AB Hernandez advances in California state championship as Save Girls' Sports activists rally nearby Tennis player Rafael Jodar accused of pushing French Open ball girl, but did he really? Steve Hilton rips Steyer for trans athlete support, leads'Save Girls Sports' rally at track title meet Umpire Dan Bellino's baffling foul tip call on Seiya Suzuki renews calls for robot review in MLB Dakich: sports media has created an'industry' out of complaining about white athletes like Caitlin Clark Oregon father issues plea as legislation could free daughter's murderer Rachel Campos-Duffy: AOC driven by'Marxist mindset,' a'true believer' Spencer Pratt responds to Newsom's Bass endorsement, calls them'alleged criminal partners' Speaker Johnson outlines plan to defeat'socialist and extremist' Democrats Trump set for'final determination' on Iran nuclear deal OutKick-Sports The 2007 Mountaineers remain college football's greatest'what-if' story nearly two decades later Rich Rodriguez's spread offense was unstoppable all season until a 13-9 loss to Pitt in the Backyard Brawl ended it all When you ask any college football fan worth their salt which season was the craziest one they can remember, most of them will answer 2007 without hesitation. And who could blame them? After all, it was a year that featured one of the most shocking upsets in college football history, with Appalachian State stunning Michigan in the Big House, and that was just the appetizer. In all, 62 ranked teams lost to lower ranked or completely unranked squads in 2007, and teams ranked No. 2 in one of the three major polls lost seven times in the final nine weeks of the season.

'I found out because I bought a new Amazon Alexa and while setting it up realized this is linked via our family prime account,' the woman shared on Reddit. 'Found in history, 'Alexa play beautiful love songs,' followed by the sound of them having sex.' To find such recordings saved to an Amazon Alexa, open the Alexa app, tap More, go to Alexa Privacy, then select Review Voice History to see recordings by date or device. Users can play back clips, delete individual recordings or delete their entire voice history. If you own a Google Home, open the Google Home app and tap Activity to review recent home events, including camera, doorbell and device activity. To check Assistant recordings, go to your Google Account activity controls and review or delete Google Assistant activity.

Is the Ferrari Luce's Design Really That Bad? 3 Italian Auto Experts Weigh In The first electric Ferrari is already this year's most divisive car. We asked three Italian auto industry professionals to explain where the EV's design makes sense, and where it doesn't add up. The Ferrari Luce, the first electric vehicle in the brand's history, has generated heated discussion online, as comments and opinions about the design continue to bounce around the web. The Luce, an electric sedan with a $650,000 price tag that Ferrari presented with pomp and circumstance at the Quirinale in Rome on Monday, has paid dearly for its coming out from behind the curtain. Since Monday, the automaker has been suffering an avalanche of complaints and skepticism about the Luce.

You probably wouldn't notice if an AI chatbot slipped ads into its responses Hundreds of millions of people consult artificial intelligence chatbots on a daily basis for everything from product recommendations to romance, making them a tempting audience to target with potentially below-the-radar advertising. Indeed, our research suggests AI chatbots could easily be used for covert advertising to manipulate their human users. We are computer scientists who have been tracking AI safety and privacy for several years. In a study we published in an Association for Computing Machinery journal, we found that chatbots trained to embed personalized product ads in replies to queries influenced people's choices about products. And most participants didn't recognize that they were being manipulated.

AI-generated vloggers like Chloe VS History (left) and Nova VS History are, their creators say, 'taking an already-proven format and applying it to history' AI-generated vloggers like Chloe VS History (left) and Nova VS History are, their creators say, 'taking an already-proven format and applying it to history' The content creators behind channels like Chloe VS History are using AI tools to'bring history to life in a really visceral way' "I have just arrived in Tudor London, 1536," a young woman in a green puffer jacket tells the camera. "I'm going to check in at my room in the inn, get into the market. Then, later I am meeting the actual king - yep, Henry VIII - in person." On YouTube and other social platforms, users are flocking to watch AI-generated "history influencers", characters that vlog their travels to historical settings. One of the most popular channels is Chloe VS History, with more than 610,000 Instagram followers and 15m views on YouTube.

PCWorld highlights 13 underutilized Google Chrome features that can significantly enhance browsing productivity and organization for billions of users. Key tools include tab groups for organization, cross-device syncing, Guest profiles for temporary use, and keyboard shortcuts like Ctrl+Shift+T to reopen closed tabs. These hidden features offer powerful customization options through Chrome flags, multiple user profiles, dark mode settings, and extension management for improved daily web interaction. Around two-thirds of all internet users use Google Chrome, according to StatCounter .

