Zobrazeno 1 - 10
of 3 976 780
pro vyhledávání: '"ZHANG, P."'
Autor:
Zou, Heqing, Luo, Tianze, Xie, Guiyang, Victor, Zhang, Lv, Fengmao, Wang, Guangcong, Chen, Juanyang, Wang, Zhuochen, Zhang, Hansheng, Zhang, Huaijian
The integration of Large Language Models (LLMs) with visual encoders has recently shown promising performance in visual understanding tasks, leveraging their inherent capability to comprehend and generate human-like text for visual reasoning. Given t
Externí odkaz:
http://arxiv.org/abs/2409.18938
Autor:
Wang, Xinlong, Zhang, Xiaosong, Luo, Zhengxiong, Sun, Quan, Cui, Yufeng, Wang, Jinsheng, Zhang, Fan, Wang, Yueze, Li, Zhen, Yu, Qiying, Zhao, Yingli, Ao, Yulong, Min, Xuebin, Li, Tao, Wu, Boya, Zhao, Bo, Zhang, Bowen, Wang, Liangdong, Liu, Guang, He, Zheqi, Yang, Xi, Liu, Jingjing, Lin, Yonghua, Huang, Tiejun, Wang, Zhongyuan
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e.g., Stable Diffusion) and compositional approaches (e.
Externí odkaz:
http://arxiv.org/abs/2409.18869
Efficiently determining the satisfiability of a boolean equation -- known as the SAT problem for brevity -- is crucial in various industrial problems. Recently, the advent of deep learning methods has introduced significant potential for enhancing SA
Externí odkaz:
http://arxiv.org/abs/2409.18778
The medium-temperature $T$ dependence of the jet transport coefficient $\hat q$ was studied via the nuclear modification factor $R_{AA}(p_{\rm T})$ and elliptical flow parameter $v_2(p_{\rm T})$ for large transverse momentum $p_{\rm T}$ hadrons in hi
Externí odkaz:
http://arxiv.org/abs/2409.18773
Autor:
Mu, Xuechen, Huang, Zhenyu, Li, Kewei, Zhang, Haotian, Wang, Xiuli, Fan, Yusi, Zhang, Kai, Zhou, Fengfeng
Recent advancements in feature representation and dimension reduction have highlighted their crucial role in enhancing the efficacy of predictive modeling. This work introduces TemporalPaD, a novel end-to-end deep learning framework designed for temp
Externí odkaz:
http://arxiv.org/abs/2409.18597
Autor:
Zhang, Haoran, Zhang, Xingjian, Eng, John, Meunier, Max, Yang, Yuzhe, Ling, Alexander, Zuniga-Perez, Jesus, Gao, Weibo
Single-photon sources (SPS) hold the potential to enhance the performance of quantum key distribution (QKD). QKD systems using SPS often require cryogenic cooling, while recent QKD attempts using SPS operating at room-temperature have failed to achie
Externí odkaz:
http://arxiv.org/abs/2409.18502
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
Autor:
Zhang, Jingwen, Zheng, Zibin, Nan, Yuhong, Ye, Mingxi, Ning, Kaiwen, Zhang, Yu, Zhang, Weizhe
Despite the increasing popularity of Decentralized Applications (DApps), they are suffering from various vulnerabilities that can be exploited by adversaries for profits. Among such vulnerabilities, Read-Only Reentrancy (called ROR in this paper), is
Externí odkaz:
http://arxiv.org/abs/2409.18468
Topological orders in 2+1d are spontaneous symmetry-breaking (SSB) phases of 1-form symmetries in pure states. The notion of symmetry is further enriched in the context of mixed states, where a symmetry can be either ``strong" or ``weak". In this wor
Externí odkaz:
http://arxiv.org/abs/2409.17530
Hierarchical search in millimeter-wave (mmWave) communications incurs significant beam training overhead and delay, especially in a dynamic environment. Deep learning-enabled beam prediction is promising to significantly mitigate the overhead and del
Externí odkaz:
http://arxiv.org/abs/2409.18429