847 research outputs found
A Fast and Accurate Nonlinear Spectral Method for Image Recognition and Registration
This article addresses the problem of two- and higher dimensional pattern
matching, i.e. the identification of instances of a template within a larger
signal space, which is a form of registration. Unlike traditional correlation,
we aim at obtaining more selective matchings by considering more strict
comparisons of gray-level intensity. In order to achieve fast matching, a
nonlinear thresholded version of the fast Fourier transform is applied to a
gray-level decomposition of the original 2D image. The potential of the method
is substantiated with respect to real data involving the selective
identification of neuronal cell bodies in gray-level images.Comment: 4 pages, 3 figure
The Spread of Opinions and Proportional Voting
Election results are determined by numerous social factors that affect the
formation of opinion of the voters, including the network of interactions
between them and the dynamics of opinion influence. In this work we study the
result of proportional elections using an opinion dynamics model similar to
simple opinion spreading over a complex network. Erdos-Renyi, Barabasi-Albert,
regular lattices and randomly augmented lattices are considered as models of
the underlying social networks. The model reproduces the power law behavior of
number of candidates with a given number of votes found in real elections with
the correct slope, a cutoff for larger number of votes and a plateau for small
number of votes. It is found that the small world property of the underlying
network is fundamental for the emergence of the power law regime.Comment: 10 pages, 7 figure
On the Efficiency of Data Representation on the Modeling and Characterization of Complex Networks
Specific choices about how to represent complex networks can have a
substantial effect on the execution time required for the respective
construction and analysis of those structures. In this work we report a
comparison of the effects of representing complex networks statically as
matrices or dynamically as spase structures. Three theoretical models of
complex networks are considered: two types of Erdos-Renyi as well as the
Barabasi-Albert model. We investigated the effect of the different
representations with respect to the construction and measurement of several
topological properties (i.e. degree, clustering coefficient, shortest path
length, and betweenness centrality). We found that different forms of
representation generally have a substantial effect on the execution time, with
the sparse representation frequently resulting in remarkably superior
performance
Relativistic model for the nonmesonic weak decay of single-lambda hypernuclei
Having in mind its future extension for theoretical investigations related to
charmed nuclei, we develop a relativistic formalism for the nonmesonic weak
decay of single- hypernuclei in the framework of the
independent-particle shell model and with the dynamics represented by the
one-meson-exchange model. Numerical results for the
one-nucleon-induced transition rates of are
presented and compared with those obtained in the analogous nonrelativistic
calculation. There is satisfactory agreement between the two approaches, and
the most noteworthy difference is that the ratio is
appreciably higher and closer to the experimental value in the relativistic
calculation. Large discrepancies between ours and previous relativistic
calculations are found, for which we do not encounter any fully satisfactory
explanation. The most recent experimental data is well reproduced by our
results. In summary, we have achieved our purpose to develop a reliable model
for the relativistic calculation of the nonmesonic weak decay of
-hypernuclei, which can now be extended to evaluate similar processes
in charmed nuclei
Analyzing Trails in Complex Networks
Even more interesting than the intricate organization of complex networks are
the dynamical behavior of systems which such structures underly. Among the many
types of dynamics, one particularly interesting category involves the evolution
of trails left by moving agents progressing through random walks and dilating
processes in a complex network. The emergence of trails is present in many
dynamical process, such as pedestrian traffic, information flow and metabolic
pathways. Important problems related with trails include the reconstruction of
the trail and the identification of its source, when complete knowledge of the
trail is missing. In addition, the following of trails in multi-agent systems
represent a particularly interesting situation related to pedestrian dynamics
and swarming intelligence. The present work addresses these three issues while
taking into account permanent and transient marks left in the visited nodes.
Different topologies are considered for trail reconstruction and trail source
identification, including four complex networks models and four real networks,
namely the Internet, the US airlines network, an email network and the
scientific collaboration network of complex network researchers. Our results
show that the topology of the network influence in trail reconstruction, source
identification and agent dynamics.Comment: 10 pages, 16 figures. A working manuscript, comments and criticisms
welcome
Seeking for Simplicity in Complex Networks
Complex networks can be understood as graphs whose connectivity deviates from
those of regular or near-regular graphs, which are understood as being
`simple'. While a great deal of the attention so far dedicated to complex
networks has been duly driven by the `complex' nature of these structures, in
this work we address the identification of simplicity, in the sense of
regularity, in complex networks. The basic idea is to seek for subgraphs
exhibiting small dispersion (e.g. standard deviation or entropy) of local
measurements such as the node degree and clustering coefficient. This approach
paves the way for the identification of subgraphs (patches) with nearly uniform
connectivity, therefore complementing the characterization of the complexity of
networks. We also performed analysis of cascade failures, revealing that the
removal of vertices in `simple' regions results in smaller damage to the
network structure than the removal of vertices in the heterogeneous regions. We
illustrate the potential of the proposed methodology with respect to four
theoretical models as well as protein-protein interaction networks of three
different species. Our results suggest that the simplicity of protein
interaction grows as the result of natural selection. This increase in
simplicity makes these networks more robust to cascade failures.Comment: 5 pages, 3 figures, 1 table. Submitted to Physical Review Letter
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