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Title: Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning, Abstract: Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building and building a communication link. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.

Title: Class-Splitting Generative Adversarial Networks, Abstract: Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserstein GAN formulation which stabilized adversarial training and allows considering high capacity network architectures such as ResNet. In this work we show how to boost conditional GAN by augmenting available class labels. The new classes come from clustering in the representation space learned by the same GAN model. The proposed strategy is also feasible when no class information is available, i.e. in the unsupervised setup. Our generated samples reach state-of-the-art Inception scores for CIFAR-10 and STL-10 datasets in both supervised and unsupervised setup.

Title: Oblivious Routing via Random Walks, Abstract: We present novel oblivious routing algorithms for both splittable and unsplittable multicommodity flow. Our algorithm for minimizing congestion for \emph{unsplittable} multicommodity flow is the first oblivious routing algorithm for this setting. As an intermediate step towards this algorithm, we present a novel generalization of Valiant's classical load balancing scheme for packet-switched networks to arbitrary graphs, which is of independent interest. Our algorithm for minimizing congestion for \emph{splittable} multicommodity flow improves upon the state-of-the-art, in terms of both running time and performance, for graphs that exhibit good expansion guarantees. Our algorithms rely on diffusing traffic via iterative applications of the random walk operator. Consequently, the performance guarantees of our algorithms are derived from the convergence of the random walk operator to the stationary distribution and are expressed in terms of the spectral gap of the graph (which dominates the mixing time).

Title: Helmholtz decomposition theorem and Blumenthal's extension by regularization, Abstract: Helmholtz decomposition theorem for vector fields is usually presented with too strong restrictions on the fields and only for time independent fields. Blumenthal showed in 1905 that decomposition is possible for any asymptotically weakly decreasing vector field. He used a regularization method in his proof which can be extended to prove the theorem even for vector fields asymptotically increasing sublinearly. Blumenthal's result is then applied to the time-dependent fields of the dipole radiation and an artificial sublinearly increasing field.

Title: A homotopy decomposition of the fibre of the squaring map on $Ω^3S^{17}$, Abstract: We use Richter's $2$-primary proof of Gray's conjecture to give a homotopy decomposition of the fibre $\Omega^3S^{17}\{2\}$ of the $H$-space squaring map on the triple loop space of the $17$-sphere. This induces a splitting of the mod-$2$ homotopy groups $\pi_\ast(S^{17}; \mathbb{Z}/2\mathbb{Z})$ in terms of the integral homotopy groups of the fibre of the double suspension $E^2:S^{2n-1} \to \Omega^2S^{2n+1}$ and refines a result of Cohen and Selick, who gave similar decompositions for $S^5$ and $S^9$. We relate these decompositions to various Whitehead products in the homotopy groups of mod-$2$ Moore spaces and Stiefel manifolds to show that the Whitehead square $[i_{2n}, i_{2n}]$ of the inclusion of the bottom cell of the Moore space $P^{2n+1}(2)$ is divisible by $2$ if and only if $2n=2, 4, 8$ or $16$.

Title: More new classes of permutation trinomials over $\mathbb{F}_{2^n}$, Abstract: Permutation polynomials over finite fields have wide applications in many areas of science and engineering. In this paper, we present six new classes of permutation trinomials over $\mathbb{F}_{2^n}$ which have explicit forms by determining the solutions of some equations.

Title: Analytical solutions for the radial Scarf II potential, Abstract: The real Scarf II potential is discussed as a radial problem. This potential has been studied extensively as a one-dimensional problem, and now these results are used to construct its bound and resonance solutions for $l=0$ by setting the origin at some arbitrary value of the coordinate. The solutions with appropriate boundary conditions are composed as the linear combination of the two independent solutions of the Schrödinger equation. The asymptotic expression of these solutions is used to construct the $S_0(k)$ s-wave $S$-matrix, the poles of which supply the $k$ values corresponding to the bound, resonance and anti-bound solutions. The location of the discrete energy eigenvalues is analyzed, and the relation of the solutions of the radial and one-dimensional Scarf II potentials is discussed. It is shown that the generalized Woods--Saxon potential can be generated from the Rosen--Morse II potential in the same way as the radial Scarf II potential is obtained from its one-dimensional correspondent. Based on this analogy, possible applications are also pointed out.

Title: AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles, Abstract: Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.

Title: Forecasting in the light of Big Data, Abstract: Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann.

Title: Position Aided Beam Alignment for Millimeter Wave Backhaul Systems with Large Phased Arrays, Abstract: Wireless backhaul communication has been recently realized with large antennas operating in the millimeter wave (mmWave) frequency band and implementing highly directional beamforming. In this paper, we focus on the alignment problem of narrow beams between fixed position network nodes in mmWave backhaul systems that are subject to small displacements due to wind flow or ground vibration. We consider nodes equipped with antenna arrays that are capable of performing only analog processing and communicate through wireless channels including a line-of-sight component. Aiming at minimizing the time needed to achieve beam alignment, we present an efficient method that capitalizes on the exchange of position information between the nodes to design their beamforming and combining vectors. Some numerical results on the outage probability with the proposed beam alignment method offer useful preliminary insights on the impact of some system and operation parameters.

Title: Deep & Cross Network for Ad Click Predictions, Abstract: Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions. In particular, DCN explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the CTR prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage.

Title: Experimental Evidence on a Refined Conjecture of the BSD type, Abstract: Let $E/\mathbb{Q}$ be an elliptic curve of level $N$ and rank equal to $1$. Let $p$ be a prime of ordinary reduction. We experimentally study conjecture $4$ of B. Mazur and J. Tate in his article "Refined Conjectures of the Birch and Swinnerton-Dyer Type". We report the computational evidence.

Title: EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract), Abstract: Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort by possibly needed marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual-reality headset. It combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a new automatically annotated and augmented dataset. Our inside-in method captures full-body motion in general indoor and outdoor scenes, and also crowded scenes.

Title: Multiphase Flows of N Immiscible Incompressible Fluids: An Outflow/Open Boundary Condition and Algorithm, Abstract: We present a set of effective outflow/open boundary conditions and an associated algorithm for simulating the dynamics of multiphase flows consisting of $N$ ($N\geqslant 2$) immiscible incompressible fluids in domains involving outflows or open boundaries. These boundary conditions are devised based on the properties of energy stability and reduction consistency. The energy stability property ensures that the contributions of these boundary conditions to the energy balance will not cause the total energy of the N-phase system to increase over time. Therefore, these open/outflow boundary conditions are very effective in overcoming the backflow instability in multiphase systems. The reduction consistency property ensures that if some fluid components are absent from the N-phase system then these N-phase boundary conditions will reduce to those corresponding boundary conditions for the equivalent smaller system. Our numerical algorithm for the proposed boundary conditions together with the N-phase governing equations involves only the solution of a set of de-coupled individual Helmholtz-type equations within each time step, and the resultant linear algebraic systems after discretization involve only constant and time-independent coefficient matrices which can be pre-computed. Therefore, the algorithm is computationally very efficient and attractive. We present extensive numerical experiments for flow problems involving multiple fluid components and inflow/outflow boundaries to test the proposed method. In particular, we compare in detail the simulation results of a three-phase capillary wave problem with Prosperetti's exact physical solution and demonstrate that the method developed herein produces physically accurate results.

Title: Shortening binary complexes and commutativity of $K$-theory with infinite products, Abstract: We show that in Grayson's model of higher algebraic $K$-theory using binary acyclic complexes, the complexes of length two suffice to generate the whole group. Moreover, we prove that the comparison map from Nenashev's model for $K_1$ to Grayson's model for $K_1$ is an isomorphism. It follows that algebraic $K$-theory of exact categories commutes with infinite products.

Title: Vibrational Density Matrix Renormalization Group, Abstract: Variational approaches for the calculation of vibrational wave functions and energies are a natural route to obtain highly accurate results with controllable errors. However, the unfavorable scaling and the resulting high computational cost of standard variational approaches limit their application to small molecules with only few vibrational modes. Here, we demonstrate how the density matrix renormalization group (DMRG) can be exploited to optimize vibrational wave functions (vDMRG) expressed as matrix product states. We study the convergence of these calculations with respect to the size of the local basis of each mode, the number of renormalized block states, and the number of DMRG sweeps required. We demonstrate the high accuracy achieved by vDMRG for small molecules that were intensively studied in the literature. We then proceed to show that the complete fingerprint region of the sarcosyn-glycin dipeptide can be calculated with vDMRG.

Title: Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization, Abstract: Recent studies on diffusion-based sampling methods have shown that Langevin Monte Carlo (LMC) algorithms can be beneficial for non-convex optimization, and rigorous theoretical guarantees have been proven for both asymptotic and finite-time regimes. Algorithmically, LMC-based algorithms resemble the well-known gradient descent (GD) algorithm, where the GD recursion is perturbed by an additive Gaussian noise whose variance has a particular form. Fractional Langevin Monte Carlo (FLMC) is a recently proposed extension of LMC, where the Gaussian noise is replaced by a heavy-tailed {\alpha}-stable noise. As opposed to its Gaussian counterpart, these heavy-tailed perturbations can incur large jumps and it has been empirically demonstrated that the choice of {\alpha}-stable noise can provide several advantages in modern machine learning problems, both in optimization and sampling contexts. However, as opposed to LMC, only asymptotic convergence properties of FLMC have been yet established. In this study, we analyze the non-asymptotic behavior of FLMC for non-convex optimization and prove finite-time bounds for its expected suboptimality. Our results show that the weak-error of FLMC increases faster than LMC, which suggests using smaller step-sizes in FLMC. We finally extend our results to the case where the exact gradients are replaced by stochastic gradients and show that similar results hold in this setting as well.

Title: On the letter frequencies and entropy of written Marathi, Abstract: We carry out a comprehensive analysis of letter frequencies in contemporary written Marathi. We determine sets of letters which statistically predominate any large generic Marathi text, and use these sets to estimate the entropy of Marathi.

Title: Single and Multiple Vortex Rings in Three-Dimensional Bose-Einstein Condensates: Existence, Stability and Dynamics, Abstract: In the present work, we explore the existence, stability and dynamics of single and multiple vortex ring states that can arise in Bose-Einstein condensates. Earlier works have illustrated the bifurcation of such states, in the vicinity of the linear limit, for isotropic or anisotropic three-dimensional harmonic traps. Here, we extend these states to the regime of large chemical potentials, the so-called Thomas-Fermi limit, and explore their properties such as equilibrium radii and inter-ring distance, for multi-ring states, as well as their vibrational spectra and possible instabilities. In this limit, both the existence and stability characteristics can be partially traced to a particle picture that considers the rings as individual particles oscillating within the trap and interacting pairwise with one another. Finally, we examine some representative instability scenarios of the multi-ring dynamics including breakup and reconnections, as well as the transient formation of vortex lines.

Title: Sequential testing for structural stability in approximate factor models, Abstract: We develop an on-line monitoring procedure to detect a change in a large approximate factor model. Our statistics are based on a well-known property of the $% \left( r+1\right) $-th eigenvalue of the sample covariance matrix of the data (having defined $r$ as the number of common factors): whilst under the null the $\left( r+1\right) $-th eigenvalue is bounded, under the alternative of a change (either in the loadings, or in the number of factors itself) it becomes spiked. Given that the sample eigenvalue cannot be estimated consistently under the null, we regularise the problem by randomising the test statistic in conjunction with sample conditioning, obtaining a sequence of \textit{i.i.d.}, asymptotically chi-square statistics which are then employed to build the monitoring scheme. Numerical evidence shows that our procedure works very well in finite samples, with a very small probability of false detections and tight detection times in presence of a genuine change-point.

Title: Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation, Abstract: We address the problem of localisation of objects as bounding boxes in images with weak labels. This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. We propose a novel framework based on Bayesian joint topic modelling. Our framework has three distinctive advantages over previous works: (1) All object classes and image backgrounds are modelled jointly together in a single generative model so that "explaining away" inference can resolve ambiguity and lead to better learning and localisation. (2) The Bayesian formulation of the model enables easy integration of prior knowledge about object appearance to compensate for limited supervision. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Extensive experiments on the challenging VOC dataset demonstrate that our approach outperforms the state-of-the-art competitors.

Title: Constraints on Super-Earths Interiors from Stellar Abundances, Abstract: Modeling the interior of exoplanets is essential to go further than the conclusions provided by mean density measurements. In addition to the still limited precision on the planets' fundamental parameters, models are limited by the existence of degeneracies on their compositions. Here we present a model of internal structure dedicated to the study of solid planets up to ~10 Earth masses, i.e. Super-Earths. When the measurement is available, the assumption that the bulk Fe/Si ratio of a planet is similar to that of its host star allows us to significantly reduce the existing degeneracy and more precisely constrain the planet's composition. Based on our model, we provide an update of the mass-radius relationships used to provide a first estimate of a planet's composition from density measurements. Our model is also applied to the cases of two well-known exoplanets, CoRoT-7b and Kepler-10b, using their recently updated parameters. The core mass fractions of CoRoT-7b and Kepler-10b are found to lie within the 10-37% and 10-33% ranges, respectively, allowing both planets to be compatible with an Earth-like composition. We also extend the recent study of Proxima Centauri b, and show that its radius may reach 1.94 Earth radii in the case of a 5 Earth masses planet, as there is a 96.7% probability that the real mass of Proxima Centauri b is below this value.

Title: On the self-duality of rings of integers in tame and abelian extensions, Abstract: Let $L/K$ be a tame and Galois extension of number fields with group $G$. It is well-known that any ambiguous ideal in $L$ is locally free over $\mathcal{O}_KG$ (of rank one), and so it defines a class in the locally free class group of $\mathcal{O}_KG$, where $\mathcal{O}_K$ denotes the ring of integers of $K$. In this paper, we shall study the relationship among the classes arising from the ring of integers $\mathcal{O}_L$ of $L$, the inverse different $\mathfrak{D}_{L/K}^{-1}$ of $L/K$, and the square root of the inverse different $A_{L/K}$ of $L/K$ (if it exists), in the case that $G$ is abelian. They are naturally related because $A_{L/K}^2 = \mathfrak{D}_{L/K}^{-1} = \mathcal{O}_L^*$, and $A_{L/K}$ is special because $A_{L/K} = A_{L/K}^*$, where $*$ denotes dual with respect to the trace of $L/K$.

Title: Multiset Combinatorial Batch Codes, Abstract: Batch codes, first introduced by Ishai, Kushilevitz, Ostrovsky, and Sahai, mimic a distributed storage of a set of $n$ data items on $m$ servers, in such a way that any batch of $k$ data items can be retrieved by reading at most some $t$ symbols from each server. Combinatorial batch codes, are replication-based batch codes in which each server stores a subset of the data items. In this paper, we propose a generalization of combinatorial batch codes, called multiset combinatorial batch codes (MCBC), in which $n$ data items are stored in $m$ servers, such that any multiset request of $k$ items, where any item is requested at most $r$ times, can be retrieved by reading at most $t$ items from each server. The setup of this new family of codes is motivated by recent work on codes which enable high availability and parallel reads in distributed storage systems. The main problem under this paradigm is to minimize the number of items stored in the servers, given the values of $n,m,k,r,t$, which is denoted by $N(n,k,m,t;r)$. We first give a necessary and sufficient condition for the existence of MCBCs. Then, we present several bounds on $N(n,k,m,t;r)$ and constructions of MCBCs. In particular, we determine the value of $N(n,k,m,1;r)$ for any $n\geq \left\lfloor\frac{k-1}{r}\right\rfloor{m\choose k-1}-(m-k+1)A(m,4,k-2)$, where $A(m,4,k-2)$ is the maximum size of a binary constant weight code of length $m$, distance four and weight $k-2$. We also determine the exact value of $N(n,k,m,1;r)$ when $r\in\{k,k-1\}$ or $k=m$.

Title: Checklists to Support Test Charter Design in Exploratory Testing, Abstract: During exploratory testing sessions the tester simultaneously learns, designs and executes tests. The activity is iterative and utilizes the skills of the tester and provides flexibility and creativity.Test charters are used as a vehicle to support the testers during the testing. The aim of this study is to support practitioners in the design of test charters through checklists. We aimed to identify factors allowing practitioners to critically reflect on their designs and contents of test charters to support practitioners in making informed decisions of what to include in test charters. The factors and contents have been elicited through interviews. Overall, 30 factors and 35 content elements have been elicited.

Title: Fast non-destructive parallel readout of neutral atom registers in optical potentials, Abstract: We demonstrate the parallel and non-destructive readout of the hyperfine state for optically trapped $^{87}$Rb atoms. The scheme is based on state-selective fluorescence imaging and achieves detection fidelities $>$98% within 10$\,$ms, while keeping 99% of the atoms trapped. For the read-out of dense arrays of neutral atoms in optical lattices, where the fluorescence images of neighboring atoms overlap, we apply a novel image analysis technique using Bayesian inference to determine the internal state of multiple atoms. Our method is scalable to large neutral atom registers relevant for future quantum information processing tasks requiring fast and non-destructive readout and can also be used for the simultaneous read-out of quantum information stored in internal qubit states and in the atoms' positions.

Title: Optimal Identity Testing with High Probability, Abstract: We study the problem of testing identity against a given distribution with a focus on the high confidence regime. More precisely, given samples from an unknown distribution $p$ over $n$ elements, an explicitly given distribution $q$, and parameters $0< \epsilon, \delta < 1$, we wish to distinguish, {\em with probability at least $1-\delta$}, whether the distributions are identical versus $\varepsilon$-far in total variation distance. Most prior work focused on the case that $\delta = \Omega(1)$, for which the sample complexity of identity testing is known to be $\Theta(\sqrt{n}/\epsilon^2)$. Given such an algorithm, one can achieve arbitrarily small values of $\delta$ via black-box amplification, which multiplies the required number of samples by $\Theta(\log(1/\delta))$. We show that black-box amplification is suboptimal for any $\delta = o(1)$, and give a new identity tester that achieves the optimal sample complexity. Our new upper and lower bounds show that the optimal sample complexity of identity testing is \[ \Theta\left( \frac{1}{\epsilon^2}\left(\sqrt{n \log(1/\delta)} + \log(1/\delta) \right)\right) \] for any $n, \varepsilon$, and $\delta$. For the special case of uniformity testing, where the given distribution is the uniform distribution $U_n$ over the domain, our new tester is surprisingly simple: to test whether $p = U_n$ versus $d_{\mathrm TV}(p, U_n) \geq \varepsilon$, we simply threshold $d_{\mathrm TV}(\widehat{p}, U_n)$, where $\widehat{p}$ is the empirical probability distribution. The fact that this simple "plug-in" estimator is sample-optimal is surprising, even in the constant $\delta$ case. Indeed, it was believed that such a tester would not attain sublinear sample complexity even for constant values of $\varepsilon$ and $\delta$.

