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高校专区

Johns Hopkins University(约翰斯·霍普金斯大学)

2026-03-26 至 2026-03-26 共收录 6
2509.13767 2026-03-26 cs.CV

VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI

VocSegMRI:多模态学习在实时MRI中的精确声带段分割

Daiqi Liu, Johannes Enk, Maureen Stone, Fangxu Xing, Tomás Arias-Vergara, Jerry L. Prince, Jana Hutter, Jonghye Woo, Andreas Maier, Paula Andrea Pérez-Toro

机构 * Friedrich-Alexander-Universität Erlangen-Nürnberg(弗里德里希-亚历山大-埃朗根-纽伦堡大学) University of Maryland School of Dentistry(马里兰大学牙科学院) Harvard Medical School/Massachusetts General Hospital(哈佛医学院/麻省总医院) Universidad de Antioquia UdeA(安蒂奥基亚大学 UdeA) Johns Hopkins University(约翰霍普金斯大学) Leibniz University Hannover(汉诺威莱布尼茨大学)

AI总结 本文提出VocSegMRI,通过跨注意力融合和对比学习目标,整合视频、音频和语音学输入,实现实时MRI中声带结构的精确分割,实验表明其优于单模态和多模态基线方法。

Comments Preprint submitted to MIDL short paper 2026

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2603.23753 2026-03-26 cs.RO

Task-Space Singularity Avoidance for Control Affine Systems Using Control Barrier Functions

任务空间奇点避免:使用控制屏障函数控制仿射系统

Kimia Forghani, Suraj Raval, Lamar Mair, Axel Krieger, Yancy Diaz-Mercado

机构 * Department of Mechanical Engineering, University of Maryland, College Park(马里兰大学机械工程系,College Park分校) Division of Magnetic Manipulation and Particle Research, Weinberg Medical Physics, Inc.(Weinberg医学物理公司磁操控与粒子研究部) Department of Mechanical Engineering, Johns Hopkins University(约翰霍普金斯大学机械工程系)

AI总结 本文提出利用控制屏障函数避免控制仿射系统中任务空间奇点,通过分析输入输出映射矩阵的特征值识别奇异配置,并构建屏障函数以保持安全距离,实验表明可实现平滑轨迹跟踪并显著降低控制输入尖峰。

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2603.23520 2026-03-26 cs.CL cs.AI

From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLM

从医生专业能力到临床代理:通过轻量级大语言模型保留、标准化和扩展医生的医学专业知识

Chanyong Luo, Jirui Dai, Zhendong Wang, Kui Chen, Jiaxi Yang, Bingjie Lu, Jing Wang, Jiaxin Hao, Bing Li, Ruiyang He, Yiyu Qiao, Chenkai Zhang, Kaiyu Wang, Zhi Liu, Zeyu Zheng, Yan Li, Xiaohong Gu

机构 * the School of Chinese Medicine, the Beijing University of Chinese Medicine, Beijing, China(中国中医药大学中医学院,北京中医药大学,北京,中国) the School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China(南京中医药大学药学院,南京,中国) the Infectious disease department, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China(北京中医药大学东四医院感染科,北京,中国) the Gulou Hospital of Traditional Chinese Medicine of Beijing,Beijing, China(北京中医药大学附属鼓楼中医医院,北京,中国) the Department of Computer Science, Johns Hopkins University, Baltimore, USA(约翰霍普金斯大学计算机科学系,美国马里兰州巴尔的摩市) the Department of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China(北京中医药大学东直门医院教育部,北京,中国) the Department of Pediatrics, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China(中国医学科学院北京协和医院儿科部,北京,中国) the School of Information Engineering, Huzhou University, Huzhou, China(湖州大学信息工程学院,湖州,中国) Research Center for Scientific Data Hub, Zhejiang Lab, Hangzhou, China(浙江省实验室科学数据中心研究中心,杭州,中国) the Frontier Basic Research Center, Zhejiang Lab, Hangzhou, China(浙江省实验室前沿基础研究中心,杭州,中国) the Research Center for High Efficiency Computing Infrastructure, Zhejiang Lab, Hangzhou, China(浙江省实验室高效计算基础设施研究中心,杭州,中国)

AI总结 本文提出Med-Shicheng框架,通过轻量级大语言模型系统学习并转移知名中医的辨证论治理念及案例依赖适应规则,实现医学知识的标准化和规模化应用。

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2512.16917 2026-03-26 cs.AI cs.CL cs.LG

Generative Adversarial Reasoner: Enhancing LLM Reasoning with Adversarial Reinforcement Learning

生成对抗推理器:通过对抗性强化学习增强大语言模型推理

Qihao Liu, Luoxin Ye, Wufei Ma, Yu-Cheng Chou, Alan Yuille

机构 * Johns Hopkins University(约翰霍普金斯大学)

AI总结 本文提出生成对抗推理器,通过对抗性强化学习联合训练LLM推理器和判别器,提升推理质量与准确性。

Comments Camera-ready version

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2505.18047 2026-03-26 cs.CV cs.AI

RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration

RestoreVAR:用于全图像修复的视觉自回归生成

Sudarshan Rajagopalan, Kartik Narayan, Vishal M. Patel

机构 * Johns Hopkins University(约翰霍普金斯大学)

AI总结 RestoreVAR通过视觉自回归模型提升全图像修复性能,实现比扩散模型快10倍的推理速度,并在修复效果和泛化能力上达到新高。

Comments Project page: https://sudraj2002.github.io/restorevarpage/

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2406.01969 2026-03-26 cs.LG

Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training

多维多切片PHATE:通过训练可视化RNN的隐藏动态

Jiancheng Xie, Lou C. Kohler Voinov, Noga Mudrik, Gal Mishne, Adam Charles

机构 * Boston University(波士顿大学) Ecole Polytechnique Fédérale de Lausanne(洛桑联邦理工学院) Johns Hopkins University(约翰霍普金斯大学) University of California San Diego(加州大学圣地亚哥分校)

AI总结 MM-PHATE通过多维分析RNN隐藏状态演变,揭示训练过程中表示几何变化,提供直观方法理解RNN架构与性能关系。

Comments Accepted at TMLR 2026. This version includes additional experiments on bifurcation and warp perturbations, revised figures, and expanded quantitative analysis. Published version: https://openreview.net/forum?id=9Yr4V7iZsq

Journal ref Transactions on Machine Learning Research (TMLR), 2026

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