Causal effect estimation seeks to determine the impact of an intervention from observational data. However, the existing causal inference literature primarily addresses treatment effects on frequently occurring events. But what if we are interested in estimating the effects of a policy intervention whose benefits, while potentially important, can only be observed and measured in rare yet impactful events, such as extreme climate events? The standard causal inference methodology is not designed for this type of inference since the events of interest may be scarce in the observed data and some degree of extrapolation is necessary. Extreme Value Theory (EVT) provides methodologies for analyzing statistical phenomena in such extreme regimes. We introduce a novel framework for assessing treatment effects in extreme data to capture the causal effect at the occurrence of rare events of interest. In particular, we employ the theory of multivariate regular variation to model extremities. We develop a consistent estimator for extreme treatment effects and present a rigorous non-asymptotic analysis of its performance. We illustrate the performance of our estimator using both synthetic and semi-synthetic data.

Giorgia Meloni rips'senseless' attacks from Trump as Italian Prime Minister refuses to back down amid G7 feud Former Olympian is arrested for allegedly vandalizing Reflecting Pool... but he claims he merely touched it Embattled Alexi Lalas makes controversial World Cup declaration amid tension with Fox colleagues: 'Makes you look like a weak poser' Cocaine scandal ripping the Hamptons apart: New York elite's dirty secret leaves mothers too afraid to let their children out... as police issue urgent warning Stingy fast food giant named America's favorite restaurant AGAIN... and experts think they know why Inside America's new fattest town: Burgers are the size of your head, gyms lie empty and custom mobility scooters carry 800lb loads... as we investigate why Ozempic just DOESN'T work Call me cynical, but the real reason Gruesome Twosome Harry and Meghan are returning to the UK is just so obvious... and highly humiliating: MAUREEN CALLAHAN Germany vs Ivory Coast - World Cup Group E RECAP: Deniz Undav's second goal seals his nation qualification to the knockouts as he nets winner in second-half stoppage time I lost 50lb without jabs using this easy but overlooked method. But I still felt dowdy - until I discovered these expert anti-ageing fashion and beauty tips. No one can see the real reason Jelly Roll divorced Bunnie XO. Blake Lively runs errands in frumpy outfit after reconciling with ex-BFF Taylor Swift... miles away from reported'bachelorette party' Three more arrested over bungee jumper's death after she was hurled from bridge without a rope Ex-partner of dad who was berated for taking his daughters into women's bathroom claims he'exploited' girls and accuses him of failing to pay child support... before he hits back Grace Kelly's lookalike granddaughter, 27, wows in bikini snaps...as she packs on the PDA during beach getaway TV star mom, 46, who appeared on'quitting everything to change your life' show died in fire at luxury Caribbean beach resort that sent 1,700 tourists running for their lives Candace Owens hits out at nasty rumors claiming she was DEAD... as fellow MAGA influencer claims her account was hacked The four mistakes that led to bungee tragedy on Skeleton Bridge: FRED KELLY saw the scene for himself, now he retraces the prelude to disaster. So was it really an accident?

Picture this: a team of camera-clad cleaners and a private chef to boot, all wired with high-tech recording apparatus show up at your home. You are not part of a reality TV show, and have not woken up in an Aldous Huxley or Margaret Atwood novel. Instead, you are a resident of New York City, where AI companies are sending free cooking and cleaning staff straight to people's doors. But, there is a catch: this AI company is gathering data to train the next generation of cooking and cleaning robots, and every inch of your apartment is now being recorded. The initiative, dubbed Shift by AI firm Micro AGI, is part of a growing number of companies developing the next generation of autonomous robots, which tech bosses hope will be able to do everything from the washing up to serving as live-in personal carers.

We consider a common case of the combinatorial semi-bandit problem, the m-set semi-bandit, where the learner exactly selects m arms from the total d arms. In the adversarial setting, the best regret bound, known to be O( nmd) for time horizon n, is achieved by the well-known Follow-the-Regularized-Leader (FTRL) policy. However, this requires to explicitly compute the arm-selection probabilities via optimizing problems at each time step and sample according to them. This problem can be avoided by the Follow-the-Perturbed-Leader (FTPL) policy, which simply pulls the m arms that rank among the m smallest (estimated) loss with random perturbation. In this paper, we show that FTPL with a Fréchet perturbation also enjoys the near optimal regret bound O( nm( p dlog(d) + m5/6)) in the adversarial setting and approaches best-of-both-world regret bounds, i.e., achieves a logarithmic regret for the stochastic setting. Moreover, our lower bounds show that the extra factors are unavoidable with our approach; any improvement would require a fundamentally different and more challenging method.