Title: Towards a Service-oriented Platform for Intelligent Apps in Intermediate Cities, Abstract: Smart cities are a growing trend in many cities in Argentina. In particular, the so-called intermediate cities present a context and requirements different from those of large cities with respect to smart cities. One aspect of relevance is to encourage the development of applications (generally for mobile devices) that enable citizens to take advantage of data and services normally associated with the city, for example, in the urban mobility domain. In this work, a platform is proposed for intermediate cities that provide "high level" services and that allow the construction of software applications that consume those services. Our platform-centric strategy focused aims to integrate systems and heterogeneous data sources, and provide "intelligent" services to different applications. Examples of these services include: construction of user profiles, recommending local events, and collaborative sensing based on data mining techniques, among others. In this work, the design of this platform (currently in progress) is described, and experiences of applications for urban mobility are discussed, which are being migrated in the form of reusable services provided by the platform

Title: Bistable reaction equations with doubly nonlinear diffusion, Abstract: Reaction-diffusion equations appear in biology and chemistry, and combine linear diffusion with different kind of reaction terms. Some of them are remarkable from the mathematical point of view, since they admit families of travelling waves that describe the asymptotic behaviour of a larger class of solutions $0\leq u(x,t)\leq 1$ of the problem posed in the real line. We investigate here the existence of waves with constant propagation speed, when the linear diffusion is replaced by the "slow" doubly nonlinear diffusion. In the present setting we consider bistable reaction terms, which present interesting differences w.r.t. the Fisher-KPP framework recently studied in \cite{AA-JLV:art}. We find different families of travelling waves that are employed to describe the wave propagation of more general solutions and to study the stability/instability of the steady states, even when we extend the study to several space dimensions. A similar study is performed in the critical case that we call "pseudo-linear", i.e., when the operator is still nonlinear but has homogeneity one. With respect to the classical model and the "pseudo-linear" case, the travelling waves of the "slow" diffusion setting exhibit free boundaries. \\ Finally, as a complement of \cite{AA-JLV:art}, we study the asymptotic behaviour of more general solutions in the presence of a "heterozygote superior" reaction function and doubly nonlinear diffusion ("slow" and "pseudo-linear").

Title: Failures of Gradient-Based Deep Learning, Abstract: In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four types of simple problems, for which the gradient-based algorithms commonly used in deep learning either fail or suffer from significant difficulties. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.

Title: Searching for the Transit of the Earth--mass exoplanet Proxima~Centauri~b in Antarctica: Preliminary Result, Abstract: Proxima Centauri is known as the closest star from the Sun. Recently, radial velocity observations revealed the existence of an Earth-mass planet around it. With an orbital period of ~11 days, the surface of Proxima Centauri b is temperate and might be habitable. We took a photometric monitoring campaign to search for its transit, using the Bright Star Survey Telescope at the Zhongshan Station in Antarctica. A transit-like signal appearing on 2016 September 8th, is identified tentatively. Its midtime, $T_{C}=2,457,640.1990\pm0.0017$ HJD, is consistent with the predicted ephemeris based on RV orbit in a 1$\sigma$ confidence interval. Time-correlated noise is pronounced in the light curve of Proxima Centauri, affecting detection of transits. We develop a technique, in a Gaussian process framework, to gauge the statistical significance of potential transit detection. The tentative transit signal reported here, has a confidence level of $2.5\sigma$. Further detection of its periodic signals is necessary to confirm the planetary transit of Proxima Centauri b. We plan to monitor Proxima Centauri in next Polar night at Dome A in Antarctica, taking the advantage of continuous darkness. \citet{Kipping17} reported two tentative transit-like signals of Proxima Centauri b, observed by the Microvariability and Oscillation of Stars space Telescope in 2014 and 2015, respectively. The midtransit time of our detection is 138 minutes later than that predicted by their transit ephemeris. If all the signals are real transits, the misalignment of the epochs plausibly suggests transit timing variations of Proxima Centauri b induced by an outer planet in this system.

Title: A Belief Propagation Algorithm for Multipath-Based SLAM, Abstract: We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem including the unknown MPC-VA/PA association. We represent this model by a factor graph, which enables the use of the belief propagation (BP) scheme for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. A core aspect of the algorithm is BP-based probabilistic MPC-VA/PA association. Moreover, for improved initialization of new VA positions, the states of unobserved potential VAs are modeled as a random finite set and propagated in time by means of a "zero-measurement" probability hypothesis density filter. The proposed BP-based SLAM algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetically generated measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

Title: End-to-end Planning of Fixed Millimeter-Wave Networks, Abstract: This article discusses a framework to support the design and end-to-end planning of fixed millimeter-wave networks. Compared to traditional techniques, the framework allows an organization to quickly plan a deployment in a cost-effective way. We start by using LiDAR data---basically, a 3D point cloud captured from a city---to estimate potential sites to deploy antennas and whether there is line-of-sight between them. With that data on hand, we use combinatorial optimization techniques to determine the optimal set of locations and how they should communicate with each other, to satisfy engineering (e.g., latency, polarity), design (e.g., reliability) and financial (e.g., total cost of operation) constraints. The primary goal is to connect as many people as possible to the network. Our methodology can be used for strategic planning when an organization is in the process of deciding whether to adopt a millimeter-wave technology or choosing between locations, or for operational planning when conducting a detailed design of the actual network to be deployed in a selected location.

Title: Spherical Functions on Riemannian Symmetric Spaces, Abstract: This paper deals with some simple results about spherical functions of type $\delta$, namely new integral formulas, new results about behavior at infinity and some facts about the related $C_\sigma$ functions.

Title: Semi-Analytical Perturbative Approaches to Third Body Resonant Trajectories, Abstract: In the framework of multi-body dynamics, successive encounters with a third body, even if well outside of its sphere of influence, can noticeably alter the trajectory of a spacecraft. Examples of these effects have already been exploited by past missions such as SMART-1, as well as are proposed to benefit future missions to Jupiter, Saturn or Neptune, and disposal strategies from Earth's High Eccentric or Libration Point Orbits. This paper revises three totally different descriptions of the effects of the third body gravitational perturbation. These are the averaged dynamics of the classical third body perturbing function, the Opik's close encounter theory and the Keplerian map approach. The first two techniques have respectively been applied to the cases of a spacecraft either always remaining very far or occasionally experiencing extremely close approaches to the third body. However, the paper also seeks solutions for trajectories that undergo one or more close approaches at distances in the order of the sphere of influence of the third body. The paper attempts to gain insight into the accuracy of these different perturbative techniques into each of these scenarios, as compared with the motion in the Circular Restricted Three Body Problem.

Title: State-dependent Priority Scheduling for Networked Control Systems, Abstract: Networked control systems (NCS) have attracted considerable attention in recent years. While the stabilizability and optimal control of NCS for a given communication system has already been studied extensively, the design of the communication system for NCS has recently seen an increase in more thorough investigation. In this paper, we address an optimal scheduling problem for a set of NCS sharing a dedicated communication channel, providing performance bounds and asymptotic stability. We derive a suboptimal scheduling policy with dynamic state-based priorities calculated at the sensors, which are then used for stateless priority queuing in the network, making it both scalable and efficient to implement on routers or multi-layer switches. These properties are beneficial towards leveraging existing IP networks for control, which will be a crucial factor for the proliferation of wide-area NCS applications. By allowing for an arbitrary number of concurrent transmissions, we are able to investigate the relationship between available bandwidth, transmission rate, and delay. To demonstrate the feasibility of our approach, we provide a proof-of-concept implementation of the priority scheduler using real networking hardware.

Title: A Survey of Deep Learning Techniques for Mobile Robot Applications, Abstract: Advancements in deep learning over the years have attracted research into how deep artificial neural networks can be used in robotic systems. This research survey will present a summarization of the current research with a specific focus on the gains and obstacles for deep learning to be applied to mobile robotics.

Title: Continuum of quantum fluctuations in a three-dimensional $S\!=\!1$ Heisenberg magnet, Abstract: Conventional crystalline magnets are characterized by symmetry breaking and normal modes of excitation called magnons with quantized angular momentum $\hbar$. Neutron scattering correspondingly features extra magnetic Bragg diffraction at low temperatures and dispersive inelastic scattering associated with single magnon creation and annihilation. Exceptions are anticipated in so-called quantum spin liquids as exemplified by the one-dimensional spin-1/2 chain which has no magnetic order and where magnons accordingly fractionalize into spinons with angular momentum $\hbar/2$. This is spectacularly revealed by a continuum of inelastic neutron scattering associated with two-spinon processes and the absence of magnetic Bragg diffraction. Here, we report evidence for these same key features of a quantum spin liquid in the three-dimensional Heisenberg antiferromagnet NaCaNi$_2$F$_7$. Through specific heat and neutron scattering measurements, Monte Carlo simulations, and analytic approximations to the equal time correlations, we show that NaCaNi$_2$F$_7$ is an almost ideal realization of the spin-1 antiferromagnetic Heisenberg model on a pyrochlore lattice with weak connectivity and frustrated interactions. Magnetic Bragg diffraction is absent and 90\% of the spectral weight forms a continuum of magnetic scattering not dissimilar to that of the spin-1/2 chain but with low energy pinch points indicating NaCaNi$_2$F$_7$ is in a Coulomb phase. The residual entropy and diffuse elastic scattering points to an exotic state of matter driven by frustration, quantum fluctuations and weak exchange disorder.

