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Title: Fuzzy gyronorms on gyrogroups Abstract: The concept of gyrogroups is a generalization of groups which do not explicitly have associativity. In this paper, the notion of fuzzy gyronorms on gyrogroups is introduced. The relations of fuzzy metrics (in the sense of George and Veeramani), fuzzy gyronorms and gyronorms on gyrogroups are studied. Also, the fuzzy metric structures on fuzzy normed gyrogroups are discussed. Finally the fuzzy metric completion of a gyrogroup with an invariant metric is studied. We mainly show that if d is an invariant metric on a gyrogroup G and (Gˆ,dˆ) is the metric completion of the metric space (G,d); then for any continuous t-norm ⁎, the standard fuzzy metric space (Gˆ,Mdˆ,⁎) of (Gˆ,dˆ) is the (up to isometry) unique fuzzy metric completion of the standard fuzzy metric space (G,Md,⁎) of (G,d); furthermore, (Gˆ,Mdˆ,⁎) is a fuzzy metric gyrogroup containing (G,Md,⁎) as a dense fuzzy metric subgyrogroup and Mdˆ is invariant on Gˆ. Applying this result, we obtain that every gyrogroup G with an invariant metric d admits an (up to isometric) unique complete metric space (Gˆ,dˆ) of (G,d) such that Gˆ with the topology introduced by dˆ is a topological gyrogroup containing G as a dense subgyrogroup and dˆ is invariant on Gˆ.

Title: Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Abstract: While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures. On the other hand, there is significant empirical evidence, e.g. in network science and bioinformatics, that substructures are often intimately related to downstream tasks. To this end, we propose ”Graph Substructure Networks” (GSN), a topologically-aware message passing scheme based on substructure encoding. We theoretically analyse the expressive power of our architecture, showing that it is strictly more expressive than the WL test, and provide sufficient conditions for universality. Importantly, we do not attempt to adhere to the WL hierarchy; this allows us to retain multiple attractive properties of standard GNNs such as locality and linear network complexity, while being able to disambiguate even hard instances of graph isomorphism. We perform an extensive experimental evaluation on graph classification and regression tasks and obtain state-of-the-art results in diverse real-world settings including molecular graphs and social networks.

Title: Tensor method for optimal control problems constrained by fractional three-dimensional elliptic operator with variable coefficients Abstract: We introduce the tensor numerical method for solving optimal control problems that are constrained by fractional two- (2D) and three-dimensional (3D) elliptic operators with variable coefficients. We solve the governing equation for the control function which includes a sum of the fractional operator and its inverse, both discretized over large 3D nxnxn spacial grids. Using the diagonalization of the arising matrix-valued functions in the eigenbasis of the one-dimensional Sturm-Liouville operators, we construct the rank-structured tensor approximation with controllable precision for the discretized fractional elliptic operators and the respective preconditioner. The right-hand side in the constraining equation (the optimal design function) is supposed to be represented in a form of a low-rank canonical tensor. Then the equation for the control function is solved in a tensor structured format by using preconditioned CG iteration with the adaptive rank truncation procedure that also ensures the accuracy of calculations, given an epsilon-threshold. This method reduces the numerical cost for solving the control problem to O(nlogn) (plus the quadratic term O(n2) with a small weight), which outperforms traditional approaches with O(n3logn) complexity in the 3D case. The storage for the representation of all 3D nonlocal operators and functions involved is also estimated by O(nlogn). This essentially outperforms the traditional methods operating with fully populated n3xn3 matrices and vectors in Double-struck capital Rn3. Numerical tests for 2D/3D control problems indicate the almost linear complexity scaling of the rank truncated preconditioned conjugate gradient iteration in the univariate grid size n.

Title: Play With One’s Feelings: A Study on Emotion Awareness for Player Experience Abstract: Affective interaction between players of video games can elicit rich and varying patterns of emotions. In multiplayer activities that take place in a common space (such as sports and board games), players are generally aware of the emotions of their teammates or opponents as they can directly observe their behavioral patterns, facial expressions, head pose, body stance, and so on. Players of onlin...

Title: Building One-Shot Semi-Supervised (BOSS) Learning Up to Fully Supervised Performance. Abstract: Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for the first time the potential for building one-shot semi-supervised (BOSS) learning on CIFAR-10 and SVHN up to attain test accuracies that are comparable to fully supervised learning. Our method combines class prototype refining, class balancing, and self-training. A good prototype choice is essential and we propose a technique for obtaining iconic examples. In addition, we demonstrate that class balancing methods substantially improve accuracy results in semi-supervised learning to levels that allow self-training to reach the level of fully supervised learning performance. Our experiments demonstrate the value with computing and analyzing test accuracies for every class, rather than only a total test accuracy. We show that our BOSS methodology can obtain total test accuracies with CIFAR-10 images and only one labeled sample per class up to 95% (compared to 94.5% for fully supervised). Similarly, the SVHN images obtains test accuracies of 97.8%, compared to 98.27% for fully supervised. Rigorous empirical evaluations provide evidence that labeling large datasets is not necessary for training deep neural networks. Our code is available at https://github.com/lnsmith54/BOSS to facilitate replication.

Title: Robust Registration of Medical Images in the Presence of Spatially-Varying Noise Abstract: Spatially-varying intensity noise is a common source of distortion in medical images and is often associated with reduced accuracy in medical image registration. In this paper, we propose two multi-resolution image registration algorithms based on Empirical Mode Decomposition (EMD) that are robust against additive spatially-varying noise. EMD is a multi-resolution tool that decomposes a signal into several principle patterns and residual components. Our first proposed algorithm (LR-EMD) is based on the registration of EMD feature maps from both floating and reference images in various resolutions. In the second algorithm (AFR-EMD), we first extract a single average feature map based on EMD and then use a simple hierarchical multi-resolution algorithm to register the average feature maps. We then showcase the superior performance of both algorithms in the registration of brain MRIs as well as retina images. For the registration of brain MR images, using mutual information as the similarity measure, both AFR-EMD and LR-EMD achieve a lower error rate in intensity (42% and 32%, respectively) and lower error rate in transformation (52% and 41%, respectively) compared to intensity-based hierarchical registration. Our results suggest that the two proposed algorithms offer robust registration solutions in the presence of spatially-varying noise.

Title: Algebraic and combinatorial expansion in random simplicial complexes Abstract: In this paper we consider the expansion properties and the spectrum of the combinatorial Laplace operator of a d-dimensional Linial-Meshulam random simplicial complex, above the cohomological connectivity threshold. We consider the spectral gap of the Laplace operator and the Cheeger constant as this was introduced by Parzanchevski, Rosenthal, and Tessler. We show that with high probability the spectral gap of the random simplicial complex as well as the Cheeger constant are both concentrated around the minimum co-degree of among all (d-1)-faces. Furthermore, we consider a random walk on such a complex, which generalizes the standard random walk on a graph. We show that the associated conductance is with high probability bounded away from 0, resulting in a bound on the mixing time that is logarithmic in the number of vertices of the complex.

Title: Non-Bipartite K-Common Graphs Abstract: A graph H is k-common if the number of monochromatic copies of H in a k-edge-coloring of Kn is asymptotically minimized by a random coloring. For every k, we construct a connected non-bipartite k-common graph. This resolves a problem raised by Jagger, Štovíček and Thomason [20]. We also show that a graph H is k-common for every k if and only if H is Sidorenko and that H is locally k-common for every k if and only if H is locally Sidorenko.

Title: SAMBA: safe model-based & active reinforcement learning Abstract: In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our method builds upon PILCO to enable active exploration using novel acquisition functions for out-of-sample Gaussian process evaluation optimised through a multi-objective problem that supports conditional-value-at-risk constraints. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. Our results show orders of magnitude reductions in samples and violations compared to state-of-the-art methods. Lastly, we provide intuition as to the effectiveness of the framework by a detailed analysis of our acquisition functions and safety constraints.

Title: A Large-scale Empirical Analysis of Browser Fingerprints Properties for Web Authentication Abstract: AbstractModern browsers give access to several attributes that can be collected to form a browser fingerprint. Although browser fingerprints have primarily been studied as a web tracking tool, they can contribute to improve the current state of web security by augmenting web authentication mechanisms. In this article, we investigate the adequacy of browser fingerprints for web authentication. We make the link between the digital fingerprints that distinguish browsers, and the biological fingerprints that distinguish Humans, to evaluate browser fingerprints according to properties inspired by biometric authentication factors. These properties include their distinctiveness, their stability through time, their collection time, their size, and the accuracy of a simple verification mechanism. We assess these properties on a large-scale dataset of 4,145,408 fingerprints composed of 216 attributes and collected from 1,989,365 browsers. We show that, by time-partitioning our dataset, more than 81.3% of our fingerprints are shared by a single browser. Although browser fingerprints are known to evolve, an average of 91% of the attributes of our fingerprints stay identical between two observations, even when separated by nearly six months. About their performance, we show that our fingerprints weigh a dozen of kilobytes and take a few seconds to collect. Finally, by processing a simple verification mechanism, we show that it achieves an equal error rate of 0.61%. We enrich our results with the analysis of the correlation between the attributes and their contribution to the evaluated properties. We conclude that our browser fingerprints carry the promise to strengthen web authentication mechanisms.

Title: Policy Evaluation and Seeking for Multiagent Reinforcement Learning via Best Response Abstract: Multiagent policy evaluation and seeking are long-standing challenges in developing theories for multiagent reinforcement learning (MARL), due to multidimensional learning goals, nonstationary environment, and scalability issues in the joint policy space. This article introduces two metrics grounded on a game-theoretic solution concept called sink equilibrium, for the evaluation, ranking, and comp...

Title: Longest and shortest cycles in random planar graphs Abstract: Let P(n,m) be a graph chosen uniformly at random from the class of all planar graphs on vertex set {1, horizontal ellipsis ,n} with m=m(n) edges. We study the cycle and block structure of P(n,m) when m similar to n/2. More precisely, we determine the asymptotic order of the length of the longest and shortest cycle in P(n,m) in the critical range when m=n/2+o(n). In addition, we describe the block structure of P(n,m) in the weakly supercritical regime when n2/3MUCH LESS-THANm-n/2MUCH LESS-THANn.

