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2021 ◽  
Vol 13 (2) ◽  
pp. 43-51
Author(s):  
Van Long Em Phan

The synchronization in complete network consisting of  nodes is studied in this paper. Each node is connected to all other ones by nonlinear coupling and is represented by a reaction-diffusion system of FitzHugh-Nagumo type which can be obtained by simplifying the famous Hodgkin-Huxley model. From this complete network, the sufficient condition on the coupling strength to achieve the synchronization is found. The result shows that the networks with bigger in-degrees of nodes synchronize more easily. The paper also presents the numerical simulations for theoretical result and shows a compromise between the theoretical and numerical results.


2020 ◽  
Vol 8 (2) ◽  
pp. 45-53
Author(s):  
Phan Van Long Em

Synchronization is a ubiquitous feature in many natural systems and nonlinear science. This paper studies the synchronization in complete network consisting of n nodes. Each node is connected to all other nodes by linear coupling and represented by a reaction-diffusion system of FitzHugh-Nagumo type which can be obtained by simplifying the famous Hodgkin-Huxley model. From this complete network, the author seeks a sufficient condition on the coupling strength to achieve synchronization. The result shows that the more easily the nodes synchronize, the bigger the degrees of the networks. Based on this consequence, the author will test the theoretical result numerically to see if there is a compromise.


Author(s):  
Ian Shapiro ◽  
Steven Richardson ◽  
Scott McClurg ◽  
Anand Sokhey

Decades of work have illuminated the influence interpersonal networks exert on voting behavior, political participation, the acquisition of political knowledge, tolerance, ambivalence, and attitude polarization. These central findings have largely been grounded in examinations of political discussion and have remained robust to measurement differences of key concepts like disagreement, various data collection methods, and multiple research designs ranging from the cross-sectional to large-scale field experiments. By comparison, scholars understand considerably less about individuals’ motivation to approach their social contacts when it comes to politics, and about why networks produce the outcomes that they do; this calls researchers to reflect on and revisit previous research, but also to consider new paths of research. Although there is a growing body of promising work focused on “whole,” or complete, networks, much can also be gained by better integrating social psychology into the study of egocentric, or “core,” political networks. Answering these (and other) questions will help connect current findings, emerging methods, and nascent theory. Such connections should advance dialogues between research on group influence, discussion networks, and individual political behavior.


2019 ◽  
Author(s):  
Peiliang Lou ◽  
Antonio Jimeno Yepes ◽  
Zai Zhang ◽  
Qinghua Zheng ◽  
Xiangrong Zhang ◽  
...  

Abstract Motivation A biochemical reaction, bio-event, depicts the relationships between participating entities. Current text mining research has been focusing on identifying bio-events from scientific literature. However, rare efforts have been dedicated to normalize bio-events extracted from scientific literature with the entries in the curated reaction databases, which could disambiguate the events and further support interconnecting events into biologically meaningful and complete networks. Results In this paper, we propose BioNorm, a novel method of normalizing bio-events extracted from scientific literature to entries in the bio-molecular reaction database, e.g. IntAct. BioNorm considers event normalization as a paraphrase identification problem. It represents an entry as a natural language statement by combining multiple types of information contained in it. Then, it predicts the semantic similarity between the natural language statement and the statements mentioning events in scientific literature using a long short-term memory recurrent neural network (LSTM). An event will be normalized to the entry if the two statements are paraphrase. To the best of our knowledge, this is the first attempt of event normalization in the biomedical text mining. The experiments have been conducted using the molecular interaction data from IntAct. The results demonstrate that the method could achieve F-score of 0.87 in normalizing event-containing statements. Availability and implementation The source code is available at the gitlab repository https://gitlab.com/BioAI/leen and BioASQvec Plus is available on figshare https://figshare.com/s/45896c31d10c3f6d857a.


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