Title: Exoplanet Atmosphere Retrieval using Multifractal Analysis of Secondary Eclipse Spectra, Abstract: We extend a data-based model-free multifractal method of exoplanet detection to probe exoplanetary atmospheres. Whereas the transmission spectrum is studied during the primary eclipse, we analyze the emission spectrum during the secondary eclipse, thereby probing the atmospheric limb. In addition to the spectral structure of exoplanet atmospheres, the approach provides information to study phenomena such as atmospheric flows, tidal-locking behavior, and the dayside-nightside redistribution of energy. The approach is demonstrated using Spitzer data for exoplanet HD189733b. The central advantage of the method is the lack of model assumptions in the detection and observational schemes.

Title: A novel distribution-free hybrid regression model for manufacturing process efficiency improvement, Abstract: This work is motivated by a particular problem of a modern paper manufacturing industry, in which maximum efficiency of the fiber-filler recovery process is desired. A lot of unwanted materials along with valuable fibers and fillers come out as a by-product of the paper manufacturing process and mostly goes as waste. The job of an efficient Krofta supracell is to separate the unwanted materials from the valuable ones so that fibers and fillers can be collected from the waste materials and reused in the manufacturing process. The efficiency of Krofta depends on several crucial process parameters and monitoring them is a difficult proposition. To solve this problem, we propose a novel hybridization of regression trees (RT) and artificial neural networks (ANN), hybrid RT-ANN model, to solve the problem of low recovery percentage of the supracell. This model is used to achieve the goal of improving supracell efficiency, viz., gain in percentage recovery. In addition, theoretical results for the universal consistency of the proposed model are given with the optimal value of a vital model parameter. Experimental findings show that the proposed hybrid RT-ANN model achieves higher accuracy in predicting Krofta recovery percentage than other conventional regression models for solving the Krofta efficiency problem. This work will help the paper manufacturing company to become environmentally friendly with minimal ecological damage and improved waste recovery.

Title: Photo-Induced Bandgap Renormalization Governs the Ultrafast Response of Single-Layer MoS2, Abstract: Transition metal dichalcogenides (TMDs) are emerging as promising two-dimensional (2d) semiconductors for optoelectronic and flexible devices. However, a microscopic explanation of their photophysics -- of pivotal importance for the understanding and optimization of device operation -- is still lacking. Here we use femtosecond transient absorption spectroscopy, with pump pulse tunability and broadband probing, to monitor the relaxation dynamics of single-layer MoS2 over the entire visible range, upon photoexcitation of different excitonic transitions. We find that, irrespective of excitation photon energy, the transient absorption spectrum shows the simultaneous bleaching of all excitonic transitions and corresponding red-shifted photoinduced absorption bands. First-principle modeling of the ultrafast optical response reveals that a transient bandgap renormalization, caused by the presence of photo-excited carriers, is primarily responsible for the observed features. Our results demonstrate the strong impact of many-body effects in the transient optical response of TMDs even in the low-excitation-density regime.

Title: Improved Quantile Regression Estimators when the Errors are Independently and Non-identically Distributed, Abstract: In a classical regression model, it is usually assumed that the explanatory variables are independent of each other and error terms are normally distributed. But when these assumptions are not met, situations like the error terms are not independent or they are not identically distributed or both of these, LSE will not be robust. Hence, quantile regression has been used to complement this deficiency of classical regression analysis and to improve the least square estimation (LSE). In this study, we consider preliminary test and shrinkage estimation strategies for quantile regression models with independently and non-identically distributed (i.ni.d.) errors. A Monte Carlo simulation study is conducted to assess the relative performance of the estimators. Also, we numerically compare their performance with Ridge, Lasso, Elastic Net penalty estimation strategies. A real data example is presented to illustrate the usefulness of the suggested methods. Finally, we obtain the asymptotic results of suggested estimators

Title: Language Modeling by Clustering with Word Embeddings for Text Readability Assessment, Abstract: We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embeddings whose training is done by observing word co-occurrences. We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression. Also, by representing features in terms of histograms, our approach can naturally address documents of varying lengths. An empirical evaluation using the Common Core Standards corpus reveals that the features formed on our clustering-based language model significantly improve the previously known results for the same corpus in readability prediction. We also evaluate the task of sentence matching based on semantic relatedness using the Wiki-SimpleWiki corpus and find that our features lead to superior matching performance.

Title: Learning Graph Representations by Dendrograms, Abstract: Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal representation of the graph defines a class of reducible linkages leading to regular dendrograms by greedy agglomerative clustering.

Title: Attitude Control of the Asteroid Origins Satellite 1 (AOSAT 1), Abstract: Exploration of asteroids and small-bodies can provide valuable insight into the origins of the solar system, into the origins of Earth and the origins of the building blocks of life. However, the low-gravity and unknown surface conditions of asteroids presents a daunting challenge for surface exploration, manipulation and for resource processing. This has resulted in the loss of several landers or shortened missions. Fundamental studies are required to obtain better readings of the material surface properties and physical models of these small bodies. The Asteroid Origins Satellite 1 (AOSAT 1) is a CubeSat centrifuge laboratory that spins at up to 4 rpm to simulate the milligravity conditions of sub 1 km asteroids. Such a laboratory will help to de-risk development and testing of landing and resource processing technology for asteroids. Inside the laboratory are crushed meteorites, the remains of asteroids. The laboratory is equipped with cameras and actuators to perform a series of science experiments to better understand material properties and asteroid surface physics. These results will help to improve our physics models of asteroids. The CubeSat has been designed to be low-cost and contains 3-axis magnetorquers and a single reaction-wheel to induce spin. In our work, we first analyze how the attitude control system will de-tumble the spacecraft after deployment. Further analysis has been conducted to analyze the impact and stability of the attitude control system to shifting mass (crushed meteorites) inside the spacecraft as its spinning in its centrifuge mode. AOSAT 1 will be the first in a series of low-cost CubeSat centrifuges that will be launched setting the stage for a larger, permanent, on-orbit centrifuge laboratory for experiments in planetary science, life sciences and manufacturing.

Title: Dynamics of cracks in disordered materials, Abstract: Predicting when rupture occurs or cracks progress is a major challenge in numerous elds of industrial, societal and geophysical importance. It remains largely unsolved: Stress enhancement at cracks and defects, indeed, makes the macroscale dynamics extremely sensitive to the microscale material disorder. This results in giant statistical uctuations and non-trivial behaviors upon upscaling dicult to assess via the continuum approaches of engineering. These issues are examined here. We will see: How linear elastic fracture mechanics sidetracks the diculty by reducing the problem to that of the propagation of a single crack in an eective material free of defects, How slow cracks sometimes display jerky dynamics, with sudden violent events incompatible with the previous approach, and how some paradigms of statistical physics can explain it, How abnormally fast cracks sometimes emerge due to the formation of microcracks at very small scales.

Title: Incompressible limit of the Navier-Stokes model with a growth term, Abstract: Starting from isentropic compressible Navier-Stokes equations with growth term in the continuity equation, we rigorously justify that performing an incompressible limit one arrives to the two-phase free boundary fluid system.

Title: Unveiling ADP-binding sites and channels in respiratory complexes: Validation of Murburn concept as a holistic explanation for oxidative phosphorylation, Abstract: Mitochondrial oxidative phosphorylation (mOxPhos) makes ATP, the energy currency of life. Chemiosmosis, a proton centric mechanism, advocates that Complex V harnesses a transmembrane potential (TMP) for ATP synthesis. This perception of cellular respiration requires oxygen to stay tethered at Complex IV (an association inhibited by cyanide) and diffusible reactive oxygen species (DROS) are considered wasteful and toxic products. With new mechanistic insights on heme and flavin enzymes, an oxygen or DROS centric explanation (called murburn concept) was recently proposed for mOxPhos. In the new mechanism, TMP is not directly harnessed, protons are a rate limiting reactant and DROS within matrix serve as the chemical coupling agents that directly link NADH oxidation with ATP synthesis. Herein, we report multiple ADP binding sites and solvent accessible DROS channels in respiratory proteins, which validate the oxygen or DROS centric power generation (ATP synthesis) system in mOxPhos. Since cyanide's heme binding Kd is high (mM), low doses (uM) of cyanide is lethal because cyanide disrupts DROS dynamics in mOxPhos. The critical study also provides comprehensive arguments against Mitchell's and Boyer's explanations and extensive support for murburn concept based holistic perspectives for mOxPhos.

Title: Regrasping by Fixtureless Fixturing, Abstract: This paper presents a fixturing strategy for regrasping that does not require a physical fixture. To regrasp an object in a gripper, a robot pushes the object against external contact/s in the environment such that the external contact keeps the object stationary while the fingers slide over the object. We call this manipulation technique fixtureless fixturing. Exploiting the mechanics of pushing, we characterize a convex polyhedral set of pushes that results in fixtureless fixturing. These pushes are robust against uncertainty in the object inertia, grasping force, and the friction at the contacts. We propose a sampling-based planner that uses the sets of robust pushes to rapidly build a tree of reachable grasps. A path in this tree is a pushing strategy, possibly involving pushes from different sides, to regrasp the object. We demonstrate the experimental validity and robustness of the proposed manipulation technique with different regrasp examples on a manipulation platform. Such a fast and flexible regrasp planner facilitates versatile and flexible automation solutions.

