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At least 55 records · Page 3

Statistics of some atmospheric turbulence records relevant to aircraft response calculations

Methods for characterizing atmospheric turbulence are described. The methods illustrated include maximum likelihood estimation of the integral scale and intensity of records obeying the von Karman transverse power spectral form, constrained least-squares estimation of the parameters of a parametric representation of autocorrelation functions, estimation of the power spectra density of the instantaneous variance of a record with temporally fluctuating variance, and estimation of the probability density functions of various turbulence components. Descriptions of the computer programs used in the computations are given, and a full listing of these programs is included.

Mark, W. D.↗

A sequential nonparametric pattern classification algorithm based on the Wald SPRT

A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.

Poage, J. L.↗

Aerodynamic parameter estimation via Fourier modulating function techniques

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.

Pearson, A. E.↗

LANDAT-4/5 image data quality analysis

LANDSAT-4/5 data quality analysis was covered. Focus was on estimation of two-dimensional point-spread function estimation. A brief description is included.

Anuta, P. E.↗

Ellipsometric characterization of In(0.52)Al(0.48)As and of modulation doped field effect transistor structures on InP substrates

The dielectric function of a thick layer of In(0.52)Al(0.48)As lattice matched to InP was measured by variable angle spectroscopic ellipsometry in the range 1.9-4.1 eV. The In(0.52)Al(0.48)As was protected from oxidation using a thin In(0.53)Ga(0.47)As cap that was mathematically removed for the dielectric function estimate. The In(0.52)Al(0.48)As dielectric function was then verified by ellipsometric measurements of other In(0.53)Ga(0.47)As/In(0.52)Al(0.48)As structures, including (MODFET), and is shown to provide accurate structure layer thicknesses.

Alterovitz, S. A.↗

Galaxy–galaxy lensing estimators and their covariance properties

Here, we study the covariance properties of real space correlation function estimators – primarily galaxy–shear correlations, or galaxy–galaxy lensing – using SDSS data for both shear catalogues and lenses (specifically the BOSS LOWZ sample). Using mock catalogues of lenses and sources, we disentangle the various contributions to the covariance matrix and compare them with a simple analytical model. We show that not subtracting the lensing measurement around random points from the measurement around the lens sample is equivalent to performing the measurement using the lens density field instead of the lens overdensity field. While the measurement using the lens density field is unbiased (in the absence of systematics), its error is significantly larger due to an additional term in the covariance. Therefore, this subtraction should be performed regardless of its beneficial effects on systematics. Comparing the error estimates from data and mocks for estimators that involve the overdensity, we find that the errors are dominated by the shape noise and lens clustering, which empirically estimated covariances (jackknife and standard deviation across mocks) that are consistent with theoretical estimates, and that both the connected parts of the four-point function and the supersample covariance can be neglected for the current levels of noise. While the trade-off between different terms in the covariance depends on the survey configuration (area, source number density), the diagnostics that we use in this work should be useful for future works to test their empirically determined covariances.

79 ASTRONOMY AND ASTROPHYSICS↗

Correlations of galaxies at z = 0.3

The angular correlation functions for galaxies with magnitudes 20 less than b(sub J) less than 21.5 and 21 less than b(sub J) less than 22.5 are measured from photographic images of nine distinct fields. New expressions for the variance of correlation function estimates are used, which show that the nine fields are consistent with a single angular correlation function w(theta) and with previous estimates of its amplitude and slope -- though the errors on previously published w(theta) values are probably underestimated. Assuming a spatial correlation function of the form xi(r,z) = (r/r(sub 0))(exp -gamma)(1 + z)(exp -3-epsilon), and using the measured N(z) for these magnitude ranges, we derive xi(r = 250 kpc/h, z = 0.18) = 48 +/- 10, and xi(r = 250 kpc/h, z = 0.27) = 55(+11; -9) for galaxies in these samples, largely independent of gamma and epsilon (1 sigma errors). This is approximately 2 times lower than the small-scale clustering seen in nearby galaxies at b(sub J) less than 18 (after allowing for modest growth in clustering since z = 0.27), yet very similar to the clustering of the nearby IRAS-selected galaxy population. The correlations of the reddest third of the b(sub J) approximately 22 galaxies, however, are larger and consistent with the b(sub J) less than 18 clustering, not the IRAS clustering, for a clustering growth rate of epsilon equal to or greater than -1. Thus to b(sub J) less than 22.5, most of the apparent evolution in clustering of blue-selected galaxies appears to be due to an increasing fraction of late-type or star-forming galaxies at fainter magnitudes, combined with weaker small-scale clustering for late types. These conclusions are entirely empirical and independent of galaxy evolution models.

