Magnetic and transport properties of iron-platinum arsenide Ca10(Pt4-{\delta}As8)(Fe2-xPtxAs2)5 single crystal

We report superconducting properties of single crystalline Ca10(Pt4-{\delta}As8)(Fe2-xPtxAs2)5 by X-ray diffraction, magnetization, resistivity, and magneto-optical imaging measurements. The magnetization measurements reveal fish-tail hysteresis loop and relatively high critical current density Jc ~ 0.8\times105 A/cm2 at low temperatures. The exponential temperature dependence of Jc, which arises from nonlinear effective flux-creep activation energy, has been observed. Upper critical field determined by resistive transition shows a relatively large anisotropy. The magneto-optical images reveal homogenous current flow within the crystal.Comment: 6 pages, 6 figures, Accepted for publication in Phys. Rev.

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov Chain Monte Carlo (MCMC) for the Bayesian model, and use the alternating direction method of multipliers (ADMM) for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods