We analyse the one-loop fermionic contribution for the scalar e ective potential in the temperature dependent Yukawa model. In order to regularize the model a mix between dimensional and analytic regularization procedures is used. We nd a general expression for the fermionic contribution in arbitrary spacetime dimension. It is found that in D = 3 this contribution is nite. Pacs number: 11.10.Ef...

Our work is focused on the joint sparsity recovery problem where the common sparsity pattern is corrupted by Poisson noise. We formulate the confidence-constrained optimization problem in both least squares (LS) and maximum likelihood (ML) frameworks and study the conditions for perfect reconstruction of the original row sparsity and row sparsity pattern. However, the confidence-constrained opt...

Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image data spaces PAT which suggests near equivalence the recovery initial pressure from compressed/subsampled measurements when assuming sparsity frame. As latter computationally more tractable, investig...

Acoustic impedance (AI) inversion is widely used in geophysics and reservoir prediction. But the traditional method cannot fully exploit sparse characteristics of geological attributes. There are problems with multiplicity low resolution. To solve this problem, a data-driven acoustic reweighted L1 norm constraints (DRL1) proposed. In process, local cross-correlation analysis introduced to above...

We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetries when or inferring differential-equation models from measurement data. Directly $\textit{interpretable}$ mathematical data has emerged as valuable modeling approach. However, in areas like biology, high noise level...