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Title: Neural Graph Matching Network: Learning Lawler’s Quadratic Assignment Problem With Extension to Hypergraph and Multiple-Graph Matching Abstract: Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's quadratic assignment problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is translated into a constrained vertex classification task. The association graph is learned by an embedding network for vertex classification, followed by Sinkhorn normalization and a cross-entropy loss for end-to-end learning. We further improve the embedding model on association graph by introducing Sinkhorn based matching-aware constraint, as well as dummy nodes to deal with unequal sizes of graphs. To our best knowledge, this is one of the first network to directly learn with the general Lawler's QAP. In contrast, recent deep matching methods focus on the learning of node/edge features in two graphs respectively. We also show how to extend our network to hypergraph matching, and matching of multiple graphs. Experimental results on both synthetic graphs and real-world images show its effectiveness. For pure QAP tasks on synthetic data and QAPLIB benchmark, our method can perform competitively and even surpass state-of-the-art graph matching and QAP solvers with notable less time cost. We provide a project homepage at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://thinklab.sjtu.edu.cn/project/NGM/index.html</uri> .

Title: Cracking in-memory database index: A case study for Adaptive Radix Tree index Abstract: Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index initialization time. However, conventional cracking algorithms are focused on simple column data structure rather than those complex index structures for in-memory databases. In order to show the feasibility of in-memory database index cracking and promote to future more extensive research, this paper conducted a case study on the Adaptive Radix Tree (ART), a popular tree index structure of in-memory databases. On the basis of carefully examining the ART index construction overhead, an algorithm using auxiliary data structures to crack the ART index is proposed. This makes it possible to build up an ART index step by step with incessant queries, and hence avoids the poor instant availability of a complete index which is constructed once and for all, but is time consuming. Furthermore, updating a cracking ART index is considered as well. Extensive experiments show that the average initialization time of the ART cracker index is reduced by 75%, and the query response time gradually approaches the original ART algorithm with the coming queries.

Title: Positivity certificates and polynomial optimization on non-compact semialgebraic sets Abstract: In a first contribution, we revisit two certificates of positivity on (possibly non-compact) basic semialgebraic sets due to Putinar and Vasilescu (C R Acad Sci Ser I Math 328(6):495–499, 1999). We use Jacobi’s technique from (Math Z 237(2):259–273, 2001) to provide an alternative proof with an effective degree bound on the sums of squares weights in such certificates. As a consequence, it allows one to define a hierarchy of semidefinite relaxations for a general polynomial optimization problem. Convergence of this hierarchy to a neighborhood of the optimal value as well as strong duality and analysis are guaranteed. In a second contribution, we introduce a new numerical method for solving systems of polynomial inequalities and equalities with possibly uncountably many solutions. As a bonus, one can apply this method to obtain approximate global optimizers in polynomial optimization.

Title: Minimal linear codes constructed from functions Abstract: In this paper, we consider minimal linear codes by a general construction of linear codes from q-ary functions. First, we give necessary and sufficient conditions for codewords which are constructed by functions to be minimal. Second, as applications, we present three constructions of minimal linear codes. Constructions on minimal linear codes in this paper generalize some recent results in Ding et al. (IEEE Trans. Inf. Theory 64(10), 6536–6545, 2018); Heng et al. (Finite Fields Appl. 54, 176–196, 2018); Bartoli and Bonini (IEEE Trans. Inf. Theory 65(7), 4152–4155, 2019); Mesnager et al. (IEEE Trans. Inf. Theory 66(9), 5404–5413, 2020); Bonini and Borello (J. Algebraic Comb. 53, 327–341, 2021). In our three constructions, the conditions of functions are much looser than theirs.

Title: The isomorphism relation of theories with S-DOP in the generalised Baire spaces Abstract: We study the Borel-reducibility of isomorphism relations in the generalised Baire space κκ. In the main result we show for inaccessible κ, that if T is a classifiable theory and T′ is superstable with the strong dimensional order property (S-DOP), then the isomorphism of models of T is Borel reducible to the isomorphism of models of T′. In fact we show the consistency of the following: If κ is inaccessible and T is a superstable theory with S-DOP, then the isomorphism of models of T is Σ11-complete.

Title: LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data Abstract: We apply methods from randomized numerical linear algebra (RandNLA) to develop improved algorithms for the analysis of large-scale time series data. We first develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes. We show that the accuracy of approximations lies within (1 + O (epsilon)) of the true leverage scores with high probability. These theoretical results are subsequently exploited to develop an efficient algorithm, called LSAR, for fitting an appropriate AR model to big time series data. Our proposed algorithm is guaranteed, with high probability, to find the maximum likelihood estimates of the parameters of the underlying true AR model and has a worst case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale synthetic as well as real data highly support the theoretical results and reveal the efficacy of this new approach.

Title: Encoding Classical Information Into Quantum Resources Abstract: We introduce and analyse the problem of encoding classical information into different resources of a quantum state. More precisely, we consider a general class of communication scenarios characterised by encoding operations that commute with a unique resource destroying map and leave free states invariant. Our motivating example is given by encoding information into coherences of a quantum system with respect to a fixed basis (with unitaries diagonal in that basis as encodings and the decoherence channel as a resource destroying map), but the generality of the framework allows us to explore applications ranging from super-dense coding to thermodynamics. For any state, we find that the number of messages that can be encoded into it using such operations in a one-shot scenario is upper bounded in terms of the information spectrum relative entropy between the given state and its version with erased resources. Furthermore, if the resource destroying map is the twirling channel over some unitary group, we find matching one-shot lower bounds as well. In the asymptotic setting where we encode into many copies of the resource state, our bounds yield an operational interpretation of resource monotones such as the relative entropy of coherence and its corresponding relative entropy variance.

Title: Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers Abstract: Viscous and gravitational flow instabilities cause a displacement front to break up into finger-like fluids. The detection and evolutionary analysis of these fingering instabilities are critical in multiple scientific disciplines such as fluid mechanics and hydrogeology. However, previous detection methods of the viscous and gravitational fingers are based on density thresholding, which provides limited geometric information of the fingers. The geometric structures of fingers and their evolution are important yet little studied in the literature. In this article, we explore the geometric detection and evolution of the fingers in detail to elucidate the dynamics of the instability. We propose a ridge voxel detection method to guide the extraction of finger cores from three-dimensional (3D) scalar fields. After skeletonizing finger cores into skeletons, we design a spanning tree based approach to capture how fingers branch spatially from the finger skeletons. Finally, we devise a novel geometric-glyph augmented tracking graph to study how the fingers and their branches grow, merge, and split over time. Feedback from earth scientists demonstrates the usefulness of our approach to performing spatio-temporal geometric analyses of fingers.

Title: Dynamical fitness models: evidence of universality classes for preferential attachment graphs Abstract: In this paper we define a family of preferential attachment models for random graphs with fitness in the following way: independently for each node, at each time step a random fitness is drawn according to the position of a moving average process with positive increments. We will define two regimes in which our graph reproduces some features of two well-known preferential attachment models: the Bianconi-Barabasi and Barabasi-Albert models. We will discuss a few conjectures on these models, including the convergence of the degree sequence and the appearance of Bose-Einstein condensation in the network when the drift of the fitness process has order comparable to the graph size.

Title: Stigmergic Independent Reinforcement Learning for Multiagent Collaboration Abstract: With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents to gradually approach the final collective objective by continuously learning from the environment based on their individual observations. In this regard, independent reinforcement learning (IRL) is often deployed in multiagent collaboration to alleviate the problem of a nonstationary learning environment. However, behavioral strategies of intelligent agents in IRL can be formulated only upon their local individual observations of the global environment, and appropriate communication mechanisms must be introduced to reduce their behavioral localities. In this article, we address the problem of communication between intelligent agents in IRL by jointly adopting mechanisms with two different scales. For the large scale, we introduce the stigmergy mechanism as an indirect communication bridge between independent learning agents, and carefully design a mathematical method to indicate the impact of digital pheromone. For the small scale, we propose a conflict-avoidance mechanism between adjacent agents by implementing an additionally embedded neural network to provide more opportunities for participants with higher action priorities. In addition, we present a federal training method to effectively optimize the neural network of each agent in a decentralized manner. Finally, we establish a simulation scenario in which a number of mobile agents in a certain area move automatically to form a specified target shape. Extensive simulations demonstrate the effectiveness of our proposed method.

Title: Action recognition via pose-based graph convolutional networks with intermediate dense supervision Abstract: •We propose a pose-based graph convolutional network (PGCN), which employs the graph convolutional module to model the spatiotemporal correlations among the pose-related features to produce a highly discriminative representation for human action recognition.•We point out the laziness problem of the backbone CNN, and further propose a novel intermediate dense supervision (IDS) to solve this problem. It is simple and effective, without the need for extra parameters and computations.•We evaluate our approach on three popular benchmarks for pose-based action recognition, where our approach achieves stateof-the-art performance on all of them.

Title: Farley–Sabalka’s Morse-Theory Model and the Higher Topological Complexity of Ordered Configuration Spaces on Trees Abstract: Using the ordered analogue of Farley–Sabalka’s discrete gradient field on the configuration space of a graph, we unravel a levelwise behavior of the generators of the pure braid group on a tree. This allows us to generalize Farber’s equivariant description of the homotopy type of the configuration space on a tree on two particles. The results are applied to the calculation of all the higher topological complexities of ordered configuration spaces on trees on any number of particles.

Title: AN ALGEBRAIC (SET) THEORY OF SURREAL NUMBERS, I Abstract: The notion of surreal number was introduced by J.H. Conway in the mid 1970's: the surreal numbers constitute a linearly ordered (proper) class No containing the class of all ordinal numbers (On) that, working within the background set theory NBG, can be defined by a recursion on the class On. Since then, have appeared many constructions of this class and was isolated a full axiomatization of this notion that has been subject of interest due to large number of interesting properties they have, including model-theoretic ones. Such constructions suggests strong connections between the class No of surreal numbers and the classes of all sets and all ordinal numbers. In an attempt to codify the universe of sets directly within the surreal number class, we have founded some clues that suggest that this class is not suitable for this purpose. The present work, that expounds parts of the PhD thesis of the first author ([28]), establishes a basis to obtain an "algebraic (set) theory for surreal numbers" along the lines of the Algebraic Set Theory - a categorial set theory introduced in the 1990's based on the concept of ZF-algebra: to establish abstract and general links between the class of all surreal numbers and a universe of "surreal sets" similar to the relations between the class of all ordinals (On) and the class of all sets (V), that also respects and expands the links between the linearly ordered class of all ordinals and of all surreal numbers. In the present work we introduce the notion of (partial) surreal algebra (SUR-algebra) and we explore some of its category theoretic properties, including (relatively) free SUR-algebras (SA, ST). In a continuation of this work ([29]) we will establish links, in both directions, between SUR-algebras and ZF-algebras (the keystone of Algebraic Set Theory) and develop the first steps of a certain kind of set theory based (or ranked) on surreal numbers, that expands the relation between V and On.