Title: Superposition solutions to the extended KdV equation for water surface waves, Abstract: The KdV equation can be derived in the shallow water limit of the Euler equations. Over the last few decades, this equation has been extended to include higher order effects. Although this equation has only one conservation law, exact periodic and solitonic solutions exist. Khare and Saxena \cite{KhSa,KhSa14,KhSa15} demonstrated the possibility of generating new exact solutions by combining known ones for several fundamental equations (e.g., Korteweg - de Vries, Nonlinear Schrödinger). Here we find that this construction can be repeated for higher order, non-integrable extensions of these equations. Contrary to many statements in the literature, there seems to be no correlation between integrability and the number of nonlinear one variable wave solutions.

Title: Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions, Abstract: Reduction of communication and efficient partitioning are key issues for achieving scalability in hierarchical $N$-Body algorithms like FMM. In the present work, we propose four independent strategies to improve partitioning and reduce communication. First of all, we show that the conventional wisdom of using space-filling curve partitioning may not work well for boundary integral problems, which constitute about 50% of FMM's application user base. We propose an alternative method which modifies orthogonal recursive bisection to solve the cell-partition misalignment that has kept it from scaling previously. Secondly, we optimize the granularity of communication to find the optimal balance between a bulk-synchronous collective communication of the local essential tree and an RDMA per task per cell. Finally, we take the dynamic sparse data exchange proposed by Hoefler et al. and extend it to a hierarchical sparse data exchange, which is demonstrated at scale to be faster than the MPI library's MPI_Alltoallv that is commonly used.

Title: Distance Measure Machines, Abstract: This paper presents a distance-based discriminative framework for learning with probability distributions. Instead of using kernel mean embeddings or generalized radial basis kernels, we introduce embeddings based on dissimilarity of distributions to some reference distributions denoted as templates. Our framework extends the theory of similarity of Balcan et al. (2008) to the population distribution case and we show that, for some learning problems, some dissimilarity on distribution achieves low-error linear decision functions with high probability. Our key result is to prove that the theory also holds for empirical distributions. Algorithmically, the proposed approach consists in computing a mapping based on pairwise dissimilarity where learning a linear decision function is amenable. Our experimental results show that the Wasserstein distance embedding performs better than kernel mean embeddings and computing Wasserstein distance is far more tractable than estimating pairwise Kullback-Leibler divergence of empirical distributions.

Title: Tropical formulae for summation over a part of SL(2, Z), Abstract: Let $f(a,b,c,d)=\sqrt{a^2+b^2}+\sqrt{c^2+d^2}-\sqrt{(a+c)^2+(b+d)^2}$, let $(a,b,c,d)$ stand for $a,b,c,d\in\mathbb Z_{\geq 0}$ such that $ad-bc=1$. Define \begin{equation} \label{eq_main} F(s) = \sum_{(a,b,c,d)} f(a,b,c,d)^s. \end{equation} In other words, we consider the sum of the powers of the triangle inequality defects for the lattice parallelograms (in the first quadrant) of area one. We prove that $F(s)$ converges when $s>1/2$ and diverges at $s=1/2$. We also prove $$\sum\limits_{\substack{(a,b,c,d),\\ 1\leq a\leq b, 1\leq c\leq d}} \frac{1}{(a+b)^2(c+d)^2(a+b+c+d)^2} = 1/24,$$ and show a general method to obtain such formulae. The method comes from the consideration of the tropical analogue of the caustic curves, whose moduli give a complete set of continuous invariants on the space of convex domains.

Title: On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis, Abstract: In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective. Although subsampling discards a large part of training data, we show provable guarantees on the accuracy of the predictive mean/variance and its generalization ability. For analysis, we consider embedding kernel matrices into graphons, which encapsulate the difference of the sample size and enables us to evaluate the approximation and generalization errors in a unified manner. The experimental results show that the subsampling approximation achieves a better trade-off regarding accuracy and runtime than the Nyström and random Fourier expansion methods.

Title: Birth of a subaqueous barchan dune, Abstract: Barchan dunes are crescentic shape dunes with horns pointing downstream. The present paper reports the formation of subaqueous barchan dunes from initially conical heaps in a rectangular channel. Because the most unique feature of a barchan dune is its horns, we associate the timescale for the appearance of horns to the formation of a barchan dune. A granular heap initially conical was placed on the bottom wall of a closed conduit and it was entrained by a water flow in turbulent regime. After a certain time, horns appear and grow, until an equilibrium length is reached. Our results show the existence of the timescales $0.5t_c$ and $2.5t_c$ for the appearance and equilibrium of horns, respectively, where $t_c$ is a characteristic time that scales with the grains diameter, gravity acceleration, densities of the fluid and grains, and shear and threshold velocities.

Title: Core2Vec: A core-preserving feature learning framework for networks, Abstract: Recent advances in the field of network representation learning are mostly attributed to the application of the skip-gram model in the context of graphs. State-of-the-art analogues of skip-gram model in graphs define a notion of neighbourhood and aim to find the vector representation for a node, which maximizes the likelihood of preserving this neighborhood. In this paper, we take a drastic departure from the existing notion of neighbourhood of a node by utilizing the idea of coreness. More specifically, we utilize the well-established idea that nodes with similar core numbers play equivalent roles in the network and hence induce a novel and an organic notion of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic framework for learning low dimensional continuous feature mapping for a node. Consequently, the nodes having similar core numbers are relatively closer in the vector space that we learn. We further demonstrate the effectiveness of core2vec by comparing word similarity scores obtained by our method where the node representations are drawn from standard word association graphs against scores computed by other state-of-the-art network representation techniques like node2vec, DeepWalk and LINE. Our results always outperform these existing methods

Title: Magneto-inductive Passive Relaying in Arbitrarily Arranged Networks, Abstract: We consider a wireless sensor network that uses inductive near-field coupling for wireless powering or communication, or for both. The severely limited range of an inductively coupled source-destination pair can be improved using resonant relay devices, which are purely passive in nature. Utilization of such magneto-inductive relays has only been studied for regular network topologies, allowing simplified assumptions on the mutual antenna couplings. In this work we present an analysis of magneto-inductive passive relaying in arbitrarily arranged networks. We find that the resulting channel has characteristics similar to multipath fading: the channel power gain is governed by a non-coherent sum of phasors, resulting in increased frequency selectivity. We propose and study two strategies to increase the channel power gain of random relay networks: i) deactivation of individual relays by open-circuit switching and ii) frequency tuning. The presented results show that both methods improve the utilization of available passive relays, leading to reliable and significant performance gains.

Title: Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces, Abstract: Transfer operators such as the Perron--Frobenius or Koopman operator play an important role in the global analysis of complex dynamical systems. The eigenfunctions of these operators can be used to detect metastable sets, to project the dynamics onto the dominant slow processes, or to separate superimposed signals. We extend transfer operator theory to reproducing kernel Hilbert spaces and show that these operators are related to Hilbert space representations of conditional distributions, known as conditional mean embeddings in the machine learning community. Moreover, numerical methods to compute empirical estimates of these embeddings are akin to data-driven methods for the approximation of transfer operators such as extended dynamic mode decomposition and its variants. One main benefit of the presented kernel-based approaches is that these methods can be applied to any domain where a similarity measure given by a kernel is available. We illustrate the results with the aid of guiding examples and highlight potential applications in molecular dynamics as well as video and text data analysis.

Title: Smallest eigenvalue density for regular or fixed-trace complex Wishart-Laguerre ensemble and entanglement in coupled kicked tops, Abstract: The statistical behaviour of the smallest eigenvalue has important implications for systems which can be modeled using a Wishart-Laguerre ensemble, the regular one or the fixed trace one. For example, the density of the smallest eigenvalue of the Wishart-Laguerre ensemble plays a crucial role in characterizing multiple channel telecommunication systems. Similarly, in the quantum entanglement problem, the smallest eigenvalue of the fixed trace ensemble carries information regarding the nature of entanglement. For real Wishart-Laguerre matrices, there exists an elegant recurrence scheme suggested by Edelman to directly obtain the exact expression for the smallest eigenvalue density. In the case of complex Wishart-Laguerre matrices, for finding exact and explicit expressions for the smallest eigenvalue density, existing results based on determinants become impractical when the determinants involve large-size matrices. In this work, we derive a recurrence scheme for the complex case which is analogous to that of Edelman's for the real case. This is used to obtain exact results for the smallest eigenvalue density for both the regular, and the fixed trace complex Wishart-Laguerre ensembles. We validate our analytical results using Monte Carlo simulations. We also study scaled Wishart-Laguerre ensemble and investigate its efficacy in approximating the fixed-trace ensemble. Eventually, we apply our result for the fixed-trace ensemble to investigate the behaviour of the smallest eigenvalue in the paradigmatic system of coupled kicked tops.

Title: Large Magellanic Cloud Near-Infrared Synoptic Survey. V. Period-Luminosity Relations of Miras, Abstract: We study the near-infrared properties of 690 Mira candidates in the central region of the Large Magellanic Cloud, based on time-series observations at JHKs. We use densely-sampled I-band observations from the OGLE project to generate template light curves in the near infrared and derive robust mean magnitudes at those wavelengths. We obtain near-infrared Period-Luminosity relations for Oxygen-rich Miras with a scatter as low as 0.12 mag at Ks. We study the Period-Luminosity-Color relations and the color excesses of Carbon-rich Miras, which show evidence for a substantially different reddening law.

