathcal C$-varieties of ordered semiautomata (i.e. no initial and final states are specified) is introduced and their correspondence with positive $\mathcal C$-varieties of languages is proved.

Title: Direct Evidence of Spontaneous Abrikosov Vortex State in Ferromagnetic Superconductor EuFe$_2$(As$_{1-x}$P$_x$)$_2$ with $x=0.21$, Abstract: Using low-temperature Magnetic Force Microscopy (MFM) we provide direct experimental evidence for spontaneous vortex phase (SVP) formation in EuFe$_2$(As$_{0.79}$P$_{0.21}$)$_2$ single crystal with the superconducting $T^{\rm 0}_{\rm SC}=23.6$~K and ferromagnetic $T_{\rm FM}\sim17.7$~K transition temperatures. Spontaneous vortex-antivortex (V-AV) pairs are imaged in the vicinity of $T_{\rm FM}$. Also, upon cooling cycle near $T_{\rm FM}$ we observe the first-order transition from the short period domain structure, which appears in the Meissner state, into the long period domain structure with spontaneous vortices. It is the first experimental observation of this scenario in the ferromagnetic superconductors. Low-temperature phase is characterized by much larger domains in V-AV state and peculiar branched striped structures at the surface, which are typical for uniaxial ferromagnets with perpendicular magnetic anisotropy (PMA). The domain wall parameters at various temperatures are estimated.

Title: A rank 18 Waring decomposition of $sM_{\langle 3\rangle}$ with 432 symmetries, Abstract: The recent discovery that the exponent of matrix multiplication is determined by the rank of the symmetrized matrix multiplication tensor has invigorated interest in better understanding symmetrized matrix multiplication. I present an explicit rank 18 Waring decomposition of $sM_{\langle 3\rangle}$ and describe its symmetry group.

Title: Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications, Abstract: When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, more robust methods based on matching or weighting have become more common. Of late, even more flexible methods based on machine learning methods have been developed for statistical adjustment. These machine learning methods are designed to be black box methods with little input from the researcher. Recent research used a data competition to evaluate various methods of statistical adjustment and found that black box methods out performed all other methods of statistical adjustment. Matching methods with covariate prioritization are designed for direct input from substantive investigators in direct contrast to black methods. In this article, we use a different research design to compare matching with covariate prioritization to black box methods. We use black box methods to replicate results from five studies where matching with covariate prioritization was used to customize the statistical adjustment in direct response to substantive expertise. We find little difference across the methods. We conclude with advice for investigators.

Title: Acoustic Impedance Calculation via Numerical Solution of the Inverse Helmholtz Problem, Abstract: Assigning homogeneous boundary conditions, such as acoustic impedance, to the thermoviscous wave equations (TWE) derived by transforming the linearized Navier-Stokes equations (LNSE) to the frequency domain yields a so-called Helmholtz solver, whose output is a discrete set of complex eigenfunction and eigenvalue pairs. The proposed method -- the inverse Helmholtz solver (iHS) -- reverses such procedure by returning the value of acoustic impedance at one or more unknown impedance boundaries (IBs) of a given domain via spatial integration of the TWE for a given real-valued frequency with assigned conditions on other boundaries. The iHS procedure is applied to a second-order spatial discretization of the TWEs derived on an unstructured grid with staggered grid arrangement. The momentum equation only is extended to the center of each IB face where pressure and velocity components are co-located and treated as unknowns. One closure condition considered for the iHS is the assignment of the surface gradient of pressure phase over the IBs, corresponding to assigning the shape of the acoustic waveform at the IB. The iHS procedure is carried out independently for each frequency in order to return the complete broadband complex impedance distribution at the IBs in any desired frequency range. The iHS approach is first validated against Rott's theory for both inviscid and viscous, rectangular and circular ducts. The impedance of a geometrically complex toy cavity is then reconstructed and verified against companion full compressible unstructured Navier-Stokes simulations resolving the cavity geometry and one-dimensional impedance test tube calculations based on time-domain impedance boundary conditions (TDIBC). The iHS methodology is also shown to capture thermoacoustic effects, with reconstructed impedance values quantitatively in agreement with thermoacoustic growth rates.

Title: Many-Body Localization: Stability and Instability, Abstract: Rare regions with weak disorder (Griffiths regions) have the potential to spoil localization. We describe a non-perturbative construction of local integrals of motion (LIOMs) for a weakly interacting spin chain in one dimension, under a physically reasonable assumption on the statistics of eigenvalues. We discuss ideas about the situation in higher dimensions, where one can no longer ensure that interactions involving the Griffiths regions are much smaller than the typical energy-level spacing for such regions. We argue that ergodicity is restored in dimension d > 1, although equilibration should be extremely slow, similar to the dynamics of glasses.

Title: Complexity of Deciding Detectability in Discrete Event Systems, Abstract: Detectability of discrete event systems (DESs) is a question whether the current and subsequent states can be determined based on observations. Shu and Lin designed a polynomial-time algorithm to check strong (periodic) detectability and an exponential-time (polynomial-space) algorithm to check weak (periodic) detectability. Zhang showed that checking weak (periodic) detectability is PSpace-complete. This intractable complexity opens a question whether there are structurally simpler DESs for which the problem is tractable. In this paper, we show that it is not the case by considering DESs represented as deterministic finite automata without non-trivial cycles, which are structurally the simplest deadlock-free DESs. We show that even for such very simple DESs, checking weak (periodic) detectability remains intractable. On the contrary, we show that strong (periodic) detectability of DESs can be efficiently verified on a parallel computer.

Title: The Knaster-Tarski theorem versus monotone nonexpansive mappings, Abstract: Let $X$ be a partially ordered set with the property that each family of order intervals of the form $[a,b],[a,\rightarrow )$ with the finite intersection property has a nonempty intersection. We show that every directed subset of $X$ has a supremum. Then we apply the above result to prove that if $X$ is a topological space with a partial order $\preceq $ for which the order intervals are compact, $\mathcal{F}$ a nonempty commutative family of monotone maps from $X$ into $X$ and there exists $c\in X$ such that $c\preceq Tc$ for every $T\in \mathcal{F}$, then the set of common fixed points of $\mathcal{F}$ is nonempty and has a maximal element. The result, specialized to the case of Banach spaces gives a general fixed point theorem that drops almost all assumptions from the recent results in this area. An application to the theory of integral equations of Urysohn's type is also given.

Title: Equality of the usual definitions of Brakke flow, Abstract: In 1978 Brakke introduced the mean curvature flow in the setting of geometric measure theory. There exist multiple variants of the original definition. Here we prove that most of them are indeed equal. One central point is to correct the proof of Brakke's §3.5, where he develops an estimate for the evolution of the measure of time-dependent test functions.

Title: Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells, Abstract: With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre-install any infrastructure. Since the base station is attached to the drone, technically it is feasible for the base station to dynamic reposition itself in response to the changing locations of users for reducing the communication distance, decreasing the probability of signal blocking, and ultimately increasing the spectral efficiency. In this paper, we first propose distributed algorithms for autonomous control of drone movements, and then model and analyse the spectral efficiency performance of a drone small cell to shed new light on the fundamental benefits of dynamic repositioning. We show that, with dynamic repositioning, the spectral efficiency of drone small cells can be increased by nearly 100\% for realistic drone speed, height, and user traffic model and without incurring any major increase in drone energy consumption.

Title: An Unsupervised Homogenization Pipeline for Clustering Similar Patients using Electronic Health Record Data, Abstract: Electronic health records (EHR) contain a large variety of information on the clinical history of patients such as vital signs, demographics, diagnostic codes and imaging data. The enormous potential for discovery in this rich dataset is hampered by its complexity and heterogeneity. We present the first study to assess unsupervised homogenization pipelines designed for EHR clustering. To identify the optimal pipeline, we tested accuracy on simulated data with varying amounts of redundancy, heterogeneity, and missingness. We identified two optimal pipelines: 1) Multiple Imputation by Chained Equations (MICE) combined with Local Linear Embedding; and 2) MICE, Z-scoring, and Deep Autoencoders.

Title: Rate-Distortion Region of a Gray-Wyner Model with Side Information, Abstract: In this work, we establish a full single-letter characterization of the rate-distortion region of an instance of the Gray-Wyner model with side information at the decoders. Specifically, in this model an encoder observes a pair of memoryless, arbitrarily correlated, sources $(S^n_1,S^n_2)$ and communicates with two receivers over an error-free rate-limited link of capacity $R_0$, as well as error-free rate-limited individual links of capacities $R_1$ to the first receiver and $R_2$ to the second receiver. Both receivers reproduce the source component $S^n_2$ losslessly; and Receiver $1$ also reproduces the source component $S^n_1$ lossily, to within some prescribed fidelity level $D_1$. Also, Receiver $1$ and Receiver $2$ are equipped respectively with memoryless side information sequences $Y^n_1$ and $Y^n_2$. Important in this setup, the side information sequences are arbitrarily correlated among them, and with the source pair $(S^n_1,S^n_2)$; and are not assumed to exhibit any particular ordering. Furthermore, by specializing the main result to two Heegard-Berger models with successive refinement and scalable coding, we shed light on the roles of the common and private descriptions that the encoder should produce and what they should carry optimally. We develop intuitions by analyzing the developed single-letter optimal rate-distortion regions of these models, and discuss some insightful binary examples.

