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Sample records for two-component gaussian mixture

Iterative Mixture Component Pruning Algorithm for Gaussian Mixture PHD Filter

Directory of Open Access Journals (Sweden)

Xiaoxi Yan

2014-01-01

Full Text Available As far as the increasing number of mixture components in the Gaussian mixture PHD filter is concerned, an iterative mixture component pruning algorithm is proposed. The pruning algorithm is based on maximizing the posterior probability density of the mixture weights. The entropy distribution of the mixture weights is adopted as the prior distribution of mixture component parameters. The iterative update formulations of the mixture weights are derived by Lagrange multiplier and Lambert W function. Mixture components, whose weights become negative during iterative procedure, are pruned by setting corresponding mixture weights to zeros. In addition, multiple mixture components with similar parameters describing the same PHD peak can be merged into one mixture component in the algorithm. Simulation results show that the proposed iterative mixture component pruning algorithm is superior to the typical pruning algorithm based on thresholds.
Gaussian Process-Mixture Conditional Heteroscedasticity.

Science.gov (United States)

Platanios, Emmanouil A; Chatzis, Sotirios P

2014-05-01

Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning. Specifically, we propose a novel nonparametric Bayesian mixture of Gaussian process regression models, each component of which models the noise variance process that contaminates the observed data as a separate latent Gaussian process driven by the observed data. This way, we essentially obtain a Gaussian process-mixture conditional heteroscedasticity (GPMCH) model for volatility modeling in financial return series. We impose a nonparametric prior with power-law nature over the distribution of the model mixture components, namely the Pitman-Yor process prior, to allow for better capturing modeled data distributions with heavy tails and skewness. Finally, we provide a copula-based approach for obtaining a predictive posterior for the covariances over the asset returns modeled by means of a postulated GPMCH model. We evaluate the efficacy of our approach in a number of benchmark scenarios, and compare its performance to state-of-the-art methodologies.
A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

International Nuclear Information System (INIS)

Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.

2013-01-01

Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability
Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

Directory of Open Access Journals (Sweden)

Changming Liu

2018-01-01

Full Text Available Acoustic emission (AE technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage.
Performance of BICM-T transceivers over Gaussian mixture noise channels

KAUST Repository

Malik, Muhammad Talha

2014-04-01

Experimental measurements have shown that the noise in many communication channels is non-Gaussian. Bit interleaved coded modulation (BICM) is very popular for spectrally efficient transmission. Recent results have shown that the performance of BICM using convolutional codes in non-fading channels can be significantly improved if the coded bits are not interleaved at all. This particular BICM design is called BICM trivial (BICM-T). In this paper, we analyze the performance of a generalized BICM-T design for communication over Gaussian mixture noise (GMN) channels. The results disclose that for an optimal bit error rate (BER) performance, the use of an interleaver in BICM for GMN channels depends upon the strength of the impulsive noise components in the Gaussian mixture. The results presented for 16-QAM show that the BICM-T can result in gains up to 1.5 dB for a target BER of 10-6 if the impulsive noise in the Gaussian mixture is below a certain threshold level. The simulation results verify the tightness of developed union bound (UB) on BER performance.
Unbiased free energy estimates in fast nonequilibrium transformations using Gaussian mixtures

International Nuclear Information System (INIS)

Procacci, Piero

2015-01-01

In this paper, we present an improved method for obtaining unbiased estimates of the free energy difference between two thermodynamic states using the work distribution measured in nonequilibrium driven experiments connecting these states. The method is based on the assumption that any observed work distribution is given by a mixture of Gaussian distributions, whose normal components are identical in either direction of the nonequilibrium process, with weights regulated by the Crooks theorem. Using the prototypical example for the driven unfolding/folding of deca-alanine, we show that the predicted behavior of the forward and reverse work distributions, assuming a combination of only two Gaussian components with Crooks derived weights, explains surprisingly well the striking asymmetry in the observed distributions at fast pulling speeds. The proposed methodology opens the way for a perfectly parallel implementation of Jarzynski-based free energy calculations in complex systems
Direct Importance Estimation with Gaussian Mixture Models

Science.gov (United States)

Yamada, Makoto; Sugiyama, Masashi

The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.
Measuring two-phase and two-component mixtures by radiometric technique

International Nuclear Information System (INIS)

Mackuliak, D.; Rajniak, I.

