Zobrazeno 1 - 10
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pro vyhledávání: '"Liu, Jun"'
Autor:
Liu, Jun
Decision-making is an essential topic for multi-robot coordination and collaboration and is also the main topic of this thesis. Examples can be found in autonomous driving, environmental monitoring, intelligent transportation, etc. To study this prob
Externí odkaz:
http://hdl.handle.net/10919/113182
Autor:
Hartnoll, Sean A., Liu, Jun
We establish a correspondence between a supersymmetric mass deformation of the IKKT matrix integral at large $N$ and a background of Euclidean type IIB string theory. Both sides have sixteen supersymmetries and an $SO(3)\times SO(7)$ symmetry. In the
Externí odkaz:
http://arxiv.org/abs/2409.18706
Autor:
Zhong, Tianyang, Liu, Zhengliang, Pan, Yi, Zhang, Yutong, Zhou, Yifan, Liang, Shizhe, Wu, Zihao, Lyu, Yanjun, Shu, Peng, Yu, Xiaowei, Cao, Chao, Jiang, Hanqi, Chen, Hanxu, Li, Yiwei, Chen, Junhao, Hu, Huawen, Liu, Yihen, Zhao, Huaqin, Xu, Shaochen, Dai, Haixing, Zhao, Lin, Zhang, Ruidong, Zhao, Wei, Yang, Zhenyuan, Chen, Jingyuan, Wang, Peilong, Ruan, Wei, Wang, Hui, Zhao, Huan, Zhang, Jing, Ren, Yiming, Qin, Shihuan, Chen, Tong, Li, Jiaxi, Zidan, Arif Hassan, Jahin, Afrar, Chen, Minheng, Xia, Sichen, Holmes, Jason, Zhuang, Yan, Wang, Jiaqi, Xu, Bochen, Xia, Weiran, Yu, Jichao, Tang, Kaibo, Yang, Yaxuan, Sun, Bolun, Yang, Tao, Lu, Guoyu, Wang, Xianqiao, Chai, Lilong, Li, He, Lu, Jin, Sun, Lichao, Zhang, Xin, Ge, Bao, Hu, Xintao, Zhang, Lian, Zhou, Hua, Zhang, Lu, Zhang, Shu, Liu, Ninghao, Jiang, Bei, Kong, Linglong, Xiang, Zhen, Ren, Yudan, Liu, Jun, Jiang, Xi, Bao, Yu, Zhang, Wei, Li, Xiang, Li, Gang, Liu, Wei, Shen, Dinggang, Sikora, Andrea, Zhai, Xiaoming, Zhu, Dajiang, Liu, Tianming
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguist
Externí odkaz:
http://arxiv.org/abs/2409.18486
This paper investigates the problem of adaptive detection of distributed targets in power heterogeneous clutter. In the considered scenario, all the data share the identical structure of clutter covariance matrix, but with varying and unknown power m
Externí odkaz:
http://arxiv.org/abs/2409.14049
Graph Neural Networks (GNNs) are powerful deep learning models designed for graph-structured data, demonstrating effectiveness across a wide range of applications.The softmax function is the most commonly used classifier for semi-supervised node clas
Externí odkaz:
http://arxiv.org/abs/2409.13544
We propose a Mamba accelerator with reconfigurable architecture, MARCA.We propose three novel approaches in this paper. (1) Reduction alternative PE array architecture for both linear and element-wise operations. For linear operations, the reduction
Externí odkaz:
http://arxiv.org/abs/2409.11440
Autor:
Serry, Mohamed, Liu, Jun
Analyzing and certifying stability and attractivity of nonlinear systems is a topic of research interest that has been extensively investigated by control theorists and engineers for many years. Despite that, accurately estimating domains of attracti
Externí odkaz:
http://arxiv.org/abs/2409.10657
Autor:
Law, Ka Nam Canaan, Yu, Mingshuo, Zhang, Lianglei, Zhang, Yiyi, Xu, Peng, Gao, Jerry, Liu, Jun
The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have p
Externí odkaz:
http://arxiv.org/abs/2409.09555
In this paper, we study the problem of Text-to-Image Person Re-identification (TIReID), which aims to find images of the same identity described by a text sentence from a pool of candidate images. Benefiting from Vision-Language Pre-training, such as
Externí odkaz:
http://arxiv.org/abs/2409.09427
Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems
Designing a stabilizing controller for nonlinear systems is a challenging task, especially for high-dimensional problems with unknown dynamics. Traditional reinforcement learning algorithms applied to stabilization tasks tend to drive the system clos
Externí odkaz:
http://arxiv.org/abs/2409.08382