Zobrazeno 1 - 10
of 104 051
pro vyhledávání: '"Tu, P. A."'
Autor:
Boer, Daniël, Flett, Chris A., Flore, Carlo, Kikoła, Daniel, Lansberg, Jean-Philippe, Nefedov, Maxim, Van Hulse, Charlotte, Bhattacharya, Shohini, Bor, Jelle, Butenschoen, Mathias, Ceccopieri, Federico, Chen, Longjie, Cheung, Vincent, D'Alesio, Umberto, Echevarria, Miguel, Hatta, Yoshitaka, Hyde, Charles E., Kishore, Raj, Kosarzewski, Leszek, Lorcé, Cédric, Li, Wenliang, Li, Xuan, Maxia, Luca, Metz, Andreas, Mukherjee, Asmita, Camacho, Carlos Muñoz, Murgia, Francesco, Nadel-Turonski, Pawel, Pisano, Cristian, Qiu, Jian-Wei, Rajesh, Sangem, Rinaldi, Matteo, West, Jennifer Rittenhouse, Saleev, Vladimir, Santiesteban, Nathaly, Setyadi, Chalis, Taels, Pieter, Tu, Zhoudunmin, Vitev, Ivan, Vogt, Ramona, Watanabe, Kazuhiro, Yao, Xiaojun, Yedelkina, Yelyzaveta, Yoshida, Shinsuke
The physics case for quarkonium-production studies accessible at the US Electron Ion Collider is described.
Comment: Latex, 84 pages. Review prepared for Progress in Particle and Nuclear Physics
Comment: Latex, 84 pages. Review prepared for Progress in Particle and Nuclear Physics
Externí odkaz:
http://arxiv.org/abs/2409.03691
Autor:
Yu, Jifan, Zhang, Zheyuan, Zhang-li, Daniel, Tu, Shangqing, Hao, Zhanxin, Li, Rui Miao, Li, Haoxuan, Wang, Yuanchun, Li, Hanming, Gong, Linlu, Cao, Jie, Lin, Jiayin, Zhou, Jinchang, Qin, Fei, Wang, Haohua, Jiang, Jianxiao, Deng, Lijun, Zhan, Yisi, Xiao, Chaojun, Dai, Xusheng, Yan, Xuan, Lin, Nianyi, Zhang, Nan, Ni, Ruixin, Dang, Yang, Hou, Lei, Zhang, Yu, Han, Xu, Li, Manli, Li, Juanzi, Liu, Zhiyuan, Liu, Huiqin, Sun, Maosong
Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widesprea
Externí odkaz:
http://arxiv.org/abs/2409.03512
Large Vision-Language Models (LVLMs) have recently garnered significant attention, with many efforts aimed at harnessing their general knowledge to enhance the interpretability and robustness of autonomous driving models. However, LVLMs typically rel
Externí odkaz:
http://arxiv.org/abs/2409.02914
Autor:
Rashid, Syed Md Mukit, Wu, Tianwei, Tu, Kai, Ishtiaq, Abdullah Al, Tanvir, Ridwanul Hasan, Dong, Yilu, Chowdhury, Omar, Hussain, Syed Rafiul
This paper proposes Proteus, a protocol state machine, property-guided, and budget-aware automated testing approach for discovering logical vulnerabilities in wireless protocol implementations. Proteus maintains its budget awareness by generating tes
Externí odkaz:
http://arxiv.org/abs/2409.02905
Autor:
Xiao, Chaojun, Zhang, Zhengyan, Song, Chenyang, Jiang, Dazhi, Yao, Feng, Han, Xu, Wang, Xiaozhi, Wang, Shuo, Huang, Yufei, Lin, Guanyu, Chen, Yingfa, Zhao, Weilin, Tu, Yuge, Zhong, Zexuan, Zhang, Ao, Si, Chenglei, Moo, Khai Hao, Zhao, Chenyang, Chen, Huimin, Lin, Yankai, Liu, Zhiyuan, Shang, Jingbo, Sun, Maosong
Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applications and evolution of these models on devices with limited computation
Externí odkaz:
http://arxiv.org/abs/2409.02877
Vision-language navigation (VLN) is a critical domain within embedded intelligence, requiring agents to navigate 3D environments based on natural language instructions. Traditional VLN research has focused on improving environmental understanding and
Externí odkaz:
http://arxiv.org/abs/2409.02561
In personalized recommendation systems, accurately capturing users' evolving interests and combining them with contextual information is a critical research area. This paper proposes a novel model called the Deep Adaptive Interest Network (DAIN), whi
Externí odkaz:
http://arxiv.org/abs/2409.02425
Autor:
Niu, Shutong, Wang, Ruoyu, Du, Jun, Yang, Gaobin, Tu, Yanhui, Wu, Siyuan, Qian, Shuangqing, Wu, Huaxin, Xu, Haitao, Zhang, Xueyang, Zhong, Guolong, Yu, Xindi, Chen, Jieru, Wang, Mengzhi, Cai, Di, Gao, Tian, Wan, Genshun, Ma, Feng, Pan, Jia, Gao, Jianqing
This technical report outlines our submission system for the CHiME-8 NOTSOFAR-1 Challenge. The primary difficulty of this challenge is the dataset recorded across various conference rooms, which captures real-world complexities such as high overlap r
Externí odkaz:
http://arxiv.org/abs/2409.02041
Autor:
Liu, Weiwen, Huang, Xu, Zeng, Xingshan, Hao, Xinlong, Yu, Shuai, Li, Dexun, Wang, Shuai, Gan, Weinan, Liu, Zhengying, Yu, Yuanqing, Wang, Zezhong, Wang, Yuxian, Ning, Wu, Hou, Yutai, Wang, Bin, Wu, Chuhan, Wang, Xinzhi, Liu, Yong, Wang, Yasheng, Tang, Duyu, Tu, Dandan, Shang, Lifeng, Jiang, Xin, Tang, Ruiming, Lian, Defu, Liu, Qun, Chen, Enhong
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. However, real function-calling data is quite challenging to collect and
Externí odkaz:
http://arxiv.org/abs/2409.00920
Tokenising continuous speech into sequences of discrete tokens and modelling them with language models (LMs) has led to significant success in text-to-speech (TTS) synthesis. Although these models can generate speech with high quality and naturalness
Externí odkaz:
http://arxiv.org/abs/2408.16373