Title: Hamiltonian decompositions of 4-regular Cayley graphs of infinite abelian groups Abstract: A well-known conjecture of Alspach says that every 2 k $2k$-regular Cayley graph of a finite abelian group can be decomposed into Hamiltonian cycles. We consider an analogous question for infinite abelian groups. In this setting one natural analogue of a Hamiltonian cycle is a spanning double-ray. However, a naive generalisation of Alspach's conjecture fails to hold in this setting due to the existence of 2 k $2k$-regular Cayley graphs with finite cuts F $F$, where divide F divide $| F| $ and k $k$ differ in parity, which necessarily preclude the existence of a decomposition into spanning double-rays. We show that every 4-regular Cayley graph of an infinite abelian group all of whose finite cuts are even can be decomposed into spanning double-rays, and so characterise when such decompositions exist. We also characterise when such graphs can be decomposed either into Hamiltonian circles, a more topological generalisation of a Hamiltonian cycle in infinite graphs, or into a Hamiltonian circle and a spanning double-ray.

Title: Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework Abstract: We present a framework to address a class of sequential decision-making problems. Our framework features learning the optimal control policy with robustness to noisy data, determining the unknown state and action parameters, and performing sensitivity analysis with respect to problem parameters. We consider two broad categories of sequential decision-making problems modeled as infinite horizon Markov decision processes (MDPs) with (and without) an absorbing state. The central idea underlying our framework is to quantify exploration in terms of the Shannon entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory. This resulting policy enhances the quality of exploration early on in the learning process, and consequently allows faster convergence rates and robust solutions even in the presence of noisy data as demonstrated in our comparisons to popular algorithms, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning, Double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning, and entropy regularized Soft <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning. The framework extends to the class of parameterized MDP and RL problems, where states and actions are parameter dependent, and the objective is to determine the optimal parameters along with the corresponding optimal policy. Here, the associated cost function can possibly be nonconvex with multiple poor local minima. Simulation results applied to a 5G small cell network problem demonstrate the successful determination of communication routes and the small cell locations. We also obtain sensitivity measures to problem parameters and robustness to noisy environment data.

Title: Order Conditions for Sampling the Invariant Measure of Ergodic Stochastic Differential Equations on Manifolds Abstract: We derive a new methodology for the construction of high-order integrators for sampling the invariant measure of ergodic stochastic differential equations with dynamics constrained on a manifold. We obtain the order conditions for sampling the invariant measure for a class of Runge–Kutta methods applied to the constrained overdamped Langevin equation. The analysis is valid for arbitrarily high order and relies on an extension of the exotic aromatic Butcher-series formalism. To illustrate the methodology, a method of order two is introduced, and numerical experiments on the sphere, the torus and the special linear group confirm the theoretical findings.

Title: CACHING WITH TIME WINDOWS AND DELAYS Abstract: We consider two generalizations of the classical weighted paging problem that incorporate the notion of delayed service of page requests. The first is the (weighted) paging with time windows (PageTW) problem, which is like the classical weighted paging problem except that each page request only needs to be served before a given deadline. This problem arises in many practical applications of online caching, such as the "deadline " I/O scheduler in the Linux kernel and video-on-demand streaming. The second, and more general, problem is the (weighted) paging with delay (PageD) problem, where the delay in serving a page request results in a penalty being added to the objective. This problem generalizes the caching problem to allow delayed service, a line of work that has recently gained traction in online algorithms (e.g., [Y. Emek, S. Kutten, and R. Wattenhofer, Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, 2016, pp. 333--344; Y. Azar et al., Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017, pp. 551-563; Y. Azar and N. Touitou, Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019, pp. 60-71]). We give O(log k log n)-competitive algorithms for both the PageTW and PageD problems on n pages with a cache of size k. This significantly improves on the previous best bounds of O(k) for both problems [Y. Azar et al., Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017, pp. 551-563]. We also consider the offline PageTW and PageD problems, for which we give O(1)-approximation algorithms and prove APX-hardness. These are the first results for the offline problems; even NP-hardness was not known before our work. At the heart of our algorithms is a novel "hitting-set " LP relaxation of the PageTW problem that overcomes the omega(k) integrality gap of the natural LP for the problem. To the best of our knowledge, this is the first example of an LP-based algorithm for an online problem with delays/deadlines.

Title: Design and Implementation of Time-Sensitive Wireless IoT Networks on Software-Defined Radio Abstract: Time-sensitive wireless networks are an important enabling building block for many emerging industrial Internet-of-Things (IoT) applications. Quick prototyping and evaluation of time-sensitive wireless technologies are desirable for research and development efforts. Software-defined radio (SDR), by allowing wireless signal processing on a personal computer (PC), has been widely used for such quick...

Title: Breaking Type Safety in Go: An Empirical Study on the Usage of the <monospace>unsafe</monospace> Package Abstract: A decade after its first release, the Go language has become a major programming language in the development landscape. While praised for its clean syntax and C-like performance, Go also contains a strong static type-system that prevents arbitrary type casting and memory access, making the language type-safe by design. However, to give developers the possibility of implementing low-level code, Go ships with a special package called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsafe</monospace> that offers developers a way around the type safety of Go programs. The package gives greater flexibility to developers but comes at a higher risk of runtime errors, chances of non-portability, and the loss of compatibility guarantees for future versions of Go. In this paper, we present the first large-scale study on the usage of the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsafe</monospace> package in 2,438 popular Go projects. Our investigation shows that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsafe</monospace> is used in 24 percent of Go projects, motivated primarily by communicating with operating systems and C code, but is also commonly used as a means of performance optimization. Developers are willing to use <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsafe</monospace> to break language specifications (e.g., string immutability) for better performance and 6 percent of the analyzed projects that use <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsafe</monospace> perform risky pointer conversions that can lead to program crashes and unexpected behavior. Furthermore, we report a series of real issues faced by projects that use <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsafe</monospace> , from crashing errors and non-deterministic behavior to having their deployment restricted from certain popular environments. Our findings can be used to understand how and why developers break type safety in Go, and help motivate further tools and language development that could make the usage of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsafe</monospace> in Go even safer.

Title: Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes Abstract: Statistical modeling of temporal point patterns is an important problem in several areas. The Cox process, a Poisson process where the intensity function is stochastic, is a common model for such data. We present a new class of unidimensional Cox process models in which the intensity function assumes parametric functional forms that switch according to a continuous-time Markov chain. A novel methodology is introduced to perform exact (up to Monte Carlo error) Bayesian inference based on MCMC algorithms. The reliability of the algorithms depends on a variety of specifications which are carefully addressed, resulting in a computationally efficient (in terms of computing time) algorithm and enabling its use with large data sets. Simulated and real examples are presented to illustrate the efficiency and applicability of the methodology. A specific model to fit epidemic curves is proposed and used to analyze data from Dengue Fever in Brazil and COVID-19 in some countries.

Title: Gender Inequality in Research Productivity During the COVID-19 Pandemic Abstract: Problem definition: We study the disproportionate impact of the lockdown as a result of the COVID-19 outbreak on female and male academic research productivity in social science. Academic/practical relevance: The lockdown has caused substantial disruptions to academic activities, requiring people to work from home. How this disruption affects productivity and the related gender equity is an important operations and societal question. Methodology: We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries over a span of two years. We use a difference-in-differences approach leveraging the exogenous pandemic shock. Results: Our results indicate that, in the 10 weeks after the lockdown in the United States, although total research productivity increased by 35%, female academics' productivity dropped by 13.2% relative to that of male academics. We also show that this intensified productivity gap is more pronounced for assistant professors and for academics in top-ranked universities and is found in six other countries. Managerial implications: Our work points out the fairness issue in productivity caused by the lockdown, a finding that universities will find helpful when evaluating faculty productivity. It also helps organizations realize the potential unintended consequences that can arise from telecommuting.

Title: Variation diminishing linear time-invariant systems Abstract: This paper studies the variation diminishing property of k-positive linear time-invariant (LTI) systems, which diminish the number of sign changes (variation) from input to output, if the input variation is at most k−1. We characterize this property for the discrete-time Toeplitz and Hankel operators of finite-dimensional causal systems. Our main result is that these operators have a dominant approximation in the form of series or parallel interconnections of k first order positive systems. This is shown by expressing the k-positivity of a LTI system as the external positivity (that is, 1-positivity) of k compound LTI systems. Our characterization generalizes well known properties of externally positive systems (k=1) and totally positive systems (k=∞; also known as relaxation systems in case of the Hankel operator). All results readily extend to continuous-time systems by considering sampled impulse responses.

Title: Decompositions of Ehrhart h*-Polynomials for Rational Polytopes Abstract: The Ehrhart quasipolynomial of a rational polytope $P$ encodes the number of integer lattice points in dilates of $P$, and the $h^*$-polynomial of $P$ is the numerator of the accompanying generating function. We provide two decomposition formulas for the $h^*$-polynomial of a rational polytope. The first decomposition generalizes a theorem of Betke and McMullen for lattice polytopes. We use our rational Betke--McMullen formula to provide a novel proof of Stanley's Monotonicity Theorem for the $h^*$-polynomial of a rational polytope. The second decomposition generalizes a result of Stapledon, which we use to provide rational extensions of the Stanley and Hibi inequalities satisfied by the coefficients of the $h^*$-polynomial for lattice polytopes. Lastly, we apply our results to rational polytopes containing the origin whose duals are lattice polytopes.

Title: A simple extrapolation method for clustered eigenvalues Abstract: This paper introduces a simple variant of the power method. It is shown analytically and numerically to accelerate convergence to the dominant eigenvalue/eigenvector pair, and it is particularly effective for problems featuring a small spectral gap. The introduced method is a one-step extrapolation technique that uses a linear combination of current and previous update steps to form a better approximation of the dominant eigenvector. The provided analysis shows the method converges exponentially with respect to the ratio between the two largest eigenvalues, which is also approximated during the process. An augmented technique is also introduced and is shown to stabilize the early stages of the iteration. Numerical examples are provided to illustrate the theory and demonstrate the methods.

