目前的行车安全场理论基于“人-车-路”三维构建势能函数,但忽略了复杂的天气因素对行车风险的复合影响,将道路状况(“路”)与天气情况(“环”)所产生的影响简单地归于一类。这低估了天气环境对行车风险的影响程度,并存在对极端天气的...目前的行车安全场理论基于“人-车-路”三维构建势能函数,但忽略了复杂的天气因素对行车风险的复合影响,将道路状况(“路”)与天气情况(“环”)所产生的影响简单地归于一类。这低估了天气环境对行车风险的影响程度,并存在对极端天气的风险解算不够敏感的问题,使得方法的实际应用受到较大局限。因此基于行车安全场(driving safety field,DSF)理论,引入了新的环境场函数,实现“人-车-路-环”的风险因素全覆盖,分别构建行为场(behavior field)、动能场(kinetic energy field)、势能场(potential energy field)和环境场(environment field),以此提出针对恶劣天气下的行车安全场BKPE模型。基于中国道路交通安全数据集对原有行车安全场相关参数进行重新标定。同时分析天气因素对行车安全影响的指数变化特征,构建环境了影响因子,并提出环境场函数。在构建包含环境场的行车安全场模型的基础上,基于Car-100数据集,对具体实例计算其人工势能函数,进行微观分析。通过2个典型事件进行多类型风险的量化分析,同时与原有行车安全场模型进行比较分析,说明原有行车安全场模型对于天气环境形成的风险存在低估。随后基于Bootstrap抽样,6次采样计算所得人工势能函数对实际交通事件的描述平均准确率达到91.7%。最终,基于BKPE模型,提出相应的行车风险控制对策。展开更多
Large language models(LLMs)have emerged as transformative tools with significant potential across healthcare and medicine.In clinical settings,they hold promises for tasks ranging from clinical decision support to pat...Large language models(LLMs)have emerged as transformative tools with significant potential across healthcare and medicine.In clinical settings,they hold promises for tasks ranging from clinical decision support to patient education.Advances in LLM agents further broaden their utility by enabling multimodal processing and multi-task handling in complex clinical workflows.However,evaluating the performance of LLMs in medical contexts presents unique challenges due to the high-risk nature of healthcare and the complexity of medical data.This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine.We contributed 3 main aspects:First,we summarized data sources used in evaluations,including existing medical resources and manually designed clinical questions,offering a basis for LLM evaluation in medical settings.Second,we analyzed key medical task scenarios:closed-ended tasks,open-ended tasks,image processing tasks,and real-world multitask scenarios involving LLM agents,thereby offering guidance for further research across different medical applications.Third,we compared evaluation methods and dimensions,covering both automated metrics and human expert assessments,while addressing traditional accuracy measures alongside agent-specific dimensions,such as tool usage and reasoning capabilities.Finally,we identified key challenges and opportunities in this evolving field,emphasizing the need for continued research and interdisciplinary collaboration between healthcare professionals and computer scientists to ensure safe,ethical,and effective deployment of LLMs in clinical practice.展开更多
Background:Proteins govern most biological functions essential for life,and achieving controllable protein editing has made great advances in probing natural systems,creating therapeutic conjugates,and generating nove...Background:Proteins govern most biological functions essential for life,and achieving controllable protein editing has made great advances in probing natural systems,creating therapeutic conjugates,and generating novel protein constructs.Recently,machine learning-assisted protein editing(MLPE)has shown promise in accelerating optimization cycles and reducing experimental workloads.However,current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions,limiting their interactivity with human feedback.Methods:To fill these gaps,we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning.Our approach comprises 2 stages:In the pretraining stage,contrastive learning aligns protein-biotext representations encoded by 2 large language models(LLMs).Subsequently,during the protein editing stage,the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences.Results:Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains,including enzyme catalytic activity,protein stability,and antibody-specific binding ability.ProtET improves the state-of-the-art results by a large margin,leading to substantial stability improvements of 16.67%and 16.90%.Conclusions:This capability positions ProtET to advance real-world artificial protein editing,potentially addressing unmet academic,industrial,and clinical needs.展开更多
