Transience in Countable MDPs

The Transient objective is not to visit any state infinitely often . While this is not possible in any finite Markov Decision Process, it can be besatisfied in countably infinite ones, e.g., if the transition graph is acyclic . Optimal strategies for Transient need not exist, even if the MDP is infinitely branching .…

Inertial Proximal ADMM for Separable Multi Block Convex Optimizations and Compressive Affine Phase Retrieval

Affine phase retrieval arises in holography, dataseparation and phaseless sampling . Separable multi-block convex optimization problem appears in manymathematical and engineering fields . In the second part of this paper, we introduce a compressive affine phaseretrieval via lifting approach . We propose an algorithm torecover sparse real signals from their (noisy) affine quadratic measurements .…

Distributed Multi object Tracking under Limited Field of View Sensors

We consider the challenging problem of tracking multiple objects using adistributed network of sensors . The proposed algorithm is fast and requires significantly less processing time than fusion methods using multi-object filtering methods . It achieves better tracking accuracy by considering trackingerrors measured by the Optimal Sub-Pattern Assignment (OSPA) metric overseveral scans rather than a single scan.…

Social media data reveals signal for public consumer perceptions

Researchers have used social media data to estimate various macroeconomic indicators about public behaviors . But the strong correlations disappeared when those models were tested with newer data according to a recent survey . By using decadal data (2008-2019)from Reddit, we show that both monthly and daily estimates of CCI can, indeed, be reliably estimated at least several months in advance, and that our modelestimates are far superior to those generated by the existing methods .…

Resonance Frequencies of a Slab with Subwavelength Slits a Fourier transformation Approach

This paper proposes a novel, rigorous and simple Fourier-transformationapproach to study resonances in a perfectly conducting slab with finite number of subwavelength slits of width $h\ll 1$. Since regions outside the slits are variable separated, by Fourier transforming the governing equation, we could express field in the outer regions in terms of field derivatives on theaperture .…

A well balanced positivity preserving cell vertex finite volume method satisfying the discrete maximum minimum principle for coupled models of surface water flow and scalar transport

Novel well-balanced positivity preservingdiscretization techniques are proposed for the water surface elevation and the concentration of the pollutant . For the hydrodynamic system, the proposedscheme preserves the steady state of a lake at rest and the positivity of the water depth .…

Approximation of Functions on Manifolds in High Dimension from Noisy Scattered Data

In this paper, we consider the fundamental problem of approximation offunctions on a low-dimensional manifold embedded in a high-dimensional space . We propose a new approximation method that leverages theadvantages of the Manifold Locally Optimal Projection (MLOP) method and the strengths of the method ofRadial Basis Functions (RBF) The method is parametrization free, requires noknownowledge regarding the manifold’s intrinsic dimension, can handle noise andoutliers in both the function values and in the location of the data, and is applied directly in the high dimensions .…

Time Fluid Field Based Coordination through Programmable Distributed Schedulers

Emerging application scenarios, such as cyber-physical systems (CPSs), theInternet of Things (IoT), and edge computing, call for coordination approaches . Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective . We propose an alternative approach wherescheduling is programmed in a natural way (along with usual field-basedcoordination) in terms of causality fields .…

Basketball Player s Value Evaluation by a Networks based Variant Parameter Hidden Markov Model

Determining the value of basketball players through analyzing the players’ behavior is important for the managers of modern basketball teams . Existing models based on dynamicnetwork theory offer major improvements to the results of such evaluations, but said models remain imprecise because they focus merely on evaluating the values of individual players rather than considering them within their current teams .…

Bayesian Inductive Learner for Graph Resiliency under uncertainty

Graph theory is widely used framework for modeling interdependent systems and to evaluatetheir resilience to disruptions . We propose a Bayesiangraph neural network-based framework for quickly identifying critical nodes in a large graph . Instead of using the observed graph for training the model, a MAP estimate of thegraph is computed based on the observed topology, and node target labels .…

Multidimensional Uncertainty Aware Evidential Neural Networks

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various domains . Unlike Bayesian NN that indirectly infer uncertainty through weightuncertainties, evidential NNs have been recently proposed to explicitlymodel the uncertainty of class probabilities and use them for classification tasks .…

Echo Chambers and Segregation in Social Networks Markov Bridge Models and Estimation

This paper deals with the modeling and estimation of the sociologicalphenomena called echo chambers and segregation in social networks . We present a novel community-based graph model that representssthe emergence of segregated echo chambers as a Markov bridge process . A Markovbridge is a one-dimensional Markov random field that facilitates modeling theformation and disassociation of communities at deterministic times .…

Variance Reduction on Adaptive Stochastic Mirror Descent

We study the idea of variance reduction applied to adaptive stochastic mirrordescent algorithms in nonsmooth nonconvex finite-sum optimization problems . Wepropose a simple yet generalized adaptive mirror descent algorithm withvariance reduction named SVRAMD . We prove that variance reduction reduces the gradientcomplexity of most adaptive mirrors descent algorithms and boost theirconvergence.…

Feature Aided Adaptive Tuning Deep Learning for Massive Device Detection

The upcomingsixth-generation (6G) wireless network is required to support grant-free random access of a massive number of sporadic traffic devices . The paper proposes a novel deep learning framework for JADCE in 6G wireless networks . Prior-feature learning followedby an adaptive-tuning strategy is proposed, where an inner network composed of the Expectation-maximization (EM) and back-propagation is introduced to jointlytune the precision and learn the precision of the device statematrix .…

Evolution Is All You Need Phylogenetic Augmentation for Contrastive Learning

Self-supervised representation learning of biological sequence embeddings can be useful for pretraining encoders . Viewingevolution as natural sequence augmentation and maximizing information acrossphylogenetic “noisy channels” is a biologically and theoretically desirable objective . We provide an illustrative example where contrastive learning using evolutionary augmentation can be used as arepresentation learning objective which maximizes the mutual informationbetween biological sequences and their conserved function, and finally outlinerationale for this approach .…

Complex Network Influence Evaluation based on extension of Grueblers Equation

Tr-centrality is a centrality measure which focuses on using thenode triangle structure and the node neighborhood information to define the strength of a node . It is greatly significant in evaluating nodes Influence ranking in complexnetworks . Toverify the validity of Tr-Centrality, we apply it to four real-world networks with different densities and shapes and it has proven to yield better results .…

Road Traffic Monitoring using DSRC Signals

A wide variety of sensor technologies are nowadays used for traffic monitoring applications . Most of these technologies rely on wired infrastructure, the installation and maintenance costs limit the deployment of traffic monitoring systems . In this paper, we introduce a trafficmonitoring approach that exploits dedicated short-range communications (DSRC)signals sent in a vehicular network and machine learning techniques .…

Fuzzing with Fast Failure Feedback

Fuzzing has become the primetechnique to detect bugs and vulnerabilities in programs . To generate inputsthat cover new functionality, fuzzers require execution feedback from theprogram . If such feedback is not available, fuzzing can only rely on chance, which isineffective .…

Concurrency measures in the era of temporal network epidemiology A review

Diseases spread over temporal networks of contacts between individuals . Structures of these temporal networks hold the keys to understanding epidemicpropagation . Early concept of the literature to aid in discussing thesestructures is concurrency — quantifying individuals’ tendency to formtime-overlapping “partnerships” An emerging body of literature is trying to connect methods to the concurrency literature .…

A Quantum Edge Detection Algorithm

Quantum computing could have exponential speed-up in comparison to their classical counterparts . In this paper, we propose an improved version of aquantum edge detection algorithm . The application of quantum computing to the field of image processing has several promising applications, such as quantum image processing, has been developed in the past .…

A discontinuous Galerkin method by patch reconstruction for convection diffusion reaction problems over polytopic meshes

In this article, we propose apatch reconstruction finite element space with only one degree of freedom perelement . It is applied to the discontinuousGalerkin methods with the upwind scheme for the steady-stateconvection-diffusion-reaction problems over polytopic meshes . The optimal errorestimates are provided in both diffusion-dominated and convection-dominated regimes .…

Performance of Dual Hop Relaying for THz RF Wireless Link

The use of Terahertz frequency bands for data transmissions between thecore network and an access point can be promising for next generation wireless systems . We analyze the performance of a dual-hop relaying forTHz-RF wireless link for backhaul applications . Using analytical results of the direct link and computer simulations, we demonstrate that theTHzRF relaying is a viable technology for wireless backhaul, providing asignificant increase of almost $25 \%$ in the spectral efficiency, compared to the direct transmissions .…

LSTM Aided Hybrid Random Access Scheme for 6G Heterogeneous MTC Networks

An LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G MTC heterogeneous networks . This scheme employs an attention-basedLSTM prediction model to predict the number of active URLLC devices, determinest the parameters of the multi-user detection algorithm dynamically, and then allows URllC devices to access the network via a two-step contention-free procedure, to meet latency and reliability access requirements .…