Vikings say Kyler Murray feeling'pretty good,' participating in team meetings despite serious concussion Ella Langley, distractions and Lane Kiffin: Pete Golding calls Ole Miss-LSU'high school homecoming' Mother Jones' WNBA'trans panic' story shows what happens when activism masquerades as journalism'A win is a win' says athlete who didn't let a little diarrhea keep her from finishing in first place Kansas City Chiefs' roster concerns linger as they host the Denver Broncos on Monday Night Football Florida State fires AD Michael Alford, as Mike Norvell's seat reaches a boiling point in Tallahassee Madison Beer's bid to become the NFL's next Taylor Swift hits road bump, Browns are a mess & a Daejon Love fan WWE broadcaster believes'the pieces are all in place' for an upset as Penta challenges Roman Reigns for title Denny Hamlin says forward-facing sonar in professional bass fishing has gone'a little overboard' Ben Shapiro warns Democrats' push for AI regulation is a'coordinated operation' Senate back in session as questions remain over Mitch McConnell's return Vance warns fraudsters stealing taxpayer money: 'Go and get a job' Vance warns fraudsters stealing taxpayer money: 'Go and get a job' Mullin weighs in on Obama, Biden judges amid Trump's immigration court fights Marc Thiessen warns US is in an'AI arms race' with China Charlie Kirk monument artist'protecting it from evil' after previous statue vandalized twice Army launches'field tested' campaign Vance reveals US has'engaged in direct conversations' with Houthis Steve Ballmer was suspended one year and fined $30M after league found Clippers paid Leonard $28M for a'no-show job' The LA Clippers have forfeited 5 1st-round picks and Owner Steve Ballmer was fined $30 million for circumventing the salary cap with Kawhi Leonard. Nick Wright, Chris Broussard, and Kevin Wildes ask if the punishments were fair, and how this will impact the Clippers in the future. Plus, they ask if the New England Patriots are in a must-win game vs. the Seattle Seahawks next week. Kawhi Leonard is officially heading back to the Toronto Raptors, and it comes after the NBA disciplined the Los Angeles Clippers for breaking the league's salary cap rules. Leonard was at the forefront of a previously agreed-upon trade between the two franchises, but there was a question about whether it would go through.

AI is already making online swindles easier. It could get much worse. Some cybersecurity researchers say it's too early to worry about AI-orchestrated cyberattacks. Others say it could already be happening. Anton Cherepanov is always on the lookout for something interesting. And in late August last year, he spotted just that.

Networked1).LetG=(V,E)beaconnected, thecommunicationE contains (i, j) ifagentsi and j can directlyviamessagest, eachagentj broadcastsmj(t) toalltheirneighbors. times G, after discarded.

In August, 1918, Virginia Woolf spent a quiet stretch at Asheham, the country house that she and her husband, Leonard, rented in rural Sussex. "We've been practically alone, which has a very spiritual effect upon the mind," Woolf wrote to a friend, the socialite Lady Ottoline Morrell. After six months spent in such isolation, Woolf quipped, "I should be a kind of Saint, and Leonard an undoubted prophet. We should shed virtue on people as we walked along the roads." Alas, any pretensions to holiness had been dispelled by the arrival of house guests the previous evening: "I had such a bath of the flesh that I am far from unspotted this morning.

This paper addresses the problem of adaptively controlling the bias parameter in nonlinear opinion dynamics (NOD) to allocate agents into groups of arbitrary sizes for the purpose of maximizing collective rewards. In previous work, an algorithm based on the coupling of NOD with an multi-objective behavior optimization was successfully deployed as part of a multi-robot system in an autonomous task allocation field experiment. Motivated by the field results, in this paper we propose and analyze a new task allocation model that synthesizes NOD with an evolutionary game framework. We prove sufficient conditions under which it is possible to control the opinion state in the group to a desired allocation of agents between two tasks through an adaptive bias using decentralized feedback. We then verify the theoretical results with a simulation study of a collaborative evolutionary division of labor game.

Detecting critical moments, such as emotional outbursts or changes in decisions during conversations, is crucial for understanding shifts in human behavior and their consequences. Our work introduces a novel problem setting focusing on these moments as turning points (TPs), accompanied by a meticulously curated, high-consensus, human-annotated multi-modal dataset. We provide precise timestamps, descriptions, and visual-textual evidence high-lighting changes in emotions, behaviors, perspectives, and decisions at these turning points. We also propose a framework, TPMaven, utilizing state-of-the-art vision-language models to construct a narrative from the videos and large language models to classify and detect turning points in our multi-modal dataset. Evaluation results show that TPMaven achieves an F1-score of 0.88 in classification and 0.61 in detection, with additional explanations aligning with human expectations.

