Turning night into day, clearing up fog, and removing cloud cover in image and video sources, in real-time, Digital Harmonic's PurePixel solution will help the Federal mission secure critical areas and communities, enabling our military to make better-informed command and control decisions. Digital Harmonic LLC, a leading image, video, and signal processing technology company, announced today they have chosen to collaborate with Dell Technologies OEM Embedded & Edge Solutions to bring PurePixel, designed on a scalable suite of hardware solutions from edge devices to rack mounted servers, to federal agencies. PurePixel is an upstream pre-processing component to enhance the quality and efficiency of machine learning (ML) and computer vision (CV) algorithms increasing the success of the output for real-time video. PurePixel also improves advanced analytics software with cloud-based ML algorithms for object recognition, object detection, image annotation/labeling, semantic image segmentation analysis, and edge computing capabilities. One of the options for delivering PurePixel to customers is on the Dell EMC PowerEdge C4140 server, which is an ultra-dense, accelerator optimized rack server system purpose-built for artificial intelligence (AI) solutions with a leading GPU-accelerated infrastructure.

UK GDP fell by 2 per cent in the first quarter of 2020, the most rapid contraction of the UK's economy since the 2008 financial crisis. Rishi Sunak, the chancellor of the exchequer, said, "It is now very likely that the UK economy will face a significant recession this year, and we're already in the middle of that as we speak." The Bank of England predicts that the UK economy could shrink by as much as 14 per cent in 2020. In England some people who aren't able to work from home returned to work today, as part of the government's recent easing of certain restrictions. Despite the government urging people to avoid public transport if they could, some commuters said buses and trains were too crowded to practice social distancing. It could be as long as "four or five years" before covid-19 is under control and the pandemic could "potentially get worse", according to the World Health Organization's chief scientist Soumya Swaminathan. Speaking at an FT conference, she said a vaccine "seems ...

We demonstrate a unified approach to rigorous design of safety-critical autonomous systems using the VerifAI toolkit for formal analysis of AI-based systems. VerifAI provides an integrated toolchain for tasks spanning the design process, including modeling, falsification, debugging, and ML component retraining. We evaluate all of these applications in an industrial case study on an experimental autonomous aircraft taxiing system developed by Boeing, which uses a neural network to track the centerline of a runway. We define runway scenarios using the Scenic probabilistic programming language, and use them to drive tests in the X-Plane flight simulator. We first perform falsification, automatically finding environment conditions causing the system to violate its specification by deviating significantly from the centerline (or even leaving the runway entirely). Next, we use counterexample analysis to identify distinct failure cases, and confirm their root causes with specialized testing. Finally, we use the results of falsification and debugging to retrain the network, eliminating several failure cases and improving the overall performance of the closed-loop system.

Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decision support systems, in particular Computer Aided Diagnosis (CADx) using machine learning (ML). Numerous ML approaches for CADx have been proposed in literature. However, these approaches assume full data availability, which is not always feasible in clinical data. To account for missing data, incomplete data samples are either removed or imputed, which could lead to data bias and may negatively affect classification performance. As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multigraph Geometric Matrix Completion (MGMC). MGMC uses multiple recurrent graph convolutional networks, where each graph represents an independent population model based on a key clinical meta-feature like age, sex, or cognitive function. Graph signal aggregation from local patient neighborhoods, combined with multigraph signal fusion via self-attention, has a regularizing effect on both matrix reconstruction and classification performance. Our proposed approach is able to impute class relevant features as well as perform accurate classification on two publicly available medical datasets. We empirically show the superiority of our proposed approach in terms of classification and imputation performance when compared with state-of-the-art approaches. MGMC enables disease prediction in multimodal and incomplete medical datasets. These findings could serve as baseline for future CADx approaches which utilize incomplete datasets.

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to \emph{stealthily} and \emph{efficiently} attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to the agent. The first technique is the \emph{critical point attack}: the adversary builds a model to predict the future environmental states and agent's actions, assesses the damage of each possible attack strategy, and selects the optimal one. The second technique is the \emph{antagonist attack}: the adversary automatically learns a domain-agnostic model to discover the critical moments of attacking the agent in an episode. Experimental results demonstrate the effectiveness of our techniques. Specifically, to successfully attack the DRL agent, our critical point technique only requires 1 (TORCS) or 2 (Atari Pong and Breakout) steps, and the antagonist technique needs fewer than 5 steps (4 Mujoco tasks), which are significant improvements over state-of-the-art methods.

