## Analysis of the Effectiveness of Face Coverings on the Death Rate of COVID 19 Using Machine Learning

The recent outbreak of the COVID-19 shocked humanity leading to the death ofmillions of people worldwide . To stave off the spread of the virus, theauthorities in the US, employed different strategies including the mask mandate(MM) order issued by the states’ governors .…

## The Limits of Computation in Solving Equity Trade Offs in Machine Learning and Justice System Risk Assessment

This paper explores how different ideas of racial equity in machine learning can present trade-offs that are difficult to solve computationally . Machine learning is often used in justice settings to create risk assessments, which are used to determine interventions, resources,and punitive actions .…

## A Convex Optimization Approach to Learning Koopman Operators

Koopman operators provide tractable means of learning linear approximationsof non-linear dynamics . Many approaches have been proposed to find theseoperators, typically based upon approximations using an a-priori fixed class of models . However, choosing appropriate models and bounding the approximationerror is far from trivial .…

## Multilevel Topological Interference Management A TIM TIN Perspective

The robust principles of treating interference as noise (TIN) and avoiding it when it is not, form the background of this work . Combining TIN with the topological interference management (TIM)framework that identifies optimal interference avoidance schemes, we formulate a TIM-TIN problem .…

## Time Dependent Wave Structure Interaction Revisited Thermo piezoelectric Scatterers

The problem of describing the way in which an incoming acoustic wave isscattered by an elastic body immersed in a fluid is one of central importance in detecting and identifying submerged objects . The problem can be treated by the boundary-field equation method, provided an appropriatescaling factor is employed .…

## 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.…

## Derivation of the Backpropagation Algorithm Based on Derivative Amplification Coefficients

The backpropagation algorithm for neural networks is widely felt hard to understand . This paper provides a new derivation of this algorithm based on the concept of derivative amplification coefficients . The concept is found to well carry over to conventional feedforward neural networks and it paves the way for the use of mathematical induction in establishing a key result that enablesbackpropagating for derivative amplification .…

## Neural Termination Analysis

We introduce a novel approach to automated termination analysis of computer programs . We train neural networks to act as ranking functions . The existence of a valid ranking function provesthat the program terminates . We present a custom loss function for learning lexicographic ranking functions and uses satisfactioniability modulo theories for verification .…

## Novel Deep neural networks for solving Bayesian statistical inverse

Markov chain Monte Carlo (MCMC) algorithms are standard techniques to solvesuch problems . MCMC techniques are computationally challenging as they require several thousands of forward PDE solves . The goal of this paper is to introduce a fractional deep neural network based approach for the forwardsolves within an MCMC routine .…

## Quantum Algorithm for DOA Estimation in Hybrid Massive MIMO

The effectiveness of the direction of arrival (DOA) estimation in array signal processing is animportant research area . In hybrid massive MIMO systems, the receivedsignals at the antennas are not sent to the receiver directly, and spatialcovariance matrix, which is essential in MUSIC algorithm, is thus unavailable .…

## 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 .…

## Higher Strong Order Methods for Itô SDEs on Matrix Lie Groups

In this paper we present a general procedure for designing higher strong order methods for It\^o stochastic differential equations on matrix Lie groups . We show how our higher order schemes can be applied in amechanical engineering as well as in a financial mathematics setting .…

## Asynchronous semi anonymous dynamics over large scale networks

We analyze a class of stochastic processes, referred to as asynchronous andsemi-anonymous dynamics (ASD) over directed labeled random networks . These processes are a natural tool to describe general best-response and noisybest-response dynamics in network games where each agent, at random timesgoverned by independent Poisson clocks, can choose among a finite set ofactions .…

## Partition based formulations for mixed integer optimization of trained ReLU neural networks

This paper introduces a class of mixed-integer formulations for trained ReLUneural networks . The approach balances model size and tightness by partitioning node inputs into groups and forming the convex hull over thepartitions via disjunctive programming . At one extreme, one partition per input covers the tightest possible formulation foreach node .…

