Surgeons exhibit distinct operating styles shaped by training, experience, and motor behavior-yet most surgical AI systems overlook this personalization signal. We propose a novel agentic modeling approach for surgeon-specific behavior prediction in robotic surgery, combining a discrete diffusion framework with a vision-language-action (VLA) pipeline. Gesture prediction is framed as a structured sequence denoising task, conditioned on multimodal inputs including surgical video, intent language, and personalized embeddings of surgeon identity and skill. These embeddings are encoded through natural language prompts using third-party language models, allowing the model to retain individual behavioral style without exposing explicit identity. We evaluate our method on the JIGSAWS dataset and demonstrate that it accurately reconstructs gesture sequences while learning meaningful motion fingerprints unique to each surgeon. To quantify the privacy implications of personalization, we perform membership inference attacks and find that more expressive embeddings improve task performance but simultaneously increase susceptibility to identity leakage. These findings demonstrate that while personalized embeddings improve performance, they also increase vulnerability to identity leakage, revealing the importance of balancing personalization with privacy risk in surgical modeling. Code is available at: https://github.com/huixin-zhan-ai/Surgeon_style_fingerprinting.

Senior medical analyst Dr. Marc Siegel discusses advancements in artificial intelligence aimed at predicting an individuals future risk of breast cancer and the increased health risks from cannabis as users age. SMARTER SCREENINGS: The U.S. Food and Drug Administration (FDA) has approved the first artificial intelligence (AI) tool to predict breast cancer risk. NOVA IN ACTION: Flock Safety has released another piece of revolutionary technology aimed at keeping everyday civilians safe from crime. The company's new product, Flock Nova, helps law enforcement with a common but often overlooked problem – a lack of data sharing and access. ROBOT NURSES RISING: The global healthcare system is expected to face a shortage of 4.5 million nurses by 2030, with burnout identified as a leading cause for this deficit.

Document-level text generation tasks are known to be more difficult than sentence-level text generation tasks as they require the understanding of longer context to generate high-quality texts. In this paper, we investigate the adaption of Minimum Bayes Risk (MBR) decoding for document-level text generation tasks. MBR decoding makes use of a utility function to estimate the output with the highest expected utility from a set of candidate outputs. Although MBR decoding is shown to be effective in a wide range of sentence-level text generation tasks, its performance on document-level text generation tasks is limited as many of the utility functions are designed for evaluating the utility of sentences. To this end, we propose MBR-OT, a variant of MBR decoding using Wasserstein distance to compute the utility of a document using a sentence-level utility function. The experimental result shows that the performance of MBR-OT outperforms that of the standard MBR in document-level machine translation, text simplification, and dense image captioning tasks. Our code is available at https://github.com/jinnaiyuu/mbr-optimal-transport

We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power (r2) and minimize pipeline complexity. Biological domain knowledge is integrated at multiple stages, including the use of nine inheritance encoding strategies to model deviations from additivity, a custom linkage disequilibrium pruning node that minimizes redundancy among features, and a dynamic variant recommendation system that prioritizes informative candidates for pipeline inclusion. We evaluate StarBASE-GP on a cohort of Rattus norvegicus (brown rat) to identify variants associated with body mass index, benchmarking its performance against a random baseline and a biologically naive version of the tool. StarBASE-GP consistently evolves Pareto fronts with superior performance, yielding higher accuracy in identifying both ground truth and novel quantitative trait loci, highlighting relevant targets for future validation. By incorporating evolutionary search and relevant biological theory into a flexible automated machine learning framework, StarBASE-GP demonstrates robust potential for advancing variant discovery in complex traits.

Waymo, the autonomous ride-hailing company that launched its services in Los Angeles late last year, is recalling more than 1,200 vehicles due to a software defect, the National Highway Traffic Safety Assn. said Wednesday. The recall comes after a series of minor crashes with gates, chains and other obstacles in the road that did not result in any injuries, the Mountain View, Calif.-based company said in a filing with the NHTSA. The recall applies to 1,212 driverless vehicles operating on Waymo's fifth-generation automated driving software. Waymo released a software update to resolve the issue, and that update has already been rolled out in all affected vehicles, the recall notice said. The company operates more than 1,500 vehicles across Los Angeles, San Francisco, Phoenix and Austin.

