33,140 research outputs found
Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation
A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems
Pairwise alignment incorporating dipeptide covariation
Motivation: Standard algorithms for pairwise protein sequence alignment make
the simplifying assumption that amino acid substitutions at neighboring sites
are uncorrelated. This assumption allows implementation of fast algorithms for
pairwise sequence alignment, but it ignores information that could conceivably
increase the power of remote homolog detection. We examine the validity of this
assumption by constructing extended substitution matrixes that encapsulate the
observed correlations between neighboring sites, by developing an efficient and
rigorous algorithm for pairwise protein sequence alignment that incorporates
these local substitution correlations, and by assessing the ability of this
algorithm to detect remote homologies. Results: Our analysis indicates that
local correlations between substitutions are not strong on the average.
Furthermore, incorporating local substitution correlations into pairwise
alignment did not lead to a statistically significant improvement in remote
homology detection. Therefore, the standard assumption that individual residues
within protein sequences evolve independently of neighboring positions appears
to be an efficient and appropriate approximation
Modeling Evolution of Crosstalk in Noisy Signal Transduction Networks
Signal transduction networks can form highly interconnected systems within
cells due to network crosstalk, the sharing of input signals between multiple
downstream responses. To better understand the evolutionary design principles
underlying such networks, we study the evolution of crosstalk and the emergence
of specificity for two parallel signaling pathways that arise via gene
duplication and are subsequently allowed to diverge. We focus on a sequence
based evolutionary algorithm and evolve the network based on two physically
motivated fitness functions related to information transmission. Surprisingly,
we find that the two fitness functions lead to very different evolutionary
outcomes, one with a high degree of crosstalk and the other without.Comment: 18 Pages, 16 Figure
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Finding Young Stellar Populations in Elliptical Galaxies from Independent Components of Optical Spectra
Elliptical galaxies are believed to consist of a single population of old
stars formed together at an early epoch in the Universe, yet recent analyses of
galaxy spectra seem to indicate the presence of significant younger populations
of stars in them. The detailed physical modelling of such populations is
computationally expensive, inhibiting the detailed analysis of the several
million galaxy spectra becoming available over the next few years. Here we
present a data mining application aimed at decomposing the spectra of
elliptical galaxies into several coeval stellar populations, without the use of
detailed physical models. This is achieved by performing a linear independent
basis transformation that essentially decouples the initial problem of joint
processing of a set of correlated spectral measurements into that of the
independent processing of a small set of prototypical spectra. Two methods are
investigated: (1) A fast projection approach is derived by exploiting the
correlation structure of neighboring wavelength bins within the spectral data.
(2) A factorisation method that takes advantage of the positivity of the
spectra is also investigated. The preliminary results show that typical
features observed in stellar population spectra of different evolutionary
histories can be convincingly disentangled by these methods, despite the
absence of input physics. The success of this basis transformation analysis in
recovering physically interpretable representations indicates that this
technique is a potentially powerful tool for astronomical data mining.Comment: 12 Pages, 7 figures; accepted in SIAM 2005 International Conference
on Data Mining, Newport Beach, CA, April 200
Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks
We outline a possible theoretical framework for the quantitative modeling of
networked embodied cognitive systems. We notice that: 1) information self
structuring through sensory-motor coordination does not deterministically occur
in Rn vector space, a generic multivariable space, but in SE(3), the group
structure of the possible motions of a body in space; 2) it happens in a
stochastic open ended environment. These observations may simplify, at the
price of a certain abstraction, the modeling and the design of self
organization processes based on the maximization of some informational
measures, such as mutual information. Furthermore, by providing closed form or
computationally lighter algorithms, it may significantly reduce the
computational burden of their implementation. We propose a modeling framework
which aims to give new tools for the design of networks of new artificial self
organizing, embodied and intelligent agents and the reverse engineering of
natural ones. At this point, it represents much a theoretical conjecture and it
has still to be experimentally verified whether this model will be useful in
practice.
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