You're viewing the US edition You can switch to the UK, AU or IE homepage at any time using this menu. Terrifying moment California college student narrowly escapes stalker who chases her home and vaults over gate in race to her front door... but police let him walk for infuriating reason Devastating untold story of Presley Gerber's final days: Extremely thin, jittering'like he was on meth' and so out his mind that his voice changed... mom Cindy Crawford tried one last panicked intervention - as reality slipped away Many missed the subtle signs in Prince William and Kate's'united front' that show us what's going on behind the scenes for the royal couple. But I've been studying them for years. Seemingly innocuous rental home in red hot California hotspot hits the market for $6,300... but it's hiding a dark secret that's left a family furious Pet rescue showcased its'incredible' work in adorable photos of cute cats... but owners were hiding a terrifying dark secret Dark new details of infertile influencer Clavicular's alleged Cape Cod rape: Read the damning claims about booze-fueled night at GRANDMA's coastal mansion with underage girl... and his mom's shock reaction Florida teacher under fire as she's accused of trying to'cancel' an 8th grader over anti-woke views Creator of AOC's infamous Tax the Rich dress owes more than $1million for A-list gala featuring Meghan Markle Trump visibly WINCES at roaring military flyover during stone-faced Xi's red carpet welcome - the same treatment he once mocked Obama for Dolly Parton's sister speaks out on family'feud' amid shock clash over late star's estate Wendy's manager, 19, accused of tying neurodivergent worker to GRILL'after saying she was too busy to babysit him' I spent my life being ashamed of my elderly father and the alarming age gap between him and my mother. Presley Gerber was discovered dead'in a bedroom' at luxurious rehab facility after going into cardiac arrest Inside soccer legend Thierry Henry's astonishing $25M NYC penthouse... including the grill to end all grills Top lawyer moved on from wife with glamorous younger bad girl who LOVES to gush over him on Instagram... until rock throwing and strangulation incident erupted during walk with baby near $2.2m mansion Meta launches an'evil Tamagotchi': Muse Charm gadget lets you use AI without your phone or computer Meta has launched a new gadget - and fans are comparing it to an'evil Tamagotchi'.

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Terrifying moment California college student narrowly escapes stalker who chases her home and vaults over gate in race to her front door... but police let him walk for infuriating reason Devastating untold story of Presley Gerber's final days: Extremely thin, jittering'like he was on meth' and so out his mind that his voice changed... mom Cindy Crawford tried one last panicked intervention - as reality slipped away Many missed the subtle signs in Prince William and Kate's'united front' that show us what's going on behind the scenes for the royal couple. But I've been studying them for years. Seemingly innocuous rental home in red hot California hotspot hits the market for $6,300... but it's hiding a dark secret that's left a family furious Pet rescue showcased its'incredible' work in adorable photos of cute cats... but owners were hiding a terrifying dark secret Dark new details of infertile influencer Clavicular's alleged Cape Cod rape: Read the damning claims about booze-fueled night at GRANDMA's coastal mansion with underage girl... and his mom's shock reaction Florida teacher under fire as she's accused of trying to'cancel' an 8th grader over anti-woke views Creator of AOC's infamous Tax the Rich dress owes more than $1million for A-list gala featuring Meghan Markle Trump visibly WINCES at roaring military flyover during stone-faced Xi's red carpet welcome - the same treatment he once mocked Obama for Dolly Parton's sister speaks out on family'feud' amid shock clash over late star's estate Wendy's manager, 19, accused of tying neurodivergent worker to GRILL'after saying she was too busy to babysit him' I spent my life being ashamed of my elderly father and the alarming age gap between him and my mother. Presley Gerber was discovered dead'in a bedroom' at luxurious rehab facility after going into cardiac arrest Inside soccer legend Thierry Henry's astonishing $25M NYC penthouse... including the grill to end all grills Top lawyer moved on from wife with glamorous younger bad girl who LOVES to gush over him on Instagram... until rock throwing and strangulation incident erupted during walk with baby near $2.2m mansion Electric car charging just got even faster: Chinese carmaker's EV battery charges in under five minutes - smashing the previous record by 30 seconds Electric car charging just got even faster - as a Chinese carmaker has unveiled a battery that charges in under five minutes.

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. 'Uncle George' Clooney rushes to Kaia Gerber's house as his heartbreaking notes to Presley are revealed: Actor in utter agony over death of'second son'... after desperate attempts to save him ended in disaster Nightmare for Kash Patel as hackers'steal data on EVERY FBI agent' and issue chilling Trump threat There's a hidden message in Diana's brother's vengeful new book. No wonder William has kept silent... Harry's role in this is so suspect: MAUREEN CALLAHAN What REALLY goes on in some Equinox steam rooms: Gym insiders reveal eye-popping indecency... secret towel signals used by experimental married men... and clubs with most'aggressive' locker rooms Married father, 56, named as man who jumped off Carnival cruise ship during 15th wedding anniversary trip with wife... as grieving sister shares heartbreaking theory on why he did it A fleeting look in Patrick Clancy's eyes during his 60 Minutes interview sent shockwaves through me... this is the truth about him and his pregnant new wife that must be said: KENNEDY SARAH VINE: My message to the four-times married colossal cad Earl Spencer: People in glass houses shouldn't throw stones Taylor Swift reveals bombshell return to music with new single Patient Zero... after sending fans into MELTDOWN over mysterious countdown NASCAR star admits he got his'a** kicked' in brutal post-race punch-up with rival... after wild video emerged of pit row brawl Why it really DOES matter what time of day you take your blood pressure pills. Truth about Adriana Lima's dramatic face transformation... and why surgeons say it could easily happen to you Trump's six-word little white lie to Zohran Mamdani reveals surprising affection for his old foe Presley Gerber's'inevitable' death: Family insiders reveal life-long secret struggle that sparked'self-destruct' spiral... and mother Cindy Crawford's desperate bid to save him before shock death at 27 Staff at leading AI companies and safety institutions are being signed off from work with stress, according to a new report. According to reporting by the Financial Times, top researchers at OpenAI, Anthropic, Google DeepMind, and the UK's AI Security Institute (AISI), have reported burnout and stress due to developing powerful AIs.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Only 15% of US-based organizations have reached scaled, orchestrated, multi-agent adoption, according to the latest Deloitte research. J. David Ake/Getty Images Add us as a preferred source Only 15% of organizations have reached scaled multi-agentic orchestration. Most business leaders are reevaluating their business models in light of advances in agentic AI. Scaling agentic AI must begin with sufficient resources to transform the workforce. Most US-based companies are under pressure and working hard to shift from experimenting with AI agents to deploying them in production, according to the latest research from Deloitte .

Hundreds of experts warn the world must prepare now for AI's impact Hundreds of experts have signed an open letter demanding that policymakers and technology leaders "must act now" to prepare for the economic impact of artificial intelligence. The brief letter, released on Monday and organised by Stanford University's digital economy lab, carries the signature of more than 200 economists and AI researchers, including 16 Nobel laureates. "It could bring risks, including large-scale job displacement, as well as opportunities such as major gains in living standards," the statement added. To address this impending disruption, the letter calls for governments and industry to create "incentives, guardrails, and institutions" that ensure AI is complementary to humans and beneficial to society. Anton Korinek, a University of Virginia professor who organised the initiative, stressed that the window for action is narrowing.

Visual prompting (VP) has emerged as a promising parameter-efficient fine-tuning approach for adapting pre-trained vision models to downstream tasks without modifying model parameters. Despite offering advantages like negligible computational overhead and compatibility with black-box models, conventional VP methods typically achieve lower accuracy than other adaptation approaches. Our analysis reveals two critical limitations: the restricted expressivity of simple additive transformation and a tendency toward overfitting when the parameter count increases. To address these challenges, we propose ACAVP (Affine, Color, and Additive Visual Prompting), which enhances VP's expressive power by introducing complementary transformation operations: affine transformation for creating task-specific prompt regions while preserving original image information, and color transformation for emphasizing task-relevant visual features. Additionally, we identify that overfitting is a critical issue in VP training and introduce TrivialAugment as an effective data augmentation, which not only benefits our approach but also significantly improves existing VP methods, with performance gains of up to 12 percentage points on certain datasets. This demonstrates that appropriate data augmentation is universally beneficial for VP training. Extensive experiments across twelve diverse image classification datasets with two different model architectures demonstrate that ACAVP achieves state-of-the-art accuracy among VP methods, surpasses linear probing in average accuracy, and exhibits superior robustness to distribution shifts, all while maintaining minimal computational overhead during inference. Our code is available at https://github.com/s-enmt/ACAVP.

Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty. Learning a separate flow for every parameter configuration quickly becomes intractable, since the number of joint settings grows exponentially with the number of parameters. We introduce Factorizable Normalizing Flows (FNFs), which represent the parameter-dependent density as a fixed, high-fidelity flow for a reference configuration composed with a learnable transformation that is polynomial in the parameters and factorized over them. This structure has a practical consequence: each parameter's effect is learned in isolation, from samples in which that parameter alone is varied. The combined response of many parameters is then recovered by summation at inference, without ever sampling their combinatorially large joint space. On a controlled problem with two interpretable deformations applied jointly to the data, the learned transformation reproduces the true deformations and matches the optimal likelihood, while optional interaction terms capture residual correlations when several parameters vary strongly at once. The resulting model is interpretable, scales linearly with the number of parameters, and keeps the likelihood tractable. This provides a general tool for any inference workflow requiring continuous density morphing, and directly enables the next generation of unbinned likelihood fits in high energy physics.

Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This paper develops a framework for characterizing when synthetic minority augmentation can improve threshold-integrated and threshold-optimized metrics, including AUROC, AUPRC, best-threshold balanced accuracy, and best-threshold \(\F_1\) score. We separate the effect of augmentation into two components: a change in effective class weighting and a discrepancy between the synthetic and true minority distributions. Under well-specified score models, the raw estimator already targets the likelihood-ratio ordering, which is population-optimal for the metrics considered. Consequently, augmentation cannot provide a fundamental population-level improvement beyond possible finite-sample variance reduction, and may introduce additional bias through synthetic distributional error. We further establish minimax lower bounds showing that the raw estimator already achieves the optimal metric-regret rate in the well-specified regime. Under misspecification, however, augmentation can play a qualitatively different role: by changing the effective class balance, it can alter the restricted-class projection and correct ranking errors induced by the raw imbalanced objective. We provide explicit improvement bounds quantifying the roles of approximation error, finite-sample estimation error, and synthetic distributional error. Simulation studies corroborate the theory, demonstrating limited gains under well-specification and nontrivial but nonmonotone improvements under misspecification.

Understanding the geometry of neural network loss landscapes is a central question in deep learning, with implications for generalization and optimization. A striking phenomenon is linear mode connectivity (LMC), where independently trained models can be connected by low-or zero-barrier paths, despite appearing to lie in separate loss basins. However, this is often obscured by symmetries in parameter space--such as neuron permutations--which make functionally equivalent models appear dissimilar. Prior work has predominantly focused on neuron reordering through permutations, but such approaches are limited in scope and fail to capture the richer symmetries exhibited by modern architectures such as Transformers. In this work, we introduce a unified framework that captures four symmetry classes--permutations, semi-permutations, orthogonal transformations, and general invertible maps--broadening the set of valid reparameterizations and subsuming many previous approaches as special cases. Crucially, this generalization enables, for the first time, the discovery of low-and zero-barrier linear interpolation paths between independently trained Vision Transformers and GPT-2 models. Furthermore, our framework extends beyond pairwise alignment, to multi-model and width-heterogeneous settings, enabling alignment across architectures of different sizes. These results reveal deeper structure in the loss landscape and underscore the importance of symmetry-aware analysis for understanding model space geometry. Our code is available here.

TF-CoVR Statistics We present detailed statistics on the distribution of video counts per label in TF-CoVR, which comprises a diverse set of 306 annotated sub-actions. Both distrib video utions distrib are ution plotted for the on a F log ineGym arithmic [3] and scale F to ineDiving emphasize [6] the subsets long-tailed of TF-CoVR nature, of label frequencies. In FineGym, many labels have several hundred to over a thousand associated videos, with a gradual decline across the distribution. By contrast, FineDiving exhibits a steeper drop in video count per label, primarily due to samples, its smaller preserving dataset enough size. Ne div v ersity ertheless, to support a substantial temporal number fine-gr of ained labels composed still contain video more retrieval. A logarithmic scale is used on the y-axis to highlight the steep drop in video counts per label due to the smaller dataset size.