Zobrazeno 1 - 10
of 36 218
pro vyhledávání: '"LI, Yun"'
Autor:
Hong-Peng, Lu, Hui, Tian, Li-Yun, Zhang, He-Chao, Chen, Ying, Li, Zi-Hao, Yang, Jia-Sheng, Wang, Jia-Le, Zhang, Zheng, Sun
We report the detection of an extreme stellar prominence eruption on the M dwarf LAMOST J044431.62+235627.9, observed through time-domain H$\alpha$ spectroscopy with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). This promine
Externí odkaz:
http://arxiv.org/abs/2411.11076
New insights from the Dark Energy Spectroscopic Instrument (DESI) 2024 baryon acoustic oscillations (BAO) data, in conjunction with cosmic microwave background (CMB) and Type Ia supernova (SN) data, suggest that dark energy may not be a cosmological
Externí odkaz:
http://arxiv.org/abs/2411.08639
Samaras, a distinct category of fruit, are composed of heavier seeds and lighter wings. Diversity in morphologies and structures subtly contributes to the flight patterns of various seeds, thereby serving as a key factor in the reproductive strategie
Externí odkaz:
http://arxiv.org/abs/2411.08997
Data deduplication, one of the key features of modern Big Data storage devices, is the process of removing replicas of data chunks stored by different users. Despite the importance of deduplication, several drawbacks of the method, such as storage ro
Externí odkaz:
http://arxiv.org/abs/2411.01407
Laryngo-pharyngeal cancer (LPC) is a highly lethal malignancy in the head and neck region. Recent advancements in tumor detection, particularly through dual-branch network architectures, have significantly improved diagnostic accuracy by integrating
Externí odkaz:
http://arxiv.org/abs/2410.21813
Autor:
Liu, Jinyu, Guo, Hongye, Li, Yun, Tang, Qinghu, Huang, Fuquan, Chen, Tunan, Zhong, Haiwang, Chen, Qixin
Over the past decade, bidding in power markets has attracted widespread attention. Reinforcement Learning (RL) has been widely used for power market bidding as a powerful AI tool to make decisions under real-world uncertainties. However, current RL m
Externí odkaz:
http://arxiv.org/abs/2410.11180
Federated Unlearning (FU) enables clients to selectively remove the influence of specific data from a trained federated learning model, addressing privacy concerns and regulatory requirements. However, existing FU methods often struggle to balance ef
Externí odkaz:
http://arxiv.org/abs/2410.06848
Autor:
Li, Yun-Qin, Zhang, Yu-Ke, Lu, Xin-Le, Shao, Ya-Ping, Bao, Zhi-qiang, Zheng, Jun-Ding, Tong, Wen-Yi, Duan, Chun-Gang
As an emerging magnetic phase, altermagnets with compensated magnetic order and non-relativistic spin-splitting have attracted widespread attention. Currently, strain engineering is considered to be an effective method for inducing valley polarizatio
Externí odkaz:
http://arxiv.org/abs/2410.03155
We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam Survey PD
Externí odkaz:
http://arxiv.org/abs/2410.00271
Autor:
Li, Jintao, Zhi, Haochang, Lyu, Ruiyu, Li, Wangzhen, Bi, Zhaori, Zhu, Keren, Zeng, Yanhan, Shan, Weiwei, Yan, Changhao, Yang, Fan, Li, Yun, Zeng, Xuan
Recent advances in machine learning (ML) for automating analog circuit synthesis have been significant, yet challenges remain. A critical gap is the lack of a standardized evaluation framework, compounded by various process design kits (PDKs), simula
Externí odkaz:
http://arxiv.org/abs/2409.08534