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Title: Spanning trees in random regular uniform hypergraphs. Abstract: Let $\\mathcal{G}_{n,r,s}$ denote a uniformly random $r$-regular $s$-uniform hypergraph on the vertex set $\\{1,2,\\ldots, n\\}$. We establish a threshold result for the existence of a spanning tree in $\\mathcal{G}_{n,r,s}$, restricting to $n$ satisfying the necessary divisibility conditions. Specifically, we show that when $s\\geq 5$, there is a positive constant $\\rho(s)$ such that for any $r\\geq 2$, the probability that $\\mathcal{G}_{n,r,s}$ contains a spanning tree tends to 1 if $r \u003e \\rho(s)$, and otherwise this probability tends to zero. The threshold value $\\rho(s)$ grows exponentially with $s$. As $\\mathcal{G}_{n,r,s}$ is connected with probability which tends to 1, this implies that when $r \\leq \\rho(s)$, most $r$-regular $s$-uniform hypergraphs are connected but have no spanning tree. When $s=3,4$ we prove that $\\mathcal{G}_{n,r,s}$ contains a spanning tree with probability which tends to 1, for any $r\\geq 2$. Our proof also provides the asymptotic distribution of the number of spanning trees in $\\mathcal{G}_{n,r,s}$ for all fixed integers $r,s\\geq 2$. This asymptotic distribution was previously only known for cubic graphs.

Title: ON THE MAXIMUM AGREEMENT SUBTREE CONJECTURE FOR BALANCED TREES Abstract: We give a counterexample to the conjecture of Martin and Thatte that two balanced rooted binary leaf-labeled trees on n leaves have a maximum agreement subtree (MAST) of size at least n(1/2). In particular, we show that for any c > 0, there exist two balanced rooted binary leaf-labeled trees on n leaves such that any MAST for these two trees has size less than cn(1/2). We also improve the lower bound of the size of such a MAST to n(1/6).

Title: Temperate fish detection and classification: a deep learning based approach Abstract: A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27% using the pre-training model. The accuracies using the post-training model are also high; 83.68% and 87.74% with and without image augmentation, respectively. This strongly indicates that the solution is viable with a more extensive dataset.

Title: Oriented hypergraphs: Balanceability Abstract: An oriented hypergraph is an oriented incidence structure that extends the concepts of signed graphs, balanced hypergraphs, and balanced matrices. We introduce hypergraphic structures and techniques that generalize the circuit classification of the signed graphic frame matroid to any oriented hypergraphic incidence matrix via its locally-signed-graphic substructure. To achieve this, Camion's algorithm is applied to oriented hypergraphs to provide a generalization of reorientation sets and frustration that is only well-defined on balanceable oriented hypergraphs. A simple partial characterization of unbalanceable circuits extends the applications to representable matroids demonstrating that the difference between the Fano and non-Fano matroids is one of balance. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

Title: On bi-embeddable categoricity of algebraic structures Abstract: In several classes of countable structures it is known that every hyperarithmetic structure has a computable presentation up to bi-embeddability. In this article we investigate the complexity of embeddings between bi-embeddable structures in two such classes, the classes of linear orders and Boolean algebras. We show that if L is a computable linear order of Hausdorff rank n, then for every bi-embeddable copy of it there is an embedding computable in 2n−1 jumps from the atomic diagrams. We furthermore show that this is the best one can do: Let L be a computable linear order of Hausdorff rank n≥1, then 0(2n−2) does not compute embeddings between it and all its computable bi-embeddable copies. We obtain that for Boolean algebras which are not superatomic, there is no hyperarithmetic degree computing embeddings between all its computable bi-embeddable copies. On the other hand, if a computable Boolean algebra is superatomic, then there is a least computable ordinal α such that 0(α) computes embeddings between all its computable bi-embeddable copies. The main technique used in this proof is a new variation of Ash and Knight's pairs of structures theorem.

Title: PROJECTION METHOD FOR DROPLET DYNAMICS ON GROOVE-TEXTURED SURFACE WITH MERGING AND SPLITTING Abstract: The geometric motion of small droplets placed on an impermeable textured substrate is mainly driven by the capillary effect, the competition among surface tensions of three phases at the moving contact lines, and the impermeable substrate obstacle. After introducing an infinite dimensional manifold with an admissible tangent space on the boundary of the manifold, by Onsager's principle for an obstacle problem, we derive the associated parabolic variational inequalities. These variational inequalities can be used to compute the contact line dynamics with unavoidable merging and splitting of droplets due to the impermeable obstacle. To efficiently solve the parabolic variational inequality, we propose an unconditional stable explicit boundary updating scheme coupled with a projection method. The explicit boundary updating efficiently decouples the computation of the motion by mean curvature of the capillary surface and the moving contact lines. Meanwhile, the projection step efficiently splits the difficulties brought by the obstacle and the motion by mean curvature of the capillary surface. Furthermore, we prove the unconditional stability of the scheme and present an accuracy check. Convergence of the proposed scheme is also proved using a nonlinear Trotter-Kato product formula under the pinning contact line assumption. After incorporating the phase transition information at splitting points, several challenging examples including splitting and merging of droplets are demonstrated.

Title: Remote State Estimation With Smart Sensors Over Markov Fading Channels Abstract: We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LTI) systems. A smart sensor forwards its local state estimate to a remote estimator over a time-correlated multistate Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state. We establish a necessary and sufficient condition for mean-square stability of the remote estimation error covariance in terms of the state transition matrix of the LTI system, the packet drop probabilities in different channel states, and the transition probability matrix of the Markov channel states. To derive this result, we propose a novel estimation-cycle based approach and provide new elementwise bounds of matrix powers. The stability condition is verified by numerical results and is shown more effective than existing sufficient conditions in the literature. We observe that the stability region in terms of the packet drop probabilities in different channel states can either be convex or nonconvex depending on the transition probability matrix of the Markov channel states. Our numerical results suggest that the stability conditions for remote estimation may coincide for setups with a smart sensor and with a conventional one (which sends raw measurements to the remote estimator) though the smart sensor setup achieves a better estimation performance.

Title: Circulant almost cross intersecting families. Abstract: Let $\mathcal{F}$ and $\mathcal{G}$ be two $t$-uniform families of subsets over $[k] = \{1,2,...,k\}$, where $|\mathcal{F}| = |\mathcal{G}|$, and let $C$ be the adjacency matrix of the bipartite graph whose vertices are the subsets in $\mathcal{F}$ and $\mathcal{G}$, and there is an edge between $A\in \mathcal{F}$ and $B \in \mathcal{G}$ if and only if $A \cap B \neq \emptyset$. The pair $(\mathcal{F},\mathcal{G})$ is $q$-almost cross intersecting if every row and column of $C$ has exactly $q$ zeros. We consider $q$-almost cross intersecting pairs that have a circulant intersection matrix $C_{p,q}$, determined by a column vector with $p > 0$ ones followed by $q > 0$ zeros. This family of matrices includes the identity matrix in one extreme, and the adjacency matrix of the bipartite crown graph in the other extreme. We give constructions of pairs $(\mathcal{F},\mathcal{G})$ whose intersection matrix is $C_{p,q}$, for a wide range of values of the parameters $p$ and $q$, and in some cases also prove matching upper bounds. Specifically, we prove results for the following values of the parameters: (1) $1 \leq p \leq 2t-1$ and $1 \leq q \leq k-2t+1$. (2) $2t \leq p \leq t^2$ and any $q> 0$, where $k \geq p+q$. (3) $p$ that is exponential in $t$, for large enough $k$. Using the first result we show that if $k \geq 4t-3$ then $C_{2t-1,k-2t+1}$ is a maximal isolation submatrix of size $k\times k$ in the $0,1$-matrix $A_{k,t}$, whose rows and columns are labeled by all subsets of size $t$ of $[k]$, and there is a one in the entry on row $x$ and column $y$ if and only if subsets $x,y$ intersect.

Title: Why Do Smart Contracts Self-Destruct? Investigating the Selfdestruct Function on Ethereum Abstract: AbstractThe selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts (SCs) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and summarize the key reasons. Their feedback shows that 66.67% of the developers will deploy an updated contract to the Ethereum after destructing the old contract. According to this information, we propose a method to find the self-destructed contracts (also called predecessor contracts) and their updated version (successor contracts) by computing the code similarity. By analyzing the difference between the predecessor contracts and their successor contracts, we found five reasons that led to the death of the contracts; two of them (i.e., Unmatched ERC20 Token and Limits of Permission) might affect the life span of contracts. We developed a tool named LifeScope to detect these problems. LifeScope reports 0 false positives or negatives in detecting Unmatched ERC20 Token. In terms of Limits of Permission, LifeScope achieves 77.89% of F-measure and 0.8673 of AUC in average. According to the feedback of developers who exclude selfdestruct functions, we propose suggestions to help developers use selfdestruct functions in Ethereum smart contracts better.