Bernstein, G. M.↗

Nonparametric Bayer-risk estimation

Nonparametric Bayes risk estimation for measurement classification, using nearest neighbor error rate and Parzen probability density function estimators

Fralick, S. C.↗

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN

Physics-informed neural network (PINN) has been successfully applied in solving a variety of nonlinear non-convex forward and inverse problems. However, the training is challenging because of the non-convex loss functions and the multiple optima in the Bayesian inverse problem. In this work, we propose a multi-variance replica exchange stochastic gradient Langevin dynamics method to tackle the challenge of the multiple local optima in the optimization and the challenge of the multiple modal posterior distribution in the inverse problem. Replica exchange methods are capable of escaping from the local traps and accelerating the convergence; two chains with different temperatures are designed where the low temperature chain aims for the local convergence, and the target of the high temperature chain is to travel globally and explore the whole loss function entropy landscape. However, it may not be efficient to solve mathematical inversion problems by using the vanilla replica method directly since the method doubles the computational cost in evaluating the forward solvers (likelihood functions) in the two chains. To address this issue, we propose to make different assumptions on the energy function estimation and this facilities one to use solvers of different fidelities in the likelihood function evaluation. More precisely, one can use a solver with low fidelity in the high temperature chain while using a solver with high fidelity in the low temperature chain. Our proposed method significantly lowers the computational cost in the high temperature chain, meanwhile preserving the accuracy and converging very fast. Here we give an unbiased estimate of the swapping rate and give an estimation of the discretization error of the scheme. To verify our idea, we design and solve four inverse problems which have multiple modes. The proposed method is also employed to train the Bayesian PINN to solve the forward and inverse problems; faster and more accurate convergence has been observed when compared to the stochastic gradient Langevin dynamics (SGLD) method and vanilla replica exchange methods.

97 MATHEMATICS AND COMPUTING↗

revengc: An R package to Reverse Engineer Summarized Data

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations, such as the World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and World Bank. The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers censored and/or decoupled data with two main functions. The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table. The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.

97 MATHEMATICS AND COMPUTING↗

Coseismic fault slip associated with the 1992 M(sub w) 6.1 Joshua Tree, California, earthquake: Implications for the Joshua Tree-Landers earthquake sequence

Coseismic surface deformation associated with the M(sub w) 6.1, April 23, 1992, Joshua Tree earthquake is well represented by estimates of geodetic monument displacements at 20 locations independently derived from Global Positioning System and trilateration measurements. The rms signal to noise ratio for these inferred displacements is 1.8 with near-fault displacement estimates exceeding 40 mm. In order to determine the long-wavelength distribution of slip over the plane of rupture, a Tikhonov regularization operator is applied to these estimates which minimizes stress variability subject to purely right-lateral slip and zero surface slip constraints. The resulting slip distribution yields a geodetic moment estimate of 1.7 x 10(exp 18) N m with corresponding maximum slip around 0.8 m and compares well with independent and complementary information including seismic moment and source time function estimates and main shock and aftershock locations. From empirical Green's functions analyses, a rupture duration of 5 s is obtained which implies a rupture radius of 6-8 km. Most of the inferred slip lies to the north of the hypocenter, consistent with northward rupture propagation. Stress drop estimates are in the range of 2-4 MPa. In addition, predicted Coulomb stress increases correlate remarkably well with the distribution of aftershock hypocenters; most of the aftershocks occur in areas for which the mainshock rupture produced stress increases larger than about 0.1 MPa. In contrast, predicted stress changes are near zero at the hypocenter of the M(sub w) 7.3, June 28, 1992, Landers earthquake which nucleated about 20 km beyond the northernmost edge of the Joshua Tree rupture. Based on aftershock migrations and the predicted static stress field, we speculate that redistribution of Joshua Tree-induced stress perturbations played a role in the spatio-temporal development of the earth sequence culminating in the Landers event.

