Robust Data-Driven Predictive Control for Mixed Platoons under Noise and Attacks

Autor: Li, Shuai, Chen, Chaoyi, Zheng, Haotian, Wang, Jiawei, Xu, Qing, Wang, Jianqiang, Li, Keqiang

Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), poses significant challenges due to the uncertain and unknown human driving behaviors. Data-driven control methods offer promi

Externí odkaz: http://arxiv.org/abs/2411.13924

Dockformer: A transformer-based molecular docking paradigm for large-scale virtual screening

Autor: Yang, Zhangfan, Ji, Junkai, He, Shan, Li, Jianqiang, He, Tiantian, Bai, Ruibin, Zhu, Zexuan, Ong, Yew Soon

Molecular docking is a crucial a crucial step in drug development, which enables the virtual screening of compound libraries to identify potential ligands that target proteins of interest. However, the computational complexity of traditional docking

Externí odkaz: http://arxiv.org/abs/2411.06740

Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

Autor: Sun, Xingwu, Chen, Yanfeng, Huang, Yiqing, Xie, Ruobing, Zhu, Jiaqi, Zhang, Kai, Li, Shuaipeng, Yang, Zhen, Han, Jonny, Shu, Xiaobo, Bu, Jiahao, Chen, Zhongzhi, Huang, Xuemeng, Lian, Fengzong, Yang, Saiyong, Yan, Jianfeng, Zeng, Yuyuan, Ren, Xiaoqin, Yu, Chao, Wu, Lulu, Mao, Yue, Xia, Jun, Yang, Tao, Zheng, Suncong, Wu, Kan, Jiao, Dian, Xue, Jinbao, Zhang, Xipeng, Wu, Decheng, Liu, Kai, Wu, Dengpeng, Xu, Guanghui, Chen, Shaohua, Chen, Shuang, Feng, Xiao, Hong, Yigeng, Zheng, Junqiang, Xu, Chengcheng, Li, Zongwei, Kuang, Xiong, Hu, Jianglu, Chen, Yiqi, Deng, Yuchi, Li, Guiyang, Liu, Ao, Zhang, Chenchen, Hu, Shihui, Zhao, Zilong, Wu, Zifan, Ding, Yao, Wang, Weichao, Liu, Han, Wang, Roberts, Fei, Hao, Yu, Peijie, Zhao, Ze, Cao, Xun, Wang, Hai, Xiang, Fusheng, Huang, Mengyuan, Xiong, Zhiyuan, Hu, Bin, Hou, Xuebin, Jiang, Lei, Ma, Jianqiang, Wu, Jiajia, Deng, Yaping, Shen, Yi, Wang, Qian, Liu, Weijie, Liu, Jie, Chen, Meng, Dong, Liang, Jia, Weiwen, Chen, Hu, Liu, Feifei, Yuan, Rui, Xu, Huilin, Yan, Zhenxiang, Cao, Tengfei, Hu, Zhichao, Feng, Xinhua, Du, Dong, Yu, Tinghao, Tao, Yangyu, Zhang, Feng, Zhu, Jianchen, Xu, Chengzhong, Li, Xirui, Zha, Chong, Ouyang, Wen, Xia, Yinben, Li, Xiang, He, Zekun, Chen, Rongpeng, Song, Jiawei, Chen, Ruibin, Jiang, Fan, Zhao, Chongqing, Wang, Bo, Gong, Hao, Gan, Rong, Hu, Winston, Kang, Zhanhui, Yang, Yong, Liu, Yuhong, Wang, Di, Jiang, Jie

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We c

Externí odkaz: http://arxiv.org/abs/2411.02265

EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications

Autor: Jiang, Junkai, Han, Zeyu, Wang, Yuning, Cai, Mengchi, Meng, Qingwen, Xu, Qing, Wang, Jianqiang

Driving risk assessment is crucial for both autonomous vehicles and human-driven vehicles. The driving risk can be quantified as the product of the probability that an event (such as collision) will occur and the consequence of that event. However, t

Externí odkaz: http://arxiv.org/abs/2410.14996