The Tate brothers left the Sunshine State Thursday ahead of an expected court appearance in Romania, but influencer and former MMA fighter Andrew Tate says he'll be back. Andrew Tate is back in America, forcing us to confront his perverse messaging to a subset of America's young men. But what we really need to come to grips with are the social conditions in our culture that created an opening for this men's rights Frankenstein. Tate, 38, is a former professional kickboxer facing sex trafficking charges in Romania, serious enough that Florida Gov. Ron DeSantis insists the podcast star is not welcome in the Sunshine State, where he landed earlier this week; the Florida attorney general is now investigating Tate and his brother Tristan. ANDREW TATE SAYS HE PLANS TO LIVE IN FLORIDA DESPITE'HEE HAW' OVER RETURN TO US SOIL Tate made a fortune off of a "webcam model" (read: porn) business, then began selling online courses ostensibly teaching alienated boys and young men how to become alpha males.

Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discourse analysis. Existing DSI approaches are often purely data-driven, deploy models that infer latent states without access to domain knowledge, underperform when the training corpus is limited/noisy, or have difficulty when test dialogs exhibit distributional shifts from the training domain. This work explores a neural-symbolic approach as a potential solution to these problems. We introduce Neural Probabilistic Soft Logic Dialogue Structure Induction (NEUPSL DSI), a principled approach that injects symbolic knowledge into the latent space of a generative neural model. We conduct a thorough empirical investigation on the effect of NEUPSL DSI learning on hidden representation quality, few-shot learning, and out-of-domain generalization performance. Over three dialog structure induction datasets and across unsupervised and semi-supervised settings for standard and cross-domain generalization, the injection of symbolic knowledge using NEUPSL DSI provides a consistent boost in performance over the canonical baselines.

No one wants to be reminded of a failed relationship by having the ex's portrait hanging around. After Amedeo Modigliani and his lover, Beatrice Hastings, broke up, the Italian artist is thought to have obliterated her memory by painting another woman's likeness over his portrait of her. So he might not be too happy to learn that science has now brought back that "lost" portrait, using artificial intelligence, an X-ray and 3D-printing to re-create the painting, with full colour and textured brushstrokes. Portrait of a Girl, a 1917 masterpiece, is owned by the Tate, which was taken aback in 2018 to discover an earlier portrait beneath the picture. X-rays revealed the ghostly outlines of a full-length figure, prompting the then curator, Nancy Ireson, to suggest that it was a portrait of Hastings, and that Modigliani "might have painted her out" after their intense two-year relationship ended in 1916.

Design-by-Analogy (DbA) is a design methodology, wherein new solutions are generated in a target domain based on inspiration drawn from a source domain through cross-domain analogical reasoning [1, 2, 3]. DbA is an active research area in engineering design and various methods and tools have been proposed to support the implement of its process [4, 5, 6, 7, 8]. Studies have shown that DbA can help designers mitigate design fixation [9] and improve design ideation outcomes [10]. Fig.1 presents an example of DbA applications [11]. This case aims to solve an engineering design problem: How might we rectify the loud sonic boom generated when trains travel at high speeds through tunnels in atmospheric conditions [11, 12]? For potential design solutions to this problem, engineers explored structures in other design fields than trains or in the nature that effectively "break" the sonic-boom effect. When looking into the nature, engineers discovered that kingfisher birds could slice through the air and dive into the water at extremely high speeds to catch prey while barely making a splash. By analogy, engineers re-designed the train's front-end nose to mimic the geometry of the kingfisher's beak. This analogical design reduced noise and eliminated tunnel booms.

Artificial intelligence (AI) and machine learning (ML) are progressing at a rate that is outstripping Moore's Law. In fact, they now are evolving faster than silicon can be designed. The industry is looking at all possibilities to provide devices that have the necessary accuracy and performance, as well as a power budget that can be sustained. FPGAs are promising, but they also have some significant problems that must be overcome. The graphics processing unit (GPU) made machine learning (ML) possible. It provided significantly more compute power and had a faster connection to memory than the CPU.

A deer and a fawn are lost far from home, with no idea where they are or how they can get back to the woods. With the help of glowing antlers, curiosity and sheer will, the animals travel through wheat fields, abandoned subway systems and surreal sewers to make it back where they belong. This is Way to the Woods, a video game for PC and Xbox One due out in 2020 from an independent, 20-year-old developer named Anthony Tan. Following in the footsteps of similar so-called "pacifist" games like Journey, Firewatch and Night in the Woods, Way to the Woods is aimed at gamers looking for adult experiences that don't rely on violence to tell a compelling story. While President Donald Trump and others continue to suggest a connection between violent video games and mass shootings despite a lack of evidence, there has been a notable spike in games like Tan's that are more about exploration, story and design than racking up a body count.

Deep learning has a DRAM problem. Systems designed to do difficult things in real time, such as telling a cat from a kid in a car's backup camera video stream, are continuously shuttling the data that makes up the neural network's guts from memory to the processor. Some systems need four or even eight DRAM chips to sling the 100s of gigabits to the processor, which adds a lot of space and consumes considerable power. Flex Logix says that the interconnect technology and tile-based architecture it developed for reconfigurable chips will lead to AI systems that need the bandwidth of only a single DRAM chip and consume one-tenth the power. Mountain View-based Flex Logix had started to commercialize a new architecture for embedded field programmable gate arrays (eFPGAs). But after some exploration, one of the founders, Cheng C. Wang, realized the technology could speed neural networks.

ACKNOWLEDGMENTS We thank the NAVIGATOR steering committee and investigators for access to the NAVIGATOR data Affiliations: Department of Biostatistics & Bioinformatics, Duke University, Durham, NC (BAG, MJP); Center For Predictive Medicine, Duke Clinical Research Institute, Durham, NC (BAG, MP, NHJ); Department of Medicine, Duke University, Durham, NC (NHJ); Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford (RRH); Department of Biostatistics, Johns Hopkins University, Baltimore, MD (EAS) Funding: This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) career development award K25 DK097279 (B.A.G.), US Department of Education Institute of Education Sciences Grant R305D150003 (EAS). The project described was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), through Grant Award Number UL1TR001117 at Duke University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. NAVIGATOR was funded by Novartis. An Outcome Model Approach to Translating a Randomized Controlled Trial Results to a Target Population Abstract Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to translate RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here we describe such an approach using source data from the 2x2 factorial NAVIGATOR trial which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a "pre-diabetic" population. Our target data consisted of people with "pre-diabetes" serviced at our institution. We used Random Survival Forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes and estimated the treatment effect in our local patient populations, as well as subpopulations, and the results compared to the traditional weighting approach.

With Zurich, Grenke, Reykjavik, and Poikovsky tournaments all taking place nearly simultaneously, you might think there's no room for "In Other News." But this is the digital age; there's room for anything! You can choose to meet a former world champion, learn about an American chess legend, help save a local chess shop, or even lead the 90,000 players of the U.S. Chess Federation. No, this doesn't mean the former world champion will be staging protests in the streets like he did while running for political office in Russia. Instead, Kasparov will be appearing at Techcrunch's "Disrupt NY" event on May 17, 2017.

We propose a novel incomplete cooperative algorithm for distributed constraint optimization problems (DCOPs) denoted as Cooperative Constraint Approximation (CoCoA). The key strategy of the algorithm is to use a semi-greedy approach in which knowledge is distributed amongst neighboring agents, and assigning a value only once instead of an iterative approach. Furthermore, CoCoA uses a unique-first approach to improve the solution quality. It is designed such that it can solve DCOPs as well as Asymmetric DCOPS, with only few messages being communicated between neighboring agents. Experimentally, through evaluating graph coloring problems, randomized (A)DCOPs, and a sensor network communication problem, we show that CoCoA is able to very quickly find solutions of high quality with a smaller communication overhead than state-of-the-art DCOP solvers such as DSA, MGM-2, ACLS, MCS-MGM and Max-Sum. In our asymmetric use case problem of a sensor network, we show that CoCoA not only finds the best solution, but also finds this solution faster than any other algorithm.