Title: Active Learning for Nonlinear System Identification with Guarantees Abstract: While the identification of nonlinear dynamical systems is a fundamental building block of modelbased reinforcement learning and feedback control, its sample complexity is only understood for systems that either have discrete states and actions or for systems that can be identified from data generated by i.i.d. random inputs. Nonetheless, many interesting dynamical systems have continuous states and actions and can only be identified through a judicious choice of inputs. Motivated by practical settings, we study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. To estimate such systems in finite time identification methods must explore all directions in feature space. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.

Title: Extraction and evaluation of formulaic expressions used in scholarly papers Abstract: Formulaic expressions, such as ‘in this paper we propose’, are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is ‘showing the aim of this paper’. Thus, resources of formulaic expressions, such as a dictionary, that could be looked up easily would be useful. However, forms of formulaic expressions can often vary to a great extent. For example, ‘in this paper we propose’, ‘in this study we propose’ and ‘in this paper we propose a new method to’ are all regarded as formulaic expressions. Such a diversity of spans and forms causes problems in both extraction and evaluation of formulaic expressions. In this paper, we propose a new approach that is robust to variation of spans and forms of formulaic expressions. Our approach regards a sentence as consisting of a formulaic part and non-formulaic part. Then, instead of trying to extract formulaic expressions from a whole corpus, by extracting them from each sentence, different forms can be dealt with at once. Based on this formulation, to avoid the diversity problem, we propose evaluating extraction methods by how much they convey specific communicative functions rather than by comparing extracted expressions to an existing lexicon. We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence. Experimental results show that the proposed extraction method achieved the best performance compared to other existing methods.

Title: A Mechanical Screwing Tool for Parallel Grippers—Design, Optimization, and Manipulation Policies Abstract: This article develops a mechanical screwing tool and its manipulation policies for two-finger parallel robotic grippers. The tool is based on a combined scissor-like element (SLE) and double-ratchet mechanism that converts the gripping motion of two-finger parallel grippers into a continuous rotation to realize tasks like fastening screws. The tool is entirely mechanical. There is no need for exte...

Title: Defective DP-colorings of sparse simple graphs Abstract: DP-coloring (also known as correspondence coloring) is a generalization of list coloring developed recently by Dvorak and Postle. We introduce and study (i, j)-defective DP-colorings of simple graphs. Let gDP(i, j, n) be the minimum number of edges in an n-vertex DP-(i, j)-critical graph. In this paper we determine sharp bound on g(DP)(i, j, n) for each i >= 3 and j >= 2i + 1 for infinitely many n. (c) 2021 Elsevier B.V. All rights reserved.

Title: Accelerated Control Using Stochastic Dual Simplex Algorithm and Genetic Filter for Drone Application Abstract: This article presents a new proportional-integral-derivative-accelerated (PIDA) control with a derivative filter to improve quadcopter/drone flight stability in a noisy environment. The mathematical model is derived from having an accurate model with a high level of fidelity by addressing the problems of nonlinearity, uncertainties, and coupling. These uncertainties and measurement noises cause instability in flight and automatic hovering. The proposed controller associated with a heuristic genetic filter (GF) addresses these challenges. The tuning of the proposed PIDA controller associated with the objective of controlling is performed by stochastic dual simplex algorithm. GF is applied to the PIDA control to estimate the observed states and parameters of quadcopters in both attitude and altitude. The simulation results show that the proposed control associated with GF has a strong ability to track the desired point in the presence of disturbances.

Title: ON THE LARGEST COMMON SUBTREE OF RANDOM LEAF-LABELED BINARY TREES Abstract: The size of the largest common subtree (maximum agreement subtree) of two independent uniform random binary trees on n leaves is known to be between orders n(1/8) and n(1/2). By a construction based on recursive splitting and analyzable by standard "stochastic fragmentation" methods, we improve the lower bound to order n(beta) for beta = root 3-1/2 = 0.366. Improving the upper bound remains a challenging problem.

Title: Multi-Density Sketch-to-Image Translation Network Abstract: Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translations, including sketch-to-photo and sketch-to-painting, can be used as powerful tools in the art design industry. However, previous methods only support S2I translation with a single level of density, which gives less flexibility to users for controlling the input sketches. In this work, we propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to microstructures. Moreover, to tackle the problem of noncontinuous representation of multi-level density input sketches, we project the density level into a continuous latent space, which can then be linearly controlled by a parameter. This allows users to conveniently control the densities of input sketches and the generation of images. Moreover, our method has been successfully verified on various datasets for different applications, including face editing, multi-modal sketch-to-photo translation, and anime colorization, providing coarse-to-fine levels of controls to these applications.

Title: Toric eigenvalue methods for solving sparse polynomial systems. Abstract: We consider the problem of computing homogeneous coordinates of points in a zero-dimensional subscheme of a compact toric variety $X$. Our starting point is a homogeneous ideal $I$ in the Cox ring of $X$, which gives a global description of this subscheme. It was recently shown that eigenvalue methods for solving this problem lead to robust numerical algorithms for solving (nearly) degenerate sparse polynomial systems. In this work, we give a first description of this strategy for non-reduced, zero-dimensional subschemes of $X$. That is, we allow isolated points with arbitrary multiplicities. Additionally, we investigate the regularity of $I$ to provide the first universal complexity bounds for the approach, as well as sharper bounds for weighted homogeneous, multihomogeneous and unmixed sparse systems, among others. We disprove a recent conjecture regarding the regularity and prove an alternative version. Our contributions are illustrated by several examples.

Title: Does causal dynamics imply local interactions? Abstract: We consider quantum systems with causal dynamics in discrete spacetimes, also known as quantum cellular automata (QCA). Due to time-discreteness this type of dynamics is not characterized by a Hamiltonian but by a one-time-step unitary. This can be written as the exponential of a Hamiltonian but in a highly non-unique way. We ask if any of the Hamiltonians generating a QCA unitary is local in some sense, and we obtain two very different answers. On one hand, we present an example of QCA for which all generating Hamiltonians are fully non-local, in the sense that interactions do not decay with the distance. We expect this result to have relevant consequences for the classification of topological phases in Floquet systems, given that this relies on the effective Hamiltonian. On the other hand, we show that all one-dimensional quasi-free fermionic QCAs have quasi-local generating Hamiltonians, with interactions decaying exponentially in the massive case and algebraically in the critical case. We also prove that some integrable systems do not have local, quasi-local nor low-weight constants of motion; a result that challenges the standard definition of integrability.

Title: Quantitative Sensitivity Bounds for Nonlinear Programming and Time-Varying Optimization Abstract: Inspired by classical sensitivity results for nonlinear optimization, we derive and discuss new quantitative bounds to characterize the solution map and dual variables of a parametrized nonlinear program. In particular, we derive explicit expressions for the local and global Lipschitz constants of the solution map of nonconvex or convex optimization problems, respectively. Our results are geared towards the study of time-varying optimization problems, which are commonplace in various applications of online optimization, including power systems, robotics, signal processing, and more. In this context, our results can be used to bound the rate of change of the optimizer. To illustrate the use of our sensitivity bounds we generalize existing arguments to quantify the tracking performance of continuous-time, monotone running algorithms. Furthermore, we introduce a new continuous-time running algorithm for time-varying constrained optimization, which we model as a so-called perturbed sweeping process. For this discontinuous scheme we establish an explicit bound on the asymptotic solution tracking for a class of convex problems.

Title: Cyclic Differentiable Architecture Search Abstract: Differentiable ARchiTecture Search, i.e., DARTS, has drawn great attention in neural architecture search. It tries to find the optimal architecture in a shallow search network and then measures its performance in a deep evaluation network. The independent optimization of the search and evaluation networks, however, leaves a room for potential improvement by allowing interaction between the two networks. To address the problematic optimization issue, we propose new joint optimization objectives and a novel Cyclic Differentiable ARchiTecture Search framework, dubbed CDARTS. Considering the structure difference, CDARTS builds a cyclic feedback mechanism between the search and evaluation networks with introspective distillation. First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized. Second, the architecture weights in the search network are further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks and thus enables the evolution of the architecture to fit the final evaluation network. The experiments and analysis on CIFAR, ImageNet and NATS-Bench [95] demonstrate the effectiveness of the proposed approach over the state-of-the-art ones. Specifically, in the DARTS search space, we achieve 97.52% top-1 accuracy on CIFAR10 and 76.3% top-1 accuracy on ImageNet. In the chain-structured search space, we achieve 78.2% top-1 accuracy on ImageNet, which is 1.1% higher than EfficientNet-B0. Our code and models are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/microsoft/Cream</uri> .

Title: Improving sequential latent variable models with autoregressive flows Abstract: We propose an approach for improving sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving frame of reference, removing temporal correlations and simplifying the modeling of higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques. We demonstrate the proposed approach both with standalone flow-based models and as a component within sequential latent variable models. Results are presented on three benchmark video datasets and three other time series datasets, where autoregressive flow-based dynamics improve log-likelihood performance over baseline models. Finally, we illustrate the decorrelation and improved generalization properties of using flow-based dynamics.

Title: Adaptive Control of Nonlinear MIMO System With Orthogonal Endocrine Intelligent Controller Abstract: In this article, a new intelligent hybrid controller is proposed. The controller is based on the combination of the orthogonal endocrine neural network (OENN) and orthogonal endocrine ANFIS (OEANFIS). The orthogonal part of the controller consists of Chebyshev orthogonal functions, which are used because of their recursive property, computational simplicity, and accuracy in nonlinear approximation...

Title: Polynomial Lyapunov Functions for Synchronization of Nonlinearly Coupled Complex Networks Abstract: In this article, we search for polynomial Lyapunov functions beyond the quadratic form to investigate the synchronization problems of nonlinearly coupled complex networks. First, with a relaxed assumption than the quadratic condition, a synchronization criterion is established for nonlinearly coupled networks with asymmetric coupling matrices. Compared with the existing synchronization criteria, o...