Title: Efficient Quantisation and Weak Covering of High Dimensional Cubes Abstract: Let $${\mathbb {Z}}_n = \{Z_1, \ldots , Z_n\}$$ be a design; that is, a collection of n points $$Z_j \in [-1,1]^d$$ . We study the quality of quantisation of $$[-1,1]^d$$ by the points of $${\mathbb {Z}}_n$$ and the problem of quality of coverage of $$[-1,1]^d$$ by $${{{\mathcal {B}}}}_d({\mathbb {Z}}_n,r)$$ , the union of balls centred at $$Z_j \in {\mathbb {Z}}_n$$ . We concentrate on the cases where the dimension d is not small, $$d\ge 5$$ , and n is not too large, $$n\le 2^d$$ . We define the design $${{\mathbb {D}}_{n,\delta }}$$ as a $$2^{d-1}$$ design defined on vertices of the cube $$[-\delta ,\delta ]^d$$ , $$0\le \delta \le 1$$ . For this design, we derive a closed-form expression for the quantisation error and very accurate approximations for the coverage area $${\text {vol}}{([-1,1]^d \cap {{{\mathcal {B}}}}_d({\mathbb {Z}}_n,r))}$$ . We provide results of a large-scale numerical investigation confirming the accuracy of the developed approximations and the efficiency of the designs  $${{\mathbb {D}}_{n,\delta }}$$ .

Title: Replica-mean-field limits of fragmentation-interaction-aggregation processes Abstract: Network dynamics with point-process-based interactions are of paramount modeling interest. Unfortunately, most relevant dynamics involve complex graphs of interactions for which an exact computational treatment is impossible. To circumvent this difficulty, the replica-mean-field approach focuses on randomly interacting replicas of the networks of interest. In the limit of an infinite number of replicas, these networks become analytically tractable under the so-called 'Poisson hypothesis'. However, in most applications this hypothesis is only conjectured. In this paper we establish the Poisson hypothesis for a general class of discrete-time, point-process-based dynamics that we propose to call fragmentation-interaction-aggregation processes, and which are introduced here. These processes feature a network of nodes, each endowed with a state governing their random activation. Each activation triggers the fragmentation of the activated node state and the transmission of interaction signals to downstream nodes. In turn, the signals received by nodes are aggregated to their state. Our main contribution is a proof of the Poisson hypothesis for the replica-mean-field version of any network in this class. The proof is obtained by establishing the propagation of asymptotic independence for state variables in the limit of an infinite number of replicas. Discrete-time Galves-Locherbach neural networks are used as a basic instance and illustration of our analysis.

Title: THE GENERALIZED RAINBOW TURAN PROBLEM FOR CYCLES Abstract: Given an edge-colored graph, we say that a subgraph is rainbow if all of its edges have different colors. Let ex(n, H, rainbow-F) denote the maximal number of copies of H that a properly edge-colored graph on n vertices can contain if it has no rainbow subgraph isomorphic to F. We determine the order of magnitude of ex(n, C-s, rainbow-C-t) for all s, t with s not equal 3. In particular, we answer a question of Gerbner, Meszaros, Methuku, and Palmer by showing that ex(n, C-2k, rainbow-C-2k) is Theta(n(k-1)) if k >= 3 and Theta(n(2)) if k = 2. We also determine the order of magnitude of ex(n, P-l, rainbow-C-2k) for all k, l >= 2, where P-l denotes the path with l edges.

Title: Attack-resilient state estimation with intermittent data authenticationx2729; Abstract: Network-based attacks on control systems may alter sensor data delivered to the controller, effectively causing degradation in control performance. As a result, having access to accurate state estimates, even in the presence of attacks on sensor measurements, is of critical importance. In this work, we analyze performance of resilient state estimators (RSEs) when any subset of sensors may be compromised by a stealthy attacker. Specifically, we consider systems with the well-known l0-based RSE and two commonly used sound intrusion detectors (IDs). For linear time-invariant plants with bounded noise, we define the notion of perfect attackability (PA) when attacks may result in unbounded estimation errors while remaining undetected by the employed ID (i.e., stealthy). We derive necessary and sufficient PA conditions, showing that a system can be perfectly attackable even if the plant is stable. While PA can be prevented with the use the standard cryptographic mechanisms (e.g., message authentication) that ensure data integrity under network-based attacks, their continuous use imposes significant communication and computational overhead. Consequently, we also study the impact that even intermittent use of data authentication has on RSE performance guarantees in the presence of stealthy attacks. We show that if messages from some of the sensors are even intermittently authenticated, stealthy attacks could not result in unbounded state estimation errors. (c) 2021 Elsevier Ltd. All rights reserved.

Title: On the Lyapunov Foster Criterion and Poincaré Inequality for Reversible Markov Chains Abstract: This article presents an elementary proof of stochastic stability of a discrete-time reversible Markov chain starting from a Foster–Lyapunov drift condition. Besides its relative simplicity, there are two salient features of the proof. 1) It relies entirely on functional-analytic non-probabilistic arguments. 2) It makes explicit the connection between a Foster–Lyapunov function and Poincaré inequa...

Title: On Loss Functions and Regret Bounds for Multi-Category Classification Abstract: We develop new approaches in multi-class settings for constructing loss functions and establishing corresponding regret bounds with respect to the zero-one or cost-weighted classification loss. We provide new general representations of losses by deriving inverse mappings from a concave generalized entropy to a loss through a convex dissimilarity function related to the multi-distribution <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> -divergence. This approach is then applied to study both hinge-like losses and proper scoring rules. In the first case, we derive new hinge-like convex losses, which are tighter extensions outside the probability simplex than related hinge-like losses and geometrically simpler with fewer non-differentiable edges. We also establish a classification regret bound in general for all losses with the same generalized entropy as the zero-one loss, thereby substantially extending and improving existing results. In the second case, we identify new sets of multi-class proper scoring rules through different types of dissimilarity functions and reveal interesting relationships between various composite losses currently in use. We also establish new classification regret bounds in general for multi-class proper scoring rules and, as applications, provide simple meaningful regret bounds for two specific sets of proper scoring rules. These results generalize, for the first time, previous two-class regret bounds to multi-class settings.

Title: Co-occurrence based texture synthesis Abstract: As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate. In this work, we turn to co-occurrence statistics, which have long been used for texture analysis, to learn a controllable texture synthesis model. We propose a fully convolutional generative adversarial network, conditioned locally on co-occurrence statistics, to generate arbitrarily large images while having local, interpretable control over texture appearance. To encourage fidelity to the input condition, we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion. We demonstrate that our solution offers a stable, intuitive, and interpretable latent representation for texture synthesis, which can be used to generate smooth texture morphs between different textures. We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.

Title: Robust subset selection Abstract: The best subset selection (or "best subsets") estimator is a classic tool for sparse regression, and developments in mathematical optimization over the past decade have made it more computationally tractable than ever. Notwithstanding its desirable statistical properties, the best subsets estimator is susceptible to outliers and can break down in the presence of a single contaminated data point. To address this issue, a robust adaption of best subsets is proposed that is highly resistant to contamination in both the response and the predictors. The adapted estimator generalizes the notion of subset selection to both predictors and observations, thereby achieving robustness in addition to sparsity. This procedure, referred to as "robust subset selection" (or "robust subsets"), is defined by a combinatorial optimization problem for which modern discrete optimization methods are applied. The robustness of the estimator in terms of the finite-sample breakdown point of its objective value is formally established. In support of this result, experiments on synthetic and real data are reported that demonstrate the superiority of robust subsets over best subsets in the presence of contamination. Importantly, robust subsets fares competitively across several metrics compared with popular robust adaptions of continuous shrinkage estimators. (C) 2021 Elsevier B.V. All rights reserved.

Title: A practical algorithm for the computation of the genus. Abstract: We describe a practical algorithm to compute the (oriented) genus of a graph, give results of the program implementing this algorithm, and compare the performance to existing algorithms. The aim of this algorithm is to be fast enough for many applications instead of focusing on the theoretical asymptotic complexity. Apart from the specific problem and the results, the article can also be seen as an example how some design principles used to carefully develop and implement standard backtracking algorithms can still result in very competitive programs.

Title: RIGOROUS JUSTIFICATION OF THE FOKKER-PLANCK EQUATIONS OF NEURAL NETWORKS BASED ON AN ITERATION PERSPECTIVE Abstract: In this work, the primary goal is to establish a rigorous connection between the Fokker-Planck equation of neural networks and its microscopic model: the diffusion-jump stochastic process that captures the mean-field behavior of collections of neurons in the integrate-and-fire model. The proof is based on a novel iteration scheme: with an auxiliary random variable counting the firing events, both the density function of the stochastic process and the solution of the PDE problem admit series representations, and thus the difficulty in verifying the link between the density function and the PDE solution in each subproblem is greatly mitigated. The iteration approach provides a generic framework for integrating the probability approach with PDE techniques, with which we prove that the density function of the diffusion-jump stochastic process is indeed the classical solution of the Fokker-Planck equation with a unique flux-shift structure.

Title: Optimal Bounds for the k-cut Problem Abstract: AbstractIn the k-cut problem, we want to find the lowest-weight set of edges whose deletion breaks a given (multi)graph into k connected components. Algorithms of Karger and Stein can solve this in roughly O(n2k) time. However, lower bounds from conjectures about the k-clique problem imply that Ω (n(1-o(1))k) time is likely needed. Recent results of Gupta, Lee, and Li have given new algorithms for general k-cut in n1.98k + O(1) time, as well as specialized algorithms with better performance for certain classes of graphs (e.g., for small integer edge weights).In this work, we resolve the problem for general graphs. We show that the Contraction Algorithm of Karger outputs any fixed k-cut of weight α λ k with probability Ωk(n-α k), where λ k denotes the minimum k-cut weight. This also gives an extremal bound of Ok(nk) on the number of minimum k-cuts and an algorithm to compute λ k with roughly nk polylog(n) runtime. Both are tight up to lower-order factors, with the algorithmic lower bound assuming hardness of max-weight k-clique.The first main ingredient in our result is an extremal bound on the number of cuts of weight less than 2 λk/k, using the Sunflower lemma. The second ingredient is a fine-grained analysis of how the graph shrinks—and how the average degree evolves—in the Karger process.

Title: UNBIASED MLMC STOCHASTIC GRADIENT-BASED OPTIMIZATION OF BAYESIAN EXPERIMENTAL DESIGNS Abstract: In this paper we propose an efficient stochastic optimization algorithm to search for Bayesian experimental designs such that the expected information gain is maximized. The gradient of the expected information gain with respect to experimental design parameters is given by a nested expectation, for which the standard Monte Carlo method using a fixed number of inner samples yields a biased estimator. In this paper, applying the idea of randomized multilevel Monte Carlo methods, we introduce an unbiased Monte Carlo estimator for the gradient of the expected information gain with finite expected squared l(2)-norm and finite expected computational cost per sample. Our unbiased estimator can be combined well with stochastic gradient descent algorithms, which results in our proposal of an optimization algorithm to search for an optimal Bayesian experimental design. Numerical experiments confirm that our proposed algorithm works well not only for a simple test problem but also for a more realistic pharmacokinetic problem.

Title: Deep Latent-Variable Kernel Learning Abstract: Deep kernel learning (DKL) leverages the connection between the Gaussian process (GP) and neural networks (NNs) to build an end-to-end hybrid model. It combines the capability of NN to learn rich representations under massive data and the nonparametric property of GP to achieve automatic regularization that incorporates a tradeoff between model fit and model complexity. However, the deterministic NN encoder may weaken the model regularization of the following GP part, especially on small datasets, due to the free latent representation. We, therefore, present a complete deep latent-variable kernel learning (DLVKL) model wherein the latent variables perform stochastic encoding for regularized representation. We further enhance the DLVKL from two aspects: 1) the expressive variational posterior through neural stochastic differential equation (NSDE) to improve the approximation quality and 2) the hybrid prior taking knowledge from both the SDE prior and the posterior to arrive at a flexible tradeoff. Extensive experiments imply that DLVKL-NSDE performs similar to the well-calibrated GP on small datasets, and shows superiority on large datasets.

Title: Semi-online scheduling: A survey Abstract: In real-life applications, neither all the inputs of an algorithm are available at the outset, as in an offline framework, nor do they occur one by one in order, as in an online setup. Semi-online is an intermediate theoretically and practically significant framework with additional information on the successive inputs to address the limitations of online and offline frameworks. One key motivation for studying semi-online algorithms is to investigate how additional information can improve the performance of online algorithms. In online scheduling, jobs are received one by one and each job must be scheduled irrevocably before the availability of the next job. Semi-online scheduling is a relaxed variant of online scheduling, where some Extra Piece Information (EPI) about the whole job sequence is known a priori, or additional algorithmic extensions are allowed. The EPI may include one or more of the following parameter(s) such as the maximum processing time, the total sum of the processing time of all jobs, arrival sequence of the jobs based on processing time, optimum makespan value, or the range of processing time. The design of improved competitive semi-online algorithms for m-machine scheduling problem has received significant research attention after the seminal works of Liu et al. (1996) and Kellerer et al. (1997). In this survey article, we highlight the scholarly contributions and stateof-the-art results for semi-online scheduling on parallel machine models such as identical, uniformly related, and unbounded batch by considering preemptive and non-preemptive as the processing formats with optimality criteria such as makespan, load balancing, machine cost, Lp-norm load balancing, early work maximization, and the sum of completion time plus total penalty cost. The survey begins with a brief introduction to the online and semi-online frameworks for the m-machine scheduling problem and presentation of a standard well-known algorithmic performance measure, the competitive analysis. Practical applications, preliminary concepts, research motivation, and taxonomy of semi-online scheduling are presented as a foundation for a basic understanding of the area. State-of-the-art results achieved by the deterministic semi-online algorithms are classified and presented based on machine models and known EPI with a special focus on novel intuitions and algorithmic development. We discuss and analyze the impact of EPI on the competitive performance of semi-online algorithms. A classification of the references based on specific EPI is outlined for further research investigation. Finally, we conclude the survey with non-trivial research challenges and open problems.

Title: Universalization of Any Adversarial Attack using Very Few Test Examples. Abstract: Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but also to input-agnostic or universal adversarial attacks. Dezfooli et al. \cite{Dezfooli17,Dezfooli17anal} construct universal adversarial attack on a given model by looking at a large number of training data points and the geometry of the decision boundary near them. Subsequent work \cite{Khrulkov18} constructs universal attack by looking only at test examples and intermediate layers of the given model. In this paper, we propose a simple universalization technique to take any input-dependent adversarial attack and construct a universal attack by only looking at very few adversarial test examples. We do not require details of the given model and have negligible computational overhead for universalization. We theoretically justify our universalization technique by a spectral property common to many input-dependent adversarial perturbations, e.g., gradients, Fast Gradient Sign Method (FGSM) and DeepFool. Using matrix concentration inequalities and spectral perturbation bounds, we show that the top singular vector of input-dependent adversarial directions on a small test sample gives an effective and simple universal adversarial attack. For VGG16 and VGG19 models trained on ImageNet, our simple universalization of Gradient, FGSM, and DeepFool perturbations using a test sample of 64 images gives fooling rates comparable to state-of-the-art universal attacks \cite{Dezfooli17,Khrulkov18} for reasonable norms of perturbation.

Title: Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling Abstract: We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, we use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the context of spatial statistics to the traffic network setting. We demonstrate our approach’s superior performance compared to the state-of-the-art methods for both synthetic and real data.

Title: Combinatorial proof of Selberg's integral formula Abstract: In this paper we present a combinatorial proof of Selberg's integral formula. We prove a theorem about the number of topological orderings of a certain related directed graph bijectively. Selberg's integral formula then follows by induction. This solves a problem posed by R. Stanley in 2009. Our proof is based on Anderson's analytic proof of the formula. As part of the proof we show a further generalisation of the generalised Vandermonde determinant.

Title: High-order gas-kinetic scheme with parallel computation for direct numerical simulation of turbulent flows Abstract: The performance of high-order gas-kinetic scheme (HGKS) has been investigated for the direct numerical simulation (DNS) of isotropic compressible turbulence up to the supersonic regime [9]. Due to the multi-scale nature and coupled temporal-spatial evolution process, HGKS provides a valid tool for the numerical simulation of compressible turbulent flow. Based on the domain decomposition and message passing interface (MPI), a parallel HGKS code is developed for large-scale computation in this paper. The standard tests from the nearly incompressible flow to the supersonic one, including Taylor-Green vortex problem, turbulent channel flow and isotropic compressible turbulence, are presented to validate the parallel scalability, efficiency, accuracy and robustness of parallel implementation. The performance of HGKS for the nearly incompressible turbulence is comparable with the high-order finite difference scheme, including the resolution of flow structure and efficiency of computation. Based on the accuracy of the numerical solution, the numerical dissipation of the scheme in the turbulence simulation is quantitatively evaluated. Meanwhile, based on the kinetic formulation HGKS shows advantage for supersonic turbulent flow simulation with its accuracy and robustness. The current work demonstrates the capability of HGKS as a powerful DNS tool from the low speed to supersonic turbulence study, which is less reported under the framework of finite volume scheme.

Title: Generating Unit Tests for Documentation Abstract: Software projects capture redundant information in various kinds of artifacts, as specifications from the source code are also tested and documented. Such redundancy provides an opportunity to reduce development effort by supporting the joint generation of different types of artifacts. We introduce a tool-supported technique, called DScribe, that allows developers to combine unit test and documentation templates, and to invoke these templates to generate documentation and unit tests. DScribe supports the detection and replacement of outdated documentation, and the use of templates can encourage extensive test suites with a consistent style. Our evaluation of 835 specifications revealed that 85 percent were not tested or correctly documented, and DScribe could be used to automatically generate 97 percent of the tests and documentation. An additional study revealed that tests generated by DScribe are more focused and readable than those written by human testers or generated by state-of-the-art automated techniques.

Title: Fault Estimation and Accommodation of Fractional-Order Nonlinear, Switched, and Interconnected Systems Abstract: This discusses the fault estimation (FE) and fault accommodation (FA) methods for fractional-order systems. First, two Lyapunov theorems of input-to-state practical stability are presented for fractional-order systems, based on which an adaptive FE/FA scheme is provided. Such a scheme ensures the faulty system is input-to-state practically stable (ISpS) with respect to estimation errors. Furthermo...

Title: A Novel Rapid-Flooding Approach With Real-Time Delay Compensation for Wireless-Sensor Network Time Synchronization Abstract: One-way-broadcast-based flooding time synchronization algorithms are commonly used in wireless-sensor networks (WSNs). However, the packet delay and clock drift pose a challenge to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome this, a rapid-flooding multibroadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this ar...

Title: Improvement of Neural-Network Classifiers Using Fuzzy Floating Centroids Abstract: In this article, a fuzzy floating centroids method (FFCM) is proposed, which uses a fuzzy strategy and the concept of floating centroids to enhance the performance of the neural-network classifier. The decision boundaries in the traditional floating centroids neural-network (FCM) classifier are “hard.” These hard boundaries force a point, such as noisy or boundary point, to be assigned to a class ...

Title: Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes Abstract: Driving pattern recognition based on features, such as GPS, gear, and speed information, is essential to develop intelligent transportation systems. However, it is usually expensive and labor intensive to collect a large amount of labeled driving data from real-world driving scenes. The lack of a labeled data problem in a driving scene substantially hinders the driving pattern recognition accuracy...

Title: An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability Abstract: This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier’s reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

Title: TOPOLOGICAL DERIVATIVE FOR PDEs ON SURFACES Abstract: In this paper we study the problem of the optimal distribution of two materials on C-2 submanifolds M of dimension d - 1 in R-d by means of the topological derivative. We consider a class of shape optimization problems which are constrained by a linear partial differential equation on the surface. We examine the configurational perturbation of the differential operator and material coefficients and derive the corresponding topological derivative. Finally, we show how the topological derivative in conjunction with a level set method on the surface can be used to solve the topology optimization problem numerically.

Title: Sham: A DSL for Fast DSLs. Abstract: Domain-specific languages (DSLs) are touted as both easy to embed in programs and easy to optimize. Yet these goals are often in tension. Embedded or internal DSLs fit naturally with a host language, while inheriting the host's performance characteristics. External DSLs can use external optimizers and languages but sit apart from the host. We present Sham, a toolkit designed to enable internal DSLs with high performance. Sham is itself a DSL embedded in Racket, but compiles transparently to LLVM at runtime. Sham is designed to be well suited as both a compilation target for other DSLs embedded in Racket as well as a language for transparently replacing DSL support code with faster versions. Sham programs interoperate seamlessly with Racket programs, and so no additional effort is required to use a DSL implemented with Sham. Finally, Sham comes with a framework for defining DSL compilers and transformations, which is also used in the implementation of Sham itself. We validate Sham's design on a series of case studies, ranging from Krishnamurthi's classic automata DSL to a sound synthesis DSL and a probabilistic programming language. All of these are existing DSLs where we replaced the backend using Sham, resulting in major performance gains.

Title: Effective Learning of a GMRF Mixture Model Abstract: Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaussian Markov Random Field Mixture Model (GMRF-MM), as well as a new method for estimating the latter's sparse precision (i.e., inverse covariance) matrices. When the sparsity pattern of each matrix is known, we propose an efficient optimization method for the Maximum Likelihood Estimate (MLE) of that matrix. When it is unknown, we utilize the popular Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) to estimate that pattern. However, we show that even for a single Gaussian, when GLASSO is tuned to successfully estimate the sparsity pattern, it does so at the price of a substantial bias of the values of the nonzero entries of the matrix, and we show that this problem only worsens in a mixture setting. To overcome this, we discard the nonzero values estimated by GLASSO, keep only its pattern estimate and use it within the proposed MLE method. This yields an effective two-step procedure that removes the bias. We show that our "debiasing" approach outperforms GLASSO in both the single-GMRF and the GMRF-MM cases. We also show that when learning priors for image patches, our method outperforms GLASSO even if we merely use an educated guess about the sparsity pattern, and that our GMRF-MM outperforms the baseline GMM on real and synthetic high-dimensional datasets.

Title: Two-view fine-grained classification of plant species Abstract: •A two-view representation method for fine-grained plant classification.•A Coarse-to-fine strategy to reduce the complexity of the classification task.•Deep metric learning used to classify plant species with a small number of labeled samples.•Scalable method since new plant species can be easily added without retraining.

Title: ExSample: Efficient Searches on Video Repositories through Adaptive Sampling Abstract: Capturing and processing video is increasingly common as cameras become cheaper to deploy. At the same time, rich video-understanding methods have progressed greatly in the last decade. As a result, many organizations now have massive repositories of video data, with applications in mapping, navigation, autonomous driving, and other areas. Because state-of-the-art object-detection methods are slow and expensive, our ability to process even simple ad-hoc object search queries ("find 100 traffic lights in dashcam video") over this accumulated data lags far behind our ability to collect the data. Processing video at reduced sampling rates is a reasonable default strategy for these types of queries; however, the ideal sampling rate is both data and query dependent. We introduce ExSample, a low cost framework for object search over un-indexed video that quickly processes search queries by adapting the amount and location of sampled frames to the particular data and query being processed. ExSample prioritizes the processing of frames in a video repository so that processing is focused in portions of video that most likely contain objects of interest. It approaches searching in a similar way to a multi-arm bandit problem where each arm corresponds to a portion of a video. On large, real-world datasets, ExSample reduces processing time by 1.9x on average and up to 6x over an efficient random sampling baseline. Moreover, we show ExSample finds many results long before sophisticated, state-of-the-art baselines based on proxy scores can begin producing their first results.

Title: CONVERGENCE IN TOTAL VARIATION OF THE EULER-MARUYAMA SCHEME APPLIED TO DIFFUSION PROCESSES WITH MEASURABLE DRIFT COEFFICIENT AND ADDITIVE NOISE Abstract: We are interested in the Euler-Maruyama discretization of a stochastic differential equation in dimension d with constant diffusion coefficient and bounded measurable drift coefficient. In the scheme, a randomization of the time variable is used to get rid of any regularity assumption of the drift in this variable. We prove weak convergence with order 1/2 in total variation distance. When the drift has a spatial divergence in the sense of distributions with \rhoth power integrable with respect to the Lebesgue measure in space uniformly in time for some rho >= d, the order of convergence at the terminal time improves to 1 up to some logarithmic factor. In dimension d = 1, this result is preserved when the spatial derivative of the drift is a measure in space with total mass bounded uniformly in time. We confirm our theoretical analysis by numerical experiments.

Title: Aggregate-based Training Phase for ML-based Cardinality Estimation Abstract: Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-based training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 90 with our aggregate-based training phase and thus outperform indexes.

Title: The number of irreducible polynomials over finite fields with vanishing trace and reciprocal trace Abstract: We present the formula for the number of monic irreducible polynomials of degree n over the finite field $${\mathbb {F}}_q$$ where the coefficients of $$x^{n-1}$$ and x vanish for $$n\ge 3$$ . In particular, we give a relation between rational points of algebraic curves over finite fields and the number of elements $$a\in {\mathbb {F}}_{q^n}$$ for which Trace $$(a)=0$$ and Trace $$(a^{-1})=0$$ .

Title: Probabilistic feasibility guarantees for solution sets to uncertain variational inequalities Abstract: We develop a data-driven approach to the computation of a-posteriori feasibility certificates for sets of solutions of variational inequalities affected by uncertainty. Specifically, we focus on variational inequalities with a deterministic mapping and an uncertain feasible set, and represent uncertainty by means of scenarios. Building upon recent advances in the scenario approach literature, we quantify the robustness properties of the entire set of solutions of a variational inequality, with feasibility set constructed using the scenario approach, against a new unseen realization of the uncertainty. Our results extend existing ones that typically impose that the solution set is a singleton and require certain non-degeneracy properties: hence, we thereby offer probabilistic feasibility guarantees for any feasible solution of the underlying variational inequality. We show that assessing the violation probability of an entire set of solutions requires enumeration of the support constraints that “shape” this set, and also propose a procedure to enumerate the support constraints that does not require a description of the solution set. We illustrate our results through numerical simulations on a robust game involving an electric vehicle charging coordination problem.

Title: Integer Linear Programming for the Tutor Allocation Problem: A practical case in a British University Abstract: In the Tutor Allocation Problem, the objective is to assign a set of tutors to a set of workshops in order to maximize tutors' preferences. The problem is solved every year by many universities, each having its own specific set of constraints. In this work, we study the tutor allocation in the School of Mathematics at the University of Edinburgh, and solve it with an integer linear programming model. We tested the model on the 2019/2020 case, obtaining a significant improvement with respect to the manual assignment in use and we showed that such improvement could be maintained while optimizing other key metrics such as load balance among groups of tutors and total number of courses assigned. Further tests on randomly created instances show that the model can be used to address cases of broad interest. We also provide meaningful insights on how input parameters, such as the number of workshop locations and the length of the tutors' preference list, might affect the performance of the model and the average number of preferences satisfied.

Title: Graphs with no induced house nor induced hole have the de Bruijn-Erdos property Abstract: A set of n $n$ points in the plane which are not all collinear defines at least n $n$ distinct lines. Chen and Chvatal conjectured in 2008 that a similar result can be achieved in the broader context of finite metric spaces. This conjecture remains open even for graph metrics. In this article we prove that graphs with no induced house nor induced cycle of length at least 5 verify the desired property. We focus on lines generated by vertices at distance at most 2, define a new notion of 'good pairs' that might have application in larger families, and finally use a discharging technique to count lines in irreducible graphs.

Title: Robust Policy Iteration for Continuous-Time Linear Quadratic Regulation Abstract: This article studies the robustness of policy iteration in the context of continuous-time infinite-horizon linear quadratic regulator (LQR) problem. It is shown that Kleinman&#39;s policy iteration algorithm is small-disturbance input-to-state stable, a property that is stronger than Sontag&#39;s local input-to-state stability but weaker than global input-to-state stability. More precisely, whenever the e...

Title: Convergence Theory for IETI-DP Solvers for Discontinuous Galerkin Isogeometric Analysis that is Explicit in h and p Abstract: In this paper, we develop a convergence theory for Dual-Primal Isogeometric Tearing and Interconnecting (IETI-DP) solvers for isogeometric multi-patch discretizations of the Poisson problem, where the patches are coupled using discontinuous Galerkin. The presented theory provides condition number bounds that are explicit in the grid sizes h and in the spline degrees p. We give an analysis that holds for various choices for the primal degrees of freedom: vertex values, edge averages, and a combination of both. If only the vertex values or both vertex values and edge averages are taken as primal degrees of freedom, the condition number bound is the same as for the conforming case. If only the edge averages are taken, both the convergence theory and the experiments show that the condition number of the preconditioned system grows with the ratio of the grid sizes on neighboring patches.

