Anyone who has ever had to navigate the NHS as a patient or carer will no doubt know the frustration and fear often caused by this vast organisation's woeful inability to communicate within itself. Your medical records are mislaid, an appointment wasn't made -- or you weren't told about it; clinics use phone numbers and addresses you've moved on from years ago. Or clinicians don't seem to know about the outcomes of previous appointments with other care teams. But could the NHS's left hand finally soon know what its right hand is doing? Early next month, NHS England is to sign a £480 million contract to build a master data-controlling system, linking up all the computer systems used across hospitals, GP practices and admin departments so they can'talk' to each other.

Syntax knowledge contributes its powerful strength in Neural machine translation (NMT) tasks. Early NMT works supposed that syntax details can be automatically learned from numerous texts via attention networks. However, succeeding researches pointed out that limited by the uncontrolled nature of attention computation, the NMT model requires an external syntax to capture the deep syntactic awareness. Although existing syntax-aware NMT methods have born great fruits in combining syntax, the additional workloads they introduced render the model heavy and slow. Particularly, these efforts scarcely involve the Transformer-based NMT and modify its core self-attention network (SAN). To this end, we propose a parameter-free, Dependency-scaled Self-Attention Network (Deps-SAN) for syntax-aware Transformer-based NMT. A quantified matrix of dependency closeness between tokens is constructed to impose explicit syntactic constraints into the SAN for learning syntactic details and dispelling the dispersion of attention distributions. Two knowledge sparsing techniques are further integrated to avoid the model overfitting the dependency noises introduced by the external parser. Experiments and analyses on IWSLT14 German-to-English and WMT16 German-to-English benchmark NMT tasks verify the effectiveness of our approach.

Machine Learning (ML) in healthcare has an affinity for patient-centred care and individual-level predictions. Both individual health and population health are not divergent, but at the same time, both are not the same and may require different approaches. ML in public health applications receives far less attention. The skills available to public health organizations to transition towards an integrated data analytics is limited. Hence the latest advances in ML and artificial intelligence (AI) have made very little impact on public health analytics and decision making.

Matthew B. Matheny, 40, of Warren, Ohio, appeared to be in good health when he was found on Mount St. Helens, authorities say. An Ohio man who went missing last week after setting out on a hike on Mount St. Helens in Washington state, was found alive Wednesday -- having survived by eating bees and berries, authorities and the man's relatives said. Matthew B. Matheny, 40, of Warren, Ohio, was reported missing by friends after he failed to return from his hike along Blue Lake Trail on the southwest side of St. Helens. His parents told reporters that he was not familiar with the terrain and got lost. They said he had not seen anyone since Aug. 9. Dozens of search-and-rescue personnel, assisted by helicopters, tracking dogs and a drone operated by the local sheriff's office searched daily for Matheny.

Imagine this scenario: Two patients need kidney transplants, and there's only one donor organ available that both their bodies will accept. One is 60 years old but in good health, except for a bout with skin cancer that's now in remission. The other is 30, but a heavy drinker -- a risk factor for kidney recipients. The question of which patient should get the kidney is a thorny one, laced with judgments about age and lifestyle. Currently, decisions like it are often made by committees of doctors who weigh many factors, from a patient's age and medical outlook to the distance an organ would need to travel, in order to match a limited supply of donated organs with the long list of people waiting for a transplant.

After a drawn-out battle, South Korea's Go grandmaster with 9-dan rank, Lee Sedol, lost his fifth game against Google's artificial intelligence (AI) program AlphaGo in Seoul on March 15, 2016. AlphaGo's win over one of the world's best players shocked the world's Go circle. Due to the complexity of the nature of Go, which requires intuition, creativity, and strategic thinking, it was believed that Go was the only board game that no computers could conquer. Hong Kong's Go champion, Lee Cheuk-leung, was surprised at the result of the fifth match, in which Lee Sedol had the upper hand in the first half of the game, but somehow lost to the computer eventually. Experts from the Go circle initially expected Lee Sedol to win all five games, but he ultimately lost four of them to the computer.

This paper proposes the concept of Well-being computing which is an information technology for improving not only our health as physical aspect but also our happiness as psy-chological aspect, and shows its potential from the sleep perspective. Concretely, this paper introduces “our personal-ized sleep monitoring system” as the well-being computing technologies and shows the following implications as its ef-fectiveness: (1) from the viewpoint of the service based on the real-time sleep, (1-a) good health is provided through a stable sleep of aged person in care house by reducing their sleep disturbance which may be occurred in diaper exchange, while happiness is provided by the smooth diaper exchange when aged person have a deep/light sleep; (1-b) good health is provided through a sufficient sleep time acquired by a fast falling asleep, while happiness is provided by releasing from anxiety of the insufficient sleep such as insomnia; and (2) from the viewpoint of the service based on the long-term sleep, (2-a) good health is provided through a deep sleep by continuing the daytime activities (such as a walking) which contribute to deriving a deep sleep, while happiness is provided by achieving a deep sleep through a change of life style; (2-b) good health is provided through a good sleep by keeping good bed condition (e.g., a change of a pillow or mattress when cotton/spring is deteriorated), while happiness is provided through a discovery of suitable bedding (such as suitable pillow or bed).