Title: Memristor Neural Networks for Linear and Quadratic Programming Problems Abstract: This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, quadratic programming (QP) and linear programming (LP) problems. The networks, which are called memristor programming NNs (MPNNs), use a set of filamentary-type memristors with sharp memristance transitions for constraint satisfaction and an additional set of memristors with smooth memristance transit...

Title: Modeling Product’s Visual and Functional Characteristics for Recommender Systems Abstract: An effective recommender system can significantly help customers to find desired products and assist business owners to earn more income. Nevertheless, the decision-making process of users is highly complex, not only dependent on the personality and preference of a user, but also complicated by the characteristics of a specific product. For example, for products of different domains (e.g., clothing versus office products), the product aspects that affect a user’s decision are very different. As such, traditional collaborative filtering methods that model only user-item interaction data would deliver unsatisfactory recommendation results. In this work, we focus on fine-grained modeling of product characteristics to improve recommendation quality. Specifically, we first divide a product’s characteristics into visual and functional aspects—i.e., the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual appearance</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">functionality</i> of the product. One insight is that, the visual characteristic is very important for products of visually-aware domain (e.g., clothing), while the functional characteristic plays a more crucial role for visually non-aware domain (e.g., office products). We then contribute a novel probabilistic model, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Visual and Functional Probabilistic Matrix Factorization</i> (VFPMF), to unify the two factors to estimate user preferences on products. Nevertheless, such an expressive model poses efficiency challenge in parameter learning from implicit feedback. To address the technical challenge, we devise a computationally efficient learning algorithm based on alternating least squares. Furthermore, we provide an online updating procedure of the algorithm, shedding some light on how to adapt our method to real-world recommendation scenario where data continuously streams in. Extensive experiments on four real-word datasets demonstrate the effectiveness of our method with both offline and online protocols.

Title: Interference Alignment for the K -User MIMO Interference Channel Abstract: We consider the $K$ -user Multiple Input Multiple Output (MIMO) Gaussian interference channel with $M$ antennas at each transmitter and $N$ antennas at each receiver. It is assumed t...

Title: Combinatorics of Antiprism Triangulations Abstract: The antiprism triangulation provides a natural way to subdivide a simplicial complex  $$\Delta $$ , similar to barycentric subdivision, which appeared independently in combinatorial algebraic topology and computer science. It can be defined as the simplicial complex of chains of multi-pointed faces of  $$\Delta $$ , from a combinatorial point of view, and by successively applying the antiprism construction, or balanced stellar subdivisions, on the faces of  $$\Delta $$ , from a geometric point of view. This paper studies enumerative invariants associated to this triangulation, such as the transformation of the h-vector of $$\Delta $$ under antiprism triangulation, and algebraic properties of its Stanley–Reisner ring. Among other results, it is shown that the h-polynomial of the antiprism triangulation of a simplex is real-rooted and that the antiprism triangulation of $$\Delta $$ has the almost strong Lefschetz property over $${{\mathbb {R}}}$$ for every shellable complex  $$\Delta $$ . Several related open problems are discussed.

Title: NULL SETS AND COMBINATORIAL COVERING PROPERTIES Abstract: A subset of the Cantor cube is null-additive if its algebraic sum with any null set is null. We construct a set of cardinality continuum such that: all continuous images of the set into the Cantor cube are null-additive, it contains a homeomorphic copy of a set that is not null-additive, and it has the property gamma, a strong combinatorial covering property. We also construct a nontrivial subset of the Cantor cube with the property. that is not null additive. Set-theoretic assumptions used in our constructions are far milder than used earlier by Galvin-Miller and Bartoszy ' nski-Reclaw, to obtain sets with analogous properties. We also consider products of Sierpi ' nski sets in the context of combinatorial covering properties.

Title: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. Abstract: Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information - for instance, the dynamics describing the effects of causal relations - which is lost when following this approach. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus makes use of the information that is shared. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under hidden confounding.

Title: NROWAN-DQN: A stable noisy network with noise reduction and online weight adjustment for exploration Abstract: Deep reinforcement learning has been applied more and more widely nowadays, especially in various complex control tasks. Effective exploration for noisy networks is one of the most important issues in deep reinforcement learning. Noisy networks tend to produce stable outputs for agents. However, this tendency is not always enough to find a stable policy for an agent, which decreases efficiency and stability during the learning process. Based on NoisyNets, this paper proposes an algorithm called NROWAN-DQN, i.e., Noise Reduction and Online Weight Adjustment NoisyNet-DQN. Firstly, we develop a novel noise reduction method for NoisyNet-DQN to make the agent perform stable actions. Secondly, we design an online weight adjustment strategy for noise reduction, which improves stable performance and gets higher scores for the agent. Finally, we evaluate this algorithm in four standard domains and analyze properties of hyper-parameters. Our results show that NROWAN-DQN outperforms prior algorithms in all these domains. In addition, NROWAN-DQN also shows better stability. The variance of the NROWAN-DQN score is significantly reduced, especially in some action-sensitive environments. This means that in some environments where high stability is required, NROWAN-DQN will be more appropriate than NoisyNets-DQN.

Title: Representing pure Nash equilibria in argumentation Abstract: In this paper we describe an argumentation-based representation of normal form games, and demonstrate how argumentation can be used to compute pure strategy Nash equilibria. Our approach builds on Modgil's Extended Argumentation Frameworks. We demonstrate its correctness, showprove several theoretical properties it satisfies, and outline how it can be used to explain why certain strategies are Nash equilibria to a non-expert human user.

Title: Bayesian analysis of mixture autoregressive models covering the complete parameter space Abstract: Mixture autoregressive (MAR) models provide a flexible way to model time series with predictive distributions which depend on the recent history of the process and are able to accommodate asymmetry and multimodality. Bayesian inference for such models offers the additional advantage of incorporating the uncertainty in the estimated models into the predictions. We introduce a new way of sampling from the posterior distribution of the parameters of MAR models which allows for covering the complete parameter space of the models, unlike previous approaches. We also propose a relabelling algorithm to deal a posteriori with label switching. We apply our new method to simulated and real datasets, discuss the accuracy and performance of our new method, as well as its advantages over previous studies. The idea of density forecasting using MCMC output is also introduced.

Title: A numerical algorithm to computationally solve the Hemker problem using Shishkin meshes Abstract: A numerical algorithm is presented to solve a benchmark problem proposed by Hemker (1996). The algorithm incorporates asymptotic information into the design of appropriate piecewise-uniform Shishkin meshes. Moreover, different co-ordinate systems are utilized due to the different geometries and associated layer structures that are involved in this problem. Numerical results are presented to demonstrate the effectiveness of the proposed numerical algorithm.

Title: Manifold feature index: A novel index based on high-dimensional data simplification Abstract: The current stock index design models typically consider financial factors, such as market capitalization and free-float, regardless of stock price changes. Their constituent selection strategies are based on the ranking and determined by experts in exchanges or financial services. In this paper, we develop a novel stock index model, namely, the manifold feature (MF) index, to reflect the overall price activity of the entire stock market. The MF index is designed using the manifold learning theory and a harmonic analysis method. To our knowledge, it is the first time to do so in expert/intelligent systems to design stock index. Specifically, according to the manifold learning theory, the studied stock dataset is assumed to be a low-dimensional manifold embedded in a higher-dimensional Euclidean space. After data preprocessing, a harmonic analysis method is performed. That is, the discrete Laplace–Beltrami operator (LBO) matrix defined on the stock dataset, which forms a low-dimensional manifold, is firstly constructed. Then, the eigenvectors of LBO are calculated, and the feature points on the eigenvectors are detected. The stocks corresponding to these feature points are considered as the constituent stocks of the MF index. Finally, the MF index is generated by a weighted formula using the price and market capitalization of these constituents. Moreover, we propose four metrics to compare the MF index series and the Shanghai Stock Exchange (SSE) index series (SSE 50, SSE 100, SSE 150, SSE 180 and SSE 380), and the MF indexes are better than the SSE indexes in two aspects: On one hand, from the perspective of data approximation, MF indexes are closer to the stock market than the SSE index series. On the other hand, from the perspective of risk premium, MF indexes have higher stability and lower risk.

Title: The fluted fragment with transitive relations Abstract: The fluted fragment is a fragment of first-order logic (without equality) in which, roughly speaking, the order of quantification of variables coincides with the order in which those variables appear as arguments of predicates. It is known that this fragment has the finite model property. We consider extensions of the fluted fragment with various numbers of transitive relations, as well as the equality predicate. In the presence of one transitive relation (together with equality), the finite model property is lost; nevertheless, we show that the satisfiability and finite satisfiability problems for this extension remain decidable. We also show that the corresponding problems in the presence of two transitive relations (with equality) or three transitive relations (without equality) are undecidable, even for the two-variable sub-fragment.

Title: A diameter-revealing proof of the Bondy-Lovász lemma Abstract: •The Bondy-Lovász lemma is strengthened with an upper bound on the graph diameter.•A constructive proof shows the upper bound on the graph diameter.•Explicit instances yield a lower bound that asymptotically matches the upper bound.