Title: Partition and Cohen-Macaulay extenders Abstract: If a pure simplicial complex is partitionable, then its h-vector has a combinatorial interpretation in terms of any partitioning of the complex. Given a non-partitionable complex increment , we construct a complex Gamma superset of increment of the same dimension such that both Gamma and the relative complex (Gamma , increment ) are partitionable. This allows us to rewrite the h-vector of any pure simplicial complex as the difference of two h-vectors of partitionable complexes, giving an analogous interpretation of the h-vector of a non-partitionable complex. By contrast, for a given complex increment it is not always possible to find a complex Gamma such that both Gamma and (Gamma , increment ) are Cohen- Macaulay. We characterize when this is possible, and we show that the construction of such a Gamma in this case is remarkably straightforward. We end with a note on a similar notion for shellability and a connection to Simon's conjecture on extendable shellability for uniform matroids. (c) 2021 Elsevier Ltd. All rights reserved.

Title: Words of Estimative Correlation: Studying Verbalizations of Scatterplots Abstract: Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two st...

Title: Decomposed structured subsets for semidefinite and sum-of-squares optimization Abstract: Semidefinite programs (SDPs) are standard convex problems that are frequently found in control and optimization applications. Interior-point methods can solve SDPs in polynomial time up to arbitrary accuracy, but scale poorly as the size of matrix variables and the number of constraints increases. To improve scalability, SDPs can be approximated with lower and upper bounds through the use of structured subsets (e.g., diagonally-dominant and scaled-diagonally dominant matrices). Meanwhile, any underlying sparsity or symmetry structure may be leveraged to form an equivalent SDP with smaller positive semidefinite constraints. In this paper, we present a notion of decomposed structured subsets to approximate an SDP with structured subsets after an equivalent conversion. The lower/upper bounds found by approximation after conversion become tighter than the bounds obtained by approximating the original SDP directly. We apply decomposed structured subsets to semidefinite and sum-of-squares optimization problems with examples of H∞ norm estimation and constrained polynomial optimization. An existing basis pursuit method is adapted into this framework to iteratively refine bounds.

Title: Learning Semantic Correspondence Exploiting an Object-Level Prior Abstract: We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal provides an object-level prior for the semantic correspondence task and offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the regression of a single global geometric transformation between images may be sensitive to image-specific details such as background clutter. We propose a new CNN architecture, dubbed SFNet, which implements this idea. It leverages a new and differentiable version of the argmax function for end-to-end training, with a loss that combines mask and flow consistency with smoothness terms. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms the state of the art on standard benchmarks.

Title: A broken circuit model for chromatic homology theories Abstract: Using the tools of algebraic Morse theory, and the thin poset approach to constructing homology theories, we give the categorification of Whitney's broken circuit theorem for the chromatic polynomial, and for Stanley's chromatic symmetric function. (C) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Title: Weight Structures Cogenerated by Weak Cocompact Objects Abstract: We study t-structures generated by sets of objects which satisfy a condition weaker than the compactness. We also study weight structures cogenerated by sets of objects satisfying the dual condition. Under some appropriate hypothesis, it turns out that the weight structure is right adjacent to the t-structure.

Title: Spin solitons in spin-1 Bose-Einstein condensates Abstract: Vector solitons in Bose-Einstein condensates are usually investigated analytically with identical intra- and interatomic interactions (for an integrable model). We obtain six families of exact spin soliton solutions for nonintegrable cases, which can be used to describe spin-1 Bose-Einstein condensates. The stability analyses and numerical simulations indicate that three families of spin solitons are robust against spin-dependent interactions and white noise. We further investigate the motion of these stable spin solitons driven by external linear potentials. Their moving trajectories demonstrate that the spin solitons admit a negative-positive mass transition. Some splitting and diffusing behaviors can emerge during the motion of a spin soliton that are absent in spin-1/2 systems. The collisions between spin solitons are exhibited with varying relative velocity and phase. The nonintegrable properties of the systems can give rise to weak amplitude and location oscillations after collision. These stable spin soliton excitations can be used to study the negative inertial mass of solitons, the dynamics of soliton-impurity systems, and the spin dynamics in Bose-Einstein condensates. (C) 2022 Elsevier B.V. All rights reserved.

Title: Some Results on Dominating Induced Matchings Abstract: Let G be a graph, a dominating induced matching (DIM) of G is an induced matching that dominates every edge of G. In this paper we show that if a graph G has a DIM, then $$\chi (G) \le 3$$ . Also, it is shown that if G is a connected graph whose all edges can be partitioned into DIM, then G is either a regular graph or a biregular graph and indeed we characterize all graphs whose edge set can be partitioned into DIM. Also, we prove that if G is an r-regular graph of order n whose edges can be partitioned into DIM, then n is divisible by $$\left( {\begin{array}{c}2r - 1\\ r - 1\end{array}}\right) $$ and $$n = \left( {\begin{array}{c}2r - 1\\ r-1\end{array}}\right) $$ if and only if G is the Kneser graph with parameters $$r-1$$ , $$2r-1$$ .

Title: Deep Learning for Visual Tracking: A Comprehensive Survey Abstract: Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years – predominantly...

Title: Benchmarking Quantum Computers and the Impact of Quantum Noise Abstract: AbstractBenchmarking is how the performance of a computing system is determined. Surprisingly, even for classical computers this is not a straightforward process. One must choose the appropriate benchmark and metrics to extract meaningful results. Different benchmarks test the system in different ways, and each individual metric may or may not be of interest. Choosing the appropriate approach is tricky. The situation is even more open ended for quantum computers, where there is a wider range of hardware, fewer established guidelines, and additional complicating factors. Notably, quantum noise significantly impacts performance and is difficult to model accurately. Here, we discuss benchmarking of quantum computers from a computer architecture perspective and provide numerical simulations highlighting challenges that suggest caution.

Title: Idealness of k-wise intersecting families Abstract: A clutter is k-wise intersecting if every k members have a common element, yet no element belongs to all members. We conjecture that, for some integer $$k\ge 4$$ , every k-wise intersecting clutter is non-ideal. As evidence for our conjecture, we prove it for $$k=4$$ for the class of binary clutters. Two key ingredients for our proof are Jaeger’s 8-flow theorem for graphs, and Seymour’s characterization of the binary matroids with the sums of circuits property. As further evidence for our conjecture, we also note that it follows from an unpublished conjecture of Seymour from 1975. We also discuss connections to the chromatic number of a clutter, projective geometries over the two-element field, uniform cycle covers in graphs, and quarter-integral packings of value two in ideal clutters.

Title: Spatially Adapted First and Second Order Regularization for Image Reconstruction: From an Image Surface Perspective Abstract: In this paper, we propose a new variational model for image reconstruction by minimizing the $$L^1$$ norm of the Weingarten map of image surface (x, y, f(x, y)) for a given image $$f:{{\Omega }}\rightarrow {\mathbb {R}}$$ . We analytically prove that the Weingarten map minimization model can not only keep the greyscale intensity contrasts of images, but also preserve edges and corners of objects. The alternating direction method of multiplier (ADMM) based algorithm is developed, where one subproblem needs to be solved by gradient descent. In what follows, we derive a hybrid nonlinear first and second order regularization from the Weingarten map, and present an efficient ADMM-based algorithm by regarding the nonlinear weights as known. By comparing with several state-of-the-art methods on synthetic and real image reconstruction problems, it confirms that the proposed models can well preserve image contrasts and features, especially the spatially adapted first and second order regularization economizing much computational cost.

Title: <italic>GeoTrackNet</italic>—A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and <italic>A Contrario</italic> Detection Abstract: Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach—referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GeoTrackNet</i> —for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a contrario</i> detection to detect abnormal events. The neural network provides a new means to capture complex and heterogeneous patterns in vessels’ behaviours, while the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a contrario</i> detector takes into account the fact that the learnt distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method compared with state-of-the-art schemes.

Title: Augmented reality for human–swarm interaction in a swarm-robotic chemistry simulation Abstract: We present a novel augmented reality (AR) framework to show relevant information about swarm dynamics to a user in the absence of markers by using blinking frequency to distinguish between groups in the swarm. In order to distinguish between groups, clusters of the same group are identified by blinking at a specific time interval that is distinct from the time interval at which their neighbors blink. The problem is thus to find blinking sequences that are distinct for each group with respect to the group’s neighbors. Selecting an appropriate sequence is an instance of the distributed graph coloring problem, which can be solved in $$O(\log (n))$$ time with n being the number of robots involved. We demonstrate our approach using a swarm chemistry simulation in which robots simulate individual atoms that form molecules following the rules of chemistry. An AR display is then used to display information about the internal state of individual swarm members as well as their topological relationship, corresponding to molecular bonds in a context that uses robot swarms to teach basic chemistry concepts.

Title: Fundamental Structure of Optimal Cache Placement for Coded Caching With Nonuniform Demands Abstract: This paper studies the caching system of multiple cache-enabled users with random demands. Under nonuniform file popularity, we thoroughly characterize the optimal uncoded cache placement structure for the coded caching scheme (CCS). Formulating the cache placement as an optimization problem to minimize the average delivery rate, we identify the file group structure in the optimal solution. We show that, regardless of the file popularity distribution, there are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">at most three file groups</i> in the optimal cache placement, where files within a group have the same cache placement. We further characterize the complete structure of the optimal cache placement and obtain the closed-form solution in each of the three file group structures. A simple algorithm is developed to obtain the final optimal cache placement by comparing a set of candidate closed-form solutions computed in parallel. We provide insight into the file groups formed by the optimal cache placement. The optimal placement solution also indicates that coding between file groups may be explored during delivery, in contrast to the existing suboptimal file grouping schemes. Using the file group structure in the optimal cache placement for the CCS, we propose a new information-theoretic converse bound for coded caching that is tighter than the existing best one. Moreover, we characterize the file subpacketization in the CCS with the optimal cache placement solution and show that the maximum subpacketization level in the worst case scales as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ O}}(2^{K}/\sqrt {K})$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users.

Title: Discovering Opioid Use Patterns From Social Media for Relapse Prevention Abstract: The United States is experiencing an unprecedented opioid crisis. Through a multidisciplinary analytic perspective, we characterize opioid addiction behavior patterns by analyzing opioid groups from Reddit.com—including modeling online discussion topics, analyzing text co-occurrence and correlations, and identifying the emotional states of people with opioid use disorder.

Title: Wasserstein Proximal Algorithms for the Schrödinger Bridge Problem: Density Control With Nonlinear Drift Abstract: In this article, we study the Schrödinger bridge problem (SBP) with nonlinear prior dynamics. In control-theoretic language, this is a problem of minimum effort steering of a given joint state probability density function (PDF) to another over a finite-time horizon, subject to a controlled stochastic differential evolution of the state vector. As such, it can be seen as a stochastic optimal contro...

Title: HITTING TIME OF EDGE DISJOINT HAMILTON CYCLES IN RANDOM SUBGRAPH PROCESSES ON DENSE BASE GRAPHS Abstract: Consider the random subgraph process on a base graph G on n vertices: a sequence {G(t)}(t=0)(vertical bar E(G)vertical bar) of random subgraphs of G obtained by choosing an ordering of the edges of G uniformly at random, and by sequentially adding edges to G(0), the empty graph on the vertex set of G, according to the chosen ordering. We show that if G has one of the following properties: 1. there is a positive constant epsilon > 0 such that delta(G) >= (1/2 + epsilon) n; 2. there are some constants alpha, beta > 0 such that every two disjoint subsets U, W of size at least an have at least beta vertical bar U vertical bar vertical bar W vertical bar edges between them, and the minimum degree of G is at least (2 alpha + beta) center dot n; or 3. G is an (n, d, lambda)-graph, with d >= C.n,log log n/log n and lambda <= c.d(2)/n for some absolute constants c, C > 0; then for a positive integer constant k with high probability the hitting time of the property of containing k edge disjoint Hamilton cycles is equal to the hitting time of having minimum degree at least 2k. These results extend prior results by Johansson and by Frieze and Krivelevich and answer a question posed by Frieze.

Title: Angle-Based Sensor Network Localization Abstract: This article studies angle-based sensor network localization (ASNL) in a plane, which is to determine locations of all sensors in a sensor network, given locations of partial sensors (called anchors) and angle measurements obtained in the local coordinate frame of each sensor. First, it is shown that a framework with a nondegenerate bilateration ordering must be angle fixable, implying that it can be uniquely determined by angles between edges up to translations, rotations, reflections, and uniform scaling. Then, ASNL is proved to have a unique solution if and only if the grounded framework is angle fixable and anchors are not all collinear. Subsequently, ASNL is solved in centralized and distributed settings, respectively. The centralized ASNL is formulated as a rank-constrained semidefinite program (SDP) in either a noise-free or a noisy scenario, with a decomposition approach proposed to deal with large-scale ASNL. The distributed protocol for ASNL is designed based on intersensor communications. Graphical conditions for equivalence of the formulated rank-constrained SDP and a linear SDP, decomposition of the SDP, as well as the effectiveness of the distributed protocol, are proposed, respectively. Finally, simulation examples demonstrate our theoretical results.

Title: Development of trust based access control models using fuzzy logic in cloud computing Abstract: Cloud computing is the technology that provides different types of services as a useful resource on the Internet. Resource trust value will help the cloud users to select the services of a cloud provider for processing and storing their essential information. Also, service providers can give access to users based on trust value to secure cloud resources from malicious users. In this paper, trust models are proposed, which comes under the subjective trust model based on the behavior of user and service provider to calculate the trust values. The trust is fuzzy, which motivated us to apply fuzzy logic for calculating the trust values of the cloud users and service providers in the cloud environment. We use a Mamdani fuzzy method with gauss membership function for fuzzification and triangular membership function for defuzzification. Parameters such as performance and elasticity are taken for trust evaluation of the resource. The attributes for calculating performance are workload and response time. And for calculating elasticity, we have taken scalability, availability, security, and usability. The fuzzy C-means clustering is applied to parameters for evaluating the trust value of users such as bad requests, bogus requests, unauthorized requests, and total requests.

Title: Analysis of semi-open queueing networks using lost customers approximation with an application to robotic mobile fulfilment systems Abstract: We consider a semi-open queueing network (SOQN), where one resource from a resource pool is needed to serve a customer. If on arrival of a customer some resource is available, the resource is forwarded to an inner network to complete the customer’s order. If no resource is available, the new customer waits in an external queue until one becomes available (“backordering”). When a resource exits the inner network, it is returned to the resource pool. We develop a new solution approach. In a first step we modify the system such that new arrivals are lost if the resource pool is empty (“lost customers”). We adjust the arrival rate of the modified system such that the throughputs in all nodes of the inner network are pairwise identical to those in the original network. Using queueing theoretical methods, in a second step we reduce this inner network to a two-station system including the resource pool. For this two-station systems, we invert the first step and obtain a standard SOQN which can be solved analytically. We apply our results to storage and delivering systems with robotic mobile fulfilment systems (RMFSs). Instead of sending pickers to the storage area to search for the ordered items and pick them, robots carry shelves with ordered items from the storage area to picking stations. We model the RMFS as an SOQN to determine the minimal number of robots.

Title: Deep Distributional Sequence Embeddings Based on a Wasserstein Loss Abstract: Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.

Title: Privacy-Preserving Search for a Similar Genomic Makeup in the Cloud Abstract: Increasing affordability of genome sequencing and, as a consequence, widespread availability of genomic data opens up new opportunities for the field of medicine, as also evident from the emergence of popular cloud-based offerings in this area, such as Google Genomics [1]. To utilize this data more efficiently, it is crucial that different entities share their data with each other. However, such data sharing is risky mainly due to privacy concerns. In this article, we attempt to provide a privacy-preserving and efficient solution for the “similar patient search” problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we propose a hierarchical index structure to index each hospital’s dataset with low memory requirement. Furthermore, we develop a novel privacy-preserving index merging mechanism that generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the high efficiency of the proposed scheme.

Title: Keyword aware influential community search in large attributed graphs Abstract: Influential community search (ICS) on a graph finds a closely connected group of vertices having a dominance over other groups of vertices. The ICS has many applications in recommendations, event organization, and so on. In this paper, we introduce a new variant of ICS, namely keyword-aware influential community query (KICQ), that finds the communities with the highest influential scores and whose keywords match with the query terms (a set of keywords) and predicates (AND or OR). It is challenging to find such communities from a large network as the traditional pre-computation approach is not applicable with the change of query terms at every instance of the search. To solve this problem, we design two efficient algorithms: (i) a branch-and-bound approach that exploits the bounds computed from already explored communities to prune the search space, and (ii) a novel index based approach that hierarchically organizes sub-communities and keywords with associated bounds to quickly identify the desired communities. We propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.

Title: Extreme Learning Machine Design for Dealing with Unrepresentative Features Abstract: Extreme Learning Machines (ELMs) have become a popular tool for the classification of electroencephalography (EEG) signals for Brain Computer Interfaces. This is so mainly due to their very high training speed and generalization capabilities. Another important advantage is that they have only one hyperparameter that must be calibrated: the number of hidden nodes. While most traditional approaches dictate that this parameter should be chosen smaller than the number of available training examples, in this article we argue that, in the case of problems in which the data contain unrepresentative features, such as in EEG classification problems, it is beneficial to choose a much larger number of hidden nodes. We characterize this phenomenon, explain why this happens and exhibit several concrete examples to illustrate how ELMs behave. Furthermore, as searching for the optimal number of hidden nodes could be time consuming in enlarged ELMs, we propose a new training scheme, including a novel pruning method. This scheme provides an efficient way of finding the optimal number of nodes, making ELMs more suitable for dealing with real time EEG classification problems. Experimental results using synthetic data and real EEG data show a major improvement in the training time with respect to most traditional and state of the art ELM approaches, without jeopardising classification performance and resulting in more compact networks.

Title: PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models Abstract: Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC algorithm is computationally expensive due to costly matrix operations. In addition, the MCMC algorithm needs to be run for more iterations because the strong cross-correlations among the spatial latent variables result in slow mixing Markov chains. To address these computational challenges, we propose a projection-based intrinsic conditional autoregression (PICAR) approach, which is a discretized and dimension-reduced representation of the underlying spatial random field using empirical basis functions on a triangular mesh. Our approach exhibits fast mixing as well as a considerable reduction in computational cost per iteration. PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models. We show, via simulation studies, that our approach performs well in terms of parameter inference and prediction. We provide several examples to illustrate the applicability of our method, including (i) a high-dimensional cloud cover dataset that showcases its computational efficiency, (ii) a spatially varying coefficient model that demonstrates the ease of implementation of PICAR in the probabilistic programming languages stan and nimble, and (iii) a watershed survey example that illustrates how PICAR applies to models that are not amenable to efficient inference via existing methods.

Title: SensorsDesign for Large-Scale Boolean Networks via Pinning Observability Abstract: In this article, a set of sensors is constructed via the pinning observability approach with the help of observability criteria given in [1] and [2], in order tomake the given Boolean network (BN)be observable. Given the assumption that system states can be accessible, an efficient pinning control scheme is developed to generate an observable BN by adjusting the network structure rather than just to check system observability. Accordingly, the sensors are constructed, of which the form is consistent with that of state feedback controllers in the designed pinning control. Since this pinning control approach only utilizes node-to-node message communication instead of global state space information, the time complexity is dramatically reduced from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(2^{2n})$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n^2+n2^d)$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n\, {\text{and}}\, d$</tex-math></inline-formula> are respectively the node number of the considered BN and the largest in-degree of vertices in its network structure. Finally, we design the sensors for the reduced D. melanogaster segmentation polarity gene network and the T-cell receptor kinetics, respectively.

Title: A linear optimization oracle for zonotope computation Abstract: A class of counting problems asks for the number of regions of a central hyperplane arrangement. By duality, this is the same as counting the vertices of a zonotope. Efficient algorithms are known that solve this problem by computing the vertices of a zonotope from its set of generators. Here, we give an efficient algorithm, based on a linear optimization oracle, that performs the inverse task and recovers the generators of a zonotope from its set of vertices. We also provide a variation of that algorithm that allows to decide whether a polytope, given as its vertex set, is a zonotope and when it is not a zonotope, to compute its greatest zonotopal summand.

