Psychometrics of an Elo-based large-scale online learning system by Hanke Vermeiren, Joost Kruis, Maria Bolsinova, Han L.J. van der Maas, Abe D. Hofman

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FedQP: Large-Scale Private and Flexible Federated Query Processing by Hussain M. J. Almohri, Layne T. Watson

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An explainable Machine Learning model for Large-Scale Travelling Ionospheric Disturbances forecasting by Ventriglia Vincenzo, Guerra Marco, Cesaroni Claudio, Spogli Luca, Altadill David, Segarra Antoni, Galkin Ivan, Barta Veronika, Verhulst Tobias G.W., de Paula Víctor, Navas-Portella Víctor, Berényi Kitti A., Belehaki Anna

“…In this work, we present a machine learning model able to forecast the occurrence of LSTIDs over the European continent up to three hours in advance. …”
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Large-scale foundation models and generative AI for BigData neuroscience by Ran Wang, Zhe Sage Chen

“…With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. …”
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A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models by Amanda Triplett, Andrew Bennett, Laura E. Condon, Peter Melchior, Reed M. Maxwell

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Indoor environmental quality and learning outcomes: protocol on large-scale sensor deployment in schools by Maartje Willeboordse, Juan Palacios Temprano, Piet Eichholtz, Nils Kok

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Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms by Aleksander M. Ivanov, Artem V. Gorbarenko, Maria B. Kireeva, Elena S. Povalishnikova

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Towards expert-level autonomous carotid ultrasonography with large-scale learning-based robotic system by Haojun Jiang, Andrew Zhao, Qian Yang, Xiangjie Yan, Teng Wang, Yulin Wang, Ning Jia, Jiangshan Wang, Guokun Wu, Yang Yue, Shaqi Luo, Huanqian Wang, Ling Ren, Siming Chen, Pan Liu, Guocai Yao, Wenming Yang, Shiji Song, Xiang Li, Kunlun He, Gao Huang

“…Here, we present UltraBot, a fully learning-based autonomous carotid ultrasound robot, achieving human-expert-level performance through four innovations: (1) A unified imitation learning framework for acquiring anatomical knowledge and scanning operational skills; (2) A large-scale expert demonstration dataset (247,000 samples, 100 × scale-up), enabling embodied foundation models with strong generalization; (3) A comprehensive scanning protocol ensuring full anatomical coverage for biometric measurement and plaque screening; (4) The clinical-oriented validation showing over 90% success rates, expert-level accuracy, up to 5.5 × higher reproducibility across diverse unseen populations. …”
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Multisensor Diffusion-Driven Optical Image Translation for Large-Scale Applications by Joao Gabriel Vinholi, Marco Chini, Anis Amziane, Renato Machado, Danilo Silva, Patrick Matgen

“…A thorough image quality assessment and comparisons with the standard DDIM framework and five other leading methods are presented. We reach a mean learned perceptual image patch similarity of 0.1884 and a Fréchet Inception Distance of 45.64, expressively outperforming all compared methods, including DDIM, ShuffleMixer, and SwinIR. …”
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CUGUV: A Benchmark Dataset for Promoting Large-Scale Urban Village Mapping with Deep Learning Models by Ziyi Wang, Qiao Sun, Xiao Zhang, Zekun Hu, Jiaoqi Chen, Cheng Zhong, Hui Li

“…This dataset can serve as a foundation for evaluating and improving the robustness and transferability of models. Subsequently, we present an innovative framework that effectively integrates and learns from multiple data sources to better address the cross-city UV mapping task. …”
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Multi-Variable Transformer-Based Meta-Learning for Few-Shot Fault Diagnosis of Large-Scale Systems by Weiyang Li, Yixin Nie, Fan Yang

“…Fault diagnosis in large-scale systems presents significant challenges due to the complexity and high dimensionality of data, as well as the scarcity of labeled fault data, which are hard to obtain during the practical operation process. …”
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Developing deep learning-based large-scale organic reaction classification model via sigma-profiles by Wenlong Wang, Chenyang Xu, Jian Du, Lei Zhang

“…In this work, a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and autoencoder. …”
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Advancing coal fire detection model for large-scale areas based on RS indices and machine learning by Jinglong Liu, Feng Zhao, Yunjia Wang, Yanan Wang, Sen Du, Libo Dang, Jordi J. Mallorqui

“…Using these TAIs alongside other remote sensing (RS) indices, a coal fire detection model (CFDM) was developed and trained using the AutoGluon machine learning (ML) framework. The model is capable of identifying large-scale coal fire target areas without relying on deformation associated with coal fires. …”
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Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data by Bas Peters, Eldad Haber, Keegan Lensink

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Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme–ligand complexes by O. Vavra, J. Tyzack, F. Haddadi, J. Stourac, J. Damborsky, S. Mazurenko, J. M. Thornton, D. Bednar

“…However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. …”
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Large-scale inventory in natural forests with mobile LiDAR point clouds by Jinyuan Shao, Yi-Chun Lin, Cameron Wingren, Sang-Yeop Shin, William Fei, Joshua Carpenter, Ayman Habib, Songlin Fei

“…Terrestrial Laser Scanning (TLS) has been used for individual tree level inventory at plot scale However, due to the inflexibility of TLS and the complex scene of natural forests, it is still challenging to localize and measure every tree at large scale. In this paper, we present a framework to conduct large-scale natural forest inventory at the individual tree level by taking advantage of deep learning models and Mobile Laser Scanning (MLS) systems. …”
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Fusing Sentinel-1 and Sentinel-2 Data With Machine Learning for Large-Scale Detection of Coastal Erosion and Accretion by Hairuo Yu

“…It is essential to analyze changes along the coast accurately to manage and develop sensitive shoreline areas in a way that lasts. This work presents a comprehensive multisensor data fusion method for detecting coastal erosion and accretion on China’s dynamic southeastern coast on a large scale and in an automated manner. …”
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Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification by Yongzhao Yan, Huazhen Cao, Boyang Zhang, Wenjun Ni, Bo Wang, Xiaoping Ma

“…The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. …”
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Large scale paired antibody language models. by Henry Kenlay, Frédéric A Dreyer, Aleksandr Kovaltsuk, Dom Miketa, Douglas Pires, Charlotte M Deane

“…This advancement marks a significant leap forward in leveraging machine learning, large scale data sets and high-performance computing for enhancing antibody design for therapeutic development.…”
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Multiphase Identification Through Automatic Classification from Large-Scale Nanoindentation Mapping Compared to an EBSD-Machine Learning Approach by Carl Slater, Bharath Bandi, Pedram Dastur, Claire Davis

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