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

Sparse x-ray CT image reconstruction using ECME hard thresholding methods

We apply expectation‐conditional maximization either (ECME) hard thresholding algorithms to X‐ray computed tomography (CT) reconstruction, where we implement the sampling operator using the nonuniform fast Fourier transform (NUFFT). 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. Our ECME schemes aim at maximizing this model’s likelihood function with respect to the sparse signal and variance of the random signal component. These schemes exploit signal sparsity in the discrete wavelet transform (DWT) domain and yield better reconstructions than the traditional filtered backprojection (FBP) approach, which is demonstrated via numerical examples. In contrast with FBP, our methods achieve artifact‐free reconstructions in undersampled and limited‐angle projection examples. We also compare the ECME schemes with a state‐of‐the‐art convex sparse signal reconstruction approach in terms of the reconstruction speed

Automatic hard thresholding for sparse signal reconstruction from NDE measurements

We propose an automatic hard thresholding (AHT) method for sparse‐signal reconstruction. The measurements follow an underdetermined linear model, where the regression‐coefficient vector is modeled as a superposition of an unknown deterministic sparse‐signal component and a zero‐mean white Gaussian component with unknown variance. Our method demands no prior knowledge about signal sparsity. Our AHT scheme approximately maximizes a generalized maximum likelihood (GML) criterion, providing an approximate GML estimate of the signal sparsity level and an empirical Bayesian estimate of the regression coefficients. We apply the proposed method to reconstruct images from sparse computerized tomography projections and compare it with existing approaches