Title: Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access, Abstract: We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. Experimental results demonstrate strong performance of the algorithm.

Title: On fibering compact manifold over the circle, Abstract: In this paper, we show that any compact manifold that carries a SL(n;R)-foliation is fibered on the circle S^1.

Title: Phonon-Induced Topological Transition to a Type-II Weyl Semimetal, Abstract: Given the importance of crystal symmetry for the emergence of topological quantum states, we have studied, as exemplified in NbNiTe2, the interplay of crystal symmetry, atomic displacements (lattice vibration), band degeneracy, and band topology. For NbNiTe2 structure in space group 53 (Pmna) - having an inversion center arising from two glide planes and one mirror plane with a 2-fold rotation and screw axis - a full gap opening exists between two band manifolds near the Fermi energy. Upon atomic displacements by optical phonons, the symmetry lowers to space group 28 (Pma2), eliminating one glide plane along c, the associated rotation and screw axis, and the inversion center. As a result, twenty Weyl points emerge, including four type-II Weyl points in the G-X direction at the boundary between a pair of adjacent electron and hole bands. Thus, optical phonons may offer control of the transition to a Weyl fermion state.

Title: Asymmetric Mach-Zehnder atom interferometers, Abstract: It is shown that using beam splitters with non-equal wave vectors results in a new recoil diagram which is qualitatively different from the well-known diagram associated with the Mach-Zehnder atom interferometer. We predict a new asymmetric Mach-Zehnder atom interferometer (AMZAI) and study it when one uses a Raman beam splitter. The main feature is that the phase of AMZAI contains a quantum part proportional to the recoil frequency. A response sensitive only to the quantum phase was found. A new technique to measure the recoil frequency and fine structure constant is proposed and studied outside of the Raman-Nath approximation.

Title: Cryptoasset Factor Models, Abstract: We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In "cryptoassets" we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market data is available. Based on our empirical analysis, we identify the leading factor that appears to strongly contribute into daily cryptoasset returns. Our results suggest that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting.

Title: Robust Detection of Covariate-Treatment Interactions in Clinical Trials, Abstract: Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process. The increasing volume of data accumulated in clinical trials provides a unique opportunity to discover new biomarkers and further the goal of personalized medicine, but it also requires innovative robust biomarker detection methods capable of detecting non-linear, and sometimes weak, signals. We propose a set of novel univariate statistical tests, based on the theory of random walks, which are able to capture non-linear and non-monotonic covariate-treatment interactions. We also propose a novel combined test, which leverages the power of all of our proposed univariate tests into a single general-case tool. We present results for both synthetic trials as well as real-world clinical trials, where we compare our method with state-of-the-art techniques and demonstrate the utility and robustness of our approach.

Title: The Future of RICH Detectors through the Light of the LHCb RICH, Abstract: The limitations in performance of the present RICH system in the LHCb experiment are given by the natural chromatic dispersion of the gaseous Cherenkov radiator, the aberrations of the optical system and the pixel size of the photon detectors. Moreover, the overall PID performance can be affected by high detector occupancy as the pattern recognition becomes more difficult with high particle multiplicities. This paper shows a way to improve performance by systematically addressing each of the previously mentioned limitations. These ideas are applied in the present and future upgrade phases of the LHCb experiment. Although applied to specific circumstances, they are used as a paradigm on what is achievable in the development and realisation of high precision RICH detectors.

Title: Stop talking to me -- a communication-avoiding ADER-DG realisation, Abstract: We present a communication- and data-sensitive formulation of ADER-DG for hyperbolic differential equation systems. Sensitive here has multiple flavours: First, the formulation reduces the persistent memory footprint. This reduces pressure on the memory subsystem. Second, the formulation realises the underlying predictor-corrector scheme with single-touch semantics, i.e., each degree of freedom is read on average only once per time step from the main memory. This reduces communication through the memory controllers. Third, the formulation breaks up the tight coupling of the explicit time stepping's algorithmic steps to mesh traversals. This averages out data access peaks. Different operations and algorithmic steps are ran on different grid entities. Finally, the formulation hides distributed memory data transfer behind the computation aligned with the mesh traversal. This reduces pressure on the machine interconnects. All techniques applied by our formulation are elaborated by means of a rigorous task formalism. They break up ADER-DG's tight causal coupling of compute steps and can be generalised to other predictor-corrector schemes.

Title: Hierarchical loss for classification, Abstract: Failing to distinguish between a sheepdog and a skyscraper should be worse and penalized more than failing to distinguish between a sheepdog and a poodle; after all, sheepdogs and poodles are both breeds of dogs. However, existing metrics of failure (so-called "loss" or "win") used in textual or visual classification/recognition via neural networks seldom view a sheepdog as more similar to a poodle than to a skyscraper. We define a metric that, inter alia, can penalize failure to distinguish between a sheepdog and a skyscraper more than failure to distinguish between a sheepdog and a poodle. Unlike previously employed possibilities, this metric is based on an ultrametric tree associated with any given tree organization into a semantically meaningful hierarchy of a classifier's classes.

Title: An Unsupervised Learning Classifier with Competitive Error Performance, Abstract: An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of small step shift and rotation operations upon selected discriminative hyperplanes at the arrival of input samples. When applied, in conjunction with a selected feature extractor, to a subset of the ImageNet dataset benchmark, it yields 6.2 % Top 3 probability of error; this exceeds by merely about 2 % the result achieved by (supervised) k-Nearest Neighbor, both using same feature extractor. This result may also be contrasted with popular unsupervised learning schemes such as k-Means which is shown to be practically useless on same dataset.

Title: Exploring the predictability of range-based volatility estimators using RNNs, Abstract: We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged as the volatility. The patterns of volatility changes are analyzed using LSTM recurrent neural networks, which are a state of the art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index, and report averaged evaluation results. We find that changes in the values of range-based estimators are more predictable than that of the estimator using daily closing values only.

Title: Robust Gesture-Based Communication for Underwater Human-Robot Interaction in the context of Search and Rescue Diver Missions, Abstract: We propose a robust gesture-based communication pipeline for divers to instruct an Autonomous Underwater Vehicle (AUV) to assist them in performing high-risk tasks and helping in case of emergency. A gesture communication language (CADDIAN) is developed, based on consolidated and standardized diver gestures, including an alphabet, syntax and semantics, ensuring a logical consistency. A hierarchical classification approach is introduced for hand gesture recognition based on stereo imagery and multi-descriptor aggregation to specifically cope with underwater image artifacts, e.g. light backscatter or color attenuation. Once the classification task is finished, a syntax check is performed to filter out invalid command sequences sent by the diver or generated by errors in the classifier. Throughout this process, the diver receives constant feedback from an underwater tablet to acknowledge or abort the mission at any time. The objective is to prevent the AUV from executing unnecessary, infeasible or potentially harmful motions. Experimental results under different environmental conditions in archaeological exploration and bridge inspection applications show that the system performs well in the field.

Title: Recent Operation of the FNAL Magnetron $H^{-}$ Ion Source, Abstract: This paper will detail changes in the operational paradigm of the Fermi National Accelerator Laboratory (FNAL) magnetron $H^{-}$ ion source due to upgrades in the accelerator system. Prior to November of 2012 the $H^{-}$ ions for High Energy Physics (HEP) experiments were extracted at ~18 keV vertically downward into a 90 degree bending magnet and accelerated through a Cockcroft-Walton accelerating column to 750 keV. Following the upgrade in the fall of 2012 the $H^{-}$ ions are now directly extracted from a magnetron at 35 keV and accelerated to 750 keV by a Radio Frequency Quadrupole (RFQ). This change in extraction energy as well as the orientation of the ion source required not only a redesign of the ion source, but an updated understanding of its operation at these new values. Discussed in detail are the changes to the ion source timing, arc discharge current, hydrogen gas pressure, and cesium delivery system that were needed to maintain consistent operation at >99% uptime for HEP, with an increased ion source lifetime of over 9 months.

Title: Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder, Abstract: Estimates of the Hubble constant, $H_0$, from the distance ladder and the cosmic microwave background (CMB) differ at the $\sim$3-$\sigma$ level, indicating a potential issue with the standard $\Lambda$CDM cosmology. Interpreting this tension correctly requires a model comparison calculation depending on not only the traditional `$n$-$\sigma$' mismatch but also the tails of the likelihoods. Determining the form of the tails of the local $H_0$ likelihood is impossible with the standard Gaussian least-squares approximation, as it requires using non-Gaussian distributions to faithfully represent anchor likelihoods and model outliers in the Cepheid and supernova (SN) populations, and simultaneous fitting of the full distance-ladder dataset to correctly propagate uncertainties. We have developed a Bayesian hierarchical model that describes the full distance ladder, from nearby geometric anchors through Cepheids to Hubble-Flow SNe. This model does not rely on any distributions being Gaussian, allowing outliers to be modeled and obviating the need for arbitrary data cuts. Sampling from the $\sim$3000-parameter joint posterior using Hamiltonian Monte Carlo, we find $H_0$ = (72.72 $\pm$ 1.67) ${\rm km\,s^{-1}\,Mpc^{-1}}$ when applied to the outlier-cleaned Riess et al. (2016) data, and ($73.15 \pm 1.78$) ${\rm km\,s^{-1}\,Mpc^{-1}}$ with SN outliers reintroduced. Our high-fidelity sampling of the low-$H_0$ tail of the distance-ladder likelihood allows us to apply Bayesian model comparison to assess the evidence for deviation from $\Lambda$CDM. We set up this comparison to yield a lower limit on the odds of the underlying model being $\Lambda$CDM given the distance-ladder and Planck XIII (2016) CMB data. The odds against $\Lambda$CDM are at worst 10:1 or 7:1, depending on whether the SNe outliers are cut or modeled, or 60:1 if an approximation to the Planck Int. XLVI (2016) likelihood is used.

