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Title: Explicitly semantic representation of pattern and combined geometrical specification Abstract: Model-based definition (MBD) containing all product and manufacturing information (PMI) has become the data source of future product development because of its incomparable advantages over traditional two-dimensional drawings. In the whole life cycle of product development, it is an urgent problem to realize the sharing of geometric tolerance specification information of PMI among different CAx (CAD, CAE, CAM, etc.) and the automatic interpretation of its semantics by computer. In this paper, the semantic representation ontology of pattern and combined geometrical specification is studied based on the new generation GPS theory and the construction process is introduced in detail through an example. Benefiting from the logic-based semantics of the web ontology language (OWL) and semantic web rule language (SWRL), the pattern and combined geometrical specification semantic can be interpreted directly by computer and has the advantages of clear, unambiguous, and consistent at different stages of the product life cycle. At the same time, it can automatically check the consistency of knowledge, reason new knowledge, and semantic query.

Title: Optimal power flow incorporating renewable uncertainty related opportunity costs Abstract: In this paper, an optimal power flow solution method incorporating a cost model that associates the uncertainty-related expense incurred with the use of renewable energy sources, viz., solar and wind, is demonstrated. Wind speed and solar radiation are assumed to follow Weibull and normal distributions and the uncertainty is simulated using Monte-Carlo approach. Wind turbine mathematical model is used to estimate the wind generator output, while the same for solar PV is estimated using PV-inverter models. The uncertainty-induced opportunity cost for both the renewable sources is composed of the costs due to both power excess and deficit. These cost components are indicative of the reserve requirement and loss of benefit, due to the unavailability of the corresponding generation. This research models and integrates the opportunity costs of renewable generation into a conventional OPF formulation, which is then solved using four variants of particle swarm optimization method. Among these, mutation-based PSO approach provided better results than others. The test system used is modified IEEE 39-bus network and the performance of the method as well as the effect of the uncertainty cost is evaluated under multiple renewable penetration levels. The results also indicate that solar generation is preferred over wind in terms of the uncertainty cost, while the use of stochastic natured renewable systems is economically justified and preferred over thermal generators.

Title: Multiplicative efficiency aggregation to evaluate Taiwanese local auditing institutions performance Abstract: This study evaluates the management efficiency and quality effectiveness of Taiwanese local auditing institutions (LAIs), whereby two outputs leave the first stage without being inputted to the second stage under the assumption of variable returns to scale. To address these considerations, this study integrates multiplicative efficiency aggregation (MEA) in a two-stage network data envelopment analysis (DEA) model into the form of second order cone programming (SOCP). Under a cone structure, our DEA findings indicate that management efficiency increased, and quality effectiveness decreased over the sample period 2013–2015. Besides, a truncated regression analysis indicates that differences in operating environments, particularly urbanization level and LAIs’ attributes, have significant impacts on the management efficiency of LAIs. The potential applications and strengths of SOCP and MEA-based two-stage network DEA in assessing the LAIs are highlighted.

Title: Dynamic Transactional Transformation Abstract: Transactional data structures support threads executing a sequence of operations atomically. Dynamic transactions allow operands to be generated on the fly and allows threads to execute code in between the operations of a transaction, in contrast to static transactions which need to know the operands in advance. A framework called lock-free transactional transformation (LFTT) allows data structures to run high-performance transactions, but it only supports static transactions. We present dynamic transactional transformation, an extension to LFTT to add support for dynamic transactions and wait-free progress while retaining its speed. The thread-helping scheme of LFTT presents a unique challenge to dynamic transactions. We overcome this challenge by changing the input of LFTT from a list of operations to a function, forcing helping threads to always start at the beginning of the transaction, and allowing threads to skip completed operations through the use of a list of return values. We thoroughly evaluate the performance impact of support for dynamic transactions and wait-free progress and find that these features do not hurt the performance of LFTT for our test cases.

Title: Original Design Manufacturer'S Warranty Strategy When Considering Retailers' Brand Power Under Different Power Structures Abstract: In recent years, the original design manufacturer (ODM) has increasing interests in multichannel cooperation with different retailers. This paper considers the problem in which the ODM cooperates with two competing retailers. The manufacturer produces two substitute products and markets them through two competing retailers. For both products, the manufacturer bundles them with basic warranties and the retailers brand them with their own branding. We quantitatively model the product demands and derive the optimal decisions under two common supply chain power structures: manufacturer Stackelberg and retailer Stackelberg. We observe that when the competition between the two retailers becomes fiercer, the manufacturer charges more for both products, the product substitutability and supply chain power structures do not affect the products' price and warranty decisions, and there does exist a brand ratio interval in which the product branded by the high-reputation retailer will not get a better warranty from the manufacturer, violating the signalization of the warranty. The leader gets the power to get higher profit while the total profit of the whole supply chain remains the same under both power structures.

Title: SOME PROPERTIES OF THE EIGENVALUES OF THE NET LAPLACIAN MATRIX OF A SIGNED GRAPH Abstract: Given a signed graph over dot(G), let A(over dot(G)) and D-over dot(G)(perpendicular to) denote its standard adjacency matrix and the diagonal matrix of vertex net-degrees, respectively. The net Laplacian matrix of over dot(G) is defined to be N-over dot(G) = D-over dot(G)(+/-) - A(over dot(G)). In this study we give some properties of the eigenvalues of N-over dot(G). In particular, we consider their behaviour under some edge perturbations, establish some relations between them and the eigenvalues of the standard Laplacian matrix and give some lower and upper bounds for the largest eigenvalue of N-over dot(G).

Title: PSSCC: Provably secure communication framework for crowdsourced industrial Internet of Things environments Abstract: Internet of things environment is adopted widely in different industries and business organizations with varying capacity. It provides a favorable environment to outsource the crowdsourced data in the cloud to minimize the cost of computation, which is called crowdsourcing. Crowdsourcing is a technique where individuals or organizations obtain goods and services. A professional or industry outsource the crowdsourced data in the cloud, where confidentiality and authenticity of data become essential. Signcryption is the cryptographic technique that serves both the authenticity and the privacy of transmitted messages. This technique ensures secure authentic data transmission and storage. Therefore, this paper proposes an identity-based signcryption scheme. In the proposed PSSCC framework, the user does pairing free computation during signcryption, which makes efficient calculation on user-side. Moreover, PSSCC framework is proved secure under modified bilinear Diffie-Hellman inversion and modified bilinear strong Diffie-Hellman problems. The performance analysis of PSSCC with related schemes indicates that the proposed system supports efficient communication along with less computation cost.

Title: Reliability estimation for the bathtub-shaped distribution based on progressively first-failure censoring sampling Abstract: In this article, we consider estimating the parameters, reliability function R(t) and failure rate function H(t) of the two-parameter bathtub-shaped distribution introduced by Chen (2000) based on the progressive first-failure censored sample. The maximum likelihood estimators and Bayes estimators under squared error loss function are derived. We obtain the asymptotic confidence intervals for the parameters using the observed Fisher information matrix. The parametric bootstrap confidence intervals of reliability characteristics are also proposed. Lindley approximation procedure is adopted to establish Bayes estimates. Furthermore, we conduct Monte Carlo simulation to compare the behaviors of different methods. A real data set is analyzed to illustrate the proposed methods.

Title: Mixed data generation packages and related computational tools in R Abstract: This paper is concerned with providing some computation-related details of the 16R packages that have been developed by Demirtas and his colleagues in the context of random number generation. The dominant theme is multivariate mixed data generation. However, univariate and multivariate data generation from different distributions as well as some other tools such as modeling the correlation transitions in latency and discretization domains are also included. This is intended for interested people who would benefit from access to a comprehensive set of data simulation tools at one single place. While the focus is on conceptual and implementation issues, the ideas are supported by appropriate references for methodological development.

Title: Assessing the performance of confidence intervals for high quantiles of Burr XII and Inverse Burr mixtures Abstract: Recent research in the area of univariate mixture modeling indicated that the finite mixture models based on Burr and Inverse Burr component distributions perform well in the modeling of heavy-tail insurance data. Mixture models are able to capture the multimodality which is quite a common characteristic of insurance losses. Through an extensive simulation study, we assess the performance of three different methods in building the confidence intervals for high quantiles of the mixtures of Burr and Inverse Burr distributions. First, we provide mathematical justification for linking the tail of the k-Burr and k-Inverse Burr mixtures to the maximum domain of attraction of the Frechet distribution which allows us to employ the Generalized Pareto Distribution (GPD) in the estimation of high quantiles and their corresponding confidence intervals. Then, we compare these results to those obtained using order statistics and the bootstrap methods. We also modified the existing Peak Over Threshold (POT) algorithm for the efficient computation of the confidence intervals in the upper tail of these mixture models. A real data set on Danish Fire Losses is used to illustrate the application of these methods in practice.

Title: The polynomial-exponential distribution: a continuous probability model allowing for occurrence of zero values Abstract: This paper deals with a new two-parameter lifetime distribution with increasing, decreasing and constant hazard rate. This distribution allows the occurrence of zero values and involves the exponential, linear exponential and other combinations of Weibull distributions as submodels. Many statistical properties of the distribution are derived. Maximum likelihood estimation of the parameters and a bias corrective approach is investigated with a simulation study for performance of the estimators. Four real data sets are analyzed for illustrative purposes and it is noted that the distribution is a highly alternative to the gamma, Weibull, Lognormal and exponentiated exponential distributions.

Title: Generalized spatial stick-breaking processes Abstract: This paper develops a Bayesian nonparametric model for skewed spatial data with nonstationary dependence structure. A transformed Gaussian model is proposed for the atoms of the kernel stick-breaking process by transforming the margins of a Gaussian process to flexible marginal distributions. This study proves that the correlation structure of the underlying spatial process is nonstationary. Results from both simulated and real datasets demonstrate that the proposed model possesses better spatial prediction performance and offers computational advantages compared to the Bayesian nonparametric model with the Gaussian base measure.

