The Dafermos regularization of a system of n hyperbolic conservation laws in one space dimension has, near a Riemann solution consisting of n Lax shock waves, a self-similar solution u=u∈(X/T ). In Lin and Schecter (2003, SIAM J. Math. Anal. 35, 884–921) it is shown that the linearized Dafermos operator at such a solution may have two kinds of eigenvalues: fast eigenvalues of order 1/∈ and slow...

Background: Quality assurance in the hematology laboratory is a must to ensure laboratory users of reliable test results with high degree of precision and accuracy. Even after so many advances in hematology laboratory practice, pre-analytical errors remain a challenge for practicing pathologists. This study was undertaken with an objective to ...

We study the behavior of the conjugate-gradient method for solving a set of linear equations, where the matrix is symmetric and positive definite with one set of eigenvalues that are large and the remaining are small. We characterize the behavior of the residuals associated with the large eigenvalues throughout the iterations, and also characterize the behavior of the residuals associated with ...

We study eigenvalues of polyharmonic operators on compact Riemannian manifolds with boundary (possibly empty). In particular, we prove a universal inequality for the eigenvalues of the polyharmonic operators on compact domains in a Euclidean space. This inequality controls the kth eigenvalue by the lower eigenvalues, independently of the particular geometry of the domain. Our inequality is shar...

In this paper, we investigate the consistency and asymptotic efficiency of an estimator of the drift matrix, F , of Ornstein-Uhlenbeck processes that are not necessarily stable. We consider all the cases. (1) The eigenvalues of F are in the right half space (i.e., eigenvalues with positive real parts). In this case the process grows exponentially fast. (2) The eigenvalues of F are on the left h...

4.1 Eigenvalues and Optimization I cannot believe that I have managed to teach three lectures on spectral graph theory without giving the characterization of eigenvalues as solutions to optimization problems. It is one of the most useful ways of understanding eigenvalues of symmetric matrices. To begin, let A be a symmetric matrix with eigenvalues α1 ≥ α2 ≥ · · · ≥ αn, and corresponding orthono...

Partial label learning aims to learn a multi-class classifier, where each training example corresponds set of candidate labels among which only one is correct. Most studies in the space have focused on difference between and non-candidate labels. So far, however, there has been little discussion about correlation partial learning. This paper begins with research correlation, followed by establi...

Regularization of (deep) learning models can be realized at the model, loss, or data level. As a technique somewhere in-between loss and data, label smoothing turns deterministic class labels into probability distributions, for example by uniformly distributing certain part mass over all classes. A predictive model is then trained on these distributions as targets, using cross-entropy function....