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Materias / palabras clave: Complex network; Dynamical systems theory; Complex system; Topology (electrical circuits); Physics; Network science; Biological network; Theoretical computer science; Statistical mechanics; Evolving networks; Computer sc...

Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and compute...

Inspired by empirical studies of networked systems such as the Internet, social networks, and bio-logical networks, researchers have in recent years developed a variety of techniques and models to help us understand or p...

"Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a ...

This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM)...

We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they ...

The proposal of this research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion. In particular, the activities developed so far can...

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law ...

Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and char...

Materias / palabras clave: Small-world network; Computer science; Topology (electrical circuits); Complex network; Dynamical systems theory; Average path length; Complex system; Network topology; Theoretical computer science; Random graph; Analogy...

Development of the efficient power aware protocol is the need of today s adhoc networks. Although developing battery efficient systems that have low cost and complexity, remains a crucial issue. In order to facilitate co...

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolut...

We develop and analyze low-complexity cooperative diversity protocols that combat fading induced by multipath propagation in wireless networks. The underlying techniques exploit space diversity available through cooperat...

Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possib...

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptabi...

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains ...

This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due ...

Evolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as rob...

Complex control tasks may be solved by dividing them into a more specific and more easily handled subtasks hierarchy. Several authors have demonstrated that the incremental layered evolution paradigm allows obtaining con...

Artificial Neural Networks belong to the sub-symbolic branch of Artificial Intelligence since they allow to find the solution of a problem without the need of knowing the algorithm necessary to solve it. This turns them ...

Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBibTexSections ToolsAdd to favoritesExport Ci...

Bowling Alone: America’s Declining Social Capital Robert D. Putnam (bio) Many students of the new democracies that have emerged over the past decade and a half have emphasized the importance of a strong and active civi...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers...