The goal of regression analysis is to predict the value of a numeric outcome variable y given a vector of joint values of other (predictor) variables x. Usually a particular x-vector does not specify a repeatable value for y, but rather a probability distribution of possible y--values, p(y|x). This distribution has a location, scale and shape, all of which can depend on x, and are needed to infer likely values for y given x. Regression methods usually assume that training data y-values are perfect numeric realizations from some well behaived p(y|x). Often actual training data y-values are discrete, truncated and/or arbitrary censored. Regression procedures based on an optimal transformation strategy are presented for estimating location, scale and shape of p(y|x) as general functions of x, in the possible presence of such imperfect training data. In addition, validation diagnostics are presented to ascertain the quality of the solutions.

Japanese railway companies are turning to artificial intelligence to help tackle potential problems for their shinkansen bullet trains caused by accumulations of snow. West Japan Railway Co. is developing an AI system to gauge the amount of snow attached to Hokuriku Shinkansen trains that cut through Niigata, Toyama and Ishikawa prefectures adjacent to the Sea of Japan. The railway operator currently decides how many personnel to deploy for snow clearance a day beforehand, based on information from meteorological data providers and past experience, but it is often not very accurate. AI will gather data from images of trains that have accumulated snow while traveling, study weather conditions and predict the number of personnel necessary for clearance work. Test operations have proved positive so far and the system is set for full introduction next winter.

The 57-year-old mystery of an infamous prison break from Alcatraz may have finally been solved using artificial-intelligence and facial-recognition technology. Rothco, the Irish creative agency owned by Accenture Interactive, teamed up with AI specialists at Identv to analyse a picture of two escapees and have, for the first time, confirmed their identities. On 11 June 1962, three prisoners – Frank Morris, along with brothers John and Clarence Anglin – broke out of their cells and escaped from the prison on Alcatraz Island, near San Francisco Bay. The trio's extraordinary escape, in which they used sharpened spoons to dig through the walls and made papier-mâché dummies to fool the guards, was made famous in the 1979 movie Escape from Alcatraz. The prison, which shut down in 1963, was famed for being supposedly impossible to escape from.

We need better ways to help people. What's the medical breakthrough that could save the most lives in the U.S. over the next ten years? In the 2020s, medical research will likely inch forward when it comes to major killers like heart disease and cancer. But the biggest potential to save lives could lie in learning to prevent suicide. The rates of reported suicides have been creeping up over the last two decades.

Anthony Levandowski makes an unlikely prophet. Dressed Silicon Valley-casual in jeans and flanked by a PR rep rather than cloaked acolytes, the engineer known for self-driving cars--and triggering a notorious lawsuit--could be unveiling his latest startup instead of laying the foundations for a new religion. But he is doing just that. Artificial intelligence has already inspired billion-dollar companies, far-reaching research programs, and scenarios of both transcendence and doom. Now Levandowski is creating its first church.

SAN FRANCISCO – The top diplomats of Japan, the United States and South Korea on Tuesday urged North Korea to refrain from military provocation and continue denuclearization talks, but ruled out any easing of crushing economic sanctions without progress in the stalled negotiations. Foreign Minister Toshimitsu Motegi held discussions with his U.S. and South Korean counterparts, Mike Pompeo and Kang Kyung-wha, in East Palo Alto, just outside San Francisco, two weeks after a deadline set by Pyongyang for progress by the end of 2019 passed. "We agreed on the importance of North Korea making positive efforts in talks with the United States rather than going through with provocative moves," Motegi told reporters. The statement appeared to contradict remarks in a New Year speech by South Korean President Moon Jae-in a day earlier in Seoul, where he said that he could seek exemptions of U.N. sanctions to bring about improved inter-Korean relations that he believes would help restart the deadlocked nuclear negotiations between Pyongyang and Washington. Moon has previously made similar comments, despite outside worries that any lifting of sanctions could undermine U.S.-led efforts to eliminate North Korea's nuclear arsenal.

