Vibrational and Thermal Properties of ZnX (X=Se, Te): Density Functional Theory (LDA and GGA) versus Experiment

We calculated the phonon dispersion relations of ZnX (X=Se, Te) employing ab initio techniques. These relations have been used to evaluate the temperature dependence of the respective specific heats of crystals with varied isotopic compositions. These results have been compared with mea- surements performed on crystals down to 2 K. The calculated and measured data are generally in excellent agreement with each other. Trends in the phonon dispersion relations and the correspond- ing densities of states for the zinc chalcogenide series of zincblende-type materials are discussed.Comment: 10 pages, submitted to PR

Computational aspects of probit model

Sometimes the maximum likelihood estimation procedure for the probit model fails. There may be two reasons: the maximum likelihood estimate (MLE) just does not exist or computer overflow error occurs during the computation of the cumulative distribution function (cdf). For example, the approximation explosive effect due to an inaccurate computation of the cdf for a large value of the argument occurs in a popular statistical package S-plus. The goal of the paper is to provide remedies for these two abnormalities. First, despite the availability of a criterion for the MLE existence, expressed in terms of a separation plane in the covariate space, there are no constructive criteria to verify whether such a separation exists. We develop constructive criteria for the MLE existence that are valid also for other link functions. Second, to avoid the overflow problem we suggest approximate formulae for the log-likelihood function and its derivatives in the case of possiblelarge value of the argument. Standard algorithms of the log-likelihood maximization like Newton-Raphson or Fisher Scoring are very sensitive to large values of the linear predictor, particularly outliers. Five algorithms are compared by the time to converge and reliability via statistical simulations. The corrected algorithms, based on the approximate formulae are more reliable and almost as fast as the standard one

Statistical Hypothesis Testing for Postreconstructed and Postregistered Medical Images

Postreconstructed and postregistered medical images are typically treated as the raw data, implicitly assuming that those operations are error free. We question this assumption and explore how the precision of reconstruction and affine registration can be assessed by the image covariance matrix and confidence interval, called the confidence eigenimage, using a statistical model-based approach. Various hypotheses may be tested after image reconstruction and registration using classical statistical hypothesis testing vehicles: Is there a statistically significant difference between images? Does the intensity at a specific location or area of interest belong to the “normal” range? Is there a tumor? Does the image require rigid registration? We illustrate statistical hypothesis testing with three examples: breast computed tomography, breast near infrared linear reconstruction, and brain magnetic resonance imaging

Microarray Enriched Gene Rank

We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R

Analysis and application of digital spectral warping in analog and mixed-signal testing

Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation