Urban traffic roughly follows a periodic pattern associated with the typical "9 to 5" work schedule. However, when an accident happens, traffic patterns are disrupted. Designing accurate traffic flow models, for use during accidents, is a major challenge for traffic engineers, who must adapt to unforeseen traffic scenarios in real time. A team of Lawrence Berkeley National Lab computer scientists are working with the California Department of Transportation (Caltrans) to use high performance computing (HPC) and machine learning to help improve Caltrans' real-time decision making when incidents occur. The research was done in conjunction with California Partners for Advanced Transportation Technology (PATH), part of UC Berkeley's Institute for Transportation Studies (ITS), and Connected Corridors, a collaborative program to research, develop, and test an Integrated Corridor Management approach to managing transportation corridors in California.

A recent video leaked from ABC appears to show anchor Amy Robach admit that her network let outside pressure influence its coverage of the case against infamous pedophile Jeffrey Epstein. Meanwhile, Epstein's rich and powerful alleged co-conspirators have managed to dodge prosecution. Police are subpoenaing an Alexa Echo device as part of a murder investigation regarding a Florida woman whose boyfriend allegedly killed her with a spear in July. A new report by the National Security Commission on Artificial Intelligence warns of the inseparability of AI development from "emerging strategic competition with China." Then former naval intelligence officer John Jordan shares his insights.

We live in a time of rapid technological change, where nearly every aspect of our lives now relies on devices that compute and connect. The resulting exponential increase in the use of cyber-physical systems has transformed industry, government, and commerce; what's more, the speed of innovation shows no signs of slowing down, particularly as the revolution in artificial intelligence (AI) stands to transform daily life even further through increasingly powerful tools for data analysis, prediction, security, and automation.1 Like past waves of extreme innovation, as this one crests, debate over ethical usage and privacy controls are likely to proliferate. So far, the intersection of AI and society has brought its own unique set of ethical challenges, some of which have been anticipated and discussed for many years, while others are just beginning to come to light. For example, academics and science fiction authors alike have long pondered the ethical implications of hyper-intelligent machines, but it's only recently that we've seen real-world problems start to surface, like social bias in automated decision-making tools, or the ethical choices made by self-driving cars.2, 5 During the past two decades, the security community has increasingly turned to AI and the power of machine learning (ML) to reap many technological benefits, but those advances have forced security practitioners to navigate a proportional number of risks and ethical dilemmas along the way. As the leader in the development of AI and ML for cybersecurity, BlackBerry Cylance is at the heart of the debate and is passionate about advancing the use of AI for good.

A new analysis of Twitter and news coverage surrounding the Democratic primary candidates for the U.S. 2020 presidential elections shows that female candidates are attacked significantly more often than male candidates by trolls and fake news accounts. The report, published Nov. 5 by Lucina Di Meco, Global Fellow at The Wilson Center, used artificial intelligence in partnership with non-partisan data analytics firm Marvelous AI to track the coverage of six Democratic candidates on Twitter, measuring the volume of conversation around each candidate between December 2018 and April 2019. Joe Biden, Bernie Sanders, Pete Buttigieg, Elizabeth Warren, Kamala Harris and Amy Klobuchar were the candidates included in the study, which forms part of the broader report titled #ShePersisted: Women, Politics and Power in the New Media World. These online conversations were analyzed for one week after each candidate's official campaign launch between December 2018 and April 2019, depending on the candidate. Marvelous AI also examined the political bias and credibility of Twitter users participating in the conversation, as well as the themes and narratives surrounding each candidate.