Title: The Internet of Things and Information Fusion: Who Talks to Who? Abstract: Problem definition: Autonomous sensors connected through the Internet of things (IoT) are deployed by different firms in the same environment. The sensors measure an important operating-condition state variable, but their measurements are noisy, so estimates are imperfect. Sensors can improve their own estimates by soliciting estimates from other sensors. The choice of which sensors to communicate with (target) is challenging because sensors (1) are constrained in the number of sensors they can target and (2) only have partial knowledge of how other sensors operate-that is, they do not know others' underlying inference algorithms/models. We study the targeting problem, examine the evolution of interfirm sensor communication patterns, and explore what drives the patterns. Academic/practical relevance: Many industries are increasingly using sensors to drive improvements in key performance metrics (e.g., asset uptime) through better information on operating conditions. Sensors will communicate among themselves to improve estimation. This IoT vision will have a major impact on operations management (OM), and OM scholars need to develop and examine models and frameworks to better understand sensor interactions. Methodology: Analytic modeling combining decision-making, estimation, optimization, and learning is used. Results: We show that when selecting its target(s), each sensor needs to consider both the measurement quality of the other sensors and its level of familiarity with their inference models. We establish that the state of the environment plays a key role in mediating quality and familiarity. When sensor qualities are public, we show that each sensor eventually settles on a constant target set, but this long-run target set is sample-path dependent (i.e., dependent on past states) and varies by sensor. The long-run network, however, can be fully defined at time zero as a random directed graph, and hence, one can probabilistically predict it. This prediction can be made perfect (i.e., the network can be identified in a deterministic way) after observing the state values for a limited number of periods. When sensor qualities are private, our results reveal that sensors may not settle on a constant target set but the subset among which it cycles can still be stochastically predicted. Managerial implications: Our work allows managers to predict (and influence) the set of other firms with which their sensors will form information links. Analogous to a manufacturer mapping its supplier base to help manage supply continuity, our work enables a firm to map its sensor-based-information suppliers to help manage information continuity.

Title: The computational strength of matchings in countable graphs Abstract: In a 1977 paper, Steffens identified an elegant criterion for determining when a countable graph has a perfect matching. In this paper, we will investigate the proof theoretic strength of this result and related theorems. We show that a number of natural variants of these theorems are equivalent, or closely related, to the "big five" subsystems of reverse mathematics. The results of this paper explore the relationship between graph theory and logic by showing the way in which specific changes to a single graph-theoretic principle impact the corresponding proof-theoretic strength. Taken together, the results and questions of this paper suggest that the existence of matchings in countable graphs provides a rich context for understanding reverse mathematics more broadly. (c) 2022 Elsevier B.V. All rights reserved.

Title: Extractors for Small Zero-Fixing Sources Abstract: Let V ⊆ [n] be a k-element subset of [n]. The uniform distribution on the 2k strings from {0, 1}n that are set to zero outside of V is called an (n, k)-zero-fixing source. An ϵ-extractor for (n, k)-zero-fixing sources is a mapping F: {0, 1}n → {0, 1}m, for some m, such that F(X) is ϵ-close in statistical distance to the uniform distribution on {0, 1}m for every (n, k)-zero-fixing source X. Zero-fixing sources were introduced by Cohen and Shinkar in [7] in connection with the previously studied extractors for bit-fixing sources. They constructed, for every μ > 0, an efficiently computable extractor that extracts a positive fraction of entropy, i.e., Ω(k) bits, from (n, k)-zero-fixing sources where k ≥ (log log n)2+μ. In this paper we present two different constructions of extractors for zero-fixing sources that are able to extract a positive fraction of entropy for k substantially smaller than log log n. The first extractor works for k ≥ C log log log n, for some constant C. The second extractor extracts a positive fraction of entropy for k ≥ log(i)n for any fixed i ∈ ℕ, where log(i) denotes i-times iterated logarithm. The fraction of extracted entropy decreases with i. The first extractor is a function computable in polynomial time in n; the second one is computable in polynomial time in n when k ≤ α log log n/log log log n, where α is a positive constant. Our results can also be viewed as lower bounds on some Ramsey-type properties. The main difference between the problems about extractors studied here and the standard Ramsey theory is that we study colorings of all subsets of size up to k while in Ramsey theory the sizes are fixed to k. However it is easy to derive results also for coloring of subsets of sizes equal to k. In Corollary 3.1 of Theorem 5.1 we show that for every l ∈ ℕ there exists β < 1 such that for every k and n, n ≤ expl (k), there exists a 2-coloring of k-tuples of elements of [n], $$\psi :\left({\matrix{{[n]} \cr k \cr}} \right) \to \left\{{- 1,1} \right\}$$ such that for every V ⊆ [n], |V| = 2k, we have $$\left| {\sum\nolimits_{X \subseteq V,\left| X \right| = k} {\psi (X)}} \right| \le {\beta ^k}\left({\matrix{{2k} \cr k \cr}} \right)$$ (Corollary 3.1 is more general — the number of colors may be more than 2).

Title: Density Estimates of 1-Avoiding Sets via Higher Order Correlations Abstract: We improve the best known upper bound on the density of a planar measurable set A containing no two points at unit distance to 0.25442. We use a combination of Fourier analytic and linear programming methods to obtain the result. The estimate is achieved by means of obtaining new linear constraints on the autocorrelation function of A utilizing triple-order correlations in A, a concept that has not been previously studied.

Title: Transporting Robotic Swarms Via Mean-Field Feedback Control Abstract: With the rapid development of Artificial Intelligence (AI) and robotics, deploying a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top–down approach, and develop a provable control strategy for transporting a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed—individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations.

Title: Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision Abstract: Pseudo-LiDAR point cloud interpolation is a novel and challenging task in autonomous driving, which aims to address the frequency mismatching problem between a camera and a LiDAR. Previous works represent the 3D spatial motion relationship with a coarse 2D optical flow, and the quality of interpolated point clouds only depends on the supervision of depth maps. As a result, the generated point clouds suffer from inferior global distributions and local appearances. To solve the above problems, we propose a Pseudo-LiDAR point cloud interpolation network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow from point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship. For a more comprehensive perception of the distribution of a point cloud, we design a novel reconstruction loss function with the chamfer distance to supervise the generation of Pseudo-LiDAR point clouds in 3D space. In addition, we introduce a multi-modal deep aggregation module to facilitate the efficient fusion of texture and depth features. As the benefits of the improved motion representation, training loss function, and model structure, our approach gains significant improvements on the Pseudo-LiDAR point cloud interpolation task. The experimental results evaluated on KITTI dataset demonstrate the state-of-the-art quantitative and qualitative performance of the proposed network.

Title: Enhancing Factorization Machines With Generalized Metric Learning Abstract: Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the inner product to model the second-order interactions between different attributes, which are represented via feature vectors. The problem is that the inner product violates the triangle inequality property of feature vectors. As a result, it cannot well capture fine-grained attribute interactions, resulting in sub-optimal performance. Recently, the euclidean distance is exploited in FMs to replace the inner product and has delivered better performance. However, previous FM methods including the ones equipped with the euclidean distance all focus on the attribute-level interaction modeling, ignoring the critical intrinsic feature correlations inside attributes. Thereby, they fail to model the complex and rich interactions exhibited in the real-world data. To tackle this problem, in this paper, we propose a FM framework equipped with generalized metric learning techniques to better capture these feature correlations. In particular, based on this framework, we present a Mahalanobis distance and a deep neural network (DNN) methods, which can effectively model the linear and non-linear correlations between features, respectively. Besides, we design an efficient approach for simplifying the model functions. Experiments on several benchmark datasets demonstrate that our proposed framework outperforms several state-of-the-art baselines by a large margin. Moreover, we collect a new large-scale dataset on second-hand trading to justify the effectiveness of our method over cold-start and data sparsity problems in recommender systems.

Title: Bayesian updating and sequential testing: overcoming inferential limitations of screening tests Abstract: Background: Bayes'theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence. Herein, we establish a mathematical model to determine whether sequential testing with a single test overcomes the aforementioned Bayesian limitations and thus improves the reliability of screening tests. Methods: We use Bayes'theorem to derive the positive predictive value equation, and apply the Bayesian updating method to obtain the equation for the positive predictive value (PPV) following repeated testing. We likewise derive the equation which determines the number of iterations of a positive test needed to obtain a desired positive predictive value, represented graphically by the tablecloth function. Results: For a given PPV (rho) approaching k, the number of positive test iterations needed given a prevalence of disease (phi) is: n(i) = lim(rho -> k) inverted right perpendicularIn[rho(phi-1)/phi(rho-1)]/In[a/1-b]inverted left perpendicular (1) where n(i) = number of testing iterations necessary to achieve rho, the desired positive predictive value, In = the natural logarithm, a = sensitivity, b = specificity, phi = disease prevalence/pre-test probability and k = constant. Conclusions: Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a rho(phi) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence/pre-test probability. Clinical validation of these concepts needs to be obtained prior to its widespread application.

Title: BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction Abstract: •The first method that predicts interpretable landmarks in unsupervised way.•Unlike pre-trained models which require large datasets for pre-training their auto-encoders, our method needs just a dozen of images to compute barycenter.•In a semi-supervised scenario, our method outperforms state-of-the-art models.•Two types of regularization (barycenter and geometric transforms) are shown to suffice for auto-encoder to produce the image landmarks in the bottleneck.•New type of cyclic/conditional GAN architecture that performs training with only one domain data and decomposes images into landmarks and style.

Title: Counterfactually Guided Policy Transfer in Clinical Settings. Abstract: Reliably transferring treatment policies learned in one clinical environment to another is currently limited by challenges related to domain shift. In this paper we address off-policy learning for sequential decision making under domain shift -- a scenario susceptible to catastrophic overconfidence -- which is highly relevant to a high-stakes clinical settings where the target domain may also be data-scarce. We propose a two-fold counterfactual regularization procedure to improve off-policy learning, addressing domain shift and data scarcity. First, we utilize an informative prior derived from a data-rich source environment to indirectly improve drawing counterfactual example observations. Then, these samples are then used to learn a policy for the target domain, regularized by the source policy through KL-divergence. In simulated sepsis treatment, our counterfactual policy transfer procedure significantly improves the performance of a learned treatment policy.

Title: A novel method for ECG signal classification via one-dimensional convolutional neural network Abstract: This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods.

Title: Exact Partitioning of High-Order Planted Models with A Tensor Nuclear Norm Constraint Abstract: We study the problem of efficient exact partitioning of the hypergraphs generated by high-order planted models. A high-order planted model assumes some underlying cluster structures, and simulates high-order interactions by placing hyperedges among nodes. Example models include the disjoint hypercliques, the densest subhypergraphs, and the hypergraph stochastic block models. We show that exact partitioning of high-order planted models (a NP-hard problem in general) is achievable through solving a computationally efficient convex optimization problem with a tensor nuclear norm constraint. Our analysis provides the conditions for our approach to succeed on recovering the true underlying cluster structures, with high probability.

