A Lattice Study of the Magnetic Moment and the Spin Structure of the Nucleon

Using an approach free from momentum extrapolation, we calculate the nucleon magnetic moment and the fraction of the nucleon spin carried by the quark angular momentum in the quenched lattice QCD approximation. Quarks with three values of lattice masses, 210, 124 and 80 MeV, are formulated on the lattice using the standard Wilson approach. At every mass, 100 gluon configurations on 16^3 x 32 lattice with \beta=6.0 are used for statistical averaging. The results are compared with the previous calculations with momentum extrapolation. The contribution of the disconnected diagrams is studied at the largest quark mass using noise theory technique.Comment: 14 pages, 3 figures, Talk given at Lattice2001, Berlin, German

On image segmentation using information theoretic criteria

Image segmentation is a long-studied and important problem in image processing. Different solutions have been proposed, many of which follow the information theoretic paradigm. While these information theoretic segmentation methods often produce excellent empirical results, their theoretical properties are still largely unknown. The main goal of this paper is to conduct a rigorous theoretical study into the statistical consistency properties of such methods. To be more specific, this paper investigates if these methods can accurately recover the true number of segments together with their true boundaries in the image as the number of pixels tends to infinity. Our theoretical results show that both the Bayesian information criterion (BIC) and the minimum description length (MDL) principle can be applied to derive statistically consistent segmentation methods, while the same is not true for the Akaike information criterion (AIC). Numerical experiments were conducted to illustrate and support our theoretical findings.Comment: Published in at http://dx.doi.org/10.1214/11-AOS925 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Distinction of The Authors of Texts Using Multilayered Feedforward Neural Networks

This paper proposes a means of using a multilayered feedforward neural network to identify the author of a text. The network has to be trained where multilayer feedforward neural network as a  powerful scheme for learning complex input-output mapping have been used in learning of the average number of words and average characters of words in a paragraphs of an author. The resulting training information we get will be used to identify the texts written by authors. The computational complexity is solved by dividing it into a number of computationally simple tasks where the input space is divided into a set of subspaces and then combining the solutions to those tasks. By this, we have been able to successfully distinguish the books authored by Leo Tolstoy, from the ones authored by George Orwell and Boris Pasternak

Divergence rates of Markov order estimators and their application to statistical estimation of stationary ergodic processes

Stationary ergodic processes with finite alphabets are estimated by finite memory processes from a sample, an n-length realization of the process, where the memory depth of the estimator process is also estimated from the sample using penalized maximum likelihood (PML). Under some assumptions on the continuity rate and the assumption of non-nullness, a rate of convergence in $\bar{d}$-distance is obtained, with explicit constants. The result requires an analysis of the divergence of PML Markov order estimators for not necessarily finite memory processes. This divergence problem is investigated in more generality for three information criteria: the Bayesian information criterion with generalized penalty term yielding the PML, and the normalized maximum likelihood and the Krichevsky-Trofimov code lengths. Lower and upper bounds on the estimated order are obtained. The notion of consistent Markov order estimation is generalized for infinite memory processes using the concept of oracle order estimates, and generalized consistency of the PML Markov order estimator is presented.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ468 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

A note on decidability of reachability for conditional Petri nets

The aim of this note is to prove that the reachability problem for Petri nets controlled by finite automata, in the sense of [5], is decidable