Zobrazeno 1 - 10
of 41 834
pro vyhledávání: '"LI, DONG"'
Autor:
Li, Dong, Liu, Xing-Yu, Ye, Xing-Guo, Pan, Zhen-Cun, Xu, Wen-Zheng, Zhu, Peng-Fei, Wang, An-Qi, Watanabe, Kenji, Taniguchi, Takashi, Liao, Zhi-Min
Publikováno v:
Physical Review B 110, L100409 (2024)
We report the synergy between orbital and spin-orbit torques in WTe2/Fe3GeTe2 heterostructures characterized by a Berry curvature dipole. By applying a current along the a axis in WTe2, we detect an out-of-plane magnetization in the system, which we
Externí odkaz:
http://arxiv.org/abs/2412.02491
Publikováno v:
Physical Review B 110, 035423 (2024)
Current-induced out of plane magnetization has been utilized for field-free switching of ferromagnets with perpendicular magnetic anisotropy. Identifying systems capable of energy-efficiently converting charge currents into out of plane orbit- or spi
Externí odkaz:
http://arxiv.org/abs/2412.02488
Autor:
Wang, An-Qi, Li, Dong, Zhao, Tong-Yang, Liu, Xing-Yu, Zhang, Jiantian, Liao, Xin, Yin, Qing, Pan, Zhen-Cun, Yu, Peng, Liao, Zhi-Min
Publikováno v:
Physical Review B 110, 155434 (2024)
We report on the observation of the linear anomalous Hall effect (AHE) in the nonmagnetic Weyl semimetal TaIrTe4. This is achieved by applying a direct current Idc and an alternating current Iac (Iac<
Externí odkaz:
http://arxiv.org/abs/2412.02937
Autor:
Liang, Chengyang, Li, Dong
Semantic communication (SemCom) has emerged as a promising technique for the next-generation communication systems, in which the generation at the receiver side is allowed without semantic features' recovery. However, the majority of existing researc
Externí odkaz:
http://arxiv.org/abs/2411.17428
As an effective approach to equip models with multi-task capabilities without additional training, model merging has garnered significant attention. However, existing methods face challenges of redundant parameter conflicts and the excessive storage
Externí odkaz:
http://arxiv.org/abs/2412.00054
Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testing and training distributions. However, existing methods often rely on
Externí odkaz:
http://arxiv.org/abs/2411.12913
Incremental graph learning has gained significant attention for its ability to address the catastrophic forgetting problem in graph representation learning. However, traditional methods often rely on a large number of labels for node classification,
Externí odkaz:
http://arxiv.org/abs/2411.06659
Autor:
Ding, Ning, Qu, Shang, Xie, Linhai, Li, Yifei, Liu, Zaoqu, Zhang, Kaiyan, Xiong, Yibai, Zuo, Yuxin, Chen, Zhangren, Hua, Ermo, Lv, Xingtai, Sun, Youbang, Li, Yang, Li, Dong, He, Fuchu, Zhou, Bowen
With the development of artificial intelligence, its contribution to science is evolving from simulating a complex problem to automating entire research processes and producing novel discoveries. Achieving this advancement requires both specialized g
Externí odkaz:
http://arxiv.org/abs/2411.03743
Autor:
Li, Dong
Recommender systems have become increasingly important with the rise of the web as a medium for electronic and business transactions. One of the key drivers of this technology is the ease with which users can provide feedback about their likes and di
Externí odkaz:
http://arxiv.org/abs/2411.01843
Out-of-distribution (OOD) detection poses a significant challenge for Graph Neural Networks (GNNs), particularly in open-world scenarios with varying distribution shifts. Most existing OOD detection methods on graphs primarily focus on identifying in
Externí odkaz:
http://arxiv.org/abs/2410.17526