You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Six-foot-10 former Knicks and Celtics star officially declares for WNBA Draft as league's trans debate explodes Trio of women - two of them transgender - tortured boy, 7, to death - but remembered to take their pet for its vaccines as victim lay at death's door, prosecutors say Watch the moment Trump's eyes go wide as he's presented with a'ball of gold' America's young dementia epidemic: The 14 lifestyle habits driving early-onset cases revealed by world-leading experts... and changes that can REVERSE your risk Why Costco's little-known in-store perk is winning over shoppers who say it's'faster, cheaper and so much nicer' than the competition Keith Urban's secret'RELIEF' about Nicole Kidman's new lover: Friends say she may finally let go of'hold over him' as daughters meet beau... and brutal question that sparked ugly split Watch the moment two middle-aged men are escorted out of ...

Claire chatted to Allison Okamura from Stanford University about developing advanced robotic systems for haptic (touch) interaction. Allison Okamura is the Richard W. Weiland Professor of Engineering at Stanford University. Her academic interests include haptics, teleoperation, virtual reality, medical robotics, soft robotics, rehabilitation, and education. Allison is Director of Graduate Studies for Mechanical Engineering at Stanford University, a deputy director of the Wu Tsai Stanford Neurosciences Institute, a Science Fellow of the Hoover Institution and a founding faculty member and executive committee member of the Stanford Robotics Center. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

In the horticultural world, some vines are especially grabby. As they grow, the woody tendrils can wrap around obstacles with enough force to pull down entire fences and trees. Inspired by vines' twisty tenacity, engineers at MIT and Stanford University have developed a robotic gripper that can snake around and lift a variety of objects, including a glass vase and a watermelon, offering a gentler approach compared to conventional gripper designs. A larger version of the robo-tendrils can also safely lift a human out of bed. The new bot consists of a pressurized box, positioned near the target object, from which long, vine-like tubes inflate and grow, like socks being turned inside out.

Datasheet Dataset URL Responsibility and statement of license Hosting/maintenance plan Data format Structured metadata UniTox Datasheet Motivation For what purpose was the dataset created? UniTox was created as a unified toxicity dataset across eight types of drug toxicities (cardiotoxicity, liver toxicity, renal toxicity, pulmonary toxicity, hematological toxicity, dermatological toxicity, ototoxicity, and infertility). We generated information across all toxicities for the same set of 2,418 drugs with the same methodology of applying LLMs. For each drug, for each toxicity, we provide an LLM-generated summary of the relevant portions of the drug label, as well as ternary (No/Less/Most) predictions and binary (No/Yes) predictions for that toxicity. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?

Like any other remote perception technology, there are also risks involved with misuse of radar-based perception especially in the context of activity monitoring. Nevertheless, we acquired approvals from Stanford University's IRB The dataset is published under CC BY -NC-ND license. The code is published under Apache License 2.0. The dataset is hosted on a Google Drive space maintained by Stanford University. Doppler snapshots are stored in hdf5 format.

Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assurance these systems require. We address this challenge with two complementary contributions. First, we introduce MedFactEval, a framework for scalable, fact-grounded evaluation where clinicians define high-salience key facts and an "LLM Jury"--a multi-LLM majority vote--assesses their inclusion in generated summaries. Second, we present MedAgentBrief, a model-agnostic, multi-step workflow designed to generate high-quality, factual discharge summaries. To validate our evaluation framework, we established a gold-standard reference using a seven-physician majority vote on clinician-defined key facts from inpatient cases. The MedFactEval LLM Jury achieved almost perfect agreement with this panel (Cohen's kappa=81%), a performance statistically non-inferior to that of a single human expert (kappa=67%, P < 0.001). Our work provides both a robust evaluation framework (MedFactEval) and a high-performing generation workflow (MedAgentBrief), offering a comprehensive approach to advance the responsible deployment of generative AI in clinical workflows.

Economists at Stanford University have found the strongest evidence yet that artificial intelligence is starting to eliminate certain jobs. But the story isn't that simple: While younger workers are being replaced by AI in some industries, more experienced workers are seeing new opportunities emerge. Erik Brynjolfsson, a professor at Stanford University, Ruyu Chen, a research scientist, and Bharat Chandar, a postgraduate student, examined data from ADP, the largest payroll provider in the US, from late 2022, when ChatGPT debuted, to mid-2025. The researchers discovered several strong signals in the data--most notably that the adoption of generative AI coincided with a decrease in job opportunities for younger workers in sectors previously identified as particularly vulnerable to AI-powered automation (think customer service and software development). In these industries, they found a 16 percent decline in employment for workers aged 22 to 25.

Transdisciplinary approaches are increasingly essential for addressing grand societal challenges, particularly in complex domains such as Artificial Intelligence (AI), urban planning, and social sciences. However, effectively validating and integrating knowledge across distinct epistemic and ontological perspectives poses significant difficulties. This article proposes a six-dimensional framework for assessing and strengthening transdisciplinary knowledge validity in AI and city studies, based on an extensive analysis of the most cited research (2014--2024). Specifically, the framework classifies research orientations according to ontological, epistemological, methodological, teleological, axiological, and valorization dimensions. Our findings show a predominance of perspectives aligned with critical realism (ontological), positivism (epistemological), analytical methods (methodological), consequentialism (teleological), epistemic values (axiological), and social/economic valorization. Less common stances, such as idealism, mixed methods, and cultural valorization, are also examined for their potential to enrich knowledge production. We highlight how early career researchers and transdisciplinary teams can leverage this framework to reconcile divergent disciplinary viewpoints and promote socially accountable outcomes.

A searchable database of public records concerning use of force and misconduct by California law enforcement officers -- some 1.5 million pages from nearly 700 law enforcement agencies -- is now available to the public. The Police Records Access Project, a database built by UC Berkeley and Stanford University, is being published by the Los Angeles Times, San Francisco Chronicle, KQED and CalMatters. It will vastly expand public access to internal affairs records that show how law enforcement agencies throughout the state handle misconduct allegations and uses of police force that result in death or serious injury. The database currently includes records from nearly 12,000 cases. The database is the product of years of work by a multidisciplinary team of journalists, data scientists, lawyers and civil liberties advocates, led by the Berkeley Institute for Data Science (BIDS), UC Berkeley Journalism's Investigative Reporting Program (IRP) and Stanford University's Big Local News.