Uncertainty Aware COVID 19 Detection from Imbalanced Sound Data

Sound-based COVID-19 detection studies have shown great promise toachieve scalable and prompt digital pre-screening . However, there are still twounsolved issues hindering the practice . Data collected for modeltraining are often imbalanced, with a considerably smaller proportion of userstested positive, making it harder to learn representative and robust features .…

City scale Simulation of Covid 19 Pandemic and Intervention Policies using Agent based Modelling

During the Covid-19 pandemic, most governments across the world imposedpolicies like lock-down of public spaces and restrictions on people’s movement . Such policies have grave social and economic costs, and so it is important topre-assess their impacts . In this work we aim to visualize the dynamics of thepandemic in a city under different intervention policies, by simulating the behavior of the residents .…

A Generalized Unscented Transformation for Probability Distributions

The unscented transform uses a weighted set of samples called sigma points to topropagate the means and covariances of nonlinear transformations of randomvariables . For correlated random vectors, the GenUT can accurately approximate the mean and covariance . The GenUT uses the same order of calculations that guarantee third-order accuracy, which makes it applicable to a wide variety of applications, such as the modeling of the coronavirus (SARS-CoV-2), causing COVID-19 .…

Data driven Contact Network Models of COVID 19 Reveal Trade offs between Costs and Infections for Optimal Local Containment Policies

Making a trade-off between economic recovery and infection control is a major challenge confronting many hard-hit counties . Understanding the transmission process and quantifying thecosts of local policies are essential to tackling this challenge . We simulate the epidemicspread with a time-varying contagion model in ten large metropolitan counties in the United States .…

Contrast enhanced MRI Synthesis Using 3D High Resolution ConvNets

Gadolinium-based contrast agents (GBCAs) have been widely used to bettervisualize disease in brain magnetic resonance imaging (MRI) However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs . The development of novel approaches that candecrease or eliminate GBCA exposure while providing similar contrastinformation would be of significant use clinically .…

Non negative matrix and tensor factorisations with a smoothed Wasserstein loss

Non-negative matrix and tensor factorisations are a classical tool in machinelearning and data science . In applications such as imaging, datasets can often be regarded as distributions in a space with metric structure . We introduce a general mathematical framework for computing non-negative factorisations of matrices and tensors with respect to an optimal transport loss, and derive an efficient method for its solution using a convexdual formulation .…

Daisen A Framework for Visualizing Detailed GPU Execution

Graphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and informationvisualization applications . Daisen is a framework that supports data collection from GPU simulators and provides visualization of thesimulator-generated GPU execution traces . The open-sourced implementation of Daisen can be found atgitlab.com/akita/vis…

Time Series Imaging for Link Layer Anomaly Classification in Wireless Networks

The number of end devices that use the last mile wireless connectivity isdramatically increasing with the rise of smart infrastructures . Toefficiently manage such massive wireless networks, more advanced and accurate monitoring and malfunction detection solutions are required . The best performing model based on recurrence plot transformation leads to up to 55% increase compared to the state of the art where classical machine learning techniques are used .…

Topological Data Analysis of Spatial Systems

In this chapter, we discuss applications of topological data analysis (TDA) to spatial systems . We briefly review the recently proposed level-setconstruction of filtered simplicial complexes . We then examine persistenthomology in two cases studies: street networks in Shanghai and hotspots ofCOVID-19 infections .…

Graph Convolutional Networks for Model Based Learning in Nonlinear Inverse Problems

The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains such as pixelated images . If the underlying model is solved on nonuniform meshes, interpolation and embeddings are needed . This gives rise to the proposed iterative Graph ConvolutionalNewton’s Method (GCNM) The GCNM has strong generalizability to different domain shapes, out of distribution data as well as experimental data, from purely simulated training data .…

Robust Pandemic Control Synthesis with Formal Specifications A Case Study on COVID 19 Pandemic

Pandemics can bring a range of devastating consequences to public health and the world economy . Various public health control strategies have been proposed and tested against pandemic diseases (e.g.,COVID-19) We study two specific pandemic control models: the susceptible,exposed, infectious, recovered (SEIR) model with vaccination control; and theSEIR model with shield immunity control .…

Model based Reconstruction with Learning From Unsupervised to Supervised and Beyond

Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements . Model-based reconstruction methods have beenparticularly popular (e.g., in magnetic resonance imaging and tomographicmodalities) and exploit models of the imaging system’s physics together withstatistical models of measurements, noise and often simple objectpriors .…

Location Data and COVID 19 Contact Tracing How Data Privacy Regulations and Cell Service Providers Work In Tandem

Governments, Healthcare, and Private Organizations in the global scale have been using digital tracking to keep COVID-19 outbreaks under control . Although this method could limit pandemic contagion, it raises significant concerns about user privacy . We found that three regulations (GDPR, COPPA, and CCPA) analyzed defined mobile location data as private information, and two (T-Mobile US, Boost Mobile) of the fiveCSPs that were analyzed did not comply with the COPPA regulation .…

