36,106 research outputs found
Towards a generic power estimator
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
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
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
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
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 (-rank)
deficient.
In this context, we characterize the state-estimate fusion and measurement
fusion under both full -rank and -rank deficient system matrices.Comment: submitted for IEEE journal publicatio
Fidelity susceptibility made simple: A unified quantum Monte Carlo approach
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- 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
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
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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