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Title: Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways Abstract: The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for prediction of the target algorithm’s quality and performance. The proposed approach was implemented and investigated as applied to an evolutionary algorithm for discovering clusters of interpretable clinical pathways in electronic health records of patients with acute coronary syndrome. Several clustering metrics and execution time were used as the target quality and performance metrics respectively. An analytical software prototype based on the proposed approach for the prediction of algorithm characteristics and feature analysis was developed to provide a more interpretable prediction of the target algorithm’s performance and quality that can be further used for parameter tuning.

Title: Event structures for the reversible early internal π-calculus Abstract: The π-calculus is a widely used process calculus, which models communications between processes and allows the passing of communication links. Various operational semantics of the π-calculus have been proposed, which can be classified according to whether transitions are unlabelled (so-called reductions) or labelled. With labelled transitions, we can distinguish early and late semantics. The early version allows a process to receive names it already knows from the environment, while the late semantics and reduction semantics do not. All existing reversible versions of the π-calculus use reduction or late semantics, despite the early semantics of the (forward-only) π-calculus being more widely used than the late. We introduce two reversible forms of the internal π-calculus; these are the first to use early semantics. The internal π-calculus is a subset of the π-calculus where every link sent by an output is private, yielding greater symmetry between inputs and outputs. One of the new reversible calculi uses static reversibility, where performing an action does not change the structure of the process, and the other uses dynamic reversibility, where performing an action moves it to a separate history. We show an operational correspondence between the two calculi. For the static calculus we define denotational event structure semantics, which generate an event structure inductively on the structure on the process. For the dynamic calculus we define operational event structure semantics, which generate an event structure based on a labelled asynchronous transition system. We describe a correspondence between the resulting event structures.

Title: The critical probability for Voronoi percolation in the hyperbolic plane tends to 1/2 Abstract: We consider percolation on the Voronoi tessellation generated by a homogeneous Poisson point process on the hyperbolic plane. We show that the critical probability for the existence of an infinite cluster tends to 1/2 as the intensity of the Poisson process tends to infinity. This confirms a conjecture of Benjamini and Schramm [5].

Title: Company classification using machine learning Abstract: The recent advancements in computational power and machine learning algorithms have led to vast improvements in manifold areas of research. Especially in finance, the application of machine learning enables both researchers and practitioners to gain new insights into financial data and well-studied areas such as company classification. In our paper, we demonstrate that unsupervised machine learning algorithms can visualize and classify company data in an economically meaningful and effective way. In particular, we implement the data-driven dimension reduction and visualization tool t-distributed stochastic neighbor embedding (t-SNE) in combination with spectral clustering. The resulting company groups can then be utilized by experts in the field for empirical analysis and optimal decision making. We are the first to demonstrate how this approach can be implemented in manifold areas of finance by developing a general decision engine. With two exemplary out-of-sample studies on portfolio optimization and company valuation with multiples, we show that the application of t-SNE and spectral clustering improves competitive benchmark models.

Title: Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency. Abstract: Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. A common goal is to share a resource, such as CPU or I/O bandwidth, among distinct users with different demand patterns as well as different quality of service requirements. To ensure these service requirements, cloud offerings often come with a service level agreement (SLA) between the provider and the users. An SLA specifies the amount of a resource a user is entitled to utilize. In many cloud settings, providers would like to operate resources at high utilization while simultaneously respecting individual SLAs. There is typically a tradeoff between these two objectives; for example, utilization can be increased by shifting away resources from idle users to “scavenger” workload, but with the risk of the former then becoming active again. We study this fundamental tradeoff by formulating a resource allocation model that captures basic properties of cloud computing systems, including SLAs, highly limited feedback about the state of the system, and variable and unpredictable input sequences. Our main result is a simple and practical algorithm that achieves near-optimal performance on the above two objectives. First, we guarantee nearly optimal utilization of the resource even if compared to the omniscient offline dynamic optimum. Second, we simultaneously satisfy all individual SLAs up to a small error. The main algorithmic tool is a multiplicative weight update algorithm, and a duality argument to obtain its guarantees. Experiments on both synthetic and real production traces demonstrate the merits of our algorithm in practical settings.

Title: Dissipativity-Based Disturbance Attenuation Control for T–S Fuzzy Markov Jumping Systems With Nonlinear Multisource Uncertainties and Partly Unknown Transition Probabilities Abstract: This article is concerned with the dissipativity-based disturbance attenuation control for a class of Takagi–Sugeno (T–S) fuzzy Markov jump systems (FMJSs) suffering from nonlinear multisource disturbances. The considered system possesses nonlinear and stochastic jumping disturbances generated by multiple sources, constituting the main challenge to control design and dissipativity analysis. By pro...

Title: Multiscale Conditional Regularization for Convolutional Neural Networks Abstract: With the increased model size of convolutional neural networks (CNNs), overfitting has become the main bottleneck to further improve the performance of networks. Currently, the weighting regularization methods have been proposed to address the overfitting problem and they perform satisfactorily. Since these regularization methods cannot be used in all the networks and they are usually not flexible...

Title: Decentralized Learning for Channel Allocation in IoT Networks Over Unlicensed Bandwidth as a Contextual Multi-Player Multi-Armed Bandit Game Abstract: We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network. In the considered network, the impoverished channel sensing/probing capability and computational resource on the IoT devices make them difficult to acquire the detailed Channel State Information (CSI) for the shared multiple channels. In pr...

Title: On the clique number of Paley graphs of prime power order Abstract: Finding a reasonably good upper bound for the clique number of Paley graphs is an open problem in additive combinatorics. A recent breakthrough by Hanson and Petridis using Stepanov's method gives an improved upper bound on Paley graphs defined on a prime field Fp, where p≡1(mod4). We extend their idea to the finite field Fq, where q=p2s+1 for a prime p≡1(mod4) and a non-negative integer s. We show the clique number of the Paley graph over Fp2s+1 is at most min⁡(ps⌈p2⌉,q2+ps+14+2p32ps−1).

Title: Directional Necessary Optimality Conditions for Bilevel Programs Abstract: The bilevel program is an optimization problem where the constraint involves solutions to a parametric optimization problem. It is well-known that the value function reformulation provides an equivalent single-level optimization problem but it results in a nonsmooth optimization problem which never satisfies the usual constraint qualification such as the Mangasarian-Fromovitz constraint qualification (MFCQ). In this paper we show that even the first order sufficient condition for metric subregularity (which is in general weaker than MFCQ) fails at each feasible point of the bilevel program. We introduce the concept of directional calmness condition and show that under {the} directional calmness condition, the directional necessary optimality condition holds. {While the directional optimality condition is in general sharper than the non-directional one,} the directional calmness condition is in general weaker than the classical calmness condition and hence is more likely to hold. {We perform the directional sensitivity analysis of the value function and} propose the directional quasi-normality as a sufficient condition for the directional calmness. An example is given to show that the directional quasi-normality condition may hold for the bilevel program.

Title: Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks Abstract: In this paper, we propose a novel economic-efficiency framework for an electric vehicle (EV) network to maximize the profits (i.e., the amount of money that can be earned) for charging stations (CSs). To that end, we first introduce an energy demand prediction method for CSs leveraging federated learning approaches, in which each CS can train its own energy transactions locally and exchange its learned model with other CSs to improve the learning quality while protecting the CS's information privacy. Based on the predicted energy demands, each CS can reserve energy from the smart grid provider (SGP) in advance to optimize its profit. Nonetheless, due to the competition among the CSs as well as unknown information from the SGP, i.e., the willingness to transfer energy, we develop a multi-principal one-agent (MPOA) contract-based method to address these issues. In particular, we formulate the CSs’ profit maximization as a non-collaborative energy contract problem under the SGP's unknown information and common constraints as well as other CSs’ contracts. To solve this problem, we transform it into an equivalent low-complexity optimization problem and develop an iterative algorithm to find the optimal contracts for the CSs. Through simulation results using a real CS dataset, we demonstrate that our proposed framework can enhance energy demand prediction accuracy up to 24.63 percent compared with other machine learning algorithms. Furthermore, our proposed framework can outperform other economic models by 48 and 36 percent in terms of the CSs’ utilities and social welfare (i.e., the total profits of all participating entities) of the network, respectively.

Title: Monitoring Constraints and Metaconstraints with Temporal Logics on Finite Traces Abstract: untime monitoring is a central operational decision support task in business process management. It helps process executors to check on-the-fly whether a running process instance satisfies business constraints of interest, providing an immediate feedback when deviations occur. We study runtime monitoring of properties expressed in ltlf, a variant of the classical ltl (Linear-time Temporal Logic) that is interpreted over finite traces, and in its extension ldlf, a powerful logic obtained by combining ltlf with regular expressions. We show that ldlf is able to declaratively express, in the logic itself, not only the constraints to be monitored, but also the de facto standard rv-LTL monitors. On the one hand, this enables us to directly employ the standard characterization of ldlf based on finite-state automata to monitor constraints in a fine-grained way. On the other hand, it provides the basis for declaratively expressing sophisticated metaconstraints that predicate on the monitoring state of other constraints, and to check them by relying on standard logical services instead of ad hoc algorithms. We then report on how this approach has been effectively implemented using Java to manipulate ldlf formulae and their corresponding monitors, and the RuM rule mining suite as underlying infrastructure.

Title: Scrambled Vandermonde convolutions of Gaussian polynomials Abstract: It is well known that Gaussian polynomials (i.e., q-binomials) describe the distribution of the AREA statistic on monotone paths in a rectangular grid. We introduce two new statistics, CORNERS and C-INdEx; attach "ornaments " to the grid that scramble the values of C-INdEx in specific fashion; and re-evaluate these statistics, in order to argue that all scrambled versions of the C-INdEx statistic are equidistributed with AREA. Our main result is a representation of the generating function for the bi-statistic (C-INdEx, CORNERS) as a new, two-variable Vandermonde convolution of the original Gaussian polynomial. The proof relies on explicit bijections between differently ornated paths. (c) 2022 Elsevier B.V. All rights reserved.

Title: Learning Deep Graph Representations via Convolutional Neural Networks Abstract: Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. R-convolution graph kernels are positive-semidefinite functions that decompose graphs into substructures and compare them. One problem in the effective implementation of this idea is that the substructures are not independent, which leads to high-dimensio...

Title: Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting Abstract: Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to alleviate the domain shift between the source and target domain. Recently, typical methods attempt to extract domain-invariant features via image translation and adversarial learning. When it comes to specific tasks, we find that the domain shifts are reflected in model parameters’ differences. To describe the domain gap directly at the parameter level, we propose a neuron linear transformation (NLT) method, exploiting domain factor and bias weights to learn the domain shift. Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters. Finally, the target neuron is generated via a linear transformation. Extensive experiments and analysis on six real-world data sets validate that NLT achieves top performance compared with other domain adaptation methods. An ablation study also shows that the NLT is robust and more effective than supervised and fine-tune training. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/taohan10200/NLT</uri> .

Title: OPENNESS, HOLDER METRIC REGULARITY, AND HOLDER CONTINUITY PROPERTIES OF SEMIALGEBRAIC SET-VALUED MAPS Abstract: Given a semialgebraic set-valued map F:R-n paired right arrows R-m with closed graph, we show that the map F is Holder metrically subregular and that the following conditions are equivalent: (i) F is an open map from its domain into its range, and the range of F is locally closed; (ii) the map F is Holder metrically regular; (iii) the inverse map F-1 is pseudo-Holder continuous; (iv) the inverse map F-1 is lower pseudo-Holder continuous. An application, via Robinson's normal map formulation, leads to the following result in the context of semialgebraic variational inequalities: if the solution map (as a map of the parameter vector) is lower semicontinuous, then the solution map is finite and pseudo-Holder continuous. In particular, we obtain a negative answer to a question mentioned in the paper of Dontchev and Rockafellar SIAM J. Optim., 4 (1996), pp. 1087-1105. As a byproduct, we show that for a (not necessarily semialgebraic) continuous single-valued map from Rn to R, the openness and the nonextremality are equivalent. This fact improves the main result of Puhl J. Math. Anal. Appl., 227 (1998), pp. 382-395, which requires the convexity of the map in question.

Title: Joint Modeling and Calibration of SPX and VIX by Optimal Transport Abstract: This paper addresses the joint calibration problem of SPX options and VIX options or futures. We show that the problem can be formulated as a semimartingale optimal transport problem under a finite number of discrete constraints, in the spirit of [Guo, Loeper, and Wang, Math. Finance, 32 (2021)]. We introduce a PDE formulation along with its dual counterpart. The solution, a calibrated diffusion process, can be represented via the solutions of Hamilton-Jacobi-Bellman equations arising from the dual formulation. The method is tested on both simulated data and market data. Numerical examples show that the model can be accurately calibrated to SPX options, VIX options, and VIX futures simultaneously.

Title: Security analysis and fault detection against stealthy replay attacks Abstract: This paper investigates the security issue of the data replay attacks on control systems with the LQG controller. The attacker tries to store measurements and replay them in further times. The main novelty in this paper is stated as proposing a different attack detection criterion under the existence of a packet-dropout feature in the network by using the Kullback-Leibler divergence method to cover more general problems and with higher-order dynamics. Formulations and numerical simulations prove the effectiveness of the newly proposed attack detection procedure. Unlike previous approaches that the trade-off between attack detection delay or LQG the performance was significant, in this approach it is proved that the difference in this trade-off is not considered in early moments when the attack happens since the attack detection rate is rapid and thus, the attacks can be stopped with defense strategies in the first moments with the proposed attack detection criterion.

Title: Entropy Stable Flux Correction for Scalar Hyperbolic Conservation Laws Abstract: It is known that Flux Corrected Transport algorithms can produce entropy-violating solutions of hyperbolic conservation laws. Our purpose is to design flux correction with maximal antidiffusive fluxes to obtain entropy solutions of scalar hyperbolic conservation laws. To do this we consider a hybrid difference scheme that is a linear combination of a monotone scheme and a scheme of high-order accuracy. The flux limiters for the hybrid scheme are calculated from a corresponding optimization problem. Constraints for the optimization problem consist of inequalities that are valid for the monotone scheme and applied to the hybrid scheme. We apply the discrete cell entropy inequality with the proper numerical entropy flux to single out a physically relevant solution of scalar hyperbolic conservation laws. A nontrivial approximate solution of the optimization problem yields expressions to compute the required flux limiters. We present examples that show that not all numerical entropy fluxes guarantee to single out a physically correct solution of scalar hyperbolic conservation laws.

Title: Enhancing Social Recommendation With Adversarial Graph Convolutional Networks Abstract: Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According to the negative findings, the failure is attributed to: (1) A majority of users only have a very limited number of neighbors in social networks and can hardly benefit from social relations; (2) Social relations are noisy but they are indiscriminately used; (3) Social relations are assumed to be universally applicable to multiple scenarios while they are actually multi-faceted and show heterogeneous strengths in different scenarios. Most existing social recommendation models only consider the homophily in social networks and neglect these drawbacks. In this paper we propose a deep adversarial framework based on graph convolutional networks (GCN) to address these problems. Concretely, for (1) and (2), a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the social profile to guarantee the validity of the identified neighborhood. After obtaining enough purified social relations for each user, a GCN-based attentive social recommendation module is designed to address (3) by capturing the heterogeneous strengths of social relations. Finally, we adopt adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance. Extensive experiments on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.

Title: Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms Abstract: We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nonsmooth proximable function with a linear operator L. This template problem has many applications, for instance, in image processing and machine learning. First, we propose a new primal–dual algorithm, which we call PDDY, for this problem. It is constructed by applying Davis–Yin splitting to a monotone inclusion in a primal–dual product space, where the operators are monotone under a specific metric depending on L. We show that three existing algorithms (the two forms of the Condat–Vũ algorithm and the PD3O algorithm) have the same structure, so that PDDY is the fourth missing link in this self-consistent class of primal–dual algorithms. This representation eases the convergence analysis: it allows us to derive sublinear convergence rates in general, and linear convergence results in presence of strong convexity. Moreover, within our broad and flexible analysis framework, we propose new stochastic generalizations of the algorithms, in which a variance-reduced random estimate of the gradient of F is used, instead of the true gradient. Furthermore, we obtain, as a special case of PDDY, a linearly converging algorithm for the minimization of a strongly convex function F under a linear constraint; we discuss its important application to decentralized optimization.

Title: Community modulated recursive trees and population dependent branching processes Abstract: We consider random recursive trees that are grown via community modulated schemes that involve random attachment or degree based attachment. The aim of this article is to derive general techniques based on continuous time embedding to study such models. The associated continuous time embeddings are not branching processes: individual reproductive rates at each time t depend on the composition of the entire population at that time, and hence vertices do not reproduce independently. Using stochastic analytic techniques we show that various key macroscopic statistics of the continuous time embedding stabilize, allowing asymptotics for a host of functionals of the original models to be derived.

Title: Sparse Linear Ensemble Systems and Structural Controllability Abstract: The article introduces and solves a structural controllability problem for continuum ensembles of linear time-invariant systems. All the individual linear systems of an ensemble are sparse, governed by the same sparsity pattern. Controllability of an ensemble system is, by convention, the capability of using a common control input to simultaneously steer every individual systems in it. A sparsity pattern is structurally controllable if it admits a controllable linear ensemble system. A main contribution of the article is to provide a graphical condition that is necessary and sufficient for a sparsity pattern to be structurally controllable. Like other structural problems, the property of being structural controllable is monotone. We provide a complete characterization of minimal sparsity patterns as well.

Title: Integrating Owicki–Gries for C11-Style Memory Models into Isabelle/HOL Abstract: Weak memory presents a new challenge for program verification and has resulted in the development of a variety of specialised logics. For C11-style memory models, our previous work has shown that it is possible to extend Hoare logic and Owicki–Gries reasoning to verify correctness of weak memory programs. The technique introduces a set of high-level assertions over C11 states together with a set of basic Hoare-style axioms over atomic weak memory statements (e.g. reads/writes), but retains all other standard proof obligations for compound statements. This paper takes this line of work further by introducing the first deductive verification environment in Isabelle/HOL for C11-like weak memory programs. This verification environment is built on the Nipkow and Nieto’s encoding of Owicki–Gries in the Isabelle theorem prover. We exemplify our techniques over several litmus tests from the literature and two non-trivial examples: Peterson’s algorithm and a read–copy–update algorithm adapted for C11. For the examples we consider, the proof outlines can be automatically discharged using the existing Isabelle tactics developed by Nipkow and Nieto. The benefit here is that programs can be written using a familiar pseudocode syntax with assertions embedded directly into the program.

Title: Adaptive Fuzzy Control for Fractional-Order Interconnected Systems With Unknown Control Directions Abstract: This article concentrates on the study of the decentralized fuzzy control method for a class of fractional-order interconnected systems with unknown control directions. To overcome the difficulties caused by the multiple unknown control directions in fractional-order systems, a novel fractional-order Nussbaum function technique is proposed. This technique is much more general than those of existin...

Title: Granular computing: An augmented scheme of degranulation through a modified partition matrix Abstract: As an important technology in artificial intelligence, Granular Computing has emerged as a new multi-disciplinary paradigm and received much attention in recent years. Information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of Granular Computing. By generating centroids (prototypes) and partition matrix, fuzzy clustering is a commonly encountered way of information granulation. As a reverse process of granulation, degranulation involves data reconstruction completed on a basis of the granular representatives (decoding information granules into numeric data). Previous studies have shown that there is a relationship between the reconstruction error and the performance of the granulation process. Typically, the lower the degranulation error is, the better performance of granulation process becomes. However, the existing methods of degranulation usually cannot restore the original numeric data, which is one of the important reasons behind the occurrence of the reconstruction error. To enhance the quality of reconstruction (degranulation), in this study, we develop an augmented scheme through modifying the partition matrix. By proposing the augmented scheme, we elaborate on a novel collection of granulation-degranulation mechanisms. In the constructed approach, the prototypes can be expressed as the product of the dataset matrix and the partition matrix. Then, in the degranulation process, the reconstructed numeric data can be decomposed into the product of the partition matrix and the matrix of prototypes. By modifying the partition matrix, the new partition matrix is constructed through a series of matrix operations. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the underlying conceptual framework. The results obtained on both synthetic and publicly available datasets are reported to show the enhancement of the data reconstruction performance thanks to the proposed method. It is pointed out that by using the proposed approach in some cases the reconstruction errors can be reduced close to zero by using the proposed approach.

Title: Testing equality of spectral density operators for functional processes Abstract: The problem of comparing the entire second order structure of two functional processes is considered and a L2-type statistic for testing equality of the corresponding spectral density operators is investigated. The test statistic evaluates, over all frequencies, the Hilbert–Schmidt distance between the two estimated spectral density operators. Under certain assumptions, the limiting distribution under the null hypothesis is derived. A novel frequency domain bootstrap method is introduced, which leads to a more accurate approximation of the distribution of the test statistic under the null than the large sample Gaussian approximation derived. Under quite general conditions, asymptotic validity of the bootstrap procedure is established for estimating the distribution of the test statistic under the null. Furthermore, consistency of the bootstrap-based test under the alternative is proved. Numerical simulations show that, even for small samples, the bootstrap-based test has a very good size and power behavior. An application to a bivariate real-life functional time series illustrates the methodology proposed.

Title: A Bertini type theorem for pencils over finite fields Abstract: We study the question of finding smooth hyperplane sections to a pencil of hypersurfaces over finite fields.

Title: Identity-based encryption with security against the KGC: A formal model and its instantiations Abstract: The key escrow problem is one of the main barriers to the widespread real-world use of identity-based encryption (IBE). Specifically, a key generation center (KGC), which generates secret keys for a given identity, has the power to decrypt all ciphertexts. At PKC 2009, Chow defined a notion of security against the KGC, that relies on assuming that it cannot discover the underlying identities behind ciphertexts. However, this is not a realistic assumption since, in practice, the KGC manages an identity list, and hence it can easily guess the identities corresponding to given ciphertexts. Chow later amended this issue by introducing a new entity called an identity-certifying authority (ICA) and proposed an anonymous keyissuing protocol. Essentially, this allows the users, KGC, and ICA to interactively generate secret keys without users ever having to reveal their identities to the KGC. Unfortunately, since Chow separately defined the security of IBE and that of the anonymous key-issuing protocol, his IBE definition did not provide any formal treatment when the ICA is used to authenticate the users. Effectively, all of the subsequent works following Chow lack the formal proofs needed to determine whether or not it delivers a secure solution to the key escrow problem. In this paper, based on Chow's work, we formally define an IBE scheme that resolves the key escrow problem and provide formal definitions of security against corrupted users, KGC, and ICA. Along the way, we observe that if we are allowed to assume a fully trusted ICA, as in Chow's work, then we can construct a trivial (and meaningless) IBE scheme that is secure against the KGC. Finally, we present two instantiations in our new security model: a lattice-based construction based on the Gentry-Peikert-Vaikuntanathan IBE scheme (STOC 2008) and Ruckert's lattice-based blind signature scheme (ASIACRYPT 2010), and a pairing-based construction based on the Boneh-Franklin IBE scheme (CRYPTO 2001) and Boldyreva's blind signature scheme (PKC 2003). (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Repudiable Ring Signature: Stronger Security And Logarithmic-Size Abstract: Ring signature, introduced by Rivest et al. [Asiacrypt'01], allows a person to sign a document on behalf of an adhoc group (or ring) while hiding the identity of the actual signer. But the anonymity provided by the ring signature scheme can be used to conceal a malicious signer and put other ring members under suspicion. Fortunately, Park et al. [Crypto'19] proposed a repudiable ring signature scheme which can overcome this disadvantage. However, the construction of Park et al. [Crypto'19] is not compact, in other word, the size of signatures and repudiations in their scheme increases with the square of the ring size. In this paper, we propose the first logarithmic-size repudiable ring signature scheme, which means the size of signatures and repudiations grows only logarithmically in the ring size. Moreover, in terms of security model, we present a new requirement (repudiation-unforgeability), which requires 'no one can forge a valid repudiation'. Our scheme is the first repudiable ring signature scheme satisfies this new requirement.

Title: Actively Secure Setup for SPDZ Abstract: We present the first actively secure, practical protocol to generate the distributed secret keys needed in the SPDZ offline protocol. As an added bonus our protocol results in the resulting distribution of the public and secret keys are such that the associated SHE 'noise' analysis is the same as if the distributed keys were generated by a trusted setup. We implemented the presented protocol for distributed BGV key generation within the SCALE-MAMBA framework. Our method makes use of a new method for creating doubly (or even more) authenticated bits in different MPC engines, which has applications in other areas of MPC-based secure computation. We were able to generate keys for two parties and a plaintext size of 64 bits in around 5 min, and a little more than 18 min for a 128-bit prime.

Title: On certain partition bijections related to Euler's partition problem Abstract: We give short elementary expositions of combinatorial proofs of some variants of Euler's partition identity that were first addressed analytically by George Andrews, and later combinatorially by others. The method using certain matrices to concisely explain these bijections, based on ideas first used in a previous manuscript by the author, enables us to also give new generalizations of two of these results. (C) 2021 Elsevier B.V. All rights reserved.

Title: JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method Abstract: We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains “4,372” images with “1.51 million” annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations, making i...

Title: Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning Abstract: This paper investigates the stochastic optimization problem focusing on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel computing strategy, coined the weighted aggregating stochastic gradient descent ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD</i> ). Following a theoretical analysis on the characteristics of the new objective function, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD</i> introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically gauges the importance of local workers and accepts them by their contributions. Furthermore, we have developed an enhanced version of the method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD+</i> , by (1) implementing a designed sample order and (2) upgrading the weight evaluation function. To validate the new method, we benchmark our pipeline against several popular algorithms including the state-of-the-art deep neural network classifier training techniques (e.g., elastic averaging SGD). Comprehensive validation studies have been conducted on four classic datasets: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CIFAR-100</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CIFAR-10</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fashion-MNIST</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MNIST</i> . Subsequent results have firmly validated the superiority of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD</i> scheme in accelerating the training of deep architecture. Better still, the enhanced version, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WASGD+</i> , is shown to be a significant improvement over its prototype.

