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of 2 274
pro vyhledávání: '"Yu James"'
Human trajectory modeling is essential for deciphering movement patterns and supporting advanced applications across various domains. However, existing methods are often tailored to specific tasks and regions, resulting in limitations related to task
Externí odkaz:
http://arxiv.org/abs/2411.03859
Autor:
Zhang, Chenhan, Wang, Weiqi, Tian, Zhiyi, Yu, James Jianqiao, Kaafar, Mohamed Ali, Liu, An, Yu, Shui
Although recent advancements in end-to-end learning-based link prediction (LP) methods have shown remarkable capabilities, the significance of traditional similarity-based LP methods persists in unsupervised scenarios where there are no known link la
Externí odkaz:
http://arxiv.org/abs/2410.19183
The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport Systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized ser
Externí odkaz:
http://arxiv.org/abs/2407.09239
Autor:
Yang, Yixuan, Lu, Junru, Zhao, Zixiang, Luo, Zhen, Yu, James J. Q., Sanchez, Victor, Zheng, Feng
Designing 3D indoor layouts is a crucial task with significant applications in virtual reality, interior design, and automated space planning. Existing methods for 3D layout design either rely on diffusion models, which utilize spatial relationship p
Externí odkaz:
http://arxiv.org/abs/2406.03866
This paper examines the alignment of inductive biases in machine learning (ML) with structural models of economic dynamics. Unlike dynamical systems found in physical and life sciences, economics models are often specified by differential equations w
Externí odkaz:
http://arxiv.org/abs/2406.01898
Autor:
Zhu, Yuanshao, Yu, James Jianqiao, Zhao, Xiangyu, Liu, Qidong, Ye, Yongchao, Chen, Wei, Zhang, Zijian, Wei, Xuetao, Liang, Yuxuan
Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses. However, existing trajectory generation methods are still in their
Externí odkaz:
http://arxiv.org/abs/2404.15380
Modeling future traffic conditions often relies heavily on complex spatial-temporal neural networks to capture spatial and temporal correlations, which can overlook the inherent noise in the data. This noise, often manifesting as unexpected short-ter
Externí odkaz:
http://arxiv.org/abs/2310.16063
Traffic forecasting is a fundamental problem in intelligent transportation systems. Existing traffic predictors are limited by their expressive power to model the complex spatial-temporal dependencies in traffic data, mainly due to the following limi
Externí odkaz:
http://arxiv.org/abs/2308.14377
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal data mining research. Nonetheless, GPS trajectories contain personal
Externí odkaz:
http://arxiv.org/abs/2304.11582
Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic forecasting models. These models are typically trained to minimize error on averaged test cases and produce a single-point prediction, such as a scalar value for t
Externí odkaz:
http://arxiv.org/abs/2303.09273