Title: INTERPRETING A FIELD IN ITS HEISENBERG GROUP Abstract: We improve on and generalize a 1960 result of Maltsev. For a field F, we denote by H(F) the Heisenberg group with entries in F. Maltsev showed that there is a copy of F defined in H(F), using existential formulas with an arbitrary non-commuting pair of elements as parameters. We show that F is interpreted in H(F) using computable Sigma(1) formulas with no parameters. We give two proofs. The first is an existence proof, relying on a result of Harrison-Trainor, Melnikov, R. Miller, and Montalb ' an. This proof allows the possibility that the elements of F are represented by tuples in H(F) of no fixed arity. The second proof is direct, giving explicit finitary existential formulas that define the interpretation, with elements of F represented by triples in H(F). Looking at what was used to arrive at this parameter-free interpretation of F in H(F), we give general conditions sufficient to eliminate parameters from interpretations.

Title: Toward the biological model of the hippocampus as the successor representation agent Abstract: The hippocampus is an essential brain region for spatial memory and learning. Recently, a theoretical model of the hippocampus based on temporal difference (TD) learning has been published. Inspired by the successor representation (SR) learning algorithms, which decompose value function of TD learning into reward and state transition, they argued that the rate of firing of CA1 place cells in the hippocampus represents the probability of state transition. This theory, called predictive map theory, claims that the hippocampus representing space learns the probability of transition from the current state to the future state. The neural correlates of expecting the future state are the firing rates of the CA1 place cells. This explanation is plausible for the results recorded in behavioral experiments, but it is lacking the neurobiological implications. Modifying the SR learning algorithm added biological implications to the predictive map theory. Similar with the simultaneous needs of information of the current and future state in the SR learning algorithm, the CA1 place cells receive two inputs from CA3 and entorhinal cortex. Mathematical transformation showed that the SR learning algorithm is equivalent to the heterosynaptic plasticity rule. The heterosynaptic plasticity phenomena in CA1 were discussed and compared with the modified SR update rule. This study attempted to interpret the TD algorithm as the neurobiological mechanism occurring in place learning, and to integrate the neuroscience and artificial intelligence approaches in the field.

Title: DO-Conv: Depthwise Over-Parameterized Convolutional Layer Abstract: Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel. The composition of the two convolutions constitutes an over-parameterization, since it adds learnable parameters, while the resulting linear operation can be expressed by a single convolution layer. We refer to this depthwise over-parameterized convolutional layer as DO-Conv, which is a novel way of over-parameterization. We show with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs on many classical vision tasks, such as image classification, detection, and segmentation. Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization. As DO-Conv introduces performance gains without incurring any computational complexity increase for inference, we advocate it as an alternative to the conventional convolutional layer. We open sourced an implementation of DO-Conv in Tensorflow, PyTorch and GluonCV at https://github.com/yangyanli/DO-Conv.

Title: Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency Abstract: This paper introduces unified models for high-dimensional factor-based Itô process, which can accommodate both continuous-time Itô diffusion and discrete-time stochastic volatility (SV) models by embedding the discrete SV model in the continuous instantaneous factor volatility process. We call it the SV-Itô model. Based on the series of daily integrated factor volatility matrix estimators, we propose quasi-maximum likelihood and least squares estimation methods. Their asymptotic properties are established. We apply the proposed method to predict future vast volatility matrix whose asymptotic behaviors are studied. A simulation study is conducted to check the finite sample performance of the proposed estimation and prediction method. An empirical analysis is carried out to demonstrate the advantage of the SV-Itô model in volatility prediction and portfolio allocation problems.

Title: On Morphisms Preserving Palindromic Richness Abstract: It is known that each word of length n contains at most n + 1 distinct palindromes. A finite rich word is a word with maximal number of palindromic factors. The definition of palindromic richness can be naturally extended to infinite words. Sturmian words and Rote complementary symmetric sequences form two classes of binary rich words, while episturmian words and words coding symmetric d-interval exchange transformations give us other examples on larger alphabets. In this paper we look for morphisms of the free monoid, which allow us to construct new rich words from already known rich words. We focus on morphisms in Class P-ret. This class contains morphisms injective on the alphabet and satisfying a particular palindromicity property: for every morphism phi in the class there exists a palindrome omega such that phi (a)omega is a first complete return word to omega for each letter a. We characterize P-ret morphisms which preserve richness over a binary alphabet. We also study marked P-ret morphisms acting on alphabets with more letters. In particular we show that every Arnoux-Rauzy morphism is conjugated to a morphism in Class P-ret and that it preserves richness.

Title: MODELING ELECTRODE HETEROGENEITY IN LITHIUM-ION BATTERIES: UNIMODAL AND BIMODAL PARTICLE-SIZE DISTRIBUTIONS Abstract: In mathematical models of lithium-ion batteries, the highly heterogeneous porous electrodes are frequently approximated as comprising spherical particles of uniform size, leading to the commonly used single-particle model (SPM) when transport in the electrolyte is assumed to be fast. Here electrode heterogeneity is modeled by extending this to a distribution of particle sizes. Unimodal and bimodal particle-size distributions (PSD) are considered. For a unimodal PSD, the effect of the spread of the distribution on the cell dynamics is investigated, and choice of effective particle radius when approximating by an SPM assessed. Asymptotic techniques are used to derive a correction to the SPM valid for narrow, but realistic, PSDs. In addition, it is shown that the heterogeneous internal states of all particles (relevant when modeling degradation, for example) can be efficiently computed after the fact. For a bimodal PSD, the results are well approximated by a double-particle model (DPM), with one size representing each mode. Results for lithium iron phosphate with a bimodal PSD show that the DPM captures an experimentally observed double plateau in the discharge curve, suggesting it is entirely due to bimodality.

Title: HOW MUCH PROPOSITIONAL LOGIC SUFFICES FOR ROSSER'S ESSENTIAL UNDECIDABILITY THEOREM? Abstract: In this paper we explore the following question: how weak can a logic be for Rosser's essential undecidability result to be provable for a weak arithmetical theory? It is well known that Robinson's Q is essentially undecidable in intuitionistic logic, and P. Hajek proved it in the fuzzy logic BL for Grzegorczyk's variant of Q which interprets the arithmetic operations as nontotal nonfunctional relations. We present a proof of essential undecidability in a much weaker substructural logic and for a much weaker arithmetic theory, a version of Robinson's R (with arithmetic operations also interpreted as mere relations). Our result is based on a structural version of the undecidability argument introduced by Kleene and we show that it goes well beyond the scope of the Boolean, intuitionistic, or fuzzy logic.

Title: Confluence in labeled chip-firing Abstract: In 2016, Hopkins, McConville, and Propp proved that labeled chip-firing on a line always leaves the chips in sorted order provided that the initial number of chips is even. We present a novel proof of this result. We then apply our methods to resolve a number of related conjectures concerning the confluence of labeled chip-firing systems.

Title: Edge Server Deployment Scheme of Blockchain in IoVs Abstract: In the Internet of Vehicles (IoVs), vehicles generate and disseminate information, which makes the related vehicular services realized. However, the IoVs is an untrusted environment. Vehicles cannot evaluate the credibility of the received information, which makes it a challenge to implement data sharing in IoVs. Blockchain, constantly directed main attention, are considered as a feasible solution...

Title: Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge Abstract: •Present the methodologies and evaluation results for the cardiac segmentation algorithms selected from the submissions to the MS-CMRSeg challenge, in conjunction with MICCAI 2019.•Provide a fair and intuitive comparison between the supervised methods and UDA algorithms for cardiac segmentation.•Provide datasets and evaluation tools for an ongoing development of MS-CMR based cardiac segmentation algorithms.

Title: Fanoos - Multi-resolution, Multi-strength, Interactive Explanations for Learned Systems. Abstract: Machine learning becomes increasingly important to tune or even synthesize the behavior of safety-critical components in highly non-trivial environments, where the inability to understand learned components in general, and neural nets in particular, poses serious obstacles to their adoption. Explainability and interpretability methods for learned systems have gained considerable academic attention, but the focus of current approaches on only one aspect of explanation, at a fixed level of abstraction, and limited if any formal guarantees, prevents those explanations from being digestible by the relevant stakeholders (e.g., end users, certification authorities, engineers) with their diverse backgrounds and situation-specific needs. We introduce Fanoos, a flexible framework for combining formal verification techniques, heuristic search, and user interaction to explore explanations at the desired level of granularity and fidelity. We demonstrate the ability of Fanoos to produce and adjust the abstractness of explanations in response to user requests on a learned controller for an inverted double pendulum and on a learned CPU usage model.

Title: Attention-based quantum tomography Abstract: With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the 'attention-based quantum tomography' (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in 'Attention is all you need' by Vaswani et al (2017 NIPS) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.

Title: Building Multiple Access Channels with a Single Particle Abstract: A multiple access channel describes a situation in which multiple senders are trying to forward messages to a single receiver using some physical medium. In this paper we consider scenarios in which this medium consists of just a single classical or quantum particle. In the quantum case, the particle ran be prepared in a superposition state thereby allowing for a richer family of encoding strategies. To make the comparison between quantum and classical channels precise, we introduce an operational framework in which all possible encoding strategies consume no more than a single particle. We apply this framework to an N-port interferometer experiment in which each party controls a path the particle can traverse. When used for the purpose of communication, this setup embodies a multiple access channel (MAC) built with a single particle. We provide a full characterization of the N-party classical MACs that can be built from a single particle, and we show that every quantum particle can generate a MAC outside the classical set. To further distinguish the capabilities of a single classical and quantum particle, we relax the locality constraint and allow for joint encodings by subsets of 1 < K <= N parties. This generates a richer family of classical MACs whose polytope dimension we compute. We identify a "generalized fingerprinting inequality" as a valid facet for this polytope, and we verify that a quantum particle distributed among N separated parties can violate this inequality even when K = N-1. Connections are drawn between the single-particle framework and multi-level coherence theory. We show that every pure state with K-level coherence can be detected in a semi-device independent manner, with the only assumption being conservation of particle number.