1984-01-01

The possibility was tried of the application of the radiometric method in measuring steam water content. The experiments were carried out in model conditions where steam was replaced with the two-component mixture of water and air. The beta radiation source was isotope 204 Tl (Esub(max)=0.765 MeV) with an activity of 19.35 MBq. Measurements were carried out within the range of the surface density of the mixture from 0.119 kg.m -2 to 0.130 kg.m -2 . Mixture speed was 5.1 m.s -1 to 7.1 m.s -1 . The observed dependence of relative pulse frequency on the specific water content in the mixture was approximated by a linear regression. (B.S.)
Gaussian Mixture Model of Heart Rate Variability

Science.gov (United States)

Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario

2012-01-01

Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters. PMID:22666386
Performance of BICM-T transceivers over Gaussian mixture noise channels

KAUST Repository

Malik, Muhammad Talha; Hossain, Md Jahangir; Alouini, Mohamed-Slim

2014-01-01

in the Gaussian mixture. The results presented for 16-QAM show that the BICM-T can result in gains up to 1.5 dB for a target BER of 10-6 if the impulsive noise in the Gaussian mixture is below a certain threshold level. The simulation results verify the tightness
Modeling text with generalizable Gaussian mixtures

DEFF Research Database (Denmark)

Hansen, Lars Kai; Sigurdsson, Sigurdur; Kolenda, Thomas

2000-01-01

We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss...
Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

KAUST Repository

Liu, Bo

2015-11-11

We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.
Gaussian mixture models and semantic gating improve reconstructions from human brain activity

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Sanne eSchoenmakers

2015-01-01

Full Text Available Better acquisition protocols and analysis techniques are making it possible to use fMRI to obtain highly detailed visualizations of brain processes. In particular we focus on the reconstruction of natural images from BOLD responses in visual cortex. We expand our linear Gaussian framework for percept decoding with Gaussian mixture models to better represent the prior distribution of natural images. Reconstruction of such images then boils down to probabilistic inference in a hybrid Bayesian network. In our set-up, different mixture components correspond to different character categories. Our framework can automatically infer higher-order semantic categories from lower-level brain areas. Furthermore the framework can gate semantic information from higher-order brain areas to enforce the correct category during reconstruction. When categorical information is not available, we show that automatically learned clusters in the data give a similar improvement in reconstruction. The hybrid Bayesian network leads to highly accurate reconstructions in both supervised and unsupervised settings.
Color Texture Segmentation by Decomposition of Gaussian Mixture Model

Czech Academy of Sciences Publication Activity Database

Grim, Jiří; Somol, Petr; Haindl, Michal; Pudil, Pavel

2006-01-01

Roč. 19, č. 4225 (2006), s. 287-296 ISSN 0302-9743. [Iberoamerican Congress on Pattern Recognition. CIARP 2006 /11./. Cancun, 14.11.2006-17.11.2006] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA MŠk 2C06019 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : texture segmentation * gaussian mixture model * EM algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 0.402, year: 2005 http://library.utia.cas.cz/separaty/historie/grim-color texture segmentation by decomposition of gaussian mixture model.pdf
Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model

KAUST Repository

Liu, Bo

2016-02-03

An ensemble-based Gaussian mixture (GM) filtering framework is studied in this paper in term of its dependence on the choice of the clustering method to construct the GM. In this approach, a number of particles sampled from the posterior distribution are first integrated forward with the dynamical model for forecasting. A GM representation of the forecast distribution is then constructed from the forecast particles. Once an observation becomes available, the forecast GM is updated according to Bayes’ rule. This leads to (i) a Kalman filter-like update of the particles, and (ii) a Particle filter-like update of their weights, generalizing the ensemble Kalman filter update to non-Gaussian distributions. We focus on investigating the impact of the clustering strategy on the behavior of the filter. Three different clustering methods for constructing the prior GM are considered: (i) a standard kernel density estimation, (ii) clustering with a specified mixture component size, and (iii) adaptive clustering (with a variable GM size). Numerical experiments are performed using a two-dimensional reactive contaminant transport model in which the contaminant concentration and the heterogenous hydraulic conductivity fields are estimated within a confined aquifer using solute concentration data. The experimental results suggest that the performance of the GM filter is sensitive to the choice of the GM model. In particular, increasing the size of the GM does not necessarily result in improved performances. In this respect, the best results are obtained with the proposed adaptive clustering scheme.
The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

Science.gov (United States)

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.
Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

DEFF Research Database (Denmark)

Khorunzhina, Natalia; Richard, Jean-Francois

The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approxima...
Estimation of Fuzzy Measures Using Covariance Matrices in Gaussian Mixtures

Directory of Open Access Journals (Sweden)