Title: Combining binary and continuous biomarkers by maximizing the area under the receiver operating characteristic curve Abstract: In any clinical case, a decision is made with the maximum possible accuracy. To achieve such accuracy, in the presence of multiple diagnostic tests or biomarkers, biomarker combinations aim to achieve maximum accuracy. As existing biomarker combination methods combine only continuous biomarkers, therefore in this study biomarker combination for binary biomarkers was created by suggesting an approach using Youden's J statistic for combining binary biomarkers. The proposed approach will facilitate binary and continuous biomarker combinations. A simulation study was conducted to compare the performance of our proposed combination approach according to different sample sizes. Both in the analysis of real data and the simulation studies for different samples, the proposed approach has been shown to yield favorable results and higher area under the curve.

Title: A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs Abstract: This study addresses the stochastic parallel assembly line balancing problem with equipment costs and presents a hyper-heuristic approach based on simulated annealing for solving it. A cost-based objective function is employed to represent the incompletion, equipment, and station installation costs. The hyper-heuristic approach is utilized to search on sequencing heuristics search space, rather than a problem-specific solution space. This study focuses on the consideration of equipment costs while balancing a stochastic parallel assembly line. The performance of the solution approach is also tested on the single-model stochastic assembly line balancing problems and stochastic parallel assembly line balancing problems due to the generalizability of hyper-heuristics. The results of the benchmark problems show that in most cases the proposed algorithm provides better solutions than the best-known solutions in literature. An extensive computational study performed to determine the parameter levels derived from the problem and the solution method. The effect of the equipment costs for stochastic parallel assembly lines is also analyzed in detail.

Title: Roadmap to distillery spent wash treatment and use of soft computing techniques Abstract: Distillery industries in several regions all over the world pose a serious risk, as it generates unpleasant compounds. Under such circumstances, it seeks an effective spent wash treatment, to eliminate the contaminants. Accordingly, this paper provides a relevant review regarding the distillery spent wash treatments, associated with the proper treatments and coagulants. At first, it reviews 67 recent research papers, from which 24 papers belong to distillery spent wash treatment and remaining belongs to other treatments. Further, it extends the valuable chronological review on distillery spent wash treatment. In addition, it describes the several processes such as anaerobic treatment, aerobic treatment, nanofiltration, reverse osmosis, adsorption and electrochemical treatments, adopted to treat distillery spent wash as reported in the literature. In the same way, it analyses the usage of different types of coagulants such as natural, electro and chemical coagulants used in distillery spent wash treatment. To the next of the coagulant analysis, it checks out the performance review of entire contributions on distillery spent wash treatment. The conventional process for distillery spent wash treatment having limitations such as limited removal efficiency, high operating cost and maintenance also it needs a high detention time that eventually increases the on the whole treatment process time. The aforesaid limitation can be overcome by adopting soft computing techniques. Soft computing has been widely studied and applied in the past three decades for engineering and scientific research computing. In environmental engineering, engineers and researchers have effectively used various techniques of soft computing like fuzzy logic, artificial neural networks, adaptive neuro-fuzzy inference systems, and support vector machines which can be useful for the researchers to achieve further research on distillery spent wash treatment.

Title: Romanized Tunisian dialect transliteration using sequence labelling techniques Abstract: In recent years, social web users in Arabic countries have been resorting to the dialects as a written language in their social exchanges. Arabic dialects derive from modern standard Arabic (MSA) and differ significantly from one country to another and one region to another. The use of these dialects has led to an increase of interest in the specificities of such informal languages and their automatic processing within the NLP community. In this work, we deal with the Tunisian dialect (TD) in particular. We address the issue of the automatic Latin to Arabic transliteration of TD language productions on the social web and propose an approach that models the transliteration as a sequence labeling task. At a word level, several techniques, based on machine and deep learning, have been tested for this study, using real word messages extracted from social networks. We experiment and compare three transliteration models: A Conditional Random Fields-based model (CRF), a Bidirectional Long Short-Term Memory based model (BLSTM), and a BLSTM based model with CRF decoding (BLSTM-CRF). The obtained results show that BLSTM-CRF, leads to the best performance, reaching 96.78% of correctly transliterated words. We also evaluate the BLSTM-CRF transliteration approach in context on a set of random TD messages extracted from the social web. We obtained a total error rate of 2.7%. 25% of which are context errors. (c) 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Using a small amount of text-independent speech data for a BiLSTM large-scale speaker identification approach Abstract: Communication between people and machines has been extended in the last two decades. Corresponding techniques have been founded to cover the need of voice understanding, including speech and speaker recognition on a large-scale. In this paper, the authors propose a simplified deep-learning approach to accomplish the large-scale speaker identification task using as little training data as possible. Fisher speech corpus has been explored to select the recordings of unique speakers having sufficient data. The authors are using the MFCC method to represent the feature vectors of a large set of more than 4 k speakers with about 343 h of speech signals. The solution includes omitting the pre-processing and considering longer segments of the voice signals. Various portions of training datasets have been tested, as well as dedicating larger percentages of the used data for testing. Bidirectional LSTM neural networks provided up to 76.9% accuracy rate for individual voice segments, and 99.5% when considering the segments of each speaker as a bundle. Doubling the amount of the training data yielded a perfect accuracy rate of 100%. (C) 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: Further evidence towards the multiplicative 1-2-3 Conjecture Abstract: The product version of the 1-2-3 Conjecture, introduced by Skowronek-Kaziow in 2012, states that, a few obvious exceptions apart, all graphs can be 3-edge-labelled so that no two adjacent vertices get incident to the same product of labels. To date, this conjecture was mainly verified for complete graphs and 3-colourable graphs. As a strong support to the conjecture, it was also proved that all graphs admit such 4-labellings. In this work, we investigate how a recent proof of the multiset version of the 1-2-3 Conjecture by Vuckovic can be adapted to prove results on the product version. We prove that 4-chromatic graphs verify the product version of the 1-2-3 Conjecture. We also prove that for all graphs we can design 3-labellings that almost have the desired property. This leads to a new problem, that we solve for some graph classes. (C) 2021 Elsevier B.V. All rights reserved.

Title: Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space Abstract: In this paper the issue of filtering and smoothing in continuous discrete time is studied when the state variable evolves in some submanifold of Euclidean space, which may not have the usual Lebesgue measure. Formal expressions for prediction and smoothing problems are reviewed, which agree with the classical results except that the formal adjoint of the generator is different in general. These results are used to generalise the projection approach to filtering and smoothing to the case when the state variable evolves in some submanifold that lacks a Lebesgue measure. The approach is used to develop projection filters and smoothers based on the von Mises–Fisher distribution, which are shown to be outperform Gaussian estimators both in terms of estimation accuracy and computational speed in simulation experiments involving the tracking of a gravity vector.

Title: Estimating the Probability That a Vehicle Reaches a Near-Term Goal State Using Multiple Lane Changes Abstract: This article proposes a model to estimate the probability of a vehicle reaching a near-term goal state using one or multiple lane changes based on parameters corresponding to traffic conditions and driving behavior. The proposed model not only has broad application in path planning and autonomous vehicle navigation, it can also be incorporated in advance warning systems to reduce traffic delay during recurrent and non-recurrent congestion. The model is first formulated for a two-lane road segment through systemic reduction of the number of parameters and transforming the problem into an abstract statistical form, for which the probability can be calculated numerically. It is then extended to cases with a higher number of lanes using the law of total probability. VISSIM simulations are used to validate the predictions of the model and study the effect of different parameters on the probability. For most cases, simulation results are within 4% of model predictions, and the effect of different parameters such as driving behavior and traffic density on the probability match our expectation. The model can be implemented with near real-time performance, with computation time increasing linearly with the number of lanes.

Title: Stabilising quasi-time-optimal nonlinear model predictive control with variable discretisation Abstract: This paper deals with novel time-optimal point-to-point model predictive control concepts for nonlinear systems. Recent approaches in the literature apply a time transformation, however, which do not maintain recursive feasibility for a piecewise constant control parameterisation. The key idea in this paper is to introduce uniform grids with variable discretisation. A shrinking-horizon grid adaptation scheme ensures convergence to a specific region around the target state and recursive feasibility. The size of the region is configurable by design parameters. This facilitates the systematic dual-mode design for quasi-time-optimal control to restore asymptotic stability and establish a smooth stabilisation. Two nonlinear programme formulations with different sparsity patterns are introduced to realise and implement the underlying optimal control problem. For a class of numerical integration schemes, even nominal asymptotic stability and true time-optimality are achieved without dual-mode. A comparative analysis as well as experimental results demonstrate the effectiveness of the proposed techniques.

Title: How Exponentially Ill-Conditioned Are Contiguous Submatrices of the Fourier Matrix? Abstract: Linear systems involving contiguous submatrices of the discrete Fourier transform (DFT) matrix arise in many applications, such as Fourier extension, superresolution, and coherent diffraction imaging. We show that the condition number of any such p x q submatrix of the N x N DFT matrix is at least exp (pi/2 [min(p, q) - pq/N]), up to algebraic prefactors. That is, fixing the shape parameters (alpha, beta) := (p/N, q/N) is an element of (0, 1)(2), the growth is e(rho N) as N -> infinity, the exponential rate being rho = pi/2 [min(alpha, beta) - alpha beta]. Our proof uses the Kaiser-Bessel transform pair (of which we give a self-contained proof), plus estimates on sums over distorted sinc functions, to construct a localized trial vector whose DFT is also localized. We warm up with an elementary proof of the above but with half the rate, via a periodized Gaussian trial vector. Using low-rank approximation of the kernel e(ixt), we also prove another lower bound (4/e pi alpha)(q), up to algebraic prefactors, which is stronger than the above for small alpha and beta. When combined, the bounds are within a factor of two of the empirical asymptotic rate, uniformly over (0, 1)(2), and become sharp in certain regions. However, the results are not asymptotic: they apply to essentially all N, p, and q, and with all constants explicit.