NASA has created a new'mini' Mars rover that will be able to climb hills even if they're covered in sand and gravel - perfect for future exploration missions. Driven by the knowledge that the rolling hills of Mars are a long way from the nearest tow truck, Georgia Tech researchers created a more resilient'wiggling' vehicle. Unlike the NASA Curiosity rover currently on Mars, or NASA Perseverance launching this summer, this'mini' design can walk, paddle and wheelspin its way up a hill. The team created the mini vehicle based on the NASA Resource Prospector 15 (RP15) rover that can wiggle its wheels to enhance and test the design. Professor Dan Goldman, from the School of Physics at the Georgia Institute of Technology, said the rover was built using 3D printed parts.

AI is seen as one of the first lines of defence in a pandemic. Given the current situation with COVID-19, hospitals and healthcare facilities are using AI to help screen and triage patients and identify those most likely to develop severe symptoms. The use of data and analytics is also helpful to track and contain diseases. As the Global Government Practice lead, Steve Bennett is helping governments around the world put their data to work for the citizens they serve. In his current role, he drives strategic industry positioning and messaging in global government markets.

Exponent is a leading engineering and scientific consulting firm. Our multidisciplinary team of scientists, engineers, physicians, and regulatory consultants brings together more than 90 different disciplines to solve complicated problems facing corporations, insurers, government entities, associations and individuals. Our approximately 900 staff members work in 26 offices across the United States and abroad. Exponent has over 700 consultants, including more than 425 that have earned a doctorate in their chosen field of specialization. Exponent's Statistical & Data Sciences Practice is currently hiring a Scientist with expertise in Machine Learning, Data Analytics, and Software Development.

Brian Kenny: At the 1939 World's Fair in New York City General Motors unveiled Futurama, an exhibit spanning an entire acre that featured a model of what US roadways would look like 20 years into the future. Networks of streamlined motorways wound through a landscape of half a million buildings, a million trees, and 50,000 miniature cars traveling on a 14-lane, multi-speed highway. It struck a chord at a time when the country was just beginning to grapple with traffic congestion. In what might have been the boldest prediction of all, Futurama depicted a future where self-driving cars would communicate directly with the road moving passengers safely and swiftly to their destination. It seemed like science fiction, but by 1958 GM made this concept a reality with one of the first full-sized self-driving vehicles. Today on Cold Call, we're doubling down to look at two cases that each look at the future for autonomous vehicles. I'm your host, Brian Kenny, and you're listening to Cold Call, recorded in Klarman Hall Studio at Harvard Business School. Joining me in studio today is Professor Bill Kerr to discuss his case entitled, Autonomous Vehicles: The Rubber Hits the Road... but When? Also in studio is Professor Elie Ofek to discuss his case entitled, Autonomous Vehicles: Smooth or Bumpy Ride Ahead? Brian Kenny: It's great to have both of you here. I found out about Bill's case first and reached out to him to do that. Elie, you and I were going to do a completely different case, but somebody brought to my attention that you had also written a case on autonomous vehicles. The two cases are really complementary, and I think this will be a really rich discussion about a topic that is certainly something that's been in the headlines a lot, and I think it's one of these things... I know, speaking for me personally, I can't wait to get into a car and just open up my newspaper and let the car take me where I'm going. For people who are listening to this while they're driving in their cars, keep your hands on the wheel.

A security lapse at controversial facial recognition startup Clearview AI meant that its source code, some of its secret keys and cloud storage credentials, and even copies of its apps were publicly accessible. TechCrunch reports that an exposed server was discovered by Mossab Hussein, Chief Security Officer at cybersecurity firm SpiderSilk, who found that it was configured to allow anyone to register as a new user and log in. Clearview AI first made headlines back in January, when a New York Times exposé detailed its massive facial recognition database, which consists of billions of images scraped from websites and social media platforms. Users upload a picture of a person of interest, and Clearview AI's software will attempt to match it with any similar images in its database, potentially revealing a person's identity from a single image. Since its work became public, Clearview AI has defended itself by saying that its software is only available to law enforcement agencies (although reports claim that Clearview has been marketing its system to private businesses including Macy's and Best Buy).