The emergence of large-scale pretrained language models has revolutionized the capabilities of new AI application, especially in the realm of crafting chatbots with distinct personas. Given the "stimulus-response" nature of chatbots, this paper unveils an innovative open-ended interview-style approach for personality assessment on role-playing chatbots, which offers a richer comprehension of their intrinsic personalities. We conduct personality assessments on 32 role-playing chatbots created by the ChatHaruhi library, across both the Big Five and MBTI dimensions, and measure their alignment with human perception. Evaluation results underscore that modern role-playing chatbots based on LLMs can effectively portray personality traits of corresponding characters, with an alignment rate of 82.8% compared with human-perceived personalities. Besides, we also suggest potential strategies for shaping chatbots' personalities. Hence, this paper serves as a cornerstone study for role-playing chatbots that intersects computational linguistics and psychology. Our resources are available at https://github.com/LC1332/Chat-Haruhi-Suzumiya

There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth system. Here, we apply a common equation-discovery technique with expansive libraries to learn closures from filtered direct numerical simulations of 2D forced turbulence and Rayleigh-B\'enard convection (RBC). Across common filters, we robustly discover closures of the same form for momentum and heat fluxes. These closures depend on nonlinear combinations of gradients of filtered variables (velocity, temperature), with constants that are independent of the fluid/flow properties and only depend on filter type/size. We show that these closures are the nonlinear gradient model (NGM), which is derivable analytically using Taylor-series expansions. In fact, we suggest that with common (physics-free) equation-discovery algorithms, regardless of the system/physics, discovered closures are always consistent with the Taylor-series. Like previous studies, we find that large-eddy simulations with NGM closures are unstable, despite significant similarities between the true and NGM-predicted fluxes (pattern correlations $> 0.95$). We identify two shortcomings as reasons for these instabilities: in 2D, NGM produces zero kinetic energy transfer between resolved and subgrid scales, lacking both diffusion and backscattering. In RBC, backscattering of potential energy is poorly predicted. Moreover, we show that SGS fluxes diagnosed from data, presumed the "truth" for discovery, depend on filtering procedures and are not unique. Accordingly, to learn accurate, stable closures from high-fidelity data in future work, we propose several ideas around using physics-informed libraries, loss functions, and metrics. These findings are relevant beyond turbulence to closure modeling of any multi-scale system.

A concert held at the Massachussetts Institute of Technology (MIT) in the fall to celebrate the opening of the university's new museum included a performer that was invisible to the audience but played a key role in forming the melodic sound: an artificial intelligence (AI) system that responded to the musicians and improvised in real time. In a piece from "Brain Opera 2.0," the system starts by growling to the trumpet, then finds pitches with the trombone, becomes melodic with the sax, and ultimately syncs with the instruments by the time everyone comes in, explains Tod Machover, a music and media professor at MIT and head of the MIT Media Lab, who served as composer/conductor of the two-night concert event. The "living, singing AI" system was designed by Manaswi Mishra, one of Machover's Ph.D. students. "We developed a machine learning-based model that could react to musician input in real time, and then'fed' this model with a vast amount of music from many countries, styles, and historic periods, as well as with all kinds of human voices making every conceivable kind of vocal sound," Machover said. The system also drew from a vast library of percussive instruments and sounds from around the world to then improvise with the performers.

We describe a general approach for maximum a posteriori (MAP) inference in a class of discrete-continuous factor graphs commonly encountered in robotics applications. While there are openly available tools providing flexible and easy-to-use interfaces for specifying and solving inference problems formulated in terms of either discrete or continuous graphical models, at present, no similarly general tools exist enabling the same functionality for hybrid discrete-continuous problems. We aim to address this problem. In particular, we provide a library, DC-SAM, extending existing tools for inference problems defined in terms of factor graphs to the setting of discrete-continuous models. A key contribution of our work is a novel solver for efficiently recovering approximate solutions to discrete-continuous inference problems. The key insight to our approach is that while joint inference over continuous and discrete state spaces is often hard, many commonly encountered discrete-continuous problems can naturally be split into a "discrete part" and a "continuous part" that can individually be solved easily. Leveraging this structure, we optimize discrete and continuous variables in an alternating fashion. In consequence, our proposed work enables straightforward representation of and approximate inference in discrete-continuous graphical models. We also provide a method to approximate the uncertainty in estimates of both discrete and continuous variables. We demonstrate the versatility of our approach through its application to distinct robot perception applications, including robust pose graph optimization, and object-based mapping and localization.