However, it seems that including the dimension of time remains challenging.

Our analysis involved a two-phase process.

Former U.S. Treasury Secretary Larry Summers says he will step back from all public commitments, adding the move is to allow him to rebuild trust and repair relationships with the people closest to me. Former U.S. Treasury Secretary Larry Summers is stepping down from a teaching post at Harvard University and as a director of one of its business and government schools, a spokesperson said on Wednesday, after Congress released documents showing Summers shared close ties with the late convicted sex offender Jeffrey Epstein. A spokesperson for Summers, Steven Goldberg, said Summers' co-teachers would complete the semester for three ongoing courses. Mr. Summers has decided it's in the best interest of the center for him to go on leave from his role as director as Harvard undertakes its review, he said. Summers, also a former president of Harvard University, is a director of the Mossavar-Rahmani Center for Business and Government at the Harvard Kennedy School. Summers has been under fire since the U.S. House Oversight Committee released documents detailing an ongoing personal correspondence between Summers and Epstein, who died by suicide in a Manhattan prison in 2019 as he faced sex-trafficking charges.

NASA's Perseverance rover discovers a shiny metallic rock nicknamed 'Phippsaksla' on Mars that scientists believe could be a meteorite from an ancient asteroid.

The draft order, obtained by WIRED, instructs the US Justice Department to sue states that pass laws regulating AI. US President Donald Trump is considering signing an executive order that would seek to challenge state efforts to regulate artificial intelligence through lawsuits and the withholding federal funding, WIRED has learned. A draft of the order viewed by WIRED directs US Attorney General Pam Bondi to create an "AI Litigation Task Force," whose purpose is to sue states in court for passing AI regulations that allegedly violate federal laws governing things like free speech and interstate commerce. Trump could sign the order, which is currently titled "Eliminating State Law Obstruction of National AI Policy," as early as this week, according to four sources familiar with the matter. A White House spokesperson told WIRED that "discussion about potential executive orders is speculation."

World's biggest company Nvidia stuns Wall Street as it gives biggest clue yet to state of US economy'Triple whammy' will decide if Wall Street crashes within the next day A senior White House official has told me the REAL threat to Trump. Epstein is a humiliating distraction. But he's losing grip fast... this could be fatal: ANDREW NEIL Secret reasons Ronaldo was desperate to meet Trump... and what he REALLY wants from the president Melania Trump delivers'dystopian' speech to troops sparking meltdown Kevin Spacey reveals he is currently homeless and'living in hotels' as he admits his financial situation is'not great' - two years after he was cleared of sexual assault allegations Female health inspector sparks internet firestorm over video of her pouring BLEACH all over unlicensed taco vendor's food Haunting final words of boy, 12, 'tortured by lesbian wives' until he shrunk and died Nancy Mace leaks wild sexts about Republican colleague: 'You will be a good girl' Full-faced Britney Spears looks unrecognizable as she carries Champagne flute from wine bar, then drives away... AGAIN: Family speak out on'nightmare' spiral'The Mamdani effect' goes berserk: Desperate New Yorkers fight over multimillion-dollar homes outside city... prices jump 24% in five DAYS All the scandals of the 1939 Wizard of Oz: How Judy Garland was drugged and starved in an'iron corset', actors DIED and one had an eyelid burned off... not to mention the drunken orgies SARAH VINE: Meghan the Domestic Goddess is back - and she's in full festive flow. Meghan Markle goes barefaced as she poses on cover of Harper's Bazaar magazine Doctors warn'overprescribed' medical test use has DOUBLED despite raising the risk of cancer by three times Deep red state of Utah will see its population swell by TWO MILLION by 2065 thanks to'net-in migration' READ MORE: Can you spot the AI-generated faces? Britain boasts some of the best authors in the world - but they could soon be replaced by AI, a disturbing report reveals.

The central task of interpretability research is to explain the functions that AI systems learn from data. Investigating these functions requires experimentation with trained models, using tools that incorporate varying degrees of human input. Hand-tooled approaches that rely on close manual inspection [Zeiler and Fergus, 2014, Zhou et al., 2014, Mahendran and V edaldi, 2015, Olah et al., 2017, 2020, Elhage et al., 2021] or search for predefined phenomena [Wang et al., 2022, Nanda

Deep learning models achieve state-of-the-art performance across domains but face scalability challenges in real-time or resource-constrained scenarios. To address this, we propose Correlation of Loss Differences (CLD), a simple and scalable metric for coreset selection that identifies the most impactful training samples by measuring their alignment with the loss trajectories of a held-out validation set. CLD is highly efficient, requiring only per-sample loss values computed at training checkpoints, and avoiding the costly gradient and curvature computations used in many existing subset selection methods. We develop a general theoretical framework that establishes convergence guarantees for CLD-based coresets, demonstrating that the convergence error is upper-bounded by the alignment of the selected samples and the representativeness of the validation set. On CIFAR-100 and ImageNet-1k, CLD-based coresets typically outperform or closely match state-of-the-art methods across subset sizes, and remain within 1% of more computationally expensive baselines even when not leading. CLD transfers effectively across architectures (ResNet, VGG, DenseNet), enabling proxy-to-target selection with <1% degradation. Moreover, CLD is stable when using only early checkpoints, incurring negligible accuracy loss. Finally, CLD exhibits inherent bias reduction via per-class validation alignment, obviating the need for additional stratified sampling. Together, these properties make CLD a principled, efficient, stable, and transferable tool for scalable dataset optimization.

Teams of cooperating autonomous underwater vehicles (AUVs) rely on acoustic communication for coordination, yet this communication medium is constrained by limited range, multi-path effects, and low bandwidth. One way to address the uncertainty associated with acoustic communication is to learn the communication environment in real-time. We address the challenge of a team of robots building a map of the probability of communication success from one location to another in real-time. This is a decentralized classification problem -- communication events are either successful or unsuccessful -- where AUVs share a subset of their communication measurements to build the map. The main contribution of this work is a rigorously derived data sharing policy that selects measurements to be shared among AUVs. We experimentally validate our proposed sharing policy using real acoustic communication data collected from teams of Virginia Tech 690 AUVs, demonstrating its effectiveness in underwater environments.