Title: Fourier-based numerical approximation of the Weertman equation for moving dislocations, Abstract: This work discusses the numerical approximation of a nonlinear reaction-advection-diffusion equation, which is a dimensionless form of the Weertman equation. This equation models steadily-moving dislocations in materials science. It reduces to the celebrated Peierls-Nabarro equation when its advection term is set to zero. The approach rests on considering a time-dependent formulation, which admits the equation under study as its long-time limit. Introducing a Preconditioned Collocation Scheme based on Fourier transforms, the iterative numerical method presented solves the time-dependent problem, delivering at convergence the desired numerical solution to the Weertman equation. Although it rests on an explicit time-evolution scheme, the method allows for large time steps, and captures the solution in a robust manner. Numerical results illustrate the efficiency of the approach for several types of nonlinearities.

Title: Design Decisions for Weave: A Real-Time Web-based Collaborative Visualization Framework, Abstract: There are many web-based visualization systems available to date, each having its strengths and limitations. The goals these systems set out to accomplish influence design decisions and determine how reusable and scalable they are. Weave is a new web-based visualization platform with the broad goal of enabling visualization of any available data by anyone for any purpose. Our open source framework supports highly interactive linked visualizations for users of varying skill levels. What sets Weave apart from other systems is its consideration for real-time remote collaboration with session history. We provide a detailed account of the various framework designs we considered with comparisons to existing state-of-the-art systems.

Title: Probing valley filtering effect by Andreev reflection in zigzag graphene nanoribbon, Abstract: Ballistic point contact (BPC) with zigzag edges in graphene is a main candidate of a valley filter, in which the polarization of the valley degree of freedom can be selected by using a local gate voltage. Here, we propose to detect the valley filtering effect by Andreev reflection. Because electrons in the lowest conduction band and the highest valence band of the BPC possess opposite chirality, the inter-band Andreev reflection is strongly suppressed, after multiple scattering and interference. We draw this conclusion by both the scattering matrix analysis and the numerical simulation. The Andreev reflection as a function of the incident energy of electrons and the local gate voltage at the BPC is obtained, by which the parameter region for a perfect valley filter and the direction of valley polarization can be determined. The Andreev reflection exhibits an oscillatory decay with the length of the BPC, indicating a negative correlation to valley polarization.

Title: LAAIR: A Layered Architecture for Autonomous Interactive Robots, Abstract: When developing general purpose robots, the overarching software architecture can greatly affect the ease of accomplishing various tasks. Initial efforts to create unified robot systems in the 1990s led to hybrid architectures, emphasizing a hierarchy in which deliberative plans direct the use of reactive skills. However, since that time there has been significant progress in the low-level skills available to robots, including manipulation and perception, making it newly feasible to accomplish many more tasks in real-world domains. There is thus renewed optimism that robots will be able to perform a wide array of tasks while maintaining responsiveness to human operators. However, the top layer in traditional hybrid architectures, designed to achieve long-term goals, can make it difficult to react quickly to human interactions during goal-driven execution. To mitigate this difficulty, we propose a novel architecture that supports such transitions by adding a top-level reactive module which has flexible access to both reactive skills and a deliberative control module. To validate this architecture, we present a case study of its application on a domestic service robot platform.

Title: Wehrl Entropy Based Quantification of Nonclassicality for Single Mode Quantum Optical States, Abstract: Nonclassical states of a quantized light are described in terms of Glauber-Sudarshan P distribution which is not a genuine classical probability distribution. Despite several attempts, defining a uniform measure of nonclassicality (NC) for the single mode quantum states of light is yet an open task. In our previous work [Phys. Rev. A 95, 012330 (2017)] we have shown that the existing well-known measures fail to quantify the NC of single mode states that are generated under multiple NC-inducing operations. Recently, Ivan et. al. [Quantum. Inf. Process. 11, 853 (2012)] have defined a measure of non-Gaussian character of quantum optical states in terms of Wehrl entropy. Here, we adopt this concept in the context of single mode NC. In this paper, we propose a new quantification of NC for the single mode quantum states of light as the difference between the total Wehrl entropy of the state and the maximum Wehrl entropy arising due to its classical characteristics. This we achieve by subtracting from its Wehrl entropy, the maximum Wehrl entropy attainable by any classical state that has same randomness as measured in terms of von-Neumann entropy. We obtain analytic expressions of NC for most of the states, in particular, all pure states and Gaussian mixed states. However, the evaluation of NC for the non-Gaussian mixed states is subject to extensive numerical computation that lies beyond the scope of the current work. We show that, along with the states generated under single NC-inducing operations, also for the broader class of states that are generated under multiple NC-inducing operations, our quantification enumerates the NC consistently.

Title: Attention-based Natural Language Person Retrieval, Abstract: Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism. More specifically, given the description of a person, the goal is to localize the person in an image. To this end, we first construct a benchmark dataset for natural language person retrieval. To do so, we generate bounding boxes for persons in a public image dataset from the segmentation masks, which are then annotated with descriptions and attributes using the Amazon Mechanical Turk. We then adopt a region proposal network in Faster R-CNN as a candidate region generator. The cropped images based on the region proposals as well as the whole images with attention weights are fed into Convolutional Neural Networks for visual feature extraction, while the natural language expression and attributes are input to Bidirectional Long Short- Term Memory (BLSTM) models for text feature extraction. The visual and text features are integrated to score region proposals, and the one with the highest score is retrieved as the output of our system. The experimental results show significant improvement over the state-of-the-art method for generic object retrieval and this line of research promises to benefit search in surveillance video footage.

Title: Large Scale Automated Forecasting for Monitoring Network Safety and Security, Abstract: Real time large scale streaming data pose major challenges to forecasting, in particular defying the presence of human experts to perform the corresponding analysis. We present here a class of models and methods used to develop an automated, scalable and versatile system for large scale forecasting oriented towards safety and security monitoring. Our system provides short and long term forecasts and uses them to detect safety and security issues in relation with multiple internet connected devices well in advance they might take place.

Title: Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data, Abstract: Machine learning algorithms such as linear regression, SVM and neural network have played an increasingly important role in the process of scientific discovery. However, none of them is both interpretable and accurate on nonlinear datasets. Here we present contextual regression, a method that joins these two desirable properties together using a hybrid architecture of neural network embedding and dot product layer. We demonstrate its high prediction accuracy and sensitivity through the task of predictive feature selection on a simulated dataset and the application of predicting open chromatin sites in the human genome. On the simulated data, our method achieved high fidelity recovery of feature contributions under random noise levels up to 200%. On the open chromatin dataset, the application of our method not only outperformed the state of the art method in terms of accuracy, but also unveiled two previously unfound open chromatin related histone marks. Our method can fill the blank of accurate and interpretable nonlinear modeling in scientific data mining tasks.

Title: Robustness against the channel effect in pathological voice detection, Abstract: Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect is a pervasive problem in machine learning for healthcare. In this study, we propose a detection system for pathological voice, which is robust against the channel effect. This system is based on a bidirectional LSTM network. To increase the performance robustness against channel mismatch, we integrate domain adversarial training (DAT) to eliminate the differences between the devices. When we train on data recorded on a high-quality microphone and evaluate on smartphone data without labels, our robust detection system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target sample labels). To the best of our knowledge, this is the first study applying unsupervised domain adaptation to pathological voice detection. Notably, our system does not need target device sample labels, which allows for generalization to many new devices.

Title: Transient flows in active porous media, Abstract: Stimuli-responsive materials that modify their shape in response to changes in environmental conditions -- such as solute concentration, temperature, pH, and stress -- are widespread in nature and technology. Applications include micro- and nanoporous materials used in filtration and flow control. The physiochemical mechanisms that induce internal volume modifications have been widely studies. The coupling between induced volume changes and solute transport through porous materials, however, is not well understood. Here, we consider advective and diffusive transport through a small channel linking two large reservoirs. A section of stimulus-responsive material regulates the channel permeability, which is a function of the local solute concentration. We derive an exact solution to the coupled transport problem and demonstrate the existence of a flow regime in which the steady state is reached via a damped oscillation around the equilibrium concentration value. Finally, the feasibility of an experimental observation of the phenomena is discussed. Please note that this version of the paper has not been formally peer reviewed, revised or accepted by a journal.