Title: Local convergence of tensor methods Abstract: In this paper, we study local convergence of high-order Tensor Methods for solving convex optimization problems with composite objective. We justify local superlinear convergence under the assumption of uniform convexity of the smooth component, having Lipschitz-continuous high-order derivative. The convergence both in function value and in the norm of minimal subgradient is established. Global complexity bounds for the Composite Tensor Method in convex and uniformly convex cases are also discussed. Lastly, we show how local convergence of the methods can be globalized using the inexact proximal iterations.

Title: Deep anomaly detection in packet payload Abstract: With the wide deployment of edge devices, a variety of emerging applications have been deployed at the edge of network. To guarantee the safe and efficient operations of the edge applications, especially the extensive web applications, it is important and challenging to detect packet payload anomalies, which can be expressed as a number of specific strings that may cause attacks. Although some approaches have achieved remarkable progress, they are with limited applications since these approaches are dependent on in-depth expert knowledge, e.g., signatures describing anomalies or communication protocol at the application level. Moreover, they might fail to detect the payload anomalies that may have long-term dependency relationships at the edge of network. To overcome these limitations and adaptively detect anomalies from packet payloads, we propose a deep learning based framework which does not rely on any in-depth expert knowledge and is capable of detecting anomalies that have long-term dependency relationships. The proposed framework consists of two parts. First, a novel block sequence construction method is proposed to obtain a valid expression of a payload. The block sequence could encapsulate both the high-dimension information and the underlying sequential information which facilitate the anomaly detection. Secondly, we design a detection model to learn two different dependency relationships within the block sequence, which is based on Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Multi-head Self Attention Mechanism. Furthermore, we cast the anomaly detection as a classification problem and employ a classifier with attention mechanism to integrate information and detect anomalies. Extensive experimental results on three public datasets indicate that our model could achieve a higher detection rate, while keeping a lower false positive rate compared with two traditional machine learning methods and three state-of-the-art methods.

Title: Universality of persistence diagrams and the bottleneck and Wasserstein distances Abstract: We undertake a formal study of persistence diagrams and their metrics. We show that barcodes and persistence diagrams together with the bottleneck distance and the Wasserstein distances are obtained via universal constructions and thus have corresponding universal properties. In addition, the 1-Wasserstein distance satisfies Kantorovich-Rubinstein duality. Our constructions and results apply to any metric space with a distinguished basepoint. For example, they can also be applied to multiparameter persistence modules.

Title: Overcoming the Curse of Dimensionality in the Numerical Approximation of Parabolic Partial Differential Equations with Gradient-Dependent Nonlinearities Abstract: Partial differential equations (PDEs) are a fundamental tool in the modeling of many real-world phenomena. In a number of such real-world phenomena the PDEs under consideration contain gradient-dependent nonlinearities and are high-dimensional. Such high-dimensional nonlinear PDEs can in nearly all cases not be solved explicitly, and it is one of the most challenging tasks in applied mathematics to solve high-dimensional nonlinear PDEs approximately. It is especially very challenging to design approximation algorithms for nonlinear PDEs for which one can rigorously prove that they do overcome the so-called curse of dimensionality in the sense that the number of computational operations of the approximation algorithm needed to achieve an approximation precision of size $${\varepsilon }> 0$$ grows at most polynomially in both the PDE dimension $$d \in \mathbb {N}$$ and the reciprocal of the prescribed approximation accuracy $${\varepsilon }$$ . In particular, to the best of our knowledge there exists no approximation algorithm in the scientific literature which has been proven to overcome the curse of dimensionality in the case of a class of nonlinear PDEs with general time horizons and gradient-dependent nonlinearities. It is the key contribution of this article to overcome this difficulty. More specifically, it is the key contribution of this article (i) to propose a new full-history recursive multilevel Picard approximation algorithm for high-dimensional nonlinear heat equations with general time horizons and gradient-dependent nonlinearities and (ii) to rigorously prove that this full-history recursive multilevel Picard approximation algorithm does indeed overcome the curse of dimensionality in the case of such nonlinear heat equations with gradient-dependent nonlinearities.

Title: Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements Abstract: AbstractChildhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children’s data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this article, we present a deep learning model designed for predicting future obesity patterns from generally available items on children’s medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the United States. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3 and 20 years using the data from 1 to 3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.

Title: The Turan number of the square of a path Abstract: The Turan number of a graph H, ex(n, H), is the maximum number of edges in a graph on n vertices which does not have H as a subgraph. Let P-k be the path with k vertices, the square P-k(2) of P-k is obtained by joining the pairs of vertices with distance one or two in P-k. The powerful theorem of Erdos, Stone and Simonovits determines the asymptotic behavior of ex(n, P-k(2)). In the present paper, we determine the exact value of ex(n, P-5(2)) and ex(n, P-6(2)) and pose a conjecture for the exact value of ex(n, P-k(2)). (C) 2021 The Authors. Published by Elsevier B.V.

Title: Efficient Semidefinite Programming with Approximate ADMM Abstract: Tenfold improvements in computation speed can be brought to the alternating direction method of multipliers (ADMM) for Semidefinite Programming with virtually no decrease in robustness and provable convergence simply by projecting approximately to the Semidefinite cone. Instead of computing the projections via “exact” eigendecompositions that scale cubically with the matrix size and cannot be warm-started, we suggest using state-of-the-art factorization-free, approximate eigensolvers, thus achieving almost quadratic scaling and the crucial ability of warm-starting. Using a recent result from Goulart et al. (Linear Algebra Appl 594:177–192, 2020. https://doi.org/10.1016/j.laa.2020.02.014 ), we are able to circumvent the numerical instability of the eigendecomposition and thus maintain tight control on the projection accuracy. This in turn guarantees convergence, either to a solution or a certificate of infeasibility, of the ADMM algorithm. To achieve this, we extend recent results from Banjac et al. (J Optim Theory Appl 183(2):490–519, 2019. https://doi.org/10.1007/s10957-019-01575-y ) to prove that reliable infeasibility detection can be performed with ADMM even in the presence of approximation errors. In all of the considered problems of SDPLIB that “exact” ADMM can solve in a few thousand iterations, our approach brings a significant, up to 20x, speedup without a noticeable increase in ADMM’s iterations.

Title: ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking Abstract: Commit messages record code changes (e.g., feature modifications and bug repairs) in natural language, and are useful for program comprehension. Due to the frequent updates of software and time cost, developers are generally unmotivated to write commit messages for code changes. Therefore, automating the message writing process is necessitated. Previous studies on commit message generation have been benefited from generation models or retrieval models, but the code structure of changed code, i.e., AST, which can be important for capturing code semantics, has not been explicitly involved. Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models. In this paper, we propose a novel commit message generation model, named ATOM, which explicitly incorporates the abstract syntax tree for representing code changes and integrates both retrieved and generated messages through hybrid ranking. Specifically, the hybrid ranking module can prioritize the most accurate message from both retrieved and generated messages regarding one code change. We evaluate the proposed model ATOM on our dataset crawled from 56 popular Java repositories. Experimental results demonstrate that ATOM increases the state-of-the-art models by 30.72 percent in terms of BLEU-4 (an accuracy measure that is widely used to evaluate text generation systems). Qualitative analysis also demonstrates the effectiveness of ATOM in generating accurate code commit messages.

Title: Knowledge extraction from the learning of sequences in a long short term memory (LSTM) architecture Abstract: Transparency and trust in machine learning algorithms have been deemed to be fundamental and yet, from a practical point of view, they remain difficult to implement. Particularly, explainability and interpretability are certainly among the most difficult capabilities to be addressed and imply to be able to understand a decision in terms of simple cues and rules. In this article, we address this specific problem in the context of sequence learning by recurrent neuronal models (and more specifically Long Short Term Memory model). We introduce a general method to extract knowledge from the latent space based on the clustering of the internal states. From these hidden states, we explain how to build and validate an automaton that corresponds to the underlying (unknown) grammar, and allows to predict if a given sequence is valid or not. Finally, we show that it is possible for such complex recurrent model, to extract the knowledge that is implicitly encoded in the sequences and we report a high rate of recognition of the sequences extracted from the original grammar. This method is illustrated on artificial grammars (Reber grammar variants) as well as on a real use-case in the electrical domain, whose underlying grammar is unknown.

Title: Learning multivariate functions with low-dimensional structures using polynomial bases Abstract: In this paper we propose a method for the approximation of high-dimensional functions over finite intervals with respect to complete orthonormal systems of polynomials. An important tool for this is the multivariate classical analysis of variance (ANOVA) decomposition. For functions with a low-dimensional structure, i.e., a low superposition dimension, we are able to achieve a reconstruction from scattered data and simultaneously understand relationships between different variables.

Title: Randomized Newton’s Method for Solving Differential Equations Based on the Neural Network Discretization Abstract: We develop a randomized Newton’s method for solving differential equations, based on a fully connected neural network discretization. In particular, the randomized Newton’s method randomly chooses equations from the overdetermined nonlinear system resulting from the neural network discretization and solves the nonlinear system adaptively. We theoretically prove that the randomized Newton’s method has a quadratic convergence locally. We also apply this new method to various numerical examples, from one to high-dimensional differential equations, to verify its feasibility and efficiency. Moreover, the randomized Newton’s method can allow the neural network to “learn” multiple solutions for nonlinear systems of differential equations, such as pattern formation problems, and provides an alternative way to study the solution structure of nonlinear differential equations overall.

