Imperial College London（帝国理工学院）

Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies

利用医学分类法测量和对齐视觉-语言模型中的抽象能力

Ben Schaper, Maxime Di Folco, Bernhard Kainz, Julia A. Schnabel, Cosmin I. Bercea

机构 * 1 School of Computation, Information ； Technology, Technical University of Munich, Germany 2 Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany 3 LTCI, Télécom Paris, Institut Polytechnique de Paris, France 4 Munich Center for Machine Learning (MCML) 5 School of Biomedical Engineering ； Imaging Sciences, King's College London, UK 6 Department of Artificial Intelligence in Biomedical Imaging, FAU Erlangen-Nuremberg, Germany 7 Department of Computing, Imperial College London, UK 纠错

AI总结 本文提出通过医学分类法量化和缓解视觉-语言模型中的抽象错误，引入灾难性抽象错误概念，并通过风险约束阈值和分类法感知微调减少严重错误至2%以下。

Cosmo-FOLD: Fast generation and upscaling of field-level cosmological maps with overlap latent diffusion

Cosmo-FOLD：利用重叠潜在扩散模型快速生成和放大场级宇宙地图

Satvik Mishra, Roberto Trotta, Matteo Viel

机构 * Theoretical and Scientific Data Science, SISSA（SISSA理论与科学数据科学） ； Astroparticle and Gravitational Physics Group, SISSA（SISSA天体粒子与引力物理组） ； INFN – National Institute for Nuclear Physics（国家核物理研究所） ； ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing（高性能计算、大数据和量子计算国家研究中心） ； Astrophysics Group, Physics Department, Blackett Lab, Imperial College London（帝国理工伦敦学院物理系天体物理学组） ； INAF – Osservatorio Astronomico di Trieste（特伦蒂诺天文台） ； IFPU – Institute for Fundamental Physics of the Universe（宇宙基本物理研究所） 纠错

AI总结 Cosmo-FOLD通过重叠潜在扩散模型快速生成和放大宇宙场，实现高效且高精度的宇宙学模拟与推断。

Comments 15 pages, 10 figures

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

人工智能/机器学习在Rubin LSST暗能量科学合作中的机遇

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz, Matthew R. Becker, Biswajit Biswas, Rahul Biswas, Boris Bolliet, Adam S. Bolton, Clecio R. Bom, Raphaël Bonnet-Guerrini, Alexandre Boucaud, Jean-Eric Campagne, Chihway Chang, Aleksandra Ćiprijanović, Johann Cohen-Tanugi, Michael W. Coughlin, John Franklin Crenshaw, Juan C. Cuevas-Tello, Juan de Vicente, Seth W. Digel, Steven Dillmann, Mariano Javier de León Dominguez Romero, Alex Drlica-Wagner, Sydney Erickson, Alexander T. Gagliano, Christos Georgiou, Aritra Ghosh, Matthew Grayling, Kirill A. Grishin, Alan Heavens, Lindsay R. House, Mustapha Ishak, Wassim Kabalan, Arun Kannawadi, François Lanusse, C. Danielle Leonard, Pierre-François Léget, Michelle Lochner, Yao-Yuan Mao, Peter Melchior, Grant Merz, Martin Millon, Anais Möller, Gautham Narayan, Yuuki Omori, Hiranya Peiris, Laurence Perreault-Levasseur, Andrés A. Plazas Malagón, Nesar Ramachandra, Benjamin Remy, Cécile Roucelle, Jaime Ruiz-Zapatero, Stefan Schuldt, Ignacio Sevilla-Noarbe, Ved G. Shah, Tjitske Starkenburg, Stephen Thorp, Laura Toribio San Cipriano, Tilman Tröster, Roberto Trotta, Padma Venkatraman, Amanda Wasserman, Tim White, Justine Zeghal, Tianqing Zhang, Yuanyuan Zhang

