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Title: A class of higher inductive types in Zermelo-Fraenkel set theory Abstract: We define a class of higher inductive types that can be constructed in the category of sets under the assumptions of Zermelo-Fraenkel set theory without the axiom of choice or the existence of uncountable regular cardinals. This class includes the example of unordered trees of any arity.

Title: A Survey on Blockchain Interoperability: Past, Present, and Future Trends Abstract: AbstractBlockchain interoperability is emerging as one of the crucial features of blockchain technology, but the knowledge necessary for achieving it is fragmented. This fact makes it challenging for academics and the industry to achieve interoperability among blockchains seamlessly. Given this new domain’s novelty and potential, we conduct a literature review on blockchain interoperability by collecting 284 papers and 120 grey literature documents, constituting a corpus of 404 documents. From those 404 documents, we systematically analyzed and discussed 102 documents, including peer-reviewed papers and grey literature. Our review classifies studies in three categories: Public Connectors, Blockchain of Blockchains, and Hybrid Connectors. Each category is further divided into sub-categories based on defined criteria. We classify 67 existing solutions in one sub-category using the Blockchain Interoperability Framework, providing a holistic overview of blockchain interoperability. Our findings show that blockchain interoperability has a much broader spectrum than cryptocurrencies and cross-chain asset transfers. Finally, this article discusses supporting technologies, standards, use cases, open challenges, and future research directions, paving the way for research in the area.

Title: CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words Abstract: AbstractTo accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that inherits the advantages of DeepCS (i.e., the capability of understanding the sequential semantics in important query words), while it can leverage the indexing technique in the IR-based model to accelerate the search response time substantially. CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query. We verified its effectiveness on a large-scale codebase with ~41K repositories. Experimental results showed that CodeMatcher achieves an MRR (a widely used accuracy measure for code search) of 0.60, outperforming DeepCS, CodeHow, and UNIF by 82%, 62%, and 46%, respectively. Our proposed model is over 1.2K times faster than DeepCS. Moreover, CodeMatcher outperforms two existing online search engines (GitHub and Google search) by 46% and 33%, respectively, in terms of MRR. We also observed that: fusing the advantages of IR-based and DL-based models is promising; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code.

Title: Bayesian Surface Warping Approach for Rectifying Geological Boundaries Using Displacement Likelihood and Evidence from Geochemical Assays Abstract: AbstractThis article presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse data, capture the global trend and provide a reasonable approximation of the stratigraphic, mineralization, and other types of boundaries for mining exploration, but they are locally inaccurate at scales typically required for grade estimation. The proposed methodology makes local spatial corrections automatically to maximize the agreement between the modeled surfaces and observed samples. Where possible, vertices on a mesh surface are moved to provide a clear delineation, for instance, between ore and waste material across the boundary based on spatial and compositional analysis using assay measurements collected from densely spaced, geo-registered blast holes. The maximum a posteriori (MAP) solution ultimately considers the chemistry observation likelihood in a given domain. Furthermore, it is guided by an a priori spatial structure that embeds geological domain knowledge and determines the likelihood of a displacement estimate. The results demonstrate that increasing surface fidelity can significantly improve grade estimation performance based on large-scale model validation.

Title: WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation. Abstract: In deep networks, the lost data details significantly degrade the performances of image segmentation. In this paper, we propose to apply Discrete Wavelet Transform (DWT) to extract the data details during feature map down-sampling, and adopt Inverse DWT (IDWT) with the extracted details during the up-sampling to recover the details. We firstly transform DWT/IDWT as general network layers, which are applicable to 1D/2D/3D data and various wavelets like Haar, Cohen, and Daubechies, etc. Then, we design wavelet integrated deep networks for image segmentation (WaveSNets) based on various architectures, including U-Net, SegNet, and DeepLabv3+. Due to the effectiveness of the DWT/IDWT in processing data details, experimental results on CamVid, Pascal VOC, and Cityscapes show that our WaveSNets achieve better segmentation performances than their vanilla versions.

Title: Counting general phylogenetic networks Abstract: We provide precise asymptotic estimates for the number of general phylogenetic networks by using analytic combinatorial methods. Recently, this approach has been studied by Fuchs, Gittenberger and the author himself, to count networks with few reticulation vertices for two subclasses: tree-child and normal networks. We follow this line of research to show how to obtain results on the enumeration of general phylogenetic networks.

Title: Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation Abstract: We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation ...

Title: Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-Identification Abstract: Existing vehicle re-identification methods commonly use spatial pooling operations to aggregate feature maps extracted via off-the-shelf backbone networks. They ignore exploring the spatial significance of feature maps, eventually degrading the vehicle re-identification performance.

Title: Data-Driven Convergence Prediction of Astrobots Swarms Abstract: Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination is so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observatio...

Title: Geometric Back-Projection Network for Point Cloud Classification Abstract: As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations. To achieve this, on the one hand, we enrich the geometric information of points in low-level 3D space explicitly. On the other hand, we apply CNN-based structures in high-level feature spaces to learn local geometric context implicitly. Specifically, we leverage an idea of error-correcting feedback structure to capture the local features of point clouds comprehensively. Furthermore, an attention module based on channel affinity assists the feature map to avoid possible redundancy by emphasizing its distinct channels. The performance on both synthetic and real-world point clouds datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency.

Title: Non-Structured DNN Weight Pruning—Is It Beneficial in Any Platform? Abstract: Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression with two main approaches. Weight pruning leverages the redundancy in the number of weights and can be performed in a non-structured, which has higher flexibility and pruning rate but incurs index accesses due to irregular weights, or structured manner, which preserves the full matrix structure with a lower pruning rate. Weight quantization leverages the redundancy in the number of bits in weights. Compared to pruning, quantization is much more hardware-friendly and has become a “must-do” step for FPGA and ASIC implementations. Thus, any evaluation of the effectiveness of pruning should be on top of quantization. The key open question is, with quantization, what kind of pruning (non-structured versus structured) is most beneficial? This question is fundamental because the answer will determine the design aspects that we should really focus on to avoid the diminishing return of certain optimizations. This article provides a definitive answer to the question for the first time. First, we build ADMM-NN-S by extending and enhancing ADMM-NN, a recently proposed joint weight pruning and quantization framework, with the algorithmic supports for structured pruning, dynamic ADMM regulation, and masked mapping and retraining. Second, we develop a methodology for fair and fundamental comparison of non-structured and structured pruning in terms of both storage and computation efficiency. Our results show that ADMM-NN-S consistently outperforms the prior art: 1) it achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$348\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$36\times $ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> overall weight pruning on LeNet-5, AlexNet, and ResNet-50, respectively, with (almost) zero accuracy loss and 2) we demonstrate the first fully binarized (for all layers) DNNs can be lossless in accuracy in many cases. These results provide a strong baseline and credibility of our study. Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structured pruning is not competitive in terms of both storage and computation efficiency. Thus, we conclude that structured pruning has a greater potential compared to non-structured pruning. We encourage the community to focus on studying the DNN inference acceleration with structured sparsity.

Title: LOCALIZATION AND DELOCALIZATION OF GROUND STATES OF BOSE-EINSTEIN CONDENSATES UNDER DISORDER Abstract: This paper studies the localization behavior of Bose-Einstein condensates in disorder potentials, modeled by a Gross-Pitaevskii eigenvalue problem on a bounded interval. In the regime of weak particle interaction, we are able to quantify exponential localization of the ground state, depending on statistical parameters and the strength of the potential. Numerical studies further show delocalization if we leave the identified parameter range, which is in agreement with experimental data. These mathematical and numerical findings allow the prediction of physically relevant regimes where localization of ground states may be observed experimentally.

Title: Gradient Approximation and Multivariable Derivative-Free Optimization Based on Noncommutative Maps Abstract: In this article, multivariable derivative-free optimization algorithms for unconstrained optimization problems are developed. A novel procedure for approximating the gradient of multivariable objective functions based on noncommutative maps is introduced. The procedure is based on the construction of an exploration sequence to specify where the objective function is evaluated and the definition of so-called gradient generating functions which are composed with the objective function, such that the procedure mimics a gradient descent algorithm. Various theoretical properties of the proposed class of algorithms are investigated and numerical examples are presented.

Title: From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma Abstract: In this article, we propose a new method to obtain feedback controllers of an unknown dynamical system directly from noisy input/state data. The key ingredient of our design is a new matrix S-lemma that will be proven in this article. We provide both strict and nonstrict versions of this S-lemma, which are of interest in their own right. Thereafter, we will apply these results to data-driven contr...

Title: Preservation of Normality by Unambiguous Transducers Abstract: We consider finite state non-deterministic but unambiguous transducers with infinite inputs and infinite outputs, and we consider the property of Borel normality of sequences of symbols. When these transducers are strongly connected, and when the input is a Borel normal sequence, the output is a sequence in which every block has a frequency given by a weighted automaton over the rationals. We provide an algorithm that decides in cubic time whether an unambiguous transducer preserves normality.

Title: AN EVANS-STYLE RESULT FOR BLOCK DESIGNS Abstract: For positive integers n and k with n >= k, an (n, k, 1)-design is a pair (V, B), where V is a set of n points and B is a collection of k-subsets of V called blocks such that each pair of points occur together in exactly one block. If we weaken this condition to demand only that each pair of points occur together in at most one block, then the resulting object is a partial (n, k, 1)-design. A completion of a partial (n, k, 1)-design (V, A) is a (complete) (n, k, 1)-design (V, B) such that A subset of B. Here, for all sufficiently large n, we determine exactly the minimum number of blocks in an uncompletable partial (n, k, 1)-design. This result is reminiscent of Evans' now-proved conjecture on completions of partial Latin squares. We also prove some related results concerning edge decompositions of almost complete graphs into copies of K-k.

