University of Pennsylvania（宾夕法尼亚大学）

Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning

Dysco：动态子空间增强以减轻联邦学习中的LoRA干扰

Haobo Zhang, Jiankun Wang, Suraj Rajendran, Weishen Pan, Lam Tsoi, Yong Chen, Fei Wang, Jiayu Zhou

机构 * University of Michigan（密歇根大学） ； Cornell University（康奈尔大学） ； University of Pennsylvania（宾夕法尼亚大学） 纠错

AI总结 研究针对联邦学习中异构客户端使LoRA聚合不稳定的问题，提出动态子空间增强方法Dysco，通过联邦动态分配特定客户端LoRA子空间，经实验验证其能减少干扰、降低训练损失、提升算法性能且开销小。

Comments 33 pages, 10 figures, 11 tables

From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphology

超越地表温度：可解释的空间机器学习揭示城市形态对以人类为中心的热压力的影响

Yuan Wang, Shengao Yi, Xiaojiang Li, Pengyuan Liu, Zhiwei Yang, Ronita Bardhan, Rudi Stouffs

机构 * Department of Architecture, National University of Singapore, Singapore 117566, Singapore ； Cambridge Centre for Advanced Research ； Sustainable Design Group, Department of Architecture, University of Cambridge, Cambridge, United Kingdom ； Department of City ； Regional Planning, University of Pennsylvania, Philadelphia, PA 19104, USA ； Urban Analytics Subject Group, Urban Studies \& Social Policy Division, University of Glasgow ； Laboratory for Earth Surface Processes, Ministry of Education, College of Urban ； Environmental Sciences, Peking University, Beijing 100871, China 纠错

AI总结 本文通过比较地表温度与通用热气候指数，揭示城市形态对人类热压力的影响，采用可解释的机器学习方法分析两者在空间分布和机制上的差异。

Comments Accepted manuscript. The final published version is available at https://doi.org/10.1016/j.scs.2026.107659