Trump on the brink of'major war' with Iran as Ayatollah defies his nuclear red line It looks like paradise... but the Costa Rica resort where a surfing legend was murdered while living with girlfriend less than half his age is hiding a seedy underbelly Courtney Love's agony over Kurt Cobain'homicide' investigation: Insiders break silence about new probe My wife showed me her extreme kink on Pornhub... then she begged me to do the unthinkable: DEAR JANE Lindsey Vonn's Winter Olympics ski crash injury is'a lot more severe than a broken leg' with her'leg in pieces' after specialists suggested she may need amputation Nancy Guthrie sheriff insists her case is'far from cold' despite no leads, arrests, or DNA matches 18 days after disappearance Unseen trove of Alexander brothers photos revealed... as horrifying sex crimes trial is rocked by jury scandal Ukraine peace talks collapse in less than two hours as Zelensky says it is'not fair' Trump wants him to compromise and not Putin How I lost eight stone by filling up on THESE two foods - and not a fat jab in sight: I will forever be haunted by my wedding and honeymoon pictures, but now I'm nine-and-a-half stone and eating more than ever Police arrest boyfriend of girl who vanished without a trace as they believe he'heinously murdered her' JFK Jr's hunky love rival kept Carolyn Bessette coming back for more... now we've found silver-haired Baywatch star on a bus bench Secret'immovable' UFO is hiding in plain sight in purpose-built structure claims US congressman The sex complaints women are too afraid to tell their husbands: The position we dread, the mistake most men make... and our favorite sneaky trick Your choice of a cheap Zinfandel Rosé over an expensive Argentinian Malbec might reveal more about your personality than your palate, according to a new study. Researchers have found that traits such as extraversion, openness and neuroticism can indicate what type of plonk you prefer. They used AI to determine personality traits based on the reviews, and compared it to the strength of wine people were buying. Analysis revealed that people who score high in agreeableness and openness tend to go for wines with a higher alcohol content. These are usually perceived as being of higher quality and have a richer body and taste - for example a Cabernet Sauvignon, Malbec, Port or Sherry.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . AI deepfake romance scam steals woman's home and life savings Your phone shares data at night: Here's how to stop it Nancy Guthrie disappearance'breaking my heart,' AG Bondi says A.I. is Moving More Quickly Than Anticipated Guthrie family'had no choice' but to do this: Retired NYPD inspector Pima County sheriff says nobody has been'ruled out yet' as a suspect in Nancy Guthrie disappearance'Everything is on the table' in Nancy Guthrie search, former FBI assistant director says Spain's Pedro Sanchez vows crackdown on social media at World Government Summit How Ring will use new'Fire Watch' tool in real time FBI director defends Georgia election probe, touts'historic' crime drop Why Trump's lawsuit against the IRS is'something you don't see every day' Fox News Flash top headlines are here.

Trump suffers Minneapolis meltdown as president flinches at ICE shooting and aides panic: 'He doesn't like it' Heiress reveals cruel outbursts her hedge funder husband made after dumping her for younger woman while sheltering from Covid at $7.5m Martha's Vineyard compound America's biggest banks line up to bash Trump over his plot to cut credit card bills as they outline obvious problem Amy Schumer sent me these late-night texts when I last wrote about her. Sorry sweetie, truth hurts... here's what I think of you now! KENNEDY I've uncovered a seedy new dating red flag. So many men are at it... I can't believe I didn't spot the warning signs sooner: JANA HOCKING Major 6.0 magnitude earthquake strikes West Coast with shockwaves felt across major cities Taylor Swift and'defeated' Travis Kelce are facing'first real test' in their relationship... as insiders say'things are changing' What Trump's desire to buy Greenland may reveal about him... psychologist unpacks the president's personality Kate leaves fans stunned with her down-to-earth nature as she's spotted driving herself to glitzy reception at Windsor Castle Kylie Kelce makes raunchy reference to her and Jason's sex life after his post-NFL glow up Gene Hackman's Santa Fe mansion listed for over $6M one year after he died in the home with his wife Students were raped after being forced to live in complex alongside 125 refugees to'aid integration': Terrified Dutch youngsters'were subjected to years of sex assaults and violence' Bitter, past his best and seething with jealousy: Horrifying'insight' into mind of surgeon accused of killing ex-wife and her dentist spouse NFL team could be forced to move over wild conspiracy theory as players' fears mount Dementia is NOT inevitable: scientists reveal how MILLIONS of cases can be prevented as they unveil a'roadmap' to beat the disease Trump threatens tariffs over Greenland if allies don't play ball: 'We need it' Titanic star with a link to Clint Eastwood emerges on rare outing in LA can you guess who she is? Karoline Leavitt takes vicious swipe at Supreme Court over bombshell hearing on trans athletes in women's sport I freaked out when my toddler dropped her baby sister... it ended up saving her life Renee Good's last moments revealed as woman suffered FOUR gunshot wounds during deadly clash with ICE Baseball is'DEAD', claim livid fans as free-spending Dodgers sign new star player to take payroll over $2 BILLION Coca-Cola's secret recipe, revealed: Scientist claims to have cracked the 139-year-old mystery formula - and it's 99% SUGAR READ MORE: Food scientist reveals why McDonald's Coke tastes better The Coca-Cola recipe is one of the world's most closely guarded trade secrets - but it may not be secret for much longer.

To improve accuracy, reduce wait times and ease worker stress, fast-food giants McDonald's and Papa Johns are deploying artificial intelligence ordering systems.