Title: Bayesian sparse factor analysis with kernelized observations Abstract: Multi-view problems can benefit from latent representations since they find low-dimensional projections that fairly capture the correlations among the multiple views that characterise the data. On the other hand, high-dimensionality and non-linear issues are traditionally handled by kernel methods, inducing a (non)-linear function between the latent projection and the data itself. However, they usually come with exposition to overfitting. Here, we combine Bayesian factor analysis with what we refer to as kernelized observations, in which the proposed model focuses on reconstructing not the data itself, but its relationship with other data points measured by a kernel function. In turn, we extend previous Bayesian FA formulations to be able to model non-linear data relationships by means of kernelized data representations and, at the same time, include additional facilities to obtain compact kernel representations by means of an automatic selection of Bayesian Relevance Vectors (RVs), feature relevance analysis and, even, obtain an automatic multiple kernel learning approach. Besides, this is flexibly included into a modular framework where we can easily adapt the model capabilities to the data needs and, even, combine them with previous FA functionalities such as heterogeneous data representations or semi-supervised learning. Using several public databases, we demonstrate the potential of the approach (and its extensions) w.r.t. common multi-view learning models such as kernel canonical correlation analysis, heterogeneous incomplete – variational autoenconder or manifold relevance determination, where the proposed model shows its ability to outperform the baselines while indistinctly combining model extensions.

Title: A new lower bound on graph gonality Abstract: We define a new graph invariant called the scramble number. We show that the scramble number of a graph is a lower bound for the gonality and an upper bound for the treewidth. Unlike the treewidth, the scramble number is not minor monotone, but it is subgraph monotone and invariant under subdivision. We compute the scramble number and gonality of several families of graphs for which these invariants are strictly greater than the treewidth. (C) 2021 Elsevier B.V. All rights reserved.

Title: End-faithful spanning trees in graphs without normal spanning trees Abstract: Schmidt characterised the class of rayless graphs by an ordinal rank function, which makes it possible to prove statements about rayless graphs by transfinite induction. Halin asked whether Schmidt's rank function can be generalised to characterise other important classes of graphs. In this paper, we address Halin's question: we characterise an important class of graphs by an ordinal function. Another largely open problem raised by Halin asks for a characterisation of the class of graphs with an end-faithful spanning tree. A well-studied subclass is formed by the graphs with a normal spanning tree. We determine a larger subclass, the class of normally traceable graphs, which consists of the connected graphs with a rayless tree-decomposition into normally spanned parts. Investigating the class of normally traceable graphs further we prove that, for every normally traceable graph, having a rayless spanning tree is equivalent to all its ends being dominated. Our proofs rely on a characterisation of the class of normally traceable graphs by an ordinal rank function that we provide.

Title: Quantum Garbled Circuits Abstract: In classical computing, garbled circuits (and their generalization known as randomized encodings) are a versatile cryptographic tool with many applications such as secure multiparty computation, delegated computation, depth-reduction of cryptographic primitives, complexity lower-bounds, and more. Quantum analogues of garbled circuits were not known prior to this work. In this work, we introduce a definition of quantum randomized encodings and present a construction which allows us to efficiently garble any quantum circuit, assuming the existence of quantum-secure one-way functions. Our construction has comparable properties to the best known classical garbling schemes. We can also achieve perfect information-theoretic security albeit with blow-up in the size of the garbled circuits. We believe that quantum garbled circuits and quantum randomized encodings can be an instrumental concept and building block for quantum computation and in particular quantum cryptography. We present some applications, including a conceptually-simple zero-knowledge proof system for QMA, a protocol for private simultaneous messages, functional encryption, and more.

Title: Choice principles in local mantles Abstract: Assume ZFC$\mathsf {ZFC}$. Let kappa be a cardinal. A <kappa${\mathord {<}\hspace{1.111pt}\kappa }$-ground is a transitive proper class W modelling ZFC$\mathsf {ZFC}$ such that V is a generic extension of W via a forcing P is an element of W$\mathbb {P}\in W$ of cardinality <kappa${\mathord {<}\hspace{1.111pt}\kappa }$. The kappa-mantle M kappa$\mathcal {M}_\kappa$ is the intersection of all <kappa${\mathord {<}\hspace{1.111pt}\kappa }$-grounds. We prove that certain partial choice principles in M kappa$\mathcal {M}_\kappa$ are the consequence of kappa being inaccessible/weakly compact, and some other related facts.

Title: Rate-optimal refinement strategies for local approximation MCMC Abstract: Many Bayesian inference problems involve target distributions whose density functions are computationally expensive to evaluate. Replacing the target density with a local approximation based on a small number of carefully chosen density evaluations can significantly reduce the computational expense of Markov chain Monte Carlo (MCMC) sampling. Moreover, continual refinement of the local approximation can guarantee asymptotically exact sampling. We devise a new strategy for balancing the decay rate of the bias due to the approximation with that of the MCMC variance. We prove that the error of the resulting local approximation MCMC (LA-MCMC) algorithm decays at roughly the expected $$1/\sqrt{T}$$ rate, and we demonstrate this rate numerically. We also introduce an algorithmic parameter that guarantees convergence given very weak tail bounds, significantly strengthening previous convergence results. Finally, we apply LA-MCMC to a computationally intensive Bayesian inverse problem arising in groundwater hydrology.

Title: Characterizing immutable sandpiles: A first look Abstract: By working with coefficients in Z or R, one can define two different notions of stability for a sandpile on a graph. We call a sandpile immutable when these notions agree. Our main results give linear-algebraic characterizations for large classes of immutable sandpiles. (c) 2021 Elsevier B.V. All rights reserved.

Title: Security Analysis for Distributed IoT-Based Industrial Automation Abstract: Internet of Things (IoT) technologies enable development of reconfigurable manufacturing systems—a new generation of modularized industrial equipment suitable for highly customized manufacturing. Sequential control in these systems is largely based on discrete events, whereas their formal execution semantics is specified as control interpreted Petri nets (CIPN). Despite industry-wide use of programming languages based on the CIPN formalism, formal verification of such control applications in the presence of adversarial activity is not supported. Consequently, in this article, we introduce security-aware modeling and verification techniques for CIPN-based sequential control applications. Specifically, we show how CIPN models of networked industrial IoT controllers can be transformed into time Petri net (TPN)-based models and composed with plant and security-aware channel models in order to enable system-level verification of safety properties in the presence of network-based attacks. Additionally, we introduce realistic channel-specific attack models that capture adversarial behavior using nondeterminism. Moreover, we show how verification results can be utilized to introduce security patches and facilitate design of attack detectors that improve system resiliency and enable satisfaction of critical safety properties. Finally, we evaluate our framework on an industrial case study. Note to Practitioners—Our main goal is to provide formal security guarantees for distributed sequential controllers. Specifically, we target smart automation controllers geared toward Industrial IoT applications that are typically programed in C/C++ and are running applications originally designed in, for example, GRAFCET (IEC 60848)/SFC (IEC 61131-3) automation programming languages. Since existing tools for the design of distributed automation do not support system-level verification of relevant safety properties, we show how security-aware transceiver and communication models can be developed and composed with distributed controller models. Then, we show how existing tools for verification of time Petri nets can be used to verify relevant properties including safety and liveness of the distributed automation system in the presence of network-based attacks. To provide an end-to-end analysis as well as security patching, results of our analysis can be used to deploy suitable firmware updates during the stage when executable code for target controllers (e.g., in C/C++) is generated based on GRAFCET/SFC control models. We also show that security guarantees can be improved as the relevant safety/liveness properties can be verified after corresponding security patches are deployed. Finally, we show applicability of our framework on a realistic distributed pneumatic manipulator.

Title: On Regularizability and Its Application to Online Control of Unstable LTI Systems Abstract: Learning, say through direct policy updates, often requires assumptions such as knowing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> that the initial policy (gain) is stabilizing, or persistently exciting (PE) input–output data, is available. In this article, we examine online regulation of (possibly unstable) partially unknown linear systems with no prior access to an initial stabilizing controller nor PE input–output data; we instead leverage the knowledge of the input matrix for online regulation. First, we introduce and characterize the notion of “regularizability” for linear systems that gauges the extent by which a system can be regulated in finite-time in contrast to its asymptotic behavior (commonly characterized by stabilizability/controllability). Next, having access only to the input matrix, we propose the data-guided regulation (DGR) synthesis procedure that—as its name suggests—regulates the underlying state while also generating informative data that can subsequently be used for data-driven stabilization or system identification. We further improve the computational performance of DGR via a rank-one update and demonstrate its utility in online regulation of the X-29 aircraft.

Title: Cyber LOPA: An Integrated Approach for the Design of Dependable and Secure Cyber-Physical Systems Abstract: Safety risk assessment is an essential process to ensure a dependable cyber-physical system (CPS) design. Traditional risk assessment considers only physical failures. For modern CPSs, failures caused by cyber attacks are on the rise. The focus of latest research effort is on safety–security lifecycle integration and the expansion of modeling formalisms for risk assessment to incorporate security failures. The interaction between safety and security lifecycles and its impact on the overall system design, as well as the reliability loss resulting from ignoring security failures, are some of the overlooked research questions. This article addresses these research questions by presenting a new safety design method named cyber layer of protection analysis (CLOPA) that extends the existing layer of protection analysis (LOPA) framework to include failures caused by cyber attacks. The proposed method provides a rigorous mathematical formulation that expresses quantitatively the tradeoff between designing a highly reliable and a highly secure CPS. We further propose a co-design lifecycle process that integrates the safety and security risk assessment processes. We evaluate the proposed CLOPA approach and the integrated lifecycle on a practical case study of a process reactor controlled by an industrial control testbed and provide a comparison between the proposed CLOPA and current LOPA risk assessment practice.

Title: Secrecy Capacity of a Gaussian Wiretap Channel With ADCs is Always Positive Abstract: We consider a complex Gaussian wiretap channel with finite-resolution analog-to-digital converters (ADCs) at both the legitimate receiver and the eavesdropper. For this channel, we show that a positive secrecy rate is always achievable as long as the channel gains at the legitimate receiver and at the eavesdropper are different, regardless of the quantization levels of the ADCs. For the achievabil...

Title: BrePartition: Optimized High-Dimensional <italic>k</italic>NN Search With Bregman Distances Abstract: Bregman distances (also known as Bregman divergences) are widely used in machine learning, speech recognition and signal processing, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN searches with Bregman distances have become increasingly important with the rapid advances of multimedia applications. Data in multimedia applications such as images and videos are commonly transformed into space of hundreds of dimensions. Such high-dimensional space has posed significant challenges for existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN search algorithms with Bregman distances, which could only handle data of medium dimensionality (typically less than 100). This paper addresses the urgent problem of high-dimensional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN search with Bregman distances. We propose a novel partition-filter-refinement framework. Specifically, we propose an optimized dimensionality partitioning scheme to solve several non-trivial issues. First, an effective bound from each partitioned subspace to obtain exact <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN results is derived. Second, we conduct an in-depth analysis of the optimized number of partitions and devise an effective strategy for partitioning. Third, we design an efficient integrated index structure for all the subspaces together to accelerate the search processing. Moreover, we extend our exact solution to an approximate version by a trade-off between the accuracy and efficiency. Experimental results on four real-world datasets and two synthetic datasets show the clear advantage of our method in comparison to state-of-the-art algorithms.

Title: Private Index Coding Abstract: We study the fundamental problem of index coding under an additional privacy constraint that requires each receiver to learn nothing more about the collection of messages beyond its demanded messages from the server and what is available to it as side information. To enable such private communication, we allow the use of a collection of independent secret keys, each of which is shared amongst a su...

Title: Solution path algorithm for twin multi-class support vector machine Abstract: The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems. However, it suffers from difficulties in effective solution of multi-classification and fast model selection. This work devotes to the fast regularization parameter tuning algorithm for the twin multi-class support vector machine. Specifically, a novel sample data set partition strategy is first adopted, which is the basis for the model construction. Then, combining the linear equations and block matrix theory, the Lagrangian multipliers are proved to be piecewise linear w.r.t. the regularization parameters, so that the regularization parameters are continuously updated by only solving the break points. Next, Lagrangian multipliers are proved to be 1 as the regularization parameter approaches infinity, thus, a simple yet effective initialization algorithm is devised. Finally, eight kinds of events are defined to seek for the starting event for the next iteration. Extensive experimental results on nine UCI data sets show that the proposed method can achieve comparable classification performance without solving any quadratic programming problem.

Title: An alternative perspective on copositive and convex relaxations of nonconvex quadratic programs Abstract: We study convex relaxations of nonconvex quadratic programs. We identify a family of so-called feasibility preserving convex relaxations, which includes the well-known copositive and doubly nonnegative relaxations, with the property that the convex relaxation is feasible if and only if the nonconvex quadratic program is feasible. We observe that each convex relaxation in this family implicitly induces a convex underestimator of the objective function on the feasible region of the quadratic program. This alternative perspective on convex relaxations enables us to establish several useful properties of the corresponding convex underestimators. In particular, if the recession cone of the feasible region of the quadratic program does not contain any directions of negative curvature, we show that the convex underestimator arising from the copositive relaxation is precisely the convex envelope of the objective function of the quadratic program, strengthening Burer's well-known result on the exactness of the copositive relaxation in the case of nonconvex quadratic programs. We also present an algorithmic recipe for constructing instances of quadratic programs with a finite optimal value but an unbounded relaxation for a rather large family of convex relaxations including the doubly nonnegative relaxation.

Title: Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning Abstract: Transductive zero-shot learning is designed to recognize unseen categories by aligning both visual and semantic information in a joint embedding space. Four types of domain biases exist in Transductive ZSL, i.e., visual bias and semantic bias in two domains, and two visual-semantic biases exist in the seen and unseen domains. However, the existing work has only focused on specific components of these topics, leading to severe semantic ambiguity during knowledge transfer. To solve this problem, we propose a novel attribute-induced bias eliminating (AIBE) module for Transductive ZSL. Specifically, for the visual bias between the two domains, the mean-teacher module is first used to bridge the visual representation discrepancy between the two domains using unsupervised learning and unlabeled images. Then, an attentional graph attribute embedding process is proposed to reduce the semantic bias between seen and unseen categories using a graph operation to describe the semantic relationship between categories. To reduce semantic-visual bias in the seen domain, we align the visual center of each category with the corresponding semantic attributes instead of with the individual visual data point, which preserves the semantic relationship in the embedding space. Finally, for the semantic-visual bias in the unseen domain, an unseen semantic alignment constraint is designed to align visual and semantic space using an unsupervised process. The evaluations on several benchmarks demonstrate the effectiveness of the proposed method, e.g., 82.8%/75.5%, 97.1%/82.5%, and 73.2%/52.1% for Conventional/Generalized ZSL settings for CUB, AwA2, and SUN datasets, respectively.

Title: Equal Higher Order Analysis of an Unfitted Discontinuous Galerkin Method for Stokes Flow Systems Abstract: In this work, we analyze an unfitted discontinuous Galerkin discretization for the numerical solution of the Stokes system based on equal higher-order discontinuous velocities and pressures. This approach combines the best from both worlds, firstly the advantages of a piece-wise discontinuous high–order accurate approximation and secondly the advantages of an unfitted to the true geometry grid around possibly complex objects and/or geometrical deformations. Utilizing a fictitious domain framework, the physical domain of interest is embedded in an unfitted background mesh and the geometrically unfitted discretization is built upon symmetric interior penalty discontinuous Galerkin formulation. To enhance stability we enrich the discrete variational formulation with a pressure stabilization term. Moreover, the present contribution adopts high order ghost penalty strategies to address the ill conditioning of the system matrix caused by small truncated elements with respect to the unfitted boundary. Motivated by continuous unfitted FEM (Burman and Hansbo in ESAIM Math Model Numer Anal 48(3):859–874, 2014; Massing et al. in J Sci Comput 61:604–628, 2014; Massing et al. in Numer Math 128:73–101, 2014) along with other unfitted mesh surveys grounded on discontinuous spaces (Becker et al. in Comput Methods Appl Mech Eng 198(41–44):3352–3360, 2009; Gürkan and Massing in Comput Methods Appl Mech Eng 348:466–499, 2019; Gürkan et al. in SIAM J Sci Comput 42(5):A2620–A2654, 2020; Massing in A cut discontinuous Galerkin method for coupled bulk-surface problems, Chapter in UCLWorkshop volume on "Geometrically Unfitted Finite Element Methods", Lecture Notes in Computational Science and Engineering, Springer, Cham, pp 259–279, 2017), we use proper velocity and pressure ghost penalties defined on faces of cut cells to establish a robust high-order method, in spite of the cell agglomeration technique usually applied on dG methods. The current presentation should prove valuable in engineering applications where special emphasis is placed on the optimal effective approximation attaining much smaller relative errors in coarser meshes. Inf-sup stability, the optimal order of convergence, and the condition number sensitivity with respect to cut configuration are investigated. Numerical examples verify the theoretical results.

Title: BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis Abstract: •We propose a fast, compact and parameter-efficient party-ignorant framework based on emotional recurrent unit.•We design generalized neural tensor block which is suitable for different structures, to perform context compositionality.•Experiments on three standard benchmarks indicate that our model outperforms the state of the art with fewer parameters.

Title: Inferring Point Cloud Quality via Graph Similarity Abstract: Objective quality estimation of media content plays a vital role in a wide range of applications. Though numerous metrics exist for 2D images and videos, similar metrics are missing for 3D point clouds with unstructured and non-uniformly distributed points. In this paper, we propose ${\sf GraphSIM}$<mml:math xmlns:mml=&#34;http://www....

Title: Staffing for many-server systems facing non-standard arrival processes Abstract: •Staffing rule for many-server systems with a realistic arrival stream model.•SEElab data used to fit realistic arrival stream model.•Arrival stream incorporates overdispersion, time-varying rate and temporal correlation.•Squareroot staffing principle based on infinite-server proxy tweaked with heuristic.•Performance tests for various examples with and without abandonments.

Title: Neural entity linking: A survey of models based on deep learning Abstract: This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in natural language processing. Its goal is to systemize design features of neural entity linking systems and compare their performance to the remarkable classic methods on common benchmarks. This work distills a generic architecture of a neural EL system and discusses its components, such as candidate generation, mention-context encoding, and entity ranking, summarizing prominent methods for each of them. The vast variety of modifications of this general architecture are grouped by several common themes: joint entity mention detection and disambiguation, models for global linking, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches. Since many neural models take advantage of entity and mention/context embeddings to represent their meaning, this work also overviews prominent entity embedding techniques. Finally, the survey touches on applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models based on the Transformer architecture.

Title: Small rainbow cliques in randomly perturbed dense graphs Abstract: For two graphs G and H, write G rbw -& RARR; H if G has the property that every proper colouring of its edges yields a rainbow copy of H. We study the thresholds for such so-called anti-Ramsey properties in randomly perturbed dense graphs, which are unions of the form G boolean OR G(n, p), where G is an n-vertex graph with edge-density at least d > 0, and d is independent of n. In a companion paper, we proved that the threshold for the property G boolean OR G(n, p) -& RARR;rbw Kl is n-1/m2(K left ceiling l/2 right ceiling ), whenever l & GE;9. For smaller l, the thresholds behave more erratically, and for 4 & LE; l & LE; 7 they deviate downwards significantly from the aforementioned aesthetic form capturing the thresholds for large cliques. In particular, we show that the thresholds for l & ISIN; {4, 5, 7} are n-5/4, n-1, and n-7/15, respectively. For l & ISIN; {6, 8} we determine the threshold up to a (1 + o(1))-factor in the exponent: they are n-(2/3+o(1)) and n-(2/5+o(1)), respectively. For l = 3, the threshold is n-2; this follows from a more general result about odd cycles in our companion paper. (c) 2021 Elsevier Ltd. All rights reserved.

Title: Bayesian Optimisation vs. Input Uncertainty Reduction Abstract: imulators often require calibration inputs estimated from real-world data, and the estimate can significantly affect simulation output. Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution. One remedy is to search for the solution that has the best performance on average over the uncertain range of inputs yielding an optimal compromise solution. We consider the more general setting where a user may choose between either running simulations or querying an external data source, improving the input estimate and enabling the search for a more targeted, less compromised solution. We explicitly examine the trade-off between simulation and real data collection to find the optimal solution of the simulator with the true inputs. Using a value of information procedure, we propose a novel unified simulation optimisation procedure called Bayesian Information Collection and Optimisation that, in each iteration, automatically determines which of the two actions (running simulations or data collection) is more beneficial. We theoretically prove convergence in the infinite budget limit and perform numerical experiments demonstrating that the proposed algorithm is able to automatically determine an appropriate balance between optimisation and data collection.

Title: A multimodal approach for multi-label movie genre classification Abstract: Movie genre classification is a challenging task that has increasingly attracted the attention of researchers. The number of movie consumers interested in taking advantage of automatic movie genre classification is overgrowing, thanks to media streaming service providers’ popularization. In this paper, we addressed the multi-label classification of movie genres in a multimodal way. To this end, we created a dataset composed of trailer video clips, subtitles, synopses, and movie posters from 152,622 movie titles of the Movie Database (TMDb). Such a large dataset was carefully curated, organized, and made available as a contribution of this work. We labeled each movie of the dataset according to a set of eighteen genre labels. In the experimental evaluation performed in this paper, we computed different kinds of descriptors, such as Mel Frequency Cepstral Coefficients (MFCCs), Statistical Spectrum Descriptor (SSD), Local Binary Pattern (LBP) from spectrograms, Long-Short Term Memory (LSTM), and Convolutional Neural Networks (CNN). With these descriptors, we trained different monolithic classifiers using BinaryRelevance and ML-kNN techniques. Besides, we also explored the combination of classifiers/features using a late fusion strategy. The fusion of a LSTM trained on synopses and another LSTM trained on the movie subtitles provided our best results in F-Score (0.674) and AUC-PR (0.725) metrics. These results corroborate the existence of complementarity among classifiers trained on different sources of information in this field of application. As far as we know, this is the most comprehensive study developed in terms of diversity of multimedia sources of information to perform movie genre classification.

Title: An efficient algorithm to compute the X-ray transform Abstract: We propose a new algorithm to compute the X-ray transform of an image represented by unit (pixel/voxel) basis functions. The fundamental task is equivalently calculating the intersection lengths of the ray with associated units. For the given ray, we derive the sufficient and necessary condition for non-vanishing intersectability. By this condition, we can distinguish the units that produce valid intersections with the ray. Only for those units, we calculate the intersection lengths by the obtained analytic formula. The proposed algorithm is adapted to various two-dimensional (2D)/three-dimensional (3D) scanning geometries, and its several issues are also discussed, including the intrinsic ambiguity, flexibility, computational cost and parallelization. The proposed method is fast and easy to implement, more complete and flexible than the existing alternatives with respect to different scanning geometries and different basis functions. Finally, we validate the correctness of the algorithm.

Title: A recurrent neural network architecture to model physical activity energy expenditure in older people Abstract: Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.

Title: INTERSECTIONS OF RANDOM SETS Abstract: We consider a variant of a classical coverage process, the Boolean model in R-d. Previous efforts have focused on convergence of the unoccupied region containing the origin to a well-studied limit C. We study the intersection of sets centered at points of a Poisson point process confined to the unit ball. Using a coupling between the intersection model and the original Boolean model, we show that the scaled intersection converges weakly to the same limit C. Along the way, we present some tools for studying statistics of a class of intersection models.

Title: Federated Learning in Vehicular Networks Abstract: Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data trans-mission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.

Title: How Twitter data sampling biases U.S. voter behavior characterizations Abstract: Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.

Title: An overview on integrated localization and communication towards 6G Abstract: while the fifth generation (5G) cellular system is being deployed worldwide, researchers have started the investigation of the sixth generation (6G) mobile communication networks. Although the essential requirements and key usage scenarios of 6G are yet to be defined, it is believed that 6G should be able to provide intelligent and ubiquitous wireless connectivity with Terabits per second (Tbps) data rate and sub-millisecond (sub-ms) latency over three-dimensional (3D) network coverage. To achieve such goals, acquiring accurate location information of the mobile terminals is becoming extremely useful, not only for location-based services but also for improving wireless communication performance in various ways such as channel estimation, beam alignment, medium access control, routing, and network optimization. On the other hand, the advancement of communication technologies also brings new opportunities to greatly improve the localization performance, as exemplified by the anticipated centimeter-level localization accuracy in 6G by extremely large-scale multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies. In this regard, a unified study on integrated localization and communication (ILAC) is necessary to unlock the full potential of wireless networks for dual purposes. While there are extensive studies on wireless localization or communications separately, the research on ILAC is still in its infancy. Therefore, this article aims to give a tutorial overview on ILAC towards 6G wireless networks. After a holistic survey on wireless localization basics, we present the state-of-the-art results on how wireless localization and communication inter-play with each other in various network layers, together with the main architectures and techniques for localization and communication co-design in current two-dimensional (2D) and future 3D networks with aerial-ground integration. Finally, we outline some promising future research directions for ILAC.

Title: Deep Learning in Target Space Abstract: Deep learning uses neural networks which are parameterised by their weights. The neural networks are usually trained by tuning the weights to directly minimise a given loss function. In this paper we propose to re-parameterise the weights into targets for the firing strengths of the individual nodes in the network. Given a set of targets, it is possible to calculate the weights which make the firing strengths best meet those targets. It is argued that using targets for training addresses the problem of exploding gradients, by a process which we call cascade untangling, and makes the loss-function surface smoother to traverse, and so leads to easier, faster training, and also potentially better generalisation, of the neural network. It also allows for easier learning of deeper and recurrent network structures. The necessary conversion of targets to weights comes at an extra computational expense, which is in many cases manageable. Learning in target space can be combined with existing neural-network optimisers, for extra gain. Experimental results show the speed of using target space, and examples of improved generalisation, for fully-connected networks and convolutional networks, and the ability to recall and process long time sequences and perform natural language processing with recurrent networks.

Title: Stochastic approximation cut algorithm for inference in modularized Bayesian models Abstract: Bayesian modelling enables us to accommodate complex forms of data and make a comprehensive inference, but the effect of partial misspecification of the model is a concern. One approach in this setting is to modularize the model and prevent feedback from suspect modules, using a cut model. After observing data, this leads to the cut distribution which normally does not have a closed form. Previous studies have proposed algorithms to sample from this distribution, but these algorithms have unclear theoretical convergence properties. To address this, we propose a new algorithm called the stochastic approximation cut (SACut) algorithm as an alternative. The algorithm is divided into two parallel chains. The main chain targets an approximation to the cut distribution; the auxiliary chain is used to form an adaptive proposal distribution for the main chain. We prove convergence of the samples drawn by the proposed algorithm and present the exact limit. Although SACut is biased, since the main chain does not target the exact cut distribution, we prove this bias can be reduced geometrically by increasing a user-chosen tuning parameter. In addition, parallel computing can be easily adopted for SACut, which greatly reduces computation time.

Title: Finite-Time H ∞ Estimator Design for Switched Discrete-Time Delayed Neural Networks With Event-Triggered Strategy Abstract: This article is concerned with the event-triggered finite-time $H_{\infty }$ estimator design for a class of discrete-time switched neural networks (SNNs) with mixed time delays and packet dropouts. To further reduce the data transmission, both the measured information of system outputs and switching signal of the SNNs are on...

Title: On Iterative Proportional Updating: Limitations and Improvements for General Population Synthesis Abstract: Population synthesis is the foundation of the agent-based social simulation. Current approaches mostly consider basic population and households, rather than other social organizations. This article starts with a theoretical analysis of the iterative proportional updating (IPU) algorithm, a representative method in this field, and then gives an extension to consider more social organization types. ...

