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Stefano Boccaletti

Bio: Stefano Boccaletti is an academic researcher from Moscow Institute of Physics and Technology. The author has contributed to research in topics: Complex network & Synchronization (computer science). The author has an hindex of 60, co-authored 348 publications receiving 25776 citations. Previous affiliations of Stefano Boccaletti include King Juan Carlos University & Istituto Nazionale di Fisica Nucleare.


Papers
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Journal ArticleDOI
TL;DR: It is analytically established and numerically show that anomalous frequency synchronization occurs in a pair of asymmetrically coupled chaotic space extended oscillators.
Abstract: We analytically establish and numerically show that anomalous frequency synchronization occurs in a pair of asymmetrically coupled chaotic space extended oscillators. The transition to anomalous behaviors is crucially dependent on asymmetries in the coupling configuration, while the presence of phase defects has the effect of enhancing the anomaly in frequency synchronization with respect to the case of merely time chaotic oscillators.

8 citations

Journal ArticleDOI
TL;DR: In this article, the authors show that weighted scale-free networks of diffusively coupled excitable elements exhibit both high synchronizability of their subthreshold dynamics and a good collective response to noise of their pulsed dynamics.
Abstract: Coupling frequently enhances noise-induced coherence and synchronization in interacting nonlinear systems, but it does so separately. In principle collective stochastic coherence and synchronizability are incompatible phenomena, since strongly synchronized elements behave identically and thus their response to noise is indistinguishable to that of a single element. Therefore one can expect systems that synchronize well to have a poor collective response to noise. Here we show that, in spite of this apparent conflict, a certain coupling architecture is able to reconcile the two properties. Specifically, our results reveal that weighted scale-free networks of diffusively coupled excitable elements exhibit both high synchronizability of their subthreshold dynamics and a good collective response to noise of their pulsed dynamics. This is established by comparing the behavior of this system to that of random, regular, and unweighted scale-free networks. We attribute the optimal response of weighted scale-free networks to a balance between degree heterogeneity, which ensures a good collective response to noise, and the coupling-strength weighting procedure, which overcomes the paradox of heterogeneity that would otherwise impair synchronization.

8 citations

Journal ArticleDOI
03 Jan 2005-Chaos
TL;DR: It is shown how the use of properly shaped parameter distributions can lead to a relevant reduction of the cross-talk between close by solitons in applications via the introduction of proper spatial distribution of the system parameters.
Abstract: The effect of phase and intensity gradients on the motion of dissipative solitons in a nonlinear interferometer is investigated. We show how the forces exerted by parameter gradients on the solitons alter their interactions. Consequently, it is possible to tune the spectrum of soliton bound states via the introduction of proper spatial distribution of the system parameters. Furthermore, we show how the use of properly shaped parameter distributions can lead to a relevant reduction of the cross-talk between close by solitons in applications.

8 citations

Journal ArticleDOI
TL;DR: In this paper, a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association is introduced, which arises when the communities detection problem is described in the probabilistic autoencoder frame.
Abstract: We introduce a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association. This cost function arises when the communities detection problem is described in the probabilistic autoencoder frame. An analogy with kernel k-means methods allows to develop an efficient optimization algorithm, based on the deterministic annealing scheme. The performance of the proposed method is shown on a real data set and on simulated networks.

7 citations

Journal ArticleDOI
TL;DR: This work distinguishes between two different hybrid synchronization regimes that emerge with parameter perturbation, and proves the existence of both classes of hybrid synchronization in numerical examples of the Rössler system, a Lorenz-like system, and also in electronic experiment.
Abstract: We evidence an interesting kind of hybrid synchronization in coupled chaotic systems where complete synchronization is restricted to only a subset of variables of two systems while other subset of variables may be in a phase synchronized state or desynchronized. Such hybrid synchronization is a generic emergent feature of coupled systems when a controller based coupling, designed by the Lyapunov function stability, is first engineered to induce complete synchronization in the identical case, and then a large parameter mismatch is introduced. We distinguish between two different hybrid synchronization regimes that emerge with parameter perturbation. The first, called hard hybrid synchronization, occurs when the coupled systems display global phase synchronization, while the second, called soft hybrid synchronization, corresponds to a situation where, instead, the global synchronization feature no longer exists. We verify the existence of both classes of hybrid synchronization in numerical examples of the R\"ossler system, a Lorenz-like system, and also in electronic experiment.

7 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

9,700 citations