Zobrazeno 1 - 10
of 28 406
pro vyhledávání: '"Wang,Bing"'
Autor:
Zhu, Jun-Peng, Niu, Boyan, Cai, Peng, Ni, Zheming, Wan, Jianwei, Xu, Kai, Huang, Jiajun, Ma, Shengbo, Wang, Bing, Zhou, Xuan, Bao, Guanglei, Zhang, Donghui, Tang, Liu, Liu, Qi
Exploratory data analysis (EDA), coupled with SQL, is essential for data analysts involved in data exploration and analysis. However, data analysts often encounter two primary challenges: (1) the need to craft SQL queries skillfully, and (2) the requ
Externí odkaz:
http://arxiv.org/abs/2412.07214
Autor:
Wang, Chongze, Yao, Shichang, Liu, Shuyuan, Wang, Bing, Liu, Liangliang, Jia, Yu, Cho, Jun-Hyung
A family of V-based kagome metals AV$_3$Sb$_5$ (A = Cs, Rb, K) presents an intriguing platform for exploring the interplay of time-reversal symmetry breaking, nontrivial topological bands, and electron correlations, resulting in a range of exotic qua
Externí odkaz:
http://arxiv.org/abs/2412.07190
Autor:
Liu, Weiwei, Su, Xiaolong, Li, Chijun, Zeng, Cheng, Wang, Bing, Wang, Yongjie, Ding, Yufan, Qin, Chengzhi, Xia, Jinsong, Lu, Peixiang
Chiral edge state is a hallmark of topological physics, which has drawn significant attention across quantum mechanics, condensed matter and optical systems. Recently, synthetic dimensions have emerged as ideal platforms for investigating chiral edge
Externí odkaz:
http://arxiv.org/abs/2412.05622
Autor:
Fang, Guangchi, Wang, Bing
In this study, we explore the essential challenge of fast scene optimization for Gaussian Splatting. Through a thorough analysis of the geometry modeling process, we reveal that dense point clouds can be effectively reconstructed early in optimizatio
Externí odkaz:
http://arxiv.org/abs/2411.12788
Autor:
Liu, Yun, Li, Peng, Yan, Xuefeng, Nan, Liangliang, Wang, Bing, Chen, Honghua, Gong, Lina, Zhao, Wei, Wei, Mingqiang
The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this paper, we integrate two prevalent methods, maske
Externí odkaz:
http://arxiv.org/abs/2411.06041
Deep Learning-Driven Microstructure Characterization and Vickers Hardness Prediction of Mg-Gd Alloys
In the field of materials science, exploring the relationship between composition, microstructure, and properties has long been a critical research focus. The mechanical performance of solid-solution Mg-Gd alloys is significantly influenced by Gd con
Externí odkaz:
http://arxiv.org/abs/2410.20402
This study presents a novel approach for quantificationally reconstructing density fields from shadowgraph images using physics-informed neural networks
Externí odkaz:
http://arxiv.org/abs/2410.20203
Autor:
Zhang, Hengxiang, Gao, Hongfu, Hu, Qiang, Chen, Guanhua, Yang, Lili, Jing, Bingyi, Wei, Hongxin, Wang, Bing, Bai, Haifeng, Yang, Lei
With the rapid development of Large language models (LLMs), understanding the capabilities of LLMs in identifying unsafe content has become increasingly important. While previous works have introduced several benchmarks to evaluate the safety risk of
Externí odkaz:
http://arxiv.org/abs/2410.18491
This paper presents a novel model-free method to solve linear quadratic Gaussian mean field social control problems in the presence of multiplicative noise. The objective is to achieve a social optimum by solving two algebraic Riccati equations (AREs
Externí odkaz:
http://arxiv.org/abs/2410.15119
Point cloud registration is a foundational task for 3D alignment and reconstruction applications. While both traditional and learning-based registration approaches have succeeded, leveraging the intrinsic symmetry of point cloud data, including rotat
Externí odkaz:
http://arxiv.org/abs/2410.05729