Trump tears into Stephen A Smith as feud grows: 'Arrogant fool, a low IQ individual' Orioles' Leody Taveras suffers most embarrassing strikeout of the pitch clock era against his former team'World's Best Ex-Girlfriend' Morgan Riddle done dating athletes, Nikki Spoelstra's selfies for haters & malls Dodgers catcher Dalton Rushing executes a slide so illegal it would've made the 1980s proud The magic of Omaha: Why the College World Series is unlike anything else in sports that's worth the trip Kyle Busch's son suffers heartbreak in emotional return to racing after father's stunning death Why the under 4.5 through five innings is the play in Nationals-Giants with Foster Griffin facing Robbie Ray Dana White brings legendary stuntman Travis Pastrana's dirt bike backflip to White House USMNT legend Landon Donovan talks World Cup, American soccer's influence overseas during Raising Cane's shift Athletics wild first game in Las Vegas leads to 29 runs, 11 home runs in ominous sign for area's MLB future LIV Golf CEO refuses to guarantee circuit's remaining events will go on as scheduled with awkward sales pitch Golf WAG Jena Sims gets excited talking about meeting Travis Kelce and reveals that he's her'hall pass' Steve Doocy traces Walmart's origins in Arkansas Pompeo warns Iranian regime will'not go away' after US helicopter downed House approves resolution to limit Trump's war powers Trump's reveals new details on Iran drone attack downing US Apache helicopter Trump warns Iran will'PAY THE PRICE' after taking too long'Fox & Friends' covers the upcoming FIFA World Cup 2026, counting down to the global soccer event. Former USMNT Midfielder Stu Holden joins live from Audi Field to discuss the Capitol Cup congressional soccer match. Holden highlights the growing excitement for soccer in the U.S. and the national team's underdog chances in the World Cup before taking part in a lighthearted penalty-kick challenge. When it comes to fandom, few can rival international soccer fanatics. It's hard to find a group of people more fervent than the ones who support a World Cup powerhouse.

Here's What You Need to Know About the Hantavirus While the outbreak aboard a cruise ship in the Atlantic is concerning, the virus isn't easily transmitted through casual contact. Cruises are so closely associated with illness that the highly contagious norovirus is commonly called the "cruise ship virus." But a ship headed for Spain's Canary Islands has attracted global attention due to a rare outbreak of hantavirus that's left three dead. While alarming, health officials and infectious disease experts say the risk to the general public right now is low because hantavirus is less contagious than other respiratory diseases like the coronavirus responsible for the Covid-19 pandemic . "This is not Covid, this is not influenza. It spreads very, very differently," Maria Van Kerkhove, director of epidemic and pandemic preparedness and prevention at the World Health Organization, said at a press conference on Thursday.

Thus, there is a need for a reference point, on which each model canbetested andfrom where potential improvements canbe derived. In this study, we select publicly available state-of-the-art visual search models and datasets in natural scenes, and provide a common framework for their evaluation. To this end, we apply a unified format and criteria, bridging the gaps between them, and we estimate the models' efficiency and similarity with humans using a specific set of metrics.

Pots, quadrants, confederation constraints, group position grids... the 2026 World Cup finals draw on Friday is not going to be a straightforward affair. There's a lot to unpack so we're going to explain it as simply as we can. Luckily, Fifa will have a computer to do most of the heavy lifting and make sure everything runs smoothly. Though as Uefa found out in 2021, sometimes technology does go wrong. Let's hope there will be no gremlins in Washington once the draw ceremony kicks off.

The UK is forecast to be the second-fastest growing of the world's most advanced economies this year and next, according to new projections from the International Monetary Fund (IMF). The rates of growth remain modest at 1.3% for both years, but that outperforms the other G7 economies apart from the US, in a torrid year of trade and geopolitical tensions. However, UK inflation is set to rise to the highest in the G7 in 2025 and 2026, the IMF predicts, driven by larger energy and utility bills. UK inflation is forecast to average 3.4% this year and 2.5% in 2026 but the IMF says this will be temporary, and fall to 2% by the end of next year. The G7 are seven advanced economies - the US, UK, France, Germany, Italy, Canada and Japan - but the group doesn't include fast-growing economies such as China and India.

