Restless multi-armed bandits (RMAB) have demonstrated success in optimizing resource allocation for large beneficiary populations in public health settings. Unfortunately, RMAB models lack flexibility to adapt to evolving public health policy priorities. Concurrently, Large Language Models (LLMs) have emerged as adept automated planners across domains of robotic control and navigation. In this paper, we propose a Decision Language Model (DLM) for RMABs, enabling dynamic fine-tuning of RMAB policies in public health settings using human-language commands. We propose using LLMs as automated planners to (1) interpret human policy preference prompts, (2) propose reward functions as code for a multi-agent RMAB environment, and (3) iterate on the generated reward functions using feedback from grounded RMAB simulations. We illustrate the application of DLM in collaboration with ARMMAN, an India-based non-profit promoting preventative care for pregnant mothers, that currently relies on RMAB policies to optimally allocate health worker calls to low-resource populations. We conduct a technology demonstration in simulation using the Gemini Pro model, showing DLM can dynamically shape policy outcomes using only human prompts as input.

Nvidia CEO Jensen Huang defends DLSS 5 against user backlash, calling critics "completely wrong" about the generative AI graphics technology's function. PCWorld notes the controversy stems from concerns that DLSS 5 applies an "AI skin" over game models rather than true enhancement. Huang clarifies DLSS 5 offers developers controllability at the geometry level, describing it as real-time neural rendering that infuses photorealism into pixels. In just a day, Nvidia's DLSS 5 technology has become the hot button for most of the PC and gaming world. Now Nvidia's chief executive has weighed in, claiming that everyone is "completely wrong" about the technology. At a question-and-answer session at Nvidia's own Game Technology Conference, Nvidia chief executive Jensen Huang said that "as I have explained very carefully, DLSS 5 fuses controllability of the of geometry and textures and everything about the game with generative AI," he said. Huang went on to say of the controversy: "They're completely wrong." Nvidia's DLSS 5 has sparked controversy because it essentially applies a generative AI filter to computer graphics. Nvidia describes DLSS 5 as a "real-time neural rendering model that infuses pixels with photoreal lighting and materials," and a "GPT moment for graphics -- blending hand-crafted rendering with generative AI".

Yellowstone, Yosemite, and Grand Canyon all make the list, but aren't number one. Yosemite National Park came in at number five on the National Parks Service list. Breakthroughs, discoveries, and DIY tips sent six days a week. In 2025, the parks received 323 million recreation visits, according to new data release by the National Parks Service. The data includes visitors to National Parks, National Historic Sites, National Memorials, National Seashores, National Parkways, and other designated public lands.

Support journalism that doesn't flinch . Support journalism that doesn't flinch . Elon Musk leaves a meeting with House Republicans in the basement of the US Capitol building on March 5, 2025 in Washington, DC. Get your news from a source that's not owned and controlled by oligarchs. Tennessee teenagers are suing Elon Musk's company xAI over allegations that its artificial intelligence tool Grok undressed photos of them as minors--the latest challenge against the wealthiest living person's chatbot .

After the rise (and fall) of reservation scalping, DoorDash and a host of apps are fighting to book you a seat at the country's most exclusive restaurants. At The Eighty-Six in Manhattan, exclusivity is the point. The luxe, 11-table steakhouse is the sort of place that lavishes caviar and aged mimolette cheese on its potatoes, and crows that your market-price duck was raised by one Dr. Taylor Swift has reportedly dined there in a Miu Miu skirt. Reservations are a scarce commodity that the restaurant, and New York law forbids you from selling one. "Access is the main asset," wrote food writer Helen Rosner in a recent New Yorker review of The Eighty-Six. "The product is the door, and what a door!

Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as well as environmental characteristics such as temperature and ambient light. These and other factors can significantly impact and decrease HR prediction reliability. In this paper, we show that state-of-the-art HR estimation methods struggle when processing representative data from everyday activities in outdoor environments, likely because they rely on existing datasets that captured controlled conditions. We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours, captured from four wearable sensors, each worn at a different location on the body, totaling 216 hours. Our recordings include accelerometer, temperature, and altitude data, as well as a synchronized Lead I-based electrocardiogram for ground-truth HR references. Participants completed a round trip from Zurich to Jungfraujoch, a tall mountain in Switzerland over the course of one day. The trip included outdoor and indoor activities such as walking, hiking, stair climbing, eating, drinking, and resting at various temperatures and altitudes (up to 3,571 m above sea level) as well as using cars, trains, cable cars, and lifts for transport--all of which impacted participants' physiological dynamics.

After grapefruit-sized hail hit Missouri, more images may help improve severe storm forecasting. A CoCoRaHS volunteer submitted this photo that displays a hand holding three large and uniquely shaped hailstones from June 14, 2023. Breakthroughs, discoveries, and DIY tips sent six days a week. Tuesday March 10th was a particularly punishing day of bad weather for the residents of Kansas City, Missouri. That evening, hailstones as large as grapefruits bombarded homes, businesses, and vehicles in the area, causing widespread damage to the community.

This study introduces the Federated Medical Knowledge Injection (FedMEKI) platform, a new benchmark designed to address the unique challenges of integrating medical knowledge into foundation models under privacy constraints. By leveraging a cross-silo federated learning approach, FedMEKI circumvents the issues associated with centralized data collection, which is often prohibited under health regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the USA. The platform is meticulously designed to handle multi-site, multi-modal, and multi-task medical data, which includes 7 medical modalities, including images, signals, texts, laboratory test results, vital signs, input variables, and output variables. The curated dataset to validate FedMEKI covers 8 medical tasks, including 6 classification tasks (lung opacity detection, COVID-19 detection, electrocardiogram (ECG) abnormal detection, mortality prediction, sepsis protection, and enlarged cardiomediastinum detection) and 2 generation tasks (medical visual question answering (MedVQA) and ECG noise clarification). This comprehensive dataset is partitioned across several clients to facilitate the decentralized training process under 16 benchmark approaches. FedMEKI not only preserves data privacy but also enhances the capability of medical foundation models by allowing them to learn from a broader spectrum of medical knowledge without direct data exposure, thereby setting a new benchmark in the application of foundation models within the healthcare sector.

We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approaches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.

The need to efficiently calculate first-and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expressive power, performance and strong usability. These include source-code transformation (SCT), flexible gradient surgery, efficient in-place array operations, and higher-order derivatives. We implement and demonstrate these ideas in the Tangent software library for Python, the first AD framework for a dynamic language that uses SCT.