Zobrazeno 1 - 10
of 896
pro vyhledávání: '"Zhou, Zhiyuan"'
Symmetry plays a fundamental role in condensed matter. The unique entanglement between magnetic sublattices and alternating crystal environment in altermagnets provides a unique opportunity for designing magnetic space symmetry. There have been exten
Externí odkaz:
http://arxiv.org/abs/2403.07396
Autor:
Yuan, Zilong, Xu, Zhiming, Li, He, Cheng, Xinle, Tao, Honggeng, Tang, Zechen, Zhou, Zhiyuan, Duan, Wenhui, Xu, Yong
Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic en
Externí odkaz:
http://arxiv.org/abs/2402.04864
Autor:
Guo, Ning, Han, Xudong, Zhong, Shuqiao, Zhou, Zhiyuan, Lin, Jian, Dai, Jian S., Wan, Fang, Song, Chaoyang
This paper presents a novel vision-based proprioception approach for a soft robotic finger capable of estimating and reconstructing tactile interactions in terrestrial and aquatic environments. The key to this system lies in the finger's unique metam
Externí odkaz:
http://arxiv.org/abs/2312.09863
Autor:
Huang, Lin, Liao, Liyang, Qiu, Hongsong, Chen, Xianzhe, Bai, Hua, Han, Lei, Zhou, Yongjian, Su, Yichen, Zhou, Zhiyuan, Pan, Feng, Jin, Biaobing, Song, Cheng
N\'eel spin-orbit torque allows a charge current pulse to efficiently manipulate the N\'eel vector in antiferromagnets, which offers a unique opportunity for ultrahigh density information storage with high speed. However, the reciprocal process of N\
Externí odkaz:
http://arxiv.org/abs/2310.03987
Autor:
Guo, Ning, Han, Xudong, Liu, Xiaobo, Zhong, Shuqiao, Zhou, Zhiyuan, Lin, Jian, Dai, Jiansheng, Wan, Fang, Song, Chaoyang
Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, ma
Externí odkaz:
http://arxiv.org/abs/2308.08510
Reinforcement-learning agents seek to maximize a reward signal through environmental interactions. As humans, our job in the learning process is to design reward functions to express desired behavior and enable the agent to learn such behavior swiftl
Externí odkaz:
http://arxiv.org/abs/2212.03733
To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward function for the environment, arguably the most important knob designers have in interacting with RL agents. Although many reward functions induce the
Externí odkaz:
http://arxiv.org/abs/2205.15400
We study the action generalization ability of deep Q-learning in discrete action spaces. Generalization is crucial for efficient reinforcement learning (RL) because it allows agents to use knowledge learned from past experiences on new tasks. But whi
Externí odkaz:
http://arxiv.org/abs/2205.05588
Autor:
Gong, Wenqi, Zhao, Deming, Chen, Feihong, Zhao, Jian, Bu, Jingjing, Zhou, Zhiyuan, Gou, Shaohua, Xu, Gang
Publikováno v:
In Dyes and Pigments July 2024 226
Autor:
Zhou, Zhiyuan, Tang, Gang, Liu, Yulu, Zhang, Xiaohong, Huang, Yuqi, Wang, Jialu, Yan, Guangyao, Hu, Gaohua, Xiao, Jianhua, Yan, Weiyao, Cao, Yongsong
Publikováno v:
In Chemical Engineering Journal 1 June 2024 489