Title: Early prediction for merged vs abandoned code changes in modern code reviews Abstract: Context: The modern code review process is an integral part of the current software development practice. Considerable effort is given here to inspect code changes, find defects, suggest an improvement, and address the suggestions of the reviewers. In a code review process, several iterations usually take place where an author submits code changes and a reviewer gives feedback until is happy to accept the change. In around 12% cases, the changes are abandoned, eventually wasting all the efforts. Objective: In this research, our objective is to design a tool that can predict whether a code change would be merged or abandoned at an early stage to reduce the waste of efforts of all stakeholders (e.g., program author, reviewer, project management, etc.) involved. The real-world demand for such a tool was formally identified by a study by Fan et al. (2018). Method: We have mined 146,612 code changes from the code reviews of three large and popular open-source software and trained and tested a suite of supervised machine learning classifiers, both shallow and deep learning-based. We consider a total of 25 features in each code change during the training and testing of the models. The features are divided into five dimensions: reviewer, author, project, text, and code. Results: The best performing model named PredCR (Predicting Code Review), a LightGBM-based classifier achieves around 85% AUC score on average and relatively improves the state-of-the-art (Fan et al., 2018) by 14%-23%. In our extensive empirical study involving PredCR on the 146,612 code changes from the three software projects, we find that (1) The new features like reviewer dimensions that are introduced in PredCR are the most informative. (2) Compared to the baseline, PredCR is more effective towards reducing bias against new developers. (3) PredCR uses historical data in the code review repository and as such the performance of PredCR improves as a software system evolves with new and more data. Conclusion: PredCR can help save time and effort by helping developers/code reviewers to prioritize the code changes that they are asked to review. Project management can use PredCR to determine how code changes can be assigned to the code reviewers (e.g., select code changes that are more likely to be merged for review before the changes that might be abandoned).

Title: Attentive Representation Learning With Adversarial Training for Short Text Clustering Abstract: Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe sparsity of short text representations, making the previous clustering approaches still far from satisfactory. In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlations between text representations and cluster representations. Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model. To further ensure robust model training for short texts, we apply adversarial training to the unsupervised clustering setting, by injecting perturbations into the cluster representations. The model parameters and perturbations are optimized alternately through a minimax game. Extensive experiments on four real-world short text datasets demonstrate the superiority of the proposed model over several strong competitors, verifying that robust adversarial training yields substantial performance gains.

Title: Efficient Approximation of High-Dimensional Functions With Neural Networks Abstract: In this article, we develop a framework for showing that neural networks can overcome the curse of dimensionality in different high-dimensional approximation problems. Our approach is based on the notion of a catalog network, which is a generalization of a standard neural network in which the nonlinear activation functions can vary from layer to layer as long as they are chosen from a predefined catalog of functions. As such, catalog networks constitute a rich family of continuous functions. We show that under appropriate conditions on the catalog, catalog networks can efficiently be approximated with rectified linear unit-type networks and provide precise estimates on the number of parameters needed for a given approximation accuracy. As special cases of the general results, we obtain different classes of functions that can be approximated with recitifed linear unit networks without the curse of dimensionality.

Title: Maximum average entropy-based quantization of local observations for distributed detection Abstract: In a wireless sensor network, multilevel quantization is necessary to find a compromise between minimizing the power consumption of sensors and maximizing the detection performance at the fusion center (FC). The previous methods have been using distance measures such as J-divergence and Bhattacharyya distance in this quantization. This work proposes a different approach based on the maximum average entropy of the output of the sensors under both hypotheses and utilizes it in a Neyman-Pearson criterion-based distributed detection scheme to detect a point source. The receiver operating characteristics of the proposed maximum average entropy (MAE) method in quantizing sensor outputs have been evaluated for multilevel quantization both when the sensor outputs are available error-free at the FC and when non-coherent M-ary frequency shift keying communication is used for transmitting MAE based multilevel quantized sensor outputs over a Rayleigh fading channel. The simulation studies show the success of the MAE in the cases of both error-free fusion and where the effect of the wireless channel has been incorporated. As expected, the performance improves as the level of quantization increases and with six-level quantization approaches the performance of non-quantized data transmission. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

Title: Good acyclic orientations of 4-regular 4-connected graphs Abstract: An st-ordering of a graph G=(V,E) is an ordering v(1),v(2), horizontal ellipsis ,v(n) of its vertex set such that s = v(1),t = v(n) and every vertex vi with i=2,3, horizontal ellipsis ,n-1 has both a lower numbered and a higher numbered neighbor. Such orderings have played an important role in algorithms for planarity testing. It is well-known that every 2-connected graph has an st-ordering for every choice of distinct vertices s,t. An st-ordering of a graph G corresponds directly to a so-called bipolar orientation of G, that is, an acyclic orientation D of G in which s is the unique source and t is the unique sink. Clearly every bipolar orientation of a graph has an out-branching rooted at the source vertex and an in-branching rooted at the sink vertex. In this paper, we study graphs which admit a bipolar orientation that contains an out-branching and in-branching which are arc-disjoint (such an orientation is called good). A 2T-graph is a graph whose edge set can be decomposed into two edge-disjoint spanning trees. Clearly a graph has a good orientation if and only if it contains a spanning 2T-graph with a good orientation, implying that 2T-graphs play a central role. It is a well-known result due to Tutte and Nash-Williams, respectively, that every 4-edge-connected graph contains a spanning 2T-graph. Vertex-minimal 2T-graphs with at least two vertices, also known as generic circuits, play an important role in rigidity theory for graphs. Recently with Bessy and Huang we proved that every generic circuit has a good orientation. In fact, we may specify the roots of the two branchings arbitrarily as long as they are distinct. Using this, several results on good orientations of 2T-graphs were obtained. It is an open problem whether there exists a polynomial algorithm for deciding whether a given 2T-graph has a good orientation. Complex constructions of 2T-graphs with no good orientation were given in work by Bang-Jensen, Bessy, Huang and Kriesell (2021) indicating that the problem might be very difficult. In this paper, we focus on so-called quartics which are 2T-graphs where every vertex has degree 3 or 4. We identify a sufficient condition for a quartic to have a good orientation, give a polynomial algorithm to recognize quartics satisfying the condition and a polynomial algorithm to produce a good orientation when this condition is met. As a consequence of these results we prove that every 4-regular and 4-connected graph has a good orientation, where, as for generic circuits, we may specify the roots of the two branchings arbitrarily as long as they are distinct. We also provide evidence that even for quartics it may be difficult to find a characterization of those instances which have a good orientation. We also show that every graph on n >= 8 vertices and of minimum degree at least [n/2] has a good orientation. Finally we pose a number of open problems.

Title: Expansion for the critical point of site percolation: the first three terms. Abstract: We expand the critical point for site percolation on the $d$-dimensional hypercubic lattice in terms of inverse powers of $2d$, and we obtain the first three terms rigorously. This is achieved using the lace expansion.

Title: DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images Abstract: Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes. It can be graded into five levels of severity according to international protocol. However, optimizing a grading model to have strong generalizability requires a large amount of balanced training data, which is difficult to collect, particularly for the high severity levels. Typical data augmentation methods, inclu...

Title: Duality of sum of nonnegative circuit polynomials and optimal SONC bounds Abstract: Circuit polynomials are polynomials with properties that make it easy to compute sharp and certifiable global lower bounds for them. Consequently, one may use them to find certifiable lower bounds for any polynomial by writing it as a sum of circuit polynomials with known lower bounds. Recent work has shown that sums of nonnegative circuit polynomials (or SONC polynomials for short) can be used to compute global lower bounds (called SONC bounds) for polynomials in this manner very efficiently both in theory and in practice, if the polynomial is bounded from below and its support satisfies a certain nondegeneracy assumption. The quality of the SONC bound depends on the circuits used in the computation but finding the set of circuits that yield the best attainable SONC bound among the astronomical number of candidate circuits is a non-trivial task that has not been addressed so far. We propose an efficient method to compute the optimal SONC lower bound by iteratively identifying the optimal circuits to use in the SONC bounding process. The method is derived from a new proof of the result that every SONC polynomial decomposes into SONC polynomials on the same support. This proof is based on convex programming duality and motivates a column generation approach that is particularly attractive for sparse polynomials of high degree and with many unknowns. The method is implemented and tested on a large set of sparse polynomial optimization problems with up to 40 unknowns, of degree up to 60, and up to 3000 monomials in the support. The results indicate that the method is efficient in practice and requires only a small number of iterations to identify the optimal circuits.

Title: Covert Channel-Based Transmitter Authentication in Controller Area Networks Abstract: In recent years, the security of automotive Cyber-Physical Systems (CPSs) is facing urgent threats due to the widespread use of legacy in-vehicle communication systems. As a representative legacy bus system, the Controller Area Network (CAN) hosts Electronic Control Units (ECUs) that are crucial for the vehicles functioning. In this scenario, malicious actors can exploit the CAN vulnerabilities, such as the lack of built-in authentication and encryption schemes, to launch CAN bus attacks (e.g., suspension, injection, and masquerade attacks) with life-threatening consequences (e.g., disabling brakes). In this article, we present TACAN (Transmitter Authentication in CAN), which provides secure authentication of ECUs on the legacy CAN bus by exploiting the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covert channels</i> , without introducing CAN protocol modifications or traffic overheads (no extra bits or CAN messages are used). TACAN turns upside-down the originally malicious concept of covert channels and exploits it to build an effective defensive technique that facilitates transmitter authentication via a centralized, trusted Monitor Node. TACAN consists of three different covert channels for ECU authentication: 1) the Inter-Arrival Time (IAT)-based, leveraging the IATs of CAN messages; 2) the Least Significant Bit (LSB)-based, concealing authentication messages into the LSBs of normal CAN data; and 3) a hybrid covert channel, exploiting the combination of the first two. In order to validate TACAN, we implement the covert channels on the University of Washington (UW) EcoCAR (Chevrolet Camaro 2016) testbed. We further evaluate the bit error, throughput, and detection performance of TACAN through extensive experiments using the EcoCAR testbed and a publicly available dataset collected from Toyota Camry 2010. We demonstrate the feasibility of TACAN and the effectiveness of detecting CAN bus attacks, highlighting no traffic overheads and attesting the regular functionality of ECUs.

Title: Detection of Collision-Prone Vehicle Behavior at Intersections Using Siamese Interaction LSTM Abstract: As a large proportion of road accidents occur at intersections, monitoring traffic safety of intersections is important. Existing approaches are designed to investigate accidents in lane-based traffic. However, such approaches are not suitable in a lane-less mixed-traffic environment where vehicles often ply very close to each other. Hence, we propose an approach called Siamese Interaction Long Sh...

Title: Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples Abstract: ABSTRACT Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics. Interviewing 13 embedding users across disciplines, we find comparing embeddings is a key task for deployment or downstream analysis but unfolds in a tedious fashion that poorly supports systematic exploration. In response, we present the Embedding Comparator, an interactive system that presents a global comparison of embedding spaces alongside fine-grained inspection of local neighborhoods. It systematically surfaces points of comparison by computing the similarity of the k-nearest neighbors of every embedded object between a pair of spaces. Through case studies across multiple modalities, we demonstrate our system rapidly reveals insights, such as semantic changes following fine-tuning, language changes over time, and differences between seemingly similar models. In evaluations with 15 participants, we find our system accelerates comparisons by shifting from laborious manual specification to browsing and manipulating visualizations.

