J Wang

Zhen Chen, Xingjian Luo, Jinlin Wu et al.

The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single specific task and scenario, and in practice require the manual combination of different functions, thus limiting the applicability. Thus, an intelligent and versatile surgical assistant is expected to accurately understand the surgeon's intentions and accordingly conduct the specific tasks to support the surgical process. In this work, by leveraging advanced multimodal large language models (MLLMs), we propose a Versatile Surgery Assistant (VS-Assistant) that can accurately understand the surgeon's intention and complete a series of surgical understanding tasks, e.g., surgical scene analysis, surgical instrument detection, and segmentation on demand. Specifically, to achieve superior surgical multimodal understanding, we devise a mixture of projectors (MOP) module to align the surgical MLLM in VS-Assistant to balance the natural and surgical knowledge. Moreover, we devise a surgical Function-Calling Tuning strategy to enable the VS-Assistant to understand surgical intentions, and thus make a series of surgical function calls on demand to meet the needs of the surgeons. Extensive experiments on neurosurgery data confirm that our VS-Assistant can understand the surgeon's intention more accurately than the existing MLLM, resulting in overwhelming performance in textual analysis and visual tasks. Source code and models will be made public.

Bingke Zhu, Xiaoxiao Wang, Minghui Jia et al.

Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited. Stellar flare forecasting can provide additional flare event samples to support research efforts. Despite this potential, no specialized models for stellar flare forecasting have been proposed to date. In this paper, we present extensive experimental evidence demonstrating that both stellar physical properties and historical flare records are valuable inputs for flare forecasting tasks. We then introduce FLARE (Forecasting Light-curve-based Astronomical Records via features Ensemble), the first-of-its-kind large model specifically designed for stellar flare forecasting. FLARE integrates stellar physical properties and historical flare records through a novel Soft Prompt Module and Residual Record Fusion Module. Our experiments on the publicly available Kepler light curve dataset demonstrate that FLARE achieves superior performance compared to other methods across all evaluation metrics. Finally, we validate the forecast capability of our model through a comprehensive case study.