Title: On Stabilizability of Switched Linear Systems Under Restricted Switching Abstract: This article deals with the stability of discrete-time switched linear systems whose all subsystems are unstable and the set of admissible switching signals obeys prespecified restrictions on switches between the subsystems and dwell times on the subsystems. We derive sufficient conditions on the subsystems matrices such that a switched system is globally exponentially stable under a set of purely...

Title: Nash equilibrium seeking over directed graphs Abstract: In this paper, we aim to develop distributed continuous-time algorithms over directed graphs to seek the Nash equilibrium in a noncooperative game. Motivated by the recent consensus-based designs, we present a distributed algorithm with a proportional gain for weight-balanced directed graphs. By further embedding a distributed estimator of the left eigenvector associated with zero eigenvalue of the graph Laplacian, we extend it to the case with arbitrary strongly connected directed graphs having possible unbalanced weights. In both cases, the Nash equilibrium is proven to be exactly reached with an exponential convergence rate. An example is given to illustrate the validity of the theoretical results.

Title: Supervised learning in the presence of concept drift: a modelling framework Abstract: We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototype-based learning vector quantization (LVQ) for classification and shallow, layered neural networks for regression tasks. We investigate so-called student-teacher scenarios in which the systems are trained from a stream of high-dimensional, labeled data. Properties of the target task are considered to be non-stationary due to drift processes while the training is performed. Different types of concept drift are studied, which affect the density of example inputs only, the target rule itself, or both. By applying methods from statistical physics, we develop a modelling framework for the mathematical analysis of the training dynamics in non-stationary environments. Our results show that standard LVQ algorithms are already suitable for the training in non-stationary environments to a certain extent. However, the application of weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes. Furthermore, we investigate gradient-based training of layered neural networks with sigmoidal activation functions and compare with the use of rectified linear units. Our findings show that the sensitivity to concept drift and the effectiveness of weight decay differs significantly between the two types of activation function.

Title: ZEROTH-ORDER REGULARIZED OPTIMIZATION (ZORO): APPROXIMATELY SPARSE GRADIENTS AND ADAPTIVE SAMPLING Abstract: We consider the problem of minimizing a high-dimensional objective function, which may include a regularization term, using only (possibly noisy) evaluations of the function. Such optimization is also called derivative-free, zeroth-order, or black-box optimization. We propose a new zeroth-order regularized optimization method, dubbed ZORO. When the underlying gradient is approximately sparse at an iterate, ZORO needs very few objective function evaluations to obtain a new iterate that decreases the objective function. We achieve this with an adaptive, randomized gradient estimator, followed by an inexact proximal-gradient scheme. Under a novel approximately sparse gradient assumption and various different convex settings, we show that the (theoretical and empirical) convergence rate of ZORO is only logarithmically dependent on the problem dimension. Numerical experiments show that ZORO outperforms existing methods with similar assumptions, on both synthetic and real datasets.

Title: Memory-Aware Denial-of-Service Attacks on Shared Cache in Multicore Real-Time Systems Abstract: In this paper, we identify that memory performance plays a crucial role in the feasibility and effectiveness for performing denial-of-service attacks on shared cache. Based on this insight, we introduce new cache DoS attacks, which can be mounted from the user-space and can cause extreme worst-case execution time (WCET) impacts to cross-core victims—even if the shared cache is partitioned—by taking advantage of the platform’s memory address mapping information and HugePage support. We deploy these enhanced attacks on two popular embedded out-of-order multicore platforms using both synthetic and real-world benchmarks. The proposed DoS attacks achieve up to 111X WCET increases on the tested platforms.

Title: Multi-Weight Nuclear Norm Minimization for Low-Rank Matrix Recovery in Presence of Subspace Prior Information Abstract: Weighted nuclear norm minimization has been recently recognized as a technique for reconstruction of a low-rank matrix from compressively sampled measurements when some prior information about the column and row subspaces of the matrix is available. We derive the conditions and the associated recovery guarantees of weighted nuclear norm minimization when multiple weights are allowed. This setup could be used when one has access to prior subspaces forming multiple angles with the column and row subspaces of the ground-truth matrix. While existing works in this field use a single weight to penalize all the angles, we propose a multi-weight problem which is designed to penalize each angle independently using a distinct weight. Specifically, we prove that our proposed multi-weight problem is robust under weaker conditions for the measurement operator than the analogous conditions for single-weight scenario and standard nuclear norm minimization. Moreover, it provides better reconstruction error than the state- of-the-art methods. We illustrate our results with extensive numerical experiments that demonstrate the advantages of allowing multiple weights in the recovery procedure. Our work has beneficial implications for channel estimation in multiple-input multiple output (MIMO) wireless communications based on Frequency Division Duplexing (FDD). The existing methods for channel estimation in this application require a huge number of pilot (training) signals to estimate the downlink channel which greatly wastes the spectrum resources in massive MIMO systems. We provide a dynamic channel estimation scenario for FDD massive MIMO systems and show how our method could be applied to enhance the spectral efficiency.

Title: Staggered DG Method with Small Edges for Darcy Flows in Fractured Porous Media Abstract: In this paper, we present and analyze a staggered discontinuous Galerkin method for Darcy flows in fractured porous media on fairly general meshes. A staggered discontinuous Galerkin method and a standard conforming finite element method with appropriate inclusion of interface conditions are exploited for the bulk region and the fracture, respectively. Our current analysis works on fairly general polygonal elements even in the presence of small edges. We prove the optimal convergence estimates in L-2 error for all the variables by exploiting the Ritz projection. Importantly, our error estimates are shown to be fully robust with respect to the heterogeneity and anisotropy of the permeability coefficients. Several numerical experiments including meshes with small edges and anisotropic meshes are carried out to confirm the theoretical findings. Finally, our method is applied in the framework of unfitted mesh.

Title: SODA: Detecting COVID-19 in Chest X-Rays With Semi-Supervised Open Set Domain Adaptation Abstract: Due to the shortage of COVID-19 viral testing kits, radiology imaging is used to complement the screening process. Deep learning based methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance for the CNN model due to two issues, first the large domain shift present in chest x-ray datasets and second the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these two important issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> emi-supervised <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> pen set <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> omain <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> dversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present initial results showing that SODA can produce better pathology localizations in the chest x-rays.

Title: On a probabilistic approach to synthesize control policies from example datasets Abstract: This paper is concerned with the design of control policies from example datasets. The case considered is when just a black box description of the system to be controlled is available and the system is affected by actuation constraints. These constraints are not necessarily fulfilled by the (possibly, noisy) example data and the system under control is not necessarily the same as the one from which these data are collected. In this context, we introduce a number of methodological results to compute a control policy from example datasets that: (i) makes the behavior of the closed-loop system similar to the one illustrated in the data; (ii) guarantees compliance with the constraints. We recast the control problem as a finite-horizon optimal control problem and give an explicit expression for its optimal solution. Moreover, we turn our findings into an algorithmic procedure. The procedure gives a systematic tool to compute the policy. The effectiveness of our approach is illustrated via a numerical example, where we use real data collected from test drives to synthesize a control policy for the merging of a car on a highway.

Title: Distributed Remote Estimation Over the Collision Channel With and Without Local Communication Abstract: Internet of Things networks are the large-scale distributed systems consisting of a massive number of simple devices communicating, typically, over a shared wireless medium. This new paradigm requires novel ways of coordinating access to limited communication resources without introducing unreasonable delays. Herein, the optimal design of a remote estimation system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> sensors communicating with a fusion center via a collision channel of limited capacity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k\leq n$</tex-math></inline-formula> is considered. In particular, for independent and identically distributed observations with a symmetric probability density function, we show that the problem of minimizing the mean-squared error with respect to a threshold strategy is quasi-convex. When coordination among sensors via a local communication network is available, the online learning of possibly unknown parameters of the probabilistic model is possible, enabling each sensor to optimize its own threshold autonomously. We propose two strategies for remote estimation with local communication: 1) one strategy swiftly reaches the performance of the optimal decentralized threshold policy and 2) the second strategy approaches the performance of the optimal centralized scheme with a slower convergence rate. A hybrid scheme that combines the best of both approaches is proposed, offering fast convergence and excellent performance.

Title: Independence of permutation limits at infinitely many scales Abstract: We introduce a new natural notion of convergence for permutations at any specified scale, in terms of the density of patterns of restricted width. In this setting we prove that limits may be chosen independently at a countably infinite number of scales.

Title: Privacy-Preserving Medical Treatment System Through Nondeterministic Finite Automata Abstract: In this article, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to cloud to deliver telemedicine service. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of patient’s illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analysis to evaluate the efficiency.

Title: A character approach to directed genus distribution of graphs: The bipartite single-black-vertex case Abstract: Given an Eulerian digraph, we consider the genus distribution of its face-oriented embeddings. We prove that such distribution is log-concave for two families of Eulerian digraphs, thus giving a positive answer for these families to a question asked in Bonnington et al. (2002) [1]. Our proof uses real-rooted polynomials and the representation theory of the symmetric group Sn. The result is also extended to some factorizations of the identity in Sn that are rotation systems of some families of one-face constellations.(C)& nbsp;2022 Elsevier B.V. All rights reserved.