Title: Multiagent Persistent Monitoring of Targets With Uncertain States Abstract: We address the problem of persistent monitoring, where a finite set of mobile agents has to persistently visit a finite set of targets. Each of these targets has an internal state that evolves with linear stochastic dynamics. The agents can observe these states, and the observation quality is a function of the distance between the agent and a given target. The goal is then to minimize the mean squared estimation error of these target states. We approach the problem from an infinite horizon perspective, where we prove that, under some natural assumptions, the covariance matrix of each target converges to a limit cycle. The goal, therefore, becomes to minimize the steady-state uncertainty. Assuming that the trajectory is parameterized, we provide tools for computing the steady-state cost gradient. We show that, in 1-D (one dimensional) environments with bounded control and nonoverlapping targets, when an optimal control exists it can be represented using a finite number of parameters. We also propose an efficient parameterization of the agent trajectories for multidimensional settings using Fourier curves. Simulation results show the efficacy of the proposed technique in 1-D, 2-D, and 3-D scenarios.

Title: The generalised Oberwolfach problem Abstract: We prove that any quasirandom dense large graph in which all degrees are equal and even can be decomposed into any given collection of two-factors (2-regular spanning subgraphs). A special case of this result gives a new solution to the Oberwolfach problem.

Title: Data Poisoning Attacks on Federated Machine Learning Abstract: Federated machine learning which enables resource-constrained node devices (e.g., Internet of Things (IoT) devices and smartphones) to establish a knowledge-shared model while keeping the raw data local, could provide privacy preservation, and economic benefit by designing an effective communication protocol. However, this communication protocol can be adopted by attackers to launch data poisoning attacks for different nodes, which has been shown as a big threat to most machine learning models. Therefore, we in this article intend to study the model vulnerability of federated machine learning, and even on IoT systems. To be specific, we here attempt to attacking a popular federated multitask learning framework, which uses a general multitask learning framework to handle statistical challenges in the federated learning setting. The problem of calculating optimal poisoning attacks on federated multitask learning is formulated as a bilevel program, which is adaptive to the arbitrary selection of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target</i> nodes and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source attacking</i> nodes. We then propose a novel systems-aware optimization method, called as attack on federated learning (AT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FL), to efficiently derive the implicit gradients for poisoned data, and further attain optimal attack strategies in the federated machine learning. This is an earlier work, to our knowledge, that explores attacking federated machine learning via data poisoning. Finally, experiments on several real-world data sets demonstrate that when the attackers directly poison the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target</i> nodes or indirectly poison the related nodes via using the communication protocol, the federated multitask learning model is sensitive to both poisoning attacks.

Title: Towards Analysis-Friendly Face Representation With Scalable Feature and Texture Compression Abstract: Compactly representing visual information plays a fundamental role in optimizing the ultimate utility of myriad visual data-centered applications. Numerous approaches have been proposed to efficiently compress the texture and visual features for human visual perception and machine intelligence, respectively; however, much less work has been dedicated to studying the interactions between them. Here, we investigate the integration of feature and texture compression and show that a universal and collaborative visual information representation can be achieved in a hierarchical way. In particular, we study feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and the enhancement layer targets to perfectly reconstruct the texture. Based on the strong generative capability of deep neural networks, the gap between the base feature layer and enhancement layer is further filled with feature-level texture reconstruction, with the goal of further constructing texture representations from features. As such, the residuals between the original and reconstructed texture could be further conveyed in the enhancement layer. To improve the efficiency of the proposed framework, the base layer neural network is trained in a multitask manner such that the learned features enjoy both high-quality reconstruction and high-accuracy analysis. The framework and optimization strategies are further applied in face image compression, and promising coding performance has been achieved in terms of both rate-fidelity and rate-accuracy evaluations.

Title: Cumulant–Cumulant Relations in Free Probability Theory from Magnus’ Expansion Abstract: Relations between moments and cumulants play a central role in both classical and non-commutative probability theory. The latter allows for several distinct families of cumulants corresponding to different types of independences: free, Boolean and monotone. Relations among those cumulants have been studied recently. In this work, we focus on the problem of expressing with a closed formula multivariate monotone cumulants in terms of free and Boolean cumulants. In the process, we introduce various constructions and statistics on non-crossing partitions. Our approach is based on a pre-Lie algebra structure on cumulant functionals. Relations among cumulants are described in terms of the pre-Lie Magnus expansion combined with results on the continuous Baker–Campbell–Hausdorff formula due to A. Murua.

Title: The (non-)existence of perfect codes in Lucas cubes Abstract: The Fibonacci cube of dimension n, denoted as Gamma(n), is the subgraph of the n-cube Q(n) induced by vertices with no consecutive 1's. Ashrafi and his co-authors proved the nonexistence of perfect codes in Gamma(n) for n >= 4. As an open problem the authors suggest to consider the existence of perfect codes in generalizations of Fibonacci cubes. The most direct generalization is the family Gamma(n)(1(s)) of subgraphs induced by strings without 1s as a substring where s = 2 is a given integer. In a precedent work we proved the existence of a perfect code in Gamma(n)(1(s)) for n = 2(p) - 1 and s >= 3.2(p-2) for any integer p >= 2. The Lucas cube.n is obtained from Gn by removing vertices that start and end with 1. Very often the same problems are studied on Fibonacci cubes and Lucas cube. In this note we prove the non-existence of perfect codes in.n for n = 4 and prove the existence of perfect codes in some generalized Lucas cube Lambda(n)(1(s)).

Title: SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images Abstract: We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but lim...

Title: Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent Abstract: Generative adversarial networks (GANs), which are famous for the capability of learning complex underlying data distribution, are, however, known to be tricky in the training process, which would probably result in mode collapse or performance deterioration. Current approaches of dealing with GANs’ issues almost utilize some practical training techniques for the purpose of regularization, which, on the other hand, undermines the convergence and theoretical soundness of GAN. In this article, we propose to stabilize GAN training via a novel particle-based variational inference—Langevin Stein variational gradient descent (LSVGD), which not only inherits the flexibility and efficiency of original SVGD but also aims to address its instability issues by incorporating an extra disturbance into the update dynamics. We further demonstrate that, by properly adjusting the noise variance, LSVGD simulates a Langevin process whose stationary distribution is exactly the target distribution. We also show that LSVGD dynamics has an implicit regularization, which is able to enhance particles’ spread-out and diversity. Finally, we present an efficient way of applying particle-based variational inference on a general GAN training procedure no matter what loss function is adopted. Experimental results on one synthetic data set and three popular benchmark data sets—Cifar-10, Tiny-ImageNet, and CelebA—validate that LSVGD can remarkably improve the performance and stability of various GAN models.

Title: The Semiring of Dichotomies and Asymptotic Relative Submajorization Abstract: We study quantum dichotomies and the resource theory of asymmetric distinguishability using a generalization of Strassen’s theorem on preordered semirings. We find that an asymptotic variant of relative submajorization, defined on unnormalized dichotomies, is characterized by real-valued monotones that are multiplicative under the tensor product and additive under the direct sum. These strong cons...

Title: The Differential Entropy of Mixtures: New Bounds and Applications Abstract: Mixture distributions are extensively used as a modeling tool in diverse areas from machine learning to communications engineering to physics, and obtaining bounds on the entropy of mixture distributions is of fundamental importance in many of these applications. This article provides sharp bounds on the entropy concavity deficit, which is the difference between the differential entropy of the mix...

Title: Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking Abstract: The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to estimate the parameters of each object when present in the tracking scene. In particular, we adopt the dependent Dirichlet process (DDP) to learn the multiple object state prior by exploiting inherent dynamic dependencies in the state transition using the dynamic clustering property of the DDP. Using the DDP to draw the mixing measures, Dirichlet process mixtures are used to learn and assign each measurement to its associated object identity. The Bayesian posterior to estimate the target trajectories is efficiently implemented using a Gibbs sampler inference scheme. A second tracking approach is proposed that replaces the DDP with the dependent Pitman-Yor process in order to allow for a higher flexibility in clustering. The improved tracking performance of the new approaches is demonstrated by comparison to the generalized labeled multi-Bernoulli filter.

Title: TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism Abstract: Effective parallelization strategies are crucial for the performance of distributed deep neural network (DNN) training. Recently, several methods have been proposed to search parallelization strategies but they all optimize a single objective (e.g., execution time, memory consumption) and produce only one strategy. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frontier Tracking</i> (FT), an efficient algorithm that finds <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a set of Pareto-optimal parallelization strategies</i> to explore the best trade-off among different objectives. FT can minimize the memory consumption when the number of devices is limited and fully utilize additional resources to reduce the execution time. Based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FT</i> , we develop a user-friendly system, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TensorOpt</i> , which allows users to run their distributed DNN training jobs without caring the details about searching and coding parallelization strategies. Experimental results show that TensorOpt is more flexible in adapting to resource availability compared with existing frameworks.

Title: Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System Abstract: Taskflow aims to streamline the building of parallel and heterogeneous applications using a lightweight task graph-based approach. Taskflow introduces an expressive task graph programming model to assist developers in the implementation of parallel and heterogeneous decomposition strategies on a heterogeneous computing platform. Our programming model distinguishes itself as a very general class of...

