We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We present the advantages of using projected gradient descent to enforce MR scanner hardware constraints as compared to using added penalties in the cost function. Further, we use the novel HybLearn scheme to jointly learn and compare our results through a retrospective study on fastMRI validation dataset.
Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage
School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；Faculty of Dentistry, The University of Hong Kong（香港大学牙科学院）；The Prince Philip Dental Hospital（菲利普王子牙科医院）；Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学利滋医学学院）；The Hong Kong University of Science and Technology（香港科学与技术大学）
EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Beijing Key Laboratory of Artificial Intelligence for Education（北京市教育人工智能重点实验室）；Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education（教育部智能技术与教育应用工程研究中心）；College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）
PreAct-Bench: Benchmarking Predictive Monitoring in LLMs
King’s College London（伦敦国王学院）；National University of Singapore（新加坡国立大学）；Southern University of Science and Technology（南方科技大学）；Thomson Reuters Foundational Research（汤姆森路透基础研究）；Imperial College London（伦敦帝国学院）；The Alan Turing Institute（艾伦·图灵研究所）
