In this paper, we propose a spectral method for deriving functions that are jointly smooth on multiple observed manifolds. This allows us to register measurements of the same phenomenon by heterogeneous sensors, and to reject sensor-specific noise. Our method is unsupervised and primarily consists of two steps. First, using kernels, we obtain a subspace spanning smooth functions on each separate manifold. Then, we apply a spectral method to the obtained subspaces and discover functions that are jointly smooth on all manifolds. We show analytically that our method is guaranteed to provide a set of orthogonal functions that are as jointly smooth as possible, ordered by increasing Dirichlet energy from the smoothest to the least smooth. In addition, we show that the extracted functions can be efficiently extended to unseen data using the Nyström method. We demonstrate the proposed method on both simulated and real measured data and compare the results to nonlinear variants of the seminal Canonical Correlation Analysis (CCA). Particularly, we show superior results for sleep stage identification. In addition, we show how the proposed method can be leveraged for finding minimal realizations of parameter spaces of nonlinear dynamical systems.
Revealing Multimodal Causality with Large Language Models
University of Technology Sydney（技术大学悉尼大学）；Tongji University（同济大学）；University of Melbourne（墨尔本大学）；University of Sydney（悉尼大学）；Macquarie University（麦考瑞大学）
Multimodal Foundation Models for Material Property Prediction and Discovery
Massachusetts Institute of Technology（麻省理工学院）；John Hopkins University Applied Physics Laboratory（约翰霍普金斯大学应用物理实验室）；University of Chicago（芝加哥大学）；Technical University of Denmark（丹麦技术大学）
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；National Institute of Health Data Science, and Institute for Artificial Intelligence, Peking University（北京大学健康医疗大数据国家研究院、人工智能研究院）
AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program
Yale University（耶鲁大学）
本研究基于All of Us研究计划数据，开发AI驱动的多模态中介框架，通过变分自编码器推导潜在表征，发现心理社会脆弱性是连接社会经济劣势与心脏代谢共病的关键中介通路，为探究健康相关复杂关系提供了新方法。
KARAMEL: Knowledge-Aware Ranking for Multimodal Entity Linking in the Arts via Heterogeneous Graphs
University of Bari Aldo Moro（巴里大学阿尔多·莫罗分校）；Hasso Plattner Institute（哈索·普拉特纳研究所）；University of Potsdam（波茨坦大学）；ZBW - Leibniz Information Centre for Economics（ZBW - 莱布尼茨经济信息中心）；University of Kiel（基尔大学）
Earth Science Foundation Models: From Perception to Reasoning and Discovery
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（数据科学与人工智能系，香港理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment
China University of Geosciences（中国地质大学）；Walmart Inc.（沃尔玛公司）；Fordham University（福特汉姆大学）
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）
L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworks
Department of Chemistry, University of Liverpool（利兹大学化学系）；Leverhulme Research Centre for Functional Materials Design, University of Liverpool（利兹大学功能性材料设计研究所以）；Department of Computer Science, University of Stanford（斯坦福大学计算机科学系）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design
Department of Applied Computer Science, The University of Winnipeg（应用计算机科学系，温尼伯大学）；Department of Mechanical Engineering, University of Manitoba（机械工程系，曼尼托巴大学）
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Biomedical Science, Hunan University（湖南大学生物医学科学学院）
Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
School of Computer Science, University of Sheffield, Sheffield, UK（计算机科学学院，谢菲尔德大学）；Centre for Machine Intelligence, University of Sheffield, Sheffield, UK（智能机器研究中心，谢菲尔德大学）；School of Chemical, Materials and Biological Engineering, University of Sheffield, Sheffield, UK（化学、材料与生物工程学院，谢菲尔德大学）；Henry Royce Institute, Royce Discovery Centre, University of Sheffield, Sheffield, UK（亨利·罗伊奇研究所，谢菲尔德大学）；Materials Nexus Ltd., Salisbury House, Cambridge, UK（材料 nexus 有限公司，剑桥，英国）；School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, UK（数学与物理科学学院，谢菲尔德大学）
Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Leibniz Institute for Prevention Research and Epidemiology – BIPS（莱比锡预防研究与流行病学研究所 – BIPS）；Faculty of Mathematics and Computer Science, University of Bremen（不莱梅大学数学与计算机科学学院）；Faculty of Physics and Electrical Engineering, University of Bremen（不莱梅大学物理与电气工程学院）
