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Abstract Risk of bias assessment is a crucial step in evidence synthesis. The traditionally adopted tool, however, is complex, resource-intensive, and unreliable. While prior investigations have focused on whether Large ...

Abstract Efficient management and optimal allocation of urban public sports facilities represents a critical challenge in modernizing public sports service governance. Current approaches to facility utilization assessmen...

Abstract We propose a method that enables large language models (LLMs) to control embodied agents through the generation of control policies that directly map continuous observation vectors to continuous action vectors. ...

Abstract Ultrasound reporting remains a manual, time-consuming process prone to errors and variability. We present UltraReporter, a compact 8B-parameter LLM pipeline that converts real-time spoken cues into structured ul...

Abstract Large language models (LLMs) demonstrate strong reasoning and planning capabilities in static textual contexts, yet they struggle significantly with dynamic decision-making tasks involving spatial elements, such...

Abstract Large language models (LLMs) like ChatGPT and DeepSeek are gaining attention for their potential in medical education. This study aims to evaluate the performance of ChatGPT and DeepSeek in the United States Med...

Abstract Cross-lingual information retrieval limits global exchange of data because of the high diversity in the methods to classify, document and encode medical procedures. Traditional keyword-based or single-language s...

Abstracts Alert fatigue remains a major barrier to the effective deployment of predictive models in emergency care, particularly in the context of rare but critical outcomes such as in-hospital mortality (IHM), which oft...

Abstract Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits the use of Integrated Computational Materials Engi...

Abstract Finding interesting phenomena is the core of scientific discovery, but the notion of interestingness is vaguely defined and heavily reliant on manual judgment. We present InterFeat, an integrative pipeline for a...

Abstract Collaborative filtering and neural recommendation models have advanced substantially over the past decade, yet they frequently struggle to capture the fine-grained structure of user preferences and the rich, mul...