LIV Golf Team Championships in Michigan'highly likely' to be canceled: we're'disappointed' Lynx star Kayla McBride becomes latest to speak out on WNBA 3-point contest drama: 'Half a-- invite' Arch Manning isn't backing down from expectations, and neither is Steve Sarkisian, as CFP semis aren't enough USA Today writer invokes Emmett Till, says Caitlin Clark puts Black and queer players'in danger' by flopping Smokin' Hot Charley Hull suffers a brutal quadruple bogey, USA Today gasbags & Angel Reese is back at it! First overall pick Fernando Mendoza signs guaranteed deal with Raiders, who seem to be an'arrow up' NFL club White House defends WNBA star Sophie Cunningham after she speaks out in support of protecting women's sports NHL's top American-born scorer Patrick Kane signs deal to return to Chicago Blackhawks Florida coach Jon Sumrall's cell phone comments cause fans and media members to lose their minds, he responds Shane Bieber's strong Rays splits make Toronto Blue Jays the smart first 5 innings MLB bet Maria Sharapova attacks the dog days of summer by hopping into the pool, it pays to be OSU's QB & Shatner Ukrainian drone strikes knock 40% of Russia's oil refining capacity offline A'bigger than ever' attack on Iran could be coming: Report Gordon Chang warns of China's'people's war' against the US Linda McMahon says we shouldn't be afraid of AI Stephen A Smith is only looking out for Stephen A Smith, not Ryan Clark | Don't @ Me w/Dan Dakich Dan Dakich reacts to Stephen A Smith's message about Ryan Clark being laid off by ESPN Welcome to our 32-team 2026 NFL Season Preview Series! As we count down to kickoff, we're breaking down every franchise division-by-division. Today, we're spotlighting the Atlanta Falcons in the NFC South. Each preview analyzes the team's offseason moves, coaching staff, projected strengths and weaknesses, schedule, win total and best futures bet. Atlanta missed the playoffs in 2025-26 for an eighth consecutive season and fired now-former head coach Raheem Morris afterward.

US fast-food chain Taco Bell is removing lettuce from its menu in some states after investigations found it could be linked to an outbreak of explosive diarrhoea caused by a parasite. The decision was taken out of an abundance of caution following discussions with health officials, Taco Bell told the BBC. The US Food and Drug Administration (FDA) says 1,644 people in five states that reported exposure to Taco Bell have been infected by cyclosporiasis, a parasitic infection that spreads through contaminated food or water. Do not eat food items with shredded iceberg lettuce from Mexico served at Taco Bell locations in Indiana, Kentucky, Michigan, Ohio, and West Virginia, the FDA said. No deaths have been reported but 94 people have been hospitalised due to cyclosporiasis infections, which were first detected on 13 May, the FDA added.

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Apple filed a lawsuit against OpenAI on Friday alleging the artificial intelligence firm stole company trade secrets in a move to create its own hardware device. The suit claims OpenAI poached Apple employees, coaxing them to hand over confidential material, product designs and other tightly held information. "Recently, significant evidence has emerged suggesting individuals employed by OpenAI wrongfully took Apple's secret and confidential information regarding our unreleased technologies, processes, and products," an Apple spokesperson said in an email. Drew Pusateri, a spokesperson for OpenAI, said the company was reviewing the court filing. "We have no interest in other companies' trade secrets," he added.

The movement to strengthen democracy in America is suffering as a surfeit of advocacy groups is crowding an ecosystem of nonprofits, coalitions, and think tanks.

Distribution-free uncertainty estimation for ensemble methods is increasingly desirable due to the widening deployment of multi-modal black-box predictive models. Conformal prediction is one approach that avoids making strong distributional assumptions. Methods for conformal aggregation have been proposed for ensembled prediction, where the prediction regions of individual models are merged to retain coverage guarantees while minimizing conservatism. Merging the prediction regions directly, however, can miss out on opportunities to further reduce conservatism by exploiting structures present in the conformal scores. We, therefore, propose a novel framework that extends the standard scalar formulation of a score function to a multivariate score that produces more efficient prediction regions. We then demonstrate that such a framework can be efficiently leveraged in both classification and predict-then-optimize regression settings downstream and empirically show the advantage over alternate conformal aggregation methods.

Correlation Clustering (CC) is a foundational problem in unsupervised learning that models binary similarity relations using labeled graphs. While classical CC has been widely studied, many real-world applications involve more nuanced relationships, either multi-class categorical interactions or varying confidence levels in edge labels. To address these, two natural generalizations have been proposed: Chromatic Correlation Clustering, which assigns semantic colors to edge labels, and pseudometric-weighted Correlation Clustering, which allows edge weights satisfying the triangle inequality. In this paper, we develop improved approximation algorithms for both settings. Our approach leverages LP-based pivoting techniques combined with problem-specific rounding functions. For the pseudometric-weighted correlation clustering problem, we present a tight 103 approximation algorithm, matching the best possible bound achievable within the framework of standard LP relaxation combined with specialized rounding. For the Chromatic Correlation Clustering (CCC) problem, we improve the approximation ratio from the previous best of 2.5 to 2.15, and we establish a lower bound of 2.11within the same analytical framework, highlighting the near-optimality of our result.

Recent progress in large language models (LLMs) highlights the power of scaling test-time compute to achieve strong performance on complex tasks, such as mathematical reasoning and code generation. This raises a critical question: how should model training be modified to optimize performance under a subsequent test-time compute strategy and budget? To explore this, we focus on pass@N, a simple test-time strategy that searches for a correct answer in N independent samples. We show, surprisingly, that training with cross-entropy (CE) loss can be misaligned with pass@N in that pass@N accuracy decreases with longer training. We explain the origins of this misalignment in terms of model overconfidence induced by CE, and experimentally verify our prediction of overconfidence as an impediment to scaling test-time compute via pass@N. Furthermore we suggest a principled, modified training loss that is better aligned to pass@N by limiting model confidence and rescuing pass@N test performance. Our algorithm demonstrates improved mathematical reasoning on MATH and MiniF2F benchmarks under several scenarios: (1) providing answers to math questions; and (2) proving theorems by searching over proof trees of varying shapes. Overall our work underscores the importance of co-designing two traditionally separate phases of LLM development: training-time protocols and test-time search and reasoning strategies.

Summary: Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remains high, particularly for classical statisticians trained in a frequentist perspective. In this letter, we demystify neural networks by describing networks that approximate a linear regression and describe common customizations that provide a foundation for further study.

We study a two-sided market, wherein, price-sensitive heterogeneous customers and servers arrive and join their respective queues. A compatible customer-server pair can then be matched by the platform, at which point, they leave the system. Our objective is to design pricing and matching algorithms that maximize the platform's profit, while maintaining reasonable queue lengths. As the demand and supply curves governing the price-dependent arrival rates may not be known in practice, we design a novel online-learning-based pricing policy and establish its near-optimality. In particular, we prove a tradeoff among three performance metrics: OpT1 γq regret, OpTγ{2q average queue length, and OpTγq maximum queue length for γ P p0,1{6s, significantly improving over existing results [1]. Moreover, barring the permissible range of γ, we show that this trade-off between regret and average queue length is optimal up to logarithmic factors under a class of policies, matching the optimal one as in [2] which assumes the demand and supply curves to be known. Our proposed policy has two noteworthy features: a dynamic component that optimizes the tradeoff between low regret and small queue lengths; and a probabilistic component that resolves the tension between obtaining useful samples for fast learning and maintaining small queue lengths.