Title: An information model for modular robots: the Hardware Robot Information Model (HRIM), Abstract: Today's landscape of robotics is dominated by vertical integration where single vendors develop the final product leading to slow progress, expensive products and customer lock-in. Opposite to this, an horizontal integration would result in a rapid development of cost-effective mass-market products with an additional consumer empowerment. The transition of an industry from vertical integration to horizontal integration is typically catalysed by de facto industry standards that enable a simplified and seamless integration of products. However, in robotics there is currently no leading candidate for a global plug-and-play standard. This paper tackles the problem of incompatibility between robot components that hinder the reconfigurability and flexibility demanded by the robotics industry. Particularly, it presents a model to create plug-and-play robot hardware components. Rather than iteratively evolving previous ontologies, our proposed model answers the needs identified by the industry while facilitating interoperability, measurability and comparability of robotics technology. Our approach differs significantly with the ones presented before as it is hardware-oriented and establishes a clear set of actions towards the integration of this model in real environments and with real manufacturers.

Title: Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition, Abstract: This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with the state-of-the-art method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the 'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.

Title: Time-domain THz spectroscopy reveals coupled protein-hydration dielectric response in solutions of native and fibrils of human lyso-zyme, Abstract: Here we reveal details of the interaction between human lysozyme proteins, both native and fibrils, and their water environment by intense terahertz time domain spectroscopy. With the aid of a rigorous dielectric model, we determine the amplitude and phase of the oscillating dipole induced by the THz field in the volume containing the protein and its hydration water. At low concentrations, the amplitude of this induced dipolar response decreases with increasing concentration. Beyond a certain threshold, marking the onset of the interactions between the extended hydration shells, the amplitude remains fixed but the phase of the induced dipolar response, which is initially in phase with the applied THz field, begins to change. The changes observed in the THz response reveal protein-protein interactions me-diated by extended hydration layers, which may control fibril formation and may have an important role in chemical recognition phenomena.

Title: Regularity of envelopes in Kähler classes, Abstract: We establish the C^{1,1} regularity of quasi-psh envelopes in a Kahler class, confirming a conjecture of Berman.

Title: Internal Model from Observations for Reward Shaping, Abstract: Reinforcement learning methods require careful design involving a reward function to obtain the desired action policy for a given task. In the absence of hand-crafted reward functions, prior work on the topic has proposed several methods for reward estimation by using expert state trajectories and action pairs. However, there are cases where complete or good action information cannot be obtained from expert demonstrations. We propose a novel reinforcement learning method in which the agent learns an internal model of observation on the basis of expert-demonstrated state trajectories to estimate rewards without completely learning the dynamics of the external environment from state-action pairs. The internal model is obtained in the form of a predictive model for the given expert state distribution. During reinforcement learning, the agent predicts the reward as a function of the difference between the actual state and the state predicted by the internal model. We conducted multiple experiments in environments of varying complexity, including the Super Mario Bros and Flappy Bird games. We show our method successfully trains good policies directly from expert game-play videos.

Title: Characterizations of quasitrivial symmetric nondecreasing associative operations, Abstract: In this paper we are interested in the class of n-ary operations on an arbitrary chain that are quasitrivial, symmetric, nondecreasing, and associative. We first provide a description of these operations. We then prove that associativity can be replaced with bisymmetry in the definition of this class. Finally we investigate the special situation where the chain is finite.

Title: Multivariate Dependency Measure based on Copula and Gaussian Kernel, Abstract: We propose a new multivariate dependency measure. It is obtained by considering a Gaussian kernel based distance between the copula transform of the given d-dimensional distribution and the uniform copula and then appropriately normalizing it. The resulting measure is shown to satisfy a number of desirable properties. A nonparametric estimate is proposed for this dependency measure and its properties (finite sample as well as asymptotic) are derived. Some comparative studies of the proposed dependency measure estimate with some widely used dependency measure estimates on artificial datasets are included. A non-parametric test of independence between two or more random variables based on this measure is proposed. A comparison of the proposed test with some existing nonparametric multivariate test for independence is presented.

Title: The nature of the tensor order in Cd2Re2O7, Abstract: The pyrochlore metal Cd2Re2O7 has been recently investigated by second-harmonic generation (SHG) reflectivity. In this paper, we develop a general formalism that allows for the identification of the relevant tensor components of the SHG from azimuthal scans. We demonstrate that the secondary order parameter identified by SHG at the structural phase transition is the x2-y2 component of the axial toroidal quadrupole. This differs from the 3z2-r2 symmetry of the atomic displacements associated with the I-4m2 crystal structure that was previously thought to be its origin. Within the same formalism, we suggest that the primary order parameter detected in the SHG experiment is the 3z2-r2 component of the magnetic quadrupole. We discuss the general mechanism driving the phase transition in our proposed framework, and suggest experiments, particularly resonant X-ray scattering ones, that could clarify this issue.

Title: Live Service Migration in Mobile Edge Clouds, Abstract: Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such as instantaneous object recognition and safety assistance in intelligent transportation systems, that require very low latency. One key issue that comes with proximity is how to ensure that users always receive good performance as they move across different locations. Migrating services between MECs is seen as the means to achieve this. This article presents a layered framework for migrating active service applications that are encapsulated either in virtual machines (VMs) or containers. This layering approach allows a substantial reduction in service downtime. The framework is easy to implement using readily available technologies, and one of its key advantages is that it supports containers, which is a promising emerging technology that offers tangible benefits over VMs. The migration performance of various real applications is evaluated by experiments under the presented framework. Insights drawn from the experimentation results are discussed.

Title: Induced density correlations in a sonic black hole condensate, Abstract: Analog black/white hole pairs, consisting of a region of supersonic flow, have been achieved in a recent experiment by J. Steinhauer using an elongated Bose-Einstein condensate. A growing standing density wave, and a checkerboard feature in the density-density correlation function, were observed in the supersonic region. We model the density-density correlation function, taking into account both quantum fluctuations and the shot-to-shot variation of atom number normally present in ultracold-atom experiments. We find that quantum fluctuations alone produce some, but not all, of the features of the correlation function, whereas atom-number fluctuation alone can produce all the observed features, and agreement is best when both are included. In both cases, the density-density correlation is not intrinsic to the fluctuations, but rather is induced by modulation of the standing wave caused by the fluctuations.

Title: Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective, Abstract: For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem has been formalized as a sequence extrapolation problem, where a number of observations are used to predict the sequence into the future. Real-world scenarios demand a model of uncertainty of such predictions, as predictions become increasingly uncertain -- in particular on long time horizons. While impressive results have been shown on point estimates, scenarios that induce multi-modal distributions over future sequences remain challenging. Our work addresses these challenges in a Gaussian Latent Variable model for sequence prediction. Our core contribution is a "Best of Many" sample objective that leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data. Beyond our analysis of improved model fit, our models also empirically outperform prior work on three diverse tasks ranging from traffic scenes to weather data.

Title: A Debt-Aware Learning Approach for Resource Adaptations in Cloud Elasticity Management, Abstract: Elasticity is a cloud property that enables applications and its execution systems to dynamically acquire and release shared computational resources on demand. Moreover, it unfolds the advantage of economies of scale in the cloud through a drop in the average costs of these shared resources. However, it is still an open challenge to achieve a perfect match between resource demand and provision in autonomous elasticity management. Resource adaptation decisions essentially involve a trade-off between economics and performance, which produces a gap between the ideal and actual resource provisioning. This gap, if not properly managed, can negatively impact the aggregate utility of a cloud customer in the long run. To address this limitation, we propose a technical debt-aware learning approach for autonomous elasticity management based on a reinforcement learning of elasticity debts in resource provisioning; the adaptation pursues strategic decisions that trades off economics against performance. We extend CloudSim and Burlap to evaluate our approach. The evaluation shows that a reinforcement learning of technical debts in elasticity obtains a higher utility for a cloud customer, while conforming expected levels of performance.

Title: Semi-simplicial spaces, Abstract: This is an exposition of homotopical results on the geometric realization of semi-simplicial spaces. We then use these to derive basic foundational results about classifying spaces of topological categories, possibly without units. The topics considered include: fibrancy conditions on topological categories; the effect on classifying spaces of freely adjoining units; approximate notions of units; Quillen's Theorems A and B for non-unital topological categories; the effect on classifying spaces of changing the topology on the space of objects; the Group-Completion Theorem.

Title: A Unified Approach to Nonlinear Transformation Materials, Abstract: The advances in geometric approaches to optical devices due to transformation optics has led to the development of cloaks, concentrators, and other devices. It has also been shown that transformation optics can be used to gravitational fields from general relativity. However, the technique is currently constrained to linear devices, as a consistent approach to nonlinearity (including both the case of a nonlinear background medium and a nonlinear transformation) remains an open question. Here we show that nonlinearity can be incorporated into transformation optics in a consistent way. We use this to illustrate a number of novel effects, including cloaking an optical soliton, modeling nonlinear solutions to Einstein's field equations, controlling transport in a Debye solid, and developing a set of constitutive to relations for relativistic cloaks in arbitrary nonlinear backgrounds.

