556 research outputs found
Properties and numerical evaluation of the Rosenblatt distribution
This paper studies various distributional properties of the Rosenblatt
distribution. We begin by describing a technique for computing the cumulants.
We then study the expansion of the Rosenblatt distribution in terms of shifted
chi-squared distributions. We derive the coefficients of this expansion and use
these to obtain the L\'{e}vy-Khintchine formula and derive asymptotic
properties of the L\'{e}vy measure. This allows us to compute the cumulants,
moments, coefficients in the chi-square expansion and the density and
cumulative distribution functions of the Rosenblatt distribution with a high
degree of precision. Tables are provided and software written to implement the
methods described here is freely available by request from the authors.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ421 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Distribution functions of Poisson random integrals: Analysis and computation
We want to compute the cumulative distribution function of a one-dimensional
Poisson stochastic integral I(\krnl) = \displaystyle \int_0^T \krnl(s) N(ds),
where is a Poisson random measure with control measure and \krnl is a
suitable kernel function. We do so by combining a Kolmogorov-Feller equation
with a finite-difference scheme. We provide the rate of convergence of our
numerical scheme and illustrate our method on a number of examples. The
software used to implement the procedure is available on demand and we
demonstrate its use in the paper.Comment: 28 pages, 8 figure
Bidirectional step torque filter with zero backlash characteristic Patent
Gearing system for eliminating backlash and filtering input torque fluctuations from high inertia loa
Numerical Computation of First-Passage Times of Increasing Levy Processes
Let be a non-decreasing L\'evy process. The
first-hitting time process (which is sometimes referred to
as an inverse subordinator) defined by is a
process which has arisen in many applications. Of particular interest is the
mean first-hitting time . This function characterizes all
finite-dimensional distributions of the process . The function can be
calculated by inverting the Laplace transform of the function
, where is the
L\'evy exponent of the subordinator . In this paper, we give two methods for
computing numerically the inverse of this Laplace transform. The first is based
on the Bromwich integral and the second is based on the Post-Widder inversion
formula. The software written to support this work is available from the
authors and we illustrate its use at the end of the paper.Comment: 31 Pages, 7 sections, 11 figures, 2 table
Recovery and quantification of mycobacterium immunogenum DNA from metalworking fluids using dual-labeled probes
Mycobacteria in metalworking fluids (MWF) are associated with hypersensitivity pneumonitis but are difficult to recover using culture. Quantitative PCR is a promising approach to quantify mycobacteria, but three challenges exist: mycobacterial cell lysis, high-yield DNA extraction, and removal of PCR inhibitors. We used Mycobacterium spp. primers to amplify polymorphic regions of 16S-rDNA flanked with highly conserved regions. A standard curve was constructed by cloning M. immunogenum amplification product. We developed single tube DNA extraction employing mixer mill cell disruption, enzymatic digestions (lysozyme, proteinase K) followed by a mechanical disruption, and column purification. MWF was spiked with M. immunogenum, and DNA was successfully extracted. Mycobacterial 16S-RNA genes were quantified by comparing PCR amplification detection (Cycle Threshold) from our samples with that obtained from the standard curve. Recovery and quantification of mycobacterial DNA from spiked samples approached 100 %. A rapid method for quantification of mycobacteria in MWF was demonstrated
The Error is the Feature: how to Forecast Lightning using a Model Prediction Error
Despite the progress within the last decades, weather forecasting is still a
challenging and computationally expensive task. Current satellite-based
approaches to predict thunderstorms are usually based on the analysis of the
observed brightness temperatures in different spectral channels and emit a
warning if a critical threshold is reached. Recent progress in data science
however demonstrates that machine learning can be successfully applied to many
research fields in science, especially in areas dealing with large datasets. We
therefore present a new approach to the problem of predicting thunderstorms
based on machine learning. The core idea of our work is to use the error of
two-dimensional optical flow algorithms applied to images of meteorological
satellites as a feature for machine learning models. We interpret that optical
flow error as an indication of convection potentially leading to thunderstorms
and lightning. To factor in spatial proximity we use various manual convolution
steps. We also consider effects such as the time of day or the geographic
location. We train different tree classifier models as well as a neural network
to predict lightning within the next few hours (called nowcasting in
meteorology) based on these features. In our evaluation section we compare the
predictive power of the different models and the impact of different features
on the classification result. Our results show a high accuracy of 96% for
predictions over the next 15 minutes which slightly decreases with increasing
forecast period but still remains above 83% for forecasts of up to five hours.
The high false positive rate of nearly 6% however needs further investigation
to allow for an operational use of our approach.Comment: 10 pages, 7 figure
Spin dynamics of the quasi two dimensional spin-1/2 quantum magnet Cs_2CuCl_4
We study dynamical properties of the anisotropic triangular quantum
antiferromagnet Cs_2CuCl_4. Inelastic neutron scattering measurements have
established that the dynamical spin correlations cannot be understood within a
linear spin wave analysis. We go beyond linear spin wave theory by taking
interactions between magnons into account in a 1/S expansion. We determine the
dynamical structure factor and carry out extensive comparisons with
experimental data. We find that compared to linear spin wave theory a
significant fraction of the scattering intensity is shifted to higher energies
and strong scattering continua are present. However, the 1/S expansion fails to
account for the experimentally observed large quantum renormalization of the
exchange energies.Comment: 13 pages, 11 figures, higher quality figures can be obtained from the
author
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