Zobrazeno 1 - 10
of 3 192
pro vyhledávání: '"Liu, Siyuan"'
Autor:
Xie, Luyuan, Lin, Manqing, Liu, Siyuan, Xu, ChenMing, Luan, Tianyu, Li, Cong, Fang, Yuejian, Shen, Qingni, Wu, Zhonghai
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns. However, existing methods often suffer from client
Externí odkaz:
http://arxiv.org/abs/2407.00462
Autor:
Wu, Yizhang, Li, Yuan, Liu, Yihan, Zhu, Dashuai, Xing, Sicheng, Lambert, Noah, Weisbecker, Hannah, Liu, Siyuan, Davis, Brayden, Zhang, Lin, Wang, Meixiang, Yuan, Gongkai, You, Chris Zhoufan, Zhang, Anran, Duncan, Cate, Xie, Wanrong, Wang, Yihang, Wang, Yong, Kanamurlapudi, Sreya, Evert, Garcia-Guzman, Putcha, Arjun, Dickey, Michael D., Huang, Ke, Bai, Wubin
Bioelectronic implants with soft mechanics, biocompatibility, and excellent electrical performance enable biomedical implants to record electrophysiological signals and execute interventions within internal organs, promising to revolutionize the diag
Externí odkaz:
http://arxiv.org/abs/2406.13956
Recent developments have introduced a groundbreaking form of collinear magnetism known as "altermagnetism". This emerging magnetic phase is characterized by robust time-reversal symmetry breaking, antiparallel magnetic order, and alternating spin-spl
Externí odkaz:
http://arxiv.org/abs/2406.02123
Autor:
Zhang, He, Liu, Chang, Wang, Zun, Wei, Xinran, Liu, Siyuan, Zheng, Nanning, Shao, Bin, Liu, Tie-Yan
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its applicability is limited by insufficient labeled data for training. I
Externí odkaz:
http://arxiv.org/abs/2403.09560
In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph. Specifically, we propose to decompose tasks defined over multiple agents which require
Externí odkaz:
http://arxiv.org/abs/2402.17585
Autor:
Lai, Qingsi, Yao, Lin, Gao, Zhifeng, Liu, Siyuan, Wang, Hongshuai, Lu, Shuqi, He, Di, Wang, Liwei, Wang, Cheng, Ke, Guolin
Crystal structure prediction (CSP) has made significant progress, but most methods focus on unconditional generations of inorganic crystal with limited atoms in the unit cell. This study introduces XtalNet, the first equivariant deep generative model
Externí odkaz:
http://arxiv.org/abs/2401.03862
This paper investigates an important class of information-flow security property called opacity for stochastic control systems. Opacity captures whether a system's secret behavior (a subset of the system's behavior that is considered to be critical)
Externí odkaz:
http://arxiv.org/abs/2401.01972
Autor:
Zhang, Duo, Liu, Xinzijian, Zhang, Xiangyu, Zhang, Chengqian, Cai, Chun, Bi, Hangrui, Du, Yiming, Qin, Xuejian, Huang, Jiameng, Li, Bowen, Shan, Yifan, Zeng, Jinzhe, Zhang, Yuzhi, Liu, Siyuan, Li, Yifan, Chang, Junhan, Wang, Xinyan, Zhou, Shuo, Liu, Jianchuan, Luo, Xiaoshan, Wang, Zhenyu, Jiang, Wanrun, Wu, Jing, Yang, Yudi, Yang, Jiyuan, Yang, Manyi, Gong, Fu-Qiang, Zhang, Linshuang, Shi, Mengchao, Dai, Fu-Zhi, York, Darrin M., Liu, Shi, Zhu, Tong, Zhong, Zhicheng, Lv, Jian, Cheng, Jun, Jia, Weile, Chen, Mohan, Ke, Guolin, E, Weinan, Zhang, Linfeng, Wang, Han
The rapid development of artificial intelligence (AI) is driving significant changes in the field of atomic modeling, simulation, and design. AI-based potential energy models have been successfully used to perform large-scale and long-time simulation
Externí odkaz:
http://arxiv.org/abs/2312.15492
Cortical surface reconstruction (CSR) from MRI is key to investigating brain structure and function. While recent deep learning approaches have significantly improved the speed of CSR, a substantial amount of runtime is still needed to map the cortex
Externí odkaz:
http://arxiv.org/abs/2312.05986
Decentralized Federated learning is a distributed edge intelligence framework by exchanging parameter updates instead of training data among participators, in order to retrain or fine-tune deep learning models for mobile intelligent applications. Con
Externí odkaz:
http://arxiv.org/abs/2311.01229