Title: IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding Abstract: In order to reach human performance on complex visual tasks, artificial systems need to incorporate a significant amount of understanding of the world in terms of macroscopic objects, movements, forces, etc. Inspired by work on intuitive physics in infants, we propose an evaluation benchmark which diagnoses how much a given system understands about physics by testing whether it can tell apart well matched videos of possible versus impossible events constructed with a game engine. The test requires systems to compute a physical plausibility score over an entire video. To prevent perceptual biases, the dataset is made of pixel matched quadruplets of videos, enforcing systems to focus on high level temporal dependencies between frames rather than pixel-level details. We then describe two Deep Neural Networks systems aimed at learning intuitive physics in an unsupervised way, using only physically possible videos. The systems are trained with a future semantic mask prediction objective and tested on the possible versus impossible discrimination task. The analysis of their results compared to human data gives novel insights in the potentials and limitations of next frame prediction architectures.

Title: TapLab: A Fast Framework for Semantic Video Segmentation Tapping Into Compressed-Domain Knowledge Abstract: Real-time semantic video segmentation is a challenging task due to the strict requirements of inference speed. Recent approaches mainly devote great efforts to reducing the model size for high efficiency. In this paper, we rethink this problem from a different viewpoint: using knowledge contained in compressed videos. We propose a simple and effective framework, dubbed TapLab, to tap into resources from the compressed domain. Specifically, we design a fast feature warping module using motion vectors for acceleration. To reduce the noise introduced by motion vectors, we design a residual-guided correction module and a residual-guided frame selection module using residuals. TapLab significantly reduces redundant computations of the state-of-the-art fast semantic image segmentation models, running 3 to 10 times faster with controllable accuracy degradation. The experimental results show that TapLab achieves 70.6 percent mIoU on the Cityscapes dataset at 99.8 FPS with a single GPU card for the 1024×2048 videos. A high-speed version even reaches the speed of 160+ FPS. Code will be available soon at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sixkplus/TapLab</uri> .

Title: Progressive Multistage Learning for Discriminative Tracking Abstract: Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multistage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intraclass variations while maintaining interclass separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.

Title: Distributionally Robust Chance Constrained Data-Enabled Predictive Control Abstract: In this article we study the problem of finite-time constrained optimal control of unknown stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive control algorithm—albeit typically having access to a model. We propose a novel distributionally robust data-enabled predictive control (DeePC) algorithm which uses noise-corrupted input/output data to predict future trajectories and compute optimal control inputs while satisfying output chance constraints. The algorithm is based on 1) a nonparametric representation of the subspace spanning the system behavior, where past trajectories are sorted in Page or Hankel matrices; and 2) a distributionally robust optimization formulation which gives rise to strong probabilistic performance guarantees. We show that for certain objective functions, DeePC exhibits strong out-of-sample performance, and at the same time respects constraints with high probability. The algorithm provides an end-to-end approach to control design for unknown stochastic LTI systems. We illustrate the closed-loop performance of the DeePC in an aerial robotics case study.

Title: Proximity in concave integer quadratic programming Abstract: A classic result by Cook, Gerards, Schrijver, and Tardos provides an upper bound of $$n \Delta $$ on the proximity of optimal solutions of an Integer Linear Programming problem and its standard linear relaxation. In this bound, n is the number of variables and $$\Delta $$ denotes the maximum of the absolute values of the subdeterminants of the constraint matrix. Hochbaum and Shanthikumar, and Werman and Magagnosc showed that the same upper bound is valid if a more general convex function is minimized, instead of a linear function. No proximity result of this type is known when the objective function is nonconvex. In fact, if we minimize a concave quadratic, no upper bound can be given as a function of n and $$\Delta $$ . Our key observation is that, in this setting, proximity phenomena still occur, but only if we consider also approximate solutions instead of optimal solutions only. In our main result we provide upper bounds on the distance between approximate (resp., optimal) solutions to a Concave Integer Quadratic Programming problem and optimal (resp., approximate) solutions of its continuous relaxation. Our bounds are functions of $$n, \Delta $$ , and a parameter $$\epsilon $$ that controls the quality of the approximation. Furthermore, we discuss how far from optimal are our proximity bounds.

Title: SL(n) Contravariant Vector Valuations Abstract: All $$\mathrm{SL}(n)$$ contravariant vector valuations on polytopes in $${\mathbb {R}}^n$$ are completely classified without any additional assumptions. The facet vector is defined. It turns out to be the unique class of such valuations for $$n\ge 3$$ . In dimension two, the classification corresponds to the known case of $$SL (2)$$ covariant valuations.

Title: Multivariate volume, Ehrhart, and h⁎-polynomials of polytropes Abstract: The univariate Ehrhart and h⁎-polynomials of lattice polytopes have been widely studied. We describe methods from toric geometry for computing multivariate versions of volume, Ehrhart and h⁎-polynomials of lattice polytropes, which are both tropically and classically convex, and are also known as alcoved polytopes of type A. These algorithms are applied to all polytropes of dimensions 2,3 and 4, yielding a large class of integer polynomials. We give a complete combinatorial description of the coefficients of volume polynomials of 3-dimensional polytropes in terms of regular central subdivisions of the fundamental polytope. Finally, we provide a partial characterization of the analogous coefficients in dimension 4.

Title: An extremal problem motivated by triangle-free strongly regular graphs Abstract: We introduce the following combinatorial problem. Let G be a triangle-free regular graph with edge density ρ. (In this paper all densities are normalized by n,n22 etc. rather than by n−1,(n2),…) What is the minimum value a(ρ) for which there always exist two non-adjacent vertices such that the density of their common neighbourhood is ≤a(ρ)? We prove a variety of upper bounds on the function a(ρ) that are tight for the values ρ=2/5,5/16,3/10,11/50, with C5, Clebsch, Petersen and Higman-Sims being respective extremal configurations. Our proofs are entirely combinatorial and are largely based on counting densities in the style of flag algebras. For small values of ρ, our bound attaches a combinatorial meaning to so-called Krein conditions that might be interesting in its own right. We also prove that for any ϵ>0 there are only finitely many values of ρ with a(ρ)≥ϵ but this finiteness result is somewhat purely existential (the bound is double exponential in 1/ϵ).

Title: Nucleus Decomposition in Probabilistic Graphs: Hardness and Algorithms Abstract: Finding dense components in graphs is of great importance in analysing the structure of networks. Popular frameworks for discovering dense subgraphs are core and truss decompositions. Recently, Sarıyüce et al. introduced nucleus decomposition, which uses <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> -cliques contained in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> -eliques, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s &gt; r$</tex> , as the basis for defining dense subgraphs. Nucleus decomposition can reveal interesting subgraphs that can be missed by core and truss decompositions. In this paper, we present nucleus decomposition in probabilistic graphs. The major questions we address are: How to define meaningfully nucleus decomposition in probabilistic graphs? How hard is computing nucleus decomposition in probabilistic graphs? Can we devise efficient algorithms for exact or approximate nucleus decomposition in large graphs? We present three natural definitions of nucleus decomposition in probabilistic graphs: local, global, and weakly-global. We show that the local version is in PTIME, whereas global and weakly-global are #P-hard and NP-hard, respectively. We present an efficient and exact dynamic programming approach for the local case. Further, we present statistical approximations that can scale to bigger datasets without much loss of accuracy. For global and weakly-global decompositions we complement our intractability results by proposing efficient algorithms that give approximate solutions based on search space pruning and Monte-Carlo sampling. Extensive experiments show the scalability and efficiency of our algorithms. Compared to probabilistic core and truss decompositions, nucleus decomposition significantly outperforms in terms of density and clustering metrics.

Title: Tight relative t-designs on two shells in hypercubes, and Hahn and Hermite polynomials Abstract: Relative t-designs in the n-dimensional hypercube Q(n) are equivalent to weighted regular t-wise balanced designs, which generalize combinatorial t-(n, k, lambda) designs by allowing multiple block sizes as well as weights. Partly motivated by the recent study on tight Euclidean t-designs on two concentric spheres, in this paper we discuss tight relative t-designs in Q(n) supported on two shells. We show under a mild condition that such a relative t-design induces the structure of a coherent configuration with two fibers. Moreover, from this structure we deduce that a polynomial from the family of the Hahn hypergeometric orthogonal polynomials must have only integral simple zeros. The Terwilliger algebra is the main tool to establish these results. By explicitly evaluating the behavior of the zeros of the Hahn polynomials when they degenerate to the Hermite polynomials under an appropriate limit process, we prove a theorem which gives a partial evidence that the non-trivial tight relative t-designs in Q(n) supported on two shells are rare for large t.

Title: A Multi-Modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store Abstract: Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information, spreading malware, or simply to increase their advertisement revenue. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation as app icons and descriptions can be quit...

Title: Canonical Conditions for <italic>K</italic>/2 Degrees of Freedom Abstract: We present a condition for 1/2 degree of freedom for each user in constant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -user single-antenna interference channels. This condition is sufficient for all and necessary for almost all channel matrices. Moreover, it applies to all channel topologies, i.e., to fully-connected channels as well as channels that have individual links absent, reflected by corresponding zeros in the channel matrix. Moreover, it captures the essence of interference alignment by virtue of being expressed in terms of a generic injectivity condition that guarantees separability of signal and interference. Finally, we provide codebook constructions achieving 1/2 degree of freedom for each user for all channel matrices satisfying the condition we identified.

