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Imperial College London(帝国理工学院)

2026-08-04 至 2026-08-04 共收录 3
2512.04452 2026-08-04 physics.ao-ph cs.AI cs.LG physics.comp-ph physics.flu-dyn 版本更新

NORi: An ML-Augmented Ocean Boundary Layer Parameterization

NORi:一种融合机器学习的海洋边界层参数化方法

Xin Kai Lee, Ali Ramadhan, Andre Souza, Gregory LeClaire Wagner, Simone Silvestri, John Marshall, Raffaele Ferrari

机构 * Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology(麻省理工学院地球、大气与行星科学系) Center for Computational Science and Engineering, Massachusetts Institute of Technology(麻省理工学院计算科学与工程中心) Department of Physics, Imperial College London(伦敦帝国学院物理系) atdepth Aeolus Labs(Aeolus实验室) Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino(托里诺理工学院环境、土地与基础设施工程系)

AI总结 NORi是一种基于物理并结合神经网络的机器学习海洋边界层湍流参数化方法,通过训练大规模涡旋模拟来捕捉边界层底部的混合过程,展示了在不同对流强度、背景层结、旋转和风力作用下的预测和泛化能力。

Comments 59 pages, 20 figures, submitted to Journal of Advances in Modeling Earth Systems (JAMES). This is version 3, updated based on reviews from 3 anonymous reviewers after initial submission to JAMES

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2506.02976 2026-08-04 cs.CV cs.AI 版本更新

Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge

利用深度学习评估视网膜退化:对MARIO挑战的全面分析

Rachid Zeghlache, Ikram Brahim, Pierre-Henri Conze, Mathieu Lamard, Mohammed El Amine Lazouni, Zineb Aziza Elaouaber, Leila Ryma Lazouni, Christopher Nielsen, Ahmad O. Ahsan, Matthias Wilms, Nils D. Forkert, Lovre Antonio Budimir, Ivana Matovinović, Donik Vršnak, Sven Lončarić, Philippe Zhang, Weili Jiang, Yihao Li, Yiding Hao, Markus Frohmann, Patrick Binder, Marcel Huber, Taha Emre, Teresa Finisterra Araújo, Marzieh Oghbaie, Hrvoje Bogunović, Amerens A. Bekkers, Nina M. van Liebergen, Hugo J. Kuijf, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Alberto J. Beltrán-Carrero, Juan J. Gómez-Valverde, Javier Torresano-Rodríquez, Álvaro Caballero-Sastre, María J. Ledesma Carbayo, Yosuke Yamagishi, Yi Ding, Robin Peretzke, Alexandra Ertl, Maximilian Fischer, Jessica Kächele, Sofiane Zehar, Karim Boukli Hacene, Thomas Monfort, Béatrice Cochener, Mostafa El Habib Daho, Anas-Alexis Benyoussef, Gwenolé Quellec

机构 * University of Western Brittany, Brest, France University of Tlemcen, Algeria Ophthalmology Department, CHRU Brest, Brest, France Imperial College London, United Kingdom Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan Evolucare Technologies, France College of Computer Science, Sichuan University, China Medical University of Vienna, Austria TNO, The Hague, The Netherlands Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands Johannes Kepler University Linz, Austria University of Zagreb, Faculty of Electrical Engineering Department of Radiology, University of Calgary, Calgary, AB, Canada Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada Department of Pediatrics, University of Calgary, Calgary, AB, Canada Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada University of Calgary, Calgary, AB, Canada German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany Medical Faculty Heidelberg, Heidelberg University, Germany Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain Ophthalmology Service of the Provincial Ophthalmic Institute, Hospital Universitario Gregorio Marañón, Madrid, Spain University of Edinburgh, Scotland Lung Institute, Faculty of Medicine, Imperial College London, United Kingdom Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom

AI总结 本文通过MARIO挑战展示了深度学习在AMD监测中的应用,验证了AI在检测AMD进展方面的有效性,但尚未实现对未来演变的预测。

Comments MARIO-MICCAI-CHALLENGE 2024

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2509.16577 2026-08-04 cs.LG eess.SP 版本更新

Learned Digital Over-the-Air Computing for Federated Edge Learning

用于联邦边缘学习的学习型数字无线聚合计算

Antonio Tarizzo, Mohammad Kazemi, Deniz Gündüz

机构 * Department of Electrical \& Electronic Engineering, Imperial College London, London, UK

AI总结 该研究针对联邦边缘学习中低信噪比下数字OTA设计性能差的问题,提出联合优化URA码本与AMP解码器的学习型框架,可扩展SNR范围约7dB且泛化性良好。

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