Nishchal K. Verma

2012-01-01

Full Text Available This paper presents a novel computational approach for estimating fuzzy measures directly from Gaussian mixtures model (GMM. The mixture components of GMM provide the membership functions for the input-output fuzzy sets. By treating consequent part as a function of fuzzy measures, we derived its coefficients from the covariance matrices found directly from GMM and the defuzzified output constructed from both the premise and consequent parts of the nonadditive fuzzy rules that takes the form of Choquet integral. The computational burden involved with the solution of λ-measure is minimized using Q-measure. The fuzzy model whose fuzzy measures were computed using covariance matrices found in GMM has been successfully applied on two benchmark problems and one real-time electric load data of Indian utility. The performance of the resulting model for many experimental studies including the above-mentioned application is found to be better and comparable to recent available fuzzy models. The main contribution of this paper is the estimation of fuzzy measures efficiently and directly from covariance matrices found in GMM, avoiding the computational burden greatly while learning them iteratively and solving polynomial equations of order of the number of input-output variables.
Multivariate spatial Gaussian mixture modeling for statistical clustering of hemodynamic parameters in functional MRI

International Nuclear Information System (INIS)

Fouque, A.L.; Ciuciu, Ph.; Risser, L.; Fouque, A.L.; Ciuciu, Ph.; Risser, L.

2009-01-01

In this paper, a novel statistical parcellation of intra-subject functional MRI (fMRI) data is proposed. The key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. To this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. Then, the extracted hemodynamic features are entered as the input data of a Multivariate Spatial Gaussian Mixture Model (MSGMM) to be fitted. The goal of the spatial aspect is to favor the recovery of connected components in the mixture. Our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized Gaussian mixtures. A specific Gibbs sampler is derived to account for different covariance structures in the feature space. On realistic artificial fMRI datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection estimation of brain activity. This allows us to overcome the classical assumption of spatial stationarity of the BOLD signal model. (authors)
Treatment of non-Gaussian tails of multiple Coulomb scattering in track fitting with a Gaussian-sum filter

International Nuclear Information System (INIS)

Strandlie, A.; Wroldsen, J.

2006-01-01

If any of the probability densities involved in track fitting deviate from the Gaussian assumption, it is plausible that a non-linear estimator which better takes the actual shape of the distribution into account can do better. One such non-linear estimator is the Gaussian-sum filter, which is adequate if the distributions under consideration can be approximated by Gaussian mixtures. The main purpose of this paper is to present a Gaussian-sum filter for track fitting, based on a two-component approximation of the distribution of angular deflections due to multiple scattering. In a simulation study within a linear track model the Gaussian-sum filter is shown to be a competitive alternative to the Kalman filter. Scenarios at various momenta and with various maximum number of components in the Gaussian-sum filter are considered. Particularly at low momenta the Gaussian-sum filter yields a better estimate of the uncertainties than the Kalman filter, and it is also slightly more precise than the latter

Large non-Gaussianity from two-component hybrid inflation

International Nuclear Information System (INIS)

Byrnes, Christian T.; Choi, Ki-Young; Hall, Lisa M.H.

2009-01-01

We study the generation of non-Gaussianity in models of hybrid inflation with two inflaton fields, (2-brid inflation). We analyse the region in the parameter and the initial condition space where a large non-Gaussianity may be generated during slow-roll inflation which is generally characterised by a large f NL , τ NL and a small g NL . For certain parameter values we can satisfy τ NL >> f NL 2 . The bispectrum is of the local type but may have a significant scale dependence. We show that the loop corrections to the power spectrum and bispectrum are suppressed during inflation, if one assume that the fields follow a classical background trajectory. We also include the effect of the waterfall field, which can lead to a significant change in the observables after the waterfall field is destabilised, depending on the couplings between the waterfall and inflaton fields
Detection Performance of Signals in Dependent Noise From a Gaussian Mixture Uncertainty Class

National Research Council Canada - National Science Library

Gerlach, K

1998-01-01

... (correlated) multivariate noise from a Gaussian mixture uncertainty class. This uncertainty class is defined using upper and lower bounding functions on the univariate Gaussian mixing distribution function...
Supervised Gaussian mixture model based remote sensing image ...

African Journals Online (AJOL)

Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...
Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures

KAUST Repository

Huser, Raphaël

2017-06-23

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.
Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures

KAUST Repository

Huser, Raphaë l; Opitz, Thomas; Thibaud, Emeric

2017-01-01

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.
XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

Science.gov (United States)

Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

2017-08-01

XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.
Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

DEFF Research Database (Denmark)

Bak, N.; Ebdrup, B.H.; Oranje, B

2017-01-01

Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically...... and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per...... day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups...
Evaluation of Distance Measures Between Gaussian Mixture Models of MFCCs

DEFF Research Database (Denmark)

Jensen, Jesper Højvang; Ellis, Dan P. W.; Christensen, Mads Græsbøll

2007-01-01

In music similarity and in the related task of genre classification, a distance measure between Gaussian mixture models is frequently needed. We present a comparison of the Kullback-Leibler distance, the earth movers distance and the normalized L2 distance for this application. Although...
TH-E-BRF-09: Gaussian Mixture Model Analysis of Radiation-Induced Parotid-Gland Injury: An Ultrasound Study of Acute and Late Xerostomia in Head-And-Neck Radiotherapy