AI struggles to write a good jingle. You'd be forgiven if you can't hum the 18th-century Cumbrian folk song "Do Ye Ken John Peel." But in 1942, a version of that tune, reworked with lyrics about Pepsi-Cola, was the most recognized melody in America. Three years earlier, two men walked into the office of Pepsi-Cola's president, carrying a phonograph. They played a demo of what would become one of America's earliest advertising jingles.

Understanding multi-image, multi-turn scenarios is a critical yet underexplored capability for Large Vision-Language Models (LVLMs). Existing benchmarks predominantly focus on static or horizontal comparisons -- e.g., spotting visual differences or assessing appropriateness -- while relying heavily on language cues. Such settings overlook progressive, context-dependent reasoning and the challenge of visual-to-visual inference. To bridge this gap, we present VisChainBench, a large-scale benchmark designed to rigorously evaluate LVLMs' ability to perform multi-step visual reasoning across sequential, interdependent tasks with minimal language guidance. VisChainBench contains 1,457 tasks spanning over 20,000 images across three diverse domains (e.g., daily scenarios, engineering troubleshooting), structured to mimic real-world decision-making processes. Uniquely, the benchmark is constructed using a multi-agent generation pipeline, ensuring high visual diversity and controlled language bias. All the benchmark data and code for benchmark construction are available for viewing and download via following Link: https://huggingface.co/datasets/eyehole/VisChainBench

In recent years, precision agriculture has been introducing groundbreaking innovations in the field, with a strong focus on automation. However, research studies in robotics and autonomous navigation often rely on controlled simulations or isolated field trials. The absence of a realistic common benchmark represents a significant limitation for the diffusion of robust autonomous systems under real complex agricultural conditions. Vineyards pose significant challenges due to their dynamic nature, and they are increasingly drawing attention from both academic and industrial stakeholders interested in automation. In this context, we introduce the TEMPO-VINE dataset, a large-scale multi-temporal dataset specifically designed for evaluating sensor fusion, simultaneous localization and mapping (SLAM), and place recognition techniques within operational vineyard environments. TEMPO-VINE is the first multi-modal public dataset that brings together data from heterogeneous LiDARs of different price levels, AHRS, RTK-GPS, and cameras in real trellis and pergola vineyards, with multiple rows exceeding 100 m in length. In this work, we address a critical gap in the landscape of agricultural datasets by providing researchers with a comprehensive data collection and ground truth trajectories in different seasons, vegetation growth stages, terrain and weather conditions. The sequence paths with multiple runs and revisits will foster the development of sensor fusion, localization, mapping and place recognition solutions for agricultural fields. The dataset, the processing tools and the benchmarking results will be available at the dedicated webpage upon acceptance.

Autonomous robots are increasingly deployed in dynamic and unstructured environments, where they must plan and execute complex tasks under uncertainty. Classical planning approaches, typically modeled in PDDL and solved with heuristic search, provide a principled foundation for task planning (Edelkamp and Schr odl, 2011; Geffner and Bonet, 2013). However, these methods rely on explicit domain models that enumerate preconditions and effects of actions. In practice, such models often omit implicit commonsense knowledge, for example, that a container must be upright before pouring, or that water must be boiled before making tea. The absence of such knowledge can lead to plans that are logically correct but physically invalid. Cognitive robotics research seeks to bridge symbolic reasoning with robot perception and control (Ghallab et al., 2004). While significant progress has been made in integrating planning with motion control and execution, robots still lack the ability to autonomously infer commonsense constraints that humans consider obvious. Large Language Models (LLMs), trained on massive corpora of human knowledge, present a promising avenue for addressing this gap. LLMs can generate likely preconditions, subgoals, and contextual constraints from natural language task descriptions, potentially enriching classical planning models. 1

We propose SemImage, a novel method for representing a text document as a two-dimensional semantic image to be processed by convolutional neural networks (CNNs). In a SemImage, each word is represented as a pixel in a 2D image: rows correspond to sentences and an additional boundary row is inserted between sentences to mark semantic transitions. Each pixel is not a typical RGB value but a vector in a disentangled HSV color space, encoding different linguistic features: the Hue with two components H_cos and H_sin to account for circularity encodes the topic, Saturation encodes the sentiment, and Value encodes intensity or certainty. We enforce this disentanglement via a multi-task learning framework: a ColorMapper network maps each word embedding to the HSV space, and auxiliary supervision is applied to the Hue and Saturation channels to predict topic and sentiment labels, alongside the main task objective. The insertion of dynamically computed boundary rows between sentences yields sharp visual boundaries in the image when consecutive sentences are semantically dissimilar, effectively making paragraph breaks salient. We integrate SemImage with standard 2D CNNs (e.g., ResNet) for document classification. Experiments on multi-label datasets (with both topic and sentiment annotations) and single-label benchmarks demonstrate that SemImage can achieve competitive or better accuracy than strong text classification baselines (including BERT and hierarchical attention networks) while offering enhanced interpretability. An ablation study confirms the importance of the multi-channel HSV representation and the dynamic boundary rows. Finally, we present visualizations of SemImage that qualitatively reveal clear patterns corresponding to topic shifts and sentiment changes in the generated image, suggesting that our representation makes these linguistic features visible to both humans and machines.

What the Rocco fridge lacks in smarts, it makes up for in looks. You have a few hours left to save on the brand's Black Friday sale. When's the last time you poured a perfect glass of Pinot Noir in your own home? Red wines should be served somewhere between 58 and 68 degrees (opinions vary). That's a bit cooler than room temperature, but unless you want to dedicate money and space to a special refrigerator, you don't have many good options.