Title: An approximate marginal spread computation approach for the budgeted influence maximization with delay Abstract: Given a social network of users with selection cost and a fixed budget, the problem of Budgeted Influence Maximization finds a subset of the nodes ( known as seed nodes) for initial activation to maximize the influence, such that the total selection cost is within the allocated budget. Existing solution methodologies for this problem make two assumptions, which are not applicable to real-life situations. First, an influenced node of the current time stamp can trigger only once in the next time stamp to its inactive neighbors and the other one is the diffusion process continues forever. To make the problem more practical, in this paper, we introduce the Budgeted Influence Maximization with Delay by relaxing the single time triggering constraint and imposing an additional constraint for maximum allowable diffusion time. For this purpose, we consider a delay distribution for each edge of the network, and consider a node is influenced, if it is so, within the allowable diffusion time. We first propose an incremental greedy strategy for solving this problem, which works based on the approximate computation of marginal gain in influence spread. Next, we make two subsequent improvements of this algorithm in terms of efficiency by exploiting the sub-modularity property of the time delayed influence function. We implement the proposed methodologies with three benchmark datasets. Reported results show that the seed set selected by the proposed methodologies can lead to more number of influenced nodes compared to that obtained by other baseline methods. We also observe that between the two improvised methodologies, the second one is more efficient for the larger datasets.

Title: BACKWARD STACKELBERG DIFFERENTIAL GAME WITH CONSTRAINTS: A MIXED TERMINAL-PERTURBATION AND LINEAR-QUADRATIC APPROACH Abstract: We discuss an open-loop backward Stackelberg differential game involving a single leader and single follower. Unlike most Stackelberg game literature, the state to be controlled is characterized by a backward stochastic differential equation for which the terminal- instead of the initialcondition is specified a priori; the decisions of the leader consist of a static terminal-perturbation and a dynamic linear-quadratic control. In addition, the terminal control is subject to (convex-closed) pointwise and (affine) expectation constraints. Both constraints arise from real applications such as mathematical finance. For the information pattern, the leader announces both terminal and openloop dynamic decisions at the initial time while taking into account the best response of the follower. Then, two interrelated optimization problems are sequentially solved by the follower (a backward linear-quadratic problem) and the leader (a mixed terminal-perturbation and backward-forward LQ problem). Our open-loop Stackelberg equilibrium is represented by some coupled backward-forward stochastic differential equations (BFSDEs) with mixed initial-terminal conditions. Our BFSDEs also involve a nonlinear projection operator (due to pointwise constraint) combining with a KarushKuhn-Tucker system (due to expectation constraint) via Lagrange multiplier. The global solvability of such BFSDEs is also discussed in some nontrivial cases. Our results are applied to one financial example.

Title: Deep Tensor CCA for Multi-View Learning Abstract: We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most CCA formulations relying solely on the pairwise correlations. Parameters of transformations of each view are jointly learned by maximizing the high-order canonical correlation. To solve the resulting problem, we reformulate it as the best sum of rank-1 approximation, which can be efficiently solved by existing tensor decomposition method. DTCCA is a nonlinear extension of tensor CCA (TCCA) via deep networks. Comparing with kernel TCCA, DTCCA not only can deal with arbitrary dimensions of the input data, but also does not need to maintain the training data for computing representations of any given data point. Hence, DTCCA as a unified model can efficiently overcome the scalable issue of TCCA for either high-dimensional multi-view data or a large amount of views, and it also naturally extends TCCA for learning nonlinear representation. Extensive experiments on four multi-view data sets demonstrate the effectiveness of the proposed method.

Title: Approximation in shift-invariant spaces with deep ReLU neural networks Abstract: We study the expressive power of deep ReLU neural networks for approximating functions in dilated shift-invariant spaces, which are widely used in signal processing, image processing, communications and so on. Approximation error bounds are estimated with respect to the width and depth of neural networks. The network construction is based on the bit extraction and data-fitting capacity of deep neural networks. As applications of our main results, the approximation rates of classical function spaces such as Sobolev spaces and Besov spaces are obtained. We also give lower bounds of the Lp(1≤p≤∞) approximation error for Sobolev spaces, which show that our construction of neural network is asymptotically optimal up to a logarithmic factor.

Title: Factor analysis of mixed data for anomaly detection Abstract: Anomaly detection aims to identify observations that deviate from the typical pattern of data. Anomalous observations may correspond to financial fraud, health risks, or incorrectly measured data in practice. We focus on unsupervised detection and the continuous and categorical (mixed) variable case. We show that detecting anomalies in mixed data is enhanced through first embedding the data then assessing an anomaly scoring scheme. We propose a kurtosis-weighted Factor Analysis of Mixed Data for anomaly detection to obtain a continuous embedding for anomaly scoring. We illustrate that anomalies are highly separable in the first and last few ordered dimensions of this space, and test various anomaly scoring experiments within this subspace. Results are illustrated for both simulated and real datasets, and the proposed approach is highly accurate for mixed data throughout these diverse scenarios.

Title: Triangularized Orthogonalization-Free Method for Solving Extreme Eigenvalue Problems Abstract: A novel orthogonalization-free method together with two specific algorithms is proposed to address extreme eigenvalue problems. On top of gradient-based algorithms, the proposed algorithms modify the multicolumn gradient such that earlier columns are decoupled from later ones. Locally, both algorithms converge linearly with convergence rates depending on eigengaps. Momentum acceleration, exact linesearch, and column locking are incorporated to accelerate algorithms and reduce their computational costs. We demonstrate the efficiency of both algorithms on random matrices with different spectrum distributions and matrices from computational chemistry.

Title: The Isometry-Dual Property in Flags of Two-Point Algebraic Geometry Codes Abstract: A flag of codes $C_{0} \subsetneq C_{1} \subsetneq \cdots \subsetneq C_{s} \subseteq \mathbb {F}_{q} ^{n}$ is said to satisfy the isometry-dual property if there exists ${\mathbf{x}}\in (\mathbb {F}_{q}^{*})^{n}$ such that the code <inli...

Title: Improved formulations and branch-and-cut algorithms for the angular constrained minimum spanning tree problem Abstract: The Angular Constrained Minimum Spanning Tree Problem ( $$\alpha $$ -MSTP) is defined in terms of a complete undirected graph $$G=(V,E)$$ and an angle $$\alpha \in (0,2\pi ]$$ . Vertices of G define points in the Euclidean plane while edges, the line segments connecting them, are weighted by the Euclidean distance between their endpoints. A spanning tree is an $$\alpha $$ -spanning tree ( $$\alpha $$ -ST) of G if, for any $$i \in V$$ , the smallest angle that encloses all line segments corresponding to its i-incident edges does not exceed $$\alpha $$ . $$\alpha $$ -MSTP consists in finding an $$\alpha $$ -ST with the least weight. In this work, we discuss families of $$\alpha $$ -MSTP valid inequalities. One of them is a lifting of existing angular constraints found in the literature and the others come from the Stable Set polytope, a structure behind $$\alpha $$ -STs disclosed here. We show that despite being already satisfied by the previously strongest known formulation, $${\mathcal {F}}_{xy}$$ , these lifted angular constraints are capable of strengthening another existing $$\alpha $$ -MSTP model so that both become equally strong, at least for the instances tested here. Inequalities from the Stable Set polytope improve the best known Linear Programming Relaxation (LPRs) bounds by about 1.6%, on average, for the hardest instances of the problem. Additionally, we indicate how formulation $${\mathcal {F}}_{xy}$$ can be more effectively used in Branch-and-cut (BC) algorithms, by reducing the number of variables explicitly enforced to be integer constrained and by eliminating constraints that do not change the quality of its LPR bounds. Extensive computational experiments conducted here suggest that the combination of the ideas above allows us to redefine the best performing $$\alpha $$ -MSTP algorithms, for almost the entire spectrum of $$\alpha $$ values, the exception being the easy instances, those with $$\alpha \ge \frac{2\pi }{3}$$ . In particular, for the hardest ones (corresponding to $$\alpha \in \{\frac{\pi }{2}, \frac{\pi }{3},\frac{2\pi }{5}\}$$ ) that could be solved to proven optimality, the best BC algorithm suggested here improves on average CPU times by factors of up to 5, on average.

Title: Attention-Based Neural Bag-of-Features Learning for Sequence Data Abstract: In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective. The proposed attention module is incorporated into the recently proposed Neural Bag of Feature (NBoF) model to enhance its learning capacity. Since 2DA acts as a plug-in layer, injecting it into different computation stages of the NBoF model results in different 2DA-NBoF architectures, each of which possesses a unique interpretation. We conducted extensive experiments in financial forecasting, audio analysis as well as medical diagnosis problems to benchmark the proposed formulations in comparison with existing methods, including the widely used Gated Recurrent Units. Our empirical analysis shows that the proposed attention formulations can not only improve performances of NBoF models but also make them resilient to noisy data.

Title: Deep Coarse-to-Fine Dense Light Field Reconstruction With Flexible Sampling and Geometry-Aware Fusion Abstract: A densely-sampled light field (LF) is highly desirable in various applications, such as 3-D reconstruction, post-capture refocusing and virtual reality. However, it is costly to acquire such data. Although many computational methods have been proposed to reconstruct a densely-sampled LF from a sparsely-sampled one, they still suffer from either low reconstruction quality, low computational efficie...

Title: Dynamic Hybrid Model to Forecast the Spread of COVID-19 Using LSTM and Behavioral Models Under Uncertainty Abstract: To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.

