Biomedical subjects

Sergey A Astakhov

Publications and source records attributed to Sergey A Astakhov.

3 recordsLinked to original sources

PubMed2006

Monte Carlo algorithm for least dependent non-negative mixture decomposition.

We propose a simulated annealing algorithm (stochastic non-negative independent component analysis, SNICA) for blind decomposition of linear mixtures of non-negative sources with non-negative coefficients. The demixing is based on a Metropolis-type Monte Carlo search for least dependent components, with the mutual information between recovered components as a cost function and their non-negativity as a hard constraint. Elementary moves are shears in two-dimensional subspaces and rotations in three-dimensional subspaces. The algorithm is geared at decomposing signals whose probability densities peak at zero, the case typical in analytical spectroscopy and multivariate curve resolution. The decomposition performance on large samples of synthetic mixtures and experimental data is much better than that of traditional blind source separation methods based on principal component analysis (MILCA, FastICA, RADICAL) and chemometrics techniques (SIMPLISMA, ALS, BTEM).

Algorithms↗

PubMed2004

Least-dependent-component analysis based on mutual information.

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand, it has the advantage, compared to other implementations of "independent" component analysis (ICA), some of which are based on crude approximations for MI, that the numerical values of the MI can be used for (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator, we use a recently proposed k -nearest-neighbor-based algorithm. For time sequences, we combine this with delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm to a real-world dataset, the ECG of a pregnant woman.

Journal Article↗

PubMed2003

Chaos-assisted capture of irregular moons.

It has been thought that the capture of irregular moons--with non-circular orbits--by giant planets occurs by a process in which they are first temporarily trapped by gravity inside the planet's Hill sphere (the region where planetary gravity dominates over solar tides). The capture of the moons is then made permanent by dissipative energy loss (for example, gas drag) or planetary growth. But the observed distributions of orbital inclinations, which now include numerous newly discovered moons, cannot be explained using current models. Here we show that irregular satellites are captured in a thin spatial region where orbits are chaotic, and that the resulting orbit is either prograde or retrograde depending on the initial energy. Dissipation then switches these long-lived chaotic orbits into nearby regular (non-chaotic) zones from which escape is impossible. The chaotic layer therefore dictates the final inclinations of the captured moons. We confirm this with three-dimensional Monte Carlo simulations that include nebular drag, and find good agreement with the observed inclination distributions of irregular moons at Jupiter and Saturn. In particular, Saturn has more prograde irregular moons than Jupiter, which we can explain as a result of the chaotic prograde progenitors being more efficiently swept away from Jupiter by its galilean moons.

Journal Article↗