International Nuclear Information System (INIS)

Liu, T; Yu, D; Beitler, J; Curran, W; Yang, X; Tridandapani, S; Bruner, D

2014-01-01

Purpose: Xerostomia (dry mouth), secondary to parotid-gland injury, is a distressing side-effect in head-and-neck radiotherapy (RT). This study's purpose is to develop a novel ultrasound technique to quantitatively evaluate post-RT parotid-gland injury. Methods: Recent ultrasound studies have shown that healthy parotid glands exhibit homogeneous echotexture, whereas post-RT parotid glands are often heterogeneous, with multiple hypoechoic (inflammation) or hyperechoic (fibrosis) regions. We propose to use a Gaussian mixture model to analyze the ultrasonic echo-histogram of the parotid glands. An IRB-approved clinical study was conducted: (1) control-group: 13 healthy-volunteers, served as the control; (2) acutetoxicity group − 20 patients (mean age: 62.5 ± 8.9 years, follow-up: 2.0±0.8 months); and (3) late-toxicity group − 18 patients (mean age: 60.7 ± 7.3 years, follow-up: 20.1±10.4 months). All patients experienced RTOG grade 1 or 2 salivary-gland toxicity. Each participant underwent an ultrasound scan (10 MHz) of the bilateral parotid glands. An echo-intensity histogram was derived for each parotid and a Gaussian mixture model was used to fit the histogram using expectation maximization (EM) algorithm. The quality of the fitting was evaluated with the R-squared value. Results: (1) Controlgroup: all parotid glands fitted well with one Gaussian component, with a mean intensity of 79.8±4.9 (R-squared>0.96). (2) Acute-toxicity group: 37 of the 40 post-RT parotid glands fitted well with two Gaussian components, with a mean intensity of 42.9±7.4, 73.3±12.2 (R-squared>0.95). (3) Latetoxicity group: 32 of the 36 post-RT parotid fitted well with 3 Gaussian components, with mean intensities of 49.7±7.6, 77.2±8.7, and 118.6±11.8 (R-squared>0.98). Conclusion: RT-associated parotid-gland injury is common in head-and-neck RT, but challenging to assess. This work has demonstrated that the Gaussian mixture model of the echo-histogram could quantify acute and
TH-E-BRF-09: Gaussian Mixture Model Analysis of Radiation-Induced Parotid-Gland Injury: An Ultrasound Study of Acute and Late Xerostomia in Head-And-Neck Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Liu, T [Department of Radiation Oncology and Winship Cancer Institute, Emory Univ, Atlanta, GA (United States); Yu, D; Beitler, J; Curran, W; Yang, X [Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA (United States); Tridandapani, S [Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA (United States); Bruner, D [School of Nursing and Winship Cancer Institute, Emory Univesity, Atlanta, GA (United States)

2014-06-15

Purpose: Xerostomia (dry mouth), secondary to parotid-gland injury, is a distressing side-effect in head-and-neck radiotherapy (RT). This study's purpose is to develop a novel ultrasound technique to quantitatively evaluate post-RT parotid-gland injury. Methods: Recent ultrasound studies have shown that healthy parotid glands exhibit homogeneous echotexture, whereas post-RT parotid glands are often heterogeneous, with multiple hypoechoic (inflammation) or hyperechoic (fibrosis) regions. We propose to use a Gaussian mixture model to analyze the ultrasonic echo-histogram of the parotid glands. An IRB-approved clinical study was conducted: (1) control-group: 13 healthy-volunteers, served as the control; (2) acutetoxicity group − 20 patients (mean age: 62.5 ± 8.9 years, follow-up: 2.0±0.8 months); and (3) late-toxicity group − 18 patients (mean age: 60.7 ± 7.3 years, follow-up: 20.1±10.4 months). All patients experienced RTOG grade 1 or 2 salivary-gland toxicity. Each participant underwent an ultrasound scan (10 MHz) of the bilateral parotid glands. An echo-intensity histogram was derived for each parotid and a Gaussian mixture model was used to fit the histogram using expectation maximization (EM) algorithm. The quality of the fitting was evaluated with the R-squared value. Results: (1) Controlgroup: all parotid glands fitted well with one Gaussian component, with a mean intensity of 79.8±4.9 (R-squared>0.96). (2) Acute-toxicity group: 37 of the 40 post-RT parotid glands fitted well with two Gaussian components, with a mean intensity of 42.9±7.4, 73.3±12.2 (R-squared>0.95). (3) Latetoxicity group: 32 of the 36 post-RT parotid fitted well with 3 Gaussian components, with mean intensities of 49.7±7.6, 77.2±8.7, and 118.6±11.8 (R-squared>0.98). Conclusion: RT-associated parotid-gland injury is common in head-and-neck RT, but challenging to assess. This work has demonstrated that the Gaussian mixture model of the echo-histogram could quantify acute and
Statistical imitation system using relational interest points and Gaussian mixture models