Title: Photo-Chemically Directed Self-Assembly of Carbon Nanotubes on Surfaces, Abstract: Transistors incorporating single-wall carbon nanotubes (CNTs) as the channel material are used in a variety of electronics applications. However, a competitive CNT-based technology requires the precise placement of CNTs at predefined locations of a substrate. One promising placement approach is to use chemical recognition to bind CNTs from solution at the desired locations on a surface. Producing the chemical pattern on the substrate is challenging. Here we describe a one-step patterning approach based on a highly photosensitive surface monolayer. The monolayer contains chromophopric group as light sensitive body with heteroatoms as high quantum yield photolysis center. As deposited, the layer will bind CNTs from solution. However, when exposed to ultraviolet (UV) light with a low dose (60 mJ/cm2) similar to that used for conventional photoresists, the monolayer cleaves and no longer binds CNTs. These features allow standard, wafer-scale UV lithography processes to be used to form a patterned chemical monolayer without the need for complex substrate patterning or monolayer stamping.

Title: The effects of subdiffusion on the NTA size measurements of extracellular vesicles in biological samples, Abstract: The interest in the extracellular vesicles (EVs) is rapidly growing as they became reliable biomarkers for many diseases. For this reason, fast and accurate techniques of EVs size characterization are the matter of utmost importance. One increasingly popular technique is the Nanoparticle Tracking Analysis (NTA), in which the diameters of EVs are calculated from their diffusion constants. The crucial assumption here is that the diffusion in NTA follows the Stokes-Einstein relation, i.e. that the Mean Square Displacement (MSD) of a particle grows linearly in time (MSD $\propto t$). However, we show that NTA violates this assumption in both artificial and biological samples, i.e. a large population of particles show a strongly sub-diffusive behaviour (MSD $\propto t^\alpha$, $0<\alpha<1$). To support this observation we present a range of experimental results for both polystyrene beads and EVs. This is also related to another problem: for the same samples there exists a huge discrepancy (by the factor of 2-4) between the sizes measured with NTA and with the direct imaging methods, such as AFM. This can be remedied by e.g. the Finite Track Length Adjustment (FTLA) method in NTA, but its applicability is limited in the biological and poly-disperse samples. On the other hand, the models of sub-diffusion rarely provide the direct relation between the size of a particle and the generalized diffusion constant. However, we solve this last problem by introducing the logarithmic model of sub-diffusion, aimed at retrieving the size data. In result, we propose a novel protocol of NTA data analysis. The accuracy of our method is on par with FTLA for small ($\simeq$200nm) particles. We apply our method to study the EVs samples and corroborate the results with AFM.

Title: Empirical regression quantile process with possible application to risk analysis, Abstract: The processes of the averaged regression quantiles and of their modifications provide useful tools in the regression models when the covariates are not fully under our control. As an application we mention the probabilistic risk assessment in the situation when the return depends on some exogenous variables. The processes enable to evaluate the expected $\alpha$-shortfall ($0\leq\alpha\leq 1$) and other measures of the risk, recently generally accepted in the financial literature, but also help to measure the risk in environment analysis and elsewhere.

Title: Role of Vanadyl Oxygen in Understanding Metallic Behavior of V2O5(001) Nanorods, Abstract: Vanadium pentoxide (V2O5), the most stable member of vanadium oxide family, exhibits interesting semiconductor to metal transition in the temperature range of 530-560 K. The metallic behavior originates because of the reduction of V2O5 through oxygen vacancies. In the present report, V2O5 nanorods in the orthorhombic phase with crystal orientation of (001) are grown using vapor transport process. Among three nonequivalent oxygen atoms in a VO5 pyramidal formula unit in V2O5 structure, the role of terminal vanadyl oxygen (OI) in the formation of metallic phase above the transition temperature is established from the temperature-dependent Raman spectroscopic studies. The origin of the metallic behavior of V2O5 is also understood due to the breakdown of pdpi bond between OI and nearest V atom instigated by the formation of vanadyl OI vacancy, confirmed from the downward shift of the bottom most split-off conduction bands in the material with increasing temperature.

Title: Graph Convolution: A High-Order and Adaptive Approach, Abstract: In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the $k$-th order convolution operator and the adaptive filtering module. Importantly, our framework of High-order and Adaptive Graph Convolutional Network (HA-GCN) is a general-purposed architecture that fits various applications on both node and graph centrics, as well as graph generative models. We conducted extensive experiments on demonstrating the advantages of our framework. Particularly, our HA-GCN outperforms the state-of-the-art models on node classification and molecule property prediction tasks. It also generates 32% more real molecules on the molecule generation task, both of which will significantly benefit real-world applications such as material design and drug screening.

Title: Almost euclidean Isoperimetric Inequalities in spaces satisfying local Ricci curvature lower bounds, Abstract: Motivated by Perelman's Pseudo Locality Theorem for the Ricci flow, we prove that if a Riemannian manifold has Ricci curvature bounded below in a metric ball which moreover has almost maximal volume, then in a smaller ball (in a quantified sense) it holds an almost-euclidean isoperimetric inequality. The result is actually established in the more general framework of non-smooth spaces satisfying local Ricci curvature lower bounds in a synthetic sense via optimal transportation.

Title: Memory Aware Synapses: Learning what (not) to forget, Abstract: Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the network, MAS accumulates an importance measure for each parameter of the network, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb's rule,which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting $<$subject, predicate, object$>$ triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.

Title: Uniform Spectral Convergence of the Stochastic Galerkin Method for the Linear Semiconductor Boltzmann Equation with Random Inputs and Diffusive Scalings, Abstract: In this paper, we study the generalized polynomial chaos (gPC) based stochastic Galerkin method for the linear semiconductor Boltzmann equation under diffusive scaling and with random inputs from an anisotropic collision kernel and the random initial condition. While the numerical scheme and the proof of uniform-in-Knudsen-number regularity of the distribution function in the random space has been introduced in [Jin-Liu-16'], the main goal of this paper is to first obtain a sharper estimate on the regularity of the solution-an exponential decay towards its local equilibrium, which then lead to the uniform spectral convergence of the stochastic Galerkin method for the problem under study.

Title: On Improving the Capacity of Solving Large-scale Wireless Network Design Problems by Genetic Algorithms, Abstract: Over the last decade, wireless networks have experienced an impressive growth and now play a main role in many telecommunications systems. As a consequence, scarce radio resources, such as frequencies, became congested and the need for effective and efficient assignment methods arose. In this work, we present a Genetic Algorithm for solving large instances of the Power, Frequency and Modulation Assignment Problem, arising in the design of wireless networks. To our best knowledge, this is the first Genetic Algorithm that is proposed for such problem. Compared to previous works, our approach allows a wider exploration of the set of power solutions, while eliminating sources of numerical problems. The performance of the algorithm is assessed by tests over a set of large realistic instances of a Fixed WiMAX Network.

Title: Quasi two-dimensional Fermi surface topography of the delafossite PdRhO$_2$, Abstract: We report on a combined study of the de Haas-van Alphen effect and angle resolved photoemission spectroscopy on single crystals of the metallic delafossite PdRhO$_2$ rounded off by \textit{ab initio} band structure calculations. A high sensitivity torque magnetometry setup with SQUID readout and synchrotron-based photoemission with a light spot size of $~50\,\mu\mathrm{m}$ enabled high resolution data to be obtained from samples as small as $150\times100\times20\,(\mu\mathrm{m})^3$. The Fermi surface shape is nearly cylindrical with a rounded hexagonal cross section enclosing a Luttinger volume of 1.00(1) electrons per formula unit.

Title: In-home and remote use of robotic body surrogates by people with profound motor deficits, Abstract: People with profound motor deficits could perform useful physical tasks for themselves by controlling robots that are comparable to the human body. Whether this is possible without invasive interfaces has been unclear, due to the robot's complexity and the person's limitations. We developed a novel, augmented reality interface and conducted two studies to evaluate the extent to which it enabled people with profound motor deficits to control robotic body surrogates. 15 novice users achieved meaningful improvements on a clinical manipulation assessment when controlling the robot in Atlanta from locations across the United States. Also, one expert user performed 59 distinct tasks in his own home over seven days, including self-care tasks such as feeding. Our results demonstrate that people with profound motor deficits can effectively control robotic body surrogates without invasive interfaces.

Title: Fermi-edge singularity and the functional renormalization group, Abstract: We study the Fermi-edge singularity, describing the response of a degenerate electron system to optical excitation, in the framework of the functional renormalization group (fRG). Results for the (interband) particle-hole susceptibility from various implementations of fRG (one- and two- particle-irreducible, multi-channel Hubbard-Stratonovich, flowing susceptibility) are compared to the summation of all leading logarithmic (log) diagrams, achieved by a (first-order) solution of the parquet equations. For the (zero-dimensional) special case of the X-ray-edge singularity, we show that the leading log formula can be analytically reproduced in a consistent way from a truncated, one-loop fRG flow. However, reviewing the underlying diagrammatic structure, we show that this derivation relies on fortuitous partial cancellations special to the form of and accuracy applied to the X-ray-edge singularity and does not generalize.

