Zobrazeno 1 - 10
of 1 198
pro vyhledávání: '"CHEN Hongyi"'
Autor:
GAN Lu (甘露), XIE Wei (谢薇), LIU Qing (刘青), JIANG Yonghong (江永红), YUAN Zheng (袁铮), XIAO Mengyun (肖孟云), MO Mei (莫梅), YUAN Dan (袁丹), CHEN Hongyi (陈红伊), JIANG Qingxia (江青霞), XU Tengfei (徐腾飞), LIU Qiulin (李秋林)
Publikováno v:
中西医结合护理, Vol 8, Iss 4, Pp 117-120 (2022)
Diabetic foot disease is a common and serious complication for diabetes and is characterized with impaired angiogenesis. Nurses are the primary providers of prevention and early diagnosis of diabetes and its complications. This paper summarized the r
Externí odkaz:
https://doaj.org/article/53538d4046b54a61b82ec5d9eb6d4ae1
We study the effect of curvature on the Parabolic Anderson model by posing it over a Cartan-Hadamard manifold. We first construct a family of noises white in time and colored in space parameterized by a regularity parameter $\alpha$, which we use to
Externí odkaz:
http://arxiv.org/abs/2411.09614
Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on integrating modalities through their complementarity but overlook the
Externí odkaz:
http://arxiv.org/abs/2410.14584
Current robot autonomy struggles to operate beyond the assumed Operational Design Domain (ODD), the specific set of conditions and environments in which the system is designed to function, while the real-world is rife with uncertainties that may lead
Externí odkaz:
http://arxiv.org/abs/2409.03966
Autor:
Chen, Hongyi, Abuduweili, Abulikemu, Agrawal, Aviral, Han, Yunhai, Ravichandar, Harish, Liu, Changliu, Ichnowski, Jeffrey
Learning dexterous manipulation skills presents significant challenges due to complex nonlinear dynamics that underlie the interactions between objects and multi-fingered hands. Koopman operators have emerged as a robust method for modeling such nonl
Externí odkaz:
http://arxiv.org/abs/2407.00548
Autor:
Ding, Jingtao, Liu, Chang, Zheng, Yu, Zhang, Yunke, Yu, Zihan, Li, Ruikun, Chen, Hongyi, Piao, Jinghua, Wang, Huandong, Liu, Jiazhen, Li, Yong
Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intert
Externí odkaz:
http://arxiv.org/abs/2402.16887
Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have achieved state-of-the-art performance in crowd simu
Externí odkaz:
http://arxiv.org/abs/2402.06680
Autor:
Chen, Hongyi, Kaptein, Maurits
In order to achieve unbiased and efficient estimators of causal effects from observational data, covariate selection for confounding adjustment becomes an important task in causal inference. Despite recent advancements in graphical criterion for cons
Externí odkaz:
http://arxiv.org/abs/2305.16908
Autor:
Chen, Hongyi, Kaptein, Maurits
We focus on the extension of bivariate causal learning methods into multivariate problem settings in a systematic manner via a novel framework. It is purposive to augment the scale to which bivariate causal discovery approaches can be applied since c
Externí odkaz:
http://arxiv.org/abs/2305.16904
Recent works have shown that sequence modeling can be effectively used to train reinforcement learning (RL) policies. However, the success of applying existing sequence models to planning, in which we wish to obtain a trajectory of actions to reach s
Externí odkaz:
http://arxiv.org/abs/2303.16189