Domain decomposition improvement of quark propagator estimation

Applying domain decomposition to the lattice Dirac operator and the associated quark propagator, we arrive at expressions which, with the proper insertion of random sources therein, can provide improvement to the estimation of the propagator. Schemes are presented for both open and closed (or loop) propagators. In the end, our technique for improving open contributions is similar to the ``maximal variance reduction'' approach of Michael and Peisa, but contains the advantage, especially for improved actions, of dealing directly with the Dirac operator. Using these improved open propagators for the Chirally Improved operator, we present preliminary results for the static-light meson spectrum. The improvement of closed propagators is modest: on some configurations there are signs of significant noise reduction of disconnected correlators; on others, the improvement amounts to a smoothening of the same correlators.Comment: 19 pages, 8 figures, version to appear in Computer Physics Communication

MML Probabilistic Principal Component Analysis

Principal component analysis (PCA) is perhaps the most widely method for data dimensionality reduction. A key question in PCA decomposition of data is deciding how many factors to retain. This manuscript describes a new approach to automatically selecting the number of principal components based on the Bayesian minimum message length method of inductive inference. We also derive a new estimate of the isotropic residual variance and demonstrate, via numerical experiments, that it improves on the usual maximum likelihood approach

A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented using a multivariate non-linear function, is initially decomposed into a number of functional components via the wellknown analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-theparameters problem, which can be solved using least-squares type methods. An efficient model structure determination approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to represent high-order and high dimensional non-linear systems

Structured penalties for functional linear models---partially empirical eigenvectors for regression

One of the challenges with functional data is incorporating spatial structure, or local correlation, into the analysis. This structure is inherent in the output from an increasing number of biomedical technologies, and a functional linear model is often used to estimate the relationship between the predictor functions and scalar responses. Common approaches to the ill-posed problem of estimating a coefficient function typically involve two stages: regularization and estimation. Regularization is usually done via dimension reduction, projecting onto a predefined span of basis functions or a reduced set of eigenvectors (principal components). In contrast, we present a unified approach that directly incorporates spatial structure into the estimation process by exploiting the joint eigenproperties of the predictors and a linear penalty operator. In this sense, the components in the regression are `partially empirical' and the framework is provided by the generalized singular value decomposition (GSVD). The GSVD clarifies the penalized estimation process and informs the choice of penalty by making explicit the joint influence of the penalty and predictors on the bias, variance, and performance of the estimated coefficient function. Laboratory spectroscopy data and simulations are used to illustrate the concepts.Comment: 29 pages, 3 figures, 5 tables; typo/notational errors edited and intro revised per journal review proces

Infrastructural Development, Poverty Reduction and Economic Growth in Nigeria

In spite of massive revenue emanating from oil wealth, Nigeria has not been able to join the league of developed nations who have made infrastructural development and poverty reduction a frontline policy of their developmental process. This has led to critical thinking as to the exact growth implications of infrastructural development and poverty reduction approaches by the successive government of the federation. This study employed the vector auto-regressive approach to analyse the times series data on the relationship between infrastructural development, poverty reduction and output growth. We also used the impulse response function and variance decomposition to explain the responses of output to shocks within the model. The findings revealed that infrastructural development and poverty reduction positively influence economic growth in Nigeria. The impulse response functions showed that poverty reduction exhibited an inverse relationship with economic growth which means that at such periods, as economic growth is rising, poverty reduction was reducing. The study suggests that access and development of infrastructural facilities must be ensured to attain an accelerated economic growth regime, and subsequently put economic development underway. Also, poverty reduction mechanisms have to be expanded and sustained to achieve an egalitarian society that we desire