36,106 research outputs found

    Towards a generic power estimator

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    International audienceData centers play an important role on worldwide electrical energy consumption. Understanding their power dissipation is a key aspect to achieve energy efficiency. Some application specific models were proposed, while other generic ones lack accuracy. The contributions of this paper are threefold. First we expose the importance of modelling alternating to direct current conversion losses. Second, a weakness of CPU proportional models is evidenced. Finally, a methodology to estimate the power consumed by applications with machine learning techniques is proposed. Since the results of such techniques are deeply data dependent, a study on devices’ power profiles was executed to generate a small set of synthetic benchmarks able to emulate generic applications’ behaviour. Our approach is then compared with two other models, showing that the percentage error of energy estimation of an application can be less than 1 %

    Group-Lasso on Splines for Spectrum Cartography

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    The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finitely-parameterized (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that ``better'' explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Simulated tests corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced.Comment: Submitted to IEEE Transactions on Signal Processin

    A novel approach to reconstructing signals of isotropy violation from a masked CMB sky

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    Statistical isotropy (SI) is one of the fundamental assumptions made in cosmological model building. This assumption is now being rigorously tested using the almost full sky measurements of the CMB anisotropies. A major hurdle in any such analysis is to handle the large biases induced due to the process of masking. We have developed a new method of analysis, using the bipolar spherical harmonic basis functions, in which we semi-analytically evaluate the modifications to SI violation induced by the mask. The method developed here is generic and can be potentially used to search for any arbitrary form of SI violation. We specifically demonstrate the working of this method by recovering the Doppler boost signal from a set of simulated, masked CMB skies.Comment: 8 pages, 3 figure

    Moment-Based Ellipticity Measurement as a Statistical Parameter Estimation Problem

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    We show that galaxy ellipticity estimation for weak gravitational lensing with unweighted image moments reduces to the problem of measuring a combination of the means of three independent normal random variables. Under very general assumptions, the intrinsic image moments of sources can be recovered from observations including effects such as the point-spread function and pixellation. Gaussian pixel noise turns these into three jointly normal random variables, the means of which are algebraically related to the ellipticity. We show that the random variables are approximately independent with known variances, and provide an algorithm for making them exactly independent. Once the framework is developed, we derive general properties of the ellipticity estimation problem, such as the signal-to-noise ratio, a generic form of an ellipticity estimator, and Cram\'er-Rao lower bounds for an unbiased estimator. We then derive the unbiased ellipticity estimator using unweighted image moments. We find that this unbiased estimator has a poorly behaved distribution and does not converge in practical applications, but demonstrates how to derive and understand the behaviour of new moment-based ellipticity estimators.Comment: 11 pages, 7 figures; v2 matches accepted version with minor change

    On the genericity properties in networked estimation: Topology design and sensor placement

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    In this paper, we consider networked estimation of linear, discrete-time dynamical systems monitored by a network of agents. In order to minimize the power requirement at the (possibly, battery-operated) agents, we require that the agents can exchange information with their neighbors only \emph{once per dynamical system time-step}; in contrast to consensus-based estimation where the agents exchange information until they reach a consensus. It can be verified that with this restriction on information exchange, measurement fusion alone results in an unbounded estimation error at every such agent that does not have an observable set of measurements in its neighborhood. To over come this challenge, state-estimate fusion has been proposed to recover the system observability. However, we show that adding state-estimate fusion may not recover observability when the system matrix is structured-rank (SS-rank) deficient. In this context, we characterize the state-estimate fusion and measurement fusion under both full SS-rank and SS-rank deficient system matrices.Comment: submitted for IEEE journal publicatio

    Fidelity susceptibility made simple: A unified quantum Monte Carlo approach

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    The fidelity susceptibility is a general purpose probe of phase transitions. With its origin in quantum information and in the differential geometry perspective of quantum states, the fidelity susceptibility can indicate the presence of a phase transition without prior knowledge of the local order parameter, as well as reveal the universal properties of a critical point. The wide applicability of the fidelity susceptibility to quantum many-body systems is, however, hindered by the limited computational tools to evaluate it. We present a generic, efficient, and elegant approach to compute the fidelity susceptibility of correlated fermions, bosons, and quantum spin systems in a broad range of quantum Monte Carlo methods. It can be applied both to the ground-state and non-zero temperature cases. The Monte Carlo estimator has a simple yet universal form, which can be efficiently evaluated in simulations. We demonstrate the power of this approach with applications to the Bose-Hubbard model, the spin-1/21/2 XXZ model, and use it to examine the hypothetical intermediate spin-liquid phase in the Hubbard model on the honeycomb lattice.Comment: new physical insight added in Sec. VI., improved data in Fig.

    Clusters and the Cosmic Web

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    We discuss the intimate relationship between the filamentary features and the rare dense compact cluster nodes in this network, via the large scale tidal field going along with them, following the cosmic web theory developed Bond et al. The Megaparsec scale tidal shear pattern is responsible for the contraction of matter into filaments, and its link with the cluster locations can be understood through the implied quadrupolar mass distribution in which the clusters are to be found at the sites of the overdense patches. We present a new technique for tracing the cosmic web, identifying planar walls, elongated filaments and cluster nodes in the galaxy distribution. This will allow the practical exploitation of the concept of the cosmic web towards identifying and tracing the locations of the gaseous WHIM. These methods, the Delaunay Tessellation Field Estimator (DTFE) and the Morphology Multiscale Filter (MMF) find their basis in computational geometry and visualization.Comment: 13 pages, 6 figures, appeared in proceedings workshop "Measuring the Diffuse Intergalactic Medium", eds. J-W. den Herder and N. Yamasaki, Hayama, Japan, October 2005. For version with high-res figures see http://www.astro.rug.nl/~weygaert/weywhim05.pd

    Astrophysical data analysis with information field theory

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    Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.Comment: 4 pages, 2 figures, accepted chapter to the conference proceedings for MaxEnt 2013, to be published by AI
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