Title: Flexibility of planar graphs-Sharpening the tools to get lists of size four Abstract: A graph where each vertex v has a list L(v) of available colors is L-colorable if there is a proper coloring such that the color of v is in L(v) for each v. A graph is k-choosable if every assignment L of at least k colors to each vertex guarantees an L-coloring. Given a list assignment L, an L-request for a vertex v is a color c is an element of L(v). In this paper, we look at a variant of the widely studied class of precoloring extension problems from Dvorak, Norin, and Postle (J. Graph Theory, 2019), wherein one must satisfy "enough'', as opposed to all, of the requested set of precolors. A graph G is epsilon-flexible for list size k if for any k-list assignment L, and any set S of L-requests, there is an L-coloring of G satisfying epsilon-fraction of the requests in S. It is conjectured that planar graphs are epsilon-flexible for list size 5, yet it is proved only for list size 6 and for certain subclasses of planar graphs. We give a stronger version of the main tool used in the proofs of the aforementioned results. By doing so, we improve upon a result by Masarik and show that planar graphs without K-4(-) are epsilon-flexible for list size 5. We also prove that planar graphs without 4-cycles and 3-cycle distance at least 2 are epsilon-flexible for list size 4. Finally, we introduce a new (slightly weaker) form of epsilon-flexibility where each vertex has exactly one request. In that setting, we provide a stronger tool and we demonstrate its usefulness to further extend the class of graphs that are epsilon-flexible for list size 5. (C) 2021 The Author(s). Published by Elsevier B.V.

Title: On a Phase Transition in General Order Spline Regression Abstract: In the Gaussian sequence model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Y= \theta _{0} + \varepsilon $ </tex-math></inline-formula> in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{n}$ </tex-math></inline-formula> , we study the fundamental limit of statistical estimation when the signal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta _{0}$ </tex-math></inline-formula> belongs to a class <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta _{n}(d,d_{0},k)$ </tex-math></inline-formula> of (generalized) splines with free knots located at equally spaced design points. Here <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is the degree of the spline, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{0}$ </tex-math></inline-formula> is the order of differentiability at each inner knot, and <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> is the maximal number of pieces. We show that, given any integer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d\geq 0$ </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">$d_{0}\in \{-1,0,\ldots,d-1\}$ </tex-math></inline-formula> , the minimax rate of estimation over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta _{n}(d,d_{0},k)$ </tex-math></inline-formula> exhibits the following phase transition: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\begin{aligned} \inf _{ \widetilde {\theta }}\sup _{\theta \in \Theta _{n}(d,d_{0}, k)} \mathbb {E}_\theta \lVert \widetilde {\theta } - \theta \rVert _{}^{2} \asymp _{d} \begin{cases} k\log \log (16n/k), &amp; 2\leq k\leq k_{0},\\ k\log (en/k), &amp; k \geq k_{0}+1. \end{cases} \end{aligned}$ </tex-math></inline-formula> The transition boundary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k_{0}$ </tex-math></inline-formula> , which takes the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\left \lfloor{ (d+1)/(d-d_{0}) }\right \rfloor + 1$ </tex-math></inline-formula> , demonstrates the critical role of the regularity parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d_{0}$ </tex-math></inline-formula> in the separation between a faster <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log \log (16n)$ </tex-math></inline-formula> and a slower <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log (en)$ </tex-math></inline-formula> rate. We further show that, once encouraging an additional ‘ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -monotonicity’ shape constraint (including monotonicity for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d = 0$ </tex-math></inline-formula> and convexity for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d=1$ </tex-math></inline-formula> ), the above phase transition is removed and the faster <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\log \log (16n/k)$ </tex-math></inline-formula> rate can be achieved for all <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> . These results provide theoretical support for developing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> -penalized (shape-constrained) spline regression procedures as useful alternatives to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </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">$\ell _{2}$ </tex-math></inline-formula> -penalized ones.

Title: MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems Abstract: Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using these Resistive Random-Access Memory (RRAM) devices can be used to efficiently implement various in-memory computing operations, such as Multiply Accumulate (MAC) and unrolled-convolutions, which are used extensively in Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). However, memristive devices face concerns of aging and non-idealities, which limit the accuracy, reliability, and robustness of Memristive Deep Learning Systems (MDLSs), that should be considered prior to circuit-level realization. This Original Software Publication(OSP) presents MemTorch, an open-source11https://github.com/coreylammie/MemTorch framework for customized large-scale memristive Deep Learning (DL) simulations, with a refined focus on the co-simulation of device non-idealities. MemTorch also facilitates co-modelling of key crossbar peripheral circuitry. MemTorch adopts a modernized software engineering methodology and integrates directly with the well-known PyTorch Machine Learning (ML) library.

Title: BOLD: an ontology-based log debugger for C programs Abstract: Program debugging is often an ad hoc activity, with a combination of manual and semi-automated processing. A challenge posed by debugging is the lack of standardized procedures for instrumenting, representing, and analyzing the execution trace. Further, debugging is often low level, getting into the nitty-gritty details of variables and their semantics-rather than at a high-level. The presence of libraries only exacerbates these issues. We propose BOLD, an Ontology-based Log Debugger, to unify various activities involved in the debugging of sequential C programs. The syntactical information of programs can be represented as Resource Description Framework (RDF) triples. BOLD automatically instruments programs by querying these triples. It represents the execution trace of the program also as RDF triples called trace triples. BOLD's novel high-level reasoning abstracts these triples as spans. A span gives a way of examining the values of a particular variable over certain portions of the program execution. The properties of the spans are defined formally as a Web Ontology Language ontology. A developer can debug a given buggy program by querying the trace triples and reasoning with the spans. To empirically assess BOLD, we debugged the programs in standard Software-artifact Infrastructure Repository. Experiments related to debugging through automated reasoning show improvement in conciseness of the developers' specifications and run time performance of BOLD compared to gdb-Python.

Title: Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Abstract: The class of random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. The body of work on random features has grown rapidly, and hence it is desirable to have a comprehensive overview on this topic explaining the connections among various algorithms and theoretical results. In this survey, we systematically review the work on random features from the past ten years. First, the motivations, characteristics and contributions of representative random features based algorithms are summarized according to their sampling schemes, learning procedures, variance reduction properties and how they exploit training data. Second, we review theoretical results that center around the following key question: how many random features are needed to ensure a high approximation quality or no loss in the empirical/expected risks of the learned estimator. Third, we provide a comprehensive evaluation of popular random features based algorithms on several large-scale benchmark datasets and discuss their approximation quality and prediction performance for classification. Last, we discuss the relationship between random features and modern over-parameterized deep neural networks (DNNs), including the use of high dimensional random features in the analysis of DNNs as well as the gaps between current theoretical and empirical results. This survey may serve as a gentle introduction to this topic, and as a users’ guide for practitioners interested in applying the representative algorithms and understanding theoretical results under various technical assumptions. We hope that this survey will facilitate discussion on the open problems in this topic, and more importantly, shed light on future research directions. Due to the page limit, we suggest the readers refer to the full version of this survey <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://arxiv.org/abs/2004.11154</uri> .

Title: A branch-and-Benders-cut algorithm for a bi-objective stochastic facility location problem Abstract: In many real-world optimization problems, more than one objective plays a role and input parameters are subject to uncertainty. In this paper, motivated by applications in disaster relief and public facility location, we model and solve a bi-objective stochastic facility location problem. The considered objectives are cost and covered demand, where the demand at the different population centers is uncertain but its probability distribution is known. The latter information is used to produce a set of scenarios. In order to solve the underlying optimization problem, we apply a Benders’ type decomposition approach which is known as the L-shaped method for stochastic programming and we embed it into a recently developed branch-and-bound framework for bi-objective integer optimization. We analyze and compare different cut generation schemes and we show how they affect lower bound set computations, so as to identify the best performing approach. Finally, we compare the branch-and-Benders-cut approach to a straight-forward branch-and-bound implementation based on the deterministic equivalent formulation.

Title: UAV-Enabled Data Collection for Wireless Sensor Networks With Distributed Beamforming Abstract: This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios with delay-tolerant and delay-sensitive applications, in which the GNs send the common/shared messages to the UAV via adaptive- and fixed-rate transmissions, respectively. For the two scenarios, we aim to maximize the average data-rate throughput and minimize the transmission outage probability, respectively, by jointly optimizing the UAV’s trajectory design and the GNs’ transmit power allocation over time, subject to the UAV’s flight speed constraints and the GNs’ individual average power constraints. However, the two formulated problems are both non-convex and thus generally difficult to be optimally solved. To tackle this issue, we first consider the relaxed problems in the ideal case with the UAV’s flight speed constraints ignored, for which the well-structured optimal solutions are obtained to reveal the fundamental performance upper bounds. It is shown that for the two approximate problems, the optimal trajectory solutions have the same multi-location-hovering structure, but with different optimal power allocation strategies. Next, for the general problems with the UAV’s flight speed constraints considered, we propose efficient algorithms to obtain high-quality solutions by using the techniques from convex optimization and approximation. Finally, numerical results show that our proposed designs significantly outperform other benchmark schemes, in terms of the achieved data-rate throughput and outage probability under the two scenarios. It is also observed that when the mission period becomes sufficiently long, our proposed designs approach the performance upper bounds when the UAV’s flight speed constraints are ignored.

Title: Path Integral Policy Improvement With Population Adaptation Abstract: Path integral policy improvement (PI2) is known to be an efficient reinforcement learning algorithm, particularly, if the target system is a high-dimensional dynamical system. However, PI2, and its existing extensions, have adjustable parameters, on which the efficiency depends significantly. This article proposes an extension of PI2 that adjusts all of the critica...

