United States Customs and Border Protection is asking tech companies to send pitches for a real-time facial recognition tool that would take photos of every single person in a vehicle at a border crossing, including anyone in the back seats, and match them to travel documents, according to a document posted in a federal register last week. The request for information, or RIF, says that CBP already has a facial recognition tool that takes a picture of a person at a port of entry and compares it to travel or identity documents that someone gives to a border officer, as well as other photos from those documents already "in government holdings." "Biometrically confirmed entries into the United States are added to the traveler's crossing record," the document says. An agency under the Department of Homeland Security, CBP says that its facial recognition tool "is currently operating in the air, sea, and land pedestrian environments." The agency's goal is to bring it to "the land vehicle environment."

U.S. President Donald Trump on Monday dismissed the backlash against an artificial intelligence-generated image of him as the pope posted by the White House on social media, saying it was a harmless joke, but communications experts said they did not see the funny side. The weekend AI-generated posts of Trump dressed in white papal vestments and another of him wielding one of the red light sabers preferred by villains in the "Star Wars" movies appeared typical of the provocation the president employs to energize supporters and troll critics. Since returning to office on Jan. 20, Trump has dominated news cycles. In an otherwise relatively quiet weekend, the two images ensured Trump stayed a major topic of conversation on social media and beyond. Throughout his political career, Trump has embraced bold visuals, from posing in a garbage truck to standing outside a church during protests against police brutality.

A U.S. lawmaker plans to introduce legislation in coming weeks to verify the location of artificial intelligence chips such as those made by Nvidia after they are sold. The effort to keep tabs on the chips, which drew bipartisan support from U.S. lawmakers, aims to address reports of widespread smuggling of Nvidia's chips into China in violation of U.S. export control laws. Nvidia's chips are a critical ingredient for creating AI systems such as chatbots, image generators and more specialized ones that can help craft biological weapons. Both U.S. President Donald Trump and his predecessor, Joe Biden, have implemented progressively tighter export controls of Nvidia's chips to China.

Russian attacks on the Donetsk and Sumy regions of eastern Ukraine killed at least three people on Monday, Ukrainian authorities said. A Ukrainian drone attack on a car in Russia's Kursk region killed two women, Governor Alexander Khinstein said in a post on Telegram. He said a 53-year-old man was also killed when an explosive device was dropped onto his car. Russian forces destroyed 105 Ukrainian drones overnight, the RIA Novosti news agency reported, citing the Russian Ministry of Defence. Moscow Mayor Sergei Sobyanin said at least 19 Ukrainian drones were destroyed as the capital was targeted for a second night in a row, prompting the closure of all airports for several hours.

Multiple explosions have been heard and a huge fire seen in Port Sudan, though the exact locations and causes were unclear, as Sudan's civil war rocks the previously quiet city for the third day. Dark plumes of smoke could be seen emerging from the vicinity of the country's main maritime port in the city, where hundreds of thousands of displaced people have sought refuge. Al Jazeera's Hiba Morgan, reporting from the Sudanese capital, Khartoum, said residents in the port city reported that attack drones launched by the paramilitary Rapid Support Forces (RSF) hit a fuel depot and other targets. "According to the residents, they believe that it was drone strikes by the paramilitary Rapid Support Forces – once again. They targeted a fuel depot in the city but also around the port and the air base," Morgan said.

Neural Operators (NOs) provide a powerful framework for computations involving physical laws that can be modelled by (integro-) partial differential equations (PDEs), directly learning maps between infinite-dimensional function spaces that bypass both the explicit equation identification and their subsequent numerical solving. Still, NOs have so far primarily been employed to explore the dynamical behavior as surrogates of brute-force temporal simulations/predictions. Their potential for systematic rigorous numerical system-level tasks, such as fixed-point, stability, and bifurcation analysis - crucial for predicting irreversible transitions in real-world phenomena - remains largely unexplored. Toward this aim, inspired by the Equation-Free multiscale framework, we propose and implement a framework that integrates (local) NOs with advanced iterative numerical methods in the Krylov subspace, so as to perform efficient system-level stability and bifurcation analysis of large-scale dynamical systems. Beyond fixed point, stability, and bifurcation analysis enabled by local in time NOs, we also demonstrate the usefulness of local in space as well as in space-time ("patch") NOs in accelerating the computer-aided analysis of spatiotemporal dynamics. We illustrate our framework via three nonlinear PDE benchmarks: the 1D Allen-Cahn equation, which undergoes multiple concatenated pitchfork bifurcations; the Liouville-Bratu-Gelfand PDE, which features a saddle-node tipping point; and the FitzHugh-Nagumo (FHN) model, consisting of two coupled PDEs that exhibit both Hopf and saddle-node bifurcations.