Title: Mutual Interpretability of Robinson Arithmetic and Adjunctive Set Theory with Extensionality, Abstract: An elementary rheory of concatenation is introduced and used to establish mutual interpretability of Robinson arithmetic, Minimal Predicative Set Theory, the quantifier-free part of Kirby's finitary set theory, and Adjunctive Set Theory, with or without extensionality.

Title: Gamorithm, Abstract: Examining games from a fresh perspective we present the idea of game-inspired and game-based algorithms, dubbed "gamorithms".

Title: Computing representation matrices for the action of Frobenius to cohomology groups, Abstract: This paper is concerned with the computation of representation matrices for the action of Frobenius to the cohomology groups of algebraic varieties. Specifically we shall give an algorithm to compute the matrices for arbitrary algebraic varieties with defining equations over perfect fields of positive characteristic, and estimate its complexity. Moreover, we propose a specific efficient method, which works for complete intersections.

Title: A Recent Survey on the Applications of Genetic Programming in Image Processing, Abstract: During the last two decades, Genetic Programming (GP) has been largely used to tackle optimization, classification, and automatic features selection related tasks. The widespread use of GP is mainly due to its flexible and comprehensible tree-type structure. Similarly, research is also gaining momentum in the field of Image Processing (IP) because of its promising results over wide areas of applications ranging from medical IP to multispectral imaging. IP is mainly involved in applications such as computer vision, pattern recognition, image compression, storage and transmission, and medical diagnostics. This prevailing nature of images and their associated algorithm i.e complexities gave an impetus to the exploration of GP. GP has thus been used in different ways for IP since its inception. Many interesting GP techniques have been developed and employed in the field of IP. To give the research community an extensive view of these techniques, this paper presents the diverse applications of GP in IP and provides useful resources for further research. Also, comparison of different parameters used in ten different applications of IP are summarized in tabular form. Moreover, analysis of different parameters used in IP related tasks is carried-out to save the time needed in future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks not only related to IP but also in other fields will increase. Additionally, guidelines are provided for applying GP in IP related tasks, pros and cons of GP techniques are discussed, and some future directions are also set.

Title: Universality in numerical computation with random data. Case studies, analytic results and some speculations, Abstract: We discuss various universality aspects of numerical computations using standard algorithms. These aspects include empirical observations and rigorous results. We also make various speculations about computation in a broader sense.

Title: A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models, Abstract: Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, typical cross entropy training procedures for these models do not directly consider the behaviour of the final decoding method. As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding. In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search. While well-defined, this "direct loss" objective is itself discontinuous and thus difficult to optimize. Hence, in our approach, we form a sub-differentiable surrogate objective by introducing a novel continuous approximation of the beam search decoding procedure. In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross entropy trained greedy decoding and cross entropy trained beam decoding baselines.

Title: Local Marchenko-Pastur Law for Random Bipartite Graphs, Abstract: This paper is the first chapter of three of the author's undergraduate thesis. We study the random matrix ensemble of covariance matrices arising from random $(d_b, d_w)$-regular bipartite graphs on a set of $M$ black vertices and $N$ white vertices, for $d_b \gg \log^4 N$. We simultaneously prove that the Green's functions of these covariance matrices and the adjacency matrices of the underlying graphs agree with the corresponding limiting law (e.g. Marchenko-Pastur law for covariance matrices) down to the optimal scale. This is an improvement from the previously known mesoscopic results. We obtain eigenvector delocalization for the covariance matrix ensemble as consequence, as well as a weak rigidity estimate.

Title: Photoelectron Yields of Scintillation Counters with Embedded Wavelength-Shifting Fibers Read Out With Silicon Photomultipliers, Abstract: Photoelectron yields of extruded scintillation counters with titanium dioxide coating and embedded wavelength shifting fibers read out by silicon photomultipliers have been measured at the Fermilab Test Beam Facility using 120\,GeV protons. The yields were measured as a function of transverse, longitudinal, and angular positions for a variety of scintillator compositions and reflective coating mixtures, fiber diameters, and photosensor sizes. Timing performance was also studied. These studies were carried out by the Cosmic Ray Veto Group of the Mu2e collaboration as part of their R\&D program.

Title: Reconstructing global fields from dynamics in the abelianized Galois group, Abstract: We study a dynamical system induced by the Artin reciprocity map for a global field. We translate the conjugacy of such dynamical systems into various arithmetical properties that are equivalent to field isomorphism, relating it to anabelian geometry.

Title: Homology theory formulas for generalized Riemann-Hurwitz and generalized monoidal transformations, Abstract: In the context of orientable circuits and subcomplexes of these as representing certain singular spaces, we consider characteristic class formulas generalizing those classical results as seen for the Riemann-Hurwitz formula for regulating the topology of branched covering maps and that for monoidal transformations which include the standard blowing-up process. Here the results are presented as cap product pairings, which will be elements of a suitable homology theory, rather than characteristic numbers as would be the case when taking Kronecker products once Poincaré duality is defined. We further consider possible applications and examples including branched covering maps, singular varieties involving virtual tangent bundles, the Chern-Schwartz-MacPherson class, the homology L-class, generalized signature, and the cohomology signature class.

Title: Mixing of odd- and even-frequency pairings in strongly correlated electron systems under magnetic field, Abstract: Even- and odd-frequency superconductivity coexist due to broken time-reversal symmetry under magnetic field. In order to describe this mixing, we extend the linearized Eliashberg equation for the spin and charge fluctuation mechanism in strongly correlated electron systems. We apply this extended Eliashberg equation to the odd-frequency superconductivity on a quasi-one-dimensional isosceles triangular lattice under in-plane magnetic field and examine the effect of the even-frequency component.

Title: Bounded height in families of dynamical systems, Abstract: Let a and b be algebraic numbers such that exactly one of a and b is an algebraic integer, and let f_t(z):=z^2+t be a family of polynomials parametrized by t. We prove that the set of all algebraic numbers t for which there exist positive integers m and n such that f_t^m(a)=f_t^n(b) has bounded Weil height. This is a special case of a more general result supporting a new bounded height conjecture in dynamics. Our results fit into the general setting of the principle of unlikely intersections in arithmetic dynamics.

Title: Input-to-State Stability of a Clamped-Free Damped String in the Presence of Distributed and Boundary Disturbances, Abstract: This note establishes the input-to-state stability (ISS) property for a clamped-free damped string with respect to distributed and boundary disturbances. While efficient methods for establishing ISS properties for distributed parameter systems with respect to distributed disturbances have been developed during the last decades, establishing ISS properties with respect to boundary disturbances remains challenging. One of the well-known methods for well-posedness analysis of systems with boundary inputs is to use an adequate lifting operator, which transfers the boundary disturbance to a distributed one. However, the resulting distributed disturbance involves time derivatives of the boundary perturbation. Thus, the subsequent ISS estimate depends on its amplitude, and may not be expressed in the strict form of ISS properties. To solve this problem, we show for a clamped-free damped string equation that the projection of the original system trajectories in an adequate Riesz basis can be used to establish the desired ISS property.

Title: Reassessing Graphene Absorption and Emission Spectroscopy, Abstract: We present a new paradigm for understanding optical absorption and hot electron dynamics experiments in graphene. Our analysis pivots on assigning proper importance to phonon assisted indirect processes and bleaching of direct processes. We show indirect processes figure in the excess absorption in the UV region. Experiments which were thought to indicate ultrafast relaxation of electrons and holes, reaching a thermal distribution from an extremely non-thermal one in under 5-10 fs, instead are explained by the nascent electron and hole distributions produced by indirect transitions. These need no relaxation or ad-hoc energy removal to agree with the observed emission spectra and fast pulsed absorption spectra. The fast emission following pulsed absorption is dominated by phonon assisted processes, which vastly outnumber direct ones and are always available, connecting any electron with any hole any time. Calculations are given, including explicitly calculating the magnitude of indirect processes, supporting these views.

Title: A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes, Abstract: We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression levels to zero. Conditional distributions are specified by neural networks, giving the proposed model enough flexibility to fit the data well. We use variational inference and stochastic optimization to approximate the posterior distribution. The inference procedure scales to over one million cells, whereas competing algorithms do not. Even for smaller datasets, for several tasks, the proposed procedure outperforms state-of-the-art methods like ZIFA and ZINB-WaVE. We also extend our framework to take into account batch effects and other confounding factors and propose a natural Bayesian hypothesis framework for differential expression that outperforms tradition DESeq2.