Title: Quasi-Developable B-Spline Surface Design with Control Rulings Abstract: We propose a method for generating a ruled B-spline surface fitting to a sequence of pre-defined ruling lines and the generated surface is required to be as developable as possible. Specifically, the terminal ruling lines are treated as hard constraints. Different from existing methods that compute a quasi-developable surface from two boundary curves and cannot achieve explicit ruling control, our method controls ruling lines in an intuitive way and serves as an effective tool for computing quasi-developable surfaces from freely-designed rulings. We treat this problem from the point of view of numerical optimization and solve for surfaces meeting the distance error tolerance allowed in applications. The performance and the efficacy of the proposed method are demonstrated by the experiments on a variety of models including an application of the method for the path planning in 5-axis computer numerical control (CNC) flank milling.

Title: Threshold Phenomena for Random Cones Abstract: We consider an even probability distribution on the d-dimensional Euclidean space with the property that it assigns measure zero to any hyperplane through the origin. Given N independent random vectors with this distribution, under the condition that they do not positively span the whole space, the positive hull of these vectors is a random polyhedral cone (and its intersection with the unit sphere is a random spherical polytope). It was first studied by Cover and Efron. We consider the expected face numbers of these random cones and describe a threshold phenomenon when the dimension d and the number N of random vectors tend to infinity. In a similar way we treat the solid angle, and more generally the Grassmann angles. We further consider the expected numbers of k-faces and of Grassmann angles of index d - k when also k tends to infinity.

Title: Optimal threshold padlock systems. Abstract: In 1968, Liu described the problem of securing the documents in a shared secret research project. In his example, at least six out of eleven participating scientists need to be present to open the lock securing the secret documents. Shamir proposed a mathematical solution to this physical problem in 1979, by designing the first efficient k-out-of-n secret sharing scheme based on a smart usage of Lagrange's interpolation. Shamir also claimed that the minimal solution using physical padlocks is clearly impractical and exponential in the number of participants. In this paper we propose an optimal physical solution to the problem of Liu that uses physical padlocks, but the number of locks is at most equal to the number of participants. This device is optimal for k-out-of-n threshold padlock systems as soon as $k > $\sqrt$ 2n$. We also propose an optimal scheme implementing a 2-out-of-n threshold padlock system requiring only about $2 log[2](n)$ padlocks. Then we derive some lower bounds required to implement threshold systems in general. Finally, we discuss more complex access schemes together with other realizations with strictly less than n padlocks.

Title: Disjoint direct product decompositions of permutation groups Abstract: Let H≤Sn be an intransitive group with orbits Ω1,Ω2,…,Ωk. Then certainly H is a subdirect product of the direct product of its projections onto each orbit, H|Ω1×H|Ω2×…×H|Ωk. Here we provide a polynomial time algorithm for computing the finest partition P of the H-orbits such that H=∏c∈PH|c and we demonstrate its usefulness in some applications.

Title: Hybrid Combining of Directional Antennas for Periodic Broadcast V2V Communication Abstract: A hybrid analog-digital combiner for broadcast vehicular communication is proposed. It has an analog part that does not require any channel state information or feedback from the receiver, and a digital part that uses maximal ratio combining (MRC). We focus on designing the analog part of the combiner to optimize the received signal strength along all azimuth angles for robust periodic vehicle-to-...

Title: Revenue maximization approaches in IaaS clouds: Research challenges and opportunities Abstract: This study critically reviews the IaaS clouds development on revenue maximization since 2012 to answer these research queries; (i) What are the main influential factors towards revenue maximization in the cloud market? (ii) What are the main challenges and resistance towards revenue maximization in cloud computing? and (iii) What are the possible solutions and potentials to these hurdles in cloud computing? The data was analyzed and the influencing factors of revenue maximization were classified into seven distinct categories, that is, the performance of the services, service level agreement and penalties management, resources scalability, resources utilization and scheduling, customers' satisfaction, cost, and pricing management, as well as advertisement and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered. These studies are compared against each other for the seven distinct categories and solutions are proposed for the clouds' obstacles to revenue maximization. Furthermore, in the light of the findings and revenue maximization categories, the main limitations, challenges, true potential, and new directions towards revenue maximization are explored.

Title: Age of Information for Single Buffer Systems With Vacation Server Abstract: In this research, we study the information freshness in M/G/1 queueing system with a single buffer and the server taking multiple vacations. This system has wide applications in communication systems. We aim to evaluate the information freshness in this system with both i.i.d. and non-i.i.d. vacations under three different scheduling policies, namely Conventional Buffer System (CBS), Buffer Relaxation System (BRS), and Conventional Buffer System with Preemption in Service (CBS-P). For the systems with i.i.d. vacations, we derive the closed-form expressions of information freshness metrics such as the expected Age of Information (AoI), the expected Peak Age of Information (PAoI), and the variance of peak age under each policy. For systems with non-i.i.d. vacations, we use the polling system as an example and provide the closed-form expression of its PAoI under each policy. We explore the conditions under which one of these policies has advantages over the others for each information freshness metric. We further perform numerical studies to validate our results and develop insights.

Title: Generalized Assignment for Multi-Robot Systems via Distributed Branch-And-Price Abstract: In this article, we consider a network of agents that has to self-assign a set of tasks while respecting resource constraints. One possible formulation is the generalized assignment problem, where the goal is to find a maximum payoff while satisfying capability constraints. We propose a purely distributed branch-and-price algorithm to solve this problem in a cooperative fashion. Inspired by classical (centralized) branch-and-price schemes, in the proposed algorithm, each agent locally solves small linear programs, generates columns by solving simple knapsack problems, and communicates to its neighbors a fixed number of basic columns. We prove finite-time convergence of the algorithm to an optimal solution of the problem. Then, we apply the proposed scheme to a generalized assignment scenario, in which a team of robots has to serve a set of tasks. We implement the proposed algorithm in a Robot Operating System testbed and provide experiments for a team of heterogeneous robots solving the assignment problem.

Title: Asymptotically Achieving Centralized Rate on the Decentralized Network MISO Channel Abstract: In this paper, we analyze the high-SNR regime of the $M\times K$ Network MISO channel in which each transmitter has access to a different channel estimate, possibly with different precision. It has been recently shown that, for some regimes, this setting attains the same Degrees-of-Freedom as the ideal centralized setting wit...

Title: Nonlinear Dynamic Systems Parameterization Using Interval-Based Global Optimization: Computing Lipschitz Constants and Beyond Abstract: Numerous state-feedback and observer designs for nonlinear dynamic systems (NDS) have been developed in the past three decades. These designs assume that NDS nonlinearities satisfy one of the following <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">function set classifications:</i> bounded Jacobian, Lipschitz continuity, one-sided Lipschitz, quadratic inner-boundedness, and quadratic boundedness. These function sets are characterized by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constant</i> scalars or matrices bounding the NDS’ nonlinearities. These constants depend on the NDS’ operating region, topology, and parameters and are utilized to synthesize observer/controller gains. Unfortunately, there is a near-complete absence of algorithms to compute such bounding constants. In this article, we develop analytical and computational methods to compute such constants. First, for every function set classification, we derive analytical expressions for these bounding constants through global maximization formulations. Second, we utilize a derivative-free interval-based global maximization algorithm based on the branch-and-bound framework to numerically obtain the bounding constants. Third, we showcase the effectiveness of our approaches to compute the corresponding parameters on some NDS such as highway traffic networks and synchronous generator models.

Title: CS-AF: A cost-sensitive multi-classifier active fusion framework for skin lesion classification Abstract: Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. Compared with single CNN classifier, combining the results of multiple classifiers via fusion approaches shows to be more effective and robust. Since the skin lesion datasets are usually limited and statistically biased, while designing an effective fusion approach, it is important to consider not only the performance of each classifier on the training/validation dataset, but also the relative discriminative power (e.g., confidence) of each classifier regarding an individual sample in the testing phase, which calls for an active fusion approach. Furthermore, in skin lesion analysis, the data of certain classes (e.g., the benign lesions) is usually abundant which makes them an over-represented majority, while the data of some other classes (e.g., the cancerous lesions) is deficient which makes them an underrepresented minority. It is more crucial to precisely identify the samples from an underrepresented (i.e., in terms of the amount of data) but more important minority class (e.g., cancerous skin lesions). In other words, misclassifying a more severe skin lesion to a benign or less severe skin lesion should have relative more cost (e.g., money, time and even lives). To address such challenges, we present CS-AF, a cost-sensitive multi-classifier active fusion framework for skin lesion classification. In the experimental evaluation, we prepared 96 base classifiers (of 12 CNN architectures) on the ISIC Challenge 2019 research dataset. Our experimental results show that our framework consistently outperforms both the static and the active fusion competitors in terms of the accuracy and total costs.

Title: SplitFed: When Federated Learning Meets Split Learning. Abstract: Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data. SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server. Moreover, the split model makes SL a better option for resource-constrained environments. However, SL performs slower than FL due to the relay-based training across multiple clients. In this regard, this paper presents a novel approach, named splitfed learning (SFL), that amalgamates the two approaches eliminating their inherent drawbacks, along with a refined architectural configuration incorporating differential privacy and PixelDP to enhance data privacy and model robustness. Our analysis and empirical results demonstrate that (pure) SFL provides similar test accuracy and communication efficiency as SL while significantly decreasing its computation time per global epoch than in SL for multiple clients. Furthermore, as in SL, its communication efficiency over FL improves with the number of clients. Besides, the performance of SFL with privacy and robustness measures is further evaluated under extended experimental settings.

Title: Urban Anomaly Analytics: Description, Detection, and Prediction Abstract: Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatical...

Title: Low-Complexity Switch Scheduling Algorithms: Delay Optimality in Heavy Traffic Abstract: Motivated by applications in data center networks, in this paper, we study the problem of scheduling in an input queued switch. While throughput maximizing algorithms in a switch are well-understood, delay analysis was developed only recently. It was recently shown that the well-known MaxWeight algorithm achieves optimal scaling of mean queue lengths in steady state in the heavy-traffic regime, an...

