Stochastic stability of viscoelastic systems under Gaussian and Poisson white noise excitations

As the use of viscoelastic materials becomes increasingly popular, stability of viscoelastic structures under random loads becomes increasingly important. This paper aims at studying the asymptotic stability of viscoelastic systems under Gaussian and Poisson white noise excitations with Lyapunov functions. The viscoelastic force is approximated as equivalent stiffness and damping terms. A stochastic differential equation is set up to represent randomly excited viscoelastic systems, from which a Lyapunov function is determined by intuition. The time derivative of this Lyapunov function is then obtained by stochastic averaging. Approximate conditions are derived for asymptotic Lyapunov stability with probability one of the viscoelastic system. Validity and utility of this approach are illustrated by a Duffing-type oscillator possessing viscoelastic forces, and the influence of different parameters on the stability region is delineated

Tuning Optical Properties of Transparent Conducting Barium Stannate by Dimensional Reduction

We report calculations of the electronic structure and optical properties of doped $n$-type perovskite BaSnO3 and layered perovskites. While doped BaSnO$_3$ retains its transparency for energies below the valence to conduction band onset, the doped layered compounds exhibit below band edge optical conductivity due to transitions from the lowest conduction band. This gives absorption in the visible for Ba2SnO4. Thus it is important to minimize this phase in transparent conducting oxide (TCO) films. Ba3Sn2O7 and Ba4Sn3O10 have strong transitions only in the red and infrared, respectively. Thus there may be opportunities for using these as wavelength filtering TCO

Robust Principal Component Analysis?

This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces

Finite difference approximations for a size-structured population model with distributed states in the recruitment

In this paper we consider a size-structured population model where individuals may be recruited into the population at different sizes. First and second order finite difference schemes are developed to approximate the solution of the mathematical model. The convergence of the approximations to a unique weak solution with bounded total variation is proved. We then show that as the distribution of the new recruits become concentrated at the smallest size, the weak solution of the distributed states-at-birth model converges to the weak solution of the classical Gurtin-McCamy-type size-structured model in the weak$^*$ topology. Numerical simulations are provided to demonstrate the achievement of the desired accuracy of the two methods for smooth solutions as well as the superior performance of the second-order method in resolving solution-discontinuities. Finally we provide an example where supercritical Hopf-bifurcation occurs in the limiting single state-at-birth model and we apply the second-order numerical scheme to show that such bifurcation occurs in the distributed model as well

The η(2225)\eta(2225) observed by the BES Collaboration

In the framework of the $^3P_0$ meson decay model, the strong decays of the $3 ^1S_0$ and $4 ^1S_0$ $s\bar{s}$ states are investigated. It is found that in the presence of the initial state mass being 2.24 GeV, the total widths of the $3 ^1S_0$ and $4 ^1S_0$ $s\bar{s}$ states are about 438 MeV and 125 MeV, respectively. Also, when the initial state mass varies from 2220 to 2400 MeV, the total width of the $4 ^1S_0$ $s\bar{s}$ state varies from about 100 to 132 MeV, while the total width of the $3 ^1S_0$ $s\bar{s}$ state varies from about 400 to 594 MeV. A comparison of the predicted widths and the experimental result of $(0.19\pm 0.03^{+0.04}_{-0.06})$ GeV, the width of the $\eta(2225)$ with a mass of $(2.24^{+0.03+0.03}_{-0.02-0.02})$ GeV recently observed by the BES Collaboration in the radiative decay $J/\psi\to\gamma\phi\phi\to\gamma K^+K^-K^0_SK^0_L$, suggests that it would be very difficult to identify the $\eta(2225)$ as the $3 ^1S_0$ $s\bar{s}$ state, and the $\eta(2225)$ seams a good candidate for the $4 ^1S_0$ $s\bar{s}$ state.Comment: 14 pages, 3 figures, typos corrected, Accepted by Physical Review

Dense Error Correction for Low-Rank Matrices via Principal Component Pursuit

We consider the problem of recovering a low-rank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It has recently been shown that this problem can be solved efficiently and effectively by a convex program named Principal Component Pursuit (PCP), provided that the fraction of corrupted entries and the rank of the matrix are both sufficiently small. In this paper, we extend that result to show that the same convex program, with a slightly improved weighting parameter, exactly recovers the low-rank matrix even if "almost all" of its entries are arbitrarily corrupted, provided the signs of the errors are random. We corroborate our result with simulations on randomly generated matrices and errors.Comment: Submitted to ISIT 201