Title: Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies, Abstract: Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. In combination with a Deep Neural Network policy learned from essentially the complete published knowledge of chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In exploratory studies, we demonstrate that MCTS with neural network policies outperforms the traditionally used best-first search with hand-coded heuristics.

Title: The quasi-Assouad dimension for stochastically self-similar sets, Abstract: The class of stochastically self-similar sets contains many famous examples of random sets, e.g. Mandelbrot percolation and general fractal percolation. Under the assumption of the uniform open set condition and some mild assumptions on the iterated function systems used, we show that the quasi-Assouad dimension of self-similar random recursive sets is almost surely equal to the almost sure Hausdorff dimension of the set. We further comment on random homogeneous and $V$-variable sets and the removal of overlap conditions.

Title: Effect of Meltdown and Spectre Patches on the Performance of HPC Applications, Abstract: In this work we examine how the updates addressing Meltdown and Spectre vulnerabilities impact the performance of HPC applications. To study this we use the application kernel module of XDMoD to test the performance before and after the application of the vulnerability patches. We tested the performance difference for multiple application and benchmarks including: NWChem, NAMD, HPCC, IOR, MDTest and IMB. The results show that although some specific functions can have performance decreased by as much as 74%, the majority of individual metrics indicates little to no decrease in performance. The real-world applications show a 2-3% decrease in performance for single node jobs and a 5-11% decrease for parallel multi node jobs.

Title: Gene regulatory network inference: an introductory survey, Abstract: Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorisation which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialised chapters of this book.

Title: Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network, Abstract: Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-to-disc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.

Title: Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks, Abstract: The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous functioning requires automatic screening and permanent optimization with processing of the huge amount of data by high-performance distributed systems. We propose new meta-algorithm of large homogeneous network analysis, its decomposition into alternative sets of loosely connected subnets, and parallel optimization of the most independent elements. This algorithm is based on a network-specific correlation function, Simulated Annealing technique, and is adapted to work in the computer cluster. On the example of large wireless network, we show that proposed algorithm essentially increases speed of parallel optimization. The elaborated general approach can be used for analysis and optimization of the wide range of networks, including such specific types as artificial neural networks or organized in networks physiological systems of living organisms.

Title: Once in a blue moon: detection of 'bluing' during debris transits in the white dwarf WD1145+017, Abstract: The first transiting planetesimal orbiting a white dwarf was recently detected in K2 data of WD1145+017 and has been followed up intensively. The multiple, long, and variable transits suggest the transiting objects are dust clouds, probably produced by a disintegrating asteroid. In addition, the system contains circumstellar gas, evident by broad absorption lines, mostly in the u'-band, and a dust disc, indicated by an infrared excess. Here we present the first detection of a change in colour of WD1145+017 during transits, using simultaneous multi-band fast-photometry ULTRACAM measurements over the u'g'r'i'-bands. The observations reveal what appears to be 'bluing' during transits; transits are deeper in the redder bands, with a u'-r' colour difference of up to ~-0.05 mag. We explore various possible explanations for the bluing. 'Spectral' photometry obtained by integrating over bandpasses in the spectroscopic data in- and out-of-transit, compared to the photometric data, shows that the observed colour difference is most likely the result of reduced circumstellar absorption in the spectrum during transits. This indicates that the transiting objects and the gas share the same line-of-sight, and that the gas covers the white dwarf only partially, as would be expected if the gas, the transiting debris, and the dust emitting the infrared excess, are part of the same general disc structure (although possibly at different radii). In addition, we present the results of a week-long monitoring campaign of the system.

Title: Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features, Abstract: Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.

Title: Superconductivity and Frozen Electronic States at the (111) LaAlO$_3$/SrTiO$_3$ Interface, Abstract: In spite of Anderson's theorem, disorder is known to affect superconductivity in conventional s-wave superconductors. In most superconductors, the degree of disorder is fixed during sample preparation. Here we report measurements of the superconducting properties of the two-dimensional gas that forms at the interface between LaAlO$_3$ (LAO) and SrTiO$_3$ (STO) in the (111) crystal orientation, a system that permits \emph{in situ} tuning of carrier density and disorder by means of a back gate voltage $V_g$. Like the (001) oriented LAO/STO interface, superconductivity at the (111) LAO/STO interface can be tuned by $V_g$. In contrast to the (001) interface, superconductivity in these (111) samples is anisotropic, being different along different interface crystal directions, consistent with the strong anisotropy already observed other transport properties at the (111) LAO/STO interface. In addition, we find that the (111) interface samples "remember" the backgate voltage $V_F$ at which they are cooled at temperatures near the superconducting transition temperature $T_c$, even if $V_g$ is subsequently changed at lower temperatures. The low energy scale and other characteristics of this memory effect ($<1$ K) distinguish it from charge-trapping effects previously observed in (001) interface samples.

Title: Nonlinear fractal meaning of the Hubble constant, Abstract: According to astrophysical observations value of recession velocity in a certain point is proportional to a distance to this point. The proportionality coefficient is the Hubble constant measured with 5% accuracy. It is used in many cosmological theories describing dark energy, dark matter, baryons, and their relation with the cosmological constant introduced by Einstein. In the present work we have determined a limit value of the global Hubble constant (in a big distance from a point of observations) theoretically without using any empirical constants on the base of our own fractal model used for the description a relation between distance to an observed galaxy and coordinate of its center. The distance has been defined as a nonlinear fractal measure with scale of measurement corresponding to a deviation of the measure from its fixed value (zero-gravity radius). We have suggested a model of specific anisotropic fractal for simulation a radial Universe expansion. Our theoretical results have shown existence of an inverse proportionality between accuracy of determination the Hubble constant and accuracy of calculation a coordinates of galaxies leading to ambiguity results obtained at cosmological observations.

Title: SEA: String Executability Analysis by Abstract Interpretation, Abstract: Dynamic languages often employ reflection primitives to turn dynamically generated text into executable code at run-time. These features make standard static analysis extremely hard if not impossible because its essential data structures, i.e., the control-flow graph and the system of recursive equations associated with the program to analyse, are themselves dynamically mutating objects. We introduce SEA, an abstract interpreter for automatic sound string executability analysis of dynamic languages employing bounded (i.e, finitely nested) reflection and dynamic code generation. Strings are statically approximated in an abstract domain of finite state automata with basic operations implemented as symbolic transducers. SEA combines standard program analysis together with string executability analysis. The analysis of a call to reflection determines a call to the same abstract interpreter over a code which is synthesised directly from the result of the static string executability analysis at that program point. The use of regular languages for approximating dynamically generated code structures allows SEA to soundly approximate safety properties of self modifying programs yet maintaining efficiency. Soundness here means that the semantics of the code synthesised by the analyser to resolve reflection over-approximates the semantics of the code dynamically built at run-rime by the program at that point.

Title: Representing numbers as the sum of squares and powers in the ring $\mathbb{Z}_n$, Abstract: We examine the representation of numbers as the sum of two squares in $\mathbb{Z}_n$ for a general positive integer $n$. Using this information we make some comments about the density of positive integers which can be represented as the sum of two squares and powers of $2$ in $\mathbb{N}$.

Title: Spatial Regression and the Bayesian Filter, Abstract: Regression for spatially dependent outcomes poses many challenges, for inference and for computation. Non-spatial models and traditional spatial mixed-effects models each have their advantages and disadvantages, making it difficult for practitioners to determine how to carry out a spatial regression analysis. We discuss the data-generating mechanisms implicitly assumed by various popular spatial regression models, and discuss the implications of these assumptions. We propose Bayesian spatial filtering as an approximate middle way between non-spatial models and traditional spatial mixed models. We show by simulation that our Bayesian spatial filtering model has several desirable properties and hence may be a useful addition to a spatial statistician's toolkit.

Title: Behaviour of electron content in the ionospheric D-region during solar X-ray flares, Abstract: One of the most important parameters in ionospheric plasma research also having a wide practical application in wireless satellite telecommunications is the total electron content (TEC) representing the columnal electron number density. The F region with high electron density provides the biggest contribution to TEC while the relatively weakly ionized plasma of the D region (60 km - 90 km above Earths surface) is often considered as a negligible cause of satellite signal disturbances. However, sudden intensive ionization processes like those induced by solar X ray flares can cause relative increases of electron density that are significantly larger in the D-region than in regions at higher altitudes. Therefore, one cannot exclude a priori the D region from investigations of ionospheric influences on propagation of electromagnetic signals emitted by satellites. We discuss here this problem which has not been sufficiently treated in literature so far. The obtained results are based on data collected from the D region monitoring by very low frequency radio waves and on vertical TEC calculations from the Global Navigation Satellite System (GNSS) signal analyses, and they show noticeable variations in the D region electron content (TECD) during activity of a solar X ray flare (it rises by a factor of 136 in the considered case) when TECD contribution to TEC can reach several percent and which cannot be neglected in practical applications like global positioning procedures by satellites.

Title: Fractional compound Poisson processes with multiple internal states, Abstract: For the particles undergoing the anomalous diffusion with different waiting time distributions for different internal states, we derive the Fokker-Planck and Feymann-Kac equations, respectively, describing positions of the particles and functional distributions of the trajectories of particles; in particular, the equations governing the functional distribution of internal states are also obtained. The dynamics of the stochastic processes are analyzed and the applications, calculating the distribution of the first passage time and the distribution of the fraction of the occupation time, of the equations are given.

Title: Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms, Abstract: Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.

Title: Interference of two co-directional exclusion processes in the presence of a static bottleneck: a biologically motivated model, Abstract: We develope a two-species exclusion process with a distinct pair of entry and exit sites for each species of rigid rods. The relatively slower forward stepping of the rods in an extended bottleneck region, located in between the two entry sites, controls the extent of interference of the co-directional flow of the two species of rods. The relative positions of the sites of entry of the two species of rods with respect to the location of the bottleneck are motivated by a biological phenomenon. However, the primary focus of the study here is to explore the effects of the interference of the flow of the two species of rods on their spatio-temporal organization and the regulations of this interference by the extended bottleneck. By a combination of mean-field theory and computer simulation we calculate the flux of both species of rods and their density profiles as well as the composite phase diagrams of the system. If the bottleneck is sufficiently stringent some of the phases become practically unrealizable although not ruled out on the basis of any fundamental physical principle. Moreover the extent of suppression of flow of the downstream entrants by the flow of the upstream entrants can also be regulated by the strength of the bottleneck. We speculate on the possible implications of the results in the context of the biological phenomenon that motivated the formulation of the theoretical model.

Title: Evolution of the Kondo lattice electronic structure above the transport coherence temperature, Abstract: The temperature-dependent evolution of the Kondo lattice is a long-standing topic of theoretical and experimental investigation and yet it lacks a truly microscopic description of the relation of the basic $f$-$d$ hybridization processes to the fundamental temperature scales of Kondo screening and Fermi-liquid lattice coherence. Here, the temperature-dependence of $f$-$d$ hybridized band dispersions and Fermi-energy $f$ spectral weight in the Kondo lattice system CeCoIn$_5$ is investigated using $f$-resonant angle-resolved photoemission (ARPES) with sufficient detail to allow direct comparison to first principles dynamical mean field theory (DMFT) calculations containing full realism of crystalline electric field states. The ARPES results, for two orthogonal (001) and (100) cleaved surfaces and three different $f$-$d$ hybridization scenarios, with additional microscopic insight provided by DMFT, reveal $f$ participation in the Fermi surface at temperatures much higher than the lattice coherence temperature, $T^*\approx$ 45 K, commonly believed to be the onset for such behavior. The identification of a $T$-dependent crystalline electric field degeneracy crossover in the DMFT theory $below$ $T^*$ is specifically highlighted.

Title: On A Conjecture Regarding Permutations Which Destroy Arithmetic Progressions, Abstract: Hegarty conjectured for $n\neq 2, 3, 5, 7$ that $\mathbb{Z}/n\mathbb{Z}$ has a permutation which destroys all arithmetic progressions mod $n$. For $n\ge n_0$, Hegarty and Martinsson demonstrated that $\mathbb{Z}/n\mathbb{Z}$ has an arithmetic-progression destroying permutation. However $n_0\approx 1.4\times 10^{14}$ and thus resolving the conjecture in full remained out of reach of any computational techniques. However, this paper using constructions modeled after those used by Elkies and Swaminathan for the case of $\mathbb{Z}/p\mathbb{Z}$ with $p$ being prime, establish the conjecture in full. Furthermore our results do not rely on the fact that it suffices to study when $n<n_0$ and thus our results completely independent of the proof given by Hegarty and Martinsson.

Title: Not even wrong: The spurious link between biodiversity and ecosystem functioning, Abstract: Resolving the relationship between biodiversity and ecosystem functioning has been one of the central goals of modern ecology. Early debates about the relationship were finally resolved with the advent of a statistical partitioning scheme that decomposed the biodiversity effect into a "selection" effect and a "complementarity" effect. We prove that both the biodiversity effect and its statistical decomposition into selection and complementarity are fundamentally flawed because these methods use a naïve null expectation based on neutrality, likely leading to an overestimate of the net biodiversity effect, and they fail to account for the nonlinear abundance-ecosystem functioning relationships observed in nature. Furthermore, under such nonlinearity no statistical scheme can be devised to partition the biodiversity effects. We also present an alternative metric providing a more reasonable estimate of biodiversity effect. Our results suggest that all studies conducted since the early 1990s likely overestimated the positive effects of biodiversity on ecosystem functioning.

Title: Evidence of Fraud in Brazil's Electoral Campaigns Via the Benford's Law, Abstract: The principle of democracy is that the people govern through elected representatives. Therefore, a democracy is healthy as long as the elected politicians do represent the people. We have analyzed data from the Brazilian electoral court (Tribunal Superior Eleitoral, TSE) concerning money donations for the electoral campaigns and the election results. Our work points to two disturbing conclusions: money is a determining factor on whether a candidate is elected or not (as opposed to representativeness); secondly, the use of Benford's Law to analyze the declared donations received by the parties and electoral campaigns shows evidence of fraud in the declarations. A better term to define Brazil's government system is what we define as chrimatocracy (govern by money).

Title: A Berkeley View of Systems Challenges for AI, Abstract: With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production. These changes have been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems software and architectures, and by the broad accessibility of these technologies. The next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (often mission-critical) decisions on our behalf, often in highly personalized contexts. Realizing this promise, however, raises daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, that are robust against sophisticated adversaries, and that can process ever increasing amounts of data across organizations and individuals without compromising confidentiality. These challenges will be exacerbated by the end of the Moore's Law, which will constrain the amount of data these technologies can store and process. In this paper, we propose several open research directions in systems, architectures, and security that can address these challenges and help unlock AI's potential to improve lives and society.

Title: Equivariant infinite loop space theory, I. The space level story, Abstract: We rework and generalize equivariant infinite loop space theory, which shows how to construct G-spectra from G-spaces with suitable structure. There is a naive version which gives naive G-spectra for any topological group G, but our focus is on the construction of genuine G-spectra when G is finite. We give new information about the Segal and operadic equivariant infinite loop space machines, supplying many details that are missing from the literature, and we prove by direct comparison that the two machines give equivalent output when fed equivalent input. The proof of the corresponding nonequivariant uniqueness theorem, due to May and Thomason, works for naive G-spectra for general G but fails hopelessly for genuine G-spectra when G is finite. Even in the nonequivariant case, our comparison theorem is considerably more precise, giving a direct point-set level comparison. We have taken the opportunity to update this general area, equivariant and nonequivariant, giving many new proofs, filling in some gaps, and giving some corrections to results in the literature.

Title: Flatness results for nonlocal minimal cones and subgraphs, Abstract: We show that nonlocal minimal cones which are non-singular subgraphs outside the origin are necessarily halfspaces. The proof is based on classical ideas of~\cite{DG1} and on the computation of the linearized nonlocal mean curvature operator, which is proved to satisfy a suitable maximum principle. With this, we obtain new, and somehow simpler, proofs of the Bernstein-type results for nonlocal minimal surfaces which have been recently established in~\cite{FV}. In addition, we establish a new nonlocal Bernstein-Moser-type result which classifies Lipschitz nonlocal minimal subgraphs outside a ball.

Title: Small subgraphs and their extensions in a random distance graph, Abstract: In previous papers, threshold probabilities for the properties of a random distance graph to contain strictly balanced graphs were found. We extend this result to arbitrary graphs and prove that the number of copies of a strictly balanced graph has asymptotically Poisson distribution at the threshold.

Title: A Fast Interior Point Method for Atomic Norm Soft Thresholding, Abstract: The atomic norm provides a generalization of the $\ell_1$-norm to continuous parameter spaces. When applied as a sparse regularizer for line spectral estimation the solution can be obtained by solving a convex optimization problem. This problem is known as atomic norm soft thresholding (AST). It can be cast as a semidefinite program and solved by standard methods. In the semidefinite formulation there are $O(N^2)$ dual variables and a standard primal-dual interior point method requires at least $O(N^6)$ flops per iteration. That has lead researcher to consider alternating direction method of multipliers (ADMM) for the solution of AST, but this method is still somewhat slow for large problem sizes. To obtain a faster algorithm we reformulate AST as a non-symmetric conic program. That has two properties of key importance to its numerical solution: the conic formulation has only $O(N)$ dual variables and the Toeplitz structure inherent to AST is preserved. Based on it we derive FastAST which is a primal-dual interior point method for solving AST. Two variants are considered with the fastest one requiring only $O(N^2)$ flops per iteration. Extensive numerical experiments demonstrate that FastAST solves AST significantly faster than a state-of-the-art solver based on ADMM.

