Zobrazeno 1 - 10
of 3 292
pro vyhledávání: '"Napoletano A"'
The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research
Externí odkaz:
http://arxiv.org/abs/2411.13181
Autor:
Bothmann, Enrico, Flower, Lois, Gütschow, Christian, Höche, Stefan, Hoppe, Mareen, Isaacson, Joshua, Knobbe, Max, Krauss, Frank, Meinzinger, Peter, Napoletano, Davide, Price, Alan, Reichelt, Daniel, Schönherr, Marek, Schumann, Steffen, Siegert, Frank
Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarise new developments, essential features, and ongoing improvements within the Sherpa 3 release series. Phy
Externí odkaz:
http://arxiv.org/abs/2410.22148
Autor:
Andersen, J., Assi, B., Asteriadis, K., Azzurri, P., Barone, G., Behring, A., Benecke, A., Bhattacharya, S., Bothmann, E., Caletti, S., Chen, X., Chiesa, M., Cooper-Sarkar, A., Cridge, T., Gomez, A. Cueto, Datta, S., Dhani, P. K., Donega, M., Engel, T., Ravasio, S. Ferrario, Forte, S., Francavilla, P., Garzelli, M. V., Ghira, A., Ghosh, A., Giuli, F., Gouskos, L., Gras, P., Gütschow, C., Haddad, Y., Harland-Lang, L., Hekhorn, F., Helenius, I., Hinzmann, A., Höche, S., Holguin, J., Huss, A., Huston, J., Ježo, T., Jones, S., Kiebacher, S., Knobbe, M., Kogler, R., Köneke, K., Kunz, L., LeBlanc, M., Loch, P., Centeno, G. Loeschcke, Löschner, M., Maas, A., Magni, G., Maier, A., Marcoli, M., Marzani, S., McFayden, J., Meinzinger, P., Mikuni, V., Moch, S., Nadolsky, P., Napoletano, D., Pellen, M., Plätzer, S., Poncelet, R., Preuss, C., Qu, H., Rabbertz, K., Reichelt, D., Rescia, A., Roloff, J., Röntsch, R., Cruz, S. Sanchez, Sarkar, T., Scyboz, L., Sforza, F., Siódmok, A., Stagnitto, G., Tarek, A., Thorne, R. S., Valassi, A., Whitehead, J., Winter, J., Delaunay, C., Herrmann, B., Re, E.
This report presents a short summary of the activities of the "Standard Model" working group for the "Physics at TeV Colliders" workshop (Les Houches, France, 12-30 June, 2023).
Comment: Proceedings of the Standard Model Working Group of the 202
Comment: Proceedings of the Standard Model Working Group of the 202
Externí odkaz:
http://arxiv.org/abs/2406.00708
Autor:
Liu, Xiaohong, Min, Xiongkuo, Zhai, Guangtao, Li, Chunyi, Kou, Tengchuan, Sun, Wei, Wu, Haoning, Gao, Yixuan, Cao, Yuqin, Zhang, Zicheng, Wu, Xiele, Timofte, Radu, Peng, Fei, Fu, Huiyuan, Ming, Anlong, Wang, Chuanming, Ma, Huadong, He, Shuai, Dou, Zifei, Chen, Shu, Zhang, Huacong, Xie, Haiyi, Wang, Chengwei, Chen, Baoying, Zeng, Jishen, Yang, Jianquan, Wang, Weigang, Fang, Xi, Lv, Xiaoxin, Yan, Jun, Zhi, Tianwu, Zhang, Yabin, Li, Yaohui, Li, Yang, Xu, Jingwen, Liu, Jianzhao, Liao, Yiting, Li, Junlin, Yu, Zihao, Lu, Yiting, Li, Xin, Motamednia, Hossein, Hosseini-Benvidi, S. Farhad, Guan, Fengbin, Mahmoudi-Aznaveh, Ahmad, Mansouri, Azadeh, Gankhuyag, Ganzorig, Yoon, Kihwan, Xu, Yifang, Fan, Haotian, Kong, Fangyuan, Zhao, Shiling, Dong, Weifeng, Yin, Haibing, Zhu, Li, Wang, Zhiling, Huang, Bingchen, Saha, Avinab, Mishra, Sandeep, Gupta, Shashank, Sureddi, Rajesh, Saha, Oindrila, Celona, Luigi, Bianco, Simone, Napoletano, Paolo, Schettini, Raimondo, Yang, Junfeng, Fu, Jing, Zhang, Wei, Cao, Wenzhi, Liu, Limei, Peng, Han, Yuan, Weijun, Li, Zhan, Cheng, Yihang, Deng, Yifan, Li, Haohui, Qu, Bowen, Li, Yao, Luo, Shuqing, Wang, Shunzhou, Gao, Wei, Lu, Zihao, Conde, Marcos V., Wang, Xinrui, Chen, Zhibo, Liao, Ruling, Ye, Yan, Wang, Qiulin, Li, Bing, Zhou, Zhaokun, Geng, Miao, Chen, Rui, Tao, Xin, Liang, Xiaoyu, Sun, Shangkun, Ma, Xingyuan, Li, Jiaze, Yang, Mengduo, Xu, Haoran, Zhou, Jie, Zhu, Shiding, Yu, Bohan, Chen, Pengfei, Xu, Xinrui, Shen, Jiabin, Duan, Zhichao, Asadi, Erfan, Liu, Jiahe, Yan, Qi, Qu, Youran, Zeng, Xiaohui, Wang, Lele, Liao, Renjie
This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major
Externí odkaz:
http://arxiv.org/abs/2404.16687
Autor:
Alioli, Simone, Bell, Guido, Billis, Georgios, Broggio, Alessandro, Dehnadi, Bahman, Lim, Matthew A., Marinelli, Giulia, Nagar, Riccardo, Napoletano, Davide, Rahn, Rudi
We present the resummation of one-jettiness for the colour-singlet plus jet production process $p p \to ( \gamma^*/Z \to \ell^+ \ell^-) + {\text{jet}}$ at hadron colliders up to the fourth logarithmic order (N$^3$LL). This is the first resummation at
Externí odkaz:
http://arxiv.org/abs/2312.06496
Autor:
Mugatwala, R., Chierichini, S., Francisco, G., Napoletano, G., Foldes, R., Giovannelli, L., De Gasperis, G., Camporeale, E., Erdélyi, R., Del Moro, D.
One of the goals of Space Weather studies is to achieve a better understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), in order to improve our ability to forecast them and mitigate the risk to our technologically driven society
Externí odkaz:
http://arxiv.org/abs/2311.13429
Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding E
Externí odkaz:
http://arxiv.org/abs/2312.09430
Autor:
Napoletano, Davide
In this talk I present a personal perspective on what the current and future challenges are for Monte Carlo event generators. I focus in particular on those aspects of Monte Carlo event generators that have not, historically, received the same scruti
Externí odkaz:
http://arxiv.org/abs/2309.14305
In this work, we assess several deep learning strategies for hyperspectral pansharpening. First, we present a new dataset with a greater extent than any other in the state of the art. This dataset, collected using the ASI PRISMA satellite, covers abo
Externí odkaz:
http://arxiv.org/abs/2307.11666
State-of-The-Art (SoTA) image captioning models often rely on the Microsoft COCO (MS-COCO) dataset for training. This dataset contains annotations provided by human annotators, who typically produce captions averaging around ten tokens. However, this
Externí odkaz:
http://arxiv.org/abs/2306.11593