In this paper, I explored how a range of regression and machine learning techniques can be applied to monthly U.S. unemployment data to produce timely forecasts. I compared seven models: Linear Regression, SGDRegressor, Random Forest, XGBoost, CatBoost, Support Vector Regression, and an LSTM network, training each on a historical span of data and then evaluating on a later hold-out period. Input features include macro indicators (GDP growth, CPI), labor market measures (job openings, initial claims), financial variables (interest rates, equity indices), and consumer sentiment. I tuned model hyperparameters via cross-validation and assessed performance with standard error metrics and the ability to predict the correct unemployment direction. Across the board, tree-based ensembles (and CatBoost in particular) deliver noticeably better forecasts than simple linear approaches, while the LSTM captures underlying temporal patterns more effectively than other nonlinear methods. SVR and SGDRegressor yield modest gains over standard regression but don't match the consistency of the ensemble and deep-learning models. Interpretability tools ,feature importance rankings and SHAP values, point to job openings and consumer sentiment as the most influential predictors across all methods. By directly comparing linear, ensemble, and deep-learning approaches on the same dataset, our study shows how modern machine-learning techniques can enhance real-time unemployment forecasting, offering economists and policymakers richer insights into labor market trends. In the comparative evaluation of the models, I employed a dataset comprising thirty distinct features over the period from January 2020 through December 2024.

Legal practice requires careful adherence to procedural rules. In the United States, few are more complex than those found in The Bluebook: A Uniform System of Citation. Compliance with this system's 500+ pages of byzantine formatting instructions is the raison d'etre of thousands of student law review editors and the bete noire of lawyers everywhere. To evaluate whether large language models (LLMs) are able to adhere to the procedures of such a complicated system, we construct an original dataset of 866 Bluebook tasks and test flagship LLMs from OpenAI, Anthropic, Google, Meta, and DeepSeek. We show (1) that these models produce fully compliant Bluebook citations only 69%-74% of the time and (2) that in-context learning on the Bluebook's underlying system of rules raises accuracy only to 77%. These results caution against using off-the-shelf LLMs to automate aspects of the law where fidelity to procedure is paramount.

The field of AI safety seeks to prevent or reduce the harms caused by AI systems. A simple and appealing account of what is distinctive of AI safety as a field holds that this feature is constitutive: a research project falls within the purview of AI safety just in case it aims to prevent or reduce the harms caused by AI systems. Call this appealingly simple account The Safety Conception of AI safety. Despite its simplicity and appeal, we argue that The Safety Conception is in tension with at least two trends in the ways AI safety researchers and organizations think and talk about AI safety: first, a tendency to characterize the goal of AI safety research in terms of catastrophic risks from future systems; second, the increasingly popular idea that AI safety can be thought of as a branch of safety engineering. Adopting the methodology of conceptual engineering, we argue that these trends are unfortunate: when we consider what concept of AI safety it would be best to have, there are compelling reasons to think that The Safety Conception is the answer. Descriptively, The Safety Conception allows us to see how work on topics that have historically been treated as central to the field of AI safety is continuous with work on topics that have historically been treated as more marginal, like bias, misinformation, and privacy. Normatively, taking The Safety Conception seriously means approaching all efforts to prevent or mitigate harms from AI systems based on their merits rather than drawing arbitrary distinctions between them.

Multilingual understanding is crucial for the cross-cultural applicability of Large Language Models (LLMs). However, evaluation benchmarks designed for Hong Kong's unique linguistic landscape, which combines Traditional Chinese script with Cantonese as the spoken form and its cultural context, remain underdeveloped. To address this gap, we introduce HKMMLU, a multi-task language understanding benchmark that evaluates Hong Kong's linguistic competence and socio-cultural knowledge. The HKMMLU includes 26,698 multi-choice questions across 66 subjects, organized into four categories: Science, Technology, Engineering, and Mathematics (STEM), Social Sciences, Humanities, and Other. To evaluate the multilingual understanding ability of LLMs, 90,550 Mandarin-Cantonese translation tasks were additionally included. We conduct comprehensive experiments on GPT-4o, Claude 3.7 Sonnet, and 18 open-source LLMs of varying sizes on HKMMLU. The results show that the best-performing model, DeepSeek-V3, struggles to achieve an accuracy of 75\%, significantly lower than that of MMLU and CMMLU. This performance gap highlights the need to improve LLMs' capabilities in Hong Kong-specific language and knowledge domains. Furthermore, we investigate how question language, model size, prompting strategies, and question and reasoning token lengths affect model performance. We anticipate that HKMMLU will significantly advance the development of LLMs in multilingual and cross-cultural contexts, thereby enabling broader and more impactful applications.