文摘目前的行车安全场理论基于“人-车-路”三维构建势能函数,但忽略了复杂的天气因素对行车风险的复合影响,将道路状况(“路”)与天气情况(“环”)所产生的影响简单地归于一类。这低估了天气环境对行车风险的影响程度,并存在对极端天气的风险解算不够敏感的问题,使得方法的实际应用受到较大局限。因此基于行车安全场(driving safety field,DSF)理论,引入了新的环境场函数,实现“人-车-路-环”的风险因素全覆盖,分别构建行为场(behavior field)、动能场(kinetic energy field)、势能场(potential energy field)和环境场(environment field),以此提出针对恶劣天气下的行车安全场BKPE模型。基于中国道路交通安全数据集对原有行车安全场相关参数进行重新标定。同时分析天气因素对行车安全影响的指数变化特征,构建环境了影响因子,并提出环境场函数。在构建包含环境场的行车安全场模型的基础上,基于Car-100数据集,对具体实例计算其人工势能函数,进行微观分析。通过2个典型事件进行多类型风险的量化分析,同时与原有行车安全场模型进行比较分析,说明原有行车安全场模型对于天气环境形成的风险存在低估。随后基于Bootstrap抽样,6次采样计算所得人工势能函数对实际交通事件的描述平均准确率达到91.7%。最终,基于BKPE模型,提出相应的行车风险控制对策。
基金supported by the Start-up Fund for RAPs under the Strategic Hiring Scheme(Grant No.P0048623)from HKSARthe Global STEM Professorship Scheme(Grant No.P0046113)。
文摘Large language models(LLMs)have emerged as transformative tools with significant potential across healthcare and medicine.In clinical settings,they hold promises for tasks ranging from clinical decision support to patient education.Advances in LLM agents further broaden their utility by enabling multimodal processing and multi-task handling in complex clinical workflows.However,evaluating the performance of LLMs in medical contexts presents unique challenges due to the high-risk nature of healthcare and the complexity of medical data.This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine.We contributed 3 main aspects:First,we summarized data sources used in evaluations,including existing medical resources and manually designed clinical questions,offering a basis for LLM evaluation in medical settings.Second,we analyzed key medical task scenarios:closed-ended tasks,open-ended tasks,image processing tasks,and real-world multitask scenarios involving LLM agents,thereby offering guidance for further research across different medical applications.Third,we compared evaluation methods and dimensions,covering both automated metrics and human expert assessments,while addressing traditional accuracy measures alongside agent-specific dimensions,such as tool usage and reasoning capabilities.Finally,we identified key challenges and opportunities in this evolving field,emphasizing the need for continued research and interdisciplinary collaboration between healthcare professionals and computer scientists to ensure safe,ethical,and effective deployment of LLMs in clinical practice.
基金supported by the National Natural Science Foundation of China under grant nos.62176231 and 82202984the Zhejiang Key R&D Program of China under grant no.2023C03053。
文摘Background:Proteins govern most biological functions essential for life,and achieving controllable protein editing has made great advances in probing natural systems,creating therapeutic conjugates,and generating novel protein constructs.Recently,machine learning-assisted protein editing(MLPE)has shown promise in accelerating optimization cycles and reducing experimental workloads.However,current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions,limiting their interactivity with human feedback.Methods:To fill these gaps,we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning.Our approach comprises 2 stages:In the pretraining stage,contrastive learning aligns protein-biotext representations encoded by 2 large language models(LLMs).Subsequently,during the protein editing stage,the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences.Results:Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains,including enzyme catalytic activity,protein stability,and antibody-specific binding ability.ProtET improves the state-of-the-art results by a large margin,leading to substantial stability improvements of 16.67%and 16.90%.Conclusions:This capability positions ProtET to advance real-world artificial protein editing,potentially addressing unmet academic,industrial,and clinical needs.