Title: Deep Direct Visual Odometry Abstract: Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. With the outstanding performance of deep learning, previous works have shown that deep neural networks can effectively learn 6-DoF (Degree of Freedom) poses between frames from monocular image sequences in the unsupervised manner. However, these unsupervised deep learning-based frameworks cannot accurately generate the full trajectory of a long monocular video because of the scale-inconsistency between each pose. To address this problem, we use several geometric constraints to improve the scale-consistency of the pose network, including improving the previous loss function and proposing a novel scale-to-trajectory constraint for unsupervised training. We call the pose network trained by the proposed novel constraint as TrajNet. In addition, a new DVO architecture, called deep direct sparse odometry (DDSO), is proposed to overcome the drawbacks of the previous direct sparse odometry (DSO) framework by embedding TrajNet. Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.

Title: Third-degree price discrimination versus uniform pricing Abstract: We compare the profit of the optimal third-degree price discrimination policy against a uniform pricing policy. A uniform pricing policy offers the same price to all segments of the market. Our main result establishes that for a broad class of third-degree price discrimination problems with concave profit functions (in the price space) and common support, a uniform price is guaranteed to achieve one half of the optimal monopoly profits. This profit bound holds for any number of segments and prices that the seller might use under third-degree price discrimination. We establish that these conditions are tight and that weakening either common support or concavity can lead to arbitrarily poor profit comparisons even for regular or monotone hazard rate distributions.

Title: Fuzzy group identification problems Abstract: The Group Identification Problem (“Who is a J?”) introduced by Kasher and Rubinstein [14] assumes a finite class of agents, each one with an opinion about the membership to a group J of the members of the society, consisting in a function that indicates for each agent, including herself, the degree of membership to J. The problem is that of aggregating those functions, satisfying different sets of axioms and characterizing different aggregators. The literature has already considered fuzzy versions of this problem. In this paper we consider alternative fuzzy presentations of the axiomatic of the original problem. While some results are analogous to those of the original crisp model, we show that our fuzzy version is able to overcome some of the main impossibility results of Kasher and Rubinstein.

Title: Short Simplex Paths in Lattice Polytopes Abstract: The goal of this paper is to design a simplex algorithm for linear programs on lattice polytopes that traces “short” simplex paths from any given vertex to an optimal one. We consider a lattice polytope P contained in $$[0,k]^n$$ and defined via m linear inequalities. Our first contribution is a simplex algorithm that reaches an optimal vertex by tracing a path along the edges of P of length in $$O(n^{4} k\, \hbox {log}\, k).$$ The length of this path is independent from m and it is the best possible up to a polynomial function. In fact, it is only polynomially far from the worst-case diameter, which roughly grows as nk. Motivated by the fact that most known lattice polytopes are defined via $$0,\pm 1$$ constraint matrices, our second contribution is a more sophisticated simplex algorithm which exploits the largest absolute value  $$\alpha $$ of the entries in the constraint matrix. We show that the length of the simplex path generated by this algorithm is in $$O(n^2k\, \hbox {log}\, ({nk} \alpha ))$$ . In particular, if $$\alpha $$ is bounded by a polynomial in n, k, then the length of the simplex path is in $$O(n^2k\, \hbox {log}\, (nk))$$ . For both algorithms, if P is “well described”, then the number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m, and $$\hbox {log}\, k$$ . If k is polynomially bounded in n and m, the algorithm runs in strongly polynomial time.

Title: Topological type of discriminants of some special families Abstract: We will describe the topological type of the discriminant curve of the morphism $$(\ell , f)$$ , where $$\ell $$ is a smooth curve and f is an irreducible curve (branch) of multiplicity less than five or a branch such that the difference between its Milnor number and Tjurina number is less than 3. We prove that for a branch of these families, the topological type of the discriminant curve is determined by the semigroup, the Zariski invariant and at most two other analytical invariants of the branch.

Title: Mission-Oriented Miniature Fixed-Wing UAV Swarms: A Multilayered and Distributed Architecture Abstract: In this article, a multilayered and distributed architecture for mission-oriented miniature fixed-wing UAV swarms is presented. Based on the concept of modularity, the proposed architecture divides the overall system into five layers: 1) low-level control layer; 2) high-level control layer; 3) coordination layer; 4) communication layer; and 5) human interaction layer, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this article is the first work which successfully demonstrates formation flight, target recognition, and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.

Title: Towards Partial Supervision for Generic Object Counting in Natural Scenes Abstract: Generic object counting in natural scenes is a challenging computer vision problem. Existing approaches either rely on instance-level supervision or absolute count information to train a generic object counter. We introduce a partially supervised setting that significantly reduces the supervision level required for generic object counting. We propose two novel frameworks, named lower-count (LC) and reduced lower-count (RLC), to enable object counting under this setting. Our frameworks are built on a novel dual-branch architecture that has an image classification and a density branch. Our LC framework reduces the annotation cost due to multiple instances in an image by using only lower-count supervision for all object categories. Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones. The RLC framework extends our dual-branch LC framework with a novel weight modulation layer and a category-independent density map prediction. Experiments are performed on COCO, Visual Genome and PASCAL 2007 datasets. Our frameworks perform on par with state-of-the-art approaches using higher levels of supervision. Additionally, we demonstrate the applicability of our LC supervised density map for image-level supervised instance segmentation.

Title: Planar graphs without normally adjacent short cycles Abstract: Let G be the class of plane graphs without triangles normally adjacent to 8(-) -cycles, without 4-cycles normally adjacent to 6(-)-cycles, and without normally adjacent 5-cycles. In this paper, it is shown that every graph in G is 3-choosable. Instead of proving this result, we directly prove a stronger result in the form of & ldquo;weakly & rdquo; DP-3-coloring. The main theorem improves the results in Dvor & aacute;k and Postle [J. Comb. Theory, Ser. B 129 (2018) 38 & ndash;54] [5]; Liu and Li [Eur. J. Comb. 82 (2019) 102995] [13]. Consequently, every planar graph without 4-, 6-, 8-cycles is 3-choosable, and every planar graph without 4-, 5-, 7-, 8-cycles is 3-choosable. In the third section, using almost the same technique, we prove that the vertex set of every graph in G can be partitioned into an independent set and a set that induces a forest, which strengthens the result in Liu and Yu [Discrete Appl. Math. 284 (2020) 626 & ndash;630] [17]. In the final section, tightness is discussed. (c) 2022 Elsevier B.V. All rights reserved.

Title: Joint Reliability-Aware And Cost Efficient Path Allocationfig And Vnf Placement Using Sharing Scheme Abstract: Network Function Virtualization (NFV) is a vital player of modern networks providing different types of services such as traffic optimization, content filtering, and load balancing. More precisely, NFV is a provisioning technology aims at reducing the large Capital Expenditure (CapEx) of network providers by moving services from dedicated hardware to commodity servers using Virtualized Network Functions (VNF). A sequence of VNFs/services following a logical goal is referred to as a Service Function Chain (SFC). The movement toward SFC introduces new challenges to those network services which require high reliability. To address this challenge, redundancy schemes are introduced. Existing redundancy schemes using dedicated protection enhance the reliability of services, however, they do not consider the cost of redundant VNFs. In this paper, we propose a novel reliability enhancement method using a shared protection scheme to reduce the cost of redundant VNFs. To this end, we mathematically formulate the problem as a Mixed Integer Linear Programming (MILP). The objective is to determine optimal reliability that could be achieved with minimum cost. Although the corresponding optimization problem can be solved using existing MILP solvers, the computational complexity is not rational for realistic scenarios. Thereafter, we propose a Reliability-aware and minimum-Cost based Genetic (RCG) algorithm to solve this problem with low computational complexity. In order to evaluate the proposed solution, we have compared it with four different solutions. Simulation results show that RCG achieves near-optimal performance at a much lower complexity compared with the optimal solution.

Title: Active object tracking using context estimation: handling occlusions and detecting missing targets Abstract: When performing visual servoing or object tracking tasks, active sensor planning is essential to keep targets in sight or to relocate them when missing. In particular, when dealing with a known target missing from the sensor’s field of view, we propose using prior knowledge related to contextual information to estimate its possible location. To this end, this study proposes a Dynamic Bayesian Network that uses contextual information to effectively search for targets. Monte Carlo particle filtering is employed to approximate the posterior probability of the target’s state, from which uncertainty is defined. We define the robot’s utility function via information theoretic formalism as seeking the optimal action which reduces uncertainty of a task, prompting robot agents to investigate the location where the target most likely might exist. Using a context state model, we design the agent’s high-level decision framework using a Partially-Observable Markov Decision Process. Based on the estimated belief state of the context via sequential observations, the robot’s navigation actions are determined to conduct exploratory and detection tasks. By using this multi-modal context model, our agent can effectively handle basic dynamic events, such as obstruction of targets or their absence from the field of view. We implement and demonstrate these capabilities on a mobile robot in real-time.

Title: Formal definitions of conservative probability distribution functions (PDFs) Abstract: Under ideal conditions, the probability density function (PDF) of a random variable, such as a sensor measurement, would be well known and amenable to computation and communication tasks. However, this is often not the case, so the user looks for some other PDF that approximates the true but intractable PDF. Conservativeness is a commonly sought property of this approximating PDF, especially in distributed or unstructured data systems where the data being fused may contain un-known correlations. Roughly, a conservative approximation is one that overestimates the uncertainty of a system. While prior work has introduced some definitions of conservativeness, these definitions either apply only to normal distributions or violate some of the intuitive appeal of (Gaussian) conservative definitions. This work provides a general and intuitive definition of conservativeness that is applicable to any probability distribution that is a measure over Rm or an infinite subset thereof, including multi-modal and uniform distributions. Unfortunately, we show that this strong definition of conservative does not hold with any of the commonly used data fusion techniques. Therefore, we also describe a weaker definition of conservative and show it is preserved through common data fusion methods, assuming the input distributions can be factored into independent and common PDFs that can be normalized over Rm. By illustrating what is possible and not possible in terms of conservativeness during data fusion, an improved understanding of data fusion methods for general PDFs can be obtained.