CSIR Research Space (South Africa)

Claassens, J

2009-11-01

Full Text Available The author proposes an imitation system that uses relational interest points (RIPs) and Gaussian mixture models (GMMs) to characterize a behaviour. The system's structure is inspired by the Robot Programming by Demonstration (RDP) paradigm...
How Many Separable Sources? Model Selection In Independent Components Analysis

Science.gov (United States)

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988
Three dimensional indoor positioning based on visible light with Gaussian mixture sigma-point particle filter technique

Science.gov (United States)

Gu, Wenjun; Zhang, Weizhi; Wang, Jin; Amini Kashani, M. R.; Kavehrad, Mohsen

2015-01-01

Over the past decade, location based services (LBS) have found their wide applications in indoor environments, such as large shopping malls, hospitals, warehouses, airports, etc. Current technologies provide wide choices of available solutions, which include Radio-frequency identification (RFID), Ultra wideband (UWB), wireless local area network (WLAN) and Bluetooth. With the rapid development of light-emitting-diodes (LED) technology, visible light communications (VLC) also bring a practical approach to LBS. As visible light has a better immunity against multipath effect than radio waves, higher positioning accuracy is achieved. LEDs are utilized both for illumination and positioning purpose to realize relatively lower infrastructure cost. In this paper, an indoor positioning system using VLC is proposed, with LEDs as transmitters and photo diodes as receivers. The algorithm for estimation is based on received-signalstrength (RSS) information collected from photo diodes and trilateration technique. By appropriately making use of the characteristics of receiver movements and the property of trilateration, estimation on three-dimensional (3-D) coordinates is attained. Filtering technique is applied to enable tracking capability of the algorithm, and a higher accuracy is reached compare to raw estimates. Gaussian mixture Sigma-point particle filter (GM-SPPF) is proposed for this 3-D system, which introduces the notion of Gaussian Mixture Model (GMM). The number of particles in the filter is reduced by approximating the probability distribution with Gaussian components.
Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model

KAUST Repository

Liu, Bo; El Gharamti, Mohamad; Hoteit, Ibrahim

2016-01-01

An ensemble-based Gaussian mixture (GM) filtering framework is studied in this paper in term of its dependence on the choice of the clustering method to construct the GM. In this approach, a number of particles sampled from the posterior
Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method

Directory of Open Access Journals (Sweden)

Abdenaceur Boudlal

2010-01-01

Full Text Available This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices are estimated by the Expectation Maximization algorithm (EM which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.
Two-component mixture model: Application to palm oil and exchange rate

Science.gov (United States)

Phoong, Seuk-Yen; Ismail, Mohd Tahir; Hamzah, Firdaus Mohamad

2014-12-01

Palm oil is a seed crop which is widely adopt for food and non-food products such as cookie, vegetable oil, cosmetics, household products and others. Palm oil is majority growth in Malaysia and Indonesia. However, the demand for palm oil is getting growth and rapidly running out over the years. This phenomenal cause illegal logging of trees and destroy the natural habitat. Hence, the present paper investigates the relationship between exchange rate and palm oil price in Malaysia by using Maximum Likelihood Estimation via Newton-Raphson algorithm to fit a two components mixture model. Besides, this paper proposes a mixture of normal distribution to accommodate with asymmetry characteristics and platykurtic time series data.
Gaussian measures of entanglement versus negativities: Ordering of two-mode Gaussian states

International Nuclear Information System (INIS)

Adesso, Gerardo; Illuminati, Fabrizio

2005-01-01

We study the entanglement of general (pure or mixed) two-mode Gaussian states of continuous-variable systems by comparing the two available classes of computable measures of entanglement: entropy-inspired Gaussian convex-roof measures and positive partial transposition-inspired measures (negativity and logarithmic negativity). We first review the formalism of Gaussian measures of entanglement, adopting the framework introduced in M. M. Wolf et al., Phys. Rev. A 69, 052320 (2004), where the Gaussian entanglement of formation was defined. We compute explicitly Gaussian measures of entanglement for two important families of nonsymmetric two-mode Gaussian state: namely, the states of extremal (maximal and minimal) negativities at fixed global and local purities, introduced in G. Adesso et al., Phys. Rev. Lett. 92, 087901 (2004). This analysis allows us to compare the different orderings induced on the set of entangled two-mode Gaussian states by the negativities and by the Gaussian measures of entanglement. We find that in a certain range of values of the global and local purities (characterizing the covariance matrix of the corresponding extremal states), states of minimum negativity can have more Gaussian entanglement of formation than states of maximum negativity. Consequently, Gaussian measures and negativities are definitely inequivalent measures of entanglement on nonsymmetric two-mode Gaussian states, even when restricted to a class of extremal states. On the other hand, the two families of entanglement measures are completely equivalent on symmetric states, for which the Gaussian entanglement of formation coincides with the true entanglement of formation. Finally, we show that the inequivalence between the two families of continuous-variable entanglement measures is somehow limited. Namely, we rigorously prove that, at fixed negativities, the Gaussian measures of entanglement are bounded from below. Moreover, we provide some strong evidence suggesting that they
Gaussian mixture clustering and imputation of microarray data.