机构 * Université Paris Cité, CNRS, CEA, Astroparticule et Cosmologie, F-75013 Paris, France ； Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA ； Leinweber Institute of Theoretical Physics, University of Michigan, Ann Arbor, MI 48109, USA ； Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA ； Cavendish Astrophysics, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK ； Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK ； SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA ； Department of Computer Science, University of Milan, Milan, Italy ； Université Paris Cité, CNRS, Astroparticule et Cosmologie, F-75013 Paris, France ； Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France ； Department of Astronomy ； Astrophysics, University of Chicago, Chicago, IL 60637, USA ； Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA ； NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA ； Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA ； Universit\'e Clermont-Auvergne, CNRS, LPCA, 63000 Clermont-Ferrand, France ； Kavli Institute for Particle Astrophysics ； Cosmology, Stanford University, Stanford, CA 94305, USA ； Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA ； Engineering Faculty, Universidad Autonoma de San Luis Potosi, Zona Universitaria, San Luis Potosi, 78290, Mexico ； Stanford Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305, USA ； Kavli Institute of Cosmological Physics, University of Chicago, Chicago, IL 60637, USA ； The NSF AI Institute for Artificial Intelligence ； Center for Astrophysics Harvard \& Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA ； Department of Physics ； Kavli Institute for Astrophysics ； Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA ； Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science ； Institute of Astronomy ； Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK ； Imperial Centre for Inference ； Cosmology (ICIC), Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK ； Data Science Institute, The University of Chicago, Chicago, IL 60615, USA ； Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA ； Department of Physics, Duke University, Durham, NC 27708, USA ； Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, F-91191 Gif-sur-Yvette, France ； School of Mathematics, Statistics ； Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom ； Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA ； Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa ； Astronomy, University of Utah, Salt Lake City, UT 84112, USA ； Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA ； Department of Astronomy, University of Illinois Urbana Champaign, 1002 W. Green St., Urbana, IL, 61801, USA ； Institute for Particle Physics ； Astrophysics, ETH Zürich, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland ； Swinburne University of Technology, Hawthorn, Victoria 3122, Australia ； Ciela - Montr\'eal Institute for Astrophysical Data Analysis ； Mila - Quebec Artificial Intelligence Institute, Montréal, QC H2S 3H1, Canada ； Advanced Research Computing Centre, University College London, 90 High Holborn, London WC1V 6LJ, UK ； Finnish Centre for Astronomy with ESO (FINCA), University of Turku, FI-20014 Turku, Finland ； Department of Physics, P.O. Box 64, University of Helsinki, FI-00014 Helsinki, Finland ； Astronomy, Northwestern University, Evanston, IL, USA ； Center for Interdisciplinary Exploration ； Research in Astrophysics, Northwestern University, Evanston, IL, USA ； Scientific Data Science, International School for Advanced Study, Via Bonomea 265, I-34136 Trieste, Italy ； Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA ； PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA ； NSF NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA 纠错

AI总结 本文探讨了AI/ML在LSST暗能量科学合作中的应用机遇，强调了大规模贝叶斯推断、物理指导方法和主动学习等关键方法学优先事项，并讨论了新兴技术在重塑工作流程中的潜力。

Comments 84 pages. This is v1.0 of the DESC's white paper on AI/ML, a collaboration document that is being made public but which is not planned for submission to a journal

PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection

PPGFlowECG: 基于跨模态编码的潜在修正流用于PPG引导的ECG生成和心血管疾病检测

Xiaocheng Fang, Jiarui Jin, Haoyu Wang, Che Liu, Jieyi Cai, Yujie Xiao, Guangkun Nie, Bo Liu, Shun Huang, Hongyan Li, Shenda Hong

机构 * National Institute of Health Data Science, Peking University, China（北京大学国家健康数据科学研究院） ； School of Intelligence Science and Technology, Peking University, China（北京大学智能科学与技术学院） ； Data Science Institute, Imperial College London, UK（伦敦帝国理工学院数据科学研究院） ； University of Chinese Academy of Sciences, China（中国科学院大学） 纠错

AI总结 PPGFlowECG通过跨模态编码和潜在修正流，实现PPG引导的ECG生成，提升心血管疾病检测的可扩展性和可靠性。

Impartial Games: A Challenge for Reinforcement Learning

impartial games: 一种对强化学习的挑战

Bei Zhou, Søren Riis

机构 * Imperial College London（帝国理工学院伦敦分校） ； Queen Mary University of London（女王玛丽大学） 纠错

AI总结 本文研究了AlphaZero风格强化学习在impartial games中的局限性，指出其在学习抽象数学原理如奇偶性时存在表示瓶颈，需发展新型算法以实现专家级AI。