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pro vyhledávání: '"Nie, Tong"'
The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning tech
Externí odkaz:
http://arxiv.org/abs/2408.17258
In the geospatial domain, universal representation models are significantly less prevalent than their extensive use in natural language processing and computer vision. This discrepancy arises primarily from the high costs associated with the input of
Externí odkaz:
http://arxiv.org/abs/2408.12116
Ensuring fault tolerance of Highly Automated Vehicles (HAVs) is crucial for their safety due to the presence of potentially severe faults. Hence, Fault Injection (FI) testing is conducted by practitioners to evaluate the safety level of HAVs. To full
Externí odkaz:
http://arxiv.org/abs/2407.21069
The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including chan
Externí odkaz:
http://arxiv.org/abs/2407.17246
$\textbf{This is the conference version of our paper: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner}$. Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation
Externí odkaz:
http://arxiv.org/abs/2406.08743
Autor:
Nie, Tong
Steroids play various roles in the body, such as regulating metabolism, immune response, and maintaining homeostasis. Cortisol, a hormone in the glucocorticoid subclass of steroids, is crucial for stress response and cognitive functions, with its lev
Externí odkaz:
https://hdl.handle.net/2144/48895
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or sourc
Externí odkaz:
http://arxiv.org/abs/2405.03185
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. This problem attracts many studies to contribute to data-driven solutions. Existing imputation solutions mainly include lo
Externí odkaz:
http://arxiv.org/abs/2312.01728
Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive advanced techniques have been designed to capture these structures for effective forecasting. However, because STTD is often massive in scale, practitioners need
Externí odkaz:
http://arxiv.org/abs/2307.01482
Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on t
Externí odkaz:
http://arxiv.org/abs/2303.05660