On β γ Chebyshev functions and points of the interval

In this paper, we introduce the class of $(\beta,\gamma)$-Chebyshev functionsand corresponding points . We prove that they are orthogonal in certain subintervals of $[-1,1]$ withrespect to a weighted arc-cosine measure . We also study the behavior of the Lebesgue constants of the polynomialinterpolant at these points on varying the parameters $beta$ and $gamma$ .…

Infinite GMRES for parameterized linear systems

Methods combine the well-established GMRES method for linear systems with algorithms for nonlineareigenvalue problems (NEPs) to generate a basis for the Krylov subspace . Weshow convergence factor bounds obtained similarly to those for the method GMRESfor linear systems. More specifically, a bound is obtained based on themagnitude of the parameter $mu$ and the spectrum of the linear companionmatrix, which corresponds to the reciprocal solutions to the corresponding NEP.…

Minimum Principle on Specific Entropy and High Order Accurate Invariant Region Preserving Numerical Methods for Relativistic Hydrodynamics

This paper explores Tadmor’s minimum entropy principle for the relativistichydrodynamics (RHD) equations . It incorporates this principle into the design of robust high-order discontinuous Galerkin (DG) and finite volume schemes for RHD on general meshes . The schemes are proven to preserve numerical solutionsin a global invariant region constituted by all the known intrinsicconstraints .…

Deep Reinforcement Learning for the Control of Robotic Manipulation A Focussed Mini Review

Deep learning has provided new ways of manipulating, processing and analyzing data . Another subfield of machine learning namedreinforcement learning, tries to find an optimal behavior strategy through interactions with the environment . Combining deep learning and reinforcementlearning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals .…

Spike based Residual Blocks

Deep Spiking Neural Networks (SNNs) are harder to train than ANNs because of discrete binary activation and spatio-temporal domain errorback-propagation . Previous Spiking ResNet used a similar residual block to the standard block of ResNet, which we regard as inadequate for SNNs .…

Datasets and Evaluation for Simultaneous Localization and Mapping Related Problems A Comprehensive Survey

Simultaneous Localization and Mapping (SLAM) has found an increasingutilization lately, such as self-driving cars, robot navigation, 3D mapping, virtual reality (VR) and augmented reality (AR) The employment of datasets is essentially a kind ofsimulation but profits many aspects – capacity of drilling algorithm hourly,exemption of costly hardware and ground truth system, and equitable benchmarkfor evaluation.…

Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks

DeepReinforcement Learning (DRL) is one of the leading robotic automation techniquethat has been able to achieve dexterous manipulation and locomotion roboticsskills . We propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks . Thesesubtasks can be parametrised as expert networks and learnt via existing DRL methods .…

Manipulation Planning Among Movable Obstacles Using Physics Based Adaptive Motion Primitives

Robot manipulation in cluttered scenes often requires contact-rich interactions with objects . For each object in a scene, depending on its properties, the robot mayor may not be allowed to make contact with, tilt, or topple it . To ensure that these constraints are satisfied during non-prehensile interactions, a planner can query a physics-based simulator to evaluate the complex multi-bodyinteractions caused by robot actions .…

Operation is the hardest teacher estimating DNN accuracy looking for mispredictions

DeepEST looks for failing test cases in the operational dataset of a DNN, with the goal of assessing the DNN expected accuracy by a small and ”informative” test suite . The results show that DeepEST provides DNNaccuracy estimates with precision close to (and often better than) those of existing sampling-based DNN testing techniques, while detecting from 5 to 30times more mispredictions with the same test suite size .…

Non linear frequency warping using constant Q transformation for speech emotion recognition

In this work, we explore the constant-Q transform (CQT) for speech emotionrecognition (SER) The CQT-based time-frequency analysis provides variablespectro-temporal resolution with higher frequency resolution at lower frequencies . Since lower-frequency regions of speech signal contain more emotion-related information, the increasedlow-frequency resolution makes it more promising for SER than standardshort-time Fourier transform (STFT) We present a comparative analysis ofshort-term acoustic features based on STFT for SER with deep neuralnetwork (DNN)…

Evaluating the robustness of source code plagiarism detection tools to pervasive plagiarism hiding modifications

A plagiarising student will commonly apply plagiarism-hidingmodifications to source code in an attempt to evade detection . Of the evaluated tools, JPlag andPlaggie demonstrates the greatest robustness to different types of plagiarism . However, the results also indicate thatgraph-based tools (specifically those that compare programs as programdependence graphs) show potentially greater robustness against pervasiveplagiarism-Hiding modifications .…

Competition Dynamics in the Meme Ecosystem

The frequency of memes has scaled almost exactly with the total amount of content created over the past decade . This means that as more data is posted, an equal proportion of memes are posted . One consequence of limited human attention in the face of agrowing number of memes is that the diversity of these memes has decreased .…

Measuring Place Connectivity Using Big Social Media Data

Social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable,and harmonized . We introduced a place connectivity index (PCI)based on spatial interactions among places revealed by geotagged tweets . We found that PCI has a strong state boundary effect and that it generally follows the distance decay effect .…

