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.

Image caption, Ukraine's military described St Petersburg's oil terminal as one of the largest in Russia A major oil terminal in Russia's second city of St Petersburg in the north-west was struck overnight by Ukraine, President Volodymyr Zelensky has said. He described it as key infrastructure that generates revenue for Russia's war. Ukraine also said a major Russian naval base in the region was hit. St Petersburg Governor Aleksandr Beglov said the city was under a massive drone attack, admitting the oil terminal was hit. Ukraine has recently intensified its long-range drone attacks on Russia's critical energy infrastructure, causing widespread fuel shortages.

Eli Lilly faces a congressional investigation into its China clinical trials at People's Liberation Army hospitals and Xinjiang facilities amid biotechnology competition concerns.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. 'The Joy of Painting' remains one of the most recognizable public television shows in U.S. history. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Public broadcasting continues to face dire funding issues across the country, but PBS hero Bob Ross is here to help.

Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolving query ambiguity and credit assignment during reward learning. In this paper, we introduce PRIMT, a PbRL framework designed to overcome these challenges by leveraging foundation models (FMs) for multimodal synthetic feedback and trajectory synthesis. Unlike prior approaches that rely on single-modality FM evaluations, PRIMT employs a hierarchical neuro-symbolic fusion strategy, integrating the complementary strengths of large language models and vision-language models in evaluating robot behaviors for more reliable and comprehensive feedback. PRIMT also incorporates foresight trajectory generation, which reduces early-stage query ambiguity by warm-starting the trajectory buffer with bootstrapped samples, and hindsight trajectory augmentation, which enables counterfactual reasoning with a causal auxiliary loss to improve credit assignment. We evaluate PRIMT on 2 locomotion and 6 manipulation tasks on various benchmarks, demonstrating superior performance over FM-based and scripted baselines.

Split Federated Learning (SFL) enables scalable training on edge devices by combining the parallelism of Federated Learning (FL) with the computational offloading of Split Learning (SL). Despite its great success, SFL suffers significantly from the well-known straggler issue in distributed learning systems. This problem is exacerbated by the dependency between Split Server and clients: the Split Server side model update relies on receiving activations from clients. Such synchronization requirement introduces significant time latency, making straggler a critical bottleneck to the scalability and efficiency of the system. To mitigate this problem, we propose MU-SplitFed, a straggler-resilient SFL algorithm in zeroth-order optimization that decouples training progress from straggler delays via a simple yet effective unbalanced update mechanism. By enabling the server to perform τ local updates per client round, MU-SplitFed achieves a convergence rate of O( p d/(τT))for non-convex objectives, demonstrating a linear speedup of τ in communication rounds. Experiments demonstrate that MU-SplitFedconsistently outperforms baseline methods with the presence of stragglers and effectively mitigates their impact through adaptive tuning of τ.

We introduce a new convex optimization framework for logistic scalar-on-matrix regression which incorporates nuclear and $\ell_1$ norm penalties to enforce simultaneously low-rank and sparse structures in the estimated coefficient matrix. The proposed method enables interpretable modeling of high-dimensional matrix-valued predictors in the presence of binary responses. We derive a custom algorithm based on the Alternating Direction Method of Multipliers (ADMM) to efficiently solve the resulting convex optimization problem and establish the theoretical properties of the obtained solution. Numerical experiments clearly demonstrate the effectiveness of our method in recovering meaningful predictive patterns. Finally, we apply our method to the brain imaging data to identify structures in functional brain connectivity matrices that are characteristic of subjects with a family history of alcohol use disorders (AUDs).

Online bipartite matching is a fundamental problem in online optimization, extensively studied both in its integral and fractional forms due to its theoretical significance and practical applications, such as online advertising and resource allocation. Motivated by recent progress in learning-augmented algorithms, we study online bipartite fractional matching when the algorithm is given advice in the form of a suggested matching in each iteration. We develop algorithms for both the vertex-weighted and unweighted variants that provably dominate the naïve "coin flip" strategy of randomly choosing between the advice-following and advice-free algorithms. Moreover, our algorithm for the vertex-weighted setting extends to the AdWords problem under the small bids assumption, yielding a significant improvement over the seminal work of Mahdian, Nazerzadeh, and Saberi (EC 2007, TALG 2012). Complementing our positive results, we establish a hardness bound on the robustness-consistency tradeoff that is attainable by any algorithm.

The first ever halftime show at a FIFA World Cup final has been curated by Coldplay's Chris Martin and will feature performances from global superstars.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Though she's not the only woman to own an NFL team, Carlie Irsay-Gordon has rapidly become the most prominent. She took over as the Indianapolis Colts' principal owner following the death of her father, Jim Irsay, in May 2025, but that was far from the beginning of her involvement with the franchise.