Title: The 10 phases of spin chains with two Ising symmetries, Abstract: We explore the topological properties of quantum spin-1/2 chains with two Ising symmetries. This class of models does not possess any of the symmetries that are required to protect the Haldane phase. Nevertheless, we show that there are 4 symmetry-protected topological phases, in addition to 6 phases that spontaneously break one or both Ising symmetries. By mapping the model to one-dimensional interacting fermions with particle-hole and time-reversal symmetry, we obtain integrable parent Hamiltonians for the conventional and topological phases of the spin model. We use these Hamiltonians to characterize the physical properties of all 10 phases, identify their local and nonlocal order parameters, and understand the effects of weak perturbations that respect the Ising symmetries. Our study provides the first explicit example of a class of spin chains with several topologically non-trivial phases, and binds together the topological classifications of interacting bosons and fermions.

Title: Generalized subspace subcodes with application in cryptology, Abstract: Most of the codes that have an algebraic decoding algorithm are derived from the Reed Solomon codes. They are obtained by taking equivalent codes, for example the generalized Reed Solomon codes, or by using the so-called subfield subcode method, which leads to Alternant codes and Goppa codes over the underlying prime field, or over some intermediate subfield. The main advantages of these constructions is to preserve both the minimum distance and the decoding algorithm of the underlying Reed Solomon code. In this paper, we propose a generalization of the subfield subcode construction by introducing the notion of subspace subcodes and a generalization of the equivalence of codes which leads to the notion of generalized subspace subcodes. When the dimension of the selected subspaces is equal to one, we show that our approach gives exactly the family of the codes obtained by equivalence and subfield subcode technique. However, our approach highlights the links between the subfield subcode of a code defined over an extension field and the operation of puncturing the $q$-ary image of this code. When the dimension of the subspaces is greater than one, we obtain codes whose alphabet is no longer a finite field, but a set of r-uples. We explain why these codes are practically as efficient for applications as the codes defined on an extension of degree r. In addition, they make it possible to obtain decodable codes over a large alphabet having parameters previously inaccessible. As an application, we give some examples that can be used in public key cryptosystems such as McEliece.

Title: Hidden long evolutionary memory in a model biochemical network, Abstract: We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

Title: Marcel Riesz on Nörlund Means, Abstract: We note that the necessary and sufficient conditions established by Marcel Riesz for the inclusion of regular Nörlund summation methods are in fact applicable quite generally.

Title: Mathematics of Isogeny Based Cryptography, Abstract: These lectures notes were written for a summer school on Mathematics for post-quantum cryptography in Thiès, Senegal. They try to provide a guide for Masters' students to get through the vast literature on elliptic curves, without getting lost on their way to learning isogeny based cryptography. They are by no means a reference text on the theory of elliptic curves, nor on cryptography; students are encouraged to complement these notes with some of the books recommended in the bibliography. The presentation is divided in three parts, roughly corresponding to the three lectures given. In an effort to keep the reader interested, each part alternates between the fundamental theory of elliptic curves, and applications in cryptography. We often prefer to have the main ideas flow smoothly, rather than having a rigorous presentation as one would have in a more classical book. The reader will excuse us for the inaccuracies and the omissions.

Title: Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap, Abstract: To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the "missing at random" and "no unmeasured confounder" assumptions. However, these assumptions are unverifiable using empirical data and pose serious threats to the validity of the qualitative conclusions of the statistical inference. A sensitivity analysis asks how the conclusions may change if the unverifiable assumptions are violated to a certain degree. In this paper we consider a marginal sensitivity model which is a natural extension of Rosenbaum's sensitivity model that is widely used for matched observational studies. We aim to construct confidence intervals based on inverse probability weighting estimators, such that asymptotically the intervals have at least nominal coverage of the estimand whenever the data generating distribution is in the collection of marginal sensitivity models. We use a percentile bootstrap and a generalized minimax/maximin inequality to transform this intractable problem to a linear fractional programming problem, which can be solved very efficiently. We illustrate our method using a real dataset to estimate the causal effect of fish consumption on blood mercury level.

Title: Cyber Risk Analysis of Combined Data Attacks Against Power System State Estimation, Abstract: Understanding smart grid cyber attacks is key for developing appropriate protection and recovery measures. Advanced attacks pursue maximized impact at minimized costs and detectability. This paper conducts risk analysis of combined data integrity and availability attacks against the power system state estimation. We compare the combined attacks with pure integrity attacks - false data injection (FDI) attacks. A security index for vulnerability assessment to these two kinds of attacks is proposed and formulated as a mixed integer linear programming problem. We show that such combined attacks can succeed with fewer resources than FDI attacks. The combined attacks with limited knowledge of the system model also expose advantages in keeping stealth against the bad data detection. Finally, the risk of combined attacks to reliable system operation is evaluated using the results from vulnerability assessment and attack impact analysis. The findings in this paper are validated and supported by a detailed case study.

Title: A New Family of Near-metrics for Universal Similarity, Abstract: We propose a family of near-metrics based on local graph diffusion to capture similarity for a wide class of data sets. These quasi-metametrics, as their names suggest, dispense with one or two standard axioms of metric spaces, specifically distinguishability and symmetry, so that similarity between data points of arbitrary type and form could be measured broadly and effectively. The proposed near-metric family includes the forward k-step diffusion and its reverse, typically on the graph consisting of data objects and their features. By construction, this family of near-metrics is particularly appropriate for categorical data, continuous data, and vector representations of images and text extracted via deep learning approaches. We conduct extensive experiments to evaluate the performance of this family of similarity measures and compare and contrast with traditional measures of similarity used for each specific application and with the ground truth when available. We show that for structured data including categorical and continuous data, the near-metrics corresponding to normalized forward k-step diffusion (k small) work as one of the best performing similarity measures; for vector representations of text and images including those extracted from deep learning, the near-metrics derived from normalized and reverse k-step graph diffusion (k very small) exhibit outstanding ability to distinguish data points from different classes.

Title: Poisoning Attacks to Graph-Based Recommender Systems, Abstract: Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacker desires. However, these poisoning attacks are either agnostic to recommendation algorithms or optimized to recommender systems that are not graph-based. Like association-rule-based and matrix-factorization-based recommender systems, graph-based recommender system is also deployed in practice, e.g., eBay, Huawei App Store. However, how to design optimized poisoning attacks for graph-based recommender systems is still an open problem. In this work, we perform a systematic study on poisoning attacks to graph-based recommender systems. Due to limited resources and to avoid detection, we assume the number of fake users that can be injected into the system is bounded. The key challenge is how to assign rating scores to the fake users such that the target item is recommended to as many normal users as possible. To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users. We also propose techniques to solve the optimization problem. We evaluate our attacks and compare them with existing attacks under white-box (recommendation algorithm and its parameters are known), gray-box (recommendation algorithm is known but its parameters are unknown), and black-box (recommendation algorithm is unknown) settings using two real-world datasets. Our results show that our attack is effective and outperforms existing attacks for graph-based recommender systems. For instance, when 1% fake users are injected, our attack can make a target item recommended to 580 times more normal users in certain scenarios.

Title: On the Deployment of Distributed Antennas for Wireless Power Transfer with Safety Electromagnetic Radiation Level Requirement, Abstract: The extremely low efficiency is regarded as the bottleneck of Wireless Power Transfer (WPT) technology. To tackle this problem, either enlarging the transfer power or changing the infrastructure of WPT system could be an intuitively proposed way. However, the drastically important issue on the user exposure of electromagnetic radiation is rarely considered while we try to improve the efficiency of WPT. In this paper, a Distributed Antenna Power Beacon (DA-PB) based WPT system where these antennas are uniformly distributed on a circle is analyzed and optimized with the safety electromagnetic radiation level (SERL) requirement. In this model, three key questions are intended to be answered: 1) With the SERL, what is the performance of the harvested power at the users ? 2) How do we configure the parameters to maximize the efficiency of WPT? 3) Under the same constraints, does the DA-PB still have performance gain than the Co-located Antenna PB (CA-PB)? First, the minimum antenna height of DA-PB is derived to make the radio frequency (RF) electromagnetic radiation power density at any location of the charging cell lower than the SERL published by the Federal Communications Commission (FCC). Second, the closed-form expressions of average harvested Direct Current (DC) power per user in the charging cell for pass-loss exponent 2 and 4 are also provided. In order to maximize the average efficiency of WPT, the optimal radii for distributed antennas elements (DAEs) are derived when the pass-loss exponent takes the typical value $2$ and $4$. For comparison, the CA-PB is also analyzed as a benchmark. Simulation results verify our derived theoretical results. And it is shown that the proposed DA-PB indeed achieves larger average harvested DC power than CA-PB and can improve the efficiency of WPT.

