10,772 research outputs found
Some Special Cases in the Stability Analysis of Multi-Dimensional Time-Delay Systems Using The Matrix Lambert W function
This paper revisits a recently developed methodology based on the matrix
Lambert W function for the stability analysis of linear time invariant, time
delay systems. By studying a particular, yet common, second order system, we
show that in general there is no one to one correspondence between the branches
of the matrix Lambert W function and the characteristic roots of the system.
Furthermore, it is shown that under mild conditions only two branches suffice
to find the complete spectrum of the system, and that the principal branch can
be used to find several roots, and not the dominant root only, as stated in
previous works. The results are first presented analytically, and then verified
by numerical experiments
Towards efficient SimRank computation on large networks
SimRank has been a powerful model for assessing the similarity of pairs of vertices in a graph. It is based on the concept that two vertices are similar if they are referenced by similar vertices. Due to its self-referentiality, fast SimRank computation on large graphs poses significant challenges. The state-of-the-art work [17] exploits partial sums memorization for computing SimRank in O(Kmn) time on a graph with n vertices and m edges, where K is the number of iterations. Partial sums memorizing can reduce repeated calculations by caching part of similarity summations for later reuse. However, we observe that computations among different partial sums may have duplicate redundancy. Besides, for a desired accuracy Ï”, the existing SimRank model requires K = [logC Ï”] iterations [17], where C is a damping factor. Nevertheless, such a geometric rate of convergence is slow in practice if a high accuracy is desirable. In this paper, we address these gaps. (1) We propose an adaptive clustering strategy to eliminate partial sums redundancy (i.e., duplicate computations occurring in partial sums), and devise an efficient algorithm for speeding up the computation of SimRank to 0(Kdn2) time, where d is typically much smaller than the average in-degree of a graph. (2) We also present a new notion of SimRank that is based on a differential equation and can be represented as an exponential sum of transition matrices, as opposed to the geometric sum of the conventional counterpart. This leads to a further speedup in the convergence rate of SimRank iterations. (3) Using real and synthetic data, we empirically verify that our approach of partial sums sharing outperforms the best known algorithm by up to one order of magnitude, and that our revised notion of SimRank further achieves a 5X speedup on large graphs while also fairly preserving the relative order of original SimRank scores
Predicting coexistence of plants subject to a tolerance-competition trade-off
Ecological trade-offs between species are often invoked to explain species
coexistence in ecological communities. However, few mathematical models have
been proposed for which coexistence conditions can be characterized explicitly
in terms of a trade-off. Here we present a model of a plant community which
allows such a characterization. In the model plant species compete for sites
where each site has a fixed stress condition. Species differ both in stress
tolerance and competitive ability. Stress tolerance is quantified as the
fraction of sites with stress conditions low enough to allow establishment.
Competitive ability is quantified as the propensity to win the competition for
empty sites. We derive the deterministic, discrete-time dynamical system for
the species abundances. We prove the conditions under which plant species can
coexist in a stable equilibrium. We show that the coexistence conditions can be
characterized graphically, clearly illustrating the trade-off between stress
tolerance and competitive ability. We compare our model with a recently
proposed, continuous-time dynamical system for a tolerance-fecundity trade-off
in plant communities, and we show that this model is a special case of the
continuous-time version of our model.Comment: To be published in Journal of Mathematical Biology. 30 pages, 5
figures, 5 appendice
High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression
Motivation: The high dimensionality of genomic data calls for the development
of specific classification methodologies, especially to prevent over-optimistic
predictions. This challenge can be tackled by compression and variable
selection, which combined constitute a powerful framework for classification,
as well as data visualization and interpretation. However, current proposed
combinations lead to instable and non convergent methods due to inappropriate
computational frameworks. We hereby propose a stable and convergent approach
for classification in high dimensional based on sparse Partial Least Squares
(sparse PLS). Results: We start by proposing a new solution for the sparse PLS
problem that is based on proximal operators for the case of univariate
responses. Then we develop an adaptive version of the sparse PLS for
classification, which combines iterative optimization of logistic regression
and sparse PLS to ensure convergence and stability. Our results are confirmed
on synthetic and experimental data. In particular we show how crucial
convergence and stability can be when cross-validation is involved for
calibration purposes. Using gene expression data we explore the prediction of
breast cancer relapse. We also propose a multicategorial version of our method
on the prediction of cell-types based on single-cell expression data.
Availability: Our approach is implemented in the plsgenomics R-package.Comment: 9 pages, 3 figures, 4 tables + Supplementary Materials 8 pages, 3
figures, 10 table
A Supersymmetry Preserving Mass-Deformation of N=1 Super Yang-Mills in D=2+1
We construct a massive non-abelian N= 1 SYM theory on R^3. This is achieved by using a non-local gauge and Poincare invariant mass term for gluons due to Nair. The underlying supersymmetry algebra is shown to be a non-central extension of the Poincare algebra by the spacetime rotation group so(3). The incorporation of Chern-Simons couplings in the formalism is also presented. The dimensional reduction of the gauge theory and the SUSY algebra is related to a massive N=2 massive matrix quantum mechanics based on euclidean
Fast, Exact Bootstrap Principal Component Analysis for p>1 million
Many have suggested a bootstrap procedure for estimating the sampling
variability of principal component analysis (PCA) results. However, when the
number of measurements per subject () is much larger than the number of
subjects (), the challenge of calculating and storing the leading principal
components from each bootstrap sample can be computationally infeasible. To
address this, we outline methods for fast, exact calculation of bootstrap
principal components, eigenvalues, and scores. Our methods leverage the fact
that all bootstrap samples occupy the same -dimensional subspace as the
original sample. As a result, all bootstrap principal components are limited to
the same -dimensional subspace and can be efficiently represented by their
low dimensional coordinates in that subspace. Several uncertainty metrics can
be computed solely based on the bootstrap distribution of these low dimensional
coordinates, without calculating or storing the -dimensional bootstrap
components. Fast bootstrap PCA is applied to a dataset of sleep
electroencephalogram (EEG) recordings (, ), and to a dataset of
brain magnetic resonance images (MRIs) ( 3 million, ). For the
brain MRI dataset, our method allows for standard errors for the first 3
principal components based on 1000 bootstrap samples to be calculated on a
standard laptop in 47 minutes, as opposed to approximately 4 days with standard
methods.Comment: 25 pages, including 9 figures and link to R package. 2014-05-14
update: final formatting edits for journal submission, condensed figure
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