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Jian Sun

5 papers indexed

openalexFrontiers in Neurology2026-07-24

Machine learning-based prediction of cerebral artery territorial infarcts in acute ischemic stroke using non-contrast CT

Yang Guo, S Liu, Sun H, Xiukun Jin, Yan Zhang, Jian Sun, et al.

Background Ischemic stroke is a leading global cause of death and disability. Accurate, rapid infarct territory localization is critical for timely treatment, but ultra-early ischemic changes on non-contrast CT (NCCT) are subtle and highly physician-dependent. Methods This retros…

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arxivcs.RO2026-07-16

AeroAct: Action-Centered World-Action Models for Language-Conditioned Quadrotor Flight

Xinhong Zhang, Qiyuan Zhu, Yubo Huang, Haolin Chen, Runqing Wang, Yuhao Mo, et al.

Language-conditioned quadrotor flight requires a policy to ground semantic goals, anticipate the visual consequences of ego-motion, and output control references that remain smooth and dynamically executable under rapidly changing first-person views. Existing aerial vision-langua…

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arxiveess.SP2026-07-15

Cross-Field Channel Parameter Estimation and Channel Characterization at THz Bands in Indoor Scenarios

Hengtai Chang, Cheng-Xiang Wang, Cunhua Pan, Jian Sun, Bingchang Hua, Yongchao He, et al.

The terahertz (THz) frequency band offers the potential for ultra-high data rate transmission in future wireless communication systems. To extend the transmission distance and enhance spectral efficiency, the deployment of large-scale antenna arrays emerges as a promising solutio…

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arxivcs.ROcs.AI2026-07-12

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning

Tong Nie, Yuewen Mei, Junlin He, Yihong Tang, Jian Sun, Wei Ma

Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data. Although adversarial training offers a feasible solution, existing methods often rely on external scenari…

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arxivcs.CV2026-07-06

Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring

Aditi Naiknaware, Jian Sun, Aminreza Khandan, Shengyang Huang, Sean Dow, Bijan Najafi, et al.

Wound monitoring is a critical yet underserved clinical challenge, where timely identification of severe adverse events (SAEs) such as infection, tissue deterioration, and delayed healing can significantly impact patient outcomes. While vision-language models (VLMs) show strong m…

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