Science.gov (United States)

Ouyang, Ming; Welsh, William J; Georgopoulos, Panos

2004-04-12

In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.
Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model.

Science.gov (United States)

Lee, Soojeong; Rajan, Sreeraman; Jeon, Gwanggil; Chang, Joon-Hyuk; Dajani, Hilmi R; Groza, Voicu Z

2017-06-01

Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients. This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities. The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE. The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates. Copyright © 2015 Elsevier Ltd. All rights reserved.
Component effects in mixture experiments

International Nuclear Information System (INIS)

Piepel, G.F.

1980-01-01

In a mixture experiment, the response to a mixture of q components is a function of the proportions x 1 , x 2 , ..., x/sub q/ of components in the mixture. Experimental regions for mixture experiments are often defined by constraints on the proportions of the components forming the mixture. The usual (orthogonal direction) definition of a factor effect does not apply because of the dependence imposed by the mixture restriction, /sup q/Σ/sub i=1/ x/sub i/ = 1. A direction within the experimental region in which to compute a mixture component effect is presented and compared to previously suggested directions. This new direction has none of the inadequacies or errors of previous suggestions while having a more meaningful interpretation. The distinction between partial and total effects is made. The uses of partial and total effects (computed using the new direction) in modification and interpretation of mixture response prediction equations are considered. The suggestions of the paper are illustrated in an example from a glass development study in a waste vitrification program. 5 figures, 3 tables

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain.

Science.gov (United States)

Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.
Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

Directory of Open Access Journals (Sweden)

Hossein Rabbani

2013-01-01

Full Text Available In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.
Segmentation of Concealed Objects in Passive Millimeter-Wave Images Based on the Gaussian Mixture Model

Science.gov (United States)

Yu, Wangyang; Chen, Xiangguang; Wu, Lei

2015-04-01

Passive millimeter wave (PMMW) imaging has become one of the most effective means to detect the objects concealed under clothing. Due to the limitations of the available hardware and the inherent physical properties of PMMW imaging systems, images often exhibit poor contrast and low signal-to-noise ratios. Thus, it is difficult to achieve ideal results by using a general segmentation algorithm. In this paper, an advanced Gaussian Mixture Model (GMM) algorithm for the segmentation of concealed objects in PMMW images is presented. Our work is concerned with the fact that the GMM is a parametric statistical model, which is often used to characterize the statistical behavior of images. Our approach is three-fold: First, we remove the noise from the image using both a notch reject filter and a total variation filter. Next, we use an adaptive parameter initialization GMM algorithm (APIGMM) for simulating the histogram of images. The APIGMM provides an initial number of Gaussian components and start with more appropriate parameter. Bayesian decision is employed to separate the pixels of concealed objects from other areas. At last, the confidence interval (CI) method, alongside local gradient information, is used to extract the concealed objects. The proposed hybrid segmentation approach detects the concealed objects more accurately, even compared to two other state-of-the-art segmentation methods.
Gaussian mixtures on tensor fields for segmentation: applications to medical imaging.

Science.gov (United States)

de Luis-García, Rodrigo; Westin, Carl-Fredrik; Alberola-López, Carlos

2011-01-01

In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic-semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results. Copyright © 2010 Elsevier Ltd. All rights reserved.
Unconventional signal detection techniques with Gaussian probability mixtures adaptation in non-AWGN channels: full resolution receiver

Science.gov (United States)

Chabdarov, Shamil M.; Nadeev, Adel F.; Chickrin, Dmitry E.; Faizullin, Rashid R.

2011-04-01

In this paper we discuss unconventional detection technique also known as «full resolution receiver». This receiver uses Gaussian probability mixtures for interference structure adaptation. Full resolution receiver is alternative to conventional matched filter receivers in the case of non-Gaussian interferences. For the DS-CDMA forward channel with presence of complex interferences sufficient performance increasing was shown.
EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

Science.gov (United States)

Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

2017-06-01

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.
EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

Energy Technology Data Exchange (ETDEWEB)

Holoien, Thomas W.-S.; /Ohio State U., Dept. Astron. /Ohio State U., CCAPP /KIPAC, Menlo Park /SLAC; Marshall, Philip J.; Wechsler, Risa H.; /KIPAC, Menlo Park /SLAC

2017-05-11

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.
Merging Mixture Components for Cell Population Identification in Flow Cytometry

Directory of Open Access Journals (Sweden)

Greg Finak

2009-01-01

Full Text Available We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project.
Two-component mixture cure rate model with spline estimated nonparametric components.

Science.gov (United States)

Wang, Lu; Du, Pang; Liang, Hua

2012-09-01

In some survival analysis of medical studies, there are often long-term survivors who can be considered as permanently cured. The goals in these studies are to estimate the noncured probability of the whole population and the hazard rate of the susceptible subpopulation. When covariates are present as often happens in practice, to understand covariate effects on the noncured probability and hazard rate is of equal importance. The existing methods are limited to parametric and semiparametric models. We propose a two-component mixture cure rate model with nonparametric forms for both the cure probability and the hazard rate function. Identifiability of the model is guaranteed by an additive assumption that allows no time-covariate interactions in the logarithm of hazard rate. Estimation is carried out by an expectation-maximization algorithm on maximizing a penalized likelihood. For inferential purpose, we apply the Louis formula to obtain point-wise confidence intervals for noncured probability and hazard rate. Asymptotic convergence rates of our function estimates are established. We then evaluate the proposed method by extensive simulations. We analyze the survival data from a melanoma study and find interesting patterns for this study. © 2011, The International Biometric Society.
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture

Science.gov (United States)

Li, Lingling; Wang, Pengchong; Chao, Kuei-Hsiang; Zhou, Yatong; Xie, Yang

2016-01-01

The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimodality. This paper proposes a novel RUL prediction method based on the Gaussian Process Mixture (GPM). It can process multimodality by fitting different segments of trajectories with different GPR models separately, such that the tiny differences among these segments can be revealed. The method is demonstrated to be effective for prediction by the excellent predictive result of the experiments on the two commercial and chargeable Type 1850 Lithium-ion batteries, provided by NASA. The performance comparison among the models illustrates that the GPM is more accurate than the SVM and the GPR. In addition, GPM can yield the predictive confidence interval, which makes the prediction more reliable than that of traditional models. PMID:27632176
Retrieving simulated volcanic, desert dust and sea-salt particle properties from two/three-component particle mixtures using UV-VIS polarization lidar and T matrix

Directory of Open Access Journals (Sweden)

G. David

2013-07-01

Full Text Available During transport by advection, atmospheric nonspherical particles, such as volcanic ash, desert dust or sea-salt particles experience several chemical and physical processes, leading to a complex vertical atmospheric layering at remote sites where intrusion episodes occur. In this paper, a new methodology is proposed to analyse this complex vertical layering in the case of a two/three-component particle external mixtures. This methodology relies on an analysis of the spectral and polarization properties of the light backscattered by atmospheric particles. It is based on combining a sensitive and accurate UV-VIS polarization lidar experiment with T-matrix numerical simulations and air mass back trajectories. The Lyon UV-VIS polarization lidar is used to efficiently partition the particle mixture into its nonspherical components, while the T-matrix method is used for simulating the backscattering and depolarization properties of nonspherical volcanic ash, desert dust and sea-salt particles. It is shown that the particle mixtures' depolarization ratio δ p differs from the nonspherical particles' depolarization ratio δns due to the presence of spherical particles in the mixture. Hence, after identifying a tracer for nonspherical particles, particle backscattering coefficients specific to each nonspherical component can be retrieved in a two-component external mixture. For three-component mixtures, the spectral properties of light must in addition be exploited by using a dual-wavelength polarization lidar. Hence, for the first time, in a three-component external mixture, the nonsphericity of each particle is taken into account in a so-called 2β + 2δ formalism. Applications of this new methodology are then demonstrated in two case studies carried out in Lyon, France, related to the mixing of Eyjafjallajökull volcanic ash with sulfate particles (case of a two-component mixture and to the mixing of dust with sea-salt and water-soluble particles
An Improved Gaussian Mixture Model for Damage Propagation Monitoring of an Aircraft Wing Spar under Changing Structural Boundary Conditions

Science.gov (United States)

