Zobrazeno 1 - 10
of 316
pro vyhledávání: '"Chen, Jiyuan"'
Autor:
Zhao, Zhiyu, Liu, Qibin, Chen, Jiyuan, Chen, Jing, Chen, Junfeng, Chen, Xiang, Fu, Changbo, Guo, Jun, Khaw, Kim Siang, Li, Liang, Li, Shu, Liu, Danning, Liu, Kun, Song, Siyuan, Sun, Tong, Tang, Jiannan, Wang, Yufeng, Wang, Zhen, Wu, Weihao, Yang, Haijun, Lin, Yuming, Yuan, Rui, Zhang, Yulei, Zhang, Yunlong, Zhou, Baihong, Zhu, Xuliang, Zhu, Yifan
This paper presents the design and optimization of a LYSO crystal-based electromagnetic calorimeter (ECAL) for the DarkSHINE experiment, which aims to search for dark photon as potential dark force mediator. The ECAL design has been meticulously eval
Externí odkaz:
http://arxiv.org/abs/2407.17800
Global wildfire spreading dynamics and severity are analyzed using the susceptible-infected-recovered (SIR) compartment model. We use the novel FireTracks (FT) Scientific Dataset covering the wildfire time series of 2002-2023.
Externí odkaz:
http://arxiv.org/abs/2404.16874
Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. %Despite the substantial advancement% While most existing work assumes a simple and fixed degradation model (e.g., bicubic downsampling), t
Externí odkaz:
http://arxiv.org/abs/2402.18929
Autor:
Song, Siyuan, Chen, Jiyuan, Liu, Jianbei, Liu, Yong, Qi, Baohua, Shi, Yukun, Wang, Jiaxuan, Wang, Zhen, Yang, Haijun
Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based on the Resi
Externí odkaz:
http://arxiv.org/abs/2310.09489
Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that t
Externí odkaz:
http://arxiv.org/abs/2305.13115
Autor:
Wang, Hongjun, Zhang, Zhiwen, Fan, Zipei, Chen, Jiyuan, Zhang, Lingyu, Shibasaki, Ryosuke, Song, Xuan
Travel time estimation from GPS trips is of great importance to order duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is not always available due to the limitation of data privacy and acquisition, while the origin destinat
Externí odkaz:
http://arxiv.org/abs/2301.05336
Autor:
Wang, Hongjun, Chen, Jiyuan, Pan, Tong, Fan, Zipei, Zhang, Boyuan, Jiang, Renhe, Zhang, Lingyu, Xie, Yi, Wang, Zhongyi, Song, Xuan
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning t
Externí odkaz:
http://arxiv.org/abs/2211.15182
Estimating the travel time of a path is an essential topic for intelligent transportation systems. It serves as the foundation for real-world applications, such as traffic monitoring, route planning, and taxi dispatching. However, building a model fo
Externí odkaz:
http://arxiv.org/abs/2207.00838
Due to the rapid development of Internet of Things (IoT) technologies, many online web apps (e.g., Google Map and Uber) estimate the travel time of trajectory data collected by mobile devices. However, in reality, complex factors, such as network com
Externí odkaz:
http://arxiv.org/abs/2206.10418
Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances. As we all know, the flow at a citywide level is in a mixed state with several basic patterns (e.g., commuting, w
Externí odkaz:
http://arxiv.org/abs/2205.07851