## 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 .…

## Improved Brain Age Estimation with Slice based Set Networks

Deep Learning for neuroimaging data is a promising but challenging direction . The proposed architecture works by encoding each 2D slice in an MRI with a deep 2D-CNN model . Next, it combines the information from these 2D slices using set networks or permutation invariant layers .…

## Reliable Probabilistic Face Embeddings in the Wild

Probabilistic Face Embeddings (PFE) can improve face recognition performance by integrating data uncertainty into the featurerepresentation . However, existing PFE methods tend to be over-confident inestimating uncertainty and is too slow to apply to large-scale face matching . This paper proposes a regularized probabilistic face embedding method to improve the robustness and speed of PFE .…

## Academic Source Code Plagiarism Detection by Measuring Program Behavioural Similarity

BPlag is designed to be both robust to pervasive plagiarism-hidingtransformations, and accurate in the detection of plagiarised source code . It is evaluated for robustness, accuracy and efficiency against 5 commonly used source code plagiarism detection tools . It was shown to be more robust to the plagiarism of source code, but is lessefficient than compared tools .…

## Learning Task Oriented Communication for Edge Inference An Information Bottleneck Approach

This paper investigates task-oriented communication for edge inference . It is critical to encode the data into an informative and compact representation for low-latency inference . We propose a learning-based communication schemethat jointly optimizes feature extraction, source coding, and channel coding in a task-orientated manner, i.e.,…

## Distributed Storage Allocations for Optimal Service Rates

This paper considers the uncertainty in nodeaccess and download service . In one access model, a user can access each node with a fixedprobability, and the other, a random fixed-size subset of nodes . For afixed redundancy level, the systems’ service rate is determined by the allocation of coded chunks over storage nodes .…

## Speaker and Direction Inferred Dual channel Speech Separation

Most speech separation methods are still far from having enough general-ization capabilities for real scenarios where the number of input sounds is usually uncertain and dynamic . In this work, we employ ideas from auditory attention with twoears and propose a speaker and direction inferred speech separation network .…

## Arbitrary Conditional Distributions with Energy

Arbitrary Conditioning with Energy (ACE) uses an energy function to specify densities . ACE is state-of-the-art for arbitrary conditional and marginal likelihoodestimation and for tabular data imputation . We also simplify the learningproblem by only learning one-dimensional conditionals, from which more complexdistributions can be recovered during inference .…

## 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 .…

## Communication efficient k Means for Edge based Machine Learning

We consider the problem of computing the k-means centers for a largehigh-dimensional dataset in the context of edge-based machine learning . Wepropose to let the data sources send small summaries, generated by jointdimensionality reduction (DR) and cardinality reduction (CR) to support approximating k-Means computation at reduced complexity and communication cost .…

## Polynomial Linear System Solving with Random Errors new bounds and early termination technique

This paper deals with the polynomial linear system solving with errors(PLSwE) problem . The number of evaluations needed to recover the solution of the linear system is crucial since it affects the number of computations . Our work is part of a series of papers about PLSwE aiming to reduce thisnumber of evaluations .…

## ICASSP 2021 Deep Noise Suppression Challenge Decoupling Magnitude and Phase Optimization with a Two Stage Deep Network

It remains a tough challenge to recover the speech signals contaminated by various noises under real acoustic environments . To this end, we propose anovel system for denoising in the complicated applications . The first pipeline is proposed to decouple the optimization problemw:r:t: magnitude and phase, i.e.,…

## Provable Model based Nonlinear Bandit and Reinforcement Learning Shelve Optimism Embrace Virtual Curvature

This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations . Global convergence is intractable even for one-layer neural net bandit with adeterministic reward . On the other hand, for convergence to localmaxima, it suffices to maximize the virtual return if the model can alsoreasonably predict the size of the gradient and Hessian of the real return .…

