3,526 research outputs found
Measuring the galaxy power spectrum and scale-scale correlations with multiresolution-decomposed covariance -- I. method
We present a method of measuring galaxy power spectrum based on the
multiresolution analysis of the discrete wavelet transformation (DWT). Since
the DWT representation has strong capability of suppressing the off-diagonal
components of the covariance for selfsimilar clustering, the DWT covariance for
popular models of the cold dark matter cosmogony generally is diagonal, or
(scale)-diagonal in the scale range, in which the second scale-scale
correlations are weak. In this range, the DWT covariance gives a lossless
estimation of the power spectrum, which is equal to the corresponding Fourier
power spectrum banded with a logarithmical scaling. In the scale range, in
which the scale-scale correlation is significant, the accuracy of a power
spectrum detection depends on the scale-scale or band-band correlations. This
is, for a precision measurements of the power spectrum, a measurement of the
scale-scale or band-band correlations is needed. We show that the DWT
covariance can be employed to measuring both the band-power spectrum and second
order scale-scale correlation. We also present the DWT algorithm of the binning
and Poisson sampling with real observational data. We show that the alias
effect appeared in usual binning schemes can exactly be eliminated by the DWT
binning. Since Poisson process possesses diagonal covariance in the DWT
representation, the Poisson sampling and selection effects on the power
spectrum and second order scale-scale correlation detection are suppressed into
minimum. Moreover, the effect of the non-Gaussian features of the Poisson
sampling can be calculated in this frame.Comment: AAS Latex file, 44 pages, accepted for publication in Ap
An Inverse Problem for Localization Operators
A classical result of time-frequency analysis, obtained by I. Daubechies in
1988, states that the eigenfunctions of a time-frequency localization operator
with circular localization domain and Gaussian analysis window are the Hermite
functions. In this contribution, a converse of Daubechies' theorem is proved.
More precisely, it is shown that, for simply connected localization domains, if
one of the eigenfunctions of a time-frequency localization operator with
Gaussian window is a Hermite function, then its localization domain is a disc.
The general problem of obtaining, from some knowledge of its eigenfunctions,
information about the symbol of a time-frequency localization operator, is
denoted as the inverse problem, and the problem studied by Daubechies as the
direct problem of time-frequency analysis. Here, we also solve the
corresponding problem for wavelet localization, providing the inverse problem
analogue of the direct problem studied by Daubechies and Paul.Comment: 18 pages, 1 figur
Multiresolution analysis in statistical mechanics. II. The wavelet transform as a basis for Monte Carlo simulations on lattices
In this paper, we extend our analysis of lattice systems using the wavelet
transform to systems for which exact enumeration is impractical. For such
systems, we illustrate a wavelet-accelerated Monte Carlo (WAMC) algorithm,
which hierarchically coarse-grains a lattice model by computing the probability
distribution for successively larger block spins. We demonstrate that although
the method perturbs the system by changing its Hamiltonian and by allowing
block spins to take on values not permitted for individual spins, the results
obtained agree with the analytical results in the preceding paper, and
``converge'' to exact results obtained in the absence of coarse-graining.
Additionally, we show that the decorrelation time for the WAMC is no worse than
that of Metropolis Monte Carlo (MMC), and that scaling laws can be constructed
from data performed in several short simulations to estimate the results that
would be obtained from the original simulation. Although the algorithm is not
asymptotically faster than traditional MMC, because of its hierarchical design,
the new algorithm executes several orders of magnitude faster than a full
simulation of the original problem. Consequently, the new method allows for
rapid analysis of a phase diagram, allowing computational time to be focused on
regions near phase transitions.Comment: 11 pages plus 7 figures in PNG format (downloadable separately
One-point Statistics of the Cosmic Density Field in Real and Redshift Spaces with A Multiresolutional Decomposition
In this paper, we develop a method of performing the one-point statistics of
a perturbed density field with a multiresolutional decomposition based on the
discrete wavelet transform (DWT). We establish the algorithm of the one-point
variable and its moments in considering the effects of Poisson sampling and
selection function. We also establish the mapping between the DWT one-point
statistics in redshift space and real space, i.e. the algorithm for recovering
the DWT one-point statistics from the redshift distortion of bulk velocity,
velocity dispersion, and selection function. Numerical tests on N-body
simulation samples show that this algorithm works well on scales from a few
hundreds to a few Mpc/h for four popular cold dark matter models.
