3,319 research outputs found
Secondary Structures in Long Compact Polymers
Compact polymers are self-avoiding random walks which visit every site on a
lattice. This polymer model is used widely for studying statistical problems
inspired by protein folding. One difficulty with using compact polymers to
perform numerical calculations is generating a sufficiently large number of
randomly sampled configurations. We present a Monte-Carlo algorithm which
uniformly samples compact polymer configurations in an efficient manner
allowing investigations of chains much longer than previously studied. Chain
configurations generated by the algorithm are used to compute statistics of
secondary structures in compact polymers. We determine the fraction of monomers
participating in secondary structures, and show that it is self averaging in
the long chain limit and strictly less than one. Comparison with results for
lattice models of open polymer chains shows that compact chains are
significantly more likely to form secondary structure.Comment: 14 pages, 14 figure
Ranking and significance of variable-length similarity-based time series motifs
The detection of very similar patterns in a time series, commonly called
motifs, has received continuous and increasing attention from diverse
scientific communities. In particular, recent approaches for discovering
similar motifs of different lengths have been proposed. In this work, we show
that such variable-length similarity-based motifs cannot be directly compared,
and hence ranked, by their normalized dissimilarities. Specifically, we find
that length-normalized motif dissimilarities still have intrinsic dependencies
on the motif length, and that lowest dissimilarities are particularly affected
by this dependency. Moreover, we find that such dependencies are generally
non-linear and change with the considered data set and dissimilarity measure.
Based on these findings, we propose a solution to rank those motifs and measure
their significance. This solution relies on a compact but accurate model of the
dissimilarity space, using a beta distribution with three parameters that
depend on the motif length in a non-linear way. We believe the incomparability
of variable-length dissimilarities could go beyond the field of time series,
and that similar modeling strategies as the one used here could be of help in a
more broad context.Comment: 20 pages, 10 figure
Link Prediction Based on Subgraph Evolution in Dynamic Social Networks
We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network subgraphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed
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