Using numerical integration in the formulation of the finite-element geometric stiffness matrix and placing movable nodes at integration points causes the geometric stiffness matrix to become lumped or diagonal. The consistent geometric stiffness matrix which is a non-diagonal matrix, is normally used in the finite-element eigenvalue buckling problem. Altering the method to deliver a diagonal (...

Algorithmic skeleton has received attention as an efficient method of parallel programming in recent years. Using the method, the programmer can implement parallel programs easily. In this study, a set of efficient algorithmic skeletons is introduced for use in implementing parallel genetic algorithm (PGA).A performance modelis derived for each skeleton that makes the comparison of skeletons po...

In this note, we show that the holonomy group of a Riemannian connection on a k-dimensional Euclidean vector bundle is transitive on the unit sphere bundle whenever the Euler class χ is spherical. We extract several consequences from this, among them that this is always the case as long as χ does not vanish, and the base of the bundle is simply connected and rationally (k + 1)/2-connected.

Deep learning has received considerable empirical successes in recent years. However, while many ad hoc tricks have been discovered by practitioners, until recently, there a lack of theoretical understanding for invented the deep literature. Known practitioners that overparameterized neural networks are easy to learn, past few years important developments analysis networks. In particular, it wa...

We introduce the concepts of second-order radial composed tangent derivative, second-order radial tangent derivative, second-order lower radial composed tangent derivative, and second-order lower radial tangent derivative for set-valued maps by means of a radial tangent cone, second-order radial tangent set, lower radial tangent cone, and second-order lower radial tangent set, respectively. Som...

Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry of the manifold u...