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University of Oxford(牛津大学)

2026-02-27 至 2026-02-27 共收录 5
2602.22731 2026-02-27 cs.RO cs.CV

Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring

Sapling-NeRF: 用于森林生态监测的地理定位幼树重建

Miguel Ángel Muñoz-Bañón, Nived Chebrolu, Sruthi M. Krishna Moorthy, Yifu Tao, Fernando Torres, Roberto Salguero-Gómez, Maurice Fallon

机构 * Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, UK(牛津大学机器人研究所、工程科学系、牛津大学、牛津、英国) Group of Automation, Robotics and Computer Vision, University of Alicante, Alicante, Spain(自动化、机器人与计算机视觉小组、阿尔瓦登特大学、阿尔瓦登特、西班牙) Department of Biology, University of Oxford, Oxford, UK(生物学系、牛津大学、牛津、英国)

AI总结 本文提出Sapling-NeRF方法,结合NeRF、LiDAR SLAM和GNSS,实现地理定位的幼树重建,提升森林生态监测的精度和长期数据采集能力。

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2510.00922 2026-02-27 cs.AI

On Discovering Algorithms for Adversarial Imitation Learning

在对抗模仿学习中发现算法

Shashank Reddy Chirra, Jayden Teoh, Praveen Paruchuri, Pradeep Varakantham

机构 * University of Oxford(牛津大学) Singapore Management University(新加坡管理大学) IIIT Hyderabad(海得拉巴印度理工学院)

AI总结 本文提出DAIL算法,通过数据驱动方法发现奖励分配函数,实现更稳定的对抗模仿学习性能。

Comments Accepted at ICLR 2026 (Poster)

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2508.20570 2026-02-27 cs.CV cs.AI

Dyslexify: A Mechanistic Defense Against Typographic Attacks in CLIP

Dyslexify: 一种针对CLIP中印刷攻击的机制性防御

Lorenz Hufe, Constantin Venhoff, Erblina Purelku, Maximilian Dreyer, Sebastian Lapuschkin, Wojciech Samek

机构 * Fraunhofer Heinrich Hertz Institute(弗劳恩霍夫海因里希·赫兹研究所) University of Oxford(牛津大学) Technological University Dublin(都柏林技术大学) Technische Universität Berlin(柏林技术大学)

AI总结 Dyslexify通过消融CLIP中的印刷电路,有效防御印刷攻击,提升性能并保持应用安全性。

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2602.22293 2026-02-27 cs.LG physics.geo-ph

Global River Forecasting with a Topology-Informed AI Foundation Model

基于拓扑信息的AI基础模型实现全球河流预报

Hancheng Ren, Gang Zhao, Shuo Wang, Louise Slater, Dai Yamazaki, Shu Liu, Jingfang Fan, Shibo Cui, Ziming Yu, Shengyu Kang, Depeng Zuo, Dingzhi Peng, Zongxue Xu, Bo Pang

机构 * College of Water Sciences, Beijing Normal University, Beijing, China(北京师范大学水科学学院) School of Geography and the Environment, University of Oxford, Oxford, UK(牛津大学地理与环境学院) Department of Transdisciplinary Science and Engineering, Institute of Science Tokyo, Tokyo, Japan(东京科学研究所跨学科科学与工程系) School of Systems Science, Beijing Normal University, Beijing, China(北京师范大学系统科学学院) Institute of Industrial Science, University of Tokyo, Tokyo, Japan(东京大学工业科学研究所) China Institute of Water Resources and Hydropower Research, Beijing, China(中国水利水电科学研究院) State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China(清华大学水利科学与工程国家重点实验室) School of Artificial Intelligence, Beijing Normal University, Beijing, China(北京师范大学人工智能学院) School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China(武汉大学水利水电学院)

AI总结 GraphRiverCast通过拓扑信息和物理对齐的神经操作符架构,实现了全球河流系统的系统性水动力模拟,无需历史数据即可进行稳健预测。

Comments 26 pages, 5 figures, 3 extended data tables, 3 extended data figures

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2602.22239 2026-02-27 stat.AP cs.LG q-bio.GN

VAE-MS: An Asymmetric Variational Autoencoder for Mutational Signature Extraction

VAE-MS:一种用于突变特征提取的非对称变分自编码器

Ida Egendal, Rasmus Froberg Brøndum, Dan J Woodcock, Christopher Yau, Martin Bøgsted

机构 * Center for Clinical Data Science(临床数据科学中心) Aalborg University(奥胡斯大学) Aalborg University Hospital(奥胡斯大学医院) Nuffield Department of Surgical Sciences(外科科学努尔菲尔德部门) University of Oxford(牛津大学) Nuffield Department of Women’s and Reproductive Health(妇女和生殖健康努尔菲尔德部门)

AI总结 VAE-MS通过结合非对称架构和概率方法,提升了突变特征提取的准确性和泛化能力。

Comments Keywords: Variational Autoencoders, Mutational Signatures

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