Title: When CNNs meet random RNNs: Towards multi-level analysis for RGB-D object and scene recognition Abstract: Recognizing objects and scenes are two challenging but essential tasks in image understanding. In particular, the use of RGB-D sensors in handling these tasks has emerged as an important area of focus for better visual understanding. Meanwhile, deep neural networks, specifically convolutional neural networks (CNNs), have become widespread and have been applied to many visual tasks by replacing hand-crafted features with effective deep features. However, it is an open problem how to exploit deep features from a multi-layer CNN model effectively. In this paper, we propose a novel two-stage framework that extracts discriminative feature representations from multi-modal RGB-D images for object and scene recognition tasks. In the first stage, a pretrained CNN model has been employed as a backbone to extract visual features at multiple levels. The second stage maps these features into high level representations with a fully randomized structure of recursive neural networks (RNNs) efficiently. To cope with the high dimensionality of CNN activations, a random weighted pooling scheme has been proposed by extending the idea of randomness in RNNs. Multi modal fusion has been performed through a soft voting approach by computing weights based on individual recognition confidences (i.e. SVM scores) of RGB and depth streams separately. This produces consistent class label estimation in final RGB-D classification performance. Extensive experiments verify that fully randomized structure in RNN stage encodes CNN activations to discriminative solid features successfully. Comparative experimental results on the popular Washington RGB-D Object and SUN RGB-D Scene datasets show that the proposed approach achieves superior or on-par performance compared to state-of-the-art methods both in object and scene recognition tasks. Code is available at https://github.com/acaglayan/CNN_randRNN.

Title: Cross-domain structure preserving projection for heterogeneous domain adaptation Abstract: •Extending locality preserving projection (LPP) to multi-domain scenarios.•Proposing a cross-domain structure preserving projection (CDSPP) algorithm for heterogeneous domain adaptation (HDA).•A progressive pseudo-labelling strategy is proposed for semi-supervised HDA.•A new benchmark for HDA with significantly more classes than the existing ones is presented.•Extensive experiments are conducted to validate the effectiveness of our proposed method for both supervised and semi-supervised HDA.

Title: <p>Hamiltonicity in Cherry-quasirandom 3-graphs</p> Abstract: We show that for any fixed alpha > 0, cherry-quasirandom 3-graphs vertex degree alpha(n of positive density and sufficiently large order n with minimum alpha((n)(2))) have a tight Hamilton cycle. This solves a & nbsp;conjecture of Aigner-Horev and Levy. (C)& nbsp;2021 Elsevier Ltd. All rights reserved.

Title: Generalization of affine feedback stock trading results to include stop-loss orders Abstract: The main question we would like to address in this paper is as follows: Given a geometric Brownian motion (GBM) as the underlying stock price model, what is the cumulative distribution function (CDF) for the trading profit or loss, call it g(t), when an affine feedback control strategy with stop-loss order is considered? Moreover, is it possible to obtain a closed-form characterization for the desired CDF for g(t) so that a theoretician or practical trader might be benefited from it? The answers to these questions are affirmative. In this paper, we provide a closed-form expression for the cumulative distribution function for the trading profit or loss. In addition, we show that the affine feedback controller with stop-loss order indeed generalizes the result without stop order in the sense of distribution function. Some simulations and illustrative examples are also provided as supporting evidence of the theory.

Title: On partitions with k corners not containing the staircase with one more corner Abstract: We give three proofs of the following result conjectured by Carriegos, De Castro-Garcia and Mufioz Castafieda in their work on enumeration of control systems: when ((k+1) (2)) <= n < ((k+2)(2)), there are as many partitions of n with k corners as pairs of partitions (alpha, beta) such that ((k+1)(2)) + vertical bar alpha vertical bar + vertical bar beta vertical bar = n. (C) 2022 Elsevier B.V. All rights reserved.

Title: Two-Stage Robust Edge Service Placement and Sizing Under Demand Uncertainty Abstract: Edge computing has emerged as a key technology to reduce network traffic, improve user experience, and enable numerous Internet of Things applications. In this article, we study an optimal resource procurement problem for a service provider (SP), who can purchase resources from various edge nodes in the edge computing market to serve its users’ requests. How to jointly optimize the service placeme...

Title: Delay-dependent Asymptotic Stability of Highly Nonlinear Stochastic Differential Delay Equations Driven by G-Brownian Motion Abstract: Based on the classical probability, the stability of stochastic differential delay equations (SDDEs) whose coefficients are growing at most linearly has been investigated intensively. Moreover, the delay-dependent stability of highly nonlinear hybrid stochastic differential equations (SDEs) has also been studied recently. In this paper, using the nonlinear expectation theory, we first explore the delay-dependent criteria on the asymptotic stability for a class of highly nonlinear SDDEs driven by G-Brownian motion (G-SDDEs). Then, the (weak) quasi-sure stability of solutions to G-SDDEs is developed. Finally, an example is analyzed by the φ-max-mean algorithm to illustrate our theoretical results.

Title: Deep Auto-Encoders With Sequential Learning for Multimodal Dimensional Emotion Recognition Abstract: Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area. However, several questions still remain unanswered for most of existing approaches including: (i) how to simultaneously learn compact yet representative features from multimodal data, (ii) how to effectively capture complementary features from multimodal streams, and (iii) how to perform all the tasks in an end-to-end manner. To address these challenges, in this paper, we propose a novel deep neural network architecture consisting of a two-stream auto-encoder and a long short term memory for effectively integrating visual and audio signal streams for emotion recognition. To validate the robustness of our proposed architecture, we carry out extensive experiments on the multimodal emotion in the wild dataset: RECOLA. Experimental results show that the proposed method achieves state-of-the-art recognition performance.

Title: Power divergence approach for one-shot device testing under competing risks Abstract: Most work on one-shot devices assume that there is only one possible cause of device failure. However, in practice, it is often the case that the products under study can experience any one of various possible causes of failure. Robust estimators and Wald-type tests are developed here for the case of one-shot devices under competing risks. An extensive simulation study illustrates the robustness of these divergence-based estimators and test procedures based on them. A data-driven procedure is proposed for choosing the optimal estimator for any given data set which is then applied to an example in the context of survival analysis.

Title: Boundary Element Methods for Helmholtz Problems With Weakly Imposed Boundary Conditions Abstract: We consider boundary element methods where the Calder\'on projector is used for the system matrix and boundary conditions are weakly imposed using a particular variational boundary operator designed using techniques from augmented Lagrangian methods. Regardless of the boundary conditions, both the primal trace variable and the flux are approximated. We focus on the imposition of Dirichlet conditions on the Helmholtz equation, and extend the analysis of the Laplace problem from \emph{Boundary element methods with weakly imposed boundary conditions} to this case. The theory is illustrated by a series of numerical examples.

Title: Topology and Geometry of Random 2-Dimensional Hypertrees Abstract: A hypertree, or $$\mathbb {Q}$$ -acyclic complex, is a higher-dimensional analogue of a tree. We study random 2-dimensional hypertrees according to the determinantal measure suggested by Lyons. We are especially interested in their topological and geometric properties. We show that with high probability, a random 2-dimensional hypertree T is aspherical, i.e., that it has a contractible universal cover. We also show that with high probability the fundamental group $$\pi _1(T)$$ is hyperbolic and has cohomological dimension 2.

Title: A Repeated Route-then-Schedule Approach to Coordinated Vehicle Platooning: Algorithms, Valid Inequalities and Computation. Abstract: Platooning of vehicles is a promising approach for reducing fuel consumption, increasing vehicle safety, and using road space more efficiently. The difficult problem of assigning optimal routes and departure schedules to a collection of vehicles is therefore important. We propose an iterative route-then-schedule approach for centralized planning that quickly converges to high-quality solutions. We also propose and analyze a collection of valid inequalities for the individual problems of assigning vehicles to routes and scheduling the times that vehicles traverse their routes. These inequalities are shown to reduce the computational time or optimality gap of solving the routing and scheduling problem instances. Our approach uses the valid inequalities in both the routing and scheduling portions of each iteration; numerical experiments highlight the speed of the approach for routing vehicles on a real-world road network.

Title: The VC-dimension of axis-parallel boxes on the Torus Abstract: We show in this paper that the VC-dimension of the family of d-dimensional axis-parallel boxes and cubes on the d-dimensional torus are both asymptotically dlog2⁡d. This is especially surprising as in most other examples the VC-dimension usually grows linearly with d in similar settings.

Title: AN AVERAGING PROCESS ON HYPERGRAPHS Abstract: Consider the following iterated process on a hypergraph H. Each vertex v starts with some initial weight x(v). At each step. uniformly at random select an edge e in H, and for each vertex v in e replace the weight of v by the average value of the vertex weights over all vertices in e. This is a generalization of an interactive process on graphs which was first introduced by Aldous and Lanoue. In this paper we use the eigenvalues of a Laplacian for hypergraphs to bound the rate of convergence for this iterated averaging process.