This study presents the development and optimization of a deep learning model based on Long Short-Term Memory (LSTM) networks to predict short-term hourly electricity demand in Córdoba, Argentina. Integrating historical consumption data with exogenous variables (climatic factors, temporal cycles, and demographic statistics), the model achieved high predictive precision, with a mean absolute percentage error of 3.20\% and a determination coefficient of 0.95. The inclusion of periodic temporal encodings and weather variables proved crucial to capture seasonal patterns and extreme consumption events, enhancing the robustness and generalizability of the model. In addition to the design and hyperparameter optimization of the LSTM architecture, two complementary analyses were carried out: (i) an interpretability study using Random Forest regression to quantify the relative importance of exogenous drivers, and (ii) an evaluation of model performance in predicting the timing of daily demand maxima and minima, achieving exact-hour accuracy in more than two-thirds of the test days and within abs(1) hour in over 90\% of cases. Together, these results highlight both the predictive accuracy and operational relevance of the proposed framework, providing valuable insights for grid operators seeking optimized planning and control strategies under diverse demand scenarios.

Large language models are, by definition, based on language. In an effort to underscore the critical need for regional localized models, this paper examines primary differences between variants of written Spanish across Latin America and Spain, with an in-depth sociocultural and linguistic contextualization therein. We argue that these differences effectively constitute significant gaps in the quotidian use of Spanish among dialectal groups by creating sociolinguistic dissonances, to the extent that locale-sensitive AI models would play a pivotal role in bridging these divides. In doing so, this approach informs better and more efficient localization strategies that also serve to more adequately meet inclusivity goals, while securing sustainable active daily user growth in a major low-risk investment geographic area. Therefore, implementing at least the proposed five sub variants of Spanish addresses two lines of action: to foment user trust and reliance on AI language models while also demonstrating a level of cultural, historical, and sociolinguistic awareness that reflects positively on any internationalization strategy.

There is justifiable interest in leveraging conversational AI (CAI) for health across the majority world, but to be effective, CAI must respond appropriately within cultur ally and linguistically diverse context s . Therefore, we need ways to address the fact that current LLMs exclude many lived experience s globally . Various advances are underway which focus on top - down approaches and increas ing training data . In this paper, we aim to complement these with a bottom - up locally - grounded approach based on qualitative data collected during participatory workshops in Latin America. Our goal is to construct a rich and human - centred understanding o f: a) potential areas of cultural misalignment in digital health; b) regional perspectives on chatbots for health and c) strategies for creating culturally - appropriate CAI; with a focus on the understudied Latin American context . Our findings show that academic boundaries on notions of cultur e lose meaning at the ground level and technologies will need to engage with a broad er framework; one that encapsulates the way economics, politics, geogr aphy and local logistics are entangled in cultural experience. To this end, we introduce a framework for ' Pluriversal Conversational AI for H ealth ' which allows for the possibility that more relationality and tolerance, rather than just more data, may be called for .

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks: (1) Race to Country retrieval, which examines the association between individuals from specific racial groups (East Asian, White, Middle Eastern, Latino, South Asian, and Black) and different countries; (2) Personal Traits to Country retrieval, where images are paired with trait-based prompts (e.g., Smart, Honest, Criminal, Violent) to investigate potential stereotypical associations; and (3) Physical Characteristics to Country retrieval, focusing on visual attributes like skinny, young, obese, and old to explore how physical appearances are culturally linked to nations. Our findings reveal persistent biases in VLMs, highlighting how visual representations may inadvertently reinforce societal stereotypes.

Knowledge Graphs have become increasingly popular due to their wide usage in various downstream applications, including information retrieval, chatbot development, language model construction, and many others. Link prediction (LP) is a crucial downstream task for knowledge graphs, as it helps to address the problem of the incompleteness of the knowledge graphs. However, previous research has shown that knowledge graphs, often created in a (semi) automatic manner, are not free from social biases. These biases can have harmful effects on downstream applications, especially by leading to unfair behavior toward minority groups. To understand this issue in detail, we develop a framework -- AuditLP -- deploying fairness metrics to identify biased outcomes in LP, specifically how occupations are classified as either male or female-dominated based on gender as a sensitive attribute. We have experimented with the sensitive attribute of age and observed that occupations are categorized as young-biased, old-biased, and age-neutral. We conduct our experiments on a large number of knowledge triples that belong to 21 different geographies extracted from the open-sourced knowledge graph, Wikidata. Our study shows that the variance in the biased outcomes across geographies neatly mirrors the socio-economic and cultural division of the world, resulting in a transparent partition of the Global North from the Global South.