Analysis of the Optimization Landscape of Linear Quadratic Gaussian LQG Control

This paper revisits the classical Linear Quadratic Gaussian (LQG) control from a modern optimization perspective . We analyze two aspects of the optimization landscape of the LQG problem: connectivity of the set ofstabilizing controllers and structure of stationary points . These results shed some light on the performanceanalysis of direct policy gradient methods for solving the problem .…

Cyber Risk in Health Facilities A Systematic Literature Review

A special emphasis is given to one of the main challenges in the healthcare sector during the COVID-19 pandemic, the cyber risk . The World Health Organization hasdetected a dramatic increase in the number of cyber-attacks . The literature lacks research contributionsto support cyber risk management in subject areas such as Business, Managementand Accounting; Social Science; and Mathematics .…

Placing Green Bridges Optimally with a Multivariate Analysis

We study the problem of placing wildlife crossings, such as green bridges, over human-made obstacles to challenge habitat fragmentation . The main taskherein is, given a graph describing habitats or routes of wildlife animals andpossibilities of building green bridges . We develop different problem models for the task and study them from a computational complexity and parameterizedalgorithmics perspective .…

Black Box Optimization via Generative Adversarial Nets

Black-box optimization (BBO) algorithms are concerned with finding the bestsolutions for the problems with missing analytical details . Most classical methods for such problems are based on strong and fixed \emph{a priori}assumptions such as Gaussian distribution . But lots of complex real-world problems are far from the distribution of the Gaussian .…

comparing card based vaccine credential systems with app based vaccine credential systems

In this early draft, we compare and contrast the technicalities present inthe implementation of a card-based vaccine credential system and an app-basedvaccine credentials system . We have chosen the domains of symptom reporting, fraud and impersonation, feasibility, scalability, equity, future dataaggregation, importability of health data, and operability to explore each system’s features and drawbacks .…

Playing the Blame Game with Robots

Recent research shows that people are willing toascribe moral blame to AI-driven systems when they cause harm . The higher the computationalsophistication of the AI system, the more blame is shifted from the human user . The results suggest (iv) that the higher the .…

Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources . Here we develop a Bayesian inversion framework that usesInterferometric Synthetic Aperture Radar (InSAR) surface deformation data to ferry the laterally heterogeneous permeability of a confined GW aquifer .…

Free space optical neural network based on thermal atomic nonlinearity

Optical implementations potentially offer extraordinary gains interms of speed and reduced energy consumption due to intrinsic parallelism offree-space optics . Physical nonlinearity, a crucialingredient of an ANN, is not easy to realize in free space optics . Here, we present afree-space optical ANN with diffraction-based linear weight summation and nonlinear activation enabled by the saturable absorption of thermal atoms .…

A Bayesian Two part Hurdle Quantile Regression Model for Citation Analysis

Quantile regression presents a complete picture of the effects on the location, scale, and shape of the dependent variable at all points, not just atthe mean . Theirmodel allows zeros and non-zeros to be modeled independently butsimultaneously . The new modeldelivers more accurate quantile regression for moderately to highly citedarticles, and enables estimates of the extent to which factors influence the chances that an article will be low cited .…

A Tutorial on 5G NR V2X Communications

The Third Generation Partnership Project (3GPP) has recently published its release 16 that includes the first Vehicle to-Everything (V2X) standard based on the 5G New Radio (NR) air interface . 5G NR V2X introduces advancedfunctionalities on top of the NR air interface to support connected andautomated driving use cases with stringent requirements .…

Timely Transmissions Using Optimized Variable Length Coding

A status updating system is considered in which a variable length code is used to transmit messages to a receiver over a noisy channel . The goal is tooptimize the codewords lengths such that successfully-decoded messages aretimely . A hybrid ARQ (HARQ) scheme is employed, in which variable-lengthincremental redundancy (IR) bits are added to the originally-transmittedcodeword until decoding is successful .…

Semiquantitative Group Testing in at Most Two Rounds

Semiquantitative group testing (SQGT) is a pooling method in which the testoutcomes represent bounded intervals for the number of defectives . Alternatively, it may be viewed as an adder channel with quantized outputs . SQGT represents a natural choice for Covid-19 group testing as it allows for astraightforward interpretation of the cycle threshold values produced by polymerase chain reactions .…

Regular Model Checking Approach to Knowledge Reasoning over Parameterized Systems technical report

We present a framework for modelling and verifying epistemicproperties over parameterized multi-agent systems that communicate by truthfulpublic announcements . In our framework, the number of agents or amount of resources are parameterized (i.e. not known a priori) and theresponding verification problem asks whether a given epistemic property istrue regardless of the instantiation of the parameters .…

Optimal Priority Assignment for Real Time Systems A Coevolution Based Approach

In real-time systems, priorities assigned to tasks deter-mine the order of task executions, by relying on an underlying task scheduling policy . In practice, priorityassignments result from an interactive process between the development and testing teams . Our approach is based on a multi-objective, competitivecoevolutionary algorithm mimicking the interactive priority assignment process .…