A method for detecting the linkage between an interval defined by a pair of markers and a nonadditive quantitative trait locus for F2 populations is presented. The method uses the EM algorithm to estimate the biometrical parameters and the locations of the quantitative trait loci. A computationally efficient approach suitable for implementation on a personal computer (PC) has been developed. The method has been applied to 100 replicated sets of 250 simulated individuals. Two criteria, the LOD score and the Fisher-Snedecor F, are compared in terms of their power and Type I error.
An efficient and accurate computational form for β which minimizes using the Moore-Penrose g-inverse is given, No rank conditions are imposed on R or X, The results are applied (i) to estimate the parameters in linear model which are subject to linear equality constraints and (ii) to obtain the generalized inverse of X″X which yields a solution of the normal equations subject to non-estimable constraints on the parameters.
Four commercial anaerobic systems (CASs) were evaluated for usefulness in identification of Eubacterium suis. Twelve strains were evaluated in each system in triplicate, and results were interpreted independently by 5 individuals. Statistically significant differences (P less than 0.01) due to strain variation and reader interpretation accounted for discrepancies encountered. The reactivity, repeatability, and unique profiles generated made both CAS-1 and CAS-2 suitable adjuncts for identification of E. suis when colony morphology and Gram reaction were considered. Limited reactivity in CAS-3 limited its use as an aid in identification. Variability in test observations and the large number of numerical profiles generated precluded use of CAS-4.
Abstract This article investigates a nonparametric procedure for testing for no treatment differences in multivariate two-way layouts with random covariates. The method is an extension of the Friedman χ r 2-test. Permutation and asymptotic permutation distributions are given and various asymptotic relative efficiency results are presented. An example illustrates the method.
In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration model-building demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model.
Abstract Maximum likelihood estimates are derived for the parameters of two normal populations with coefficients of variation equal but unknown. Formulas are provided for calculating the asymptotic variances and covariances of the estimates. The relative efficiency of the proposed mean estimator with respect to the sample mean is shown to be greater than one. The effect of departures from the assumptions of normality and of equal coefficients of variation on this relative efficiency are studied. An example is provided with data from wildlife populations to illustrate the proposed methods.
In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model.
Software Reliability: With the advent of highly complex computer programs whose correct execution are essential to the proper functioning of a critical system, the concept of reliability has been extended to software. This is necessary for complex software because it is impossible to verify that it will execute correctly under all conceivable inputs. By testing a software product extensively and attempting to correct the errors that are discovered, confidence that it will execute correctly in a give situation is increased. The "time to failure" between corrections (measured, for example, in numbers of executions or CPU cycles) may be used to gauge the increasing reliability of the product. Indeed, contracts for the purchase of custom software frequently include specifications on the minimum allowable mean time to failure.
