The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL.
Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models
Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland（瑞士苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems, Tübingen, Germany（德国图宾根智能系统研究所）；ELLIS Institute, Tübingen, Germany（德国图宾根ELLIS研究所）；Stanford School of Medicine, CA, USA（美国斯坦福医学院）
MIEO: encoding clinical data to enhance cardiovascular event prediction
Department of Computer Science, University of Pisa（计算机科学系，比萨大学）；Clinical Physiology Institute, CNR（临床生理研究所，意大利国家研究委员会）；Department of Computer Science and Engineering, University of Bologna（计算机科学与工程系，博洛尼亚大学）
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
Inria（法国国家信息与自动化技术研究所）；Université Côte d’Azur（蔚蓝海岸大学）；PhySense（PhySense公司）；Department of Information and Communication Technologies（信息与通信技术系）；Universitat Pompeu Fabra（庞培法布拉大学）
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Biomedical Informatics（生物医学信息学系）；Department of Neurology（神经病学系）；Translational Data Analytics Institute（转化数据分析研究所）
Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates
Dublin City University（都柏林城市大学）；Insight Research Ireland Centre for Data Analytics（爱尔兰Insight研究中心数据分析中心）；School of Electronic Engineering（电子工程学院）；School of Chemical Sciences（化学科学学院）；School of Computing（计算机学院）；Entwine Centre（Entwine中心）
Multitask Battery Management with Flexible Pretraining
IIIS, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室、车辆与运载学院）；School of Advanced Manufacturing and Robotics, National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学先进制造与机器人学院、大数据分析与应用技术国家工程实验室）
Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration
Computational Bioscience Research Center, King Abdullah University of Science and Technology（国王阿卜杜勒·阿齐兹科技大学计算生物科学研究中心）；Institute for Infocomm Research, A*STAR（信息与通信研究机构，A*STAR）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算机与信息系统学院）
Impute-MACFM: Imputation based on Mask-Aware Flow Matching
Dept. of Graduate Computer Science and Engineering, Yeshiva University（耶鲁大学研究生计算机科学与工程系）；Dept. of Computer and Information Science, University of Massachusetts Dartmouth（马萨诸塞大学达特茅斯分校计算机与信息科学系）；University of Massachusetts Chan Medical School（马萨诸塞大学查普曼医学学校）
本文提出掩码感知条件流匹配框架 Impute-MACFM，通过缺失项轨迹、观测值稳定约束、一致性正则化和投影 ODE 推理，在多种表格基准上实现更鲁棒高效的 SOTA 插补。
Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images
Tianjin University of Technology（天津理工大学）；Peking University（北京大学）；HeartVoice Medical Technology（心语医疗科技）；Capital Medical University（首都医科大学）；Tianjin Medical University（天津医科大学）；Tsinghua University（清华大学）
scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；Guangzhou Laboratory（广州实验室）
A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random
Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam（越南科技大学数学与计算机科学学院）；Vietnam National University Ho Chi Minh City, Vietnam（越南胡志明市国家大学）；School of Information and Communications Technology, Hanoi University of Science and Technology, Ha Noi, Vietnam（河内科学技术大学信息与通信技术学院）；ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems（细胞系统数学分析卓越中心）；School of Mathematical Sciences, Queensland University of Technology, Brisbane City, Australia（昆士兰科技大学数学科学学院）
