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.

Identification of network modules by optimization of ratio association.

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 us to develop an efficient optimization algorithm, based on the deterministic annealing scheme. The performance of the proposed method is shown on real data sets and on simulated networks.

The control of chaos: theoretical schemes and experimental realizations

Abstract: Controlling chaos is a process wherein an unstable periodic orbit embedded in a chaotic attractor is stabilized by means of tiny perturbations of the system. These perturbations imply goal oriented feedback techniques which act either on the state variables of the system or on the control parameters. We review some theoretical schemes and experimental implementations for the control of chaos.

Macroscopic and microscopic spectral properties of brain networks during local and global synchronization.

Abstract: We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators, which evolve adaptively according to homophilic and homeostatic adaptive principles, we give evidence that the increase of synchronization within groups of nodes (and the corresponding formation of synchronous clusters) causes also the defragmentation of the wavelet energy spectrum of the macroscopic signal. Our methodology is then applied to getting a glance into the microscopic interactions occurring in a neurophysiological system, namely, in the thalamocortical neural network of an epileptic brain of a rat, where the group electrical activity is registered by means of multichannel EEG. We demonstrate that it is possible to infer the degree of interaction between the interconnected regions of the brain during different types of brain activities and to estimate the regions' participation in the generation of the different levels of consciousness.

Introduction: Control and synchronization in chaotic dynamical systems

Abstract: exopl athe unof me ics r nd ired n a its onby esobded rian n tal of a The last ten years have seen remarkable developmen the research of chaotic dynamics, particularly with respec the interaction of chaotic dynamics with other fields of r search and with applications. There is now a developed ence of chaos that has as an essential underpinning the s interaction of theory and experiment. This is a depart from earlier times in which theoretical work existed large in the absence of substantial experimental realizations. Al with this new orientation has come increased apprecia and concern for the implications of chaotic dynamics in pr tical applications. Issues in topics such as the active con of chaotic systems in a broad variety of situations, the us chaos for communication, and the synchronization of cha dynamics for various purposes, are at the forefront of rec application topics in nonlinear science. The common thr through those topics is the marriage between knowledg the basic mathematical properties of chaos and specific p tical considerations of various applications. This Focus Issue resulted from a six-week event at Max Planck Institute for Physics of Complex Systems Dresden in the Fall of 2001. During that Worshop/Semin especially interesting and challenging topics on control a synchronization were addressed. We believe that success the research work coming out from that program will ha far-reaching technological and economical impact for broad area of important practical systems ranging from sers, via engineering to neuroscience and medicine. This issue focuses onControl and Synchronization in Chaotic Dynamical Systems . The fundamentals and the ma jor concepts involved in this area were reviewed in Chaosin a Focus Issue in December 1997 @Chaos7 ~4!#. Since that time, the then novel topics and applications have matu making this area well-established within nonlinear scien Elements and concepts from the theory of systems con and the theory of communication have been brought in, g ing the whole topic a firmer foundation. Therefore, the p

Reconstructing embedding spaces of coupled dynamical systems from multivariate data.

Abstract: A method for reconstructing dimensions of subspaces for weakly coupled dynamical systems is offered. The tool is able to extrapolate the subspace dimensions from the zero coupling limit, where the division of dimensions as per the algorithm is exact. Implementation of the proposed technique to multivariate data demonstrates its effectiveness in disentangling subspace dimensionalities also in the case of emergent synchronized motions, for both numerical and experimental systems.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

Pattern Recognition and Machine Learning

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.

Complex brain networks: graph theoretical analysis of structural and functional systems

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.