13,384 research outputs found
Evaluation of aerothermal modeling computer programs
Various computer programs based upon the SIMPLE or SIMPLER algorithm were studied and compared for numerical accuracy, efficiency, and grid dependency. Four two-dimensional and one three-dimensional code originally developed by a number of research groups were considered. In general, the accuracy and computational efficieny of these TEACH type programs were improved by modifying the differencing schemes and their solvers. A brief description of each program is given. Error reduction, spline flux and second upwind differencing programs are covered
Precursor luminescence near the collapse of laser-induced bubbles in alkali-salt solutions
A precursor luminescence pulse consisting of atomic line emission is observed as much as 150 nanoseconds prior to the collapse point of laser-induced bubbles in alkali-metal solutions. The timing of the emission from neutral Na, Li, and K atoms is strongly dependent on the salt concentration, which appears to result from resonant radiation trapping by the alkali atoms in the bubble. The alkali emission ends at the onset of the blackbody luminescence pulse at the bubble collapse point, and the duration of the blackbody pulse is found to be reduced by up to 30% as the alkali salt concentration is increased.http://deepblue.lib.umich.edu/bitstream/2027.42/84214/1/CAV2009-final166.pd
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
Subsequence clustering of multivariate time series is a useful tool for
discovering repeated patterns in temporal data. Once these patterns have been
discovered, seemingly complicated datasets can be interpreted as a temporal
sequence of only a small number of states, or clusters. For example, raw sensor
data from a fitness-tracking application can be expressed as a timeline of a
select few actions (i.e., walking, sitting, running). However, discovering
these patterns is challenging because it requires simultaneous segmentation and
clustering of the time series. Furthermore, interpreting the resulting clusters
is difficult, especially when the data is high-dimensional. Here we propose a
new method of model-based clustering, which we call Toeplitz Inverse
Covariance-based Clustering (TICC). Each cluster in the TICC method is defined
by a correlation network, or Markov random field (MRF), characterizing the
interdependencies between different observations in a typical subsequence of
that cluster. Based on this graphical representation, TICC simultaneously
segments and clusters the time series data. We solve the TICC problem through
alternating minimization, using a variation of the expectation maximization
(EM) algorithm. We derive closed-form solutions to efficiently solve the two
resulting subproblems in a scalable way, through dynamic programming and the
alternating direction method of multipliers (ADMM), respectively. We validate
our approach by comparing TICC to several state-of-the-art baselines in a
series of synthetic experiments, and we then demonstrate on an automobile
sensor dataset how TICC can be used to learn interpretable clusters in
real-world scenarios.Comment: This revised version fixes two small typos in the published versio
Lattice study of vacuum polarization function and determination of strong coupling constant
We calculate the vacuum polarization functions on the lattice using the
overlap fermion formulation.By matching the lattice data at large momentum
scales with the perturbative expansion supplemented by Operator Product
Expansion (OPE), we extract the strong coupling constant in
two-flavor QCD as =
GeV, where the errors are statistical and systematic, respectively. In
addition, from the analysis of the difference between the vector and
axial-vector channels, we obtain some of the four-quark condensates.Comment: 24 pages, 9 figures, enlarged version published in Phys. Rev.
Validation of nonlinear PCA
Linear principal component analysis (PCA) can be extended to a nonlinear PCA
by using artificial neural networks. But the benefit of curved components
requires a careful control of the model complexity. Moreover, standard
techniques for model selection, including cross-validation and more generally
the use of an independent test set, fail when applied to nonlinear PCA because
of its inherent unsupervised characteristics. This paper presents a new
approach for validating the complexity of nonlinear PCA models by using the
error in missing data estimation as a criterion for model selection. It is
motivated by the idea that only the model of optimal complexity is able to
predict missing values with the highest accuracy. While standard test set
validation usually favours over-fitted nonlinear PCA models, the proposed model
validation approach correctly selects the optimal model complexity.Comment: 12 pages, 5 figure
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