570 research outputs found
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
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 . 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
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