Title: Energy-Efficient Resource Allocation for Mobile Edge Computing With Multiple Relays Abstract: The merging of Internet of Things (IoT) and mobile edge computing (MEC) enables resource-limited IoT devices to complete computation-intensive or urgent task through offloading the task to the adjacent edge server, and is becoming popular recently. Due to blockage or deep fading, one IoT device may not be able to build direct link with the edge server. On the other hand, many IoT devices can serve as relay nodes as there may exist massive ones in the neighborhood. In this article, we study an MEC system with the IoT device aided by multiple relay nodes for task offloading. Specifically, the modes of decode-and-forward (DF) with time-division-multiple-access (TDMA) and frequency-division-multiple-access (FDMA), and the mode of amplify-and-forward (AF) are investigated, which are denoted as DF-TDMA, DF-FDMA, and AF, respectively. The allocation of computation and communication resources is optimized in order to minimize the weighted sum of energy consumption of all the IoT devices. Associated optimization problems are formulated but shown to be nonconvex, which are challenging to solve. For the DF-TDMA mode, we transform the original nonconvex problem to be convex and further develop a low complexity yet optimal solution. In DF-FDMA mode, with some transformation on the original problem, we prove the mathematical equivalence between the problems in DF-FDMA and DF-TDMA mode. In AF mode, the convergent solution is found by decomposing the associated optimization problem into two levels, with monotonic optimization and successive convex approximation (SCA) utilized for upper level and lower level, respectively. The numerical results prove the effectiveness of our proposed methods.

Title: Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-Based Approach Abstract: In this article, we propose a general framework for the unsupervised fuzzy rule-based dimensionality reduction primarily for data visualization. This framework has the following important characteristics relevant to the dimensionality reduction for visualization: preserves neighborhood relationships; effectively handles data on nonlinear manifolds; capable of projecting out-of-sample test points; can reject test points, when it is appropriate; and interpretable to a reasonable extent. We use the first-order Takagi–Sugeno model. Typically, fuzzy rules are either provided by experts or extracted using an input–output training set. Here, neither the output data nor experts are available. This makes the problem challenging. We estimate the rule parameters minimizing a suitable objective function that preserves the interpoint geodesic distances (distances over the manifold) as Euclidean distances on the projected space. In this context, we propose a new variant of the geodesic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering algorithm. The proposed method is tested on several synthetic and real-world datasets and compared with the results of six state-of-the-art data visualization methods. The proposed method is the only method that performs equally well on all the datasets tried. Our method is found to be robust to the initial conditions. The predictability of the method is validated by suitable experiments. We also assess the ability of our method to reject test points when it should. The scalability issue of the scheme is also discussed. Due to the general nature of the framework, we can use different objective functions to obtain projections satisfying different goals. To the best of our knowledge, this is the first attempt to manifold learning using unsupervised fuzzy rule-based modeling.

Title: Tensor network models of AdS/qCFT Abstract: The study of critical quantum many-body systems through conformal field theory (CFT) is one of the pil-lars of modern quantum physics. Certain CFTs are also understood to be dual to higher-dimensional the-ories of gravity via the anti-de Sitter/conformal field theory (AdS/CFT) correspondence. To reproduce var-ious features of AdS/CFT, a large number of discrete models based on tensor networks have been proposed. Some recent models, most notably including toy mod-els of holographic quantum error correction, are con-structed on regular time-slice discretizations of AdS. In this work, we show that the symmetries of these models are well suited for approximating CFT states, as their geometry enforces a discrete subgroup of con-formal symmetries. Based on these symmetries, we in-troduce the notion of a quasiperiodic conformal field theory (qCFT), a critical theory less restrictive than a full CFT and with characteristic multi-scale quasiperi-odicity. We discuss holographic code states and their renormalization group flow as specific implementations of a qCFT with fractional central charges and argue that their behavior generalizes to a large class of ex-isting and future models. Beyond approximating CFT properties, we show that these can be best understood as belonging to a paradigm of discrete holography.

Title: Robust Linear Precoder Design for 3D Massive MIMO Downlink With A Posteriori Channel Model Abstract: In this paper, we investigate the linear precoder design for three dimensional (3D) massive multi-input multi-output (MIMO) downlink with uniform planar array (UPA) and imperfect channel state information (CSI). We introduce a beam based statistical channel model (BSCM) by using sampled steering vectors, and then an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> channel model which includes the channel aging is established. On the basis of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> channel model, we consider the robust precoder design by maximizing an upper bound of the expected weighted sum-rate under a total power constraint. We derive two concave minorizing functions of the objective function. With these minorizing functions and the minorize-maximization (MM) methodology, we derive two iterative algorithms that converge to stationary points of the optimization problem. Simulation results show that the proposed precoders can achieve a significant performance gain than the widely used regularized zero forcing (RZF) precoder and the signal to leakage noise ratio (SLNR) precoder in median to high mobility scenarios.

Title: Learnable Subspace Clustering Abstract: This article studies the large-scale subspace clustering (LS2C) problem with millions of data points. Many popular subspace clustering methods cannot directly handle the LS2C problem although they have been considered to be state-of-the-art methods for small-scale data points. A simple reason is that these methods often choose all data points as a large dictionary to build hu...

Title: Rethinking the Mathematical Framework and Optimality of Set-Membership Filtering Abstract: Set-membership filter (SMF) has been extensively studied for state estimation in the presence of bounded noises with unknown statistics. Since it was first introduced in the 1960s, the studies on SMF have used the set-based description as its mathematical framework. One important issue that has been overlooked is the optimality of SMF. In this article, we put forward a new mathematical framework f...

Title: Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems With Imperfect Cascaded CSI Abstract: In this paper, intelligent reflecting surface (IRS) is introduced to enhance the network performance of cognitive radio (CR) systems. Specifically, we investigate robust beamforming design based on both bounded channel state information (CSI) error model and statistical CSI error model for primary user (PU)-related channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) ...

Title: A Generalized Graph Regularized Non-Negative Tucker Decomposition Framework for Tensor Data Representation Abstract: Non-negative Tucker decomposition (NTD) is one of the most popular techniques for tensor data representation. To enhance the representation ability of NTD by multiple intrinsic cues, that is, manifold structure and supervisory information, in this article, we propose a generalized graph regularized NTD (GNTD) framework for tensor data representation. We first develop the unsupervised GNTD (UGNTD) ...

Title: Fuzzy-Based Concept Learning Method: Exploiting Data With Fuzzy Conceptual Clustering Abstract: Concepts have been adopted in concept-cognitive learning (CCL) and conceptual clustering for concept classification and concept discovery. However, the standard CCL algorithms are incapable of tackling continuous data directly, and some standard conceptual clustering methods mainly focus on the attribute information, ignoring the object information that is also important to improve clustering anal...

Title: Homotopic Convex Transformation: A New Landscape Smoothing Method for the Traveling Salesman Problem Abstract: This article proposes a novel landscape smoothing method for the symmetric traveling salesman problem (TSP). We first define the homotopic convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP. The simple TSP, called the convex-hull TSP, is constructed by transforming a known local or global optimum. We observe that controlled by the coef...

Title: Image Hallucination From Attribute Pairs Abstract: Recent image-generation methods have demonstrated that realistic images can be produced from captions. Despite the promising results achieved, existing caption-based generation methods confront a dilemma. On the one hand, the image generator should be provided with sufficient details for realistic hallucination, meaning that longer sentences with rich content are preferred, but on the other hand, ...

Title: Guaranteed Cost Finite-Time Control of Uncertain Coupled Neural Networks Abstract: This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback contro...

Title: Design of Fuzzy State Observer and Mobile Sensor Guidance for Semilinear Parabolic PDE Systems Abstract: This article investigates the issue of the fuzzy observer design for the semilinear parabolic partial differential equation (PDE) systems with mobile sensing measurements. Initially, we employ a Takagi–Sugeno (T–S) fuzzy PDE model to represent the semilinear parabolic PDE system accurately in a local region. Afterward, via the T–S fuzzy model and under the hypothesis that the spatial domain is div...

Title: Bearing-Only Formation Control With Prespecified Convergence Time Abstract: This article considers the bearing-only formation control problem, where the control of each agent only relies on relative bearings of their neighbors. A new control law is proposed to achieve target formations in finite time. Different from the existing results, the control law is based on a time-varying scaling gain. Hence, the convergence time can be arbitrarily chosen by users, and the derivat...

Title: Distributed Event-Triggered Consensus of General Linear Multiagent Systems Under Directed Graphs Abstract: This article investigates the consensus problem of general linear multiagent systems under directed communication graphs with event-triggered mechanisms. First, a novel distributed static event-triggered mechanism with a state-dependent threshold is proposed to solve the consensus problem, both with a positive lower bound on the average time interval of the communication among agents and updates o...