Title: Quotients of Uniform Positroids. Abstract: Flag matroids are a rich family of Coxeter matroids that can be characterized using pairs of matroids that form a quotient. We consider a class of matroids called positroids, introduced by Postnikov, and utilize their combinatorial representations to explore characterizations of flag positroids. Given a uniform positroid, we give a purely combinatorial characterization of a family of positroids that form quotients with it. We state this in terms of their associated decorated permutations. In proving our characterization we also fully describe the circuits of this family.

Title: The crawler: Three equivalence results for object (re)allocation problems when preferences are single-peaked Abstract: For object reallocation problems, if preferences are strict but otherwise unrestricted, the Top Trading Cycles rule (TTC) is the leading rule: It is the only rule satisfying efficiency, individual rationality, and strategy-proofness. However, on the subdomain of single-peaked preferences, Bade (2019) defines a new rule, the “crawler”, which also satisfies these three properties. (i) The crawler selects an allocation by “visiting” agents in a specific order. A natural “dual” rule can be defined by proceeding in the reverse order. Our first theorem states that the crawler and its dual are actually the same. (ii) Single-peakedness of a preference profile may in fact hold for more than one order and its reverse. Our second theorem states that the crawler is invariant to the choice of the order. (iii) For object allocation problems (as opposed to reallocation problems), we define a probabilistic version of the crawler by choosing an endowment profile at random according to a uniform distribution, and applying the original definition. Our third theorem states that this rule is the same as the “random priority rule”.

Title: Self-triggered adaptive model predictive control of constrained nonlinear systems: A min–max approach Abstract: In this paper, a self-triggered adaptive model predictive control (MPC) method is proposed for constrained discrete-time nonlinear systems subject to parametric uncertainties and additive disturbances. Firstly, a real-time zonotope-based set-membership parameter estimator is developed to refine a set-valued description of the time-varying parametric uncertainty based on the available measurements. By approximating the reachable sets for the states between two successive triggering time instants, the proposed estimator can be used for dynamic systems sampled in an aperiodic manner, including the self-triggered scheduling. We leverage this estimation scheme to design a novel self-triggered adaptive MPC (ST-AMPC) approach for uncertain nonlinear systems. Compared with the existing self-triggered robust MPC methods, the proposed ST-AMPC method can further reduce the average sampling frequency while preserving comparable closed-loop performance. We theoretically show that, under some reasonable assumptions, the proposed ST-AMPC algorithm is recursively feasible, and the closed-loop system is input-to-state practical stable (ISpS) at triggering time instants. Numerical experiments and comparisons are conducted to demonstrate the efficacy of the proposed method.

Title: BOOSTED OPTIMAL WEIGHTED LEAST-SQUARES Abstract: This paper is concerned with the approximation of a function u in a given subspace Vm of dimension m from evaluations of the function at n suitably chosen points. The aim is to construct an approximation of u in Vm which yields an error close to the best approximation error in Vm and using as few evaluations as possible. Classical least-squares regression, which defines a projection in Vm from n random points, usually requires a large n to guarantee a stable approximation and an error close to the best approximation error. This is a major drawback for applications where u is expensive to evaluate. One remedy is to use a weighted least-squares projection using n samples drawn from a properly selected distribution. In this paper, we introduce a boosted weighted least-squares method which allows to ensure almost surely the stability of the weighted least-squares projection with a sample size close to the interpolation regime n = m. It consists in sampling according to a measure associated with the optimization of a stability criterion over a collection of independent n -samples, and resampling according to this measure until a stability condition is satisfied. A greedy method is then proposed to remove points from the obtained sample. Quasi-optimality properties in expectation are obtained for the weighted least-squares projection, with or without the greedy procedure. The proposed method is validated on numerical examples and compared to state-of-the-art interpolation and weighted least-squares methods.

Title: Penalized-Likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding Abstract: Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors into PET image reconstruction, especially with the development of hybrid PET/CT and PET/MRI systems. In this work, we proposed a cube-based 3D structural convolutional sparse coding (CSC) concept for penalized-likelihood ...

Title: Detecting and classifying outliers in big functional data Abstract: We propose two new outlier detection methods, for identifying and classifying different types of outliers in (big) functional data sets. The proposed methods are based on an existing method called Massive Unsupervised Outlier Detection (MUOD). MUOD detects and classifies outliers by computing for each curve, three indices, all based on the concept of linear regression and correlation, which measure outlyingness in terms of shape, magnitude and amplitude, relative to the other curves in the data. ‘Semifast-MUOD’, the first method, uses a sample of the observations in computing the indices, while ‘Fast-MUOD’, the second method, uses the point-wise or $$L_1$$ median in computing the indices. The classical boxplot is used to separate the indices of the outliers from those of the typical observations. Performance evaluation of the proposed methods using simulated data show significant improvements compared to MUOD, both in outlier detection and computational time. We show that Fast-MUOD is especially well suited to handling big and dense functional datasets with very small computational time compared to other methods. Further comparisons with some recent outlier detection methods for functional data also show superior or comparable outlier detection accuracy of the proposed methods. We apply the proposed methods on weather, population growth, and video data.

Title: Analyzing Offline Social Engagements: An Empirical Study of Meetup Events Related to Software Development Abstract: Software developers use a variety of social media channels and tools in order to keep themselves up to date, collaborate with other developers, and find projects to contribute to. Meetup is one of such social media used by software developers to organize community gatherings. We in this work, investigate the dynamics of Meetup groups and events related to software development. Our work is different from previous work as we focus on the actual event and group data that was collected using Meetup API. In this work, we performed an empirical study of events and groups present on Meetup which are related to software development. First, we identified 6,327 Meetup groups related to software development and extracted 250,36 9 events organized by them. Then we took a sample of 452 events on which we performed open coding, based on which we were able to develop 9 categories of events (8 main categories +"Others"). Next, we did a popularity analysis of the categories of events and found that Talks by Domain Experts, Hands-on Sessions, and Open Discussions are the most popular categories of events organized by Meetup groups related to software development. Our findings show that more popular categories are those where developers can learn and gain knowledge. On doing a diversity analysis of Meetup groups we found 20.46% of the members on average are female, and 20.34% of the actual event participants are female, which is a larger proportion as compared to numbers reported in previous studies on gender representation in software engineering communities. We also found evidence that the gender of Meetup group organizer affects gender distribution of group members and event participants. Finally, we also looked at some data on how COVID-19 has affected the Meetup activity and found that the event activity has dropped, but not stalled. A substantial number of events are now being organized virtually. The results and insights uncovered in our work can guide future studies related to software communities, groups, and diversity-related studies.

Title: Support Vector Machine Classifier via <inline-formula><tex-math notation="LaTeX">$L_{0/1}$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mo>/</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq1-3092177.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula> Soft-Margin Loss Abstract: Support vector machines (SVM) have drawn wide attention for the last two decades due to its extensive applications, so a vast body of work has developed optimization algorithms to solve SVM with various soft-margin losses. To distinguish all, in this paper, we aim at solving an ideal soft-margin loss SVM: <inline-formula><tex-math notation="LaTeX">$L_{0/1}$</tex-math></inline-formula> soft-margin loss SVM (dubbed as <inline-formula><tex-math notation="LaTeX">$L_{0/1}$</tex-math></inline-formula> -SVM). Many of the existing (non)convex soft-margin losses can be viewed as one of the surrogates of the <inline-formula><tex-math notation="LaTeX">$L_{0/1}$</tex-math></inline-formula> soft-margin loss. Despite its discrete nature, we manage to establish the optimality theory for the <inline-formula><tex-math notation="LaTeX">$L_{0/1}$</tex-math></inline-formula> -SVM including the existence of the optimal solutions, the relationship between them and P-stationary points. These not only enable us to deliver a rigorous definition of <inline-formula><tex-math notation="LaTeX">$L_{0/1}$</tex-math></inline-formula> support vectors but also allow us to define a working set. Integrating such a working set, a fast alternating direction method of multipliers is then proposed with its limit point being a locally optimal solution to the <inline-formula><tex-math notation="LaTeX">$L_{0/1}$</tex-math></inline-formula> -SVM. Finally, numerical experiments demonstrate that our proposed method outperforms some leading classification solvers from SVM communities, in terms of faster computational speed and a fewer number of support vectors. The bigger the data size is, the more evident its advantage appears.

Title: Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data Abstract: The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognized to be three key ingredients for deep learning. Most of the recent theoretical studies for deep learning focus on the necessity and advantages of depth and structures of neural networks. In this article, we aim at rigorous verification of the importance of massive data in embodying the outperformance of deep learning. In particular, we prove that the massiveness of data is necessary for realizing the spatial sparseness, and deep nets are crucial tools to make full use of massive data in such an application. All these findings present the reasons why deep learning achieves great success in the era of big data though deep nets and numerous network structures have been proposed at least 20 years ago.

Title: Algorithms that "Don't See Color": Measuring Biases in Lookalike and Special Ad Audiences. Abstract: Today, algorithmic models are shaping important decisions in domains such as credit, employment, or criminal justice. At the same time, these algorithms have been shown to have discriminatory effects. Some organizations have tried to mitigate these effects by removing demographic features from an algorithm's inputs. If an algorithm is not provided with a feature, one might think, then its outputs should not discriminate with respect to that feature. This may not be true, however, when there are other correlated features. In this paper, we explore the limits of this approach using a unique opportunity created by a lawsuit settlement concerning discrimination on Facebook's advertising platform. Facebook agreed to modify its Lookalike Audiences tool - which creates target sets of users for ads by identifying users who share "common qualities" with users in a source audience provided by an advertiser - by removing certain demographic features as inputs to its algorithm. The modified tool, Special Ad Audiences, is intended to reduce the potential for discrimination in target audiences. We create a series of Lookalike and Special Ad audiences based on biased source audiences - i.e., source audiences that have known skew along the lines of gender, age, race, and political leanings. We show that the resulting Lookalike and Special Ad audiences both reflect these biases, despite the fact that Special Ad Audiences algorithm is not provided with the features along which our source audiences are skewed. More broadly, we provide experimental proof that removing demographic features from a real-world algorithmic system's inputs can fail to prevent biased outputs. Organizations using algorithms to mediate access to life opportunities should consider other approaches to mitigating discriminatory effects.