Title: Easy and efficient preconditioning of the isogeometric mass matrix Abstract: This paper deals with the fast solution of linear systems associated with the mass matrix, in the context of isogeometric analysis. We propose a preconditioner that is both efficient and easy to implement, based on a diagonal-scaled Kronecker product of univariate parametric mass matrices. Its application is faster than a matrix–vector product involving the mass matrix itself. We prove that the condition number of the preconditioned matrix converges to 1 as the mesh size is reduced, that is, the preconditioner is asymptotically equivalent to the exact inverse. Moreover, we give numerical evidence of its good behaviour with respect to the spline degree and the (possibly singular) geometry parametrization. We also extend the preconditioner to the multipatch case through an Additive Schwarz method.

Title: Shallow Neural Hawkes: Non-parametric kernel estimation for Hawkes processes Abstract: The Multi-dimensional Hawkes Process (MHP) is a class of self and mutually exciting point processes that find many applications–from predicting earthquakes to modelling order books in high-frequency trading. This paper makes two significant contributions; we first find an unbiased estimator for the gradient of the Hawkes process’s log-likelihood estimator. The estimator enables the efficient implementation of the stochastic gradient descent method for the maximum likelihood estimation. The second contribution is that we propose a specific neural network for the non-parametric estimation of the underlying kernels of the MHP. We evaluate the proposed model on synthetic and natural datasets and find the method has comparable or better performance than existing estimation methods. The use of neural networks for modelling the excitation kernel ensures that we do not compromise on the Hawkes model’s interpretability. At the same time, the proposed algorithm has the flexibility to estimate any non-standard Hawkes excitation kernel.

Title: Time Dependent Biased Random Walks Abstract: AbstractWe study the biased random walk where at each step of a random walk a “controller” can, with a certain small probability, move the walk to an arbitrary neighbour. This model was introduced by Azar et al. [STOC’1992]; we extend their work to the time dependent setting and consider cover times of this walk. We obtain new bounds on the cover and hitting times. Azar et al. conjectured that the controller can increase the stationary probability of a vertex from p to p1-ε; while this conjecture is not true in full generality, we propose a best-possible amended version of this conjecture and confirm it for a broad class of graphs. We also consider the problem of computing an optimal strategy for the controller to minimise the cover time and show that for directed graphs determining the cover time is PSPACE-complete.

Title: CAN'T TOUCH THIS: UNCONDITIONAL TAMPER EVIDENCE FROM SHORT KEYS Abstract: Storing data on an external server with information-theoretic security, while using a key shorter than the data itself, is impossible. As an alternative, we propose a scheme that achieves information-theoretically secure tamper evidence: The server is able to obtain information about the stored data, but not while staying undetected. Moreover, the client only needs to remember a key whose length is much shorter than the data. We provide a security proof for our scheme, based on an entropic uncertainty relation, similar to QKD proofs. Our scheme works if Alice is able to (reversibly) randomise the message to almost-uniformity with only a short key. By constructing an explicit attack we show that short-key unconditional tamper evidence cannot be achieved without this randomisability.

Title: DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions Abstract: Robust traffic sign detection and recognition (TSDR) is of paramount importance for the successful realization of autonomous vehicle technology. The importance of this task has led to vast amount of research efforts and many promising methods have been proposed in the existing literature. However, most of these methods have been evaluated on clean and challenge-free datasets and overlooked the performance deterioration associated with different challenging conditions (CCs) that obscure the traffic-sign images captured in the wild. In this paper, we look at the TSDR problem under CCs and focus on the performance degradation associated with them. To this end, we propose a Convolutional Neural Network (CNN) based prior enhancement focused TSDR framework. Our modular approach consists of a CNN-based challenge classifier, Enhance-Net–an encoder-decoder CNN architecture for image enhancement, and two separate CNN architectures for sign-detection and classification. We propose a novel training pipeline for Enhance-Net that focuses on the enhancement of the traffic sign regions (instead of the whole image) in the challenging images subject to their accurate detection. We used CURE-TSD dataset consisting of traffic videos captured under different CCs to evaluate the efficacy of our approach. We experimentally show that our method obtains an overall precision and recall of 91.1% and 70.71% that is 7.58% and 35.90% improvement in precision and recall, respectively, compared to the current benchmark. Furthermore, we compare our approach with different CNN-based TSDR methods and show that our approach outperforms them by a large margin.

Title: Hamilton Cycles In The Semi-Random Graph Process Abstract: The semi-random graph process is a single player game in which the player is initially presented an empty graph on n vertices. In each round, a vertex u is presented to the player independently and uniformly at random. The player then adaptively selects a vertex v, and adds the edge uv to the graph. For a fixed monotone graph property, the objective of the player is to force the graph to satisfy this property with high probability in as few rounds as possible. We focus on the problem of constructing a Hamilton cycle in as few rounds as possible. In particular, we present a novel strategy for the player which achieves a Hamiltonian cycle in c*n rounds, where the value of c* is the result of a high dimen-sional optimization problem. Numerical computations indicate that c* < 2.61135. This improves upon the previously best known upper bound of 3 n rounds. We also show that the previously best lower bound of (ln 2 + ln(1 + ln 2) + o(1)) n is not tight. (c) 2021 Elsevier Ltd. All rights reserved.

Title: Height Estimation From Single Aerial Images Using a Deep Ordinal Regression Network Abstract: Understanding the 3-D geometric structure of the Earth's surface has been an active research topic in photogrammetry and remote sensing community for decades, serving as an essential building block for various applications such as 3-D digital city modeling, change detection, and city management. Previous research studies have extensively studied the problem of height estimation from aerial images based on stereo or multiview image matching. These methods require two or more images from different perspectives to reconstruct 3-D coordinates with camera information provided. In this letter, we deal with the ambiguous and unsolved problem of height estimation from a single aerial image. Driven by the great success of deep learning, especially deep convolutional neural networks (CNNs), some research studies have proposed to estimate height information from a single aerial image by training a deep CNN model with large-scale annotated data sets. These methods treat height estimation as a regression problem and directly use an encoder-decoder network to regress the height values. In this letter, we propose to divide height values into spacing-increasing intervals and transform the regression problem into an ordinal regression problem, using an ordinal loss for network training. To enable multiscale feature extraction, we further incorporate an Atrous Spatial Pyramid Pooling (ASPP) module to extract features from multiple dilated convolution layers. After that, a postprocessing technique is designed to transform the predicted height map of each patch into a seamless height map. Finally, we conduct extensive experiments on International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam data sets. Experimental results demonstrate significantly better performance of our method compared to state-of-the-art methods.

Title: Gradient-bounded dynamic programming for submodular and concave extensible value functions with probabilistic performance guarantees Abstract: We consider stochastic dynamic programming problems with high-dimensional, discrete state-spaces and finite, discrete-time horizons that prohibit direct computation of the value function from a given Bellman equation for all states and time steps due to the “curse of dimensionality”. For the case where the value function of the dynamic program is concave extensible and submodular in its state-space, we present a new algorithm that computes deterministic upper and stochastic lower bounds of the value function in the realm of dual dynamic programming. We show that the proposed algorithm terminates after a finite number of iterations. Furthermore, we derive probabilistic guarantees on the value accumulated under the associated policy for a single realisation of the dynamic program and for the expectation of this value. Finally, we demonstrate the efficacy of our approach on a high-dimensional numerical example from delivery slot pricing in attended home delivery.

Title: On the number of minimal codewords in codes generated by the adjacency matrix of a graph Abstract: Minimal codewords have applications in decoding linear codes and in cryptography. We study the number of minimal codewords in binary linear codes that arise by appending a unit matrix to the adjacency matrix of a graph. (c) 2021 Elsevier B.V. All rights reserved. <comment>Superscript/Subscript Available</comment

Title: An integer program and new lower bounds for computing the strong rainbow connection numbers of graphs Abstract: We present an integer programming model to compute the strong rainbow connection number, src(G), of any simple graph G. We introduce several enhancements to the proposed model, including a fast heuristic, and a variable elimination scheme. Moreover, we present a novel lower bound for src(G) which may be of independent research interest. We solve the integer program both directly and using an alternative method based on iterative lower bound improvement, the latter of which we show to be highly effective in practice. To our knowledge, these are the first computational methods for the strong rainbow connection problem. We demonstrate the efficacy of our methods by computing the strong rainbow connection numbers of graphs containing up to 379 vertices.

Title: Force-Based Algorithm for Motion Planning of Large Agent Abstract: This article presents a distributed, efficient, scalable, and real-time motion planning algorithm for a large group of agents moving in 2-D or 3-D spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: 1) collision avo...

Title: Discovering Parametric Activation Functions Abstract: Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks. However, the benefits of novel activation functions have been inconsistent and task dependent, and therefore the rectified linear unit (ReLU) is still the most commonly used. This paper proposes a technique for customizing activation functions automatically, resulting in reliable improvements in performance. Evolutionary search is used to discover the general form of the function, and gradient descent to optimize its parameters for different parts of the network and over the learning process. Experiments with four different neural network architectures on the CIFAR-10 and CIFAR-100 image classification datasets show that this approach is effective. It discovers both general activation functions and specialized functions for different architectures, consistently improving accuracy over ReLU and other activation functions by significant margins. The approach can therefore be used as an automated optimization step in applying deep learning to new tasks.

Title: Efficient semi-external depth-first search Abstract: •A novel and efficient semi-external DFS algorithm EP-DFS is presented.•EP-DFS requires simpler CPU calculation and less memory space.•A novel index is devised to reduce the disk random accesses.•Extensive experiments are conducted on both real and synthetic datasets.