Developing Apps for Researching the COVID 19 Pandemic with the TrackYourHealth Platform

TrackYourHealth (TYH) is a highly configurable, generic, and modular mobile data collection and EMA platform . It enabled us to develop and release two mobile multi-platform applications related to COVID-19 in just afew weeks . We present TYH and highlight specific challenges researchers and developers of similar apps may also face, especially when developing apps related to the medical field .…

Can Twitter Give Insights into International Differences in Covid 19 Vaccination Eight countries English tweets to 21 March 2021

Wordassociation thematic analysis (WATA) was applied to English-languagevaccine-related tweets from eight countries gathered between 5 December 2020and 21 March 2021 . The method was able to quickly identify multipleinternational differences . Ireland seemed to be the only country in which university presidents were widelytweeted about in vaccine discussions .…

Privacy preserving Identity Broadcast for Contact Tracing Applications

Wireless contact tracing has emerged as an important tool for managing theCOVID-19 pandemic and relies on continuous broadcasting of a person’s presence using Bluetooth Low Energy beacons . The limitation of current contact tracingsystems is in that a reception of a single beacon is sufficient to reveal the user’s identity, potentially exposing users to malicious trackers installed along theroads, passageways, and other infrastructure .…

Scam Pandemic How Attackers Exploit Public Fear through Phishing

Phishing attack traffic in March and April 2020 skyrocketed upto 220\% of its pre-COVID-19 rate, far exceeding typical seasonal spikes . Attackers exploited victims’ uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystem’s collective response .…

LaCulturaNonsiFerma Report on Use and Diffusion of Hashtags from the Italian Cultural Institutions during the COVID 19 outbreak

Report presents analysis of #hashtags used by Italian CulturalHeritage institutions to promote and communicate cultural content during theCOVID-19 lock-down period in Italy . Results show that on one side Italian institutions have been proactive in adapting to the pandemic scenario and on the other side users’ reacted very positively increasing their participation in the proposed activities .…

Triage and diagnosis of COVID 19 from medical social media

This study aims to develop an end-to-end natural languageprocessing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts . The text processing pipeline firstextracts symptoms and related concepts such as severity, duration,negations, and body parts from patients posts using conditional random fields .…

Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras

The proposed approach outperformsmonocular and conventional techniques for dealing with 4D imagery . It yields more accurate odometry and depth maps and delivers these with metric scale . We anticipate our technique to generalise to a broad class of LF and sparse LFcameras, and to enable unsupervised recalibration for coping with shifts incamera behaviour over the lifetime of a robot.…

EXSCLAIM An automated pipeline for the construction of labeled materials imaging datasets from literature

Material microscopy is experiencing an explosion of published imaging data . The standard publication format is not conducive to large-scale data aggregation oranalysis, hindering data sharing and reuse . We highlight the methodology behind the construction of the EXSCLAIM! Python toolkit for the automatic EXtraction,Separation, and Caption-based natural Language Annotation of IMages from scientific literature.…

Congolese Swahili Machine Translation for Humanitarian Response

In this paper we describe our efforts to make a bidirectional CongoleseSwahili (SWC) to French (FRA) neural machine translation system . We recorded improvementsof up to 2.4 and 3.5 BLEU points in the SWC-FRA and FRA-SWC directions, respectively . We performed human evaluations to assess the usability of our models in a COVID-domain chatbot that operates in the Democratic Republic ofCongo (DRC) We make our models, datasets containing up to 1million sentences, our development pipeline, and a translator web-app available for public use .…

Exploring the Drivers and Barriers to Uptake for Digital Contact Tracing

Digital contact tracing has been deployed as a public health intervention to suppress the spread of COVID-19 in many jurisdictions . Mostgovernments have struggled with low uptake and participation rates, limiting the effectiveness of the tool . This paper characterises a number of systems developed around the world, comparing the uptake rates for systems with different technology, data architectures, and mandates .…

Uncertainty Based Biological Age Estimation of Brain MRI Scans

Current BA estimation approaches are restricted to skeletal images or rely on non-imaging modalities that yield a whole-body BA assessment . Various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors . In thisinitial study, we propose a new framework for organ-specific BA estimation using 3D magnetic resonance image (MRI) scans .…

Modeling and forecasting Spread of COVID 19 epidemic in Iran until Sep 22 2021 based on deep learning

The recent global outbreak of covid-19 is affecting many countries around the world . Due to the growing number of newly infected individuals and the health-care system bottlenecks, it will be useful to predict the upcoming number of patients . The study aims to efficiently forecast the is used to estimate new cases, number of deaths, and number of recovered patients in Iran for 180 days .…