Pattern recognition on a quantum computer

By means of a simple example it is demonstrated that the task of finding and identifying certain patterns in an otherwise (macroscopically) unstructured picture (data set) can be accomplished efficiently by a quantum computer. Employing the powerful tool of the quantum Fourier transform the proposed quantum algorithm exhibits an exponential speed-up in comparison with its classical counterpart. The digital representation also results in a significantly higher accuracy than the method of optical filtering. PACS: 03.67.Lx, 03.67.-a, 42.30.Sy, 89.70.+c.Comment: 6 pages RevTeX, 1 figure, several correction

A multi-layer extension of the stochastic heat equation

Motivated by recent developments on solvable directed polymer models, we define a 'multi-layer' extension of the stochastic heat equation involving non-intersecting Brownian motions.Comment: v4: substantially extended and revised versio

Self Duality and Oblique Confinement in Planar Gauge Theories

We investigate the non-perturbative structure of two planar $Z_p \times Z_p$ lattice gauge models and discuss their relevance to two-dimensional condensed matter systems and Josephson junction arrays. Both models involve two compact U(1) gauge fields with Chern-Simons interactions, which break the symmetry down to $Z_p \times Z_p$. By identifying the relevant topological excitations (instantons) and their interactions we determine the phase structure of the models. Our results match observed quantum phase transitions in Josephson junction arrays and suggest also the possibility of {\it oblique confining ground states} corresponding to quantum Hall regimes for either charges or vortices.Comment: 32 pages, harvma

On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection

Abstract. This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact model based on Markov chains is pro-posed to formulate the variation of gene frequency. This model iden-tifies the correlation between the adopted number of parents and the mean convergence time. Moreover, it reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs. The good fit between theoretical and experi-mental results further verifies the capability of this model.

The Trail Pheromone of the Venomous Samsum Ant, Pachycondyla sennaarensis

Ant species use branching networks of pheromone trails for orientation between nest and resources. The current study demonstrated that workers of the venomous samsum ant, Pachycondyla sennaarensis (Mayr) (Hymenoptera: Formicidae: Ponerinae), employ recruitment trail pheromones discharged from the Dufour's gland. Secretions of other abdomen complex glands, as well as hindgut gland secretions, did not evoke trail following. The optimum concentration of trail pheromone was found to be 0.1 gland equivalent/40 cm trail. This concentration demonstrated effective longevity for about one hour. This study also showed that P. sennaarensis and Tapinoma simrothi each respond to the trail pheromones of the other species as well as their own

The G0 Experiment: Apparatus for Parity-Violating Electron Scattering Measurements at Forward and Backward Angles

In the G0 experiment, performed at Jefferson Lab, the parity-violating elastic scattering of electrons from protons and quasi-elastic scattering from deuterons is measured in order to determine the neutral weak currents of the nucleon. Asymmetries as small as 1 part per million in the scattering of a polarized electron beam are determined using a dedicated apparatus. It consists of specialized beam-monitoring and control systems, a cryogenic hydrogen (or deuterium) target, and a superconducting, toroidal magnetic spectrometer equipped with plastic scintillation and aerogel Cerenkov detectors, as well as fast readout electronics for the measurement of individual events. The overall design and performance of this experimental system is discussed.Comment: Submitted to Nuclear Instruments and Method

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization