Browns pick Deshaun Watson as their starting QB despite home crowd booing and his '17 away games' comments Why Wyndham Clark deserves PGA Tour Player of the Year, LIV Golf's final hoorah, and Scottie Scheffler's money Sam Burns is one of America's best golfers, but faith, family and the USA matter more to him than golf Donald Trump waves a patriotic green flag and the liberals are furious, Bubba Wallace locks up & 'F-K HIM!' Scottie Scheffler reveals he played through'pretty painful' illness during the BMW Championship Fever coach dismisses Enes Kanter Freedom's women's sports fight as a'social media thing' Harrison Butker's 69-yard field goal demonstrates the NFL's latest big scoring controversy Nationals outfielder does his best Spider-Man impression for one of baseball's best catches of the year US'entering the endgame' with Iran as economy targeted New York City taxpayers could pay twice under Mamdani's grocery plan US announces'Economic D-Day' against Iran Steve Doocy explores the US Air Force Academy's elite military training Dr. Marc Siegel analyzes new study suggesting GLP-1 may slow down aging Humanoid robot beats Usain Bolt's 100m dash record at Beijing games Bessent SENDS WARNING to Iran: 'We are entering the ENDGAME' South Carolina Sen. Darline Graham makes her case ahead of GOP runoff Ali's mind games included stealing a stuffed animal from Jason Hotch's daughter before pinning him for his second reign TNA Wrestling's newest signee Gabby Forza explains to OutKick why she decided to join the company. Mustafa Ali and Jason Hotch went above and beyond to inflict damage on each other during their match at Total Nonstop Action Wrestling's (TNA) Lockdown for the International Championship. Ali and Hotch's feud got personal after Hotch initially dethroned Ali as the international champion. Hotch held the title for a few days before Ali got his revenge at Lockdown. As the match moved beyond the cage, Ali stole a stuffed animal that Hotch's daughter was holding in the stands.

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We study online prediction under distribution shift, where inputs arrive chronologically and outcomes are revealed only after prediction. In this setting, predictors must remain stable in quiet regimes yet adapt when regimes shift, and the right adaptation memory is unknown in advance. We propose MELO (Memory-hedged Exponentially Weighted Least-Squares Online aggregation), a model-agnostic method that hedges across adaptation scales: it wraps any non-anticipating base-predictor pool with exponentially weighted least-squares (EWLS) adaptation experts at multiple forgetting factors, and aggregates raw and EWLS-adapted forecasts with MLpol which is a parameter-free online aggregation rule. Under boundedness conditions, we establish deterministic oracle inequalities showing that it competes with both the best raw predictor and the best bounded, time-varying affine combinations of the base predictions, up to a path-length-dependent tracking cost and a sublinear aggregation overhead. We evaluate MELO on French national electricity-load forecasting through the COVID-19 lockdown using no regime indicators, lockdown dates, or policy covariates. MELO reduces overall RMSE by 34.7%relative to base-only MLpol and achieves lower overall RMSE than a TabICL reference supplied with an external COVID policy-response covariate. MELO requires only lightweight per-step recursive updates without model retraining.

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Large-scale simulation models of complex socio-technical systems provide decision-makerswith high-fidelity testbeds inwhich policyinterventions canbe evaluated andwhat-if scenarios explored.

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed computing nature. Existing defense solution usually requires larger amount of computation in either the training or testing phase, which limits their practicality in the resource-constrain scenarios. A more practical defense, i.e., neural network (NN) pruning based defense has been proposed in centralized backdoor setting. However, our empirical study shows that traditional pruning-based solution suffers \textit{poison-coupling} effect in FL, which significantly degrades the defense performance.This paper presents Lockdown, an isolated subspace training method to mitigate the poison-coupling effect. Lockdown follows three key procedures.