Online dating giants are set to offer digital health passports to millions of UK singletons to prove they are free of coronavirus. Manchester-based cyber firm VST Enterprises (VSTE), is pioneering technology which it says can be used to safeguard daters when coronavirus restrictions are eased. The company says it has been approached for its digital health passports by several leading dating app companies. Tinder and Grindr are believed to be two of the dating apps that are waiting to launch them. The technology, called'VCode', would enable a doctor or nurse to upload the results of a government-approved Covid-19 test to the digital health passport.

Sphero, a toy manufacturer known for making simple, programmable robots for kids, has launched a new spinoff business to develop AI and robotics for law enforcement, first responders, and other government agencies. The new entity is called Company Six (CO6) and will build on technology Sphero had previously developed through its Public Safety Division. The company hasn't announced any clients or new projects, but promises to focus on'lightweight, yet highly advanced robotic solution that provides critical awareness for those we depend on the most, including police, fire, EMT, military, and others with dangerous jobs.' Sphero's Paul Berberian, who previously served in the US Air Force, will step down from his role as CEO and take a new title as Chairman of both companies, according to a report in CNet. 'This is an opportunity to continue to bring revolutionary robotics technology to new markets to improve the lives of more people, our future leaders, and people with essential and sometimes dangerous job functions,' he said in a prepared statement. Sphero says the company has sold more than four million robots since it was founded in 2010.

The decision, handed down Monday by Rebecca Beach Smith of the U.S. District Court for the Eastern District of Virginia, modifies a ruling in 2000 by a previous judge that prohibits the salvage firm, RMS Titanic, Inc. (RMST), from cutting into or detaching pieces from the wreck. Removal of the telegraph machine, located inside the ship's officers' quarters, may require cutting or widening holes in the hull and detaching equipment from interior walls.

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 ...

If you can't meet a date in person, then where they live doesn't really matter. That's what Tinder seems to think, anyway. The company is reportedly planning to test a new Global Mode, which will allow profiles to show up around the world, The Verge reports. Users will be able to match with people in other cities, states and even countries. Tinder says it will begin rolling out the "first steps" of Global Mode in late May but that it will take some time before it is available to all members. To start, Global Mode will be a free opt-in feature.

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.

Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. In this study, a joint model of the two types of heterogeneity in car-following behavior is proposed as an approach of driver profiling and identification. It is assumed that all drivers share a pool of driver states; under each state a car-following data sequence obeys a specific probability distribution in feature space; each driver has his/her own probability distribution over the states, called driver profile, which characterize the intradriver heterogeneity, while the difference between the driver profile of different drivers depict the inter-driver heterogeneity. Thus, the driver profile can be used to distinguish a driver from others. Based on the assumption, a stochastic car-following model is proposed to take both intra-driver and inter-driver heterogeneity into consideration, and a method is proposed to jointly learn parameters in behavioral feature extractor, driver states and driver profiles. Experiments demonstrate the performance of the proposed method in driver identification on naturalistic car-following data: accuracy of 82.3% is achieved in an 8-driver experiment using 10 car-following sequences of duration 15 seconds for online inference. The potential of fast registration of new drivers are demonstrated and discussed.

Deep Interest Network (DIN) is a state-of-the-art model which uses attention mechanism to capture user interests from historical behaviors. User interests intuitively follow a hierarchical pattern such that users generally show interests from a higher-level then to a lower-level abstraction. Modeling such an interest hierarchy in an attention network can fundamentally improve the representation of user behaviors. We, therefore, propose an improvement over DIN to model arbitrary interest hierarchy: Deep Interest with Hierarchical Attention Network (DHAN). In this model, a multi-dimensional hierarchical structure is introduced on the first attention layer which attends to an individual item, and the subsequent attention layers in the same dimension attend to higher-level hierarchy built on top of the lower corresponding layers. To enable modeling of multiple dimensional hierarchies, an expanding mechanism is introduced to capture one to many hierarchies. This design enables DHAN to attend different importance to different hierarchical abstractions thus can fully capture user interests at different dimensions (e.g. category, price, or brand).To validate our model, a simplified DHAN has applied to Click-Through Rate (CTR) prediction and our experimental results on three public datasets with two levels of the one-dimensional hierarchy only by category. It shows the superiority of DHAN with significant AUC uplift from 12% to 21% over DIN. DHAN is also compared with another state-of-the-art model Deep Interest Evolution Network (DIEN), which models temporal interest. The simplified DHAN also gets slight AUC uplift from 1.0% to 1.7% over DIEN. A potential future work can be a combination of DHAN and DIEN to model both temporal and hierarchical interests.

The combination of Monte-Carlo Tree Search (MCTS) and deep reinforcement learning is state-of-the-art in two-player perfect-information games. In this paper, we describe a search algorithm that uses a variant of MCTS which we enhanced by 1) a novel action value normalization mechanism for games with potentially unbounded rewards (which is the case in many optimization problems), 2) defining a virtual loss function that enables effective search parallelization, and 3) a policy network, trained by generations of self-play, to guide the search. We gauge the effectiveness of our method in "SameGame"---a popular single-player test domain. Our experimental results indicate that our method outperforms baseline algorithms on several board sizes. Additionally, it is competitive with state-of-the-art search algorithms on a public set of positions.

In order to keep trace of information, the brain has to resolve the problem where information is and how to index new ones. We propose that the neural mechanism used by the prefrontal cortex (PFC) to detect structure in temporal sequences, based on the temporal order of incoming information, has served as second purpose to the spatial ordering and indexing of brain networks. We call this process, apparent to the manipulation of neural 'addresses' to organize the brain's own network, the 'digitalization' of information. Such tool is important for information processing and preservation, but also for memory formation and retrieval.