Title: A proximal-gradient algorithm for crystal surface evolution Abstract: As a counterpoint to recent numerical methods for crystal surface evolution, which agree well with microscopic dynamics but suffer from significant stiffness that prevents simulation on fine spatial grids, we develop a new numerical method based on the macroscopic partial differential equation, leveraging its formal structure as the gradient flow of the total variation energy, with respect to a weighted H-1 norm. This gradient flow structure relates to several metric space gradient flows of recent interest, including 2-Wasserstein flows and their generalizations to nonlinear mobilities. We develop a novel semi-implicit time discretization of the gradient flow, inspired by the classical minimizing movements scheme (known as the JKO scheme in the 2-Wasserstein case). We then use a primal dual hybrid gradient (PDHG) method to compute each element of the semi-implicit scheme. In one dimension, we prove convergence of the PDHG method to the semi-implicit scheme, under general integrability assumptions on the mobility and its reciprocal. Finally, by taking finite difference approximations of our PDHG method, we arrive at a fully discrete numerical algorithm, with iterations that converge at a rate independent of the spatial discretization: in particular, the convergence properties do not deteriorate as we refine our spatial grid. We close with several numerical examples illustrating the properties of our method, including facet formation at local maxima, pinning at local minima, and convergence as the spatial and temporal discretizations are refined.

Title: Density-Based Stochastic Reachability Computation for Occupancy Prediction in Automated Driving Abstract: We propose a stochastic reachability computation framework for occupancy prediction in automated driving by directly solving the underlying transport partial differential equation (PDE) governing the advection of the closed-loop joint density functions. The resulting nonparametric gridless computation is based on integration along the characteristic curves, and it allows online computation of the time-varying collision probabilities. We provide numerical simulations for multi-lane highway driving scenarios to highlight the scope of the proposed method.

Title: When distributed formation control is feasible under hard constraints on energy and time? Abstract: This paper studies distributed optimal formation control with hard constraints on energy levels and termination time, in which the formation error is to be minimized jointly with the energy cost. The main contributions include a globally optimal distributed formation control law and a comprehensive analysis of the resulting closed-loop system under those hard constraints. It is revealed that the energy levels, the task termination time, the steady-state error tolerance, as well as the network topology impose inherent limitations in achieving the formation control mission. Most notably, the lower bounds on the achievable termination time and the required minimum energy levels are derived, which are given in terms of the initial formation error, the steady-state error tolerance, and the largest eigenvalue of the Laplacian matrix. These lower bounds can be employed to assert whether an energy and time constrained formation task is achievable and how to accomplish such a task. Furthermore, the monotonicity of those lower bounds in relation to the control parameters is revealed.

Title: iffDetector: Inference-Aware Feature Filtering for Object Detection Abstract: Modern convolutional neural network (CNN)-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this article, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages. We introduce a generic inference-aware feature filtering (IFF) module that can be easily combined with existing detectors, resulting in our iffDetector. Unlike conventional open-loop feature calculation approaches without feedback, the proposed IFF module performs the closed-loop feature optimization by leveraging high-level semantics to enhance the convolutional features. By applying the Fourier transform to analyze our detector, we prove that the IFF module acts as a negative feedback that can theoretically guarantee the stability of the feature learning. IFF can be fused with CNN-based object detectors in a plug-and-play manner with little computational cost overhead. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that our iffDetector consistently outperforms state-of-the-art methods with significant margins.

Title: CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks Abstract: Cameras currently allow access to two image states: (i) a minimally processed linear raw-RGB image state (i.e., raw sensor data); or (ii) a highly-processed nonlinear image state (e.g., sRGB). There are many computer vision tasks that work best with a linear image state, such as image deblurring and image dehazing. Unfortunately, the vast majority of images are saved in the nonlinear image state. Because of this, a number of methods have been proposed to “unprocess” nonlinear images back to a raw-RGB state. However, existing unprocessing methods have a drawback because raw-RGB images are sensor-specific. As a result, it is necessary to know which camera produced the sRGB output and use a method or network tailored for that sensor to properly unprocess it. This paper addresses this limitation by exploiting another camera image state that is not available as an output, but it is available inside the camera pipeline. In particular, cameras apply a colorimetric conversion step to convert the raw-RGB image to a device-independent space based on the CIE XYZ color space before they apply the nonlinear photo-finishing. Leveraging this canonical image state, we propose a deep learning framework, CIE XYZ Net, that can unprocess a nonlinear image back to the canonical CIE XYZ image. This image can then be processed by any low-level computer vision operator and re-rendered back to the nonlinear image. We demonstrate the usefulness of the CIE XYZ Net on several low-level vision tasks and show significant gains that can be obtained by this processing framework. Code and dataset are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mahmoudnafifi/CIE_XYZ_NET</uri> .

Title: Identification of Matrix Diffusion Coefficient in a Parabolic PDE Abstract: An inverse problem of identifying the diffusion coefficient in matrix form in a parabolic PDE is considered. Following the idea of natural linearization, considered by Cao and Pereverzev (2006), the nonlinear inverse problem is transformed into a problem of solving an operator equation where the operator involved is linear. Solving the linear operator equation turns out to be an ill-posed problem. The method of Tikhonov regularization is employed for obtaining stable approximations and its finite-dimensional analysis is done based on the Galerkin method, for which an orthogonal projection on the space of matrices with entries from L-2(Omega) is defined. Since the error estimates in Tikhonov regularization method rely heavily on the adjoint operator, an explicit representation of adjoint of the linear operator involved is obtained. For choosing the regularizing parameter, the adaptive technique is employed in order to obtain order optimal rate of convergence. For the relaxed noisy data, we describe a procedure for obtaining a smoothed version so as to obtain the error estimates. Numerical experiments are carried out for a few illustrative examples.

Title: Canonical double covers of circulants Abstract: The canonical double cover B(X) of a graph X is the direct product of X and K2. If Aut(B(X))≅Aut(X)×Z2 then X is called stable; otherwise X is called unstable. An unstable graph is nontrivially unstable if it is connected, non-bipartite and distinct vertices have different neighborhoods. A circulant is a Cayley graph on a cyclic group. Qin et al. (2019) [18] conjectured that there are no nontrivially unstable circulants of odd order. In this paper we prove this conjecture.

Title: Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. Abstract: We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: L0-bounded perturbations, adversarial patches, and adversarial frames. The L0-version of untargeted Sparse-RS outperforms all black-box and even all white-box attacks for different models on MNIST, CIFAR-10, and ImageNet. Moreover, our untargeted Sparse-RS achieves very high success rates even for the challenging settings of 20x20 adversarial patches and 2-pixel wide adversarial frames for 224x224 images. Finally, we show that Sparse-RS can be applied to generate targeted universal adversarial patches where it significantly outperforms the existing approaches. Our code is available at https://github.com/fra31/sparse-rs.

Title: Analytic Solution to the Piecewise Linear Interface Construction Problem and Its Application in Curvature Calculation for Volume-of-Fluid Simulation Codes Abstract: The plane-cube intersection problem has been discussed in the literature since 1984 and iterative solutions to it have been used as part of piecewise linear interface construction (PLIC) in computational fluid dynamics simulation codes ever since. In many cases, PLIC is the bottleneck of these simulations regarding computing time, so a faster analytic solution to the plane-cube intersection would greatly reduce the computing time for such simulations. We derive an analytic solution for all intersection cases and compare it to the previous solution from Scardovelli and Zaleski (Scardovelli, R.; Zaleski, S. Analytical relations connecting linear interfaces and volume fractions in rectangular grids. J. Comput. Phys. 2000, 164, 228-237), which we further improve to include edge cases and micro-optimize to reduce arithmetic operations and branching. We then extend our comparison regarding computing time and accuracy to include two different iterative solutions as well. We find that the best choice depends on the employed hardware platform: on the CPU, Newton-Raphson is fastest with compiler optimization enabled, while analytic solutions perform better than iterative solutions without. On the GPU, the fastest method is our optimized version of the analytic SZ solution. We finally provide details on one of the applications of PLIC-curvature calculation for the Volume-of-Fluid model used for free surface fluid simulations in combination with the lattice Boltzmann method.

Title: Multi-source domain adaptation via weighted joint distributions optimal transport. Abstract: This work addresses the problem of domain adaptation on an unlabeled target dataset using knowledge from multiple labelled source datasets. Most current approaches tackle this problem by searching for an embedding that is invariant across source and target domains, which corresponds to searching for a universal classifier that works well on all domains. In this paper, we address this problem from a new perspective: instead of crushing diversity of the source distributions, we exploit it to adapt better to the target distribution. Our method, named Multi-Source Domain Adaptation via Weighted Joint Distribution Optimal Transport (MSDA-WJDOT), aims at finding simultaneously an Optimal Transport-based alignment between the source and target distributions and a re-weighting of the sources distributions. We discuss the theoret- ical aspects of the method and propose a conceptually simple algorithm. Numerical experiments indicate that the proposed method achieves state-of- the-art performance on simulated and real datasets.

Title: Weakly binary expansions of dense meet-trees Abstract: We compute the domination monoid in the theory DMT of dense meet-trees. In order to show that this monoid is well-defined, we prove weak binarity of DMT and, more generally, of certain expansions of it by binary relations on sets of open cones, a special case being the theory DTR from [7]. We then describe the domination monoids of such expansions in terms of those of the expanding relations.

Title: Deep Polynomial Neural Networks Abstract: Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math></inline-formula> -Nets, a new class of function approximators based on polynomial expansions. <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math></inline-formula> -Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. The unknown parameters, which are naturally represented by high-order tensors, are estimated through a collective tensor factorization with factors sharing. We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks. We empirically demonstrate that <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math></inline-formula> -Nets are very expressive and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math></inline-formula> -Nets produce state-of-the-art results in three challenging tasks, i.e., image generation, face verification and 3D mesh representation learning. The source code is available at <uri>https://github.com/grigorisg9gr/polynomial_nets</uri> .

