This interesting study is similar to the study by Bazant and Najjar (1973) [extensively reviewed in Section 5.5 of Bazant (1975)], which apparently escaped the attention of the authors. However, the conclusion in which the authors recommend the effective modulus method, is different. It appears unwarranted and conflicts with more extensive evidence. The authors dismiss the age-adjusted effectiv...

2 Sparse Matrices ……………………………………………………………………………… 4 2.1 The direct solution of sparse unsymmetric linear sets of equations (I.S. Duff and J.K. Reid) …………………………………………………………………………… 4 2.2 The design and use of algorithms for permuting large entries to the diagonal 2.6 Element resequencing for use with a multiple front solver (J. A. Scott) ………… 10 2.7 Exploiting zeros on the diagonal in the direct s...

In many regression settings the unknown coefficients may have some known structure, for instance they be ordered in space or correspond to a vectorized matrix tensor. At same time, sparse, with nearly exactly equal zero. However, commonly used priors and corresponding penalties do not encourage simultaneously structured sparse estimates. this paper we develop shrinkage that generalize multivari...

We propose a method for super-resolution of range image. Our approach leverages the interpretation of LR image as sparse samples on the HR grid. Based on this interpretation, we demonstrate that our recently reported approach which reconstructs dense range images from sparse range data by exploiting a registered colour image, can be applied for the task of resolution enhancement of range images...

We propose a Bayesian methodology for estimating spiked covariance matrices with jointly sparse structure in high dimensions. The matrix is reparameterized terms of the latent factor model, where loading equipped novel spike-and-slab LASSO prior, which continuous shrinkage prior modeling matrices. establish rate-optimal posterior contraction respect to spectral norm as well that principal subsp...