arXivDaily arXiv每日学术速递 周一至周五更新

高校专区

Harvard University(哈佛大学)

2026-06-26 至 2026-06-26 共收录 7
2606.27334 2026-06-26 cs.AI 新提交

Language-Based Digital Twins for Elderly Cognitive Assistance

基于语言的数字孪生用于老年人认知辅助

Mohammad Mehdi Hosseini, Mohammad H. Mahoor, Hiroko H. Dodge

机构 * Ritchie School of Engineering and Computer Science, University of Denver(丹佛大学里奇工程与计算机科学学院) Department of Neurology, Massachusetts General Hospital, Harvard Medical School(哈佛医学院麻省总医院神经内科)

AI总结 提出基于大语言模型的数字孪生框架,通过风格测量和上下文元数据模拟老年人对话行为,并引入多条件变分自编码器评估保真度和认知一致性,实现非侵入式认知健康监测。

Comments Accepted and published in the Proceedings of the ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2026). The final published version is available through the ACM Digital Library

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2606.26898 2026-06-26 cs.CV cs.LG 新提交

Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology

基于纤维束成像的示踪组织学纤维束分割合成数据生成

Kyriaki-Margarita Bintsi, Sparsh Makharia, Yaël Balbastre, Joselyn Romero Avila, Julia F. Lehman, Suzanne N. Haber, Anastasia Yendiki

机构 * Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School(马萨诸塞总医院和哈佛医学院 Athinoula A. Martinos 生物医学影像中心) Krea University(Krea 大学) Department of Experimental Psychology, University College London(伦敦大学学院实验心理学系) Universidad Nacional Mayor de San Marcos(国立圣马科斯大学) Department of Pharmacology and Physiology, University of Rochester School of Medicine(罗切斯特大学医学院药理学与生理学系) McLean Hospital(麦克莱恩医院)

AI总结 提出利用离体dMRI纤维束成像作为生成先验合成2D图像块,结合真实背景和域随机化训练U-Net,实现猕猴示踪组织学中纤维束的自动分割,在减少3倍标注数据下达到与现有方法相当的性能。

Comments MICCAI 2026

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2606.26392 2026-06-26 cs.RO 新提交

MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins

MPC-Injection: 将离线运动学RL偏向控制器诱导的行为盆地

Roy Xing, Seyoung Ree, Brian Plancher

机构 * Dartmouth College(达特茅斯学院) Harvard University(哈佛大学)

AI总结 提出MPC-Injection方法,通过将模型预测控制器的轨迹注入回放缓冲区,引导强化学习策略学习期望步态,无需奖励塑形或对抗模仿学习,在仿真和真实机器人上验证有效性。

Comments 22 pages, 10 figures

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2606.26130 2026-06-26 cs.CL cs.AI cs.DL 新提交

Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods

像科学家一样思考?LLM生成研究方法的结构性研究

Francesca Carlon, Brecht Verbeken, Vincent Ginis, Andres Algaba

机构 * Data Analytics Lab, Vrije Universiteit Brussel(布鲁塞尔自由大学数据分析实验室) imec-SMIT, Vrije Universiteit Brussel(布鲁塞尔自由大学imec-SMIT) School of Engineering and Applied Sciences, Harvard University(哈佛大学工程与应用科学学院)

AI总结 通过对比LLM在最小提示下生成的方法建议与论文实际方法,发现LLM在模型选择上存在显著偏差,且方法多样性大幅缩减。

Comments 46 pages, 13 figures, 18 tables

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2606.18640 2026-06-26 cs.LG q-bio.QM 新提交

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes

MetaboNet-Bench:1型糖尿病血糖预测的多模态基准

Nathaniel Jeffries, Miriam Wolff, Sam Royston, Elizabeth Healey, Caleb Mayer, David Klonoff, Michael Snyder, Tao Wang

机构 * Department of Genetics, Stanford University School of Medicine(斯坦福大学医学院遗传学系) Replica Health Boston Children’s Hospital, Harvard Medical School(哈佛医学院波士顿儿童医院) Diabetes Research Institute, Mills-Peninsula Medical Center(米尔斯半岛医学中心糖尿病研究所)

AI总结 针对1型糖尿病血糖预测算法缺乏标准化评估基准的问题,提出MetaboNet-Bench多模态基准,集成血糖、胰岛素和碳水化合物数据,通过多个模型对比验证多模态数据对模型性能的影响。

Comments main content in 10 pages with 5 figures; supplementary section with 11 more pages and 5 more figures. v2: fix figure 4 and 5's misordering

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2602.16220 2026-06-26 cs.LG 版本更新

SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting

SEMixer: 语义增强的MLP-Mixer用于多尺度混合和长期时间序列预测

Xu Zhang, Qitong Wang, Peng Wang, Wei Wang

机构 * Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence Fudan University(上海数据科学 key 实验室,复旦大学计算机科学与人工智能学院) Harvard University(哈佛大学)

AI总结 提出SEMixer模型,通过随机注意力机制和多尺度渐进混合链,有效建模多尺度时间依赖并解决语义鸿沟问题,在10个公开数据集和真实无线网络数据上取得优异性能。

Comments This work is accepted by the proceedings of the ACM Web Conference 2026 (WWW 2026). The code is available at the link https://github.com/Meteor-Stars/SEMixer

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2502.18966 2026-06-26 cs.LG 版本更新

Bayesian Optimization for General Reaction Conditions

通用反应条件的贝叶斯优化

Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alán Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff

机构 * Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich(苏黎世联邦理工学院化学与生物工程学院,化学与应用生物科学系) NCCR Catalysis, Switzerland(瑞士催化联合体) Department of Chemistry, University of Toronto(多伦多大学化学系) Vector Institute, Toronto, Canada(多伦多向量研究所) Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School(哈佛医学院生物化学与分子药理学系) Dana-Farber Cancer Institute, Boston, MA, USA(波士顿马萨诸塞州 Dana-Farber 癌症研究所) School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University(南洋理工大学化学系、化工与生物技术学院) Department of Computer Science, University of Toronto(多伦多大学计算机科学系) Department of Chemical Engineering and Applied Chemistry, University of Toronto(多伦多大学化学工程与应用化学系) Department of Materials Science and Engineering, University of Toronto(多伦多大学材料科学与工程系) Acceleration Consortium, University of Toronto(多伦多大学加速联盟) Canadian Institute for Advanced Research (CIFAR)(加拿大高级研究研究院) Institute of Medical Science, Medical Sciences Building, Toronto, Canada(多伦多大学医学科学研究院,医学科学大楼) NVIDIA, Toronto, Canada(多伦多NVIDIA) Department of Computer Science, Western University(温哥华大学计算机科学系)

AI总结 提出CurryBO框架,通过curried函数的贝叶斯优化实现通用反应条件的高效搜索,在多个基准上显著提升样本效率。

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