This paper proposed a new methodology for machine learning in 2-dimensional space (2-D ML) in inline coordinates. It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. Specifically, it can be done with the inline based coordinates in different modifications, including static and dynamic ones. The classification and regression algorithms based on these inline coordinates were introduced. A successful case study based on a benchmark data demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning as a promising ML methodology. It has advantages of abilities to involve actively the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models.
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Princeton University（普林斯顿大学）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing, A*STAR（A*STAR高性能计算研究院）
Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)
Institute for Computational and Mathematical Engineering, Stanford University（计算与数学工程研究所，斯坦福大学）；Department of Mechanical Engineering, Stanford University（机械工程系，斯坦福大学）；National Renewable Energy Laboratory（国家可再生能源实验室）；Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder（安与H.J.斯迈德航空航天工程科学系，科罗拉多大学，博尔德）
Reaction coordinate flows for model reduction of molecular kinetics
Shanghai Jiao Tong University（上海交通大学）；Tongji University（同济大学）；Freie Universität Berlin（柏林自由大学）；Rice University（莱斯大学）；Microsoft Research AI4Science（微软研究院AI4Science）
MountainLion: A Multi-Modal LLM-Based Agent System for Interpretable and Adaptive Financial Trading
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Sichuan University（四川大学）；Northwestern University（西北大学）；Indiana University（印第安纳大学）；Emory University（埃默里大学）；Nankai University（南开大学）；MountainLion Research（MountainLion研究机构）；Xi’an University of Electronic Science and Technology（西安电子科技大学）；Kyoto University（京都大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Nanjing university（南京大学）；Tsinghua University（清华大学）；University of Toronto（多伦多大学）
A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer
Department of Data Science, Dana-Farber Cancer Institute（数据科学部，达纳-法伯癌症研究所）；Department of Biostatistics, Harvard T.H. Chan School of Public Health（生物统计学部，哈佛T.H. 潘克学校公共卫生学院）