Title: Learning Cognitive Map Representations for Navigation by Sensory–Motor Integration Abstract: How to transform a mixed flow of sensory and motor information into memory state of self-location and to build map representations of the environment are central questions in the navigation research. Studies in neuroscience have shown that place cells in the hippocampus of the rodent brains form dynamic cognitive representations of locations in the environment. We propose a neural-network model ca...

Title: Combinatorics of quasi-hereditary structures Abstract: A quasi-hereditary algebra is an Artin algebra together with a partial order on its set of isomorphism classes of simple modules which satisfies certain conditions. In this article we investigate all the possible choices that yield quasi-hereditary structures on a given algebra, in particular we introduce and study what we call the poset of quasi-hereditary structures. Our techniques involve certain quiver decompositions and idempotent reductions. For a path algebra of Dynkin type A, we provide a full classification of its quasi-hereditary structures. For types D and E, we give a counting method for the number of quasi-hereditary structures. In the case of a hereditary incidence algebra, we present a necessary and sufficient condition for its poset of quasi-hereditary structures to be a lattice.

Title: A load balancing system in the many-server heavy-traffic asymptotics Abstract: We study a load balancing system in the many-server heavy-traffic regime. We consider a system with N servers, where jobs arrive to the system according to a Poisson process and have an exponentially distributed size with mean 1. We parametrize the arrival rate so that the arrival rate per server is $$1-N^{-\alpha }$$ , where $$\alpha >0$$ is a parameter that represents how fast the load grows with respect to the number of servers. The many-server heavy-traffic regime corresponds to the limit as $$N\rightarrow \infty $$ , and subsumes several regimes, such as the Halfin–Whitt regime ( $$\alpha =1/2$$ ), the NDS regime ( $$\alpha =1$$ ), as $$\alpha \downarrow 0$$ it approximates mean field and as $$\alpha \rightarrow \infty $$ it approximates the classical heavy-traffic regime. Most of the prior work focuses on regimes with $$\alpha \in [0,1]$$ . In this paper, we focus on the case when $$\alpha >1$$ and the routing algorithm is power-of-d choices with $$d=\lceil cN^\beta \rceil $$ for some constants $$c>0$$ and $$\beta \ge 0$$ . We prove that $$\alpha +\beta >3$$ is sufficient to observe that the average queue length scaled by $$N^{1-\alpha }$$ converges to an exponential random variable. In other words, if $$\alpha +\beta >3$$ , the scaled average queue length behaves similarly to the classical heavy-traffic regime. In particular, this result implies that if d is constant, we require $$\alpha >3$$ and if routing occurs according to JSQ we require $$\alpha >2$$ . We provide two proofs to our result: one based on the Transform method introduced in Hurtado-Lange and Maguluri (Stoch Syst 10(4):275–309, 2020) and one based on Stein’s method. In the second proof, we also compute the rate of convergence in Wasserstein’s distance. In both cases, we additionally compute the rate of convergence in expected value. All of our proofs are powered by state space collapse.

Title: Modular and fractional L-intersecting families of vector spaces Abstract: This paper is divided into two logical parts. In the first part of this paper, we prove the following theorem which is the q-analogue of a generalized modular Ray-Chaudhuri-Wilson Theorem shown in [Alon, Babai, Suzuki, J. Combin. Theory Series A, 1991]. It is also a generalization of the main theorem in [Frankl and Graham, European J. Combin. 1985] under certain circumstances. Let V be a vector space of dimension n over a finite field of size q. Let K = {k(1), ..., k(r)}, L = {mu(1), ..., mu(s)} be two disjoint subsets of {0, 1, ..., b - 1} with k(1) < ... < k(r). Let F = {V-1, V-2, ..., V-m} be a family of subspaces of V such that (a) for every i is an element of [m], dim(V-i) mod b = k(t), for some k(t) is an element of K, and (b) for every distinct i, j is an element of [m], dim(V-i boolean AND V-j)mod b = mu(t), for some mu(t) is an element of L. Moreover, it is given that neither of the following two conditions hold: (i) q + 1 is a power of 2, and b = 2 (ii) q = 2, b = 6. Then, vertical bar F vertical bar <= {N(n, s, r, q), N(n, s, r, q) + Sigma(t is an element of[r]) [n k(t)](q), if (s + k(r) <= n and r(s - r + 1) <= b - 1) or (s < k(1) + r ) otherwise, where N(n, s, r, q) := [n s](q) + [n s - 1](q) + ... + [n s - r + 1](q). In the second part of this paper, we prove q-analogues of results on a recent notion called fractional L-intersecting family of sets for families of subspaces of a given vector space over a finite field of size q. We use the above theorem to obtain a general upper bound to the cardinality of such families. We give an improvement to this general upper bound in certain special cases.

Title: ModuleNet: Knowledge-Inherited Neural Architecture Search Abstract: Although neural the architecture search (NAS) can bring improvement to deep models, it always neglects precious knowledge of existing models. The computation and time costing property in NAS also means that we should not start from scratch to search, but make every attempt to reuse the existing knowledge. In this article, we discuss what kind of knowledge in a model can and should be used for a new architecture design. Then, we propose a new NAS algorithm, namely, ModuleNet, which can fully inherit knowledge from the existing convolutional neural networks. To make full use of the existing models, we decompose existing models into different modules, which also keep their weights, consisting of a knowledge base. Then, we sample and search for a new architecture according to the knowledge base. Unlike previous search algorithms, and benefiting from inherited knowledge, our method is able to directly search for architectures in the macrospace by the NSGA-II algorithm without tuning parameters in these modules. Experiments show that our strategy can efficiently evaluate the performance of a new architecture even without tuning weights in convolutional layers. With the help of knowledge we inherited, our search results can always achieve better performance on various datasets (CIFAR10, CIFAR100, and ImageNet) over original architectures.

Title: Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing Abstract: Domination-based multiobjective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods, however, stagnate when the majority of the population becomes nondominated, preventing further convergence to the Pareto set. Hypervolume-based MO optimization has shown promising results to overcome this. Direct use of the hypervolume, however, results in no selection pressure for dominated solutions. The recently introduced Sofomore framework overcomes this by solving multiple interleaved single-objective dynamic problems that iteratively improve a single approximation set, based on the uncrowded hypervolume improvement (UHVI). It thereby however loses many advantages of population-based MO optimization, such as handling multimodality. Here, we reformulate the UHVI as a quality measure for approximation sets, called the uncrowded hypervolume (UHV), which can be used to directly solve MO optimization problems with a single-objective optimizer. We use the state-of-the-art gene-pool optimal mixing evolutionary algorithm (GOMEA) that is capable of efficiently exploiting the intrinsically available grey-box properties of this problem. The resulting algorithm, UHV-GOMEA, is compared with Sofomore equipped with GOMEA, and the domination-based MO-GOMEA. In doing so, we investigate in which scenarios either domination-based or hypervolume-based methods are preferred. Finally, we construct a simple hybrid approach that combines MO-GOMEA with UHV-GOMEA and outperforms both.

Title: On the identifiability of Bayesian factor analytic models Abstract: A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. We introduce a post-processing scheme in order to deal with rotation, sign and permutation invariance of the MCMC sample. The exact version of the contributed algorithm requires to solve $$2^q$$ assignment problems per (retained) MCMC iteration, where q denotes the number of factors of the fitted model. For large numbers of factors two approximate schemes based on simulated annealing are also discussed. We demonstrate that the proposed method leads to interpretable posterior distributions using synthetic and publicly available data from typical factor analytic models as well as mixtures of factor analyzers. An R package is available online at CRAN web-page.

Title: Combined Eco-Routing and Power-Train Control of Plug-In Hybrid Electric Vehicles in Transportation Networks Abstract: We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. In order to show the effectiveness of our method in practice, we use a HERE Maps API to apply our algorithms based on traffic data in the city of Boston with more than 110,000 links. Moreover, we validate the performance of our eco-routing algorithm using speed profiles collected from a traffic simulator (SUMO) as input to a high-fidelity energy model to calculate energy consumption costs. Our results show significant energy savings (around 12%) for PHEVs with a near real-time execution time for the algorithm.

Title: A Review on Deep Learning Techniques for Video Prediction Abstract: The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demo...

Title: Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review Abstract: Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this article devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions.

Title: Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification Abstract: Analyzing a large amount of malware is a major burden for security analysts. Since emerging malware is often a variant of existing malware, automatically classifying malware into known families greatly reduces a part of their burden. Image-based malware classification with deep learning is an attractive approach due to its simplicity, versatility, and affinity with the latest technologies. However, the impact of differences in deep learning models and the degree of transfer learning on the classification accuracy of malware variants has not been fully studied. In this paper, we conducted an exhaustive study of deep learning models using 24 ImageNet pre-trained models and five fine-tuning parameters, totaling 120 combinations, on two platforms. As a result, we found that the highest classification accuracy was obtained by fine-tuning one of the latest deep learning models with a relatively low degree of transfer learning, and we achieved the highest classification accuracy ever in cross-validation on the Malimg and Drebin datasets. We also confirmed that this trend is true for recent malware variants using the VirusTotal 2020 Windows and Android datasets.