Title: Distributed Resource Scheduling for Large-Scale MEC Systems: A Multiagent Ensemble Deep Reinforcement Learning With Imitation Acceleration Abstract: In large-scale mobile edge computing (MEC) systems, the task latency, and energy consumption are important for massive resource-consuming and delay-sensitive Internet of Things Devices (IoTDs). Against this background, we propose a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task latency and energy consumption for all IoTDs, which can be formulated as a mixe...

Title: VC-saturated set systems Abstract: The well-known Sauer lemma states that a family F subset of & nbsp; 2([n]) of VC-dimension at most d has size at most & nbsp; sigma(d)(i=0)((n)(i)). We obtain both random and explicit constructions to prove that the corresponding saturation number, i.e., the size of the smallest maximal family with VC-dimension d >= 2, is at most 4(d+1), and thus is independent of n. (c) 2022 The Author(s). Published by Elsevier Ltd.& nbsp;

Title: Upper bounds for the necklace folding problems Abstract: A necklace can be considered as a cyclic list of n red and n blue beads in an arbitrary order. In the necklace folding problem the goal is to find a large crossing-free matching of pairs of beads of different colors in such a way that there exists a “folding” of the necklace, that is a partition into two contiguous arcs, which splits the beads of any matching edge into different arcs.

Title: GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model Abstract: The spatio-temporal epidemic type aftershock sequence (ETAS) model is widely used to describe the self-exciting nature of earthquake occurrences. While traditional inference methods provide only point estimates of the model parameters, we aim at a fully Bayesian treatment of model inference, allowing naturally to incorporate prior knowledge and uncertainty quantification of the resulting estimates. Therefore, we introduce a highly flexible, non-parametric representation for the spatially varying ETAS background intensity through a Gaussian process (GP) prior. Combined with classical triggering functions this results in a new model formulation, namely the GP-ETAS model. We enable tractable and efficient Gibbs sampling by deriving an augmented form of the GP-ETAS inference problem. This novel sampling approach allows us to assess the posterior model variables conditioned on observed earthquake catalogues, i.e., the spatial background intensity and the parameters of the triggering function. Empirical results on two synthetic data sets indicate that GP-ETAS outperforms standard models and thus demonstrate the predictive power for observed earthquake catalogues including uncertainty quantification for the estimated parameters. Finally, a case study for the l’Aquila region, Italy, with the devastating event on 6 April 2009, is presented.

Title: Dynamic Immunization Node Model for Complex Networks Based on Community Structure and Threshold Abstract: In the information age of big data, and increasingly large and complex networks, there is a growing challenge of understanding how best to restrain the spread of harmful information, for example, a computer virus. Establishing models of propagation and node immunity are important parts of this problem. In this article, a dynamic node immune model, based on the community structure and threshold (NI...

Title: Sampling Rate Decay in Hindsight Experience Replay for Robot Control Abstract: Training agents via deep reinforcement learning with sparse rewards for robotic control tasks in vast state space are a big challenge, due to the rareness of successful experience. To solve this problem, recent breakthrough methods, the hindsight experience replay (HER) and aggressive rewards to counter bias in HER (ARCHER), use unsuccessful experiences and consider them as successful experiences ...

Title: Joint Optimal Transport With Convex Regularization for Robust Image Classification Abstract: The critical step of learning the robust regression model from high-dimensional visual data is how to characterize the error term. The existing methods mainly employ the nuclear norm to describe the error term, which are robust against structure noises (e.g., illumination changes and occlusions). Although the nuclear norm can describe the structure property of the error term, global distribution i...

Title: Investigating Strategies for Robot Persuasion in Social Human–Robot Interaction Abstract: Persuasion is a fundamental aspect of how people interact with each other. As robots become integrated into our daily lives and take on increasingly social roles, their ability to persuade will be critical to their success during human–robot interaction (HRI). In this article, we present a novel HRI study that investigates how a robot’s persuasive behavior influences people’s decision making. The ...

Title: Set-Membership Global Estimation of Networked Systems Abstract: This article is concerned with set-membership global estimation for a networked system under unknown-but-bounded process and measurement noises. First, a group of local set-membership estimators is deployed to obtain the local ellipsoidal estimate of the true system state. Each estimator is capable of communicating with its neighbors within its communication range. Second, a global estimation appr...

Title: Improved AET Robust Control for Networked T–S Fuzzy Systems With Asynchronous Constraints Abstract: This article proposes a novel improved adaptive event-triggered (AET) control algorithm for networked Takagi–Sugeno (T–S) fuzzy systems with asynchronous constraints. First, taking the limited bandwidth of the network into consideration, an improved AET mechanism is proposed to save the communication resource. Superior to the existing event-triggered mechanism, the improved AET scheme introduces t...

Title: System Transformation-Based Neural Control for Full-State-Constrained Pure-Feedback Systems via Disturbance Observer Abstract: In this article, a novel disturbance observer-based adaptive neural control (ANC) scheme is proposed for full-state-constrained pure-feedback nonlinear systems using a new system transformation method. A nonlinear transformation function in a uniformed design framework is constructed to convert the original states with constrained bounds into the ones without any constraints. By combining an auxil...

Title: Video Saliency Prediction via Joint Discrimination and Local Consistency Abstract: While saliency detection on static images has been widely studied, the research on video saliency detection is still in an early stage and requires more efforts due to the challenge to bring both local and global consistency of salient objects into full consideration. In this article, we propose a novel dynamic saliency network based on both local consistency and global discriminations, via which ...

Title: Asynchronous Distributed Finite-Time H ∞ Filtering in Sensor Networks With Hidden Markovian Switching and Two-Channel Stochastic Attack Abstract: This article investigates the asynchronous distributed finite-time $H_{\infty }$ filtering problem for nonlinear Markov jump systems over sensor networks under stochastic attacks. The stochastic attacks, called two-channel deception attacks, exist not only between the Markov jump plant and the sensors but also among the senso...

Title: Association rules of fuzzy soft set based classification for text classification problem Abstract: Text classification is imperative in order to search for more accessible and appropriate information. It utilized in various fields, including marketing, security, biomedical, etc. Apart from its usefulness, the available classifiers are vulnerable to two major problems, namely long processing time and low accuracy. They can result from a large amount of data presented in the text classification problem. In this paper, we propose a model called Class-Based Fuzzy Soft Associative (CBFSA). This model is a combination of the association rules method and fuzzy soft set model. We used Fuzzy Soft Set Association Rules Mining for generating classifiers and Fuzzy Decision Set of an FP-Soft Set for building classifiers. Our experiment for the 20 Newsgroups dataset on 20 class documents has shown that CBFSA is more accurate than the other soft set classifiers: Soft Set Classifier (SCC), Fuzzy Soft Set Classifier (FSSC) and Hybrid Fuzzy Classifier (HFC). Besides that, it has also shown that CBFSA is more accurate and efficient compared to other associative classifiers such as the classification Based on Association (CBA) method. (C) 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: Multirobot Search For A Stationary Object Placed In A Known Environment With A Combination Of Grasp And Vnd Abstract: This paper addresses the problem of multirobot search for a stationary object in ana prioriknown environment. Two variants of the problem are studied given the working environment represented by a graph. The first variant is an extension of the traveling deliveryman problem for multiple vehicles, while the second variant is a generalization of the graph search problem. A novel algorithm is presented to solve both problems, which is based on a combination of greedy randomized adaptive search procedure with variable neighborhood descent. A set of experimental evaluations were conducted over the benchmark instances derived from the TSPLIB library. For both problems, the results obtained show that the proposed approach is comparable to state-of-the-art heuristics. Moreover, for problems of a few hundred vertices, the solution times suggest that the approach is suitable for online decision-making in search and rescue.

Title: Evaluation of C-pm estimators in ranked set sampling designs Abstract: Capability analysis allows evaluating the conformity of the production to the project specifications in industrial processes. Different indices can be used to assess the process capability, among them the C-pm (or Taguchi) index. In this work we propose the estimation of C-pm for normally distributed processes using ranked set sampling (RSS) and two extensions: pair ranked set sampling (PRSS), as an economical alternative; and double ranked set sampling (DRSS), as a more efficient (and expensive) strategy. Also, three different C-pm estimators were considered. Their performances regarding bias, mean squared error, and relative efficiency were evaluated through Monte Carlo simulation. The results indicated that: (i) There was a substantial variation in performances for different C-pm estimators, particularly for small samples; (ii) RSS based estimators outperformed their simple random sampling counterparts; (iii) DRSS estimator presented the lowest mean square error; and (iv) PRSS estimator showed competitive performance to its counterparts in different scenarios.

Title: Integer and constraint programming model formulations for flight-gate assignment problem Abstract: Flight-gate assignment problems are complex real world problems involving different constraints. Some of these constraints include plane-gate eligibility, assigning planes of the same airline and planes getting service from the same ground handling companies to adjacent gates, buffers for changes in flight schedules, night stand flights, priority of some gates over others, and so on. In literature there are numerous models to solve this highly complicated problem and tackle its complexity. In this study, first, we propose two different integer programming models, namely, timetabling and assignment based models, and then a scheduling based constraint programming model to solve the problem to optimality. These models prove to be highly efficient in that the computational times are quite short. We also present the results for one day operation of an airport using real data. Finally, we present our conclusions based on our study along with the possible further research.