Title: mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks Abstract: Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images. To improve adversarial image generation for DNNs, we develop a novel method, called mFI-PSO, which utilizes a Manifold-based First-order Influence measure for vulnerable image and pixel selection and the Particle Swarm Optimization for various objective functions. Our mFI-PSO can thus effectively design adversarial images with flexible, customized options on the number of perturbed pixels, the misclassification probability, and the targeted incorrect class. Experiments demonstrate the flexibility and effectiveness of our mFI-PSO in adversarial attacks and its appealing advantages over some popular methods.

Title: Joint Performance Analysis of Ages of Information in a Multi-Source Pushout Server Abstract: Age of information (AoI) has been widely accepted as a measure quantifying freshness of status information in real-time status update systems. In many of such systems, multiple sources share a limited network resource and therefore the AoIs defined for the individual sources should be correlated with each other. However, there are not found any results in the literature studying the correlation of...

Title: Computed Origami Tomography Abstract: In this paper, we provide assembly instructions for an easy-to-build experimental setup in order to gain practical experience with tomography. As such, this paper can be seen as a complementary work to excellent undergraduate-level mathematical textbooks concerned with the basic mathematical principles of tomography. Since the setup uses light for tomographic imaging, the objects investigated need to be light transparent, such as origami figures. Should the reader want to experiment with computational tomographic reconstructions without assembling the device, we provide a database of several objects together with their tomographic measurements and publicly available software. Moreover, recent advances in cryo-imaging have enabled three-dimensional high-resolution visualization of single particles such as, for instance, viruses. To exemplify, and demonstrate, single particle cryo-electron microscopy, we provide an advanced assembly that we use to generate data simulating a cryo-recording. We also discuss some of the major practical difficulties in reconstructing particles from cryo-microscopic data.

Title: A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization Abstract: The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of $n$ local cost functions by using local information exchange is considered. This problem is an important component of many machine learning techniques with data parallelism, such as deep learning and federated learning. We propose a distributed primal-dual stochastic gradient descent (...

Title: Lexicon-Based Sentiment Convolutional Neural Networks for Online Review Analysis Abstract: With the growing availability and popularity of sentiment-rich resources like blogs and online reviews, new opportunities and challenges have emerged regarding the identification, extraction, and organization of sentiments from user-generated documents or sentences. Recently, many studies have exploited lexicon-based methods or supervised learning algorithms to conduct sentiment analysis tasks separately; however, the former approaches ignore contextual information of sentences and the latter ones do not take sentiment information embedded in sentiment words into consideration. To tackle these limitations, we propose a new model named Sentiment Convolutional Neural Network (SentiCNN) to analyze the sentiments of sentences with both contextual and sentiment information of sentiment words, in which, contextual information is captured from word embeddings and sentiment information is identified using existing lexicons. We incorporate a Highway Network into our model to adaptively combine sentiment and contextual information from sentences by strengthening the connection between features of both sentences and their sentiment words. Furthermore, we propose three lexicon-based attention mechanisms (LBAMs) for our SentiCNN model to find the most important indicators of sentiments and make predictions more effectively. Experiments over two well-known datasets indicate that sentiment words, the Highway Network, and LBAMs contribute to sentiment analysis.

Title: Differentially private partition selection. Abstract: Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation. To make such a query differentially private, adding noise to each aggregation is not enough: we also need to make sure that the set of partitions released is also differentially private. This problem is not new, and it was recently formally introduced as differentially private set union. In this work, we continue this area of study, and focus on the common setting where each user is associated with a single partition. In this setting, we propose a simple, optimal differentially private mechanism that maximizes the number of released partitions. We discuss implementation considerations, as well as the possible extension of this approach to the setting where each user contributes to a fixed, small number of partitions.

Title: Some exact results for generalized Turan problems Abstract: Fix a k-chromatic graph F. In this paper we consider the question to determine for which graphs H does the Turan graph Tk-1(n) have the maximum number of copies of H among all n-vertex F-free graphs (for n large enough). We say that such a graph H is F-Turan-good. In addition to some general results, we give (among others) the following concrete results:& nbsp;(i) For every complete multipartite graph H, there is k large enough such that H is K-k-Turan-good.& nbsp;(ii) The path P-3 is F-Turan-good for F with chi(F) >= 4.& nbsp;(iii) The path P-4 and cycle C-4 are C5-Turan-good.& nbsp;(iv) The cycle C-4 is F-2-Turan-good where F-2 is the graph of two triangles sharing exactly one vertex. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.

Title: THE WAVE BREAKING FOR WHITHAM-TYPE EQUATIONS REVISITED Abstract: We prove wave breaking (shock formation) for some Whitham-type equations which include the Burgers???Hilbert equation, the fractional Korteweg???de Vries equation, and the classical Whitham equation. The result seems to be new for the Burgers???Hilbert equation and has been proven independently in (Yang, SIAM J. Math. Anal., 53 (2021), pp. 5756???5802). In all cases we provide simpler proofs than the known ones.

Title: The Impact of Global Structural Information in Graph Neural Networks Applications Abstract: Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours. A known limitation of GNNs is that, as the number of layers increases, information gets smoothed and squashed and node embeddings become indistinguishable, negatively affecting performance. Therefore, practical GNN models employ few layers and only leverage the graph structure in terms of limited, small neighbourhoods around each node. Inevitably, practical GNNs do not capture information depending on the global structure of the graph. While there have been several works studying the limitations and expressivity of GNNs, the question of whether practical applications on graph structured data require global structural knowledge or not remains unanswered. In this work, we empirically address this question by giving access to global information to several GNN models, and observing the impact it has on downstream performance. Our results show that global information can in fact provide significant benefits for common graph-related tasks. We further identify a novel regularization strategy that leads to an average accuracy improvement of more than 5% on all considered tasks.

Title: On the maximum cardinality cut problem in proper interval graphs and related graph classes Abstract: Although it has been claimed in two different papers that the maximum cardinality cut problem is polynomial-time solvable for proper interval graphs, both of them turned out to be erroneous. In this work we consider the parameterized complexity of this problem. We show that the maximum cardinality cut problem in proper/unit interval graphs is FPT when parameterized by the maximum number of non-empty bubbles in a column of its bubble model. We then generalize this result to a more general graph class by defining new parameters related to the well-known clique-width parameter. Specifically, we define an (alpha, beta, delta)-clique-width decomposition of a graph as a clique-width decomposition in which at each step the following invariant is preserved: after discarding at most delta labels, a) every label consists of at most beta sets of twin vertices, and b) all the labels together induce a graph with independence number at most alpha. We show that for every two constants alpha, delta > 0 the problem is FPT when parameterized by beta plus the smallest width of an (alpha, beta, delta)-clique-width decomposition. (C) 2021 Elsevier B.V. All rights reserved.

Title: Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself? Abstract: In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

Title: Local Stackelberg Equilibrium Seeking in Generalized Aggregative Games Abstract: We propose a two-layer, semidecentralized algorithm to compute a local solution to the Stackelberg equilibrium problem in aggregative games with coupling constraints. Specifically, we focus on a single-leader, multiple-follower problem, and after equivalently recasting the Stackelberg game as a mathematical program with complementarity constraints (MPCC), we iteratively convexify a regularized version of the MPCC as the inner problem, whose solution generates a sequence of feasible descent directions for the original MPCC. Thus, by pursuing a descent direction at every outer iteration, we establish convergence to a local Stackelberg equilibrium. Finally, the proposed algorithm is tested on a numerical case study, a hierarchical instance of the charging coordination problem of plug-in electric vehicles.

Title: Graph Neural Network Encoding for Community Detection in Attribute Networks Abstract: In this article, we first propose a graph neural network encoding method for the multiobjective evolutionary algorithm (MOEA) to handle the community detection problem in complex attribute networks. In the graph neural network encoding method, each edge in an attribute network is associated with a continuous variable. Through nonlinear transformation, a continuous valued vector (i.e., a concatenation of the continuous variables associated with the edges) is transferred to a discrete valued community grouping solution. Further, two objective functions for the single-attribute and multiattribute network are proposed to evaluate the attribute homogeneity of the nodes in communities, respectively. Based on the new encoding method and the two objectives, a MOEA based upon NSGA-II, called continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables. Experimental results on single-attribute and multiattribute networks with different types show that the developed algorithm performs significantly better than some well-known evolutionary- and nonevolutionary-based algorithms. The fitness landscape analysis verifies that the transformed community detection problems have smoother landscapes than those of the original problems, which justifies the effectiveness of the proposed graph neural network encoding method.

Title: Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples Abstract: In this article, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy. On the one hand, current OOD detection approaches usually do not directly fix the SoftMax loss drawbacks, but rather build techniques to circumvent it. Unfortunately, those methods usually produce undesired side effects (e.g., classification accuracy drop, additional hyperparameters, slower inferences, and collecting extra data). On the other hand, we propose replacing SoftMax loss with a novel loss function that does not suffer from the mentioned weaknesses. The proposed IsoMax loss is isotropic (exclusively distance-based) and provides high entropy posterior probability distributions. Replacing the SoftMax loss by IsoMax loss requires no model or training changes. Additionally, the models trained with IsoMax loss produce as fast and energy-efficient inferences as those trained using SoftMax loss. Moreover, no classification accuracy drop is observed. The proposed method does not rely on outlier/background data, hyperparameter tuning, temperature calibration, feature extraction, metric learning, adversarial training, ensemble procedures, or generative models. Our experiments showed that IsoMax loss works as a seamless SoftMax loss drop-in replacement that significantly improves neural networks’ OOD detection performance. Hence, it may be used as a baseline OOD detection approach to be combined with current or future OOD detection techniques to achieve even higher results.

