Bear cubs spar on woman's front porch in adorable viral nature video, reactions pour in Show Tiffany Stratton some respect -- a boob job doesn't mean the WWE champ is made of plastic Britney Spears stuns with a post-plea deal Instagram dance, college baseball HOT mic & is this dream normal? Landlord in a tenant's home for repairs was caught on a security camera getting it on with a woman instead Paige Spiranac continues her generational golf content influencing run in 2026, Mike Alstott is ripped & MEAT! 'World's sexiest fan' drops her World Cup anthem and here's why you never assist a bike thief Wearing only a watch, a headlamp and flip-flops isn't a great disguise when trashing a neighbor's motion light Paige Spiranac's swing is so hot it gets flagged as she hits the course in country club approved attire Hannah Jeter makes rare public appearance and still fires heat, Shania Twain's new look stuns & HOA Karen! Minnesota fraud mastermind sentenced to 41.5 years in prison America 250: One Step - Armstrong's Walk on the Moon Democratic Senate candidate Graham Platner's old social posts stir up controversy Mideast awaits Trump's'critical' next move as US-Iran negotiations fail OutKick-Culture Robot's lifeless corpse hauled off stage after fall during disastrous Michael Jackson impression Bizarre footage captured the chaotic moment a service robot appeared to spin out of control at a restaurant near San Jose, California, leaving staff struggling restrain the uncontrollable humanoid. We're in the era of robotics before they enslave the human race, when we make them do fun stuff like fold our clothes and board Southwest flights . However, I think we're playing it fast and loose because all it's going to take is one embarrassing Michael Jackson impression for a robot to go, You know what?

A.1 Creation of the Multimodal Web Document Dataset A.1.1 Collecting of a Large Number of HTMLFiles Our data collection process begins by considering the 25 most recent Common Crawl6 dumps available at the time of dataset creation. It contains webpages spanning from February 2020 to January/February 2023. We use a modified version of readability-lxml7 to extract the main text from the pages, discarding any pages that contain text of excessively high perplexity. This process yields a total of 41.2 billion documents. Selection of English content To identify non-English content, we apply the FastText classifier (Joulin et al., 2017) to the extracted text, e ectively filtering out 63.6% of the documents. Early text deduplication Often, a set of URLs is crawled repeatedly across di erent Common Crawl snapshots. However, the content of these websites may vary as web administrators make changes over time. Hence, at this stage, we refrain from deduplicating documents based on their URLs. Instead, we perform MinHash (Broder, 1997) deduplication with 16 hashes calculated over 5-grams. To further refine the data, we eliminate documents containing substantial proportions of repeated paragraphs and n-grams, employing the methodology described in MassiveText (Rae et al., 2022).

Andrew Yang criticizes TKO Group Holdings for its handling of WrestleMania 42, saying the company moved away from the heart of pro wrestling amid a wave of WWE departures.

UFC fighter Tim Means was arrested in Albuquerque on a child abuse charge, according to online court records. He is accused of strangling and striking a teenager.

China's annual gala on Lunar New Year's Eve has showcased Beijing's giant leap in technology as humanoid robots took centre stage to perform a joint martial arts routine featuring several firsts. China's Spring Festival Gala, which aired on Monday on state broadcaster CGTN, has gone viral, drawing nearly half a million views on YouTube. The performance marked a stark contrast with last year's show, when robots twirled handkerchiefs and performed simple movements. The first robots to appear were Noetix's Bumi models, who performed a comedy sketch. Unitree's robots later exhibited martial arts alongside child artists, including backflips and trampoline jumps, followed by Magiclab's humanoids in a musical segment.

How a gift for shooting the shit turned into an online empire--and a political force. Trust in American mass media has plummeted; more than three thousand newspapers have disappeared in the past two decades, and many people get their news from social platforms. In this chaotic media multiverse, Rogan has emerged as a figure of singular influence. For a long time, I stayed up through the night listening to tall-tale tellers, U.F.O. I could not get enough of it. I was a fairly ordinary kid, Jersey-born, but the house I lived in was shadowed by illness. My mother had been diagnosed with a debilitating neurological disease when she was in her early thirties. Every year, she got worse. During the day, I wanted nothing more than to please my mother, do well in school, lighten her load. At night, I wanted only to climb into the shelter of my bed and turn on the radio. I was hungry for elsewhere, for other lives--for what was being said down the street, over the bridge, beyond the horizon. On clear nights, the signal was strong. You could hear the country expressing itself incessantly: everyone was phoning in, suggesting three-way trades, bitching about the mayor, speaking in tongues, raging, joking, climbing out on a ledge and threatening to jump. When I wanted a few hours of sleep before school, I tuned in to a ballgame on the West Coast. The staticky murmur of the crowd in Anaheim or Chavez Ravine was a sure slide to oblivion. Mostly, though, I wanted nothing to do with sleep. Mostly, I was tuned in, midnight to five-thirty, to "The Long John Nebel Show."

We describe a theory and implementation of an intuitionistic decentralized framework for causal discovery using judo calculus, which is formally defined as j-stable causal inference using j-do-calculus in a topos of sheaves. In real-world applications -- from biology to medicine and social science -- causal effects depend on regime (age, country, dose, genotype, or lab protocol). Our proposed judo calculus formalizes this context dependence formally as local truth: a causal claim is proven true on a cover of regimes, not everywhere at once. The Lawvere-Tierney modal operator j chooses which regimes are relevant; j-stability means the claim holds constructively and consistently across that family. We describe an algorithmic and implementation framework for judo calculus, combining it with standard score-based, constraint-based, and gradient-based causal discovery methods. We describe experimental results on a range of domains, from synthetic to real-world datasets from biology and economics. Our experimental results show the computational efficiency gained by the decentralized nature of sheaf-theoretic causal discovery, as well as improved performance over classical causal discovery methods.

Fair, transparent, and explainable decision-making remains a critical challenge in Olympic and Paralympic combat sports. This paper presents \emph{FST.ai 2.0}, an explainable AI ecosystem designed to support referees, coaches, and athletes in real time during Taekwondo competitions and training. The system integrates {pose-based action recognition} using graph convolutional networks (GCNs), {epistemic uncertainty modeling} through credal sets, and {explainability overlays} for visual decision support. A set of {interactive dashboards} enables human--AI collaboration in referee evaluation, athlete performance analysis, and Para-Taekwondo classification. Beyond automated scoring, FST.ai~2.0 incorporates modules for referee training, fairness monitoring, and policy-level analytics within the World Taekwondo ecosystem. Experimental validation on competition data demonstrates an {85\% reduction in decision review time} and {93\% referee trust} in AI-assisted decisions. The framework thus establishes a transparent and extensible pipeline for trustworthy, data-driven officiating and athlete assessment. By bridging real-time perception, explainable inference, and governance-aware design, FST.ai~2.0 represents a step toward equitable, accountable, and human-aligned AI in sports.

Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major barrier has been the lack of demographic annotations in web-scale datasets such as LAION-400M. We address this gap by creating person-centric annotations for the full dataset, including over 276 million bounding boxes, perceived gender and race/ethnicity labels, and automatically generated captions. These annotations are produced through validated automatic labeling pipelines combining object detection, multimodal captioning, and finetuned classifiers. Using them, we uncover demographic imbalances and harmful associations, such as the disproportionate linking of men and individuals perceived as Black or Middle Eastern with crime-related and negative content. We also show that 60-70% of gender bias in CLIP and Stable Diffusion can be linearly explained by direct co-occurrences in the data. Our resources establish the first large-scale empirical link between dataset composition and downstream model bias.