Pentagon officials are weighing whether to disperse U.S. military bases in the Gulf after Iranian missile and drone attacks exposed vulnerabilities at major Middle East installations.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens.

New York City's Summer of Ludd festival is teaching people how to live offline amid the suffocating presence of Big Tech. A papier-mâché woman is the backdrop to a play about the Luddite movement. On a Sunday evening in the middle of Tompkins Square Park in New York City's East Village, hundreds of people gather in front of a giant papier-mâché face of a woman wearing a crown. She's the backdrop of a play, her body made up of curtains that look like a dress but serve a dual purpose, allowing actors to scurry on and offstage. I'm here to watch a performance called " Luddite Recreations," which is a history of the Luddite movement--a group of artisans and textile workers who resisted the adoption of machines during the early years of the Industrial Revolution in England and whose resistance to being displaced from their work was met with violence by the British monarchy.

Meta Is Charging a Subscription for Smart Glasses Features. Now you'll need to subscribe for "expanded access" to the most advanced features. So you paid a few hundred bucks for a neat little gadget and think you're good to go. But it turns out you'll need to subscribe to a monthly plan to unlock its advanced features. That where AI-powered consumer electronics are increasingly heading, and Meta is the latest to prove it with its smart glasses .

Russian forces have launched a major drone and missile attack on the Ukrainian capital Kyiv overnight, killing at least eight people, officials say. Several neighbourhoods were evacuated as strikes rocked buildings throughout the city, hours after Ukrainian President Volodymyr Zelensky warned Russia was preparing for a massive attack. Children were among the significant number of casualties, Tymur Tkachenko, the head of Kyiv's military administration, said. The enemy is once again deliberately targeting residential areas and killing civilians, he said in the early hours of Thursday. On Thursday morning, daylight brought clearer images of a crater which appeared to be caused by the impact of explosions.

Is the war entering a new phase? Kyiv came under a ballistic missile and drone attack overnight, with at least two people killed and 11 injured after Ukrainian President Volodymyr Zelenskyy warned of an impending "massive" attack by Russia. Kyiv Mayor Vitali Klitschko, writing on the Telegram messaging platform, said the roof of a hotel was on fire early on Thursday morning. "Kyiv is under attack from ballistic missiles and UAVs," Klitschko wrote, using the acronym for unmanned aerial vehicles, or drones. Klitschko later said that 11 people were injured, with others trapped in a damaged nine-storey residential building, and the roof of another high-rise apartment building on fire.

Smoke rises during a Russian missile strike on Kyiv on Thursday. Kyiv - Russian missile and drone strikes rocked Kyiv early on Thursday, setting off fires and wounding at least five people, after Ukrainian President Volodymyr Zelenskyy warned that Moscow was preparing a "massive attack." Russia has routinely launched waves of missiles and drones at Ukrainian cities, including Kyiv, during its more than four-year invasion, which has become Europe's deadliest conflict since World War II. The attack came after the Ukrainian air force warned that ballistic missiles were headed toward the capital and followed Zelenskyy cutting short a visit to Dublin Wednesday, citing intelligence reports of an impending Russian strike. Journalists in central and eastern Kyiv heard more than a dozen explosions and saw residents -- some with children and pets -- rushing into metro stations being used as shelters. "Kyiv is under attack from ballistic missiles and UAVs," Kyiv Mayor Vitali Klitschko said on Telegram, adding that blasts could be heard across the city.

We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a transparent parametric structure while simultaneously accounting for complex and nonstationary patterns in the observed phenomenon. Our analysis covers two specifications of the noise process. Besides the standard Gaussian setting, we also consider Laplace-distributed noise, which can offer a more adequate description in the presence of heavier tails and sharper local fluctuations. For both cases, we formulate the predictive scheme of the model and analyze the associated uncertainty quantification, including the construction of prediction intervals. The results illustrate that a relatively simple model, when combined with time-dependent parameter estimation, can serve as a mathematically tractable and practically flexible tool for forecasting complex dynamics under different noise assumptions. The general model is stated for TVAR($p$), while the prediction-interval formulas and the numerical experiments are developed for the TVAR(1) case.

Machine learning algorithms and their training datasets have grown substantially in both size and complexity over the past decade. This increased model complexity has made it challenging to interpret and predict their behavior in unobserved scenarios. Hence, many applications that involve societal decisions still rely on simple, interpretable models like linear regression, often after feature engineering. Examples of such applications include predicting national housing prices, estimating wages across industries, forecasting loan amounts across banks, predicting life insurance premiums across groups, and projecting energy consumption across communities [CGKMN24]. A shared safety and sometimes legal concern across the above applications is the potential for wildly different model qualities for different distributions, i.e., outputting a notably worse model for some source data distributions [Dat14; BS16; HPS16; VVB18; SBFVV19; BHJKR21; CGNSG23; Cho16; KLMR18; ADW19; CGKMN24; SVWZ24].

Kornelius Raeth 1 Nicole Ludwig 1 2 Abstractscoring rules distribute the training gradient in proportion to Sample-based generative models are increasingly data density, with no awareness of the decision maker's cost structure. The model's limited capacity is allocated globused for probabilistic forecasting in high-stakes ally, leaving decision-critical regions of the output space decision settings, yet their training objectives are potentially underserved. These models are commonly trained with strictly proper Given a forecast, a decision maker with cost function c(a,y), scoring rules, such as the energy score, which al-of action aand outcome y, selects the action that minimises locate their training signal in proportion to dataexpected cost under the forecast distribution; a point forecast density, with no awareness of where forecast eris insufficient to evaluate this expectation. A good forecast rors are most costly for downstream decisions. Crucially, the energy score objective with a differentiable deci-observed cost of the optimal action is itself a proper scoring sion loss that directly penalises the cost incurredrule (Hartline et al., 2025; Kleinberg et al., 2023), placing by acting on the model's forecast. This combinedit in the same family as the energy score which licenses loss is theoretically grounded, as the decision losstheir combination as a theoretically well-founded training is itself a proper scoring rule. Introduction score acts as that anchor, preventing the model from collapsing outside cost-sensitive regions. Our method is theo-tion based on a temperature forecast, balancing asset loss against the cost of intervention. In the weather domain, retically grounded and leads to better downstream decisions state-of-the-art forecasting systems (Lang et al., 2024; Pricewhile retaining full probabilistic forecasts, as validated on et al., 2023) are trained with strictly proper scoring rulessynthetic and real-world forecasting tasks. A gradient analysis showing which regions benefitscore reduces to the continuous ranked probability score from the decision loss and why, based on the cost (CRPS), widely used in meteorological forecast verificafunction structure. Both model classes introduced above are commonly trained by minimising strictly proper sion calibration.