Zobrazeno 1 - 10
of 113 958
pro vyhledávání: '"WANG, Jing"'
Autor:
Wang, Jing Yi, Sukiennik, Nicholas, Li, Tong, Su, Weikang, Hao, Qianyue, Xu, Jingbo, Huang, Zihan, Xu, Fengli, Li, Yong
The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally perf
Externí odkaz:
http://arxiv.org/abs/2411.14491
We construct a minimal lattice model to provide an orbital description of lowest and first Landau levels. With the maximally localized Wannier functions with $s$, $p_-$, $p_+$ orbital characteristics, a three-orbital model is developed, where the low
Externí odkaz:
http://arxiv.org/abs/2411.13071
Autor:
Ding, Zhengyao, Hu, Yujian, Xu, Youyao, Zhao, Chengchen, Li, Ziyu, Mao, Yiheng, Li, Haitao, Li, Qian, Wang, Jing, Chen, Yue, Chen, Mengjia, Wang, Longbo, Chu, Xuesen, Pan, Weichao, Liu, Ziyi, Wu, Fei, Zhang, Hongkun, Chen, Ting, Huang, Zhengxing
Cardiovascular diseases (CVDs) present significant challenges for early and accurate diagnosis. While cardiac magnetic resonance imaging (CMR) is the gold standard for assessing cardiac function and diagnosing CVDs, its high cost and technical comple
Externí odkaz:
http://arxiv.org/abs/2411.13602
Autor:
Ma, Wenlin, Guo, Hong, Xu, Haojie, Jones, Michael G., Zhang, Chuan-Peng, Zhu, Ming, Wang, Jing, Wang, Jie, Jiang, Peng
We present the first HI mass function (HIMF) measurement for the recent FAST All Sky HI (FASHI) survey and the most complete measurements of HIMF in the local universe so far by combining the HI catalogues from HI Parkes All Sky Survey (HIPASS), Arec
Externí odkaz:
http://arxiv.org/abs/2411.09903
Autor:
Li, Xiaopeng, Wang, Shangwen, Li, Shasha, Ma, Jun, Yu, Jie, Liu, Xiaodong, Wang, Jing, Ji, Bin, Zhang, Weimin
Large Language Models for Code (LLMs4Code) have been found to exhibit outstanding performance in the software engineering domain, especially the remarkable performance in coding tasks. However, even the most advanced LLMs4Code can inevitably contain
Externí odkaz:
http://arxiv.org/abs/2411.06638
Autor:
Tang, Peng, Liu, Jiacheng, Hou, Xiaofeng, Pu, Yifei, Wang, Jing, Heng, Pheng-Ann, Li, Chao, Guo, Minyi
The Mixture-of-Experts (MoE) architecture has demonstrated significant advantages in the era of Large Language Models (LLMs), offering enhanced capabilities with reduced inference costs. However, deploying MoE-based LLMs on memoryconstrained edge dev
Externí odkaz:
http://arxiv.org/abs/2411.01433
``Creative'' remains an inherently abstract concept for both humans and diffusion models. While text-to-image (T2I) diffusion models can easily generate out-of-domain concepts like ``a blue banana'', they struggle with generating combinatorial object
Externí odkaz:
http://arxiv.org/abs/2410.24160
Autor:
Huang, Qifeng, Wang, Jing, Lin, Xuchen, Oh, Se-Heon, Chen, Xinkai, Catinella, Barbara, Deg, Nathan, Dénes, Helga, For, Bi-Qing, Koribalski, Baerbel, Lee-Waddell, Karen, Rhee, Jonghwan, Shen, Austin, Shao, Li, Spekkens, Kristine, Staveley-Smith, Lister, Westmeier, Tobias, Wong, O. Ivy, Bosma, Albert
Galaxy interactions can significantly affect the star formation in galaxies, but it remains a challenge to achieve a consensus on the star formation rate (SFR) enhancement in galaxy pairs. Here, we investigate the SFR enhancement of gas-rich galaxy p
Externí odkaz:
http://arxiv.org/abs/2410.22406
In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This comprehensive first-principles many-b
Externí odkaz:
http://arxiv.org/abs/2410.16791
Previous studies have highlighted significant advancements in multimodal fusion. Nevertheless, such methods often encounter challenges regarding the efficacy of feature extraction, data integrity, consistency of feature dimensions, and adaptability a
Externí odkaz:
http://arxiv.org/abs/2410.15475