Title: Topology analysis and routing algorithms design for PTNet network Abstract: Data center network (DCN) is used for transmission, storage, and processing of big data, which plays an important role in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server-centric DCN topology can ensure the scale of the DCN by recursively increasing the number of network nodes and links. Relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server-centric DCN topology, which has more advantages in scalability, fault tolerance, and so on. The PTNet network topology is theoretically analyzed in terms of network diameter, bottleneck throughput, and total number of links in the network. Based on the deep research of PTNet network, this article analyzes and studies the network topology, multicast, and broadcast routing algorithm.

Title: Finger vein identification using deeply-fused Convolutional Neural Network Abstract: Finger vein identification is a recently developed biometric technology and has become an essential field in biometrics, garnering increasing attention in recent years. As a biometric trait, using vein patterns allows for personal recognition with high security. In this paper, we have employed an improved deep network, named Merge Convolutional Neural Network (Merge CNN), which uses several CNNs with short paths. The scheme is based on the use of multiple identical CNNs with different input images qualities, and the unification of their outputs into a single layer. To achieve this, we designed different networks and trained them with the FV-USM dataset. The most optimal CNN architecture was used to build our final merged CNN labeled A, which is a combination of original image and image enhanced with Contrast Limited Adaptive Histogram (CLAH) method. Using six images for training, satisfactory performances were obtained from the FV-USM database with a recognition rate of 96.75%. Our proposed approach showed better performance than other methods exist in the literature, for the SDUMLA-HMT database with a recognition rate of 99.48%, when using five images for learning. Our proposed scheme can compete with state-of-the-art methods with recognition rate of 99.56% for the THU-FVFDT2 database.(c) 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Transfer learning meets sales engagement email classification: Evaluation, analysis, and strategies Abstract: Enterprise email classification in the sales engagement platform is a challenge due to its evolving asynchronous conversational context during the sales process and differences across industries and organizations. This is further exacerbated by the limited amount of labeled emails due to security and privacy constraints. The leaderboard success of using pretrained language models (LMs) such as BERT and various transfer learning techniques promises a paradigm shift to natural language processing, yet the recipe for applying high performance transfer learning (HPTL) in practical applications remains unclear. This article investigates applying HPTL to sales engagement email classification through a series of experiments and analysis. The experiment datasets include two different organizations' emails. The contribution of this paper is 4-fold: (a) analysis and characterization of the email corpora from different organizations; (b) identification of the best combinations of pre-trained LMs under different modeling architectures; (c) study of the impact and trade-off of limited labeled data on the model accuracy and training time; and (d) characterization and study of the impact of different orgs' datasets on the model accuracy. Our results showed that a practical winning recipe that uses BERT-finetuning with as few as 500 labeled training examples can consistently outperform significantly with reasonable training time among all models evaluated.

Title: A nonparametric statistical method for two crossing survival curves Abstract: Background: In comparative research on time-to-event data for two groups, when two survival curves cross each other, it may be difficult to use the log-rank test and hazard ratio (HR) to properly assess the treatment benefit. Our aim was to identify a method for evaluating the treatment benefits for two groups in the above situation. Methods: We quantified treatment benefits based on an intuitive measure called the area between two survival curves (ABS), which is a robust measure of treatment benefits in clinical trials regardless of whether the proportional hazards assumption is violated or two survival curves cross each other. Additionally, we propose a permutation test based on the ABS, and we evaluate the effectiveness and reliability of this test with simulated data. Results: The ABS permutation test is a robust statistical inference method with an acceptable type I error rate and superior power to detect differences in treatment effects, especially when the proportional hazards assumption is violated. Conclusion: The ABS can be used to intuitively quantify treatment differences over time and provide reliable conclusions in complicated situations, such as crossing survival curves.

Title: BVI-DVC: A Training Database for Deep Video Compression Abstract: Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. In this paper, a new extensive and representative video database, BVI-DVC,is presented for training CNN-based video compression systems, with specific emphasis on machine learning tools that enhance conventional coding architectures, including spatial resolution and bit depth up-sampling, post-processing and in-loop filtering. BVI-DVC contains 800 sequences at various spatial resolutions from 270p to 2160p and has been evaluated on ten existing network architectures for four different coding tools. Experimental results show that this database produces significant improvements in terms of coding gains over five existing (commonly used) image/video training databases under the same training and evaluation configurations. The overall additional coding improvements by using the proposed database for all tested coding modules and CNN architectures are up to 10.3% based on the assessment of PSNR and 8.1% based on VMAF.

Title: Cross validation for uncertain autoregressive model Abstract: Uncertain time series models have been investigated to predict future values based on imprecise observations. The existing researches focus on how to estimate unknown parameters in the uncertain time series model without considering how to determine the lag order. This paper proposes three types of cross validation methods, i.e. fixed origin cross validation, rolling origin cross validation, and rolling window cross validation to choose the lag order considering the model's prediction ability, and derives corresponding calculation methods under the framework of uncertainty theory. A numerical example and a real data example illustrate our methods in detail.

Title: Combinatorial approximation algorithms for the submodular multicut problem in trees with submodular penalties Abstract: In this paper, we introduce the submodular multicut problem in trees with submodular penalties, which generalizes the prize-collecting multicut problem in trees and the submodular vertex cover with submodular penalties. We present a combinatorial approximation algorithm, based on the primal-dual algorithm for the submodular set cover problem. In addition, we present a combinatorial 3-approximation algorithm for a special case where the edge cost is a modular function, based on the primal-dual scheme for the multicut problem in trees.

Title: Trust Decision-Making in Online Social Communities: A Network-Based Model Abstract: The unique characteristics of online social communities call for a reexamination and adaptation of established behavioral theories of trust decision-making. Guided by relevant social science and computational graph theories, we propose a conceptual model of trust decision-making in online social networks. This is the first study that integrates the existing graph-based view of trust decision-making in social networks into socio-psychological theories of trust to provide a richer understanding of trusting decisions in online social networks. We introduce new behavioral antecedents of trusting decisions, and redefine and integrate existing graph-based concepts to develop our proposed conceptual model. We introduce new behavioral antecedents of trusting decisions that have not been identified in previous research. We also identify novel operationalization methods to measure behavioral trust-inducing factors for online social networks. Our empirical findings indicate that both behavioral and network-specific trust decision-making factors should be considered in studying trusting decisions in online social networks.

Title: Blockchain-based intelligent contract for factoring business in supply chains Abstract: Factoring business, an important aspect in the supply chain finance field, has significant potential in adopting blockchain-based intelligent contract technology. Based on the existing theories of factoring business in supply chains, this paper conducts a coupling analysis between blockchain technology and supply chain factoring business. Specifically, we propose the application scenarios of blockchain-based intelligent contract technology in the supply chain factoring business from three aspects: the division and transfer of creditors' rights certificate, the factoring financing of upstream suppliers, and the due payment of core enterprises, and elaborate their implementation processes. Furthermore, from a game theoretical perspective, we analyze the mechanism of the key technology implementation of the intelligent contract to verify whether the nodes on the blockchains will follow the relevant protocols to automatically execute them. Finally, we conduct a three-way game analysis of the supply chain factor financing process and obtain an equilibrium solution based on the principle of utility maximization, which highlights the optimization effect of the intelligent contract technology on the decision-making behavior of individual entities in the supply chain.

Title: Impact of artificial intelligence adoption on online returns policies Abstract: The shift to e-commerce has led to an astonishing increase in online sales for retailers. However, the number of returns made on online purchases is also increasing and have a profound impact on retailers' operations and profit. Hence, retailers need to balance between minimizing and allowing product returns. This study examines an offline showroom versus an artificial intelligence (AI) online virtual-reality webroom and how the settings affect customers' purchase and retailers' return decisions. A case study is used to illustrate the AI application. Our results show that adopting artificial intelligence helps sellers to make better returns policies, maximize reselling returns, and reduce the risks of leftovers and shortages. Our findings unlock the potential of artificial intelligence applications in retail operations and should interest practitioners and researchers in online retailing, especially those concerned with online returns policies and the consumer personalized service experience.

Title: A parent-generalized family of chain ratio exponential estimators in stratified random sampling using supplementary variables Abstract: In this article, we propose a parent-generalized family of chain exponential ratio type estimators in stratified random sampling to estimate the finite population mean using known information on two supplementary variables. The proposed family covered all the well-known family of existing ratio, product, chain ratio, chain product, chain exponential ratio and chain exponential product type estimators and some new families of estimators are generated from the proposed family of estimators. Properties of the proposed parent family of estimators are studied theoretically and empirically using real data and simulation study.

Title: Obstacles of On-Premise Enterprise Resource Planning Systems and Solution Directions Abstract: The article presents the results of a Systematic Literature Review (SLR) that has been carried out to identify and present the state-of-the-art of ERP systems, describe the obstacles of on-premise ERP systems, and provide general solutions to tackle these challenges. Based on this SLR, 22 obstacles are identified, the dependencies and interactions among these obstacles are described, and finally the corresponding solutions as described in the primary studies are discussed in detail. Our study shows that there is a general agreement on the obstacles of on-premise ERP systems and further research is needed to provide satisfactory solutions to the obstacles.

Title: A news image captioning approach based on multimodal pointer-generator network Abstract: News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer-generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer-generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word-image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on theDailyMailtest dataset and the out-of-domainBBCtest dataset show that the proposed model outperforms the original pointer-generator network, the generic image captioning method, the extractive news image captioning method, and theLDA-based method accordingBLEU,METEOR, andROUGL-Levaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model.