EBAC-DCC Analysis of World Data of pi N, gamma N, and N(e,e') Reactions

The development, results, and prospect of the Dynamical Coupled-Channels analysis at Excited Baryon Analysis Center (EBAC-DCC) are reported.Comment: 6 pages, 7 figures. Contribution to the proceedings of the 8th International Workshop on the Physics of Excited Nucleons (NSTAR2011), Newport News, VA, USA, May 17-20, 201

Status of Dynamical Coupled-Channel Analysis by Collaboration@EBAC

The development and results of the Dynamical Coupled-Channels analysis by a collaboration at the Excited Baryon Analysis Center (EBAC) are reported.Comment: 5 pages, 4 figures. Contribution to Eleventh Conference on the Intersections of Particle and Nuclear Physics --- CIPANP 2012, May 28, 2012 - June 3, 2012, St. Petersburg, FL, US

Characterization of the asymptotic distribution of semiparametric M-estimators

This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator. © 2010 Elsevier B.V. All rights reserved

Interferometric distillation and determination of unknown two-qubit entanglement

We propose a scheme for both distilling and quantifying entanglement, applicable to individual copies of an arbitrary unknown two-qubit state. It is realized in a usual two-qubit interferometry with local filtering. Proper filtering operation for the maximal distillation of the state is achieved, by erasing single-qubit interference, and then the concurrence of the state is determined directly from the visibilities of two-qubit interference. We compare the scheme with full state tomography

Studies in optical parallel processing

Threshold and A/D devices for converting a gray scale image into a binary one were investigated for all-optical and opto-electronic approaches to parallel processing. Integrated optical logic circuits (IOC) and optical parallel logic devices (OPA) were studied as an approach to processing optical binary signals. In the IOC logic scheme, a single row of an optical image is coupled into the IOC substrate at a time through an array of optical fibers. Parallel processing is carried out out, on each image element of these rows, in the IOC substrate and the resulting output exits via a second array of optical fibers. The OPAL system for parallel processing which uses a Fabry-Perot interferometer for image thresholding and analog-to-digital conversion, achieves a higher degree of parallel processing than is possible with IOC

Optical recognition of statistical patterns

Optical implementation of the Fukunaga-Koontz transform (FKT) and the Least-Squares Linear Mapping Technique (LSLMT) is described. The FKT is a linear transformation which performs image feature extraction for a two-class image classification problem. The LSLMT performs a transform from large dimensional feature space to small dimensional decision space for separating multiple image classes by maximizing the interclass differences while minimizing the intraclass variations. The FKT and the LSLMT were optically implemented by utilizing a coded phase optical processor. The transform was used for classifying birds and fish. After the F-K basis functions were calculated, those most useful for classification were incorporated into a computer generated hologram. The output of the optical processor, consisting of the squared magnitude of the F-K coefficients, was detected by a T.V. camera, digitized, and fed into a micro-computer for classification. A simple linear classifier based on only two F-K coefficients was able to separate the images into two classes, indicating that the F-K transform had chosen good features. Two advantages of optically implementing the FKT and LSLMT are parallel and real time processing