Generalized Background-Field Method

The graphical method discussed previously can be used to create new gauges not reachable by the path-integral formalism. By this means a new gauge is designed for more efficient two-loop QCD calculations. It is related to but simpler than the ordinary background-field gauge, in that even the triple-gluon vertices for internal lines contain only four terms, not the usual six. This reduction simplifies the calculation inspite of the necessity to include other vertices for compensation. Like the ordinary background-field gauge, this generalized background-field gauge also preserves gauge invariance of the external particles. As a check of the result and an illustration for the reduction in labour, an explicit calculation of the two-loop QCD $\beta$-function is carried out in this new gauge. It results in a saving of 45% of computation compared to the ordinary background-field gauge.Comment: 17 pages, Latex, 18 figures in Postscrip

Hybrid biomedical intelligent systems

"Copyright © 2012 Maysam Abbod et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited."The purpose of this special issue is to promote research and developments of the best work in the field of hybrid intelligent systems for biomedical applications

Phase II of the ASCE Benchmark Study on SHM

The task group on structural health monitoring of the Dynamic Committee of ASCE was formed in 1999 at the 12 th Engineering Mechanics Conference. The task group has designed a number of analytical studies on a benchmark structure and there are plans to follow these with an experimental program. The first phase of the analytical studies was completed in 2001. The second phase, initiated in the summer of 2001, was formulated in the light of the experience gained on phase I and focuses on increasing realism in the simulation of the discrepancies between the actual structure and the mathematical model used in the analysis. This paper describes the rational that lead the SHM task group to the definition of phase II and presents the details of the cases that are being considered

Direct measurement of penetration length in ultra-thin and/or mesoscopic superconducting structures

We describe a method for direct measurement of the magnetic penetration length in thin (10 - 100 nm) superconducting structures having overall dimensions in the range 1 to 100 micrometers. The method is applicable for broadband magnetic fields from dc to MHz frequencies.Comment: Accepted by Journal of Applied P:hysics (Jun 2006).5 pages, 5 figure

Controlling the Momentum Current of an Off-resonant Ratchet

We experimentally investigate the phenomenon of a quantum ratchet created by exposing a Bose-Einstein Condensate to short pulses of a potential which is periodic in both space and time. Such a ratchet is manifested by a directed current of particles, even though there is an absence of a net bias force. We confirm a recent theoretical prediction [M. Sadgrove and S. Wimberger, New J. Phys. \textbf{11}, 083027 (2009)] that the current direction can be controlled by experimental parameters which leave the underlying symmetries of the system unchanged. We demonstrate that this behavior can be understood using a single variable containing many of the experimental parameters and thus the ratchet current is describable using a single universal scaling law.Comment: arXiv admin note: substantial text overlap with arXiv:1210.565

Fast algorithms for large scale generalized distance weighted discrimination

High dimension low sample size statistical analysis is important in a wide range of applications. In such situations, the highly appealing discrimination method, support vector machine, can be improved to alleviate data piling at the margin. This leads naturally to the development of distance weighted discrimination (DWD), which can be modeled as a second-order cone programming problem and solved by interior-point methods when the scale (in sample size and feature dimension) of the data is moderate. Here, we design a scalable and robust algorithm for solving large scale generalized DWD problems. Numerical experiments on real data sets from the UCI repository demonstrate that our algorithm is highly efficient in solving large scale problems, and sometimes even more efficient than the highly optimized LIBLINEAR and LIBSVM for solving the corresponding SVM problems