Zobrazeno 1 - 10
of 18 939
pro vyhledávání: '"LIN, Yan"'
Autor:
Guo, Shengnan, Wei, Tonglong, Huang, Yiheng, Zhao, Miaomiao, Chen, Ran, Lin, Yan, Lin, Youfang, Wan, Huaiyu
Traffic data imputation is a critical preprocessing step in intelligent transportation systems, enabling advanced transportation services. Despite significant advancements in this field, selecting the most suitable model for practical applications re
Externí odkaz:
http://arxiv.org/abs/2412.04733
Autor:
Li, Shu-Yue, Zhang, Qing-Min, Ying, Bei-Li, Feng, Li, Su, Ying-Na, Zhang, Mu-Sheng Lin. Yan-Jie
In this paper, we perform a follow-up investigation of the solar eruption originating from active region (AR) 13575 on 2024 February 9. The primary eruption of a hot channel (HC) generates an X3.4 class flare, a full-halo coronal mass ejection (CME),
Externí odkaz:
http://arxiv.org/abs/2412.01123
Autor:
Gong, Letian, Lin, Yan, Zhang, Xinyue, Lu, Yiwen, Han, Xuedi, Liu, Yichen, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, existing models analyzing check-in sequ
Externí odkaz:
http://arxiv.org/abs/2411.00823
Spatiotemporal trajectory data is vital for web-of-things services and is extensively collected and analyzed by web-based hardware and platforms. However, issues such as service interruptions and network instability often lead to sparsely recorded tr
Externí odkaz:
http://arxiv.org/abs/2410.14281
In order to avoid repeated task offloading and realize the reuse of popular task computing results, we construct a novel content caching-assisted vehicular edge computing (VEC) framework. In the face of irregular network topology and unknown environm
Externí odkaz:
http://arxiv.org/abs/2410.10071
We present a framework for learning to generate background music from video inputs. Unlike existing works that rely on symbolic musical annotations, which are limited in quantity and diversity, our method leverages large-scale web videos accompanied
Externí odkaz:
http://arxiv.org/abs/2409.07450
Autor:
Mao, Xiaowei, Lin, Yan, Guo, Shengnan, Chen, Yubin, Xian, Xingyu, Wen, Haomin, Xu, Qisen, Lin, Youfang, Wan, Huaiyu
Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T). Accurately quantifying this uncertainty requires generating the most l
Externí odkaz:
http://arxiv.org/abs/2408.12809
Autor:
Lin, Yan, Wei, Tonglong, Zhou, Zeyu, Wen, Haomin, Hu, Jilin, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Vehicle trajectories provide valuable movement information that supports various downstream tasks and powers real-world applications. A desirable trajectory learning model should transfer between different regions and tasks without retraining, thus i
Externí odkaz:
http://arxiv.org/abs/2408.15251
Autor:
Lin, Yan, Liu, Yichen, Zhou, Zeyu, Wen, Haomin, Zheng, Erwen, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Vehicle trajectories provide crucial movement information for various real-world applications. To better utilize vehicle trajectories, it is essential to develop a trajectory learning approach that can effectively and efficiently extract rich semanti
Externí odkaz:
http://arxiv.org/abs/2408.04916
Autor:
Gong, Letian, Wan, Huaiyu, Guo, Shengnan, Li, Xiucheng, Lin, Yan, Zheng, Erwen, Wang, Tianyi, Zhou, Zeyu, Lin, Youfang
The rapid growth of location-based services (LBS) has yielded massive amounts of data on human mobility. Effectively extracting meaningful representations for user-generated check-in sequences is pivotal for facilitating various downstream services.
Externí odkaz:
http://arxiv.org/abs/2407.15899