Title: The Two-fold Role of Observables in Classical and Quantum Kinematics, Abstract: Observables have a dual nature in both classical and quantum kinematics: they are at the same time \emph{quantities}, allowing to separate states by means of their numerical values, and \emph{generators of transformations}, establishing relations between different states. In this work, we show how this two-fold role of observables constitutes a key feature in the conceptual analysis of classical and quantum kinematics, shedding a new light on the distinguishing feature of the quantum at the kinematical level. We first take a look at the algebraic description of both classical and quantum observables in terms of Jordan-Lie algebras and show how the two algebraic structures are the precise mathematical manifestation of the two-fold role of observables. Then, we turn to the geometric reformulation of quantum kinematics in terms of Kähler manifolds. A key achievement of this reformulation is to show that the two-fold role of observables is the constitutive ingredient defining what an observable is. Moreover, it points to the fact that, from the restricted point of view of the transformational role of observables, classical and quantum kinematics behave in exactly the same way. Finally, we present Landsman's general framework of Poisson spaces with transition probability, which highlights with unmatched clarity that the crucial difference between the two kinematics lies in the way the two roles of observables are related to each other.

Title: Minimum energy path calculations with Gaussian process regression, Abstract: The calculation of minimum energy paths for transitions such as atomic and/or spin re-arrangements is an important task in many contexts and can often be used to determine the mechanism and rate of transitions. An important challenge is to reduce the computational effort in such calculations, especially when ab initio or electron density functional calculations are used to evaluate the energy since they can require large computational effort. Gaussian process regression is used here to reduce significantly the number of energy evaluations needed to find minimum energy paths of atomic rearrangements. By using results of previous calculations to construct an approximate energy surface and then converge to the minimum energy path on that surface in each Gaussian process iteration, the number of energy evaluations is reduced significantly as compared with regular nudged elastic band calculations. For a test problem involving rearrangements of a heptamer island on a crystal surface, the number of energy evaluations is reduced to less than a fifth. The scaling of the computational effort with the number of degrees of freedom as well as various possible further improvements to this approach are discussed.

Title: Evaluating Roles of Central Users in Online Communication Networks: A Case Study of #PanamaLeaks, Abstract: Social media has changed the ways of communication, where everyone is equipped with the power to express their opinions to others in online discussion platforms. Previously, a number of stud- ies have been presented to identify opinion leaders in online discussion networks. Feng ("Are you connected? Evaluating information cascade in online discussion about the #RaceTogether campaign", Computers in Human Behavior, 2016) identified five types of central users and their communication patterns in an online communication network of a limited time span. However, to trace the change in communication pattern, a long-term analysis is required. In this study, we critically analyzed framework presented by Feng based on five types of central users in online communication network and their communication pattern in a long-term manner. We take another case study presented by Udnor et al. ("Determining social media impact on the politics of developing countries using social network analytics", Program, 2016) to further understand the dynamics as well as to perform validation . Results indicate that there may not exist all of these central users in an online communication network in a long-term manner. Furthermore, we discuss the changing positions of opinion leaders and their power to keep isolates interested in an online discussion network.

Title: Best polynomial approximation on the triangle, Abstract: Let $E_n(f)_{\alpha,\beta,\gamma}$ denote the error of best approximation by polynomials of degree at most $n$ in the space $L^2(\varpi_{\alpha,\beta,\gamma})$ on the triangle $\{(x,y): x, y \ge 0, x+y \le 1\}$, where $\varpi_{\alpha,\beta,\gamma}(x,y) := x^\alpha y ^\beta (1-x-y)^\gamma$ for $\alpha,\beta,\gamma > -1$. Our main result gives a sharp estimate of $E_n(f)_{\alpha,\beta,\gamma}$ in terms of the error of best approximation for higher order derivatives of $f$ in appropriate Sobolev spaces. The result also leads to a characterization of $E_n(f)_{\alpha,\beta,\gamma}$ by a weighted $K$-functional.

Title: SecureTime: Secure Multicast Time Synchronization, Abstract: Due to the increasing dependency of critical infrastructure on synchronized clocks, network time synchronization protocols have become an attractive target for attackers. We identify data origin authentication as the key security objective and suggest to employ recently proposed high-performance digital signature schemes (Ed25519 and MQQ-SIG)) as foundation of a novel set of security measures to secure multicast time synchronization. We conduct experiments to verify the computational and communication efficiency for using these signatures in the standard time synchronization protocols NTP and PTP. We propose additional security measures to prevent replay attacks and to mitigate delay attacks. Our proposed solutions cover 1-step mode for NTP and PTP and we extend our security measures specifically to 2-step mode (PTP) and show that they have no impact on time synchronization's precision.

Title: Topologically Invariant Double Dirac States in Bismuth based Perovskites: Consequence of Ambivalent Charge States and Covalent Bonding, Abstract: Bulk and surface electronic structures, calculated using density functional theory and a tight-binding model Hamiltonian, reveal the existence of two topologically invariant (TI) surface states in the family of cubic Bi perovskites (ABiO$_3$; A = Na, K, Rb, Cs, Mg, Ca, Sr and Ba). The two TI states, one lying in the valence band (TI-V) and other lying in the conduction band (TI-C) are formed out of bonding and antibonding states of the Bi-$\{$s,p$\}$ - O-$\{$p$\}$ coordinated covalent interaction. Below a certain critical thickness of the film, which varies with A, TI states of top and bottom surfaces couple to destroy the Dirac type linear dispersion and consequently to open surface energy gaps. The origin of s-p band inversion, necessary to form a TI state, classifies the family of ABiO$_3$ into two. For class-I (A = Na, K, Rb, Cs and Mg) the band inversion, leading to TI-C state, is induced by spin-orbit coupling of the Bi-p states and for class-II (A = Ca, Sr and Ba) the band inversion is induced through weak but sensitive second neighbor Bi-Bi interactions.

Title: Blockchain and human episodic memory, Abstract: We relate the concepts used in decentralized ledger technology to studies of episodic memory in the mammalian brain. Specifically, we introduce the standard concepts of linked list, hash functions, and sharding, from computer science. We argue that these concepts may be more relevant to studies of the neural mechanisms of memory than has been previously appreciated. In turn, we also highlight that certain phenomena studied in the brain, namely metacognition, reality monitoring, and how perceptual conscious experiences come about, may inspire development in blockchain technology too, specifically regarding probabilistic consensus protocols.

Title: The Shattered Gradients Problem: If resnets are the answer, then what is the question?, Abstract: A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients. Although, the problem has largely been overcome via carefully constructed initializations and batch normalization, architectures incorporating skip-connections such as highway and resnets perform much better than standard feedforward architectures despite well-chosen initialization and batch normalization. In this paper, we identify the shattered gradients problem. Specifically, we show that the correlation between gradients in standard feedforward networks decays exponentially with depth resulting in gradients that resemble white noise whereas, in contrast, the gradients in architectures with skip-connections are far more resistant to shattering, decaying sublinearly. Detailed empirical evidence is presented in support of the analysis, on both fully-connected networks and convnets. Finally, we present a new "looks linear" (LL) initialization that prevents shattering, with preliminary experiments showing the new initialization allows to train very deep networks without the addition of skip-connections.

Title: A 2-edge partial inverse problem for the Sturm-Liouville operators with singular potentials on a star-shaped graph, Abstract: Boundary value problems for Sturm-Liouville operators with potentials from the class $W_2^{-1}$ on a star-shaped graph are considered. We assume that the potentials are known on all the edges of the graph except two, and show that the potentials on the remaining edges can be constructed by fractional parts of two spectra. A uniqueness theorem is proved, and an algorithm for the constructive solution of the partial inverse problem is provided. The main ingredient of the proofs is the Riesz-basis property of specially constructed systems of functions.

Title: Jastrow form of the Ground State Wave Functions for Fractional Quantum Hall States, Abstract: The topological morphology--order of zeros at the positions of electrons with respect to a specific electron--of Laughlin state at filling fractions $1/m$ ($m$ odd) is homogeneous as every electron feels zeros of order $m$ at the positions of other electrons. Although fairly accurate ground state wave functions for most of the other quantum Hall states in the lowest Landau level are quite well-known, it had been an open problem in expressing the ground state wave functions in terms of flux-attachment to particles, {\em a la}, this morphology of Laughlin state. With a very general consideration of flux-particle relations only, in spherical geometry, we here report a novel method for determining morphologies of these states. Based on these, we construct almost exact ground state wave-functions for the Coulomb interaction. Although the form of interaction may change the ground state wave-function, the same morphology constructs the latter irrespective of the nature of the interaction between electrons.