Judges describe a campaign of firings and interference which threatens the system's independence. On a Thursday morning last month, Patrick O'Brien, a federal immigration judge, walked into his courtroom in downtown San Francisco. He was scheduled for a master-calendar hearing, a roll call, essentially, to get cases ready for trial. O'Brien was wearing a matte-black robe that seemed to absorb the artificial light overhead. He took his seat, scanned the room, and angled himself toward a computer monitor. The court was leanly staffed. There was a judicial clerk but no bailiff or stenographer. Opposite the judge were tables for the prosecution--the Department of Homeland Security--and for the respondent, a succession of immigrants who were applying for asylum. A Spanish interpreter appeared as a faceless box on a big screen. About ten people, all Latino, sat in wooden pews, gripping folders full of esoteric documents.

The center hosts the world's largest wind tunnel and a rich history of aerospace innovation, preserved in a striking visual archive in the heart of Silicon Valley. At the southern tip of San Francisco Bay, surrounded by the tech giants Google, Apple, and Microsoft, sits the historic NASA Ames Research Center . Its rich history includes a grab bag of fascinating scientific research involving massive wind tunnels, experimental aircraft, supercomputing, astrobiology, and more. Founded in 1939 as a West Coast lab for the National Advisory Committee for Aeronautics (NACA), NASA Ames was built to close the US gap with Germany in aeronautics research. Named for NACA founding member Joseph Sweetman Ames, the facility grew from a shack on Moffett Field into a sprawling compound with thousands of employees. A key motivation for the new lab was the need for huge wind tunnels to jump-start America's aeronautical research, which was far behind Germany's.

Quite recently, large language models have made a significant breakthrough across various disciplines. However, training them is an extremely resource-intensive task, even for major players with vast computing resources. One of the methods gaining popularity in light of these challenges is Sign-SGD. This method can be applied both as a memory-efficient approach in single-node training and as a gradient compression technique in the distributed learning. Nevertheless, it is impossible to automatically determine the effective stepsize from the theoretical standpoint. Indeed, it depends on the parameters of the dataset to which we do not have access in the real-world learning paradigm. To address this issue, we design several variants of single-node deterministic Sign-SGD. We extend our approaches to practical scenarios: stochastic single-node and multi-node learning, methods with incorporated momentum. We conduct extensive experiments on real machine learning problems that emphasize the practical applicability of our ideas.

What makes a place seem'haunted'? Psychology, setting, and the power of suggestion all help make certain places feel more spooky. From the ghost of Al Capone to the crying Lady in Green, many otherworldly entities are said to populate Alcatraz Island. Breakthroughs, discoveries, and DIY tips sent every weekday. With its long history of incarceration, brutal conditions, and several grisly murders, the stories of hauntings on Alcatraz Island are a dime a dozen.

Anthropic Has a Plan to Keep Its AI From Building a Nuclear Weapon. Anthropic partnered with the US government to create a filter meant to block Claude from helping someone build a nuke. Experts are divided on whether its a necessary protection--or a protection at all. At the end of August, the AI company Anthropic announced that its chatbot Claude wouldn't help anyone build a nuclear weapon. According to Anthropic, it had partnered with the Department of Energy (DOE) and the National Nuclear Security Administration (NNSA) to make sure Claude wouldn't spill nuclear secrets.

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The LED lights reduced entanglements by 63 percent, according to a new study. Breakthroughs, discoveries, and DIY tips sent every weekday. For fishers working the inky dark night, it can be difficult to keep endangered or unwanted animals out of their nets. While lighted nets can reduce the bycatch of sharks and sea turtles, their batteries are short lived, expensive to replace, and not always easy to dispose of. The lights themselves are also heavy, can make the nets sag, and not easy for fishers to work with.

Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps.

Unlike traditional backdoor attacks that focus on specific class labels, our approach aims to induce poisoned models to predict future data as a predefined target pattern.