Despite enormous efforts over the last decades to establish the relationship between concrete proportioning and strength, a robust knowledge-based model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, data-driven machine learning (ML) methods offer a new solution to this problem. Although this approach is promising for handling the complex, non-linear, non-additive relationship between concrete mixture proportions and strength, a major limitation of ML lies in the fact that large datasets are needed for model training. This is a concern as reliable, consistent strength data is rather limited, especially for realistic industrial concretes. Here, based on the analysis of a large dataset (>10,000 observations) of measured compressive strengths from industrially-produced concretes, we compare the ability of select ML algorithms to "learn" how to reliably predict concrete strength as a function of the size of the dataset. Based on these results, we discuss the competition between how accurate a given model can eventually be (when trained on a large dataset) and how much data is actually required to train this model.
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；The Royal Brompton & Harefield NHS Foundation Trust（皇家布朗普顿与哈菲尔德国民保健服务基金会信托）；University College London（伦敦大学学院）
Learning from Neighbors with PHIBP: Predicting Infectious Disease Dynamics in Data-Sparse Environments
Department of Statistics and Actuarial Science, HKU（香港大学统计与精算科学系）；Department of ISOM, HKUST（香港科技大学工业系统与管理系）；The Graduate School of AI, KAIST（韩国科学技术院人工智能研究生院）
Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy
Arizona State University（亚利桑那州立大学）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Southeast University（东南大学）；Children’s Hospital Los Angeles（洛杉矶儿童医院）；Mayo Clinic Arizona（梅奥诊所亚利桑那分院）；University of Southern California（南加州大学）
生物标志物辅助诊断和干预阿尔茨海默病（AD）可能是预防突破的关键。AD的标志之一是tau斑块在人脑中的积累。然而，目前检测tau病理学的方法要么是侵入性的（腰椎穿刺），要么是相当昂贵且不广泛可用的（Tau PET）。在我们以前的工作中，基于结构MRI的海马多变量形态统计学（MMS）显示出优越的性能，作为临床前AD的有效神经退行性生物标志物，基于斑块分析的表面Correntropy-induced Sparse coding and max-pooling（PASCS-MP）具有出色的能力，能够生成具有强大统计能力的低维表示，用于脑淀粉样蛋白预测。在这项工作中，我们将这个框架与岭回归模型一起应用于分别预测Braak 12和Braak 34脑区的Tau沉积。我们评估了我们的框架925个科目从阿尔茨海默病神经影像学倡议（ADNI）。每名受试者都有一对由PET图像和MRI扫描组成的图像，它们是在大约相同的时间收集的。实验结果表明，从我们的MMS和PASCS-MP的表示有更强的预测能力和他们的预测Braak 12和Braak 34更接近真实值相比，从其他方法，如海马表面积和体积，形状形态特征的基础上，球谐函数（SPHARM）。
Less can be more for predicting properties with large language models
Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学）；Intel labs（英特尔实验室）；Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena)（耶拿亥姆霍兹能源应用聚合物研究所）
Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations
Southeast University（东南大学）；Queen Mary University of London（伦敦玛丽女王大学）；Università di Catania and INFN（卡塔尼亚大学及国家核物理研究所）；Complexity Science Hub Vienna（维也纳复杂性科学中心）；Fudan University（复旦大学）
High correlated variables creator machine: Prediction of the compressive strength of concrete
Institute of Structural Mechanics, Bauhaus-University Weimar（魏玛包豪斯大学结构力学研究所）；School of Civil Engineering, Iran University of Science and Technology（伊朗科技大学土木工程学院）；Ale Taha Institute of Higher Education（阿勒塔哈高等教育学院）；Department of Geotechnical Engineering, Tongji University（同济大学岩土工程系）；School of Civil Engineering, Khajeh Nasir Toosi University of Technology（哈杰·纳赛尔·图西工业大学土木工程学院）
Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA（机械与工业工程系，东北大学，波士顿，马萨诸塞州，02115，美国）；School of Mechanical Engineering, University of Leeds, Leeds, UK（机械工程学院，利兹大学，利兹，英国）
Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
Engineering and Technology Institute Groningen, University of Groningen（Groningen大学工程与技术学院）；Department of Human Movement Sciences, University of Groningen, University Medical Center Groningen（Groningen大学人类运动科学系）
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
National University of Singapore（新加坡国立大学）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；MIT（麻省理工学院）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（蒂宾根人工智能中心）；Miller Institute for Basic Research in Science, University of California, Berkeley（加州大学伯克利分校米勒基础科学研究所）
