Fury as NYC on course to join Detroit, Chicago and Puerto Rico with woke mayor Mamdani's latest reckless plan Hidden $65bn lithium motherlode mapped beneath America's oldest mountains could power nation for centuries A quarter of US stock market gets report cards from Wall Street on same day this week. Even one bad grade can spell catastrophe for your 401(k). Here's EXACTLY what you need to do I was constantly burned out and kept cancelling plans because I was so tired. Doctors said it was just hormones... then I was diagnosed with this aggressive cancer. Nicole Kidman's daughters have'CUT OFF' dad Keith Urban: Insiders reveal why they are'SO angry'... and how he is utterly'distraught' but finally admitting'guilt' Florida go-kart park ordered to pay hefty settlement after mom and daughter, 6, broke two important rules that resulted in little girl's death King Charles leaves White House roaring with laughter with jokes to Trump about'speaking French' and the Boston Tea Party in dazzling state dinner Brace for the'Big Crunch': Scientists predict when the universe will end - and it's TRILLIONS of years sooner than we thought The $1.50 fruit that can protect you from deadly heart disease Why Donald Trump Jr and Bettina Anderson's wedding is'on hold' just weeks after extravagant'enchanted garden' bridal shower Serena Williams leaves fans split with controversial parenting confession as tennis legend opens up on'discipline' incident with daughter'No more Mr Nice Guy!': Trump warns Iran to'get smart' and'sign non-nuclear deal' with image of him brandishing assault rifle - as oil prices spike once more The surprise state cashing in big as Californians flee in droves... and the $672-a-month reason why What REALLY goes on in some Equinox steam rooms: Gym insiders reveal eye-popping indecency... secret towel signals used by experimental married men... and clubs with most'aggressive' locker rooms Fox News's Jesse Watters, 47, takes his young wife, 33, to state dinner after causing stir with story of how he seduced her Truth about Jordan Peterson's catastrophic decline: Inside his living hell, dumbstruck and in'overwhelming pain' locked up on $50m estate... as friends point finger about REAL cause Worrying shift as restaurant chain rolls out no-seating stores - sparking fears this is just the start of a'corporate purge of the American dining room' Shocking footage has revealed the moment a dancing robot went rogue at a school sports day.

Then we perform more ablation studies on our MixFormerV2 framework and the model pruning route during the distillation-based model reduction. We also provide some visualization results of the prediction-token-to-search and prediction-token-to-template attention maps.

Why is Israel still in southern Lebanon? A war to shape Lebanon's future How Hezbollah's fibre optic drones test Israel's sophisticated radar system In the skies over the Lebanese town of Taybeh, Israel's multibillion-dollar defence systems were rendered useless by a spool of cable, according to a report by the Israeli daily Yedioth Ahronoth (Ynet). As an Israeli medical evacuation helicopter rushed to rescue soldiers wounded in a drone attack, another unmanned aerial vehicle (UAV) hurtled towards them. With their electronic countermeasures failing, soldiers on the ground were forced to point their rifles at the sky, firing at the incoming threat before it detonated just metres away. The chaotic scene underscores a lethal new reality in the escalating conflict.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' The world's militaries spent $2.88 trillion in 2025, an increase of 2.9 percent from the year before, according to the Stockholm International Peace Research Institute's (SIPRI) latest report. To put that number into perspective, $2.88 trillion amounts to $350 of military spending for each person on the planet. In this visual explainer, Al Jazeera unpacks the rise of global militarisation, including how much each nation spends, which countries sell the most weapons, and how military spending compares with spending on healthcare and education. In 2025, the five biggest military spenders were the United States ($954bn), China ($336bn), Russia ($190bn), Germany ($114bn) and India ($92bn), accounting for more than half (58 percent) of world military spending. The US is by far the biggest spender, as it has been every year since World War II.

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy.

HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.

As a nonlinear dimension reduction technique, the diffusion map (DM) has been widely used. In DM, kernels play an important role for capturing the nonlinear relationship of data. However, only symmetric kernels can be used now, which prevents the use of DM in directed graphs, trophic networks, and other real-world scenarios where the intrinsic and extrinsic geometries in data are asymmetric. A promising technique is the magnetic transform which converts an asymmetric matrix to a Hermitian one. However, we are facing essential problems, including how diffusion distance could be preserved and how divergence could be avoided during diffusion process. Via theoretical proof, we successfully establish a diffusion representation framework with the magnetic transform, named MagDM. The effectiveness and robustness for dealing data endowed with asymmetric proximity are demonstrated on three synthetic datasets and two trophic networks.