Title: A sample efficient sparse FFT for arbitrary frequency candidate sets in high dimensions Abstract: In this paper, a sublinear time algorithm is presented for the reconstruction of functions that can be represented by just few out of a potentially large candidate set of Fourier basis functions in high spatial dimensions, a so-called high-dimensional sparse fast Fourier transform. In contrast to many other such algorithms, our method works for arbitrary candidate sets and does not make additional structural assumptions on the candidate set. Our transform significantly improves upon the other approaches available for such a general framework in terms of the scaling of the sample complexity. Our algorithm is based on sampling the function along multiple rank-1 lattices with random generators. Combined with a dimension-incremental approach, our method yields a sparse Fourier transform whose computational complexity only grows mildly in the dimension and can hence be efficiently computed even in high dimensions. Our theoretical analysis establishes that any Fourier s-sparse function can be accurately reconstructed with high probability. This guarantee is complemented by several numerical tests demonstrating the high efficiency and versatile applicability for the exactly sparse case and also for the compressible case.

Title: AN ENTROPY STRUCTURE PRESERVING SPACE-TIME FORMULATION FOR CROSS-DIFFUSION SYSTEMS: ANALYSIS AND GALERKIN DISCRETIZATION Abstract: Cross-diffusion systems are systems of nonlinear parabolic partial differential equations that are used to describe dynamical processes in several application, including chemical concentrations and cell biology. We present a space-time approach to the proof of existence of bounded weak solutions of cross-diffusion systems, making use of the system entropy to examine long-term behavior and to show that the solution is nonnegative, even when a maximum principle is not available. This approach naturally gives rise to a novel space-time Galerkin method for the numerical approximation of cross-diffusion systems that conserves their entropy structure. We prove existence and convergence of the discrete solutions and present numerical results for the porous medium, the Fisher-KPP, and the Maxwell-Stefan problem.

Title: A Catalogue of Game-Specific Anti-Patterns. Abstract: With the ever-increasing use of games, game developers are expected to write efficient code supporting several qualities such as security, maintainability, and performance. However, lack of time and continuous need to update the features of games may result in bad practices that may affect the functional and non-functional requirements of the game. These bad practices are often termed as Anti-patterns, which can cause technical debt, poor program comprehension, and can lead to several issues during software maintenance. While there exists empirical research on games, we are not aware of any work on understanding and cataloguing anti-patterns in games. Thus, we propose a catalogue of game-specific anti-patterns by mining commits, pull requests, and issues from 229 popular GitHub game repositories. We use regular expressions to create an initial dataset of open-source games and did thematic analysis on text records for cataloguing game-specific anti-patterns. We applied LDA (Latent Dirichlet Allocation) to validate further and refine the categories. We present a catalogue of 11 anti-patterns with a total of 20 subcategories involved in it and provide examples for them. We believe this catalogue would help game developers in making educated decisions during game development that can result in enhanced quality of games.

Title: Experience Replay with Likelihood-free Importance Weights. Abstract: The use of past experiences to accelerate temporal difference (TD) learning of value functions, or experience replay, is a key component in deep reinforcement learning. Prioritization or reweighting of important experiences has shown to improve performance of TD learning algorithms.In this work, we propose to reweight experiences based on their likelihood under the stationary distribution of the current policy. Using the corresponding reweighted TD objective, we implicitly encourage small approximation errors on the value function over frequently encountered states. We use a likelihood-free density ratio estimator over the replay buffer to assign the prioritization weights. We apply the proposed approach empirically on two competitive methods, Soft Actor Critic (SAC) and Twin Delayed Deep Deterministic policy gradient (TD3) -- over a suite of OpenAI gym tasks and achieve superior sample complexity compared to other baseline approaches.

Title: A stabilized finite element method for inverse problems subject to the convection–diffusion equation. II: convection-dominated regime Abstract: We consider the numerical approximation of the ill-posed data assimilation problem for stationary convection–diffusion equations and extend our previous analysis in Burman et al. (Numer. Math. 144:451–477, 2020) to the convection-dominated regime. Slightly adjusting the stabilized finite element method proposed for dominant diffusion, we draw upon a local error analysis to obtain quasi-optimal convergence along the characteristics of the convective field through the data set. The weight function multiplying the discrete solution is taken to be Lipschitz continuous and a corresponding super approximation result (discrete commutator property) is proven. The effect of data perturbations is included in the analysis and we conclude the paper with some numerical experiments.

Title: Leader-Following Mean-Square Consensus of Stochastic Multiagent Systems With ROUs and RONs via Distributed Event-Triggered Impulsive Control Abstract: Based on the distributed event-triggered impulsive mechanism, the leader-following mean-square consensus of stochastic multiagent systems with randomly occurring uncertainties and randomly occurring nonlinearities is investigated for the first time in this article. In order to make better use of the limited communication resources, we proposed some novel communication rules among agents and corres...

Title: Semi-Grant-Free NOMA: A Stochastic Geometry Model Abstract: Grant-free (GF) transmission holds promise in terms of low latency communication by directly transmitting messages without waiting for any permissions. However, collision situations may frequently happen when limited spectrum is occupied by numerous GF users. The non-orthogonal multiple access (NOMA) technique can be a promising solution to achieve massive connectivity and fewer collisions for GF transmission by multiplexing users in power domain. We utilize a semi-grant-free (semi-GF) NOMA scheme for enhancing network connectivity and spectral efficiency by enabling grant-based (GB) and GF users to share the same spectrum resources. With the aid of semi-GF protocols, uplink NOMA networks are investigated by invoking stochastic geometry techniques. We propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic protocol</i> to interpret which part of the GF users are allocated in NOMA transmissions via transmitting various channel quality thresholds by an added handshake. We utilize open-loop protocol with a fixed average threshold as the benchmark to investigate performance improvement. It is observed that dynamic protocol provides more accurate channel quality thresholds than open-loop protocol, thereby the interference from the GF users is reduced to a large extent. We analyze the outage performance and diversity gains under two protocols. Numerical results demonstrate that dynamic protocol is capable of enhancing the outage performance than open-loop protocol.

Title: Balanced Truncation of $k$ -Positive Systems Abstract: This article considers balanced truncation of discrete-time Hankel $k$-positive systems, characterized by Hankel matrices whose minors up to order $k$ are nonnegative. Our main result shows that if the truncated system has order <tex-math not...

Title: A Fourfold Refined Enumeration of Alternating Sign Trapezoids Abstract: Alternating sign trapezoids have recently been introduced as a generalisation of alternating sign triangles. Fischer established a threefold refined enumeration of alternating sign trapezoids and provided three statistics on column strict shifted plane partitions with the same joint distribution. In this paper, we are able to add a new pair of statistics to these results. More precisely, we consider the number of ???1s on alternating sign trapezoids and introduce a corresponding statistic on column strict shifted plane partitions that has the same distribution. More generally, we show that the joint distributions of the two quadruples of statistics on alternating sign trapezoids and column strict shifted plane partitions, respectively, coincide. In addition, we provide a closed-form expression for the 2-enumeration of alternating sign trapezoids.

Title: Online Competitive Influence Maximization Abstract: Online influence maximization has attracted much attention as a way to maximize influence spread through a social network while learning the values of unknown network parameters. Most previous works focus on single-item diffusion. In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e.g., products, news stories) propagate in the same network and influence probabilities on edges are unknown. We adopt a combinatorial multi-armed bandit (CMAB) framework for OCIM, but unlike the non-competitive setting, the important monotonicity property (influence spread in- creases when influence probabilities on edges increase) no longer holds due to the competitive nature of propagation, which brings a significant new challenge to the problem. We provide a nontrivial proof showing that the Triggering Probability Modulated (TPM) condition for CMAB still holds in OCIM, which is instrumental for our proposed algorithms OCIM-TS and OCIM-OFU to achieve sublinear Bayesian and frequentist regret, respectively. We also design an OCIM-ETC algorithm that requires less feedback and easier offline computation, at the expense of a worse frequentist regret bound. Experimental evaluations demonstrate the effectiveness of our algorithms.

Title: Distributionally-robust machine learning using locally differentially-private data Abstract: We consider machine learning, particularly regression, using locally-differentially private datasets. The Wasserstein distance is used to define an ambiguity set centered at the empirical distribution of the dataset corrupted by local differential privacy noise. The radius of the ambiguity set is selected based on privacy budget, spread of data, and size of the problem. Machine learning with private dataset is rewritten as a distributionally-robust optimization. For general distributions, the distributionally-robust optimization problem can be relaxed as a regularized machine learning problem with the Lipschitz constant of the machine learning model as a regularizer. For Gaussian data, the distributionally-robust optimization problem can be solved exactly to find an optimal regularizer. Training with this regularizer can be posed as a semi-definite program.

Title: Neural Schrödinger Equation: Physical Law as Deep Neural Network Abstract: We show a new family of neural networks based on the Schrödinger equation (SE-NET). In this analogy, the trainable weights of the neural networks correspond to the physical quantities of the Schrödinger equation. These physical quantities can be trained using the complex-valued adjoint method. Since the propagation of the SE-NET can be described by the evolution of physical systems, its outputs can be computed by using a physical solver. The trained network is transferable to actual optical systems. As a demonstration, we implemented the SE-NET with the Crank–Nicolson finite difference method on Pytorch. From the results of numerical simulations, we found that the performance of the SE-NET becomes better when the SE-NET becomes wider and deeper. However, the training of the SE-NET was unstable due to gradient explosions when SE-NET becomes deeper. Therefore, we also introduced phase-only training, which only updates the phase of the potential field (refractive index) in the Schrödinger equation. This enables stable training even for the deep SE-NET model because the unitarity of the system is kept under the training. In addition, the SE-NET enables a joint optimization of physical structures and digital neural networks. As a demonstration, we performed a numerical demonstration of end-to-end machine learning (ML) with an optical frontend toward a compact spectrometer. Our results extend the application field of ML to hybrid physical-digital optimizations.

Title: Affinity Fusion Graph-Based Framework for Natural Image Segmentation Abstract: This paper proposes an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation. The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels. These affinity nodes are selected base...