The NHS Confederation, a membership body that represents people who commission or provide NHS services, has warned of the urgent need for a UK contact tracing strategy. "Our members are concerned that unless there is a clear strategy, then there must be a greater risk of a second wave of infections and serious health consequences," chief executive Niall Dickson wrote in a letter sent to the UK's health and social care minister Matt Hancock yesterday. "We would therefore urge you to produce such a strategy with a clear implementation plan ahead of any further easing of the lockdown." Dickson welcomed Prime Minister Boris Johnson's new commitment to trace 10,000 new coronavirus cases per day by 1 June, adding that "delivery and implementation will be critical, and we await further details." However, he said that a strategy for tracing contacts "should have been in place much sooner". An international randomised controlled trial investigating whether hydroxychloroquine and chloroquine ...

The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this large-scale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.

Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset. We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset---including the introduction of biases that state-of-the-art models exploit. Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for. Finally, our findings emphasize the need to augment our current model training and evaluation toolkit to take such misalignments into account. To facilitate further research, we release our refined ImageNet annotations at https://github.com/MadryLab/ImageNetMultiLabel.

In this paper, novel gradient based online learning algorithms are developed to investigate an important environmental application: real-time river pollution source identification, which aims at estimating the released mass, the location and the released time of a river pollution source based on downstream sensor data monitoring the pollution concentration. The problem can be formulated as a non-convex loss minimization problem in statistical learning, and our online algorithms have vectorized and adaptive step-sizes to ensure high estimation accuracy on dimensions having different magnitudes. In order to avoid gradient-based method sticking into the saddle points of non-convex loss, the "escaping from saddle points" module and multi-start version of algorithms are derived to further improve the estimation accuracy by searching for the global minimimals of the loss functions. It can be shown theoretically and experimentally $O(N)$ local regret of the algorithms, and the high probability cumulative regret bound $O(N)$ under particular error bound condition on loss functions. A real-life river pollution source identification example shows superior performance of our algorithms than the existing methods in terms of estimating accuracy. The managerial insights for decision maker to use the algorithm in reality are also provided.

A special, peer-reviewed edition of OMICS: A Journal of Integrative Biology, has highlighted the importance of key digital technologies, including Artificial Intelligence (AI), machine learning, and blockchain for innovation in healthcare in response to the challenges posed by COVID-19. Vural Özdemir, MD, PhD, Editor-in-Chief of OMICS, said: "COVID-19 is undoubtedly among the ecological determinants of planetary health. Digital health is a veritable opportunity for integrative biology and systems medicine to broaden its scope from human biology to ecological determinants of health. Articles in the special issue include an interview on'Responsible Innovation and Future Science in Australia' by Justine Lacey, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Erik Fisher, Arizona State University, Tempe, 'Blockchain for Digital Health: Prospects and Challenges' and'Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine.' In'Blockchain for Digital Health: Prospects and Challenges' the article explores the challenges that can be faced with the use of blockchain technology.

A robot dog designed for search and rescue missions has had a go at herding sheep in New Zealand.

"Cells move and change in very weird ways during the five days the embryo is in an incubator and it's completely beyond any human to work out what all that means," Dr Peter Illingworth, medical director at IVF Australia, who is leading the trial, said. "What the AI tool can do is analyse all the embryos. The embryo with the highest score can then be selected for transfer by the embryologist with the aim of accelerating the chance to a successful pregnancy." Dr Illingworth said the purpose of the study was to determine whether the technology can shorten the time it takes a woman to fall pregnant, ultimately saving aspiring parents thousands of dollars in fertility treatment. "It's a very promising tool, but does it really help women, that's the question," he said.

This article is part of Privacy in the Pandemic, a Future Tense series. Since the pandemic began, authorities in New Delhi, Italy, Oman, Connecticut, and China have begun to experiment with fever-finding drones as a means of mass COVID-19 screening. They're claiming the aircraft can be used to better understand the health of the population at large and even to identify potentially sick individuals, who can then be pulled aside for further diagnostic testing. In Italy, police forces are reportedly using drones to read the temperatures of people who are out and about during quarantine, while officials in India are hoping to use thermal-scanner-equipped drones to search for "temperature anomalies" in people on the ground. A Lithuanian drone pilot even used a thermal-scanning drone to read the temperature of a sick friend who didn't own a thermometer. Unfortunately, there's almost no evidence that these fever-detecting drones actually work.

In this work, we address the problem of learning to seek novel policies in reinforcement learning tasks. Instead of following the multi-objective framework used in previous methods, we propose to rethink the problem under a novel perspective of constrained optimization. We first introduce a new metric to evaluate the difference between policies, and then design two practical novel policy seeking methods following the new perspective, namely the Constrained Task Novel Bisector (CTNB), and the Interior Policy Differentiation (IPD), corresponding to the feasible direction method and the interior point method commonly known in constrained optimization problems. Experimental comparisons on the MuJuCo control suite show our methods achieve substantial improvements over previous novelty-seeking methods in terms of both novelty and primal task performance.

Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them are trained based on labeled reasoning path. This hinders the system's performance as many correct reasoning paths are not labeled as ground truth, and thus they cannot be learned. In this paper, we introduce an end-to-end KBQA system which can leverage multiple reasoning paths' information and only requires labeled answer as supervision. We conduct experiments on several benchmark datasets containing both single-hop simple questions as well as muti-hop complex questions, including WebQuestionSP (WQSP), ComplexWebQuestion-1.1 (CWQ), and PathQuestion-Large (PQL), and demonstrate strong performance.