Fox News contributor Dr. Marc Siegel joins'Fox News Live' to discuss the FDA approving a new Alzheimer treatment drug and the FDA banning bromide vegetable oils. "Normal" levels of vitamin B12 may not be enough to ward off dementia, new research finds. Researchers at University of California San Francisco studied 231 healthy older adults (averaging 71 years of age) who did not have dementia or mild cognitive impairment. Blood tests showed that their B12 levels averaged 414.8 pmol/L, while the recommended minimum level in the U.S. is just 148 pmol/L. Participants who had lower B12 levels were found to have "slower cognitive and visual processing speeds" when taking tests, which is linked to "subtle cognitive decline," according to a UCSF press release.

Gert-Jan Oskam, paralyzed for 12 years, is able to walk again thanks to the brain-spine "digital bridge" interface developed at France's Atomic Energy Commission (CEA). For the first time ever, a human has successfully received an implanted device to enable movement of the arms, hands and fingers after a paralyzing spinal cord injury. Onward Medical NV, a medical technology company based in the Netherlands, announced on Wednesday the surgical implant of its ARC-IM Stimulator, which is designed to restore function to the upper extremities of paralyzed patients. The patient, a 46-year-old man, suffered a spinal cord injury nearly two years ago, which left his left side almost fully paralyzed, doctors told Fox News Digital. The ARC-IM implantation took place on Aug. 14 at Centre Hospitalier Universitaire Vaudois (CHUV) in Lausanne, Switzerland.

Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks in recent years. Existing efforts propose metrics that allow a user to choose one model from a pool of pre-trained models without having to fine-tune each model individually and identify one explicitly. With the growth in the number of available pre-trained models and the popularity of model ensembles, it also becomes essential to study the transferability of multiple-source models for a given target task. The few existing efforts study transferability in such multi-source ensemble settings using just the outputs of the classification layer and neglect possible domain or task mismatch. Moreover, they overlook the most important factor while selecting the source models, viz., the cohesiveness factor between them, which can impact the performance and confidence in the prediction of the ensemble. To address these gaps, we propose a novel Optimal tranSport-based suBmOdular tRaNsferability metric (OSBORN) to estimate the transferability of an ensemble of models to a downstream task. OSBORN collectively accounts for image domain difference, task difference, and cohesiveness of models in the ensemble to provide reliable estimates of transferability. We gauge the performance of OSBORN on both image classification and semantic segmentation tasks. Our setup includes 28 source datasets, 11 target datasets, 5 model architectures, and 2 pre-training methods. We benchmark our method against current state-of-the-art metrics MS-LEEP and E-LEEP, and outperform them consistently using the proposed approach.

Four years ago, a few hundred miles off the coast of West Africa, a crane lifted a bulbous yellow submarine from the research vessel Poseidon and lowered it into the Atlantic. Inside the sub, Karen Osborn, a zoologist at the Smithsonian Institution who was swaddled in warm clothes, tried to ward off nausea. During half an hour of safety checks, Osborn watched water slosh across the submarine's round window, washing-machine style. Then the crew gave the all-clear and the vessel descended. In the waters of Cape Verde, a volcanic archipelago that is famous for its marine life, Osborn felt the seasickness dissipate.

The U.S. Navy and U.S. Air Force are working on a new generation of training technologies to prepare their fighter aircraft for new Russian and Chinese air threats posed by the Su-57 fighter and J-20 fifth-generation stealth aircraft, respectively. Over the next two years, the U.S. Air Force plans to use a cutting-edge computer technology called the P5 Combat Training System (P5CTS), made by a firm called Cubic Mission and Performance Solutions. Information from Cubic describes the P5 as an encryption solution intended to improve U.S. Air Force and U.S. Navy pilot training for advanced, high-threat combat scenarios using advanced computer simulations, wireless networks, and artificial intelligence (AI)-enabled data organization. Interestingly, the P5 pod can be seen in the now-famous Top Gun Maverick movie on a F/A-18 fighter. "Over the course of the last 13 years, we've learned some critical lessons about integrating fast movers with virtual environments to provide a realistic presentation to the aircrew in their cockpits. Having a wireless network that allows you to sustain that environment without interruptions. In other words, a low, flat latency is a very important feature," said Cubic's training expert, Paul Averna.

