4,910,333 research outputs found

    The power of data in Aboriginal hands

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    This paper explores the critical role that data can play in development scenarios when Aboriginal people are in control of collecting, managing and interpreting data. It was first presented as a pleniary paper at the conference Social Science Perspectives on the 2008 National Aboriginal and Torres Strait Islander Social Survey, held at The ANU on 11–12 April 2011. At the time of writing, Peter Yu was Chief Executive Officer, Nyamba Buru Yawuru Ltd, Broome and a member of the Australian Statistics Advisory Council of the Australian Bureau of Statistics

    Bayesian Power Spectrum Analysis of the First-Year WMAP data

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    We present the first results from a Bayesian analysis of the WMAP first year data using a Gibbs sampling technique. Using two independent, parallel supercomputer codes we analyze the WMAP Q, V and W bands. The analysis results in a full probabilistic description of the information the WMAP data set contains about the power spectrum and the all-sky map of the cosmic microwave background anisotropies. We present the complete probability distributions for each C_l including any non-Gaussianities of the power spectrum likelihood. While we find good overall agreement with the previously published WMAP spectrum, our analysis uncovers discrepancies in the power spectrum estimates at low l multipoles. For example we claim the best-fit Lambda-CDM model is consistent with the C_2 inferred from our combined Q+V+W analysis with a 10% probability of an even larger theoretical C_2. Based on our exact analysis we can therefore attribute the "low quadrupole issue" to a statistical fluctuation.Comment: 5 pages. 4 figures. For additional information and data see http://www.astro.uiuc.edu/~iodwyer/research#wma

    The Angular Power Spectrum of the First-Year WMAP Data Reanalysed

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    We measure the angular power spectrum of the WMAP first-year temperature anisotropy maps. We use SpICE (Spatially Inhomogeneous Correlation Estimator) to estimate Cl's for multipoles l=2-900 from all possible cross-correlation channels. Except for the map-making stage, our measurements provide an independent analysis of that by Hinshaw etal (2003). Despite the different methods used, there is virtually no difference between the two measurements for l < 700 ; the highest l's are still compatible within 1-sigma errors. We use a novel intra-bin variance method to constrain Cl errors in a model independent way. When applied to WMAP data, the intra-bin variance estimator yields diagonal errors 10% larger than those reported by the WMAP team for 100 < l < 450. This translates into a 2.4 sigma detection of systematics since no difference is expected between the SpICE and the WMAP team estimator window functions in this multipole range. With our measurement of the Cl's and errors, we get chi^2/d.o.f. = 1.042 for a best-fit LCDM model, which has a 14% probability, whereas the WMAP team obtained chi^2/d.o.f. = 1.066, which has a 5% probability. We assess the impact of our results on cosmological parameters using Markov Chain Monte Carlo simulations. From WMAP data alone, assuming spatially flat power law LCDM models, we obtain the reionization optical depth tau = 0.145 +/- 0.067, spectral index n_s = 0.99 +/- 0.04, Hubble constant h = 0.67 +/- 0.05, baryon density Omega_b h^2 = 0.0218 +/- 0.0014, cold dark matter density Omega_{cdm} h^2 = 0.122 +/- 0.018, and sigma_8 = 0.92 +/- 0.12, consistent with a reionization redshift z_{re} = 16 +/- 5 (68% CL).Comment: Matches version accepted by ApJ Letters. Main changes: emphasizes chi2 value for best-fit model given our estimate of Cls and errors vs. WMAP team's. Potential detection of systematics in WMAP data quantified. Power spectrum and other data files available at http://www.ifa.hawaii.edu/cosmowave/wmap.htm

    Power Management Techniques for Data Centers: A Survey

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    With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

    The harmonic power spectrum of the soft X-ray background I. The data analysis

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    Fluctuations of the soft X-ray background are investigated using harmonic analysis. A section of the ROSAT All-Sky Survey around the north galactic pole is used. The flux distribution is expanded into a set of harmonic functions and the power spectrum is determined. Several subsamples of the RASS have been used and the spectra for different regions and energies are presented. The effects of the data binning in pixels are assessed and taken into account. The spectra of the analyzed samples reflect both small scale effects generated by strong discrete sources and the large scale gradients of the XRB distribution. Our results show that the power spectrum technique can be effectively used to investigate anisotropy of the XRB at various scales. This statistics will become a useful tool in the investigation of various XRB components.Comment: 12 pages, A&A accepte

    Citizen Science 2.0 : Data Management Principles to Harness the Power of the Crowd

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    Citizen science refers to voluntary participation by the general public in scientific endeavors. Although citizen science has a long tradition, the rise of online communities and user-generated web content has the potential to greatly expand its scope and contributions. Citizens spread across a large area will collect more information than an individual researcher can. Because citizen scientists tend to make observations about areas they know well, data are likely to be very detailed. Although the potential for engaging citizen scientists is extensive, there are challenges as well. In this paper we consider one such challenge – creating an environment in which non-experts in a scientific domain can provide appropriate and accurate data regarding their observations. We describe the problem in the context of a research project that includes the development of a website to collect citizen-generated data on the distribution of plants and animals in a geographic region. We propose an approach that can improve the quantity and quality of data collected in such projects by organizing data using instance-based data structures. Potential implications of this approach are discussed and plans for future research to validate the design are described

    Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

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    Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data
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