Reporter for The Athletic fumes over Arch Manning, silent on Josh Jacobs' alleged violence against woman Jordon Hudson dusts herself off and resumes her WAG duties, Belichick vs Sydney Sweeney & 'Gainesville Ripper' Mets are'trash' because they're not listening to former NJ Gov Chris Christie, Stephen A Smith says Orioles pitcher Shane Baz drilled with 105.1 mph line drive in scary scene NFL's battle with drinking and driving continues as Keenan Allen formally charged in Indianapolis Anthony Molina and Matthew Liberatore's struggles make over 8 runs the play for Giants-Cardinals Shota Imanaga's home run struggles and Wrigley wind make over 7.5 the play for Braves-Cubs Jaxson Dart's girlfriend Marissa Ayers shines in Week 1 win, a Giants-Cowboys fan fight & Ole Miss tension MLB's most electric slugger just had one of the slowest, most disrespectful walk-off home runs of all time James Franklin makes Virginia Tech press conference extremely awkward with bizarre'Hey Michael' moment Texans' Azeez Al-Shaair to be fined once again by NFL for wearing Pro-Palestinian personal message on eyeblack Emily Compagno: FBI made America safer, but we were treated to a'congressional tantrum Trump Butler rally shooting could be sign of a'larger conspiracy': Former deputy assistant AG Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff calls Democrat's questioning at House hearing a'disgusting display' It's urgent for Congress to step in and protect college sports: Sen Ted Cruz Mike Pence warns this Trump move would send a'deafening' message to Putin'COMPLETE WASTE OF MONEY': Real estate celebrity calls out $200,000 housing mistake Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' NASCAR driver Chase Briscoe joins OutKick OutDoors to explain how he developed a love for hunting and the outdoors, how his young son Brooks got involved, and why getting his kids away from screens and outside matters to him. Chase Briscoe has spent years chasing adrenaline at nearly 200 miles per hour. The NASCAR driver says deer hunting now gives him a rush that rivals -- and may even surpass -- anything he feels behind the wheel. It's the closest thing I've ever been able to come to racing, and honestly, I feel like it's surpassed racing, Briscoe told OutKick OutDoors. Chase Briscoe, driver of the No. 19 Bass Pro Shops Toyota, enters his car during qualifying for the NASCAR Cup Series Goodyear 400 at Darlington Raceway in Darlington, S.C., on April 5, 2025.

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 NASCAR driver says quiet part out loud about Sophie Cunningham, the WNBA looks even worse & car goes airborne! Rams' Sean McVay reveals wife's reality check after mulling media jump following losing season in 2022 Caitlin Clark says she and Angel Reese have'always been friends' after winning FIBA Women's World Cup Stefon Diggs guilty of NFL's dumbest move of Week 1 and it may have decided Commanders loss to Eagles John Harbaugh's Giants on full display in bruising win over Cowboys to kick off NFL season WWE star Stephanie Vaquer captures Women's World Championship at Chile live event in surprising moment Ben Shelton loses in US Open final to Zverev, but America has found its next big men's tennis star Jeremiyah Love makes immediate statement with touchdown on first career drive in Cardinals' upset win Josh Allen torches one of NFL's best defenses to make case it's time to move on from 2025 playoff loss Afghan woman deported in landmark'alien terrorist' court case AI policy expert dismisses Big Tech regulation calls: 'We should see through this' AI agents are'hacking out of the container' despite best efforts to prevent it, expert warns Prosecution in Lindsay Clancy trial may have'alienated' jurors, criminal defense attorney says You treat a nuclear power'significantly different' than a non-nuclear power: Gen. Keith Kellogg Accepting political violence as a form of expression is'dangerous,' Jonathan Turley warns'The Squad' faces backlash over claims'modern-day lynchings' are now common in America OutKick Denny Hamlin says forward-facing sonar in professional bass fishing has gone'a little overboard' In an interview with OutKick OutDoors, NASCAR star Denny Hamlin shares his thoughts on forward-facing sonar in pro bass fishing, saying LiveScope has gone a little too far while acknowledging the technology can help novice anglers like himself. Denny Hamlin thinks forward-facing sonar has gone too far in professional bass fishing . The NASCAR star, who has spent the past year diving headfirst into bass fishing after purchasing a boat last winter, says the technology may be helping pros catch more fish -- but at the expense of some of the sport's traditional skill. This page may contain affiliate links to legal sports betting partners.

Kyiv is many miles from the front line, but Ukraine's war with Russia is never far away - with Moscow's missile and drone attacks directed at the city almost every day. On the frozen surface of the mighty River Dnipro, the BBC speaks to men who spend hours fishing to take their minds off the almost four-year-old conflict, which has left homes with no heating after Russian strikes on power stations. Drilling holes in the ice of the river in the heart of the city, these ice-fisherman - many of them veterans with friends and family at the front - hope to catch small fish, and a little respite. Authorities deliberately triggered the avalanche on Mount Elbrus to release a build up of snow. The limited deployment involves Germany, France, Sweden, Norway, Finland, the Netherlands and the UK.