The Navy is progressively phasing artificial intelligence (AI) into its ship systems, weapons, networks, and command and control infrastructure as computer automation becomes more reliable and advanced algorithms make once-impossible discernments and analyses. Previously segmented data streams on ships, drones, aircraft, and even submarines are now increasingly able to share organized data in real-time, in large measure due to breakthrough advances in AI and machine learning. AI can, for instance, enable command and control systems to identify moments of operational relevance from among hours or days or surveillance data in milliseconds, something which saves time, maximizes efficiency, and performs time-consuming procedural tasks autonomously at an exponentially faster speed. "Multiple data bytes of information will be passed around on the networks here in the near future. So as we think about big data, and how do we handle all that data and turn it into information without getting overloaded, this will be a key part of AI, then we're talking about handling decentralized systems," Nathan Husted of the Naval Surface Warfare Center, Carderock told an audience at the 2022 Sea Air Space Symposium.

We introduce Playspecs, an application of omega-regular expressions to specifying play traces (sequences of game states or events unfolding over time). This connects the automated analysis and model checking of games to the literature on formal software verification via Bu chi automata. We show how to define desirable or undesirable sequences of game events with Playspecs and how associated algorithms can find examples (or prove the impossibility) of such sequences. Playspecs have two main benefits over existing techniques for specifying the behaviors of a game over time. First, they offer a scalable commitment to formal modeling: the same Playspecs can filter existing traces gathered by telemetry, search for satisfying traces using existing game code, or drive formal verification when paired with a logical model of a game. Second, Playspecs' syntax can be customized for the game engine or game in question so designers may write specifications using their game's native vocabulary. We define Playspecs' syntax and semantics (modulo gamespecific customizations) and outline algorithms for each of the applications mentioned above, providing examples from the social simulation game Prom Week and the puzzle game engine PuzzleScript.

We extend the Expressionist project, and thereby the re-emerging area of grammar-based text generation, by applying a technique from software verification to a critical search problem related to content generation from grammars. In Expressionist, authors attach tags (corresponding to pertinent meanings) to nonterminal symbols in a context-free grammar, which enables the targeted generation of content that expresses requested meanings (i.e., has the requested tags). While previous work has demonstrated methods for requesting content with a single required tag, requests for multiple tags yields a search task over domains that may realistically span quintillions or more elements. In this paper, we reduce Expressionist grammars to symbolic visibly pushdown automata, which allows us to locate in massive search spaces generable outputs that satisfy moderately complex criteria related to tags. While the satisficing of more complex tag criteria is still not feasible using this technique, we forecast a number of opportunities for future directions.

General videogame playing has come a long way in a short period of time, but remains at the level of solving relatively short games made up of distinct and isolated episodes. Even simple console role-playing games (RPGs) are far beyond the reach of current techniques, requiring the synthesis of cultural knowledge with compositional reasoning over several interconnected sub-games. We explore how the challenges of playing these games could spark new advances in compositional analysis of games and common-sense reasoning. General RPG playing can leverage advances in episodic general game playing and in areas like text understanding, image classification, and automated game design learning. It has direct applications in design support and AI-based game design, and the techniques used to enable it could generalize to other families of games such as adventure, open-world, and simulation games. In this paper, we describe the motivation behind general RPG playing in a sub-domain of Nintendo Entertainment System (NES) RPGs, some promising approaches to some of its fundamental issues, and immediate next steps; we conclude by describing a few concrete benchmark problems on the path towards automated play of these complex games.

Platformers and action-adventure games have high-dimensional state spaces with difficult, non-linear constraints on character movement; even worse, game environments often respond to the player in complex ways that can cause exponential expansion of the planning search space. Planning problems in these high-dimensional spaces generally require domain-specific knowledge and manually abstracted models of game rules to replicate the intuition of human designers or playtesters. In this work, we outline a system for modeling these complex games at a precise and low level in terms of hybrid automata. With this representation, standard incremental search algorithms can be used to answer reachable-region queries, taking advantage of the domain information embedded in the system.