Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. Various scholarly search websites, citation recommendation engines, and research databases have been created to simplify the text search tasks. However, it is still difficult for researchers to be able to identify potential research topics without doing intensive reviews on a tremendous number of articles published by journals, conferences, meetings, and workshops. In this paper, we consider a novel topic diffusion discovery technique that incorporates sparseness-constrained Non-negative Matrix Factorization with generalized Jensen-Shannon divergence to help understand term-topic evolutions and identify topic diffusions. Our experimental result shows that this approach can extract more prominent topics from large article databases, visualize relationships between terms of interest and abstract topics, and further help researchers understand whether given terms/topics have been widely explored or whether new topics are emerging from literature.

Nowadays, huge amounts of tracking data in the mobility domain are being generated by Global Positioning System (GPS) enabled devices and collected in data repositories; tracked moving entities could be pedestrians, cars, vessels, planes, animals, robots, etc. These datasets constitute a rich source for inferring mobility patterns and characteristics for a wide spectrum of novel applications and services, from social networking applications [5][46] to aviation traffic monitoring [61][67]. During the recent years, this kind of information has attracted great interest by data scientists, both in industry and in academia, and is being used in order to extract useful knowledge about what, how and for how long the moving entities are conducting individual activities related with specific circumstances. The most challenging task is to make this information actionable, by means of exploiting historical mobility patterns in order to gauge how the moving entities may evolve in short-or long-term, whether the individual forecasted movement is typical or anomalous, whether there exists a high probability for congestion in the near future, etc. As a consequence, predictive analytics over mobility data has become increasingly important and turns out to be a'hot' field in several application domains [4][74][111]. The problem of predictive analytics over mobility data finds two broad categories of application scenarios. The first scenario involves cases where the moving entities are traced in real-time to produce analytics and compute short-term predictions, which are time-critical and need immediate response. The prediction includes either location-or trajectory-related tasks.

Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data. Inside these networks, gates are used to control the flow of information, allowing to model even very long-term dependencies in the data. In this paper, we investigate whether the original gate equation (a linear projection followed by an element-wise sigmoid) can be improved. In particular, we design a more flexible architecture, with a small number of adaptable parameters, which is able to model a wider range of gating functions than the classical one. To this end, we replace the sigmoid function in the standard gate with a non-parametric formulation extending the recently proposed kernel activation function (KAF), with the addition of a residual skip-connection. A set of experiments on sequential variants of the MNIST dataset shows that the adoption of this novel gate allows to improve accuracy with a negligible cost in terms of computational power and with a large speed-up in the number of training iterations.

Like in a Tough Mudder, you've got a few strategies when it comes to the race to launch a taxi-like service with autonomous vehicles. You can start early and keep a slow but steady pace. You can show up a bit late, then try to sprint through it. Or you can hold back, see what trips up other contenders, and then slowly work your way through the obstacles. The big automakers tend to fall into the third category. They may have taken a few years to recognize that shared autonomous vehicles could annihilate their business model--selling human-driven cars to individual humans--but they're now making real progress toward the finish line.

LONDON – Most people think the world is more dangerous today than it was two years ago as concerns rise over politically motivated violence and weapons of mass destruction, according to a survey released on Tuesday. Six out of 10 respondents to the survey, commissioned by the Global Challenges Foundation, said the dangers had increased, with conflict and nuclear or chemical weapons seen as more pressing risks than population growth or climate change. The results come as NATO leaders prepare to meet in Brussels on Wednesday amid growing tensions between the United States and fellow members over defense spending, which some fear could damage morale and play into the hands of Russia. "It's clear that our current systems of global cooperation are no longer making people feel safe," said Mats Andersson, vice chairman of the Global Challenges Foundation, in a statement. Andersson said turbulence between NATO powers and Russia, ongoing conflict in Syria, Yemen and Ukraine and nuclear tensions with North Korea and Iran were making people feel unsafe.

Daimler's, the makers of Mercedes-Benz, and automotive supplies Bosch are soon to join the race of the self-driving cars market. Maria Mercedes Galuppo (@mariamgaluppo) has more. As self-driving vehicle experiments are launched in a few cities around the U.S., Mercedes-Benz maker Daimler and a prominent auto supplier are launching a new one in the place that would seem most receptive: Silicon Valley. Daimler and auto components maker Bosch will start a self-driving vehicle shuttle service in one of the cities south of San Francisco that comprises the heart of the nation's tech industry beginning in the second half of 2019. The move marks a concrete step forward for a partnership announced in April 2017 with the ultimate goal of delivering a self-driving car by 2021.

WASHINGTON--(BUSINESS WIRE)--The following is an opinion editorial provided by Naveen Rao of Intel Corporation. Most people agree that artificial intelligence (AI) will transform modern society in positive ways. From autonomous cars that will save thousands of lives, to data analytics programs that may finally discover a cure for cancer, to machines that give voice to those who can't speak, AI will be known as one of the most revolutionary innovations of mankind. But this fantastic future is a long way off, and the path to get us there is still under construction. Never before has society undertaken such a significant transformation so deliberately, and no blueprints exist to guide us.

A team from Huddersfield has pioneered the use of artificial intelligence to predict those mental health patients most likely to take their own lives. Prof Grigoris Antoniou and a team at the University of Huddersfield worked with South West Yorkshire Partnership NHS Foundation Trust (SWYPFT) to examine how AI could help reduce the risk of suicide among mental health patients. Now further work will be carried out into ways of deploying the new computer-based technique. Although clinical decisions about suicide risk will always be based on clinical judgement, findings from this research should aid decision-making by identifying high risk patients based on learning from previous suicides. Prof Antoniou, a globally-acknowledged expert in AI technologies, and his team analysed data from more than 100 suicide cases in order to compile a list of the key risk factors.

Apple and Google must tell users how much they are spying on them, according to US lawmakers. The Energy and Commerce Committee has written to the heads of both companies to demand they make clear whether iPhones and Android devices are collecting information about their users, and how that data is used. The letters suggest that devices might be tracking their users, including tracking their location and listening in to what they are doing through their microphone. It asks that both companies make clear their data policies, including whether that information is tracked and who it might be passed on to. The letters make reference to suggestions that – despite tech companies' claim that devices only listen if they hear their wake word – they are actually collecting more audio than that and that unknown companies might have access to recordings of people's personal lives.