## Participation Analysis in Impedance Models The Grey Box Approach for Power System Stability

This paper develops a grey-box approach to stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the system . The approach is favoured by manufacturers of wind and solar systems for the limited disclosure required .…

## Subjective and Objective Visual Quality Assessment of Textured 3D Meshes

Quality assessment metrics may allow a wide range of processes to be guided and evaluated, such as level of detail creation,compression, filtering, and so on . Almost no research has been conducted on the evaluation of texture-mapped 3D models . We introduce both texture and geometry distortions on aset of 5 reference models to produce a database of 136 distorted models .…

## Learning N M Fine grained Structured Sparse Neural Networks From Scratch

Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments . Fine-grained sparsity can achieve a high compression ratio but is not hardware friendly and hence receives limited speed gains . We propose a noveland effective ingredient, sparse-refined straight-through estimator (SR-STE) We also define a metric, Sparse ArchitectureDivergence (SAD), to measure the sparse network’s topology change during the training process .…

## 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 .…

## The role of mesh quality and mesh quality indicators in the Virtual Element Method

Virtual Element Method (VEM) was shown to be able to deal with a large variety of polygons while achieving good convergencerates . The regularity assumptions proposed in the VEM literature to guaranteethe convergence on a theoretical basis are quite general .…

## DroneTrap Drone Catching in Midair by Soft Robotic Hand with Color Based Force Detection and Hand Gesture Recognition

The paper proposes a novel concept of docking drones to make this process assafe and fast as possible . The idea behind the project is that a robot with thegripper grasps the drone in midair . The soft hand has a unique technology of providing force information through the color of the fingers to the remote computer vision (CV)system .…

## Sensor Planning for Large Numbers of Robots

After a disaster, locating and extracting victims quickly is critical because mortality rises rapidly after the first two days . To assist search and rescueteams and improve response times, teams of camera-equipped aerial robots canengage in tasks such as mapping buildings and locating victims .…

## RL Scope Cross Stack Profiling for Deep Reinforcement Learning Workloads

RL has made groundbreaking advancements in robotic, datacenter managements and other applications . System-level bottlenecks in RL workloads are poorly understood . We observe fundamental structural differences in RLworkloads that make them inherently less GPU-bound than supervised learning . RL-Scope is an open-source tool available at https://://://github.com/UofT-EcoSystem/rlscope…

## 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 .…

## Doubly Residual Neural Decoder Towards Low Complexity High Performance Channel Decoding

Deep neural networks have been successfully applied in channelcoding to improve the decoding performance . However, state-of-the-artneural channel decoders cannot achieve high decoding performance and lowcomplexity simultaneously . To overcome this challenge, in this paper we proposedoubly residual neural (DRN) decoder .…

## SceML A Graphical Modeling Framework for Scenario based Testing of Autonomous Vehicles

Scenariobased testing is an approach to tackle this problem and reduce necessary test drives by replacing driven kilometers with simulations of relevant or interesting scenarios . These scenarios can be begenerated or extracted from recorded data with machine learning algorithms or created by experts .…

## 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 .…

## Identifying the Origin of Finger Vein Samples Using Texture Descriptors

Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and initiating sensor-specific processing pipelines . Motivated by shortcomings of the photo response non-uniformity(PRNU) based method in the biometric context, we use a texture classificationapproach to detect the origin .…

## Contrastive Embeddings for Neural Architectures

The performance of algorithms for neural architecture search strongly depend on the parametrization of the search space . We use contrastive learning to identify networks across different initializations based on their dataJacobians . We show that traditional black-box optimization algorithms, without modification, can reach state-of-the-art performance in Neural ArchitectureSearch .…

## 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 .…

## Overhead MNIST A Benchmark Satellite Dataset

The research presents an overhead view of 10 important objects and followsthe general formatting requirements of the most popular machine learning task:digit recognition with MNIST . A prototype deep learning approach with transfer learning and convolutional neural networks (MobileNetV2)correctly identifies the ten overhead classes with an average accuracy of96.7%.…