Taking the advantage that the DWT one-point variable is dependent on both the
scale and the shape (configuration) of decomposition modes, one can design
estimators of the redshift distortion parameter (beta) from combinations of DWT
modes. When the non-linear redshift distortion is not negligible, the beta
estimator from quadrupole-to-monopole ratio is a function of scale. This
estimator would not work without adding information about the scale-dependence,
such as the power-spectrum index or the real-space correlation function of the
random field. The DWT beta estimators, however, do not need such extra
information. Numerical tests show that the proposed DWT estimators are able to
determine beta robustly with less than 15% uncertainty in the redshift range 0
< z < 3.Comment: 39 pages, 12 figures, ApJ accepte
Multiscale 3D Shape Analysis using Spherical Wavelets
©2005 Springer. The original publication is available at www.springerlink.com:
http://dx.doi.org/10.1007/11566489_57DOI: 10.1007/11566489_57Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data
Discrepancy between sub-critical and fast rupture roughness: a cumulant analysis
We study the roughness of a crack interface in a sheet of paper. We
distinguish between slow (sub-critical) and fast crack growth regimes. We show
that the fracture roughness is different in the two regimes using a new method
based on a multifractal formalism recently developed in the turbulence
literature. Deviations from monofractality also appear to be different in both
regimes
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Numerical treatment of seismic accelerograms and of inelastic seismic structural responses using harmonic wavelets
The harmonic wavelet transform is employed to analyze various kinds of nonstationary signals common in aseismic design. The effectiveness of the harmonic wavelets for capturing the temporal evolution of the frequency content of strong ground motions is demonstrated. In this regard, a detailed study of important earthquake accelerograms is undertaken and smooth joint time-frequency spectra are provided for two near-field and two far-field records; inherent in this analysis is the concept of the mean instantaneous frequency. Furthermore, as a paradigm of usefulness for aseismic structural purposes, a similar analysis is conducted for the response of a 20-story steel frame benchmark building considering one of the four accelerograms scaled by appropriate factors as the excitation to simulate undamaged and severely damaged conditions for the structure. The resulting joint time-frequency representation of the response time histories captures the influence of nonlinearity on the variation of the effective natural frequencies of a structural system during the evolution of a seismic event. In this context, the potential of the harmonic wavelet transform as a detection tool for global structural damage is explored in conjunction with the concept of monitoring the mean instantaneous frequency of records of critical structural responses
Quantitative Regular Expressions for Arrhythmia Detection Algorithms
Motivated by the problem of verifying the correctness of arrhythmia-detection
algorithms, we present a formalization of these algorithms in the language of
Quantitative Regular Expressions. QREs are a flexible formal language for
specifying complex numerical queries over data streams, with provable runtime
and memory consumption guarantees. The medical-device algorithms of interest
include peak detection (where a peak in a cardiac signal indicates a heartbeat)
and various discriminators, each of which uses a feature of the cardiac signal
to distinguish fatal from non-fatal arrhythmias. Expressing these algorithms'
desired output in current temporal logics, and implementing them via monitor
synthesis, is cumbersome, error-prone, computationally expensive, and sometimes
infeasible.
In contrast, we show that a range of peak detectors (in both the time and
wavelet domains) and various discriminators at the heart of today's
arrhythmia-detection devices are easily expressible in QREs. The fact that one
formalism (QREs) is used to describe the desired end-to-end operation of an
arrhythmia detector opens the way to formal analysis and rigorous testing of
these detectors' correctness and performance. Such analysis could alleviate the
regulatory burden on device developers when modifying their algorithms. The
performance of the peak-detection QREs is demonstrated by running them on real
patient data, on which they yield results on par with those provided by a
cardiologist.Comment: CMSB 2017: 15th Conference on Computational Methods for Systems
Biolog
Deep Regionlets for Object Detection
In this paper, we propose a novel object detection framework named "Deep
Regionlets" by establishing a bridge between deep neural networks and
conventional detection schema for accurate generic object detection. Motivated
by the abilities of regionlets for modeling object deformation and multiple
aspect ratios, we incorporate regionlets into an end-to-end trainable deep
learning framework. The deep regionlets framework consists of a region
selection network and a deep regionlet learning module. Specifically, given a
detection bounding box proposal, the region selection network provides guidance
on where to select regions to learn the features from. The regionlet learning
module focuses on local feature selection and transformation to alleviate local
variations. To this end, we first realize non-rectangular region selection
within the detection framework to accommodate variations in object appearance.
Moreover, we design a "gating network" within the regionlet leaning module to
enable soft regionlet selection and pooling. The Deep Regionlets framework is
trained end-to-end without additional efforts. We perform ablation studies and
conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets.
The proposed framework outperforms state-of-the-art algorithms, such as
RetinaNet and Mask R-CNN, even without additional segmentation labels.Comment: Accepted to ECCV 201
On the efficient Monte Carlo implementation of path integrals
We demonstrate that the Levy-Ciesielski implementation of Lie-Trotter
products enjoys several properties that make it extremely suitable for
path-integral Monte Carlo simulations: fast computation of paths, fast Monte
Carlo sampling, and the ability to use different numbers of time slices for the
different degrees of freedom, commensurate with the quantum effects. It is
demonstrated that a Monte Carlo simulation for which particles or small groups
of variables are updated in a sequential fashion has a statistical efficiency
that is always comparable to or better than that of an all-particle or
all-variable update sampler. The sequential sampler results in significant
computational savings if updating a variable costs only a fraction of the cost
for updating all variables simultaneously or if the variables are independent.
In the Levy-Ciesielski representation, the path variables are grouped in a
small number of layers, with the variables from the same layer being
statistically independent. The superior performance of the fast sampling
algorithm is shown to be a consequence of these observations. Both mathematical
arguments and numerical simulations are employed in order to quantify the
computational advantages of the sequential sampler, the Levy-Ciesielski
implementation of path integrals, and the fast sampling algorithm.Comment: 14 pages, 3 figures; submitted to Phys. Rev.
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