Zobrazeno 1 - 10
of 11 244
pro vyhledávání: '"Yan, Peng"'
It is widely believed that the skyrmion Hall effect is absent in antiferromagnets because of the vanishing topological charge. However, the Aharonov-Casher theory indicates the possibility of topological effects for neutral particles. In this work, w
Externí odkaz:
http://arxiv.org/abs/2407.03959
Tackling non-IID data is an open challenge in federated learning research. Existing FL methods, including robust FL and personalized FL, are designed to improve model performance without consideration of interpreting non-IID across clients. This pape
Externí odkaz:
http://arxiv.org/abs/2406.19631
Autor:
Li, Zhixiong, Yan, Peng
Topological excitations in periodic magnetic crystals have received significant recent attention. However, it is an open question on their fate once the lattice periodicity is broken. In this work, we theoretically study the topological properties em
Externí odkaz:
http://arxiv.org/abs/2406.13953
Autor:
Zeng, Zhaozhuo, Yan, Peng
It is a well-established notion that the spin of a magnon should be flipped when it passes through a $180^{\circ}$ domain wall (DW) in both ferromagnets and antiferromagnets, while the magnon spin transport through ferrimagnetic DW is still elusive.
Externí odkaz:
http://arxiv.org/abs/2406.09298
Recently, topolectrical circuits (TECs) boom in studying the topological states of matter. The resemblance between circuit Laplacians and tight-binding models in condensed matter physics allows for the exploration of exotic topological phases on the
Externí odkaz:
http://arxiv.org/abs/2405.14643
Autor:
Bindal, Akanksha, Ramanujam, Sudarshan, Golland, Dave, Hazen, TJ, Jiang, Tina, Zhang, Fengyu, Yan, Peng
In enhancing LinkedIn core content recommendation models, a significant challenge lies in improving their semantic understanding capabilities. This paper addresses the problem by leveraging multi-task learning, a method that has shown promise in vari
Externí odkaz:
http://arxiv.org/abs/2405.11344
Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even outside the application. Multi
Externí odkaz:
http://arxiv.org/abs/2404.08361
Autor:
Zhang, Mozhi, Huang, Mianqiu, Shi, Rundong, Guo, Linsen, Peng, Chong, Yan, Peng, Zhou, Yaqian, Qiu, Xipeng
Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately cali
Externí odkaz:
http://arxiv.org/abs/2404.02655
Autor:
Yan, Peng, Long, Guodong
Personalized Federated Learning (PerFL) is a new machine learning paradigm that delivers personalized models for diverse clients under federated learning settings. Most PerFL methods require extra learning processes on a client to adapt a globally sh
Externí odkaz:
http://arxiv.org/abs/2403.19499
Autor:
Chen, Yan-Peng, Jiang, Ji-an, Luo, Wen-Tao, Zheng, Xian Zhong, Fang, Min, Yang, Chao, Hong, Yuan-Yu, Lv, Zong-Fei
Aiming at improving the survey efficiency of the Wide Field Survey Telescope, we have developed a basic scheduling strategy that takes into account the telescope characteristics, observing conditions, and weather conditions at the Lenghu site. The sk
Externí odkaz:
http://arxiv.org/abs/2312.03421