Perturbations of bounce inflation scenario from f(T)f(T) modified gravity revisited

In this work, we revisit the perturbations that are generated in the bounce inflation scenario constructed within the framework of $f(T)$ theory. It has been well known that pure $f(T)$ theory cannot give rise to bounce inflation behavior, so aside from the gravity part, we also employ a canonical scalar field for minimal extension. We calculate the perturbations in $f(T)$ theory using the well-established ADM formalism, and find various conditions to avoid their pathologies. We find that it is indeed very difficult to obtain a healthy model without those pathologies, however, one may find a way out if a potential requirement, say, to keep every function continuous, is abandoned.Comment: 5 pages, 1 figures. Comments are welcom

Experimental analysis for the effect of dynamic capillarity on stress transformation in porous silicon

The evolution of real-time stress in porous silicon(PS) during drying is investigated using micro-Raman spectroscopy. The results show that the PS sample underwent non-negligible stress when immersed in liquid and suffered a stress impulsion during drying. Such nonlinear transformation and nonhomogeneneous distribution of stress are regarded as the coupling effects of several physical phenomena attributable to the intricate topological structure of PS. The effect of dynamic capillarity can induce microcracks and even collapse in PSstructures during manufacture and storage.This work is funded by the National Natural Science Foundation of China Contract Nos. 10732080 and 10502014

ECME Hard Thresholding Methods for Image Reconstruction from Compressive Samples

We propose two hard thresholding schemes for image reconstruction from compressive samples. The measurements follow an underdetermined linear model, where the regression-coefficient vector is a sum of an unknown deterministic sparse signal component and a zero-mean white Gaussian component with an unknown variance. We derived an expectation-conditional maximization either (ECME) iteration that converges to a local maximum of the likelihood function of the unknown parameters for a given image sparsity level. Here, we present and analyze a double overrelaxation (DORE) algorithm that applies two successive overrelaxation steps after one ECME iteration step, with the goal to accelerate the ECME iteration. To analyze the reconstruction accuracy, we introduce minimum sparse subspace quotient (minimum SSQ), a more flexible measure of the sampling operator than the well-established restricted isometry property (RIP). We prove that, if the minimum SSQ is sufficiently large, the DORE algorithm achieves perfect or near-optimal recovery of the true image, provided that its transform coefficients are sparse or nearly sparse, respectively. We then describe a multiple-initialization DORE algorithm (DOREMI) that can significantly improve DORE’s reconstruction performance. We present numerical examples where we compare our methods with existing compressive sampling image reconstruction approaches