Title: A proof of the theta operator conjecture Abstract: In the context of the (generalized) Delta Conjecture and its compositional form, D'Adderio, Iraci, and Vanden Wyngaerd recently stated a conjecture relating two symmetric function operators, Dk and Θk. We prove this Theta Operator Conjecture, finding it as a consequence of the five-term relation of Mellit and Garsia. As a result, we find surprising ways of writing the Dk operators. Even though we deal specifically with the relation between the Dk and Θk operators, our work introduces a method for finding relations between Θk and other plethystic operators which are important in this area of study.

Title: DNN-aided read-voltage threshold optimization for MLC flash memory with finite block length Abstract: The error-correcting performance of multi-level-cell (MLC) NAND flash memory is closely related to the block length of error-correcting codes (ECCs) and log-likelihood-ratios of the read-voltage thresholds. Driven by this issue, this paper optimizes the read-voltage thresholds for MLC flash memory to improve the decoding performance of ECCs with finite block length. First, through the analysis of channel coding rate and decoding error probability under finite block length, the optimization problem of read-voltage thresholds to minimize the maximum decoding error probability is formulated. Second, a cross-iterative search algorithm to optimize read-voltage thresholds under the perfect knowledge of flash memory channel is developed. However, it is challenging to analytically characterize the voltage distribution under the effect of data retention noise. To address this problem, a deep neural network (DNN)-aided optimization strategy to optimize the read-voltage thresholds is developed, where a multi-layer perception network is employed to learn the relationship between voltage distribution and read-voltage thresholds. Simulation results show that, compared with the existing schemes, the proposed DNN-aided read-voltage threshold optimization strategy with a well-designed Low Density Parity Check (LDPC) code can not only improve the program-and-erase endurance but also reduce the read latency.

Title: <p>A lower bound on the saturation number, and graphs for which it is sharp</p> Abstract: Let H be a fixed graph. We say that a graph G is H-saturated if it has no subgraph isomorphic to H, but the addition of any edge to G results in an H-subgraph. The saturation number sat(H, n) is the minimum number of edges in an H-saturated graph on n vertices. Kaszonyi and Tuza, in 1986, gave a general upper bound on the saturation number of a graph H, but a nontrivial lower bound has remained elusive. In this paper we give a general lower bound on sat(H, n) and prove that it is asymptotically sharp (up to an additive constant) on a large class of graphs. This class includes all threshold graphs and many graphs for which the saturation number was previously determined exactly. Our work thus gives an asymptotic common generalization of several earlier results. The class also includes disjoint unions of cliques, allowing us to address an open problem of Faudree, Ferrara, Gould, and Jacobson. (C) 2022 Elsevier B.V. All rights reserved.

Title: How to Secure Distributed Filters Under Sensor Attacks Abstract: In this article, we study how to secure distributed filters for linear time-invariant systems with bounded noise under false-data injection attacks. A malicious attacker is able to arbitrarily manipulate the observations for a time-varying and unknown subset of the sensors. We first propose a recursive distributed filter consisting of two steps at each update. The first step employs a saturation-like scheme, which gives a small gain if the innovation is large corresponding to a potential attack. The second step is a consensus operation of state estimates among neighboring sensors. We prove the estimation error is upper bounded if the filter parameters satisfy a condition. We further analyze the feasibility of the condition and connect it to sparse observability in the centralized case. When the attacked sensor set is known to be time-invariant, the secured filter is modified by adding an online local attack detector. The detector is able to identify the attacked sensors whose observation innovations are larger than the detection thresholds. Also, with more attacked sensors being detected, the thresholds will adaptively adjust to reduce the space of the stealthy attack signals. The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors. Moreover, for the noise-free case, we prove that the state estimate of each sensor asymptotically converges to the system state under certain conditions. Numerical simulations are provided to illustrate the developed results.

Title: Meta-Learning in Neural Networks: A Survey Abstract: The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research.

Title: Sharing Matters for Generalization in Deep Metric Learning Abstract: Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes. However, the main challenge is to learn a metric that not only generalizes from training to novel, but related, test samples. It should also transfer to different object classes. So wh...

Title: Behavior Variations and Their Implications for Popularity Promotions: From Elites to Mass on Weibo Abstract: The boom in social media with regard to producing and consuming information simultaneously implies the crucial role of online user influence in determining content popularity. In particular, understanding behavior variations between the influential elites and the mass grassroots is an important issue in communication. However, how their behavior varies across user categories and content domains and how these differences influence content popularity are rarely addressed. From a novel view of seven content domains, a detailed picture of the behavior variations among five user groups, from the views of both the elites and mass, is drawn on Weibo, one of the most popular Twitter-like services in China. Interestingly, elites post more diverse content with video links, while the mass possess retweeters of higher loyalty. According to these variations, user-oriented actions for enhancing content popularity are discussed and testified. The most surprising finding is that the diverse content does not always bring more retweets, and the mass and elites should promote content popularity by increasing their retweeter counts and loyalty, respectively. For the first time, our results demonstrate the possibility of highly individualized strategies of popularity promotions in social media, instead of a universal principle.

Title: Normal approximation and fourth moment theorems for monochromatic triangles Abstract: Given a graph sequence {Gn}n >= 1 denote by T-3(G(n)) the number of monochromatic triangles in a uniformly random coloring of the vertices of G(n) with c >= 2 colors. In this paper we prove a central limit theorem (CLT) for T-3(G(n)) with explicit error rates, using a quantitative version of the martingale CLT. We then relate this error term to the well-known fourth-moment phenomenon, which, interestingly, holds only when the number of colors satisfies c >= 5. We also show that the convergence of the fourth moment is necessary to obtain a Gaussian limit for any c >= 2, which, together with the above result, implies that the fourth-moment condition characterizes the limiting normal distribution of T-3(G(n)), whenever c >= 5. Finally, to illustrate the promise of our approach, we include an alternative proof of the CLT for the number of monochromatic edges, which provides quantitative rates for the results obtained in [7].

Title: MulayCap: Multi-Layer Human Performance Capture Using a Monocular Video Camera Abstract: We introduce MulayCap, a novel human performance capture method using a monocular video camera without the need for pre-scanning. The method uses “multi-layer” representations for geometry reconstruction and texture rendering, respectively. For geometry reconstruction, we decompose the clothed human into multiple geometry layers, namely a body mesh layer and a garment piece layer. The key techniqu...

Title: Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems Abstract: Multivariate geo-sensory time series prediction is challenging because of the complex spatial and temporal correlations. In urban water distribution systems (WDSs), numerous spatial-correlated sensors have been deployed to continuously collect hydraulic data. Forecasts of the monitored flow and pressure time series are of vital importance for operational decision making, alerts, and anomaly detect...

Title: Spectral symmetry in conference matrices Abstract: A conference matrix of order n is an $$n\times n$$ matrix C with diagonal entries 0 and off-diagonal entries $$\pm 1$$ satisfying $$CC^\top =(n-1)I$$ . If C is symmetric, then C has a symmetric spectrum $$\Sigma $$ (that is, $$\Sigma =-\Sigma $$ ) and eigenvalues $$\pm \sqrt{n-1}$$ . We show that many principal submatrices of C also have symmetric spectrum, which leads to examples of Seidel matrices of graphs (or, equivalently, adjacency matrices of complete signed graphs) with a symmetric spectrum. In addition, we show that some Seidel matrices with symmetric spectrum can be characterized by this construction.

Title: Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks Abstract: Deep neural models, in recent years, have been successful in almost every field, even solving the most complex problem statements. However, these models are huge in size with millions (and even billions) of parameters, demanding heavy computation power and failing to be deployed on edge devices. Besides, the performance boost is highly dependent on redundant labeled data. To achieve faster speeds ...

Title: A General Framework for Approximating Min Sum Ordering Problems Abstract: We consider a large family of problems in which an ordering of a finite set must be chosen to minimize some weighted sum of costs. This family includes variations of Min Sum Set Cover, several scheduling and search problems, and problems in Boolean function evaluation. We define a new problem, called the Min Sum Ordering Problem (MSOP) which generalizes all these problems using a cost and a weight function on subsets of a finite set. Assuming a polynomial time $\alpha$-approximation algorithm for the problem of finding a subset whose ratio of weight to cost is maximal, we show that under very minimal assumptions, there is a polynomial time $4 \alpha$-approximation algorithm for MSOP. This approximation result generalizes a proof technique used for several distinct problems in the literature. We apply our approximation result to obtain a number of new approximation results.

Title: Gradient-free distributed optimization with exact convergence Abstract: In this paper, a gradient-free distributed algorithm is introduced to solve a set constrained optimization problem under a directed communication network. Specifically, at each time-step, the agents locally compute a so-called pseudo-gradient to guide the updates of the decision variables, which can be applied in the fields where the gradient information is unknown, not available or non-existent. A surplus-based method is adopted to remove the doubly stochastic requirement on the weighting matrix, which enables the implementation of the algorithm in graphs having no associated doubly stochastic weighting matrix. For the convergence results, the proposed algorithm is able to obtain the exact convergence to the optimal value with any positive, non-summable and non-increasing step-sizes. Furthermore, when the step-size is also square-summable, the proposed algorithm is guaranteed to achieve the exact convergence to an optimal solution. In addition to the standard convergence analysis, the convergence rate of the proposed algorithm is also investigated. Finally, the effectiveness of the proposed algorithm is verified through numerical simulations.

Title: PLASMA ECHOES NEAR STABLE PENROSE DATA Abstract: In this paper we construct particular solutions to the classical Vlasov-Poisson system near stable Penrose initial data on T x R that are a combination of elementary waves with arbitrarily high frequencies. These waves mutually interact giving birth, eventually, to an infinite cascade of echoes of smaller and smaller amplitude. The echo solutions do not belong to the analytic or Gevrey classes studied by Mouhot and Villani but do, nonetheless, exhibit damping phenomena for large times.

Title: A Survey of Single-Scene Video Anomaly Detection Abstract: This article summarizes research trends on the topic of anomaly detection in video feeds of a single scene. We discuss the various problem formulations, publicly available datasets and evaluation criteria. We categorize and situate past research into an intuitive taxonomy and provide a comprehensive comparison of the accuracy of many algorithms on standard test sets. Finally, we also provide best ...

Title: A Robust Reputation-Based Group Ranking System and Its Resistance to Bribery Abstract: AbstractThe spread of online reviews and opinions and its growing influence on people’s behavior and decisions boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this article, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.

Title: Gelato: Feedback-driven and Guided Security Analysis of Client-side Web Applications Abstract: Modern web applications are getting more sophisticated by using frameworks that make development easy, but pose challenges for security analysis tools. New analysis techniques are needed to handle such frameworks that grow in number and popularity. In this paper, we describe Gelato that addresses the most crucial challenges for a security-aware client-side analysis of highly dynamic web applications. In particular, we use a feedback-driven and state-aware crawler that is able to analyze complex framework-based applications automatically, and is guided to maximize coverage of security-sensitive parts of the program. Moreover, we propose a new lightweight client-side taint analysis that outperforms the state-of-the-art tools, requires no modification to browsers, and reports non-trivial taint flows on modern JavaScript applications. Gelato reports vulnerabilities with higher accuracy than existing tools and achieves significantly better coverage on 12 applications of which three are used in production.

Title: Stochastic batch size for adaptive regularization in deep network optimization Abstract: •Adaptive regularization for deep network optimization via parameter-wise batch size.•The stochastic batch size reflects local and global properties of each parameter.•Beneficial for practical studies where the number of training examples is small.

Title: The R Package stagedtrees for Structural Learning of Stratified Staged Trees Abstract: stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and clustering-based algorithms are implemented, as well as various functionalities to plot the models and perform inference. The capabilities of stagedtrees are illustrated using mainly two datasets both included in the package or bundled in R.

Title: Faster Stochastic Quasi-Newton Methods Abstract: Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems, such as training neural networks, due to low per-iteration computational complexity. In fact, the Newton or quasi-newton (QN) methods leveraging the second-order information are able to achieve a better solution than the first-order methods. Thus, stochastic QN (SQN) methods have been developed to achieve a better solution efficiently than the stochastic first-order methods by utilizing approximate second-order information. However, the existing SQN methods still do not reach the best known stochastic first-order oracle (SFO) complexity. To fill this gap, we propose a novel faster stochastic QN method (SpiderSQN) based on the variance reduced technique of SIPDER. We prove that our SpiderSQN method reaches the best known SFO complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n+n^{1/2}\epsilon ^{-2})$ </tex-math></inline-formula> in the finite-sum setting to obtain an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -first-order stationary point. To further improve its practical performance, we incorporate SpiderSQN with different momentum schemes. Moreover, the proposed algorithms are generalized to the online setting, and the corresponding SFO complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\epsilon ^{-3})$ </tex-math></inline-formula> is developed, which also matches the existing best result. Extensive experiments on benchmark data sets demonstrate that our new algorithms outperform state-of-the-art approaches for nonconvex optimization.

Title: Quantization Analysis and Robust Design for Distributed Graph Filters Abstract: Distributed graph filters have recently found applications in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and reconstruction. However, when implemented over WSNs, the graph filters should deal with network limited energy constraints as well as processing and communication capabilities. Quantization plays a fundamental role to improve the latter but its effects on distributed graph filtering are little understood. WSNs are also prone to random link losses due to noise and interference. In this instance, the filter output is affected by both the quantization error and the topological randomness error, which, if it is not properly accounted in the filter design phase, may lead to an accumulated error through the filtering iterations and significantly degrade the performance. In this paper, we analyze how quantization affects distributed graph filtering over both time-invariant and time-varying graphs. We bring insights on the quantization effects for the two most common graph filters: the finite impulse response (FIR) and autoregressive moving average (ARMA) graph filter. Besides providing a comprehensive analysis, we devise theoretical performance guarantees on the filter performance when the quantization stepsize is fixed or changes dynamically over the filtering iterations. For FIR filters, we show that a dynamic quantization stepsize leads to more reduction of the quantization noise than in the fixed-stepsize quantization. For ARMA graph filters, we show that decreasing the quantization stepsize over the iterations reduces the quantization noise to zero at the steady-state. In addition, we propose robust filter design strategies that minimize the quantization noise for both time-invariant and time-varying networks. Numerical experiments on synthetic and two real data sets corroborate our findings and show the different trade-offs between quantization bits, filter order, and robustness to topological randomness.

Title: Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis Abstract: Agent-based microsimulation has become the standard to analyze intelligent transportation systems, using disaggregate travel demand data for entire populations, data that are not typically readily available. Population synthesis approaches are thus needed. We present Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequential mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared with other recently proposed methods such as the Variational Autoencoders (VAE) method, which has shown success in high dimensional tabular population synthesis. We evaluate the performance of the synthesized outputs based on distribution similarity, multi-variate correlations and spatio-temporal metrics. The results show the consistent and accurate generation of synthetic populations and their tabular and spatially sequential attributes, generated over varying spatial scales and dimensions.

Title: On the Complexity of the Plantinga–Vegter Algorithm Abstract: We introduce tools from numerical analysis and high dimensional probability for precision control and complexity analysis of subdivision-based algorithms in computational geometry. We combine these tools with the continuous amortization framework from exact computation. We use these tools on a well-known example from the subdivision family: the adaptive subdivision algorithm due to Plantinga and Vegter. The only existing complexity estimate on this rather fast algorithm was an exponential worst-case upper bound for its interval arithmetic version. We go beyond the worst-case by considering both average and smoothed analysis, and prove polynomial time complexity estimates for both interval arithmetic and finite-precision versions of the Plantinga–Vegter algorithm.

Title: State Observation of Power Systems Equipped With Phasor Measurement Units: The Case of Fourth-Order Flux-Decay Model Abstract: The problem of effective use of phasor measurement units (PMUs) to enhance power systems awareness and security is a topic of key interest. The central question to solve is how to use these new measurements to reconstruct the state of the system. In this article, we provide the first solution to the problem of (globally convergent) state estimation of multimachine power systems equipped wit...

Title: A Function Space Analysis of Finite Neural Networks With Insights From Sampling Theory Abstract: This work suggests using sampling theory to analyze the function space represented by interpolating mappings. While the analysis in this paper is general, we focus it on neural networks with bounded weights that are known for their ability to interpolate (fit) the training data. First, we show, under the assumption of a finite input domain, which is the common case in training neural networks, that the function space generated by multi-layer networks with bounded weights, and non-expansive activation functions are smooth. This extends over previous works that show results for the case of infinite width ReLU networks. Then, under the assumption that the input is band-limited, we provide novel error bounds for univariate neural networks. We analyze both deterministic uniform and random sampling showing the advantage of the former.

Title: Anti-Unwinding Sliding Mode Attitude Maneuver Control for Rigid Spacecraft Abstract: In this article, anti-unwinding attitude maneuver control for rigid spacecraft is considered. First, in order to avoid the unwinding phenomenon when the system states are restricted to the switching surface, a novel switching function is designed by a hyperbolic sine function such that the switching surface contains the two equilibriums. Then, a sliding mode attitude maneuver controller is developed based on the proposed switching function to ensure the robustness of the closed-loop system to disturbance and inertia uncertainty. Another important feature of the presented attitude control law is that a dynamic parameter is constructed to guarantee the unwinding-free performance before the system states reach the switching surface. Furthermore, a boundary layer is introduced for the designed controller to avoid the chattering phenomenon. Moreover, the convergence property and unwinding-free performance when the system states within the boundary layer are proven. The simulation results demonstrate that the unwinding problem is settled during attitude maneuver for rigid spacecraft by adopting the newly developed switching function and the presented attitude control scheme.

Title: Fully convolutional online tracking Abstract: Online learning has turned out to be effective for improving tracking performance. However, it could be simply applied for classification branch, but still remains challenging to adapt to regression branch due to its complex design and intrinsic requirement for high-quality online samples. To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target filter based tracking paradigm. Our key contribution is to introduce an online regression model generator (RMG) for initializing weights of the target filter in the regression branch with online samples, and then optimizing this target filter weights based on the ground-truth samples at the first frame. Specifically, we devise a simple fully online tracker, composed of a feature extraction backbone, an up-sampling decoder, a multi-scale classification branch, and an anchor-free regression branch. Thanks to the unique design of RMG, our FCOT can not only handle the target variation along temporal dimension, but also overcome the issue of error accumulation during the tracking procedure. In addition, due to its simplicity in design, our FCOT could be trained and deployed in a fully convolutional manner with a real-time running speed. Our FCOT achieves promising performance on seven benchmarks, including VOT2018, LaSOT, TrackingNet, GOT-10k, OTB100, UAV123, and NFS. Code and models of our FCOT are available at: https://github.com/MCG-NJU/FCOT.

Title: Complete edge-colored permutation graphs. Abstract: We introduce the concept of complete edge-colored permutation graphs as complete graphs that are the edge-disjoint union of "classical" permutation graphs. We show that a graph $G=(V,E)$ is a complete edge-colored permutation graph if and only if each monochromatic subgraph of $G$ is a "classical" permutation graph and $G$ does not contain a triangle with~$3$ different colors. Using the modular decomposition as a framework we demonstrate that complete edge-colored permutation graphs are characterized in terms of their strong prime modules, which induce also complete edge-colored permutation graphs. This leads to an $\mathcal{O}(|V|^2)$-time recognition algorithm. We show, moreover, that complete edge-colored permutation graphs form a superclass of so-called symbolic ultrametrics and that the coloring of such graphs is always a Gallai coloring.

Title: A local adaptive discontinuous Galerkin method for convection-diffusion-reaction equations Abstract: •A posteriori error analysis for a local method for elliptic PDEs.•Automatic local domain identification.•Efficiency in singularly perturbed regimes.•Error estimators based on flux reconstructions with weakened regularity.

Title: Spatial Linear Dynamic Relationship of Strongly Connected Multiagent Systems and Adaptive Learning Control for Different Formations Abstract: This article addresses an important problem of how to improve the learnability of an intelligent agent in a strongly connected multiagent network. A novel spatial-dimensional linear dynamic relationship (SLDR) is developed to formulate the spatial dynamic relationship of an agent with respect to all the related agents. The obtained SLDR virtually exists in the computer to describe the input–output...

Title: Observer-Based Consensus Protocol for Directed Switching Networks With a Leader of Nonzero Inputs Abstract: We aim to address the consensus tracking problem for multiple-input–multiple-output (MIMO) linear networked systems under directed switching topologies, where the leader is subject to some nonzero but norm bounded inputs. First, based on the relative outputs, a full-order unknown input observer (UIO) is designed for each agent to track the full states’ error among neighboring agents. With the aid ...

Title: Optimal Design of High-Order Control for Fuzzy Dynamical Systems Based on the Cooperative Game Theory Abstract: In this article, we propose a high-order robust control for fuzzy dynamical systems. The time varying but bounded uncertainty in this system is described by the fuzzy set theory. The control is deterministic and is not based on IF-THEN fuzzy rules. By the Lyapunov approach, we prove that the control is able to guarantee uniform boundedness and uniform ultimate boundedness. In addition, the tunable...

Title: Asynchronous Adaptive Fault-Tolerant Sliding-Mode Control for T–S Fuzzy Singular Markovian Jump Systems With Uncertain Transition Rates Abstract: In this article, the problem of asynchronous sliding-mode control (SMC) for a class of nonlinear singular Markovian jump systems (SMJSs) with actuator faults and uncertain transition rates (TRs) is investigated. Based on Takagi–Sugeno (T–S) fuzzy models, the nonlinear SMJSs are transformed to a set of local linear SMJSs connected by the so-called IF-THEN rules. The hidden Markov model is employed ...

Title: Adaptive Finite-Time Containment Control of Uncertain Multiple Manipulator Systems Abstract: This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping. To further improve the control performance, the filtering...

Title: Self-Triggered Scheduling for Boolean Control Networks Abstract: It has been shown that self-triggered control has the ability to deal with cases with constrained resources by properly setting up the rules for updating the system control when necessary. In this article, self-triggered stabilization of the Boolean control networks (BCNs), including the deterministic BCNs, probabilistic BCNs, and Markovian switching BCNs, is first investigated via the semitensor product of matrices and the Lyapunov theory of the Boolean networks. The self-triggered mechanism with the aim to determine when the controller should be updated is provided by the decrease of the corresponding Lyapunov functions between two consecutive samplings. Rigorous theoretical analysis is presented to prove that the designed self-triggered control strategy for BCNs is well defined and can make the controlled BCNs be stabilized at the equilibrium point.

Title: On the Stability Problem of Equilibrium Discrete Planar Curves Abstract: We study planar polygonal curves with the variational methods. We show a unified interpretation of discrete curvatures and the Steiner-type formula by extracting the notion of the discrete curvature vector from the first variation of the length functional. Moreover, we determine the equilibrium curves for the length functional under the area-constraint condition and study their stability.

Title: Explainable image classification with evidence counterfactual Abstract: The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. A counterfactual explanation highlights the parts of an image which, when removed, would change the predicted class. Both legal scholars and data scientists are increasingly turning to counterfactual explanations as these provide a high degree of human interpretability, reveal what minimal information needs to be changed in order to come to a different prediction and do not require the prediction model to be disclosed. Our literature review shows that existing counterfactual methods for image classification have strong requirements regarding access to the training data and the model internals, which often are unrealistic. Therefore, SEDC is introduced as a model-agnostic instance-level explanation method for image classification that does not need access to the training data. As image classification tasks are typically multiclass problems, an additional contribution is the introduction of the SEDC-T method that allows specifying a target counterfactual class. These methods are experimentally tested on ImageNet data, and with concrete examples, we illustrate how the resulting explanations can give insights in model decisions. Moreover, SEDC is benchmarked against existing model-agnostic explanation methods, demonstrating stability of results, computational efficiency and the counterfactual nature of the explanations.

Title: Classify and generate: Using classification latent space representations for image generations Abstract: Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area. In general, discriminative representations are rich in class specific features but are too sparse for reconstruction, whereas, in autoencoders the representations are dense but has limited indistinguishable class specific features, making it less suitable for classification. In this work, we propose a discriminative modelling framework that employs manipulated supervised latent representations to reconstruct and generate new samples belonging to a given class. Unlike generative modelling approaches such as GANs and VAEs that aim to model the data manifold distribution, Representation based Generations (ReGene) directly represents the given data manifold in the classification space. Such supervised representations, under certain constraints, allow for reconstructions and controlled generations using an appropriate decoder without enforcing any prior distribution. Theoretically, given a class, we show that these representations when smartly manipulated using convex combinations retain the same class label. Furthermore, they also lead to novel generation of visually realistic images. Extensive experiments on datasets of varying resolutions demonstrate that ReGene has higher classification accuracy than existing conditional generative models while being competitive in terms of FID. (c) 2021 Elsevier B.V. All rights reserved.

Title: A tight Hermite–Hadamard inequality and a generic method for comparison between residuals of inequalities with convex functions Abstract: We present a tight parametrical Hermite–Hadamard type inequality with probability measure, which yields a considerably closer upper bound for the mean value of convex function than the classical one. Our inequality becomes equality not only with affine functions, but also with a family of V-shaped curves determined by the parameter. The residual (a distance between two sides) of this inequality is strictly smaller than in the classical Hermite–Hadamard inequality under any probability measure and with all nonaffine convex functions. In the framework of Karamata’s theorem on the inequalities with convex functions, we propose a method of measuring a global performance of inequalities in terms of average residuals over functions of the type $$x\mapsto |x-u|$$ . Using average residuals enables comparing two or more inequalities as themselves, with same or different measures and without referring to a particular function. Our method is applicable to all Karamata’s type inequalities, with integrals or sums. A numerical experiment with three different measures indicates that the average residual in our inequality is about 4 times smaller than in classical right Hermite–Hadamard, and also is smaller than in Jensen’s inequality, with all three measures. Some topics from history and priority are discussed.