96,159 research outputs found
Mixed Cumulative Distribution Networks
Directed acyclic graphs (DAGs) are a popular framework to express
multivariate probability distributions. Acyclic directed mixed graphs (ADMGs)
are generalizations of DAGs that can succinctly capture much richer sets of
conditional independencies, and are especially useful in modeling the effects
of latent variables implicitly. Unfortunately there are currently no good
parameterizations of general ADMGs. In this paper, we apply recent work on
cumulative distribution networks and copulas to propose one one general
construction for ADMG models. We consider a simple parameter estimation
approach, and report some encouraging experimental results.Comment: 11 pages, 4 figure
Grouping complex systems: a weighted network comparative analysis
In this study, the authors compare two inter-municipal commuting networks (MCN) pertaining to the Italian islands of Sardinia and Sicily, by approaching their characterization through a weighted network analysis. They develop on
the results obtained for the MCN of Sardinia (De Montis et al. 2007) and attempt to use network analysis as a mean of detection of similarities or dissimilarities between the systems at hand
Molecular motors robustly drive active gels to a critically connected state
Living systems often exhibit internal driving: active, molecular processes
drive nonequilibrium phenomena such as metabolism or migration. Active gels
constitute a fascinating class of internally driven matter, where molecular
motors exert localized stresses inside polymer networks. There is evidence that
network crosslinking is required to allow motors to induce macroscopic
contraction. Yet a quantitative understanding of how network connectivity
enables contraction is lacking. Here we show experimentally that myosin motors
contract crosslinked actin polymer networks to clusters with a scale-free size
distribution. This critical behavior occurs over an unexpectedly broad range of
crosslink concentrations. To understand this robustness, we develop a
quantitative model of contractile networks that takes into account network
restructuring: motors reduce connectivity by forcing crosslinks to unbind.
Paradoxically, to coordinate global contractions, motor activity should be low.
Otherwise, motors drive initially well-connected networks to a critical state
where ruptures form across the entire network.Comment: Main text: 21 pages, 5 figures. Supplementary Information: 13 pages,
8 figure
MISEP - Linear and Nonlinear ICA Based on Mutual Information
MISEP is a method for linear and nonlinear ICA, that is able to handle a large variety of situations. It is an extension of the well known INFOMAX method, in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs. The method can therefore separate linear and nonlinear mixtures of components with a wide range of statistical distributions.
This paper presents the basis of the MISEP method, as well as experimental results obtained with it. The results illustrate the applicability of the method to various situations, and show that, although the nonlinear blind separation problem is ill-posed, use of regularization allows the problem to be solved when the nonlinear mixture is relatively smooth
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