Yang-Mills theory constructed from Cho--Faddeev--Niemi decomposition

We give a new way of looking at the Cho--Faddeev--Niemi (CFN) decomposition of the Yang-Mills theory to answer how the enlarged local gauge symmetry respected by the CFN variables is restricted to obtain another Yang-Mills theory with the same local and global gauge symmetries as the original Yang-Mills theory. This may shed new light on the fundamental issue of the discrepancy between two theories for independent degrees of freedom and the role of the Maximal Abelian gauge in Yang-Mills theory. As a byproduct, this consideration gives new insight into the meaning of the gauge invariance and the observables, e.g., a gauge-invariant mass term and vacuum condensates of mass dimension two. We point out the implications for the Skyrme--Faddeev model.Comment: 17pages, 1 figure; English improved; a version appeared in Prog. Theor. Phy

Single and Double Universal Seesaw Mechanisms with Universal Strength for Yukawa Couplings

Single and double universal seesaw mechanisms and the hypothesis of universal strength for Yukawa couplings are applied to formulate a unified theory of fermion mass spectrum in a model based on an extended Pati-Salam symmetry. Five kinds of Higgs fields are postulated to mediate scalar interactions among electroweak doublets of light fermions and electroweak singlets of heavy exotic fermions with relative Yukawa coupling constants of exponential form. At the first-order seesaw approximation, quasi-democratic mass matrices with equal diagonal elements are derived for all charged fermion sectors and a diagonal mass matrix is obtained for the neutrino sector under an additional ansatz. Assuming the vacuum neutrino oscillation, the problems of solar and atmospheric neutrino anomalies are investigated.Comment: 13 pages, LaTeX; a reference adde

Nuclear Excitations Described by Randomly Selected Multiple Slater Determinants

We propose a new stochastic method to describe low-lying excited states of finite nuclei superposing multiple Slater determinants without assuming generator coordinates a priori. We examine accuracy of our method by using simple BKN interaction.Comment: Talk at International Symposium on Correlation Dynamics in Nuclei, Tokyo, Japan, 31 Jan.-- 4 Feb. 200

Solving the Schwinger-Dyson Equations for Gluodynamics in the Maximal Abelian Gauge

We derive the Schwinger-Dyson equations for the SU(2) Yang-Mills theory in the maximal Abelian gauge and solve them in the infrared asymptotic region. We find that the infrared asymptotic solutions for the gluon and ghost propagators are consistent with the hypothesis of Abelian dominance.Comment: 3 pages, 1 figure; Lattice2003(topology

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions

This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this voxel does not enhance", to a well-defined and numerically testable null hypothesis based on existing data. We call the procedure "soft null hypothesis" testing as opposed to the standard "hard null hypothesis" testing. This problem is fundamentally different from: 1) testing when a quantitative null hypothesis is given; 2) clustering using a mixture distribution; or 3) identifying a reasonable threshold with a parametric null assumption. We analyze a total of 20 subjects scanned at 63 visits (~30Gb), the largest population of such clinical brain images