1+1-dimensional p-wave superconductors from intersecting D-branes

In this work we explore 1+1 dimensional p-wave superconductors using the probe D-brane construction. Specifically, we choose three intersecting D-brane models: D1/D5, D2/D4 and D3/D3 systems. According to the dilaton running behavior, we denote the former two systems as nonconformal models and the last system as conformal. We find that all three models are qualitatively similar in describing superconducting condensate as well as some basic features (such as the gap formation and DC superconductivity) of superconducting conductivity. There also exist some differences among the three models as far as the AC conductivity is concerned. Specifically, for D3/D3 model there is no peak at nonzero frequency for the imaginary part of the conductivity, which is present in the nonconformal models; their asymptotic behaviors are different-for D1/D5 the real part of the AC conductivity approaches one at large frequency limit, for D2/D4 it slowly goes to a certain nonzero constant smaller than one and for D3/D3 it goes to zero. We find the profile of the AC conductivity for the D1/D5 system is very similar to that of higher dimensional p-wave superconductors.Comment: v2: matched with the published versio

Pregelix: Big(ger) Graph Analytics on A Dataflow Engine

There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up to 35x speedup compared to distributed GraphLab), and makes more effective use of available machine resources to support Big(ger) Graph Analytics

Thermodynamic conditions during growth determine the magnetic anisotropy in epitaxial thin-films of La0.7_{0.7}Sr0.3_{0.3}MnO3_{3}

The suitability of a particular material for use in magnetic devices is determined by the process of magnetization reversal/relaxation, which in turn depends on the magnetic anisotropy. Therefore, designing new ways to control magnetic anisotropy in technologically important materials is highly desirable. Here we show that magnetic anisotropy of epitaxial thin-films of half-metallic ferromagnet La$_{0.7}$Sr$_{0.3}$MnO$_{3}$ (LSMO) is determined by the proximity to thermodynamic equilibrium conditions during growth. We performed a series of X-ray diffraction and ferromagnetic resonance (FMR) experiments in two different sets of samples: the first corresponds to LSMO thin-films deposited under tensile strain on (001) SrTiO$_{3}$ by Pulsed Laser Deposition (PLD; far from thermodynamic equilibrium); the second were deposited by a slow Chemical Solution Deposition (CSD) method, under quasi-equilibrium conditions. Thin films prepared by PLD show a in-plane cubic anisotropy with an overimposed uniaxial term. A large anisotropy constant perpendicular to the film plane was also observed in these films. However, the uniaxial anisotropy is completely suppressed in the CSD films. The out of plane anisotropy is also reduced, resulting in a much stronger in plane cubic anisotropy in the chemically synthesized films. This change is due to a different rotation pattern of MnO$_{6}$ octahedra to accomodate epitaxial strain, which depends not only on the amount of tensile stress imposed by the STO substrate, but also on the growth conditions. Our results demonstrate that the nature and magnitude of the magnetic anisotropy in LSMO can be tuned by the thermodynamic parameters during thin-film deposition.Comment: 6 pages, 8 Figure

Astronomy in the Cloud: Using MapReduce for Image Coaddition

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computation challenges such as anomaly detection and classification, and moving object tracking. Since such studies benefit from the highest quality data, methods such as image coaddition (stacking) will be a critical preprocessing step prior to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources or transient objects, these data streams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this paper we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data is partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources, e.g., Amazon's EC2. We report on our experience implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multi-terabyte imaging dataset provides a good testbed for algorithm development since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image coaddition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table

Specification analysis in regime-switching continuous-time diffusion models for market volatility

We examine model specification in regime-switching continuous-time diffusions for modeling S&P 500 Volatility Index (VIX). Our investigation is carried out under two nonlinear diffusion frameworks, the NLDCEV and the CIRCEV frameworks, and our focus is on the nonlinearity in regime-dependent drift and diffusion terms, the switching components, and the endogeneity in regime changes. While we find strong evidence of regime-switching effects, models with a switching diffusion term capture the VIX dynamics considerably better than models with only a switching drift, confirming the presence and importance of volatility regimes. Strong evidence of nonlinear endogeneity in regime changes is also detected. Meanwhile, we find significant nonlinearity in the regime-dependent diffusion specification, suggesting that the nonlinearity in the VIX dynamics cannot be accounted for by regime-switching effects alone. Finally, we find that models based on the CIRCEV specification are significantly closer to the true data generating process of VIX than models based on the NLDCEV specification uniformly across all regime-switching specifications

Assessment of household energy utilized for cooking in Ikeja, Lagos state, Nigeria

Household cooking energy accounts for a major part of the total energy consumed in Nigeria. Factors affecting the choice of Household energy utilized for cooking and the type preferred in Ikeja area of Lagos state were investigated in this study. Data were obtained through oral interview and administration of structured questionnaire on 250 randomly sampled households in the study area. MATLAB was used to conduct descriptive statistics, inferential statistics and percentage difference between used energy and preference energy. The study revealed that kerosene and Gas (LPG) were mostly used for daily cooking (48.60%) and (36.30%) respectively. Only a small proportion use Charcoal, firewood and electricity for their daily cooking, the percentage being 7.10%, 5.7% and 2.4% for charcoal, firewood and electricity respectively. However preference rating of household energy was highest in Gas followed by electricity, kerosene, charcoal and firewood respectively. Chi-test, linear-by-linear relationship test, likelihood ratio test revealed that level of income, level of education and type of employment affects the choice of fuel used for cooking and the type preferred. http://dx.doi.org/10.4314/njt.v35i4.1

Polymeric routes to silicon carbide and silicon oxycarbide CMC

An overview of two approaches to the formation of ceramic composite matrices from polymeric precursors is presented. Copolymerization of alkyl- and alkenylsilanes (RSiH3) represents a new precursor system for the production of Beta-SiC on pyrolysis, with copolymer composition controlling polymer structure, char yield, and ceramic stoichiometry and morphology. Polysilsesquioxanes which are synthesized readily and can be handled in air serve as precursors to Si-C-O ceramics. Copolymers of phenyl and methyl silsesquioxanes display rheological properties favorable for composite fabrication; these can be tailored by control of pH, water/methoxy ratio and copolymer composition. Composites obtained from these utilize a carbon coated, eight harness satin weave Nicalon cloth reinforcement. The material exhibits nonlinear stress-strain behavior in tension

Keratin 6a marks mammary bipotential progenitor cells that can give rise to a unique tumor model resembling human normal-like breast cancer.

Progenitor cells are considered an important cell of origin of human malignancies. However, there has not been any single gene that can define mammary bipotential progenitor cells, and as such it has not been possible to use genetic methods to introduce oncogenic alterations into these cells in vivo to study tumorigenesis from them. Keratin 6a is expressed in a subset of mammary luminal epithelial cells and body cells of terminal end buds. By generating transgenic mice using the Keratin 6a (K6a) gene promoter to express tumor virus A (tva), which encodes the receptor for avian leukosis virus subgroup A (ALV/A), we provide direct evidence that K6a(+) cells are bipotential progenitor cells, and the first demonstration of a non-basal location for some biopotential progenitor cells. These K6a(+) cells were readily induced to form mammary tumors by intraductal injection of RCAS (an ALV/A-derived vector) carrying the gene encoding the polyoma middle T antigen. Tumors in this K6a-tva line were papillary and resembled the normal breast-like subtype of human breast cancer. This is the first model of this subtype of human tumors and thus may be useful for preclinical testing of targeted therapy for patients with normal-like breast cancer. These observations also provide direct in vivo evidence for the hypothesis that the cell of origin affects mammary tumor phenotypes