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Deep Learning Models for Global Coordinate Transformations that Linearize PDEs Craig Gin 1, Bethany Lusch 2, Steven L. Brunton 1,3, and J. Nathan Kutz 1 1 Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA 2 Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL, USA 3 Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA (Received 11 November 2019) We develop a deep autoencoder architecture that can be used to find a coordinate transformation which turns a nonlinear PDE into a linear PDE. Our architecture is motivated by the linearizing transformations provided by the Cole-Hopf transform for Burgers equation and the inverse scattering transform for completely integrable PDEs. By leveraging a residual network architecture, a near-identity transformation can be exploited to encode intrinsic coordinates in which the dynamics are linear. The resulting dynamics are given by a Koopman operator matrix K. The decoder allows us to transform back to the original coordinates as well. Multiple time step prediction can be performed by repeated multiplication by the matrix K in the intrinsic coordinates. We demonstrate our method on a number of examples, including the heat equation and Burgers equation, as well as the substantially more challenging Kuramoto-Sivashinsky equation, showing that our method provides a robust architecture for discovering interpretable, linearizing transforms for nonlinear PDEs. Key Words: Koopman theory, deep neural nets, residual networks, linearizing transforms, Cole-Hopf transform 2010 Mathematics Subject Classification: 35A22, 35A35, 37M99, 65P99, 68T99 1 Introduction Partial differential equations (PDEs) provide a theoretical framework for modeling spatiotemporal systems across the biological, physical and engineering sciences. Analytic solution techniques are readily available for PDEs that are linear and have constant coefficients [12]. These PDEs include canonical models such as the heat equation, wave equation and Laplace's equation which are amenable to standard separation of variable techniques and linear superposition. In contrast, there is no general mathematical architecture for solving nonlinear PDEs as methods like separation of variables fail to hold, thus recourse to computational solutions is necessary. There are a few, but notable, exceptions: (i) the Cole-Hopf transformation [14, 6] for solving diffusively regularized Burgers equation, and (ii) the Inverse Scattering Transform (IST) [1] for solving a class of completely integrable PDEs such as Korteweg deVries (KdV), nonlinear Schr odinger arXiv:1911.02710v1 A deep autoencoder is used to find coordinate transformations to linearize PDEs. The encoder finds a set of intrinsic coordinates for which the dynamics are linear.

Graph representation learning for hypergraphs can be used to extract patterns among higher-order interactions that are critically important in many real world problems. Current approaches designed for hypergraphs, however, are unable to handle different types of hypergraphs and are typically not generic for various learning tasks. Indeed, models that can predict variable-sized heterogeneous hyperedges have not been available. Here we develop a new self-attention based graph neural network called Hyper-SAGNN applicable to homogeneous and heterogeneous hypergraphs with variable hyperedge sizes. We perform extensive evaluations on multiple datasets, including four benchmark network datasets and two single-cell Hi-C datasets in genomics. We demonstrate that Hyper-SAGNN significantly outperforms the state-of-the-art methods on traditional tasks while also achieving great performance on a new task called outsider identification. Hyper-SAGNN will be useful for graph representation learning to uncover complex higher-order interactions in different applications.

Deep neural networks frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Consequently, they can often be compressed using techniques such as weight pruning and quantization that reduce both model size and inference time without appreciable loss in accuracy. Compressing models before they are deployed can therefore result in significantly more efficient systems. However, while the results are desirable, finding the best compression strategy for a given neural network, target platform, and optimization objective often requires extensive experimentation. Moreover, finding optimal hyperparameters for a given compression strategy typically results in even more expensive, frequently manual, trial-and-error exploration. In this paper, we introduce a programmable system for model compression called Condensa. Users programmatically compose simple operators, in Python, to build complex compression strategies. Given a strategy and a user-provided objective, such as minimization of running time, Condensa uses a novel sample-efficient constrained Bayesian optimization algorithm to automatically infer desirable sparsity ratios. Our experiments on three real-world image classification and language modeling tasks demonstrate memory footprint reductions of up to 65x and runtime throughput improvements of up to 2.22x using at most 10 samples per search. We have released a reference implementation of Condensa at https://github.com/NVlabs/condensa.

America's national security depends on the government getting access to the artificial intelligence breakthroughs made by the technology industry. So says a report submitted to Congress on Monday by the National Security Commission on Artificial Intelligence. The group, which includes executives from Google, Microsoft, Oracle, and Amazon, says the Pentagon and intelligence agencies need a better relationship with Silicon Valley to stay ahead of China. "AI adoption for national security is imperative," said Eric Schmidt, chair of the commission and formerly CEO of Google, at a news briefing Monday. "The private sector and government officials need to build a shared sense of responsibility."

In today's digital evolution, traditional methods of verification while still vital is ineffective in combating today's fraudsters. There's now an ever increasing need to apply a layered approach and leverage probabilistic techniques such as machine learning, device and telephony intelligence to help combat fraudsters who have access to the same traditional data you're verifying as part of your existing fraud strategy.