Toward the beginning of the 2002 film, a domineering lawyer (played by James Spader) barges into the office of his assistant (Maggie Gyllenhaal) with evidence of a work infraction: a memo she has written that has "three typing errors." "Do you know what this makes me look like to the people who receive these letters?" Setting aside that his screed turns out to be foreplay, Spader's character was channeling a widespread cultural revulsion: Typos were the ultimate shorthand for careless work. A spelling mistake was proof that the writer hadn't bothered putting much effort into a piece of correspondence, that their instructions or advice shouldn't be taken seriously--and perhaps that the recipient shouldn't invest time in reading their note at all. More than two decades later, as AI-generated writing has flooded workplaces, social media, and dating apps, old hallmarks of sloppiness--typos chief among them--are getting a new gloss. Some job applicants are intentionally adding typos to their cover letters to prove that they, and not an AI program, wrote them.

Medium-horizon Alzheimer's disease progression prediction is difficult because future clinical scores can remain tied to baseline severity, while biomarker histories are irregular and incompletely observed. We develop an anchor-based analysis of 24-month Clinical Dementia Rating Sum of Boxes (CDR-SB) change using harmonized Alzheimer's Disease Neuroimaging Initiative (ADNI) tables. Each labeled sample is anchored at a mild cognitive impairment visit, uses only clinical and biomarker history observed at or before that anchor, and defines the response as CDR-SB at the future visit closest to 24 months within an 18--30 month window minus anchor CDR-SB. The analytic cohort contains 2,600 labeled anchors from 858 participants and 7,276 longitudinal rows. We propose a residual gap-aware transformer that combines a mixed-effects statistical reference with transformer-based residual learning from pre-anchor clinical and biomarker histories. The model uses participant-level random intercepts in the mixed-effects reference, observation-level triplet tokenization for irregular histories, and a learned nonnegative time-gap penalty inside self-attention. We compare the proposed model with a Bayesian-information-criterion-selected linear mixed-effects baseline, GRU-D, and STraTS under repeated participant-level train--test splits. Across five participant-level random seeds, the proposed model achieves the best mean test performance across all reported metrics, reducing MSE by 13.1% and increasing prediction--observation correlation by 26.4% relative to the mixed-effects baseline. It also improves over both GRU-D and STraTS in mean error and correlation. These results show that statistical anchoring and gap-aware residual learning provide a useful structure for medium-horizon Alzheimer's disease progression prediction.

Neural operators provide fast surrogate models for time-dependent partial differential equations, but their standard autoregressive use usually assumes that the instantaneous field $u(t,\cdot)$ is a complete state. This assumption fails for delay equations, distributed-memory systems, and other non-Markovian dynamics: two trajectories may agree at time $t$ and nevertheless have different futures because their histories differ. We introduce the History-Space Fourier Neural Operator (HS-FNO), a neural operator for delay and memory-driven PDEs formulated on the lifted state $u_t(θ,x)=u(t+θ,x)$, $θ\in[-τ,0]$. The key computational step is to decompose one history-state update into a learned predictor for the newly exposed future slice and an exact shift-append transport for the portion of the history window already known from the previous state. This avoids learning deterministic history coordinates, reduces the learned output dimension, and enforces the natural discrete history update. We test HS-FNO on five benchmark families covering delayed reaction--diffusion, spatial epidemiology, nonlocal neural-field dynamics, delayed waves, and distributed-memory closures. Across ten random seeds, HS-FNO attains the lowest aggregate one-step, history-space, and rollout errors among the principal baselines. The largest gain occurs in autoregressive prediction, where aggregate rollout error decreases from $0.241$, $0.188$, and $0.185$ for current-state, lag-stack, and unconstrained history-to-history operators, respectively, to $0.094$. The same model uses fewer parameters than unconstrained history prediction. These results indicate that enforcing the discrete shift structure of history-state evolution is an effective inductive bias for non-Markovian PDE surrogate modeling.

Information-theoretic generalization bounds based on the supersample construction are a central tool for algorithm-dependent generalization analysis in the batch i.i.d.~setting. However, existing supersample conditional mutual information (CMI) bounds do not directly apply to sequential decision-making problems such as online learning, streaming active learning, and bandits, where data are revealed adaptively and the learner evolves along a causal trajectory. To address this limitation, we develop a sequential supersample framework that separates the learner filtration from a proof-side enlargement used for ghost-coordinate comparisons. Under a row-wise exchangeability assumption, the sequential generalization gap is controlled by sequential CMI, a sum of roundwise selector--loss information terms. We also establish a Bernstein-type refinement that yields faster rates under suitable variance conditions. The selector-SCMI proof strategy applies to online learning, streaming active learning with importance weighting, and stochastic multi-armed bandits.