Modeling Spatio-temporal Network Computations: A Summary of Results

2007 ◽  
pp. 177-194
Author(s):  
Betsy George ◽  
Shashi Shekhar
Keyword(s):  
Temporal Network ◽  
Spatio Temporal

MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction

2021 ◽  
pp. 1-14
Author(s):  
Shen Fang ◽  
Veronique Prinet ◽  
Jianlong Chang ◽  
Michael Werman ◽  
Chunxia Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Temporal Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal

scholarly journals Residual Invertible Spatio-Temporal Network for Video Super-Resolution

2019 ◽  
Vol 33 ◽  
pp. 5981-5988 ◽  
Author(s):  
Xiaobin Zhu ◽  
Zhuangzi Li ◽  
Xiao-Yu Zhang ◽  
Changsheng Li ◽  
Yaqi Liu ◽  
...  
Keyword(s):  
Spatial Information ◽  
Super Resolution ◽  
Temporal Consistency ◽  
Temporal Network ◽  
Convolutional Network ◽  
Feature Representations ◽  
Video Frames ◽  
Temporal Features ◽  
Benchmark Datasets ◽  
Spatio Temporal

Video super-resolution is a challenging task, which has attracted great attention in research and industry communities. In this paper, we propose a novel end-to-end architecture, called Residual Invertible Spatio-Temporal Network (RISTN) for video super-resolution. The RISTN can sufficiently exploit the spatial information from low-resolution to high-resolution, and effectively models the temporal consistency from consecutive video frames. Compared with existing recurrent convolutional network based approaches, RISTN is much deeper but more efficient. It consists of three major components: In the spatial component, a lightweight residual invertible block is designed to reduce information loss during feature transformation and provide robust feature representations. In the temporal component, a novel recurrent convolutional model with residual dense connections is proposed to construct deeper network and avoid feature degradation. In the reconstruction component, a new fusion method based on the sparse strategy is proposed to integrate the spatial and temporal features. Experiments on public benchmark datasets demonstrate that RISTN outperforms the state-ofthe-art methods.

scholarly journals On dynamic linkages of the state natural gas markets in the USA: Evidence from an empirical spatio-temporal network quantile analysis

2019 ◽  
Vol 80 ◽  
pp. 234-252 ◽  
Author(s):  
Xiaohang Ren ◽  
Zudi Lu ◽  
Cheng Cheng ◽  
Yukun Shi ◽  
Jian Shen
Keyword(s):  
Natural Gas ◽  
The State ◽  
Temporal Network ◽  
Natural Gas Markets ◽  
The Usa ◽  
Gas Markets ◽  
Spatio Temporal ◽  
Dynamic Linkages
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