scholarly journals Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 172 ◽  
Author(s):  
Johann H. Martínez ◽  
David Garrido ◽  
José L Herrera-Diestra ◽  
Javier Busquets ◽  
Ricardo Sevilla-Escoboza ◽  
...  
Keyword(s):  
Network Science ◽  
Permutation Entropy ◽  
Network Parameter ◽  
Temporal Fluctuations ◽  
Entropy Changes ◽  
Network Parameters ◽  
Spatial Entropy ◽  
Statistical Complexity ◽  
Football Team ◽  
Number Of Passes

We quantified the spatial and temporal entropy related to football teams and their players by means of a pass-based interaction. First, we calculated the spatial entropy associated to the positions of all passes made by a football team during a match, obtaining a spatial entropy ranking of Spanish teams during the 2017/2018 season. Second, we investigated how the player’s average location in the field is related to the amount of entropy of his passes. Next, we constructed the temporal passing networks of each team and computed the deviation of their network parameters along the match. For each network parameter, we obtained the permutation entropy and the statistical complexity of its temporal fluctuations. Finally, we investigated how the permutation entropy (and statistical complexity) of the network parameters was related to the total number of passes made by a football team. Our results show that (i) spatial entropy changes according to the position of players in the field, and (ii) the organization of passing networks change during a match and its evolution can be captured measuring the permutation entropy and statistical complexity of the network parameters, allowing to identify what parameters evolve more randomly.

scholarly journals A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

2021 ◽  
Vol 11 (17) ◽  
pp. 8074
Author(s):  
Tierui Zou ◽  
Nader Aljohani ◽  
Keerthiraj Nagaraj ◽  
Sheng Zou ◽  
Cody Ruben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
State Of The Art ◽  
Real Life ◽  
The State ◽  
Wide Area ◽  
Network Parameter ◽  
False Data Injection ◽  
Network Parameters ◽  
Injection Attacks

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

2022 ◽  
pp. 202-226
Author(s):  
Leema N. ◽  
Khanna H. Nehemiah ◽  
Elgin Christo V. R. ◽  
Kannan A.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Neural Network Training ◽  
Network Parameter ◽  
Network Parameters ◽  
Network Training ◽  
Rate Minimum ◽  
Hidden Layer ◽  
Function Number

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

scholarly journals Consistency and identifiability of football teams: a network science perspective

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
D. Garrido ◽  
D. R. Antequera ◽  
J. Busquets ◽  
R. López del Campo ◽  
R. Resta Serra ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Network Science ◽  
Adjacency Matrices ◽  
Number Of Passes ◽  
At Home

AbstractWe investigated the ability of football teams to develop a particular playing style by looking at their passing patterns. Using the information contained in the pass sequences during matches, we constructed the pitch passing networks of teams, whose nodes are the divisions of the pitch for a given spatial scale and links account for the number of passes from region to region. We translated football passings networks into their corresponding adjacency matrices. We calculated the correlations between matrices of the same team to quantify how consistent the passing patterns of a given team are. Next, we quantified the differences with other teams’ matrices and obtained an identifiability parameter that indicates how unique are the passing patterns of a given team. Consistency and identifiability rankings were calculated during a whole season, allowing to detect those teams of a league whose passing patterns are different from the rest. Furthermore, we found differences between teams playing at home or away. Finally, we used the identifiability parameter to investigate what teams imposed their passing patterns over the rivals during a given match.

scholarly journals Research and Verification of Convolutional Neural Network Lightweight in BCI

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shipu Xu ◽  
Runlong Li ◽  
Yunsheng Wang ◽  
Yong Liu ◽  
Wenwen Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Classification Accuracy ◽  
Original Network ◽  
Network Parameter ◽  
Network Parameters ◽  
Network Operation ◽  
Depth And Complexity ◽  
Convergence Stability

With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convolution and uses channel shuffle between blocks to alleviate the information jam. The method is aimed at reducing the dimensionality of parameters of in an original network structure and improving the efficiency of network operation. The verification performance of the ORL dataset shows that the classification accuracy and convergence efficiency are not reduced or even slightly improved when the network parameters are reduced, which supports the validity of block convolution in structure lightweight. Moreover, using a classic CIFAR-10 dataset, this network decreases parameter dimensionality while accelerating computational processing, with excellent convergence stability and efficiency when the network accuracy is only reduced by 1.3%.

