Having been collected for its primary purpose in patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics. However, the potential for secondary usage of OHD continues to be hampered by the fiercely private nature of patient-related data. Generative Adversarial Networks (GAN) have Generative Adversarial Networks (GAN) have recently emerged as a groundbreaking approach to efficiently learn generative models that produce realistic Synthetic Data (SD). However, the application of GAN to OHD seems to have been lagging in comparison to other fields. We conducted a review of GAN algorithms for OHD in the published literature, and report our findings here.

The statistical imprecision of quarterly gross domestic product (GDP) estimates, along with the fact that the first estimate is available with a delay of nearly a month, pose a significant challenge to policy makers, market participants, and other observers with an interest in monitoring the state of the economy in real time; see, e.g., Ghysels, Horan, and Moench (2018) for a recent discussion of macroeconomic data revision and publication delays. A term originated in meteorology, nowcasting pertains to the prediction of the present and very near future. Nowcasting is intrinsically a mixed frequency data problem as the object of interest is a low-frequency data series (e.g., quarterly GDP), whereas the real-time information (e.g., daily, weekly, or monthly) can be used to update the state, or to put it differently, to nowcast the low-frequency series of interest. Traditional methods used for nowcasting rely on dynamic factor models that treat the underlying low frequency series of interest as a latent process with high frequency data noisy observations. These models are naturally cast in a state-space form and inference can be performed using likelihood-based methods and Kalman filtering techniques; see Bańbura, Giannone, Modugno, and Reichlin (2013) for a recent survey.

Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a result, recent research about adversarial examples has raised great concerns. Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction. While several whitebox attacks have demonstrated their effectiveness, which assume that the attackers have full access to the machine learning models; blackbox attacks are more realistic in practice. In this paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based only on model's final prediction labels. We theoretically show why previous boundary-based attack with gradient estimation on the whole gradient space is not efficient in terms of query numbers, and provide optimality analysis for our dimension reduction-based gradient estimation. On the other hand, we conducted extensive experiments on ImageNet and CelebA datasets to evaluate QEBA. We show that compared with the state-of-the-art blackbox attacks, QEBA is able to use a smaller number of queries to achieve a lower magnitude of perturbation with 100% attack success rate. We also show case studies of attacks on real-world APIs including MEGVII Face++ and Microsoft Azure.

"We didn't learn the lesson on SARS and MERS," UK prime minister Boris Johnson said today as he faced questions from the House of Commons Liaison Committee, referencing the government's pandemic planning and a lack of capacity at Public Health England to detect outbreaks of coronavirus around the country. He also said that there would not be an official inquiry to investigate whether his senior aide Dominic Cummings broke lockdown rules. More than 40 Conservative party MPs have now called for Cummings' resignation. During the meeting, Johnson announced that England's test and trace system will be launched tomorrow. Under the new system, contact tracers will ask people who test positive for coronavirus to self-isolate for 14 days, regardless of symptoms, and to provide details of any recent close contacts. The secretary of state will have the power to "mandate" people to isolate if they do not isolate voluntarily. The government announced earlier today that localised lockdowns, ...

Drone firm Zipline has been given the go-ahead to deliver medical supplies and personal protective equipment to hospitals in North Carolina. The firm will be allowed to use drones on two specified routes after the Federal Aviation Administration granted it an emergency waiver. It is the first time the FAA has allowed beyond-line-of-sight drone deliveries in the US. Experts say the pandemic could help ease some drone-flight regulations. Zipline, which has been negotiating with the FAA, wants to expand to other hospitals and eventually offer deliveries to people's homes.

The Diet enacted a bill Wednesday to create "super cities" where artificial intelligence, big data and other technologies are utilized to resolve social problems. The bill revising the national strategic special zone law passed the House of Councilors by a majority vote with support mainly from the ruling coalition. The revision stipulates procedures to speed up the changing of regulations in various fields to facilitate the creating of such smart cities. The government hopes to utilize cutting-edge technologies to address issues such as depopulation and the aging of society. In such cities, data-linking platforms to collect and organize various kinds of data from administrative organizations and companies will be established for autonomous driving, cashless payments, telemedicine and other services.

Only 38 per cent of people supported the UK government's change to coronavirus restrictions announced on 10 May, compared to 90 per cent of people who said they supported the lockdown measures announced on 23 March, according to a survey conducted by researchers at King's College London and Ipsos MORI. The measures brought in on 10 May largely affected England. They included a stronger emphasis on people going to work if they are unable to work from home, encouraging people to avoid public transport as much as possible, letting people exercise outside more than once a day and allowing people to meet up with one person from a household other than their own, providing the meeting takes place outside and at a distance of at least 2 metres. The poll, which surveyed 2254 people in the UK aged 16 to 75, was conducted between 20 and 22 May, before it emerged that prime ministerial aide Dominic Cummings drove more than 260 miles from home with his son and ill wife in March, at a time when the ...

In Michael Crichton's 1969 novel The Andromeda Strain, a deadly alien microbe hitches a ride to Earth aboard a downed military satellite and scientists must race to contain it. While fictional, the plot explores a very real and longstanding concern shared by NASA and world governments: that spacefaring humans, or our robotic emissaries, may unwittingly contaminate Earth with extraterrestrial life or else biologically pollute other planets we visit. It's an old fear that's taken on a new relevance in the era of COVID-19, said Scott Hubbard, an adjunct professor of aeronautics and astronautics at Stanford University. "I have heard from some colleagues in the human spaceflight area that they can see how, in the current environment, the general public could become more concerned about bringing back some alien microbe, virus or contamination," said Hubbard, who is also the former director of NASA Ames and the first Mars program director. Hubbard is a co-author of a new report published last month by the National Academies of Sciences, Engineering and Medicine that reviews recent findings and recommendations related to "planetary protection" or "planetary quarantine" -- the safeguarding of Earth and other worlds from biological cross-contamination.

In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The cultural division between these two statistical learning frameworks has been growing at a steady pace in recent years. What is the way forward? It has become blatantly obvious that this widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole. This article presents a solution by establishing a link between the two cultures. Through examples, we describe the challenges and potential gains of this new integrated statistical thinking.

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products.