Thermal relaxation in charge ordered Pr0.63_{0.63} Ca0.37_{0.37} MnO3_3 in presence of a magnetic field

We report observation of substantial thermal relaxation in single crystal of charge ordered system Pr$_{0.63}$Ca$_{0.37}$MnO$_3$ in an applied magnetic field of H = 8T. The relaxation is observed when the temperature is scanned in presence of a magnetic field in the temperature interval $T_{MH}<T<T_{CO}$ where $T_{CO}$ is the charge ordering temperature and $T_{MH}$ is charge melting temperature in a field. In this temperature range the system has coexisting charged ordered insulator (COI) and ferromagnetic metallic (FMM) phases. No such relaxation is observed in the COI state in H = 0T or in the FMM phase at $T < T_{MH}$ in presence of a magnetic field. We conclude that the thermal relaxation is due to two coexisting phases with nearly same free energies but separated by a potential barrier. This barrier makes the transformation from one phase to the other time-dependent in the scale of the specific heat experiment and gives rise to the thermal relaxation.Comment: 4 pages LaTEX, 3 eps figure

Structural RFV: Recovery Form and Defaultable Debt Analysis

Receiving the same fractional recovery of par at default for bonds of the same issuer and seniority, regardless of remaining maturity, has been labelled in the academic literature as a Recovery of Face Value at Default (RFV).Such a recovery form results from language found in typical bond indentures and is supported by empirical evidence from defaulted bond values.We incorporate RFV into an exogenous boundary structural credit risk model and compare its e ect to more typical recovery forms found in such models.We find that the chosen recovery form can significantly a ect valuation and the sensitivities produced by these models, thus having important implications for empirical studies attempting to validate structural credit risk models.We show that some features of existing structural models are a result of the recovery form assumed in the model and do not necessarily hold under an RFV recovery form.Some of our results complement those found in the literature which examines the endogeneity of the default boundary.We find that some features that may have been solely attributed to modelling the boundary as an optimal decision by the firm can be obtained in an exogenous boundary framework with RFV.This has direct implications for studies which attempt to determine whether endogenous or exogenous models are better supported empirically.We extend our results to incorporate a multifactor default-free term structure model and examine the impact of the recovery form in estimating the cost of debt capital within a structural model framework.bonds;credit;risk;capital costs;debt

Magnetic Field resulting from non-linear electrical transport in single crystals of charge-ordered Pr0.63_{0.63} Ca0.37_{0.37} MnO3_{3}}

In this letter we report that the current induced destabilization of the charge ordered (CO) state in a rare-earth manganite gives rise to regions with ferromagnetic correlation. We did this experiment by measurement of the I-V curves in single crystal of the CO system Pr$_{0.63}$Ca$_{0.37}$MnO$_{3}$ and simultanously measuring the magnetization of the current carrying conductor using a high T$_c$ SQUID working at T = 77K. We have found that the current induced destabilization of the CO state leads to a regime of negative differential resistance which leads to a small enhancement of the magnetization of the sample, indicating ferromagnetically aligned moments.Comment: 4 pages LateX, 4 eps figure

An Unsupervised Learning Model for Deformable Medical Image Registration

We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data. We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest. Given a new pair of scans, we can quickly compute a registration field by directly evaluating the function using the learned parameters. We model this function using a convolutional neural network (CNN), and use a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field. The proposed method does not require supervised information such as ground truth registration fields or anatomical landmarks. We demonstrate registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Our method promises to significantly speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is available at https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201