Impact of the Delta (1232) resonance on neutral pion photoproduction in chiral perturbation theory

We present an ongoing project to assess the importance of D-waves and the $\Delta (1232)$ resonance for descriptions of neutral pion photoproduction in Heavy Baryon Chiral Perturbation Theory. This research has been motivated by data published by the A2 and CB-TAPS collaborations at MAMI [1]. This data has reached unprecedented levels of accuracy from threshold through to the $\Delta$ resonance. Accompanying the experimental work, there has also been a series of publications studying the theory that show that, to go beyond an energy of $E_\gamma=170$ MeV, it is necessary to include other aspects, in particular the $\Delta (1232)$ as a degree of freedom [2] and possibly higher partial waves [3].Comment: Proceedings to the 8th International Workshop on Chiral Dynamics 201

Parabolic and Hyperbolic Contours for Computing the Bromwich Integral

Some of the most effective methods for the numerical inversion of the Laplace transform are based on the approximation of the Bromwich contour integral. The accuracy of these methods often hinges on a good choice of contour, and several such contours have been proposed in the literature. Here we analyze two recently proposed contours, namely a parabola and a hyperbola. Using a representative model problem, we determine estimates for the optimal parameters that define these contours. An application to a fractional diffusion equation is presented.\ud \ud JACW was supported by the National Research Foundation in South Africa under grant FA200503230001

Numerical Evidence for Robustness of Environment-Assisted Quantum Transport

Recent theoretical studies show that decoherence process can enhance transport efficiency in quantum systems. This effect is known as environment-assisted quantum transport (ENAQT). The role of ENAQT in optimal quantum transport is well investigated, however, it is less known how robust ENAQT is with respect to variations in the system or its environment characteristic. Toward answering this question, we simulated excitonic energy transfer in Fenna-Matthews-Olson (FMO) photosynthetic complex. We found that ENAQT is robust with respect to many relevant parameters of environmental interactions and Frenkel-exciton Hamiltonian including reorganization energy, bath frequency cutoff, temperature, and initial excitations, dissipation rate, trapping rate, disorders, and dipole moments orientations. Our study suggests that the ENAQT phenomenon can be exploited in robust design of highly efficient quantum transport systems.Comment: arXiv admin note: substantial text overlap with arXiv:1104.481

Automatic Detection of Outliers in Multibeam Echo Sounding Data

The data volumes produced by new generation multibeam systems are very large, especially for shallow water systems. Results from recent multibeam surveys indicate that the ratio of the field survey time, to the time used in interactive editing through graphical editing tools, is about 1:1. An important reason for the large amount of processing time is that users subjectively decide which soundings are outliers. There is an apparent need for an automated approach for detecting outliers that would reduce the extensive labor and obtain consistent results from the multibeam data cleaning process, independent of the individual that has processed the data. The proposed automated algorithm for cleaning multibeam soundings was tested using the SAX-99 (Destin FL) multibeam survey data [2]. Eight days of survey data (6.9 Gigabyte) were cleaned in 2.5 hours on an SGI platform. A comparison of the automatically cleaned data with the subjective, interactively cleaned data indicates that the proposed method is, if not better, at least equivalent to interactive editing as used on the SAX-99 multibeam data. Furthermore, the ratio of acquisition to processing time is considerably improved since the time required for cleaning the data was decreased from 192 hours to 2.5 hours (an improvement by a factor of 77)

Faster K-Means Cluster Estimation

There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose a fast heuristic to overcome this bottleneck with only marginal increase in MSE. We observe that across all iterations of K-means, a data point changes its membership only among a small subset of clusters. Our heuristic predicts such clusters for each data point by looking at nearby clusters after the first iteration of k-means. We augment well known variants of k-means with our heuristic to demonstrate effectiveness of our heuristic. For various synthetic and real-world datasets, our heuristic achieves speed-up of up-to 3 times when compared to efficient variants of k-means.Comment: 6 pages, Accepted at ECIR 201

Entanglement in a Valence-Bond-Solid State

We study entanglement in Valence-Bond-Solid state. It describes the ground state of Affleck, Kennedy, Lieb and Tasaki quantum spin chain. The AKLT model has a gap and open boundary conditions. We calculate an entropy of a subsystem (continuous block of spins). It quantifies the entanglement of this block with the rest of the ground state. We prove that the entanglement approaches a constant value exponentially fast as the size of the subsystem increases. Actually we proved that the density matrix of the continuous block of spins depends only on the length of the block, but not on the total size of the chain [distance to the ends also not essential]. We also study reduced density matrices of two spins both in the bulk and on the boundary. We evaluated concurrencies.Comment: 4pages, no figure

Feature subset selection: a correlation based filter approach

Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or redundant to the learning task. Feature subset selection, then, is a method of enhancing the performance of learning algorithms, reducing the hypothesis search space, and, in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based heuristic to determine the goodness of feature subsets, and evaluates its effectiveness with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner(IBI). Experiments using a number of standard data sets drawn from real and artificial domains are presented. Feature subset selection gave significant improvement for all three algorithms; C4.5 generated smaller decision trees

Quantum metrology

We point out a general framework that encompasses most cases in which quantum effects enable an increase in precision when estimating a parameter (quantum metrology). The typical quantum precision-enhancement is of the order of the square root of the number of times the system is sampled. We prove that this is optimal and we point out the different strategies (classical and quantum) that permit to attain this bound.Comment: 4 pages, 2 figure