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Li He, Qian Zhang, Jianyong Duan and Hao Wang
Open-domain event extraction is a fundamental task that aims to extract non-predefined types of events from news clusters. Some researchers have noticed that its performance can be enhanced by improving dependency relationships. Recently, graphical convo...
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Yang Shen, Changjiang Zheng and Fei Wu
Urban highway tunnels are frequent accident locations, and predicting and analyzing road conditions after accidents to avoid traffic congestion is a key measure for tunnel traffic operation management. In this paper, 200 traffic accident data from the Yi...
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Yifei Wang, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu and Pengyu Hong
This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real appli...
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Jiawei Kang, Shangwen Yang, Xiaoxuan Shan, Jie Bao and Zhao Yang
Exploring the delay causality between airports and comparing the delay propagation patterns across different airport networks is critical to better understand delay propagation mechanisms and provide effective delay mitigation strategies. A novel attenti...
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Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a...
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Wei Li, Xi Zhan, Xin Liu, Lei Zhang, Yu Pan and Zhisong Pan
Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. ...
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Junwei Zhou, Xizhong Qin, Kun Yu, Zhenhong Jia and Yan Du
Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current resea...
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Tingting Wang, Zhuolin Li, Xiulin Geng, Baogang Jin and Lingyu Xu
The accurate prediction of sea surface temperature (SST) is the basis for our understanding of local and global climate characteristics. At present, the existing sea temperature prediction methods fail to take full advantage of the potential spatial depe...
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Ruoming Zhai, Jingui Zou, Yifeng He and Liyuan Meng
Point-based networks have been widely used in the semantic segmentation of point clouds owing to the powerful 3D convolution neural network (CNN) baseline. Most of the current methods resort to intermediate regular representations for reorganizing the st...
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Wenzhuo Zhang, Mingyang Yu, Xiaoxian Chen, Fangliang Zhou, Jie Ren, Haiqing Xu and Shuai Xu
Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and internal c...
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