17,945 research outputs found
Cluster-based reduced-order modelling of a mixing layer
We propose a novel cluster-based reduced-order modelling (CROM) strategy of
unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's
group (Burkardt et al. 2006) and and transition matrix models introduced in
fluid dynamics in Eckhardt's group (Schneider et al. 2007). CROM constitutes a
potential alternative to POD models and generalises the Ulam-Galerkin method
classically used in dynamical systems to determine a finite-rank approximation
of the Perron-Frobenius operator. The proposed strategy processes a
time-resolved sequence of flow snapshots in two steps. First, the snapshot data
are clustered into a small number of representative states, called centroids,
in the state space. These centroids partition the state space in complementary
non-overlapping regions (centroidal Voronoi cells). Departing from the standard
algorithm, the probabilities of the clusters are determined, and the states are
sorted by analysis of the transition matrix. Secondly, the transitions between
the states are dynamically modelled using a Markov process. Physical mechanisms
are then distilled by a refined analysis of the Markov process, e.g. using
finite-time Lyapunov exponent and entropic methods. This CROM framework is
applied to the Lorenz attractor (as illustrative example), to velocity fields
of the spatially evolving incompressible mixing layer and the three-dimensional
turbulent wake of a bluff body. For these examples, CROM is shown to identify
non-trivial quasi-attractors and transition processes in an unsupervised
manner. CROM has numerous potential applications for the systematic
identification of physical mechanisms of complex dynamics, for comparison of
flow evolution models, for the identification of precursors to desirable and
undesirable events, and for flow control applications exploiting nonlinear
actuation dynamics.Comment: 48 pages, 30 figures. Revised version with additional material.
Accepted for publication in Journal of Fluid Mechanic
Towards a Topological Mechanism of Quark Confinement
We report on new analyses of the topological and chiral vacuum structure of
four-dimensional QCD on the lattice. Correlation functions as well as
visualization of monopole currents in the maximally Abelian gauge emphasize
their topological origin and gauge invariant characterization. The
(anti)selfdual character of strong vacuum fluctuations is reveiled by
smoothing. In full QCD, (anti)instanton positions are also centers of the local
chiral condensate and quark charge density. Most results turn out generically
independent of the action and the cooling/smoothing method.Comment: 14 pages, Contribution to YKIS9
Bayesian non-linear large scale structure inference of the Sloan Digital Sky Survey data release 7
In this work we present the first non-linear, non-Gaussian full Bayesian
large scale structure analysis of the cosmic density field conducted so far.
The density inference is based on the Sloan Digital Sky Survey data release 7,
which covers the northern galactic cap. We employ a novel Bayesian sampling
algorithm, which enables us to explore the extremely high dimensional
non-Gaussian, non-linear log-normal Poissonian posterior of the three
dimensional density field conditional on the data. These techniques are
efficiently implemented in the HADES computer algorithm and permit the precise
recovery of poorly sampled objects and non-linear density fields. The
non-linear density inference is performed on a 750 Mpc cube with roughly 3 Mpc
grid-resolution, while accounting for systematic effects, introduced by survey
geometry and selection function of the SDSS, and the correct treatment of a
Poissonian shot noise contribution. Our high resolution results represent
remarkably well the cosmic web structure of the cosmic density field.
Filaments, voids and clusters are clearly visible. Further, we also conduct a
dynamical web classification, and estimated the web type posterior distribution
conditional on the SDSS data.Comment: 18 pages, 11 figure
Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experiments
© 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.The binarization of consensus partition matrices (Bi-CoPaM) method has, among its unique features, the ability to perform ensemble clustering over the same set of genes from multiple microarray datasets by using various clustering methods in order to generate tunable tight clusters. Therefore, we have used the Bi-CoPaM method to the most synchronized 500 cell-cycle-regulated yeast genes from different microarray datasets to produce four tight, specific and exclusive clusters of co-expressed genes. We found 19 genes formed the tightest of the four clusters and this included the gene CMR1/YDL156W, which was an uncharacterized gene at the time of our investigations. Two very recent proteomic and biochemical studies have independently revealed many facets of CMR1 protein, although the precise functions of the protein remain to be elucidated. Our computational results complement these biological results and add more evidence to their recent findings of CMR1 as potentially participating in many of the DNA-metabolism processes such as replication, repair and transcription. Interestingly, our results demonstrate the close co-expressions of CMR1 and the replication protein A (RPA), the cohesion complex and the DNA polymerases α, δ and ɛ, as well as suggest functional relationships between CMR1 and the respective proteins. In addition, the analysis provides further substantial evidence that the expression of the CMR1 gene could be regulated by the MBF complex. In summary, the application of a novel analytic technique in large biological datasets has provided supporting evidence for a gene of previously unknown function, further hypotheses to test, and a more general demonstration of the value of sophisticated methods to explore new large datasets now so readily generated in biological experiments.National Institute for Health Researc
Randomizing world trade. II. A weighted network analysis
Based on the misleading expectation that weighted network properties always
offer a more complete description than purely topological ones, current
economic models of the International Trade Network (ITN) generally aim at
explaining local weighted properties, not local binary ones. Here we complement
our analysis of the binary projections of the ITN by considering its weighted
representations. We show that, unlike the binary case, all possible weighted
representations of the ITN (directed/undirected, aggregated/disaggregated)
cannot be traced back to local country-specific properties, which are therefore
of limited informativeness. Our two papers show that traditional macroeconomic
approaches systematically fail to capture the key properties of the ITN. In the
binary case, they do not focus on the degree sequence and hence cannot
characterize or replicate higher-order properties. In the weighted case, they
generally focus on the strength sequence, but the knowledge of the latter is
not enough in order to understand or reproduce indirect effects.Comment: See also the companion paper (Part I): arXiv:1103.1243
[physics.soc-ph], published as Phys. Rev. E 84, 046117 (2011
Randomizing world trade. II. A weighted network analysis
Based on the misleading expectation that weighted network properties always
offer a more complete description than purely topological ones, current
economic models of the International Trade Network (ITN) generally aim at
explaining local weighted properties, not local binary ones. Here we complement
our analysis of the binary projections of the ITN by considering its weighted
representations. We show that, unlike the binary case, all possible weighted
representations of the ITN (directed/undirected, aggregated/disaggregated)
cannot be traced back to local country-specific properties, which are therefore
of limited informativeness. Our two papers show that traditional macroeconomic
approaches systematically fail to capture the key properties of the ITN. In the
binary case, they do not focus on the degree sequence and hence cannot
characterize or replicate higher-order properties. In the weighted case, they
generally focus on the strength sequence, but the knowledge of the latter is
not enough in order to understand or reproduce indirect effects.Comment: See also the companion paper (Part I): arXiv:1103.1243
[physics.soc-ph], published as Phys. Rev. E 84, 046117 (2011
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