Title: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. Abstract: Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and the projection methods in previous works fail to establish the relationships between the local point sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly contains voxel-graph module and sparse-to-dense regression module to achieve comparable 3D detection tasks from raw LIDAR data. Specifically, SVGA-Net constructs the local complete graph within each divided 3D spherical voxel and global KNN graph through all voxels. The local and global graphs serve as the attention mechanism to enhance the extracted features. In addition, the novel sparse-to-dense regression module enhances the 3D box estimation accuracy through feature maps aggregation at different levels. Experiments on KITTI detection benchmark and Waymo Open dataset demonstrate the efficiency of extending the graph representation to 3D object detection and the proposed SVGA-Net can achieve decent detection accuracy.

Title: An Autonomous Path Planning Method for Unmanned Aerial Vehicle Based on a Tangent Intersection and Target Guidance Strategy Abstract: Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles and reach the target efficiently. To generate high-quality paths without obstacle collision for UAVs, this article proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT). Guided by a target, the elliptic tangent graph method is used to generate two sub-path...

Title: MIM: A deep mixed residual method for solving high-order partial differential equations Abstract: In recent years, a significant amount of attention has been paid to solve partial differential equations (PDEs) by deep learning. For example, deep Galerkin method (DGM) uses the PDE residual in the least-squares sense as the loss function and a deep neural network (DNN) to approximate the PDE solution. In this work, we propose a deep mixed residual method (MIM) to solve PDEs with high-order derivatives. Notable examples include Poisson equation, Monge-Ampére equation, biharmonic equation, and Korteweg-de Vries equation. In MIM, we first rewrite a high-order PDE into a first-order system, very much in the same spirit as local discontinuous Galerkin method and mixed finite element method in classical numerical methods for PDEs. We then use the residual of the first-order system in the least-squares sense as the loss function, which is in close connection with least-squares finite element method. For aforementioned classical numerical methods, the choice of trial and test functions is important for stability and accuracy issues in many cases. MIM shares this property when DNNs are employed to approximate unknowns functions in the first-order system. In one case, we use nearly the same DNN to approximate all unknown functions and in the other case, we use totally different DNNs for different unknown functions. Numerous results of MIM with different loss functions and different choices of DNNs are given for four types of PDEs. In most cases, MIM provides better approximations (not only for high-order derivatives of the PDE solution but also for the PDE solution itself) than DGM with nearly the same DNN and the same execution time, sometimes by more than one order of magnitude. MIM with multiple DNNs often provides better approximations than MIM with only one DNN, sometimes by more than one order of magnitude. Numerical results also indicate interesting connections between MIM and classical numerical methods. Therefore, we expect MIM to open up a possibly systematic way to understand and improve deep learning for solving PDEs from the perspective of classical numerical analysis.

Title: Sumsets of Wythoff sequences, Fibonacci representation, and beyond Abstract: Let alpha = (1 + root 5)/2 and define the lower and upper Wythoff sequences by a(i) = left perpendiculari alpha right perpendicular, b(i) = left perpendiculari alpha(2)right perpendicular for i >= 1. In a recent interesting paper, Kawsumarng et al. proved a number of results about numbers representable as sums of the form a(i) +a(j), b(i) + b(j), a(i) + b(j), and so forth. In this paper I show how to derive all of their results, using one simple idea and existing free software called Walnut. The key idea is that for each of their sumsets, there is a relatively small automaton accepting the Fibonacci representation of the numbers represented. I also show how the automaton approach can easily prove other results.

Title: Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs Abstract: Backpropagation of error (backprop) is a powerful algorithm for training machine learning architectures through end-to-end differentiation. Recently it has been shown that backprop in multilayer perceptrons (MLPs) can be approximated using predictive coding, a biologically plausible process theory of cortical computation that relies solely on local and Hebbian updates. The power of backprop, however, lies not in its instantiation in MLPs but in the concept of automatic differentiation, which allows for the optimization of any differentiable program expressed as a computation graph. Here, we demonstrate that predictive coding converges asymptotically (and in practice, rapidly) to exact backprop gradients on arbitrary computation graphs using only local learning rules. We apply this result to develop a straightforward strategy to translate core machine learning architectures into their predictive coding equivalents. We construct predictive coding convolutional neural networks, recurrent neural networks, and the more complex long short-term memory, which include a nonlayer-like branching internal graph structure and multiplicative interactions. Our models perform equivalently to backprop on challenging machine learning benchmarks while using only local and (mostly) Hebbian plasticity. Our method raises the potential that standard machine learning algorithms could in principle be directly implemented in neural circuitry and may also contribute to the development of completely distributed neuromorphic architectures.

Title: Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces Abstract: Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as $k$k<inline-graphic xlink:hr...

Title: Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under STL specifications Abstract: We propose a counterexample-guided inductive synthesis framework for the formal synthesis of closed-form sampled-data controllers for nonlinear systems to meet STL specifications over finite-time trajectories. Rather than stating the STL specification for a single initial condition, we consider an (infinite and bounded) set of initial conditions. Candidate solutions are proposed using genetic programming, which evolves controllers based on a finite number of simulations. Subsequently, the best candidate is verified using reachability analysis; if the candidate solution does not satisfy the specification, an initial condition violating the specification is extracted as a counterexample. Based on this counterexample, candidate solutions are refined until eventually a solution is found (or a user-specified number of iterations is met). The resulting sampled-data controller is expressed as a closed-form expression, enabling both interpretability and the implementation in embedded hardware with limited memory and computation power. The effectiveness of our approach is demonstrated for multiple systems.

Title: EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks Abstract: Dropout is a well-known regularization method by sampling a sub-network from a larger deep neural network and training different sub-networks on different subsets of the data. Inspired by the dropout concept, we propose EDropout as an energy-based framework for pruning neural networks in classification tasks. In this approach, a set of binary pruning state vectors (population) represents a set of corresponding sub-networks from an arbitrary original neural network. An energy loss function assigns a scalar energy loss value to each pruning state. The energy-based model (EBM) stochastically evolves the population to find states with lower energy loss. The best pruning state is then selected and applied to the original network. Similar to dropout, the kept weights are updated using backpropagation in a probabilistic model. The EBM again searches for better pruning states and the cycle continuous. This procedure is a switching between the energy model, which manages the pruning states, and the probabilistic model, which updates the kept weights, in each iteration. The population can dynamically converge to a pruning state. This can be interpreted as dropout leading to pruning the network. From an implementation perspective, unlike most of the pruning methods, EDropout can prune neural networks without manually modifying the network architecture code. We have evaluated the proposed method on different flavors of ResNets, AlexNet, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> pruning, ThinNet, ChannelNet, and SqueezeNet on the Kuzushiji, Fashion, CIFAR-10, CIFAR-100, Flowers, and ImageNet data sets, and compared the pruning rate and classification performance of the models. The networks trained with EDropout on average achieved a pruning rate of more than 50% of the trainable parameters with approximately < 5% and < 1% drop of Top-1 and Top-5 classification accuracy, respectively.

Title: Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations Abstract: Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we shape a blueprint of the decisions and processes to be considered in the context of equality of recommended learning opportunities, based on principles that need to be empirically-validated (no evaluation with live learners has been performed). To this end, we first provide a formalization of educational principles that model recommendations’ learning properties, and a novel fairness metric that combines them to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. This paper provides a theoretical foundation for future studies of learners’ preferences and limits concerning the equality of recommended learning opportunities.

Title: Generalized Dissections and Monsky’s Theorem Abstract: Monsky’s celebrated equidissection theorem follows from his more general proof of the existence of a polynomial relation f among the areas of the triangles in a dissection of the unit square. More recently, the authors studied a different polynomial p, also a relation among the areas of the triangles in such a dissection, that is invariant under certain deformations of the dissection. In this paper we study the relationship between these two polynomials. We first generalize the notion of dissection, allowing triangles whose orientation differs from that of the plane. We define a deformation space of these generalized dissections and we show that this space is an irreducible algebraic variety. We then extend the theorem of Monsky to the context of generalized dissections, showing that Monsky’s polynomial f can be chosen to be invariant under deformation. Although f is not uniquely defined, the interplay between p and f then allows us to identify a canonical pair of choices for the polynomial f. In many cases, all of the coefficients of the canonical f polynomials are positive. We also use the deformation-invariance of f to prove that the polynomial p is congruent modulo 2 to a power of the sum of its variables.

Title: DYNAMICS OF THRESHOLD SOLUTIONS FOR ENERGY CRITICAL NLS WITH INVERSE SQUARE POTENTIAL Abstract: We consider the focusing energy critical nonlinear Schrodinger equation (NLS) with inverse square potential in dimension d = 3, 4, 5 with the details given in d = 3 and remarks on results in other dimensions. Solutions on an energy surface of the ground state are characterized. We prove that solutions with kinetic energy less than that of the ground state must scatter to zero or belong to the stable/unstable manifolds of the ground state. In the latter case they converge to the ground state exponentially in the energy space as t -> infinity or t -> -infinity. (In three-dimensions without radial assumption, this holds under the compactness assumption of nonscattering solutions on the energy surface.) When the kinetic energy is greater than that of the ground state, we show that all radial H-1 solutions blow up in finite time, with the only two exceptions being in the case of five-dimensions which belong to the stable/unstable manifold of the ground state. The proof relies on detailed spectral analysis, local invariant manifold theory, and a global Virial analysis.