Large Language Models (LLMs) are predominantly evaluated on Standard American English (SAE), often overlooking the diversity of global English varieties. This narrow focus may raise fairness concerns as degraded performance on nonstandard varieties can lead to unequal benefits for users worldwide. Therefore, it is critical to extensively evaluate the linguistic robustness of LLMs on multiple non-standard English varieties. We introduce Trans-EnV, a framework that automatically transforms SAE datasets into multiple English varieties to evaluate the linguistic robustness. Our framework combines (1) linguistics expert knowledge to curate variety-specific features and transformation guidelines from linguistic literature and corpora, and (2) LLM-based transformations to ensure both linguistic validity and scalability. Using Trans-EnV, we transform six benchmark datasets into 38 English varieties and evaluate seven state-of-the-art LLMs. Our results reveal significant performance disparities, with accuracy decreasing by up to 46.3% on non-standard varieties.

Serial dependence reflects how recent sensory history shapes current perception, producing two opposing biases: repulsion, where perception is repelled from recent stimuli, and attraction, where perception is drawn toward them. Repulsion typically occurs at the sensory perception stage, while attraction arises at the post-perception stage. To uncover the neural basis of these effects, we developed a two-layer continuous attractor neural network model incorporating synaptic short-term plasticity (STP). The lower layer, dominated by synaptic depression, models sensory processing and drives repulsion due to sustained neurotransmitter depletion. The higher layer, dominated by synaptic facilitation, models post-perception processing and drives attraction by sustained high neurotransmitter release probability. Our model successfully explains the serial dependence phenomena observed in the visual orientation judgment experiments, highlighting STP as the critical mechanism, with its time constants defining the temporal windows of repulsion and attraction. Furthermore, the model provides a neural foundation for the Bayesian interpretation of serial dependence. This study advances our understanding of how the neural system leverages STP to balance sensitivity in sensory perception with stability in post-perceptual cognition.

A huge fire at a luxury beach resort in the Dominican Republic killed one woman and forced nearly 1,700 guests to be evacuated on Friday. In a statement to local media, the DAEH emergency services said that a 46-year-old Italian tourist died, three people were taken to medical facilities and six others were treated on site. Drone footage shows how widespread the fire was, with buildings spanning the Viva Wyndham Dominicus Beach in Bayahibe on fire and thick black smoke billowing into the air. What sparked the early-morning blaze is not yet known, but an initial investigation found the flames spread quickly due to wind conditions and the flammable nature of the thatched roofs on some buildings. The country's Emergency Operations Center (COE) said the fire had been brought under control and guests had been moved to other hotels. It added that tourist activities in the town and surrounding area have been unaffected and can continue as normal.

The company behind the UK's newest video game console is not concerned with the latest state-of-the-art graphics or hardware. Instead, David Lee, chief executive of US technology firm Nex, says its cube-shaped machine, the Nex Playground, is designed to get children moving. The relatively little-known device surprised the games industry when research firm Circana revealed it was the third best-selling console in the US over Black Friday 2025, outselling the Xbox Series S and X. While motion-controlled gaming is nothing new - Nintendo's Wii launched in 2006 - concerns around children's passive screen time remain a hot topic for many parents and politicians. Ahead of the UK launch I spoke to parents who already own the console in the US, and tried the machine myself to find out how it works - and if it can really get families feeling fitter.

Traditional reinforcement learning (RL) assumes the agents make decisions based on Markov decision processes (MDPs) with one-step transition models. In many real-world applications, such as energy management and stock investment, agents can access multi-step predictions of future states, which provide additional advantages for decision making. However, multi-step predictions are inherently high-dimensional: naively embedding these predictions into an MDP leads to an exponential blow-up in state space and the curse of dimensionality. Moreover, existing RL theory provides few tools to analyze prediction-augmented MDPs, as it typically works on one-step transition kernels and cannot accommodate multi-step predictions with errors or partial action-coverage. We address these challenges with three key innovations: First, we propose the Bayesian value function to characterize the optimal prediction-aware policy tractably. Second, we develop a novel BellmanJensen Gap analysis on the Bayesian value function, which enables characterizing the value of imperfect predictions. Third, we introduce BOLA (Bayesian Offline Learning with Online Adaptation), a two-stage model-based RL algorithm that separates offline Bayesian value learning from lightweight online adaptation to real-time predictions. We prove that BOLA remains sample-efficient even under imperfect predictions.

Emergency services are at the scene of a collision involving two trains in the Bedford area, British Transport Police has confirmed. Operator East Midlands Railway has said two of its trains were involved in the crash. Footage taken from the scene shows where the two trains collided and passengers who appear to have been evacuated. Speaking to the BBC, passenger Pete Knapp said the crash felt like [he'd] been in a bomb explosion. The designer behind DR Congo's World Cup suit: 'I wanted to change people's views on Africa' Alvin Junior Mak explains the inspiration behind the stylish suits he designed for DR Congo's World Cup team.