Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Kehan Long"'
Publikováno v:
Frontiers in Nutrition, Vol 11 (2024)
BackgroundThis study examines the indirect causal relationships between dietary habits and osteoporosis, mediated through liposomes, utilizing a two-sample Mendelian randomization (MR) approach. The research leverages genetic variations as instrument
Externí odkaz:
https://doaj.org/article/e31b6ba9170842ddab4a3e7b0f532146
Publikováno v:
Frontiers in Immunology, Vol 15 (2024)
BackgroundThis study aims to assess the causal relationship between immune cell characteristics and malignant tumors of bone and articular cartilage, focusing on the mediating role of metabolites. Using Mendelian randomization, we evaluated these rel
Externí odkaz:
https://doaj.org/article/d26004692e5244dcafb721b3881c9dc4
Publikováno v:
IEEE Open Journal of Control Systems, Vol 3, Pp 375-388 (2024)
This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. A key challenge in designing controllers with stability guarantees for uncertain
Externí odkaz:
https://doaj.org/article/02e405ca43c3491c98921cc92745dedf
Publikováno v:
Frontiers in Genetics, Vol 15 (2024)
Background:Osteomyelitis is a severe bone marrow infection, whose pathogenesis is not yet fully understood. This study aims to explore the causal relationship between immune cell characteristics and osteomyelitis, hoping to provide new insights for t
Externí odkaz:
https://doaj.org/article/13b83d8b6c404a5ca23498e250f3ab9d
Publikováno v:
IEEE Robotics and Automation Letters. 6:4931-4938
Control barrier functions are widely used to enforce safety properties in robot motion planning and control. However, the problem of constructing barrier functions online and synthesizing safe controllers that can deal with the associated uncertainty
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81552c2e4459265f29578921f977dd8d
https://doi.org/10.1109/lra.2021.3070250
https://doi.org/10.1109/lra.2021.3070250
Publikováno v:
2022 International Joint Conference on Neural Networks (IJCNN).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::306aa7b462b191cf785ba7a405a3ed31
https://doi.org/10.1109/ijcnn55064.2022.9892003
https://doi.org/10.1109/ijcnn55064.2022.9892003
This paper considers safe control synthesis for dynamical systems with either probabilistic or worst-case uncertainty in both the dynamics model and the safety constraints. We formulate novel probabilistic and robust (worst-case) control Lyapunov fun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc79863e77e57ac9999035ad4054aceb
http://arxiv.org/abs/2202.09557
http://arxiv.org/abs/2202.09557
Autor:
Hyung-Jin Yoon, Aditya Gahlawat, Donghwan Lee, Huaiyu Chen, Naira Hovakimyan, Heling Zhang, Kehan Long
We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11addb292188b2c907819135d064bc57
http://arxiv.org/abs/1812.05256
http://arxiv.org/abs/1812.05256