Variational techniques on classes of Lagrangians

We present canonical procedures for the manipulation of whole classes of Lagrangians that share the same transformation law and functional dependence but are otherwise arbitrary in functional form, and for the derivation therefrom of generalized conserved quantities. The techniques are demonstrated on the class of scalar density LagrangiansL=L _G+L _EM, whereL _G is a function of the metric and its first and second derivatives andL _EM is a function of the metric and a vector potential and its first derivative, which generate the Einstein-Maxwell equations (without cosmological constant). These procedures should be of interest to those studying alternate formulations of general relativity, those deriving new field theories, and others working with general of modified Lagrangians.     

Bayesian stratified random sampling using auxiliary information

Summary Smith (1976,J. R. Statist. Soc., A,139, 183–204) has argued that survey statisticians should attempt to model finite population structures in the same way that statisticians in other disciplines have to provide models of finite or infinite populations. Following this argument, we suggest in this paper that an obvious model for a stratified population when auxiliary information regarding variate values is available, is the one way analysis of covariance model with unequal variances and we consider the problem of estimating the finite population mean. Finally a possible extension of this result is discussed.     

Morphological correlates of injury-induced reorganization in primate somatosensory cortex

Background  

Topographic reorganization of central maps following peripheral nerve injury has been well characterized. Despite extensive documentation of these physiological changes, the underlying anatomical correlates have yet to be fully explored. In this study, we used Golgi impregnation and light microscopy to assess dendritic morphology following denervation of the glabrous hand surface in adult primates.     

Estimation and Uncertainty Quantification for Piecewise Smooth Signal Recovery

This paper presents an application of the sparse Bayesian learning (SBL) algorithm to linear inverse problems with a high order total variation (HOTV) sparsity prior. For the problem of sparse signal recovery, SBL often produces more accurate estimates than maximum a posterioriestimates, including those that use $\ell_1$ regularization. Moreover, rather than a single signal estimate, SBL yields a full posterior density estimate which can be used for uncertainty quantification. However, SBL is only immediately applicable to problems having a directsparsity prior, or to those that can be formed via synthesis. This paper demonstrates how a problem with an HOTV sparsity prior can be formulated via synthesis, and then utilizes SBL. This expands the class of problems available to Bayesian learning to include, e.g., inverse problems dealing with the recovery of piecewise smooth functions or signals from data. Numerical examples are provided to demonstrate how this new technique is effectively employed.     

Nachweis und Bestimmung der Alkalien

Ohne Zusammenfassung     

Nachweis und Bestimmung des Aluminiums

Ohne Zusammenfassung     

Nachweis und Bestimmung des Berylliums und seine Trennung von anderen Elementen

Ohne Zusammenfassung