Title: Preconditioned Legendre Spectral Galerkin Methods for the Non-separable Elliptic Equation Abstract: The Legendre spectral Galerkin method of self-adjoint second order elliptic equations usually results in a linear system with a dense and ill-conditioned coefficient matrix. In this paper, the linear system is solved by a preconditioned conjugate gradient (PCG) method where the preconditioner M is constructed by approximating the variable coefficients with a (T+1)-term Legendre series in each direction to a desired accuracy. A feature of the proposed PCG method is that the iteration step increases slightly with the size of the resulting matrix when reaching a certain approximation accuracy. The efficiency of the method lies in that the system with the preconditioner M is approximately solved by a one-step method based on the incomplete LU factorization technique with no fill-in, denoted by ILU(0). The ILU(0) factorization of M is an element of R(N-1)dx(N-1)d can be computed using O(T-2d N-d) operations, and the number of nonzeros in the factorization factors is of O(T-d N-d), d = 1, 2, 3. A conclusion of the algorithm is to fast solve the resulting system from the Legendre Galerkin spectral method for Poisson equations with Dirichlet boundary conditions, which has a complexity of O(N-d). To further speed up the PCG method, an algorithm is developed for fast matrix-vector multiplications by the resulting matrix of Legendre-Galerkin spectral discretization, without the need to explicitly form it. The complexity of the fast matrix-vector multiplications is of O(N-d (log(2) N)(2)). In view that T is independent of N in one dimension and is set to be of order O(log(2) N) in two and three dimensions, the PCG method has a O(N-d (log(2) N)(2d)) quasi-optimal complexity for a d dimensional domain with (N-1)(d) unknows, d = 1, 2, 3. In addition, a fast direct solver for the three-dimensional Poisson equation is developed, which is of O(N-3(log(2) N)(2)) and improves the existing results on the computational complexity. Numerical examples are given to demonstrate the efficiency of proposed preconditioners and the algorithm for fast matrix-vector multiplications.

Title: Bounds on the Lattice Point Enumerator via Slices and Projections Abstract: Gardner et al. posed the problem to find a discrete analogue of Meyer’s inequality bounding from below the volume of a convex body by the geometric mean of the volumes of its slices with the coordinate hyperplanes. Motivated by this problem, for which we provide a first general bound, we study in a more general context the question of bounding the number of lattice points of a convex body in terms of slices, as well as projections.

Title: Resolving the Feedback Bottleneck of Multi-Antenna Coded Caching Abstract: Multi-antenna cache-aided wireless networks were thought to suffer from a severe feedback bottleneck, since achieving the maximal Degrees-of-Freedom (DoF) performance required feedback from all served users for the known transmission schemes. These feedback costs match the caching gains and thus scale with the number of users. In the context of the $L$ <...

Title: Whittle index based Q-learning for restless bandits with average reward Abstract: A novel reinforcement learning algorithm is introduced for multiarmed restless bandits with average reward, using the paradigms of Q-learning and Whittle index. Specifically, we leverage the structure of the Whittle index policy to reduce the search space of Q-learning, resulting in major computational gains. Rigorous convergence analysis is provided, supported by numerical experiments. The numerical experiments show excellent empirical performance of the proposed scheme.

Title: Wavelet-based estimation in a semiparametric regression model Abstract: In this paper, we introduce a wavelet-based method for estimating the effective dimension reduction (EDR) space in the semiparametric regression model introduced by Li [Sliced inverse regression for dimension reduction, J. Amer. Statist. Assoc. 86 (1991) 316-327]. This method is obtained by using linear wavelet estimators of the density and regression functions that are involved in the covariance matrix of conditional expectation whose eigenvectors span the EDR space. Then, consistency of the proposed estimators is proved. A simulation study that allows one to evaluate the performance of the proposal with comparison to existing methods is presented.

Title: Learning Deformable Image Registration From Optimization: Perspective, Modules, Bilevel Training and Beyond Abstract: Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning-based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints which are indispensable to generate plausible deformations, e.g., topology-preserving. Moreover, these learning-based approaches typically pose hyper-parameter learning as a black-box problem and require considerable computational and human effort to perform many training runs. To tackle the aforementioned problems, we propose a new learning-based framework to optimize a diffeomorphic model via multi-scale propagation. Specifically, we introduce a generic optimization model to formulate diffeomorphic registration and develop a series of learnable architectures to obtain propagative updating in the coarse-to-fine feature space. Further, we propose a new bilevel self-tuned training strategy, allowing efficient search of task-specific hyper-parameters. This training strategy increases the flexibility to various types of data while reduces computational and human burdens. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data. Extensive results demonstrate the state-of-the-art performance of the proposed method with diffeomorphic guarantee and extreme efficiency. We also apply our framework to challenging multi-modal image registration, and investigate how our registration to support the down-streaming tasks for medical image analysis including multi-modal fusion and image segmentation.

Title: Generation of Accessible Sets in the Dynamical Modeling of Quantum Network Systems Abstract: In this article, we consider the dynamical modeling of a class of quantum network systems consisting of qubits, where information extraction is allowed by performing measurement on several selected qubits of the system. For a variety of applications, a state space model is a useful approach to modeling the system dynamics. To construct a state space model for a quantum network system, the major task is to find an accessible set containing all of the operators coupled to the measurement operators. This article focuses on the generation of a proper accessible set for a given system and measurement scheme. We provide analytic results on simplifying the process of generating accessible sets for systems with a time-independent Hamiltonian. Since the order of elements in the accessible set determines the form of state space matrices, guidance is provided to effectively arrange the ordering of elements in the state vector. Defining a system state according to the accessible set, one can develop a state space model with a special pattern inherited from the system structure. As a demonstration, we specifically consider a typical 1-D-chain system with several common measurements and employ the proposed method to determine its accessible set.

Title: The Lipschitz constant of perturbed anonymous games Abstract: The Lipschitz constant of a game measures the maximal amount of influence that one player has on the payoff of some other player. The worst-case Lipschitz constant of an n-player k-action $$\delta $$ -perturbed game, $$\lambda (n,k,\delta )$$ , is given an explicit probabilistic description. In the case of $$k\ge 3$$ , it is identified with the passage probability of a certain symmetric random walk on $${\mathbb {Z}}$$ . In the case of $$k=2$$ and n even, $$\lambda (n,2,\delta )$$ is identified with the probability that two i.i.d. binomial random variables are equal. The remaining case, $$k=2$$ and n odd, is bounded through the adjacent (even) values of n. Our characterization implies a sharp closed-form asymptotic estimate of $$\lambda (n,k,\delta )$$ as $$\delta n /k\rightarrow \infty $$ .

Title: The Structure of $I_4$-Free and Triangle-Free Binary Matroids Abstract: A simple binary matroid is called $I_4$-free if none of its rank-4 flats are independent sets. These objects can be equivalently defined as the sets $E$ of points in $PG(n-1,2)$ for which $|E \cap F|$ is not a basis of $F$ for any four-dimensional flat $F$. We prove a decomposition theorem that exactly determines the structure of all $I_4$-free and triangle-free matroids. In particular, our theorem implies that the $I_4$-free and triangle-free matroids have critical number at most $2$.

Title: Equilibrium customer and socially optimal balking strategies in a constant retrial queue with multiple vacations and N-policy Abstract: In this paper, equilibrium strategies and optimal balking strategies of customers in a constant retrial queue with multiple vacations and the N-policy under two information levels, respectively, are investigated. We assume that there is no waiting area in front of the server and an arriving customer is served immediately if the server is idle; otherwise (the server is either busy or on a vacation) it has to leave the system to join a virtual retrial orbit waiting for retrials according to the FCFS rules. After a service completion, if the system is not empty, the server becomes idle, available for serving the next customer, either a new arrival or a retried customer from the virtual retrial orbit; otherwise (if the system is empty), the server starts a vacation. Upon the completion of a vacation, the server is reactivated only if it finds at least N customers in the virtual orbit; otherwise, the server continues another vacation. We study this model at two levels of information, respectively. For each level of information, we obtain both equilibrium and optimal balking strategies of customers, and make corresponding numerical comparisons. Through Particle Swarm Optimization (PSO) algorithm, we explore the impact of parameters on the equilibrium and social optimal thresholds, and obtain the trend in changes, as a function of system parameters, for the optimal social welfare, which provides guiding significance for social planners. Finally, by comparing the social welfare under two information levels, we find that whether the system information should be disclosed to customers depends on how to maintain the growth of social welfare.

Title: Higher Convexity and Iterated Sum Sets Abstract: Let f be a smooth real function with strictly monotone first k derivatives. We show that for a finite set A, with ∣A + A∣ ≤K∣A∣, $$\left| {{2^k}f(A) - ({2^k} - 1)f(A)} \right|{ \gg _k}\,{\left| A \right|^{k + 1 - o(1)}}/{K^{{O_k}(1)}}.$$ We deduce several new sum-product type implications, e.g. that A+A being small implies unbounded growth for a many enough times iterated product set A ⋯ A.

Title: A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics Abstract: In this article, we consider an optimization problem where the objective function is evaluated at the fixed-point of a contraction mapping parameterized by a control variable, and optimization takes place over this control variable. Since the derivative of the fixed-point with respect to the parameter can usually not be evaluated exactly, an adjoint dynamical system can be used to estimate gradients. Using this estimation procedure, the optimization algorithm alternates between derivative estimation and an approximate gradient descent step. We analyze a variant of this approach involving dynamic time-scaling, where after each parameter update the adjoint system is iterated until a convergence threshold is passed. We prove that, under certain conditions, the algorithm can find approximate stationary points of the objective function. We demonstrate the approach in the settings of an inverse problem in chemical kinetics, and learning in attractor networks.

Title: Energy-Efficient Wireless Communications With Distributed Reconfigurable Intelligent Surfaces Abstract: This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients m...

Title: Lie Algebraic Unscented Kalman Filter for Pose Estimation Abstract: An unscented Kalman filter (UKF) for matrix Lie groups is proposed where the time propagation of the state is formulated on the Lie algebra. This is done with the kinematic differential equation of the logarithm, where the inverse of the right Jacobian is used. The sigma points can then be expressed as logarithms in vector form, and time propagation of the sigma points and the computation of the mean and the covariance can be done on the Lie algebra. The resulting formulation is to a large extent based on logarithms in vector form and is, therefore, closer to the UKF for systems in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {R}^n$</tex-math></inline-formula> . This gives an elegant and well-structured formulation, which provides additional insight into the problem, and which is computationally efficient. The proposed method is in particular formulated and investigated on the matrix Lie group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> . A discussion on right and left Jacobians is included, and a novel closed-form solution for the inverse of the right Jacobian on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> is derived, which gives a compact representation involving fewer matrix operations. The proposed method is validated in simulations.

Title: Scaled Vecchia Approximation for Fast Computer-Model Emulation Abstract: Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia's ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.

Title: Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks Abstract: A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

Title: Optimal Scheduling of Age-Centric Caching: Tractability and Computation Abstract: The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache for making updates. Each content is associated with a utility function that depends on the AoI and the time duration of absence from the cache. For this combinatorial optimization problem, we present the following contributions. First, we provide theoretical results of problem tractability. Whereas the problem is NP-hard, we prove solution tractability in polynomial time for a special case where all contents have the same size, by a reformulation using network flows. Second, we derive an integer linear formulation for the problem, of which the optimal solution can be obtained for small-scale scenarios. Next, via a mathematical reformulation, we derive a scalable optimization algorithm using repeated column generation. In addition, the algorithm computes a bound of global optimum, that can be used to assess the performance of any scheduling solution. Performance evaluation of large-scale scenarios demonstrates the strengths of the algorithm in comparison to a greedy schedule. Finally, we extend the applicability of our work to cyclic scheduling.

Title: Distributions of restricted rotation distances. Abstract: Rotation distances measure the differences in structure between rooted ordered binary trees. The one-dimensional skeleta of associahedra are rotation graphs, where two vertices representing trees are connected by an edge if they differ by a single rotation. There are no known efficient algorithms to compute rotation distance between trees and thus distances in rotation graphs. Limiting the allowed locations of where rotations are permitted gives rise to a number of notions related to rotation distance. Allowing rotations at a minimal such set of locations gives restricted rotation distance. There are linear-time algorithms to compute restricted rotation distance, where there are only two permitted locations for rotations to occur. The associated restricted rotation graph has an efficient distance algorithm. There are linear upper and lower bounds on restricted rotation distance with respect to the sizes of the reduced tree pairs. Here, we experimentally investigate the expected restricted rotation distance between two trees selected at random of increasing size and find that it lies typically in a narrow band well within the earlier proven linear upper and lower bounds.

Title: TREVERSE: TRial-and-Error Lightweight Secure ReVERSE Authentication With Simulatable PUFs Abstract: A physical unclonable function (PUF) generates hardware intrinsic volatile secrets by exploiting uncontrollable manufacturing randomness. Although PUFs provide the potential for lightweight and secure authentication for increasing numbers of low-end Internet of Things devices, practical and secure mechanisms remain elusive. We aim to explore simulatable PUFs (SimPUFs) that are physically unclonable but efficiently modeled mathematically through privileged one-time PUF access to address the above problem. Given a challenge, a securely stored SimPUF in possession of a trusted server computes the corresponding response and its bit-specific reliability. Consequently, naturally noisy PUF responses generated by a resource limited prover can be immediately processed by a one-way function (OWF) and transmitted to the server, because the resourceful server can exploit the SimPUF to perform a trial-and-error search over likely error patterns to recover the noisy response to authenticate the prover. Security of trial-and-error reverse (TREVERSE) authentication under the random oracle model is guaranteed by the hardness of inverting the OWF. We formally evaluate the TREVERSE authentication capability with two SimPUFs experimentally derived from popular silicon PUFs.

Title: A Novel Self-Organizing Fuzzy Neural Network to Learn and Mimic Habitual Sequential Tasks Abstract: In this article, a new self-organizing fuzzy neural network (FNN) model is presented which is able to simultaneously and accurately learn and reproduce different sequences. Multiple sequence learning is important in performing habitual and skillful tasks, such as writing, signing signatures, and playing piano. Generally, it is indispensable for pattern generation applications. Since multiple seque...

Title: Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning Abstract: Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing. However, efficient processing and learning of discrete spikes still remain a challenging problem. In this article, ...

Title: Cubic vertex-transitive graphs of girth six Abstract: In this paper, a complete classification of finite simple cubic vertex-transitive graphs of girth 6 is obtained. It is proved that every such graph, with the exception of the Desargues graph on 20 vertices, is either a skeleton of a hexagonal tiling of the torus, the skeleton of the truncation of an arc-transitive triangulation of a closed hyperbolic surface, or the truncation of a 6-regular graph with respect to an arc-transitive dihedral scheme. Cubic vertex-transitive graphs of girth larger than 6 are also discussed. (c) 2021 Elsevier B.V. All rights reserved.

Title: Cross-Validation-based Adaptive Sampling for Gaussian Process Models Abstract: In many real-world applications, we are interested in approximating black-box, costly functions as accurately as possible with the smallest number of function evaluations. A complex computer code is an example of such a function. In this work, a Gaussian process (GP) emulator is used to approximate the output of complex computer code. We consider the problem of extending an initial experiment sequentially to improve the emulator. A sequential sampling approach based on leave-one-out (LOO) cross-validation is proposed that can be easily extended to a batch mode. This is a desirable property since it saves the user time when parallel computing is available. After fitting a GP to training data points, the expected squared LOO error ($ESE_{LOO}$) is calculated at each design point. $ESE_{LOO}$ is used as a measure to identify important data points. More precisely, when this quantity is large at a point it means that the quality of prediction depends a great deal on that point and adding more samples in the nearby region could improve the accuracy of the GP model. As a result, it is reasonable to select the next sample where $ESE_{LOO}$ is maximum. However, such quantity is only known at the experimental design and needs to be estimated at unobserved points. To do this, a second GP is fitted to the $ESE_{LOO}$s and where the maximum of the modified expected improvement (EI) criterion occurs is chosen as the next sample. EI is a popular acquisition function in Bayesian optimisation and is used to trade-off between local/global search. However, it has tendency towards exploitation, meaning that its maximum is close to the (current) "best" sample. To avoid clustering, a modified version of EI, called pseudo expected improvement, is employed which is more explorative than EI and allows us to discover unexplored regions. The results show that the proposed sampling method is promising.

Title: Tractable Learning in Underexcited Power Grids Abstract: Estimating the structure of physical flow networks, such as power grids, is critical to secure delivery of energy. This article discusses statistical structure estimation in power grids in the “underexcited” regime, where a subset of internal nodes has zero injection fluctuations. Prior estimation algorithms based on nodal voltages fail for such grids as the voltage covariance matrix is not invertible. We propose a novel topology learning algorithm for learning underexcited general networks. Our algorithm uses physics-informed conservation laws to first identify the zero-injection buses and their neighbors, and then estimates the remaining edges in the grid. We prove the asymptotic correctness of our algorithm for grids with nonadjacent internal zero-injection nodes. More important, we theoretically analyze our algorithm’s efficacy under noisy measurements, and determine bounds on maximum noise under which asymptotically correct recovery is guaranteed. Our approach is validated through simulations with voltage samples generated on test distribution grids with real injection data and nonlinear power flow models.

Title: Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection Abstract: Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error of variational autoencoder (VAE) by maximizing the evidence lower bound. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error and finally arrive at a simpler yet effective model for anomaly detection. In addition, to enhance the effectiveness of detecting anomalies, we incorporate a practical model uncertainty measure into the anomaly score. We show empirically the competitive performance of our approach on benchmark data sets.

Title: The oriented swap process and last passage percolation Abstract: We present new probabilistic and combinatorial identities relating three random processes: the oriented swap process (OSP) on n particles, the corner growth process, and the last passage percolation (LPP) model. We prove one of the probabilistic identities, relating a random vector of LPP times to its dual, using the duality between the Robinson-Schensted-Knuth and Burge correspondences. A second probabilistic identity, relating those two vectors to a vector of "last swap times" in the OSP, is conjectural. We give a computer-assisted proof of this identity for n <= 6 after first reformulating it as a purely combinatorial identity, and discuss its relation to the Edelman-Greene correspondence. The conjectural identity provides precise finite-n and asymptotic predictions on the distribution of the absorbing time of the OSP, thus conditionally solving an open problem posed by Angel, Holroyd, and Romik.

Title: Deep Constraint-Based Propagation in Graph Neural Networks Abstract: The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs). We focus our attention on the originally proposed GNN model of Scarselli et al. 2009, which encodes the state of the nodes of the graph by means of an iterative diffusion procedure...

Title: On New Record Graphs Close to Bipartite Moore Graphs Abstract: The modelling of interconnection networks by graphs motivated the study of several extremal problems that involve well known parameters of a graph (degree, diameter, girth and order) and optimising one of the parameters given restrictions on some of the others. Here we focus on bipartite Moore graphs, that is, bipartite graphs attaining the optimum order, fixed either the degree/diameter or degree/girth. The fact that there are very few bipartite Moore graphs suggests the relaxation of some of the constraints implied by the bipartite Moore bound. First we deal with local bipartite Moore graphs. We find in some cases those local bipartite Moore graphs with local girths as close as possible to the local girths given by a bipartite Moore graph. Second, we construct a family of $$(q+2)$$ -bipartite graphs of order $$2(q^2+q+5)$$ and diameter 3, for q a power of prime. These graphs attain the record value for $$q=9$$ and improve the values for $$q=11$$ and $$q=13$$ .

Title: Joint Multi-Dimensional Model for Global and Time-Series Annotations Abstract: Crowdsourcing is a popular approach to collect annotations for unlabeled data instances. It involves collecting a large number of annotations from several, often naive untrained annotators for each data instance which are then combined to estimate the ground truth. Further, annotations for constructs such as affect are often multi-dimensional with annotators rating multiple dimensions, such as val...