Bennett, Richard A.↗

Estimating crop proportions from remotely sensed data

The classification/pixel-count method for estimating the proportion of wheat in each segment is theoretically biased even if all distributional assumptions are met. Alternative ways to estimate crop proportions are examined and their performance testing is considered. Topics covered include general linear functional estimates, the method of moments, and maximum likelihood estimators.

Feiveson, A. H.↗

Rayleigh-Brillouin scattering to determine one-dimensional temperature and number density profiles of a gas flow field

Rayleigh-Brillouin spectra for heated nitrogen gas were measured by imaging the output of a Fabry-Perot interferometer onto a CCD array. The spectra were compared with the theoretical 6-moment model of Rayleigh-Brillouin scattering convolved with the Fabry-Perot instrument function. Estimates of the temperature and a dimensionless parameter proportional to the number density of the gas as functions of position in the laser beam were calculated by least-squares deviation fits between theory and experiment.

Lock, James A.↗

Identification of flexible aircraft from flight data

This paper describes a frequency-domain method for the estimation of the aeroelastic equations of motion of an aircraft using discrete sinusoidal inputs to the control surface actuators. The theory of estimation of the signal inputs and outputs is described as well as the method of obtaining accurate transfer functions from the resulting Bode plots. Methods of quantifying the accuracy of the results from more than one control input are discussed along with ways to obtain state-space and reduced-order models from the transfer function estimates. Presented is a summary of the results obtained in using the technique to develop a model of the USAF Total In-Flight Simulator (TIFS) aircraft which includes five symmetrical elastic modes of motion of the aircraft.

Eulrich, B. J.↗

Theoretical studies of solar-pumped lasers

The investigation of stimulated emission causing transitions from the B(1) pi sub u state of sodium to the overlapping 2(1) sigma(+) sub g electronic state has been continued. A new method of estimating the Franck-Condon factors has been developed which instead of fitting the molecular potential curves with Morse functions, estimates the V(r) dependence by interpolation from given potential curves. The results for the sum of the rates from one vibrational level in the upper state to all the levels in the lower state show good agreement with the previous method, implying that curve crossing by stimulated emission due to photons from the oven is an important mechanism in sodium.

Harries, Wynford L.↗

Out of Distribution Detection with Neural Network Anchoring

This is code to reproduce and build on OOD detection from the paper "Out of Distribution Detection with Neural Network Anchoring". Our goal here is to exploit heteroscedastic temperature scaling as a calibration strategy for out of distribution (OOD) detection. Heteroscedasticity here refers to the fact that the optimal temperature parameter for each sample can be different, as opposed to conventional approaches that use the same value for the entire distribution. To enable this, we propose a new training strategy called anchoring that can estimate appropriate temperature values for each sample, leading to state-of-the-art OOD detection performance across several benchmarks. Using NTK theory, we show that this temperature function estimate is closely linked to the epistemic uncertainty of the classifier, which explains its behavior. In contrast to some of the best-performing OOD detection approaches, our method does not require exposure to additional outlier datasets, custom calibration objectives, or model ensembling. Through empirical studies with different OOD detection settings - far OOD, near OOD, and semantically coherent OOD - we establish a highly effective OOD detection approach.

Thiagarajan, Jayaraman↗