This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction.

Numerous images of atarget human subject or ascene are required for training. Second, a learned model has limited generalization capability.

A team of Nvidia researchers this week are demonstrating how they've used deep learning to tackle a common challenge: producing a slow-motion video with existing video footage. Nvidia AI system fills in missing frames between the left and right images (in green borders), enabling high quality video play back in slow motion. The research team, presenting their paper at the 2018 Conference on Computer Vision and Pattern Recognition (CVPR), developed a deep learning-based system that can produce slow-motion videos -- slowed down to any frame rate -- from a 30-frame-per-second video. The result is a high-quality video that looks smooth and seamless in comparison to the existing state-of-the-art methods. Check out the video below.

Recently, a number of non-uniform random satisfiability models have been proposed that are closer to practical satisfiability problems in some characteristics. In contrast to uniform random Boolean formulas, scale-free formulas have a variable occurrence distribution that follows a power law. It has been conjectured that such a distribution is a more accurate model for some industrial instances than the uniform random model. Though it seems that there is already an awareness of a threshold phenomenon in such models, there is still a complete picture lacking. In contrast to the uniform model, the critical density threshold does not lie at a single point, but instead exhibits a functional dependency on the power-law exponent. For scale-free formulas with clauses of length k=2, we give a lower bound on the phase transition threshold as a function of the scaling parameter. We also perform computational studies that suggest our bound is tight and investigate the critical density for formulas with higher clause lengths. Similar to the uniform model, on formulas with k>=3, we find that the phase transition regime corresponds to a set of formulas that are difficult to solve by backtracking search.

"As my father lost the ability to do things for himself, my mother would give him gentle prompts to keep him on track," recalled Kautz, associate professor in the University of Washington's Department of Computer Science & Engineering. "So at a stage of the disease where, according to the clinical scales, it would seem he couldn't do anything for himself, he could still perform many of the functions of life. He could shower, get dressed, and so forth because my mother would monitor him and give a prompt when needed." It's a recollection that has guided Kautz in initiating a research effort at the UW to explore ways in which computer science can compensate for diminished mental capacity. The Assisted Cognition Project is a collaborative effort by the UW, Intel Computers and Elite Care, a private company developing a state-of-the-art retirement community in the Portland area that utilizes so-called ubiquitous computing to keep tabs on residents' needs.

These results far outperform alternative models. This work is an important step towards the development Recent work has demonstrated that micro-blogging data can of automated methods that identify disease vectors, trace the be used to predict a variety of phenomena, including movie transmission between concrete individuals, and ultimately box-office revenues (Asur and Huberman 2010), elections help us understand and predict the spread of infectious diseases (Tumasjan et al. 2010), and flu epidemics (Lampos, De Bie, with fine granularity. It provides a foundation for and Cristianini 2010). Most research to date has focused on research on fundamental questions of public health, such predicting aggregate properties of the population from the as: How does an epidemic on a population scale emerge activity of the bloggers. A different kind of problem one can from low-level interactions between people in the course pose, however, is to predict the behavior or state of particular of their everyday lives? Can we identify a potentially noncooperative individuals within the social network. For instance, one individual who is a vector of a dangerous disease, could try to predict whether a person will go to a movie or i.e., a "Typhoid Mary"? What is the interaction between vote for a particular candidate based on micro-blog data. The friendship, location, and co-location in the spread of individual's own data may or may not be accessible.

In the last few years, we have witnessed a major growth in the use of empirical methods in AI. In part, this growth has arisen from the availability of fast networked computers that allow certain problems of a practical size to be tackled for the first time. There is also a growing realization that results obtained empirically are no less valuable than theoretical results. Experiments can, for example, offer solutions to problems that have defeated a theoretical attack and provide insights that are not possible from a purely theoretical analysis. I identify some of the emerging trends in this area by describing a recent workshop that brought together researchers using empirical methods as far apart as robotics and knowledge-based systems.

The difficulty of finding information on the World Wide Web by browsing hypertext documents has led to the development and deployment of various search engines and indexing techniques. However, many information-gathering tasks are better handled by finding a referral to a human expert rather than by simply interacting with online information sources. A personal referral allows a user to judge the quality of the information he or she is receiving as well as to potentially obtain information that is deliberately not made public. The process of finding an expert who is both reliable and likely to respond to the user can be viewed as a search through the net-work of social relationships between individuals as opposed to a search through the network of hypertext documents. The goal of the REFERRAL WEB Project is to create models of social networks by data mining the web and develop tools that use the models to assist in locating experts and related information search and evaluation tasks.