We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs. The motion is modeled by a newly proposed component, motion guider, which plays the role of both learner and teacher. Specifically, it {\em learns} the temporal features from real data and {\em guides} the generator to predict future frames. The spatial consistency in video is modeled by an adaptive filtering network. To further ensure the spatio-temporal consistency of the prediction, a discriminator is also adopted to distinguish the real and generated frames. Further, the discriminator leaks information to the motion guider and the generator to help the learning of motion. The proposed LMVP can effectively learn the static and temporal features in videos without the need for human labeling. Experiments on synthetic and real data demonstrate that LMVP can yield state-of-the-art results.

We seek to improve the data efficiency of neural networks and present novel implementations of parameterized piece-wise polynomial activation functions. The parameters are the y-coordinates of n 1 Chebyshev nodes per hidden unit and Lagrangian interpolation between the nodes produces the polynomial on [ 1, 1]. We show results for different methods of handling inputs outside [ 1, 1] on synthetic datasets, finding significant improvements in capacity of expression and accuracy of interpolation in models that compute some form of linear extrapolation from either ends. We demonstrate competitive or state-of-the-art performance on the classification of images (MNIST and CIFAR-10) and minimally-correlated vectors (DementiaBank) when we replace ReLU or tanh with linearly extrapolated Chebyshev-Lagrange activations in deep residual architectures.

This paper presents the first end-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. All of these components, learned end-to-end, help produce realistic videos with good temporal stability. Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively.

DUBAI, UNITED ARAB EMIRATES - One person was killed and seven others were wounded in an attack by Iranian-allied Yemeni rebels on an airport in the kingdom Sunday evening as U.S. Secretary of State was on his way to the country for talks on Iran, Saudi Arabia said. Regional tensions have flared in recent days, The U.S. abruptly called off military strikes against Iran in response to the shooting down of an unmanned American surveillance drone. The Trump administration has vowed to combine a "maximum pressure" campaign of economic sanctions with a buildup of American forces in the region, following the U.S. withdrawal from the 2015 nuclear deal between Iran and world powers. A new set of U.S. sanctions on Iran are expected to be announced Monday. The Sunday attack by the Yemeni rebels, known as Houthis, targeted the Saudi airport in Abha.

There are many never-ending debates between Republicans and Democrats. Impeach vs. don't impeach; capital punishment vs. life in prison; wall vs. no wall; legalizing marijuana vs. not; self-driving cars vs. human drivers; Red Sox vs. Yankees; takeout vs. home-cooked; or Gone With the Wind vs. any other movie. All of these issues are stunningly important, right up to the second where cataclysm falls and creates a nightmare scenario that so many fear. That cataclysm is a complete loss of electricity and every mode of convenience and survival we take for granted. IS NORTH KOREA'S EMP THREAT REAL OR'SOMETHING OUT OF A JAMES BOND MOVIE'?

Investment in artificial intelligence (A.I.) has skyrocketed over the past several years. One study suggests 80 percent of the enterprises it surveyed have some form of A.I. in production today and 30 percent plan to expand A.I. investment over the next 36 months. Health care has the most robust A.I. startup scene of any sector: as of February 2017, there were 106 A.I. startups in the industry. Seventy launched in the last year alone. While there is tremendous excitement surrounding A.I. activity, there is also considerable fear, confusion and resistance.

Walmart is using computer vision technology to monitor checkouts and deter potential theft in more than 1,000 stores, the company confirmed to Business Insider. The surveillance program, which Walmart refers to internally as Missed Scan Detection, uses cameras to help identify checkout scanning errors and failures. The cameras track and analyze activities at both self-checkout registers and those manned by Walmart cashiers. When a potential issue arises, such as an item moving past a checkout scanner without getting scanned, the technology notifies checkout attendants so they can intervene. The program is designed to reduce shrinkage, which is the term retailers use to define losses due to theft, scanning errors, fraud, and other causes.

How organizations can improve processes and capture value through enterprise cognitive computing. Artificial intelligence invariably conjures up visions of self-driving vehicles, obliging personal assistants, and intelligent robots. But AI's effect on how companies operate is no less transformational than its impact on such products. Enterprise cognitive computing -- the use of AI to enhance business operations -- involves embedding algorithms into applications that support organizational processes.1 ECC applications can automate repetitive, formulaic tasks and, in doing so, deliver orders-of-magnitude improvements in the speed of information analysis and in the reliability and accuracy of outputs. For example, ECC call center applications can answer customer calls within 5 seconds on a 24-7-365 basis, accurately address their issues on the first call 90% of the time, and transfer complex issues to employees, with less than half of the customers knowing that they are interacting with a machine.2

BI tools play a vital role in the growth of retail businesses helping them to enhance their overall efficiency. The combination of these tools and analytics will continue to modify retail. FREMONT, CA: With the influence of online retailers, the retail sector has always been competitive. The retailers should always be ready to handle the dynamism of the market and continuously upgrade offerings. Retailers should be capable enough to remodel themselves as trends, demands, and how customer habits change.

The language of artificial intelligence (AI) and automation is misused and incites fear among the workforce, according to a panel on day two of the 2018 CIPD Annual Conference and Exhibition. Speaking during a panel session called'Will automation and artificial intelligence (AI) help or hinder good people management?', Andrew Spence, HR transformation director at Glass Bead Consulting, said that there's "fear-mongering" in the rhetoric that "the robots will take our jobs". "The word robot comes from the word'robota', which means slave so the language puts fear in people," he said. But the "term AI has a naming problem in itself", he continued, pointing out that there are multiple ways people would define the word intelligence. Cheryl Allen, HR director transformation at Atos, agreed that "people use AI as a general [term] and so many words are used interchangeably".