A Bayesian Multifactor Spatio-Temporal Model for Estimating Time-Varying Network Interdependence

2021 ◽  
Author(s):  
Xun Pang ◽  
Licheng Liu
Keyword(s):  
Time Varying ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Network Interdependence

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill

2016 ◽  
Vol 12 (6) ◽  
pp. e1004969 ◽  
Author(s):  
Zhihui Wang ◽  
Romica Kerketta ◽  
Yao-Li Chuang ◽  
Prashant Dogra ◽  
Joseph D. Butner ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Experimental Validation ◽  
Cell Kill ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Enhance Tumor Cell

scholarly journals Spatio-temporal integration in plant tropisms

2019 ◽  
Vol 16 (154) ◽  
pp. 20190038 ◽  
Author(s):  
Yasmine Meroz ◽  
Renaud Bastien ◽  
L. Mahadevan
Keyword(s):  
Response Function ◽  
Shoot Growth ◽  
Analytic Solution ◽  
Temporal Integration ◽  
Time Varying ◽  
Local Response ◽  
History Of ◽  
Non Local ◽  
Spatio Temporal ◽  
Plant Shoots

Tropisms, growth-driven responses to environmental stimuli, cause plant organs to respond in space and time and reorient themselves. Classical experiments from nearly a century ago reveal that plant shoots respond to the integrated history of light and gravity stimuli rather than just responding instantaneously. We introduce a temporally non-local response function for the dynamics of shoot growth formulated as an integro-differential equation whose solution allows us to qualitatively reproduce experimental observations associated with intermittent and unsteady stimuli. Furthermore, an analytic solution for the case of a pulse stimulus expresses the response function as a function of experimentally tractable variables, which we calculate for the case of the phototropic response of Arabidopsis hypocotyls. All together, our model enables us to predict tropic responses to time-varying stimuli, manifested in temporal integration phenomena, and sets the stage for the incorporation of additional effects such as multiple stimuli, gravitational sagging, etc.

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

scholarly journals Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Heri Kuswanto ◽  
Muhammad Hisyam Lee
Keyword(s):  
Forecast Accuracy ◽  
Oil Production ◽  
Training Data ◽  
Temporal Data ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Temporal Models ◽  
Accurate Forecast ◽  
Temporal Model ◽  
Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

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