Nearly isotropic<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>s</mml:mi></mml:math>-wave gap in the bulk of the optimally electron-doped superconductor<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>Nd</mml:mtext></mml:mrow><mml:mrow><mml:mn>1.85</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mtext>Ce</mml:mtext></mml:mrow><mml:mrow><mml:mn>0.15</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><m

Zhao, Guangyao

doi:10.1103/physrevb.82.012506

Cited by 17 publications

(14 citation statements)

References 39 publications

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“…(7) represents the complexity of WS, ER and SF networks in terms of reduced noise intensity. The complexity is defined by Shannon entropy of p(D) [27]:…”

Section: The Effect Of Random Forcementioning

confidence: 99%

Noise-induced synchronization in small world networks of phase oscillators

2012

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A small-world (SW) network of similar phase oscillators, interacting according to the Kuramoto model, is studied numerically. It is shown that deterministic Kuramoto dynamics on SW networks has various stable stationary states. This can be attributed to the so-called defect patterns in an SW network, which it inherits from deformation of helical patterns in its regular parent. Turning on an uncorrelated random force causes vanishing of the defect patterns, hence increasing the synchronization among oscillators for moderate noise intensities. This phenomenon, called stochastic synchronization, is generally observed in some natural networks such as the brain neural network.

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“…(7) represents the complexity of WS, ER and SF networks in terms of reduced noise intensity. The complexity is defined by Shannon entropy of p(D) [27]:…”

Section: The Effect Of Random Forcementioning

confidence: 99%

Noise-induced synchronization in small world networks of phase oscillators

2012

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“…One straightforward way to study such a balance in complex systems is to represent them as dynamical networks, endowing them with well-studied topological and dynamical properties (see [8,9] for a review). For instance, Zhao et al [10] characterized systems of coupled phase oscillators in terms of a complexity index based on the entropy of the distribution of pairwise synchronization. Heterogeneous and modular networks were shown to be characterized by high complexity, for intermediate levels of modularity, in a regime marked by the formation of dynamical clusters and the coordination between them.…”

mentioning

confidence: 99%

“…An alternative way to measure the combination of dynamical segregation and integration is by means of the complexity index E, introduced in Ref. [10] in the context of oscillatory networks. Here, E is calculated using the Shannon entropy of the distribution P (ω) of the average frequencies of all oscillators as E = (− m l=1 P l ln P l )/ ln m, where m is the number of bins in the histogram of P (ω).…”

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confidence: 99%

Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network

et al. 2012

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We introduce an easily computable topological measure which locates the effective crossover between segregation and integration in a modular network. Segregation corresponds to the degree of network modularity, while integration is expressed in terms of the algebraic connectivity of an associated hypergraph. The rigorous treatment of the simplified case of cliques of equal size that are gradually rewired until they become completely merged, allows us to show that this topological crossover can be made to coincide with a dynamical crossover from cluster to global synchronization of a system of coupled phase oscillators. The dynamical crossover is signaled by a peak in the product of the measures of intracluster and global synchronization, which we propose as a dynamical measure of complexity. This quantity is much easier to compute than the entropy (of the average frequencies of the oscillators), and displays a behavior which closely mimics that of the dynamical complexity index based on the latter. The proposed topological measure simultaneously provides information on the dynamical behavior, sheds light on the interplay between modularity and total integration, and shows how this affects the capability of the network to perform both local and distributed dynamical tasks.

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“…[19] is that the coupled dynamical units may get synchronized because of inter-cluster or intra-cluster couplings. Whereas self-organized mechanism has more commonly been thought as a reason of synchronization from seminal work by Kaneko [16] to recent work by several scientists [18,20,21], [19] identified a new mechanism of cluster formation that is driven synchronization and did extensive studies of the two mechanisms of cluster formation for various networks [19,22,23]. Recently self and driven-synchronization has been investigated in the relevance of brain cortical networks [24].…”

Section: Introductionmentioning

confidence: 99%

Transition from the self-organized to the driven dynamical clusters

Singh

Jalan

2012

Physica A: Statistical Mechanics and its Applications

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We study the mechanism of formation of synchronized clusters in coupled maps on networks with various connection architectures. The nodes in a cluster are self- synchronized or driven-synchronized, based on the coupling strength and underlying network structures. A smaller coupling strength region shows driven clusters independent of the network rewiring strategies, whereas a larger coupling strength region shows the transition from the self-organized cluster to the driven cluster as network connections are rewired to the bi-partite type. Lyapunov function analysis is performed to understand the dynamical origin of cluster formation. The results provide insights into the relationship between the topological clusters which are based on the direct connections between the nodes, and the dynamical clusters which are based on the functional behavior of these nodes.Comment: 18 pages, 7 figure

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Nearly isotropics-wave gap in the bulk of the optimally electron-doped superconductorNd1.85Ce0.15

Cited by 17 publications

References 39 publications

Noise-induced synchronization in small world networks of phase oscillators

Noise-induced synchronization in small world networks of phase oscillators

Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network

Transition from the self-organized to the driven dynamical clusters

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