The Fact-Checking Universe in Spring 2012: An Overview

By almost any measure, the 2012 presidential race is shaping up to be the most fact-checked electoral contest in American history. Every new debate and campaign ad yields a blizzard of fact-checking from the new full-time fact-checkers, from traditional news outlets in print and broadcast, and from partisan political organizations of various stripes. And though fact-checking still peaks before elections it is now a year-round enterprise that challenges political claims beyond the campaign trail.This increasingly crowded and contentious landscape raises at least two fundamental questions. First, who counts as a legitimate fact-checker? The various kinds of fact-checking at work both inside and outside of journalism must be considered in light of their methods, their audiences, and their goals. And second, how effective are fact-checkers -- or how effective could they be -- in countering widespread misinformation in American political life? The success of the fact-checkers must be assessed in three related areas: changing people's minds, changing journalism, and changing the political conversation. Can fact-checking really stop a lie in its tracks? Can public figures be shamed into being more honest? Or has the damage been done by the time the fact-checkers intervene?This report reviews the shape of the fact-checking landscape today. It pays special attention to the divide between partisan and nonpartisan fact-checkers, and between fact-checking and conventional reporting. It then examines what we know and what we don't about the effectiveness of fact-checking, using the media footprint of various kinds of fact-checkers as an initial indicator of the influence these groups wield. Media analysis shows how political orientation limits fact-checkers' impact in public discourse

Impact of random and targeted disruptions on information diffusion during outbreaks

Outbreaks are complex multi-scale processes that are impacted not only by cellular dynamics and the ability of pathogens to effectively reproduce and spread, but also by population-level dynamics and the effectiveness of mitigation measures. A timely exchange of information related to the spread of novel pathogens, stay-at-home orders, and other containment measures can be effective at containing an infectious disease, particularly during in the early stages when testing infrastructure, vaccines, and other medical interventions may not be available at scale. Using a multiplex epidemic model that consists of an information layer (modeling information exchange between individuals) and a spatially embedded epidemic layer (representing a human contact network), we study how random and targeted disruptions in the information layer (\eg, errors and intentional attacks on communication infrastructure) impact outbreak dynamics. We calibrate our model to the early outbreak stages of the SARS-CoV-2 pandemic in 2020. Mitigation campaign can still be effective under random disruptions, such as failure of information channels between a few individuals. However, targeted disruptions or sabotage of hub nodes that exchange information with a large number of individuals can abruptly change outbreak characteristics such as the time to reach the peak infection. Our results emphasize the importance of using a robust communication infrastructure that can withstand both random and targeted disruptions.Comment: 10 pages, 6 figure

ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation

Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods. Additionally, lung lesions have large inter-patient variations, with some pathologies having similar visual appearance as healthy lung tissues. This poses a challenge when applying existing semi-automatic interactive segmentation techniques for data labelling. To address these challenges, we propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction. To accelerate learning from only the samples labelled through user-interactions, a patch-based approach is used for training the network. Moreover, we use weighted cross-entropy loss to address the class imbalance that may result from user-interactions. During online inference, the learned network is applied to the whole input volume using a fully convolutional approach. We compare our proposed method with state-of-the-art using synthetic scribbles and show that it outperforms existing methods on the task of annotating lung lesions associated with COVID-19, achieving 16% higher Dice score while reducing execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled voxels. Due to the online learning aspect, our approach adapts quickly to user input, resulting in high quality segmentation labels. Source code for ECONet is available at: https://github.com/masadcv/ECONet-MONAILabel.Comment: Accepted at MIDL 202

Breaking Rayleigh's law with spatially correlated disorder to control phonon transport

Controlling thermal transport in insulators and semiconductors is crucial for many technological fields such as thermoelectrics and thermal insulation, for which a low thermal conductivity ($\kappa$) is desirable. A major obstacle for realizing low $\kappa$ materials is Rayleigh's law, which implies that acoustic phonons, which carry most of the heat, are insensitive to scattering by point defects at low energy. We demonstrate, with large scale simulations on tens of millions of atoms, that isotropic long-range spatial correlations in the defect distribution can dramatically reduce phonon lifetimes of important low-frequency heat-carrying modes, leading to a large reduction of $\kappa$ -- potentially an order of magnitude at room temperature. We propose a general and quantitative framework for controlling thermal transport in complex functional materials through structural spatial correlations, and we establish the optimal functional form of spatial correlations that minimize $\kappa$. We end by briefly discussing experimental realizations of various correlated structures