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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: In this article, a high Fresnel number laser close to threshold can be set at a detuning value where it displays only the first transverse pattern with zero intensity in its center, which corresponds to the so-called Takens-Bogdanov bifurcation.
Abstract: A high Fresnel number laser close to threshold can be set at a detuning value where it displays only the first transverse pattern with zero intensity in its center. However, the availability of a large phase space gives rise to a new type of dynamics. Precisely, for this highly symmetric situation, the intensity pattern undergoes a transition between stable and oscillating superpositions of traveling and standing waves. Such a transition corresponds to the so-called Takens-Bogdanov bifurcation. We report experimental evidence of such a behavior. @S1050-2947~97!01409-1#

3 citations

Book ChapterDOI
01 Jan 1995
TL;DR: In this article, the authors review a recent series of investigations on pattern formation in an extended optical system, where modes compete or mix in a weakly correlated or uncorrelated way, thus giving rise to complex patterns.
Abstract: This paper reviews a recent series of investigations on pattern formation in an extended optical system. While early phenomena studied in lasers or other nonlinear optical devices were either single mode or multimode, but with coherent phase relations among modes (mode locking), here we consider phenomena in which modes compete or mix in a weakly correlated or uncorrelated way, thus giving rise to “complex” patterns.

3 citations

Posted Content
TL;DR: In this paper, it was shown that there is a direct connection between the elements of the eigenvector centrality and the clusters of a network. And they proposed a new framework for cluster analysis in undirected and connected graphs, whose computational cost is linear in the number of nodes.
Abstract: Symmetries in a network connectivity regulate how the graph's functioning organizes into clustered states. Classical methods for tracing the symmetry group of a network require very high computational costs, and therefore they are of hard, or even impossible, execution for large sized graphs. We here unveil that there is a direct connection between the elements of the eigen-vector centrality and the clusters of a network. This gives a fresh framework for cluster analysis in undirected and connected graphs, whose computational cost is linear in $N$. We show that the cluster identification is in perfect agreement with symmetry based analyses, and it allows predicting the sequence of synchronized clusters which form before the eventual occurrence of global synchronization.

3 citations

01 Jan 2010
TL;DR: In this article, the authors present an overview of the work of the authors of this paper and their colleagues in the field of functional neuroscience and neuroheuristic research in the context of the Spanish Network of Excellence on Neurodegenerative Diseases.
Abstract: 1. CNR-Institute of Complex Systems, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy, and Italian Embassy in Israel, 25 Hamered Street, Tel-Aviv, Israel. E-mail: stefano.boccaletti@isc.cnr.it 2. Laboratory of Functional Neuroscience, Spanish Network of Excellence on Neurodegenerative Diseases (CIBERNED), University Pablo de Olavide, 41013 Seville, Spain. E-mail: jlcanlor@upo.es 3. Centre National de la Recherche Scientifique, UPR 640 Laboratoire de Neurosciences Cognitives et Imagerie Cerebrale, Hopital de la PitieSalpetriere, 75651 Paris, France. E-mail: mario.chavez@upmc.fr 4. Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland. E-mail: german.gomezherrero@tut.fi 5. Instituto de Fisica Interdisciplinar y Sistemas Complejos (UIB-CSIC), Campus Universitat de les Illes Balears, 07122 Palma de Mallorca, Spain. E-mail: laserdynamics@gmail.com 6. Department of Neurophysiology, Max Planck Institute for Brain Research, Deutschordenstr. 46, 60528, Frankfurt am Main, Germany. E-mail: mail@g-pipa.com 7. Neuroheuristic Research Group, Institute of Computer and Organizational Sciences, Inforge CP1, Universite de Lausanne, 1005 Lausanne, Switzerland. E-mail: avilla@neuroheuristic.org 8. Departament de Fisica i Enginyeria Nuclear, Universitat Politecnica de Catalunya, Campus de Terrassa, 08222 Terrassa, Spain. E-mail: jordi.g.ojalvo@upc.edu

3 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