Title: PETSC TSADJOINT: A DISCRETE ADJOINT ODE SOLVER FOR FIRST-ORDER AND SECOND-ORDER SENSITIVITY ANALYSIS Abstract: We present a new software system, PETSc TSAdjoint, for first-order and second-order adjoint sensitivity analysis of time-dependent nonlinear differential equations. The derivative calculation in PETSc TSAdjoint is essentially a high-level algorithmic differentiation process. The adjoint models are derived by differentiating the timestepping algorithms and implementing them based on the parallel infrastructure in PETSc. Full differentiation of the library code, including MPI routines, is avoided, and users do not need to derive their own adjoint models for their specific applications. PETSc TSAdjoint can compute the first-order derivative, that is, the gradient of a scalar functional, and the Hessian-vector product, which carries second-order derivative information, while requiring minimal input (a few callbacks) from the users. The adjoint model employs optimal checkpointing schemes in a manner that is transparent to users. Usability, efficiency, and scalability are demonstrated through examples from a variety of applications.

Title: Explaining Machine Learning Models for Clinical Gait Analysis Abstract: AbstractMachine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character. This article investigates the usefulness of Explainable Artificial Intelligence (XAI) methods to increase transparency in automated clinical gait classification based on time series. For this purpose, predictions of state-of-the-art classification methods are explained with a XAI method called Layer-wise Relevance Propagation (LRP). Our main contribution is an approach that explains class-specific characteristics learned by ML models that are trained for gait classification. We investigate several gait classification tasks and employ different classification methods, i.e., Convolutional Neural Network, Support Vector Machine, and Multi-layer Perceptron. We propose to evaluate the obtained explanations with two complementary approaches: a statistical analysis of the underlying data using Statistical Parametric Mapping and a qualitative evaluation by two clinical experts. A gait dataset comprising ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls is utilized. Our experiments show that explanations obtained by LRP exhibit promising statistical properties concerning inter-class discriminativity and are also in line with clinically relevant biomechanical gait characteristics.

Title: Asymptotic confirmation of the Faudree-Lehel conjecture on irregularity strength for all but extreme degrees Abstract: The irregularity strength of a graph G, s(G), is the least k admitting a {1,2, horizontal ellipsis ,k}-weighting of the edges of G assuring distinct weighted degrees of all vertices, or equivalently the least possible maximal edge multiplicity in an irregular multigraph obtained of G via multiplying some of its edges. The most well-known open problem concerning this graph invariant is the conjecture posed in 1987 by Faudree and Lehel that there exists a constant C such that s(G)<= nd+C for each d-regular graph G with n vertices and d >= 2 (while a straightforward counting argument yields s(G)>= n+d-1d). The best known results towards this imply that s(G)<= 6 left ceiling nd right ceiling for every d-regular graph G with n vertices and d >= 2, while s(G)<=(4+o(1))nd+4 if d >= n0.5lnn. We show that the conjecture of Faudree and Lehel holds asymptotically in the cases when d is neither very small nor very close to n. We in particular prove that for large enough n and d is an element of[ln8n,nln3n], s(G)<= nd(1+8lnn), and thereby we show that s(G)=nd(1+o(1)) then. We moreover prove the latter to hold already when d is an element of[ln1+epsilon n,nln epsilon n], where epsilon is an arbitrary positive constant.

Title: Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation Abstract: We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to $$50\%$$ , and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics.

Title: The inverse Kakeya problem Abstract: We prove that the largest convex shape that can be placed inside a given convex shape Q subset of R-d in any desired orientation is the largest inscribed ball of Q. The statement is true both when "largest" means "largest volume" and when it means "largest surface area". The ball is the unique solution, except when maximizing the perimeter in the two-dimensional case.

Title: Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network Abstract: With the development of Earth observation technology, a very-high-resolution (VHR) image has become an important data source of change detection (CD). These days, deep learning (DL) methods have achieved conspicuous performance in the CD of VHR images. Nonetheless, most of the existing CD models based on DL require annotated training samples. In this article, a novel unsupervised model, called kernel principal component analysis (KPCA) convolution, is proposed for extracting representative features from multitemporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multiclass CD. In the KPCA-MNet, the high-level spatial–spectral feature maps are extracted by a deep siamese network consisting of weight-shared KPCA convolutional layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the CD results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet do not require labeled data. The theoretical analysis and experimental results in two binary CD datasets and one multiclass CD datasets demonstrate the validity, robustness, and potential of the proposed method.

Title: Generalized residual ratio thresholding Abstract: •Sparse recovery without sparsity and noise variance.•Finite sample performance guarantees.•LASSO, orthogonal matching pursuit.•Group sparsity.

Title: An Analytic Interpolation Approach to Stability Margins With Emphasis on Time Delay Abstract: Unlike the situation with gain and phase margins in robust stabilization, the problem to determine an exact maximum delay margin is still an open problem, although extensive work has been done to establish upper and lower bounds. The problem is that the corresponding constraints in the Nyquist plot are frequency dependent, and encircling the point $s=-1$<...

Title: Real time evolution with neural-network quantum states Abstract: A promising application of neural-network quantum states is to describe the time dynamics of many-body quantum systems. To realize this idea, we employ neural-network quantum states to approximate the implicit midpoint rule method, which preserves the symplectic form of Hamiltonian dynamics. We ensure that our complex-valued neural networks are holomorphic functions, and exploit this property to efficiently compute gradients. Application to the transverse-field Ising model on a one- and two-dimensional lattice exhibits an accuracy comparable to the stochastic configuration method proposed in [Carleo and Troyer, Science 355, 602-606 (2017)], but does not require computing the (pseudo-)inverse of a matrix.

Title: The group C-p(4) x C-q is a DCI-group Abstract: We prove that the group C-p(4) x C-q is a DCI-group for distinct primes p and q, that is, two Cayley digraphs over C-p(4) x C-q are isomorphic if and only if their connection sets are conjugate by a group automorphism. (c) 2021 Elsevier B.V. All rights reserved.

Title: Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis Abstract: Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner. In this work, we pr...

Title: Online Route Choice Modeling for Mobility-as-a-Service Networks With Non-Separable, Congestible Link Capacity Effects Abstract: With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of ``congestible capacity''; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data from Citi Bike in New York City. The results show that the model can fit to the data quite well and performs better than a baseline modeling approach that ignores congestible capacity effects. By relating the route choice to congestible capacities using a random utility model, modelers can monitor and quantify the impacts to traveler consumer surplus in real time. Applications of the model and online method include monitoring capacity effects on consumer surplus, using the model to direct incentives programs for rebalancing and other revenue management strategies, and to guide resource allocation to mitigate consumer surplus impacts due to disruptions from incidents.

Title: CLOAKING PROPERTY OF A PLASMONIC STRUCTURE IN DOUBLY COMPLEMENTARY MEDIA AND THREE-SPHERE INEQUALITIES WITH PARTIAL DATA Abstract: We investigate the cloaking property of negative-index metamaterials in the time-harmonic electromagnetic setting for the so-called doubly complementary media. These are media consisting of negative-index metamaterials in a shell (plasmonic structure) and positive-index materials in its complement for which the shell is complementary to a part of the core and a part of the exterior of the core-shell structure. We show that an arbitrary object is invisible when it is placed close to a plasmonic structure of a doubly complementary medium as long as its cross section is smaller than a threshold given by the property of the plasmonic structure. To handle the loss of the compactness and of the ellipticity of the modeling Maxwell equations with sign-changing coefficients, we first obtain Cauchy's problems associated with two Maxwell systems using reflections. We then derive information from them, and combine it with the removing localized singularity technique to deal with the localized resonance. A central part of the analysis on Cauchy's problems is to establish three-sphere inequalities with partial data for general elliptic systems, which are interesting in themselves. The proof of these inequalities first relies on an appropriate change of variables, inspired by conformal maps, and is then based on Carleman's estimates for a class of degenerate elliptic systems.

Title: Coanalytic ultrafilter bases Abstract: We study the definability of ultrafilter bases on $$\omega $$ in the sense of descriptive set theory. As a main result we show that there is no coanalytic base for a Ramsey ultrafilter, while in L we can construct $$\Pi ^1_1$$ P-point and Q-point bases. We also show that the existence of a $${\varvec{\Delta }}^1_{n+1}$$ ultrafilter is equivalent to that of a $${\varvec{\Pi }}^1_n$$ ultrafilter base, for $$n \in \omega $$ . Moreover we introduce a Borel version of the classical ultrafilter number and make some observations.

Title: Piecewise Linear Valued CSPs Solvable by Linear Programming Relaxation Abstract: AbstractValued constraint satisfaction problems (VCSPs) are a large class of combinatorial optimisation problems. The computational complexity of VCSPs depends on the set of allowed cost functions in the input. Recently, the computational complexity of all VCSPs for finite sets of cost functions over finite domains has been classified. Many natural optimisation problems, however, cannot be formulated as VCSPs over a finite domain. We initiate the systematic investigation of the complexity of infinite-domain VCSPs with piecewise linear homogeneous cost functions. Such VCSPs can be solved in polynomial time if the cost functions are improved by fully symmetric fractional operations of all arities. We show this by reducing the problem to a finite-domain VCSP which can be solved using the basic linear program relaxation. It follows that VCSPs for submodular PLH cost functions can be solved in polynomial time; in fact, we show that submodular PLH functions form a maximally tractable class of PLH cost functions.

Title: ZEROTH-ORDER STOCHASTIC COMPOSITIONAL ALGORITHMS FOR RISK-AWARE LEARNING Abstract: We present Free-MESSAGE(p), the first zeroth-order algorithm for (weakly) convex mean-semideviation-based risk-aware learning, which is also the first-ever three-level zeroth-order compositional stochastic optimization algorithm. Using a nontrivial extension of Nesterov's classical results on Gaussian smoothing, we develop the Free-MESSAGE(p) algorithm from first principles and show that it essentially solves a smoothed surrogate to the original problem, the former being a uniform approximation of the latter, in a useful, convenient sense. We then present a complete analysis of the Free-MESSAGE(p) algorithm, which establishes convergence in a user-tunable neighborhood of the optimal solutions of the original problem for convex costs, as well as explicit convergence rates for convex, weakly convex, and strongly convex costs, in a unified way. Orderwise, and for fixed problem parameters, our results demonstrate no sacrifice in convergence speed as compared to existing first-order methods, while striking a certain balance among the condition of the problem, its dimensionality, and the accuracy of the obtained results, naturally extending previous results in zeroth-order risk-neutral learning.