Estimating temporal patterns in travel times along road segments in urban settings is of central importance to traffic engineers and city planners. In this work, we propose a methodology to leverage coarse-grained and aggregated travel time data to estimate the street-level travel times of a given metropolitan area. Our main focus is to estimate travel times along the arterial road segments where relevant data are often unavailable. The central idea of our approach is to leverage easy-to-obtain, aggregated data sets with broad spatial coverage, such as the data published by Uber Movement, as the fabric over which other expensive, fine-grained datasets, such as loop counter and probe data, can be overlaid. Our proposed methodology uses a graph representation of the road network and combines several techniques such as graph-based routing, trip sampling, graph sparsification, and least-squares optimization to estimate the street-level travel times. Using sampled trips and weighted shortest-path routing, we iteratively solve constrained least-squares problems to obtain the travel time estimates. We demonstrate our method on the Los Angeles metropolitan-area street network, where aggregated travel time data is available for trips between traffic analysis zones. Additionally, we present techniques to scale our approach via a novel graph pseudo-sparsification technique.

Wildlife is flourishing in the exclusion zone around the disabled Fukushima Daichii nuclear reactor in Japan, images from remotely-operated cameras have revealed. Researchers spotted more than 20 species in areas around the reactor, including wild boar, macaques and fox-like raccoon dogs. The findings help reveal how wildlife populations respond in the wake of catastrophic nuclear disaster like those that occurred at Fukushima and Chernobyl. Humans were evacuated from certain zones around the the Fukushima reactor following radiation leaks caused by the Tōhoku earthquake and tsunami of 2011. Wildlife ecologist James Beasley of the University of Georgia, in the US, and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area around the Fukushima Daiichi power plant over a four-month period.

Aviation Maintenance Administrationman 3rd Class Shea Wright, assigned to the Skinny Dragons of Patrol Squadron (VP) 4, recovers a squadron P-8A Poseidon maritime patrol and reconnaissance aircraft following an anti-submarine warfare mission over the Atlantic Ocean, Nov. 30, 2019. The increasingly global reach of Chinese nuclear-armed ballistic missile submarines, armed with JL-2 weapons reportedly able to hit parts of the U.S., continues to inspire an ongoing Navy effort to accelerate production of attack submarines, prepare long-dwell drones for deployment to the Pacific and continue acquisition of torpedo-armed sub-hunting planes such as the P-8/A Poseidon. The Navy has been moving quickly to increase its fleet of Poseidon's on an accelerated timetable; in the Navy's 2020 budget, the service was authorized for a near term increase in Poseidon production by three, moving funding for the year up for nine Poseidons, as cited in a report from USNI news. Last year, the Navy awarded Boeing a $2.4 billion deal to produce 19 more P-8A Poseidon surveillance and attack planes. The Poseidon increase appears to align with the service's overall Pacific theater strategy, which makes a point to sustain peaceful, yet vital surveillance and Freedom of Navigation missions in the region.

--Multivariate time series prediction has applications in a wide variety of domains and is considered to be a very challenging task, especially when the variables have correlations and exhibit complex temporal patterns, such as seasonality and trend. Many existing methods suffer from strong statistical assumptions, numerical issues with high dimensionality, manual feature engineering efforts, and scalability. In this work, we present a novel deep learning architecture, known as T emporal T ensor Transformation Network, which transforms the original multivariate time series into a higher order of tensor through the proposed T emporal-Slicing Stack Transformation. This yields a new representation of the original multivariate time series, which enables the convolution kernel to extract complex and nonlinear features as well as variable interactional signals from a relatively large temporal region. Experimental results show that T emporal T ensor Transformation Network outperforms several state-of-the-art methods on window-based predictions across various tasks. The proposed architecture also demonstrates robust prediction performance through an extensive sensitivity analysis. Index T erms--multivariate time series, prediction, convolution, deep learning, tensor transformation I. I NTRODUCTION Multivariate time series analysis has gained wide spread applications in many fields, e.g., financial market prediction, weather forecasting, and energy consumption prediction. It is used to model and explain the underlying temporal patterns among a group of time series variables in dynamical systems. V arious methods have been proposed to predict multivariate time series based on statistical modeling and deep neural networks. Classical statistical models assume that the time series is stationary, i.e., the summary statistics of data points are consistent over time. Preprocessing procedures are usually needed to remove trend, seasonality, and other time-dependent structures from the raw series in order to make the data stationary. In addition, these models also assume the independence condition in the underlying linear regression problem, i.e., the random errors in the model are not correlated over time.