11,761 research outputs found
Modularity from Fluctuations in Random Graphs and Complex Networks
The mechanisms by which modularity emerges in complex networks are not well
understood but recent reports have suggested that modularity may arise from
evolutionary selection. We show that finding the modularity of a network is
analogous to finding the ground-state energy of a spin system. Moreover, we
demonstrate that, due to fluctuations, stochastic network models give rise to
modular networks. Specifically, we show both numerically and analytically that
random graphs and scale-free networks have modularity. We argue that this fact
must be taken into consideration to define statistically-significant modularity
in complex networks.Comment: 4 page
A machine learning approach for mapping and accelerating multiple sclerosis research
The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research.info:eu-repo/semantics/publishedVersio
Ising Model on Edge-Dual of Random Networks
We consider Ising model on edge-dual of uncorrelated random networks with
arbitrary degree distribution. These networks have a finite clustering in the
thermodynamic limit. High and low temperature expansions of Ising model on the
edge-dual of random networks are derived. A detailed comparison of the critical
behavior of Ising model on scale free random networks and their edge-dual is
presented.Comment: 23 pages, 4 figures, 1 tabl
Canalizing Kauffman networks: non-ergodicity and its effect on their critical behavior
Boolean Networks have been used to study numerous phenomena, including gene
regulation, neural networks, social interactions, and biological evolution.
Here, we propose a general method for determining the critical behavior of
Boolean systems built from arbitrary ensembles of Boolean functions. In
particular, we solve the critical condition for systems of units operating
according to canalizing functions and present strong numerical evidence that
our approach correctly predicts the phase transition from order to chaos in
such systems.Comment: to be published in PR
Prediction of cattle density and location at the frontier of Brazil and Paraguay using remote sensing
In this paper, we explore the potential of remote sensing to map pastures areas and by this way establish models for predicting cattle density and location. First, an object based classification (OB) was made in Landsat 5 images for three different municipalities to provide a land-cover map. Second, on the basis of Brazilian official livestock database, a statistical model to predict number of cattle in function of declared pasture area by the farmers was produced. Finally, this model was applied to the pasture areas detected by remote sensing to predict cattle density. Coefficient of determination of the model was 0.63. The results indicate that the methodology used for estimating cattle density has a potential to be applied in regions where no information about farm location and cattle density exists. (Résumé d'auteur
Directed Surfaces in Disordered Media
The critical exponents for a class of one-dimensional models of interface
depinning in disordered media can be calculated through a mapping onto directed
percolation (DP). In higher dimensions these models give rise to directed
surfaces, which do not belong to the directed percolation universality class.
We formulate a scaling theory of directed surfaces, and calculate critical
exponents numerically, using a cellular automaton that locates the directed
surfaces without making reference to the dynamics of the underlying interface
growth models.Comment: 4 pages, REVTEX, 2 Postscript figures avaliable from [email protected]
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