Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems – that are in general NP-hard – efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact...

This paper proposes a subspace decomposition method based on an over-complete dictionary in sparse representation, called “sparse signal subspace decomposition” (or 3SD) method. This method makes use of a novel criterion based on the occurrence frequency of atoms of the dictionary over the data set. This criterion, well adapted to subspace decomposition over a dependent basis set, adequately re...

Abstract Because the characteristic of wavelet transform is multi-resolution, their unique advantage of the data model is multi-scale analysis. Then, it’s widely used the Wavelet-based multi-scale sensor data fusion technology in many fields. There are two problems in operating process, which are the choice of wavelet base and the choice of decomposition level. The wavelet decomposition level i...

Applying domain decomposition to the lattice Dirac operator and the associated quark propagator, we arrive at expressions which, with the proper insertion of random sources therein, can provide improvement to the estimation of the propagator. Schemes are considered for both open and closed (or loop) propagators. In the end, our technique for improving open contributions is similar to the “maxim...

INTRODUCTION The diffusion tensor is a 3x3 positive definite matrix and, therefore, possesses several distinct matrix decompositions, e.g. the Cholesky, and the Eigenvalue decompositions. To date, the Eigenvalue decomposition has been used only in computing tensor-derived quantities [1-4] but not as a parametrization (or equivalently, a representation) in DTI error propagation [5]. Treating a m...

Abstract Soil carbon diversity can be an important property for the stability of soil carbon. A problem is lack techniques measuring this diversity. I suggest here use a combination general statistical principle, MAXimum ENTropy (MaxEnt), and mechanistic model organic matter decomposition, Q model. The provides temporal development average quality litter amount C, which applied in MaxEnt calcul...

An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error in [3]. The bias/variance decomposition includes the concept of the average predictor. The bias is the error of the average predictor, and the systematic part of the generalization error, while the variability around the average predictor ...

We present a review of empirical evidence that suggests that a substantial portion of phenotypic variance is due to non-linear (epigenetic) processes during ontogenesis. The role of such processes as a source of phenotypic variance in human behavior genetic studies is not fully appreciated. In addition to our review, we present simulation studies of nonlinear epigenetic variance using a computa...

Regression-based studies of inequality model only between-group differences, yet often these differences are far exceeded by residual inequality. Residual inequality is usually attributed to measurement error or the influence of unobserved characteristics. We present a regression that includes covariates for both the mean and variance of a dependent variable. In this model, the residual varianc...