Grindr, the dating app that caters to gay men, cannot be held responsible for the rape of a 15-year-old boy who the company matched with sexual predators, the U.S. 9th Circuit Court of Appeals ruled this week; it is the latest teens-versus-tech spat in a fight over internet immunity experts say could soon come before the U.S. Supreme Court. The appellate court's ruling upheld a 2023 decision by U.S. District Judge Otis D. Wright II of the Central District of California, who dismissed the suit, saying Grindr was shielded by broad immunity protections passed almost a decade before the plaintiff was born. In a series of events Wright called "alarming and tragic," a closeted Nova Scotia teen downloaded the LGBTQ hookup app in an attempt to meet other gay kids in his rural Canadian town. Instead, over the course of four days, he was assaulted by four adult men, including a man who picked him up after the teen sent him pictures from his high school cafeteria. LGBTQ social networking platform Grindr last year told its all-remote staff they had to return to the office or lose their jobs.

The field of text privatization often leverages the notion of $\textit{Differential Privacy}$ (DP) to provide formal guarantees in the rewriting or obfuscation of sensitive textual data. A common and nearly ubiquitous form of DP application necessitates the addition of calibrated noise to vector representations of text, either at the data- or model-level, which is governed by the privacy parameter $\varepsilon$. However, noise addition almost undoubtedly leads to considerable utility loss, thereby highlighting one major drawback of DP in NLP. In this work, we introduce a new sentence infilling privatization technique, and we use this method to explore the effect of noise in DP text rewriting. We empirically demonstrate that non-DP privatization techniques excel in utility preservation and can find an acceptable empirical privacy-utility trade-off, yet cannot outperform DP methods in empirical privacy protections. Our results highlight the significant impact of noise in current DP rewriting mechanisms, leading to a discussion of the merits and challenges of DP in NLP, as well as the opportunities that non-DP methods present.

Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical snippets while processing thousands of irrelevant tokens. We introduce LongProc (Long Procedural Generation), a new benchmark that requires both the integration of highly dispersed information and long-form generation. LongProc consists of six diverse procedural generation tasks, such as extracting structured information from HTML pages into a TSV format and executing complex search procedures to create travel plans. These tasks challenge LCLMs by testing their ability to follow detailed procedural instructions, synthesize and reason over dispersed information, and generate structured, long-form outputs (up to 8K tokens). Furthermore, as these tasks adhere to deterministic procedures and yield structured outputs, they enable reliable rule-based evaluation. We evaluate 17 LCLMs on LongProc across three difficulty levels, with maximum numbers of output tokens set at 500, 2K, and 8K. Notably, while all tested models claim a context window size above 32K tokens, open-weight models typically falter on 2K-token tasks, and closed-source models like GPT-4o show significant degradation on 8K-token tasks. Further analysis reveals that LCLMs struggle to maintain long-range coherence in long-form generations. These findings highlight critical limitations in current LCLMs and suggest substantial room for improvement. Data and code available at: https://princeton-pli.github.io/LongProc

As urbanization and climate change progress, urban heat becomes a priority for climate adaptation efforts. High temperatures concentrated in urban heat can drive increased risk of heat-related death and illness as well as increased energy demand for cooling. However, estimating the effects of urban heat is an ongoing field of research typically burdened by an imprecise description of the built environment, significant computational cost, and a lack of high-resolution estimates of the impacts of climate change. Here, we present open-source, computationally efficient machine learning methods that can improve the accuracy of urban temperature estimates when compared to historical reanalysis data. These models are applied to residential buildings in Los Angeles, and we compare the energy benefits of heat mitigation strategies to the impacts of climate change. We find that cooling demand is likely to increase substantially through midcentury, but engineered high-albedo surfaces could lessen this increase by more than 50%. The corresponding increase in heating demand complicates this narrative, but total annual energy use from combined heating and cooling with electric heat pumps in the Los Angeles urban climate is shown to benefit from the engineered cooling strategies under both current and future climates.

Benjamin Valdez, a rideshare driver with Uber and Lyft in the Los Angeles area, used to drive seven days a week when the gig was more lucrative--but he says he makes far less per ride these days. When Valdez started driving, around nine years ago, he told me that he could earn anywhere from 60 to 85 to drive from West Hollywood to downtown Los Angeles at peak surge, a roughly 6-to-10-mile trip depending on the specific route. Now, if "the stars align," he can earn between 25 and 35 for the same trip. "It's gotten harder and harder to make money," he said. In recent years, rideshare drivers like Valdez have experienced shrinking incomes as the companies continue to increase their cut from each ride.