scholarly journals Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 26
Author(s):  
Hongjian Xiao ◽  
Danilo P. Mandic
Keyword(s):  
Real World ◽  
Complexity Analysis ◽  
Structural Complexity ◽  
Conditional Entropy ◽  
Sample Entropy ◽  
Embedding Dimension ◽  
World Systems ◽  
Entropy Methods ◽  
Data Channel ◽  
Noise Resilience

Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.

scholarly journals Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 605
Author(s):  
Carmen González ◽  
Erik Jensen ◽  
Pedro Gambús ◽  
Montserrat Vallverdú
Keyword(s):  
Healthy Subjects ◽  
A Priori ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
P Value ◽  
Embedding Dimension ◽  
P Values ◽  
Entropy Measures ◽  
Inexpensive Technique

Rheoencephalography (REG) is a simple and inexpensive technique that intends to monitor cerebral blood flow (CBF), but its ability to reflect CBF changes has not been extensively proved. Based on the hypothesis that alterations in CBF during apnea should be reflected in REG signals under the form of increased complexity, several entropy metrics were assessed for REG analysis during apnea and resting periods in 16 healthy subjects: approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), corrected conditional entropy (CCE) and Shannon entropy (SE). To compute these entropy metrics, a set of parameters must be defined a priori, such as, for example, the embedding dimension m, and the tolerance threshold r. A thorough analysis of the effects of parameter selection in the entropy metrics was performed, looking for the values optimizing differences between apnea and baseline signals. All entropy metrics, except SE, provided higher values for apnea periods (p-values < 0.025). FuzzyEn outperformed all other metrics, providing the lowest p-value (p = 0.0001), allowing to conclude that REG signals during apnea have higher complexity than in resting periods. Those findings suggest that REG signals reflect CBF changes provoked by apneas, even though further studies are needed to confirm this hypothesis.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

scholarly journals Asymmetry underlies stability in power grids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ferenc Molnar ◽  
Takashi Nishikawa ◽  
Adilson E. Motter
Keyword(s):  
Symmetry Breaking ◽  
Real World ◽  
Power Grids ◽  
Stable Operation ◽  
World Systems ◽  
Symmetric State ◽  
Network Systems ◽  
Additional Improvement ◽  
The Stability ◽  
General Method

AbstractBehavioral homogeneity is often critical for the functioning of network systems of interacting entities. In power grids, whose stable operation requires generator frequencies to be synchronized—and thus homogeneous—across the network, previous work suggests that the stability of synchronous states can be improved by making the generators homogeneous. Here, we show that a substantial additional improvement is possible by instead making the generators suitably heterogeneous. We develop a general method for attributing this counterintuitive effect to converse symmetry breaking, a recently established phenomenon in which the system must be asymmetric to maintain a stable symmetric state. These findings constitute the first demonstration of converse symmetry breaking in real-world systems, and our method promises to enable identification of this phenomenon in other networks whose functions rely on behavioral homogeneity.

scholarly journals Principled approach to the selection of the embedding dimension of networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Aditya Tandon ◽  
Yong-Yeol Ahn ◽  
Filippo Radicchi
Keyword(s):  
Real World ◽  
Structural Information ◽  
General Purpose ◽  
Embedding Dimension ◽  
Network Embedding ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Low Dimensional ◽  
Large Corpus ◽  
Selection Of

AbstractNetwork embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension – small enough to be efficient and large enough to be effective – is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

scholarly journals Modeling and simulation of economic processes

2010 ◽  
Author(s):  
Bogdan Brumar
Keyword(s):  
Decision Analysis ◽  
Modeling And Simulation ◽  
Real World ◽  
Analytical Methods ◽  
Simulation Experiment ◽  
Management Decision ◽  
Economic Systems ◽  
World Systems ◽  
Simulation Techniques ◽  
Laborious Work

In general, any activity requires a longer action often characterized by a degree of uncertainty, insecurity, in terms of size of the objective pursued. Because of the complexity of real economic systems, the stochastic dependencies between different variables and parameters considered, not all systems can be adequately represented by a model that can be solved by analytical methods and covering all issues for management decision analysis-economic horizon real. Often in such cases, it is considered that the simulation technique is the only alternative available. Using simulation techniques to study real-world systems often requires a laborious work. Making a simulation experiment is a process that takes place in several stages.

scholarly journals Identifying important nodes from content-associated heterogeneous graph by LeaderRank

2021 ◽  
Vol 2113 (1) ◽  
pp. 012082
Author(s):  
Yulong Dai ◽  
Qiyou Shen ◽  
Xiangqian Xu ◽  
Jun Yang
Keyword(s):  
Real World ◽  
Experimental Analysis ◽  
Differential Information ◽  
World Systems ◽  
Ranking Problem ◽  
Data Set ◽  
Importance Ranking ◽  
Basic Concepts ◽  
Important Nodes ◽  
Graph Nodes

Abstract Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.

Absence of DOA Effect but No Proper Test of the Lumberjack Effect: A Reply to Jamieson and Skraaning (2019)

2020 ◽  
Vol 62 (4) ◽  
pp. 530-534
Author(s):  
Christopher D. Wickens ◽  
Linda Onnasch ◽  
Angelina Sebok ◽  
Dietrich Manzey
Keyword(s):  
Situation Awareness ◽  
Real World ◽  
Statistical Power ◽  
Meta Analysis ◽  
Performance Measure ◽  
World Systems ◽  
Complex Environments ◽  
Level Of Automation ◽  
Key Aspects ◽  
Inappropriate Choice

Objective The aim was to evaluate the relevance of the critique offered by Jamieson and Skraaning (2019) regarding the applicability of the lumberjack effect of human–automation interaction to complex real-world settings. Background The lumberjack effect, based upon a meta-analysis, identifies the consequences of a higher degree of automation—to improve performance and reduce workload—when automation functions as intended, but to degrade performance more, as mediated by a loss of situation awareness (SA) when automation fails. Jamieson and Skraaning provide data from a process control scenario that they assert contradicts the effect. Approach We analyzed key aspects of their simulation, measures, and results which we argue limit the strength of their conclusion that the lumberjack effect is not applicable to complex real-world systems. Results Our analysis revealed limits in their inappropriate choice of automation, the lack of a routine performance measure, support for the lumberjack effect that was actually provided by subjective measures of the operators, an inappropriate assessment of SA, and a possible limitation of statistical power. Conclusion We regard these limitations as reasons to temper the strong conclusions drawn by the authors, of no applicability of the lumberjack effect to complex environments. Their findings should be used as an impetus for conducting further research on human–automation interaction in these domains. Applications The collective findings of both Jamieson and Skraaning and our study are applicable to system designers and users in deciding upon the appropriate level of automation to deploy.

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