Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' 'These are not pets!' Wildlife experts urge Brits to keep their distance from HIGHLAND COWS following a surge in TikTokers trying to capture content with the rustic cattle A flock of Highland cows have become unexpected social media stars, but wildlife experts are urging Brits to keep their distance. The rustic cattle can be found'free grazing' on the Hothfield Heathlands Nature Reserve near Ashford, Kent. However Kent Wildlife Trust, which manages the site, says a recent viral video has led to a surge of TikTokers trying to film themselves with the adorable animals.

AI helps drive record $11.8 billion in Black Friday online spending People line up outside of a store during Black Friday in Woodbury, New York, on Friday. Although some shoppers queued up, many others used AI-powered shopping tools to help drive a surge in online spending on Black Friday. AI-powered shopping tools helped drive a surge in U.S. online spending on Black Friday, as shoppers bypassed crowded stores and turned to chatbots to compare prices and secure discounts amid concerns about tariff-driven price hikes. U.S. shoppers spent a record $11.8 billion online, up 9.1% from 2024 on the year's biggest shopping day, according to Adobe Analytics, which tracks 1 trillion visits that shoppers make to online retail websites. The holiday shopping season arrives amid tighter budgets, unemployment nearing a four-year high, U.S. consumer confidence sagging to a seven-month low and price tags that have shoppers watching every dollar. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

This paper introduces an approach to regularize 2.5D surface normal and depth predictions at each pixel given a single input image. The approach infers and reasons about the underlying 3D planar surfaces depicted in the image to snap predicted normals and depths to inferred planar surfaces, all while maintaining fine detail within objects. Our approach comprises two components: (i) a fourstream convolutional neural network (CNN) where depths, surface normals, and likelihoods of planar region and planar boundary are predicted at each pixel, followed by (ii) a dense conditional random field (DCRF) that integrates the four predictions such that the normals and depths are compatible with each other and regularized by the planar region and planar boundary information. The DCRF is formulated such that gradients can be passed to the surface normal and depth CNNs via backpropagation. In addition, we propose new planar wise metrics to evaluate geometry consistency within planar surfaces, which are more tightly related to dependent 3D editing applications. We show that our regularization yields a 30% relative improvement in planar consistency on the NYU v2 dataset.

A deadly conflict between bears and humans is playing out across Japan, where authorities have deployed the military to protect locals who are using drone-based alert and surveillance systems to track the bears. Since April this year, at least 13 people have been killed and more than 100 have been injured in bear attacks in the country, according to an October report by the Ministry of Environment. The ministry added that the death toll is the highest since Japan began keeping records of bear attacks in 2006. It is also home to Asiatic black bears - also known as Moon bears - which are smaller in size, weighing between 80-200kg (176-440 pounds), and are found on the mainland, which is more densely populated. Both types of bear have been involved in incidents this year, and both are dangerous to humans to varying degrees.

The'Surge' of Troops May Not Come to San Francisco, but the City Is Ready Anyway San Francisco is preparing for federal law enforcement's invasion of the Bay Area, whether it happens or not. Citizens protesting the threat of federal troop deployments in the San Francisco Bay Area held a rally on Thursday at SF City Hall. After months of deployments by US Immigration and Customs Enforcement and the National Guard across American cities, federal agents have been preparing to descend into San Francisco . Local resistance groups have been coordinating with activists in other cities across the country that have been besieged by federal law enforcement. Thousands of volunteers, coordinating through Signal group chats, Zoom calls, and social media posts, planned protests and spread the word that federal troops are on their way to San Francisco.

Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100-year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.

Chipmaker Nvidia became the first public company in history to scale a 4tn market value on Wednesday as its stock price continues a years-long stratospheric rise. Shares of the top chip designer rose roughly 2.4% to 164, benefiting from the ongoing surge in demand for artificial intelligence technologies. Nvidia's chips and associated software are considered world leaders for building artificial intelligence products. Nvidia achieved a 1tn market value for the first time back in June 2023 and the surge continued unabated with its market value – the total value of its shares – more than tripling in about a year, faster than Apple and Microsoft, the only other US firms with a market value of more than 3tn. Apple was the first company to reach a valuation of 3tn, back in 2022.

Storm surge forecasting plays a crucial role in coastal disaster preparedness, yet existing machine learning approaches often suffer from limited spatial resolution, reliance on coastal station data, and poor generalization. Moreover, many prior models operate directly on unstructured spatial data, making them incompatible with modern deep learning architectures. In this work, we introduce a novel approach that projects unstructured water elevation fields onto structured Red Green Blue (RGB)-encoded image representations, enabling the application of Convolutional Long Short Term Memory (ConvLSTM) networks for end-to-end spatiotemporal surge forecasting. Our model further integrates ground-truth wind fields as dynamic conditioning signals and topo-bathymetry as a static input, capturing physically meaningful drivers of surge evolution. Evaluated on a large-scale dataset of synthetic storms in the Gulf of Mexico, our method demonstrates robust 48-hour forecasting performance across multiple regions along the Texas coast and exhibits strong spatial extensibility to other coastal areas. By combining structured representation, physically grounded forcings, and scalable deep learning, this study advances the frontier of storm surge forecasting in usability, adaptability, and interpretability.

'The Art of the Surge' is available to stream on Fox Nation, taking viewers behind the scenes of President Donald Trump's campaign and road back to the White House. President Donald Trump's 2024 presidential campaign took a deadly turn when an assassin made an attempt on his life during a July rally in Butler, Pennsylvania. Now, Fox Nation subscribers can uncover new, previously unreleased footage from the shocking event. As shown on Wednesday's edition of "Fox & Friends," new drone footage featured in Fox Nation's "Art of the Surge: The Donald Trump Comeback" captures the scene of the Butler rally, as well as close-up footage of the Secret Service protecting Trump after shots rang out. Also shown are shocking videos of the crowd's reaction as everything unfolded, highlighting the fear of those who were there that day.

The COVID-19 pandemic response relied heavily on statistical and machine learning models to predict key outcomes such as case prevalence and fatality rates. These predictions were instrumental in enabling timely public health interventions that helped break transmission cycles. While most existing models are grounded in traditional epidemiological data, the potential of alternative datasets, such as those derived from genomic information and human behavior, remains underexplored. In the current study, we investigated the usefulness of diverse modalities of feature sets in predicting case surges. Our results highlight the relative effectiveness of biological (e.g., mutations), public health (e.g., case counts, policy interventions) and human behavioral features (e.g., mobility and social media conversations) in predicting country-level case surges. Importantly, we uncover considerable heterogeneity in predictive performance across countries and feature modalities, suggesting that surge prediction models may need to be tailored to specific national contexts and pandemic phases. Overall, our work highlights the value of integrating alternative data sources into existing disease surveillance frameworks to enhance the prediction of pandemic dynamics.