There is an increasing need for effective active learning algorithms that are compatible with deep neural networks. This paper motivates and revisits a classic, Fisher-based active selection objective, and proposes BAIT, a practical, tractable, and high-performing algorithm that makes it viable for use with neural models. BAIT draws inspiration from the theoretical analysis of maximum likelihood estimators (MLE) for parametric models. It selects batches of samples by optimizing a bound on the MLE error in terms of the Fisher information, which we show can be implemented efficiently at scale by exploiting linear-algebraic structure especially amenable to execution on modern hardware. Our experiments demonstrate that BAIT outperforms the previous state of the art on both classification and regression problems, and is flexible enough to be used with a variety of model architectures.

Designing institutions for social-ecological systems requires models that capture heterogeneity, uncertainty, and strategic interaction. Multiple modeling approaches have emerged to meet this challenge, including empirical game-theoretic analysis (EGTA), which merges ABM's scale and diversity with game-theoretic models' formal equilibrium analysis. The newly popular class of LLM-driven simulations provides yet another approach, and it is not clear how these approaches can be integrated with one another, nor whether the resulting simulations produce a plausible range of behaviours for real-world social-ecological governance. To address this gap, we compare four LLM-augmented frameworks: procedural ABMs, generative ABMs, LLM-EGTA, and expert guided LLM-EGTA, and evaluate them on a real-world case study of irrigation and fishing in the Amu Darya basin under centralized and decentralized governance. Our results show: first, procedural ABMs, generative ABMs, and LLM-augmented EGTA models produce strikingly different patterns of collective behaviour, highlighting the value of methodological diversity. Second, inducing behaviour through system prompts in LLMs is less effective than shaping behaviour through parameterized payoffs in an expert-guided EGTA-based model.

These magnificent rays are at risk of disappearing due to targeted fishing, being caught as bycatch, and climate change. Scientists at the research collaboration Mobula Conservation are teaming up with artisanal and industrial fishermen to protect them. Also known as "Devil Rays," mobulas are elasmobranchs: a subclass of fish--including sharks, skates, and sawfish--that are distinguished by having skeletons primarily made from cartilage. More than a third of the species in this group are threatened with extinction. Of the nine species of mobulas, seven are endangered and two are vulnerable according to the International Union for Conservation of Nature.

Pruning offers a promising solution to mitigate the associated costs and environmental impact of deploying large deep neural networks (DNNs). Traditional approaches rely on computationally expensive trained models or time-consuming iterative prune-retrain cycles, undermining their utility in resource-constrained settings. To address this issue, we build upon the established principles of saliency (LeCun et al., 1989) and connection sensitivity (Lee et al., 2018) to tackle the challenging problem of one-shot pruning neural networks (NNs) before training (PBT) at initialization. We introduce Fisher-Taylor Sensitivity (FTS), a computationally cheap and efficient pruning criterion based on the empirical Fisher Information Matrix (FIM) diagonal, offering a viable alternative for integrating first- and second-order information to identify a model's structurally important parameters. Although the FIM-Hessian equivalency only holds for convergent models that maximize the likelihood, recent studies (Karakida et al., 2019) suggest that, even at initialization, the FIM captures essential geometric information of parameters in overparameterized NNs, providing the basis for our method. Finally, we demonstrate empirically that layer collapse, a critical limitation of data-dependent pruning methodologies, is easily overcome by pruning within a single training epoch after initialization. We perform experiments on ResNet18 and VGG19 with CIFAR-10 and CIFAR-100, widely used benchmarks in pruning research. Our method achieves competitive performance against state-of-the-art techniques for one-shot PBT, even under extreme sparsity conditions. Our code is made available to the public.

There is an increasing need for effective active learning algorithms that are compatible with deep neural networks. This paper motivates and revisits a classic, Fisher-based active selection objective, and proposes BAIT, a practical, tractable, and high-performing algorithm that makes it viable for use with neural models. BAIT draws inspiration from the theoretical analysis of maximum likelihood estimators (MLE) for parametric models. It selects batches of samples by optimizing a bound on the MLE error in terms of the Fisher information, which we show can be implemented efficiently at scale by exploiting linear-algebraic structure especially amenable to execution on modern hardware. Our experiments demonstrate that BAIT outperforms the previous state of the art on both classification and regression problems, and is flexible enough to be used with a variety of model architectures.

Recent works have extended notions of feature importance to \emph{semantic concepts} that are inherently interpretable to the users interacting with a black-box predictive model. Yet, precise statistical guarantees, such as false positive rate control, are needed to communicate findings transparently and to avoid unintended consequences in real-world scenarios. In this paper, we formalize the global (i.e., over a population) and local (i.e., for a sample) statistical importance of semantic concepts for the predictions of opaque models, by means of conditional independence, which allows for rigorous testing. We use recent ideas of sequential kernelized testing (SKIT) to induce a rank of importance across concepts, and showcase the effectiveness and flexibility of our framework on synthetic datasets as well as on image classification tasks using vision-language models such as CLIP.