Far infrared spectroscopy of heavy fermion superconductor CeCoIn5

The optical response to far infrared radiation has been measured on a mosaic of heavy fermion CeColnssingle crystals. The superconducting transition temperature of the crystals has been determined by van der Pauw resistivity and ac-susceptibility measurements as Tc = 2.3 K. The optical measurements were taken above and below the transition temperature using a 3He cryostat and step and integrate Martin-Puplett type polarizing interferometer. The absolute reflectance of the heavy fermion CeColns in the superconducting state in range (0, 100)cm-1 was calculated from the measured thermal reflectance, using the normal state data of Singley et al and a low frequency extrapolation for a metallic material in the Hagen-Rubens regime. By means of Kramers-Kronig analysis the absolute reflectance was used to calculate the optical conductivity of the sample. The real part of the calculated complex conductivity 0-(w) ofCeColns indicates a possible opening of an energy gap close to 50 em-I.

Far infrared spectroscopy of heavy fermion superconductor CeCoIn5 /

The optical response to far infrared radiation has been measured on a mosaic of heavy fermion CeCoIns single crystals. The superconducting transition temperature of the crystals has been determined by van der Pauw resistivity and ac-susceptibility measurements as Tc = 2.3 K. The optical measurements were taken above and below the transition temperature using a ^He cryostat and step and integrate Martin-Puplett type polarizing interferometer. The absolute reflectance of the heavy fermion CeCoIns in the superconducting state in range (0, 100)cm~^ was calculated from the measured thermal reflectance, using the normal state data of Singley et al and a low frequency extrapolation for a metallic material in the Hagen-Rubens regime. By means of Kramers-Kronig analysis the absolute reflectance was used to calculate the optical conductivity of the sample. The real part of the calculated complex conductivity a{u)) of CeCoIns indicates a possible opening of an energy gap close to 50 cm~^.

Infrared Spectroscopy of Gadolinium

Measurements of the optical reflectivity of the normal incident light along c-axis [0001] have been made on a Gadolinium single crystal, for temperatures between 50 K and room temperature just above the Curie temperature of Gd, which is 293 K. And covering the spectrum range between 100 -11000 cm-I . This work is the first study of Gd in the far infrared range. In fact it fills the gap below 0.2 eV which has never been measured before. Extreme attention was paid to the fact that Gadolinium is a very reactive metal with air. Thus, the sample was mechanically polished and carefully handled during the measurement. However, temperature dependent optical measurements have been made in the same frequency range for a sample of Gd2O3. For comparison, both samples of Gd and Gd2O3 were examined by X-Ray diffraction. XRD analysis showed that the sample was pure gadolinium and the oxide layer either does not exist, or is very thin. Furthermore, this fact was supported by the absence of any of Gd2O3 features in the Gd sample reflectivity. Kramers Kronig analysis was applied to extract the optical functions from the reflectance data. The optical conductivity shows a strong temperature dependence feature in the mid-infrared. This feature disappears completely at room temperature which supports a magnetic origin.

Far-infrared spectroscopy of nanoscopic InAs rings

We have employed time-dependent local-spin-density theory to analyze the far-infrared transmission spectrum of InAs self-assembled nanoscopic rings recently reported [A. Lorke et al., Phys. Rev. Lett. (to be published)]. The overall agreement between theory and experiment is fairly good, which on the one hand confirms that the experimental peaks indeed reflect the ringlike structure of the sample, and on the other hand, asseses the suitability of the theoretical method to describe such nanostructures. The addition energies of one- and two-electron rings are also reported and compared with the corresponding capacitance spectra

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

On the relationship between the Method of Least Squares and Gram-Schmidt orthogonalization

The method of Least Squares is due to Carl Friedrich Gauss. The Gram-Schmidt orthogonalization method is of much younger date. A method for solving Least Squares Problems is developed which automatically results in the appearance of the Gram-Schmidt orthogonalizers. Given these orthogonalizers an induction-proof is available for solving Least Squares Problems.

Approximate Gauss-Newton methods for nonlinear least squares problems

The Gauss–Newton algorithm is an iterative method regularly used for solving nonlinear least squares problems. It is particularly well suited to the treatment of very large scale variational data assimilation problems that arise in atmosphere and ocean forecasting. The procedure consists of a sequence of linear least squares approximations to the nonlinear problem, each of which is solved by an “inner” direct or iterative process. In comparison with Newton’s method and its variants, the algorithm is attractive because it does not require the evaluation of second-order derivatives in the Hessian of the objective function. In practice the exact Gauss–Newton method is too expensive to apply operationally in meteorological forecasting, and various approximations are made in order to reduce computational costs and to solve the problems in real time. Here we investigate the effects on the convergence of the Gauss–Newton method of two types of approximation used commonly in data assimilation. First, we examine “truncated” Gauss–Newton methods where the inner linear least squares problem is not solved exactly, and second, we examine “perturbed” Gauss–Newton methods where the true linearized inner problem is approximated by a simplified, or perturbed, linear least squares problem. We give conditions ensuring that the truncated and perturbed Gauss–Newton methods converge and also derive rates of convergence for the iterations. The results are illustrated by a simple numerical example. A practical application to the problem of data assimilation in a typical meteorological system is presented.

On integral equation and least squares methods for scattering by diffraction gratings

In this paper we consider the scattering of a plane acoustic or electromagnetic wave by a one-dimensional, periodic rough surface. We restrict the discussion to the case when the boundary is sound soft in the acoustic case, perfectly reflecting with TE polarization in the EM case, so that the total field vanishes on the boundary. We propose a uniquely solvable first kind integral equation formulation of the problem, which amounts to a requirement that the normal derivative of the Green's representation formula for the total field vanish on a horizontal line below the scattering surface. We then discuss the numerical solution by Galerkin's method of this (ill-posed) integral equation. We point out that, with two particular choices of the trial and test spaces, we recover the so-called SC (spectral-coordinate) and SS (spectral-spectral) numerical schemes of DeSanto et al., Waves Random Media, 8, 315-414 1998. We next propose a new Galerkin scheme, a modification of the SS method that we term the SS* method, which is an instance of the well-known dual least squares Galerkin method. We show that the SS* method is always well-defined and is optimally convergent as the size of the approximation space increases. Moreover, we make a connection with the classical least squares method, in which the coefficients in the Rayleigh expansion of the solution are determined by enforcing the boundary condition in a least squares sense, pointing out that the linear system to be solved in the SS* method is identical to that in the least squares method. Using this connection we show that (reflecting the ill-posed nature of the integral equation solved) the condition number of the linear system in the SS* and least squares methods approaches infinity as the approximation space increases in size. We also provide theoretical error bounds on the condition number and on the errors induced in the numerical solution computed as a result of ill-conditioning. Numerical results confirm the convergence of the SS* method and illustrate the ill-conditioning that arises.