scholarly journals Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

2020 ◽  
Vol 11 (4) ◽  
pp. 62-85
Author(s):  
Leema N. ◽  
Khanna H. Nehemiah ◽  
Elgin Christo V. R. ◽  
Kannan A.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Neural Network Training ◽  
Network Parameter ◽  
Network Parameters ◽  
Network Training ◽  
Hidden Layer ◽  
Function Number ◽  
The Impact

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

scholarly journals Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 116 ◽  
Author(s):  
Ignacio Echegoyen ◽  
David López-Sanz ◽  
Johann H. Martínez ◽  
Fernando Maestú ◽  
Javier M. Buldú
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Frequency Band ◽  
Local Level ◽  
Permutation Entropy ◽  
Frequency Bands ◽  
Individual Level ◽  
Statistical Complexity

We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.

Investigating anthropically induced effects in streamflow dynamics by using permutation entropy and statistical complexity analysis: A case study

2016 ◽  
Vol 540 ◽  
pp. 1136-1145 ◽  
Author(s):  
Tatijana Stosic ◽  
Luciano Telesca ◽  
Diego Vicente de Souza Ferreira ◽  
Borko Stosic
Keyword(s):  
Complexity Analysis ◽  
Permutation Entropy ◽  
Statistical Complexity

scholarly journals Permutation Entropy and Statistical Complexity Analysis of Brazilian Agricultural Commodities

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1220 ◽  
Author(s):  
Fernando Henrique Antunes de Araujo ◽  
Lucian Bejan ◽  
Osvaldo A. Rosso ◽  
Tatijana Stosic
Keyword(s):  
Market Integration ◽  
Complexity Analysis ◽  
Commodity Prices ◽  
Food Prices ◽  
Permutation Entropy ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Abrupt Increase ◽  
Statistical Complexity ◽  
Cattle Markets

Agricultural commodities are considered perhaps the most important commodities, as any abrupt increase in food prices has serious consequences on food security and welfare, especially in developing countries. In this work, we analyze predictability of Brazilian agricultural commodity prices during the period after 2007/2008 food crisis. We use information theory based method Complexity/Entropy causality plane (CECP) that was shown to be successful in the analysis of market efficiency and predictability. By estimating information quantifiers permutation entropy and statistical complexity, we associate to each commodity the position in CECP and compare their efficiency (lack of predictability) using the deviation from a random process. Coffee market shows highest efficiency (lowest predictability) while pork market shows lowest efficiency (highest predictability). By analyzing temporal evolution of commodities in the complexity–entropy causality plane, we observe that during the analyzed period (after 2007/2008 crisis) the efficiency of cotton, rice, and cattle markets increases, the soybeans market shows the decrease in efficiency until 2012, followed by the lower predictability and the increase of efficiency, while most commodities (8 out of total 12) exhibit relatively stable efficiency, indicating increased market integration in post-crisis period.

Permutation entropy and statistical complexity in characterising low-aspect-ratio reversed-field pinch plasma

2017 ◽  
Vol 92 (5) ◽  
pp. 055601 ◽  
Author(s):  
T Onchi ◽  
A Fujisawa ◽  
A Sanpei ◽  
H Himura ◽  
S Masamune ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Permutation Entropy ◽  
Reversed Field Pinch ◽  
Pinch Plasma ◽  
Low Aspect Ratio ◽  
Statistical Complexity ◽  
Reversed Field

scholarly journals Asymmetries in Football: The Pass—Goal Paradox

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1052
Author(s):  
Daniel R. Antequera ◽  
David Garrido ◽  
Ignacio Echegoyen ◽  
Roberto López del Campo ◽  
Ricardo Resta Serra ◽  
...  
Keyword(s):  
Football Team ◽  
National League ◽  
Number Of Passes ◽  
Strong Asymmetry ◽  
Made In

We investigate the relation between the number of passes made by a football team and the number of goals. We analyze the 380 matches of a complete season of the Spanish national league “LaLiga" (2018/2019). We observe how the number of scored goals is positively correlated with the number of passes made by a team. In this way, teams on the top (bottom) of the ranking at the end of the season make more (less) passes than the rest of the teams. However, we observe a strong asymmetry when the analysis is made depending on the part of the match. Interestingly, fewer passes are made in the second half of a match, while, at the same time, more goals are scored. This paradox appears in the majority of teams, and it is independent of the number of passes made. These results confirm that goals in the first half of matches are more “costly” in terms of passes than those scored in second halves.

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