A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling

Moghari, Somaye; Ghorani, Maryam

doi:10.1016/j.chaos.2021.111660

Cited by 7 publications

(6 citation statements)

References 51 publications

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“…Such early warning signals can be used in a real-time early-warning system of self-organization to the edge of a phase transition in a real-world system if its structure is similar to that of sandpile cellular automata, i.e., the systems are isomorphic in the context of systems theory. Such systems are a segment of a stock exchange (e.g., see the paper [41]), epidemiological networks (e.g., see the papers [5,42]), continuous media systems (e.g., see the paper [43]), and complex networks (e.g., see the paper [44]). Te known models of information dynamics in Twitter (e.g., see the papers [45,46]) do not allow us to explain the selforganization of a network on the edge of a phase transition and therefore cannot be used as models for testing EWMs.…”

Section: Discussionmentioning

confidence: 99%

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Dmitriev,

Lebedev,

Kornilov

et al. 2023

Complexity

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Many real-world systems of various origins are capable of self-organization to the edge of a phase transition, characterized by avalanche-like behavior. Therefore, it is important, by observing the behavior of early warning measures for dynamical series generated by systems, to timely see the early warning signals (precursors) of such self-organization and, if necessary, take preventive measures. To date, convincing evidence of self-organization to the edge of a phase transition has been obtained, but no effective precursors for this self-organization have been found. This research explores precursors for the Twitter self-organization based on the analysis of the behavior of measures directly related to the critical slowdown of the network and measures of the phase space reconstructed by the Takens method for the series of the number of network users creating avalanches of retweets in the network, corresponding to the three debates of the 2016 United States Presidential Election. We hydrated the relevant Tweet IDs, which were obtained from the Harvard Dataverse using the Social Feed Manager, to form this series. Preliminarily, we explore the potential of measures for early detection of self-organization of sandpile cellular automata as systems with Twitter-equivalent self-organization mechanisms. The equivalence is justified in the proposed discrete-time model for Twitter self-organization to the edge of a phase transition. It is found that there are more moments of the Twitter self-organization than the moments of time when debates started, and Twitter stays at the edge of a phase transition longer than the debate lasts. The effective measures, as the measures with the lowest number of false early warning signals, among all studied measures and for all studied systems, are dispersion and correlation dimension. Obtained results are practically important in the design and implementation of early warning systems for the systems with similar mechanisms for sandpile cellular automata self-organization to the edge of a phase transition.

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Section: Discussionmentioning

confidence: 99%

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Dmitriev,

Lebedev,

Kornilov

et al. 2023

Complexity

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“…Cellular automata (CA) is a computational model for systems that changes occur at any point (cell) based on the status or history of its neighboring points [6,30]. A CA consists of a collection of cells arranged in a multidimensional space and a dynamical rule (transition function) that updates their configuration synchronously.…”

Section: Related Workmentioning

confidence: 99%

“…A CA consists of a collection of cells arranged in a multidimensional space and a dynamical rule (transition function) that updates their configuration synchronously. The transition function calculates the state of each cell based on the state of its neighbors, where these simple local interactions and computations between cells, form a complex global behavior [30]. The set of transition rules of a CA is a type of knowledge that Li and Yeh [26] applied data mining technique for reconstructing it.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing fuzzy tree automata

Moghari

2022

RAIRO-Theor. Inf. Appl.

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Fuzzy tree automata are mathematical devices for modeling and analyzing vaguely defined tree structures. The behavior of a fuzzy tree automaton generates a fuzzy tree language by mapping a set of regular trees on a ranked alphabet to fuzzy membership values. It calculates the membership grade of trees using a set of rules that process their structural characteristics. This paper deals with constructing fuzzy tree automata models that their behavior satisfies a set of given logical propositions (called properties) on the structure of trees. Our goal is uncertainty modeling by synthesizing fuzzy tree automata whose behavior is described by fuzzy linguistic variables. In this regard, we first provide several patterns and heuristic tricks and techniques for constructing fuzzy tree automata that satisfy simple properties. Then, we develop a method for modeling complex propositional formulas based on the conversion of a logical formula into a computation tree, as well as a step-by-step combination of models.

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“…Additionally, FISs play a pivotal role in financial applications by predicting stock prices and analyzing market trends. In medical diagnosis systems, they assist doctors in interpreting medical test results and making informed treatment decisions [13,14]. In light of their critical role and widespread utilization, there is a compelling need to enhance the design of FISs for greater efficiency and sustainability.…”

Section: Introductionmentioning

confidence: 99%

A Hardware Realization Framework for Fuzzy Inference System Optimization

Gorgin,

Karvandi,

Moghari

et al. 2024

Electronics

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Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.

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A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling

Cited by 7 publications

References 51 publications

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Synthesizing fuzzy tree automata

A Hardware Realization Framework for Fuzzy Inference System Optimization

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