The iteratively reweighted least square (IRLS) method is mostly identical to maximum likelihood (ML) method in terms of parameter estimation and power of quantitative trait locus (QTL) detection. But the IRLS is greatly superior to ML in terms of computing speed and the robustness of parameter estimation. In conjunction with the priors of parameters, ML can analyze multiple QTL model based on B...

IR remote sensing and reconnaissance applications require transmission and/or store of line-scan thermal (IRLS) images in real-time. IRLS images exhibit a high degree of spatial redundancy that a codec reduces optimizing trade-off between bitrate and image distortion. In this paper, we propose adaptive coding based on joint optimization of image segmentation scheme and operational codec paramet...

A response surface method for reliability analysis based on iteratively-reweighted-least-square extreme learning machines (IRLS-ELM) is explored in this paper, which, highly nonlinear implicit performance functions of structures are approximated by the IRLS-ELM. Monte Carlo simulation then carried out approximate IRLS-ELM structural analysis. Some numerical examples given to illustrate proposed...

Mixture of experts (ME) is a modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME architecture for adjusting the parameters and the iteratively reweighted least squares (IRLS) algorithm is used to perform maximization in the inner loop [Jordan, M.I., Jacobs, R.A. (1994). Hierarchical mixture of experts a...

Based on our systematic studies of IRLS (InfraRed Line-Scanner) image statistical properties and comparative analyzes of state-of-the-art transform based image coders, we propose coding performance improvements by spatial image segmentation and operational rate-distortion optimization. We present in this paper, a new approach for an adaptive coding of IRLS images which combines the advantages o...

The pathogenesis of idiopathic restless legs syndrome (iRLS) is not well established, but the most important hypothesis suggests dopaminergic dysfunction and iron deficiency. However, recent reports suggest a possible role for several neurotransmitters or neuromodulators, such as aspartate, glutamate, gamma-hydroxybutyric acid (GABA) and opiates, as well as relation with vitamin D deficiency. I...

Iteratively reweighted least squares (IRLS) is a widely-used method in machine learning to estimate the parameters in the generalised linear models. In particular, IRLS for L1 minimisation under the linear model provides a closed-form solution in each step, which is a simple multiplication between the inverse of the weighted second moment matrix and the weighted first moment vector. When dealin...

Alternating minimization algorithms with a shrinkage step, derived within the Split Bregman (SB) or Alternating Direction Method of Multipliers (ADMM) frameworks, have become very popular for `-regularized problems, including Total Variation and Basis Pursuit Denoising. It appears to be generally assumed that they deliver much better computational performance than older methods such as Iterativ...

Iteratively reweighted least squares (IRLS) is one of the most effective methods to minimize the lp regularized linear inverse problem. Unfortunately, the regularizer is nonsmooth and nonconvex when 0 < p < 1. In spite of its properties and mainly due to its high computation cost, IRLS is not widely used in image deconvolution and reconstruction. In this paper, we first derive the IRLS method f...

In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology. The first is the famous Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery while the second is the dynamics of a slime mold (Physarum polycephalum). Both of these dynamics are geared towar...