Title: Asynchronous Byzantine Machine Learning (the case of SGD), Abstract: Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, corrupt data, or even malicious attacks. We introduce \emph{Kardam}, the first distributed asynchronous stochastic gradient descent (SGD) algorithm that copes with Byzantine workers. Kardam consists of two complementary components: a filtering and a dampening component. The first is scalar-based and ensures resilience against $\frac{1}{3}$ Byzantine workers. Essentially, this filter leverages the Lipschitzness of cost functions and acts as a self-stabilizer against Byzantine workers that would attempt to corrupt the progress of SGD. The dampening component bounds the convergence rate by adjusting to stale information through a generic gradient weighting scheme. We prove that Kardam guarantees almost sure convergence in the presence of asynchrony and Byzantine behavior, and we derive its convergence rate. We evaluate Kardam on the CIFAR-100 and EMNIST datasets and measure its overhead with respect to non Byzantine-resilient solutions. We empirically show that Kardam does not introduce additional noise to the learning procedure but does induce a slowdown (the cost of Byzantine resilience) that we both theoretically and empirically show to be less than $f/n$, where $f$ is the number of Byzantine failures tolerated and $n$ the total number of workers. Interestingly, we also empirically observe that the dampening component is interesting in its own right for it enables to build an SGD algorithm that outperforms alternative staleness-aware asynchronous competitors in environments with honest workers.

Title: Revealing Hidden Potentials of the q-Space Signal in Breast Cancer, Abstract: Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.

Title: Surface tension of flowing soap films, Abstract: The surface tension of flowing soap films is measured with respect to the film thickness and the concentration of soap solution. We perform this measurement by measuring the curvature of the nylon wires that bound the soap film channel and use the measured curvature to parametrize the relation between the surface tension and the tension of the wire. We find the surface tension of our soap films increases when the film is relatively thin or made of soap solution of low concentration, otherwise it approaches an asymptotic value 30 mN/m. A simple adsorption model with only two parameters describes our observations reasonably well. With our measurements, we are also able to measure Gibbs elasticity for our soap film.

Title: FluxMarker: Enhancing Tactile Graphics with Dynamic Tactile Markers, Abstract: For people with visual impairments, tactile graphics are an important means to learn and explore information. However, raised line tactile graphics created with traditional materials such as embossing are static. While available refreshable displays can dynamically change the content, they are still too expensive for many users, and are limited in size. These factors limit wide-spread adoption and the representation of large graphics or data sets. In this paper, we present FluxMaker, an inexpensive scalable system that renders dynamic information on top of static tactile graphics with movable tactile markers. These dynamic tactile markers can be easily reconfigured and used to annotate static raised line tactile graphics, including maps, graphs, and diagrams. We developed a hardware prototype that actuates magnetic tactile markers driven by low-cost and scalable electromagnetic coil arrays, which can be fabricated with standard printed circuit board manufacturing. We evaluate our prototype with six participants with visual impairments and found positive results across four application areas: location finding or navigating on tactile maps, data analysis, and physicalization, feature identification for tactile graphics, and drawing support. The user study confirms advantages in application domains such as education and data exploration.

Title: A Survey on Content-Aware Video Analysis for Sports, Abstract: Sports data analysis is becoming increasingly large-scale, diversified, and shared, but difficulty persists in rapidly accessing the most crucial information. Previous surveys have focused on the methodologies of sports video analysis from the spatiotemporal viewpoint instead of a content-based viewpoint, and few of these studies have considered semantics. This study develops a deeper interpretation of content-aware sports video analysis by examining the insight offered by research into the structure of content under different scenarios. On the basis of this insight, we provide an overview of the themes particularly relevant to the research on content-aware systems for broadcast sports. Specifically, we focus on the video content analysis techniques applied in sportscasts over the past decade from the perspectives of fundamentals and general review, a content hierarchical model, and trends and challenges. Content-aware analysis methods are discussed with respect to object-, event-, and context-oriented groups. In each group, the gap between sensation and content excitement must be bridged using proper strategies. In this regard, a content-aware approach is required to determine user demands. Finally, the paper summarizes the future trends and challenges for sports video analysis. We believe that our findings can advance the field of research on content-aware video analysis for broadcast sports.

Title: Learning Combinations of Sigmoids Through Gradient Estimation, Abstract: We develop a new approach to learn the parameters of regression models with hidden variables. In a nutshell, we estimate the gradient of the regression function at a set of random points, and cluster the estimated gradients. The centers of the clusters are used as estimates for the parameters of hidden units. We justify this approach by studying a toy model, whereby the regression function is a linear combination of sigmoids. We prove that indeed the estimated gradients concentrate around the parameter vectors of the hidden units, and provide non-asymptotic bounds on the number of required samples. To the best of our knowledge, no comparable guarantees have been proven for linear combinations of sigmoids.

Title: Heads or tails in zero gravity: an example of a classical contextual "measurement", Abstract: Playing the game of heads or tails in zero gravity demonstrates that there exists a contextual "measurement" in classical mechanics. When the coin is flipped, its orientation is a continuous variable. However, the "measurement" that occurs when the coin is caught by clapping two hands together gives a discrete value (heads or tails) that depends on the context (orientation of the hands). It is then shown that there is a strong analogy with the spin measurement of the Stern-Gerlach experiment, and in particular with Stern and Gerlach's sequential measurements. Finally, we clarify the analogy by recalling how the de Broglie-Bohm interpretation simply explains the spin "measurement".

Title: Linear complexity of Legendre-polynomial quotients, Abstract: We continue to investigate binary sequence $(f_u)$ over $\{0,1\}$ defined by $(-1)^{f_u}=\left(\frac{(u^w-u^{wp})/p}{p}\right)$ for integers $u\ge 0$, where $\left(\frac{\cdot}{p}\right)$ is the Legendre symbol and we restrict $\left(\frac{0}{p}\right)=1$. In an earlier work, the linear complexity of $(f_u)$ was determined for $w=p-1$ under the assumption of $2^{p-1}\not\equiv 1 \pmod {p^2}$. In this work, we give possible values on the linear complexity of $(f_u)$ for all $1\le w<p-1$ under the same conditions. We also state that the case of larger $w(\geq p)$ can be reduced to that of $0\leq w\leq p-1$.

Title: The Time Dimension of Science: Connecting the Past to the Future, Abstract: A central question in science of science concerns how time affects citations. Despite the long-standing interests and its broad impact, we lack systematic answers to this simple yet fundamental question. By reviewing and classifying prior studies for the past 50 years, we find a significant lack of consensus in the literature, primarily due to the coexistence of retrospective and prospective approaches to measuring citation age distributions. These two approaches have been pursued in parallel, lacking any known connections between the two. Here we developed a new theoretical framework that not only allows us to connect the two approaches through precise mathematical relationships, it also helps us reconcile the interplay between temporal decay of citations and the growth of science, helping us uncover new functional forms characterizing citation age distributions. We find retrospective distribution follows a lognormal distribution with exponential cutoff, while prospective distribution is governed by the interplay between a lognormal distribution and the growth in the number of references. Most interestingly, the two approaches can be connected once rescaled by the growth of publications and citations. We further validate our framework using both large-scale citation datasets and analytical models capturing citation dynamics. Together this paper presents a comprehensive analysis of the time dimension of science, representing a new empirical and theoretical basis for all future studies in this area.

Title: Stochastic Ratcheting on a Funneled Energy Landscape is Necessary for Highly Efficient Contractility of Actomyosin Force Dipoles, Abstract: Current understanding of how contractility emerges in disordered actomyosin networks of non-muscle cells is still largely based on the intuition derived from earlier works on muscle contractility. This view, however, largely overlooks the free energy gain following passive cross-linker binding, which, even in the absence of active fluctuations, provides a thermodynamic drive towards highly overlapping filamentous states. In this work, we shed light on this phenomenon, showing that passive cross-linkers, when considered in the context of two anti-parallel filaments, generate noticeable contractile forces. However, as binding free energy of cross-linkers is increased, a sharp onset of kinetic arrest follows, greatly diminishing effectiveness of this contractility mechanism, allowing the network to contract only with weakly resisting tensions at its boundary. We have carried out stochastic simulations elucidating this mechanism, followed by a mean-field treatment that predicts how contractile forces asymptotically scale at small and large binding energies, respectively. Furthermore, when considering an active contractile filament pair, based on non-muscle myosin II, we found that the non-processive nature of these motors leads to highly inefficient force generation, due to recoil slippage of the overlap during periods when the motor is dissociated. However, we discovered that passive cross-linkers can serve as a structural ratchet during these unbound motor time spans, resulting in vast force amplification. Our results shed light on the non-equilibrium effects of transiently binding proteins in biological active matter, as observed in the non-muscle actin cytoskeleton, showing that highly efficient contractile force dipoles result from synergy of passive cross-linker and active motor dynamics, via a ratcheting mechanism on a funneled energy landscape.

Title: Mitigation of Phase Noise in Massive MIMO Systems: A Rate-Splitting Approach, Abstract: This work encompasses Rate-Splitting (RS), providing significant benefits in multi-user settings in the context of huge degrees of freedom promised by massive Multiple-Input Multiple-Output (MIMO). However, the requirement of massive MIMO for cost-efficient implementation makes them more prone to hardware imperfections such as phase noise (PN). As a result, we focus on a realistic broadcast channel with a large number of antennas and hampered by the unavoidable PN. Moreover, we employ the RS transmission strategy, and we show its robustness against PN, since the sum-rate does not saturate at high signal-to-noise ratio (SNR). Although, the analytical results are obtained by means of the deterministic equivalent analysis, they coincide with simulation results even for finite system dimensions.