Qiu, Lei; Yuan, Shenfang; Mei, Hanfei; Fang, Fang

2016-01-01

Structural Health Monitoring (SHM) technology is considered to be a key technology to reduce the maintenance cost and meanwhile ensure the operational safety of aircraft structures. It has gradually developed from theoretic and fundamental research to real-world engineering applications in recent decades. The problem of reliable damage monitoring under time-varying conditions is a main issue for the aerospace engineering applications of SHM technology. Among the existing SHM methods, Guided Wave (GW) and piezoelectric sensor-based SHM technique is a promising method due to its high damage sensitivity and long monitoring range. Nevertheless the reliability problem should be addressed. Several methods including environmental parameter compensation, baseline signal dependency reduction and data normalization, have been well studied but limitations remain. This paper proposes a damage propagation monitoring method based on an improved Gaussian Mixture Model (GMM). It can be used on-line without any structural mechanical model and a priori knowledge of damage and time-varying conditions. With this method, a baseline GMM is constructed first based on the GW features obtained under time-varying conditions when the structure under monitoring is in the healthy state. When a new GW feature is obtained during the on-line damage monitoring process, the GMM can be updated by an adaptive migration mechanism including dynamic learning and Gaussian components split-merge. The mixture probability distribution structure of the GMM and the number of Gaussian components can be optimized adaptively. Then an on-line GMM can be obtained. Finally, a best match based Kullback-Leibler (KL) divergence is studied to measure the migration degree between the baseline GMM and the on-line GMM to reveal the weak cumulative changes of the damage propagation mixed in the time-varying influence. A wing spar of an aircraft is used to validate the proposed method. The results indicate that the crack
Mixed Platoon Flow Dispersion Model Based on Speed-Truncated Gaussian Mixture Distribution

Directory of Open Access Journals (Sweden)

Weitiao Wu

2013-01-01

Full Text Available A mixed traffic flow feature is presented on urban arterials in China due to a large amount of buses. Based on field data, a macroscopic mixed platoon flow dispersion model (MPFDM was proposed to simulate the platoon dispersion process along the road section between two adjacent intersections from the flow view. More close to field observation, truncated Gaussian mixture distribution was adopted as the speed density distribution for mixed platoon. Expectation maximum (EM algorithm was used for parameters estimation. The relationship between the arriving flow distribution at downstream intersection and the departing flow distribution at upstream intersection was investigated using the proposed model. Comparison analysis using virtual flow data was performed between the Robertson model and the MPFDM. The results confirmed the validity of the proposed model.
Quantifying entanglement in two-mode Gaussian states

Science.gov (United States)

Tserkis, Spyros; Ralph, Timothy C.

2017-12-01

Entangled two-mode Gaussian states are a key resource for quantum information technologies such as teleportation, quantum cryptography, and quantum computation, so quantification of Gaussian entanglement is an important problem. Entanglement of formation is unanimously considered a proper measure of quantum correlations, but for arbitrary two-mode Gaussian states no analytical form is currently known. In contrast, logarithmic negativity is a measure that is straightforward to calculate and so has been adopted by most researchers, even though it is a less faithful quantifier. In this work, we derive an analytical lower bound for entanglement of formation of generic two-mode Gaussian states, which becomes tight for symmetric states and for states with balanced correlations. We define simple expressions for entanglement of formation in physically relevant situations and use these to illustrate the problematic behavior of logarithmic negativity, which can lead to spurious conclusions.
Gaussian Mixture Random Coefficient model based framework for SHM in structures with time-dependent dynamics under uncertainty

Science.gov (United States)

Avendaño-Valencia, Luis David; Fassois, Spilios D.

2017-12-01

The problem of vibration-based damage diagnosis in structures characterized by time-dependent dynamics under significant environmental and/or operational uncertainty is considered. A stochastic framework consisting of a Gaussian Mixture Random Coefficient model of the uncertain time-dependent dynamics under each structural health state, proper estimation methods, and Bayesian or minimum distance type decision making, is postulated. The Random Coefficient (RC) time-dependent stochastic model with coefficients following a multivariate Gaussian Mixture Model (GMM) allows for significant flexibility in uncertainty representation. Certain of the model parameters are estimated via a simple procedure which is founded on the related Multiple Model (MM) concept, while the GMM weights are explicitly estimated for optimizing damage diagnostic performance. The postulated framework is demonstrated via damage detection in a simple simulated model of a quarter-car active suspension with time-dependent dynamics and considerable uncertainty on the payload. Comparisons with a simpler Gaussian RC model based method are also presented, with the postulated framework shown to be capable of offering considerable improvement in diagnostic performance.
Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

Directory of Open Access Journals (Sweden)

Chin-Teng Lin

2018-01-01

Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
Kinect Posture Reconstruction Based on a Local Mixture of Gaussian Process Models.

Science.gov (United States)

Liu, Zhiguang; Zhou, Liuyang; Leung, Howard; Shum, Hubert P H

2016-11-01

Depth sensor based 3D human motion estimation hardware such as Kinect has made interactive applications more popular recently. However, it is still challenging to accurately recognize postures from a single depth camera due to the inherently noisy data derived from depth images and self-occluding action performed by the user. In this paper, we propose a new real-time probabilistic framework to enhance the accuracy of live captured postures that belong to one of the action classes in the database. We adopt the Gaussian Process model as a prior to leverage the position data obtained from Kinect and marker-based motion capture system. We also incorporate a temporal consistency term into the optimization framework to constrain the velocity variations between successive frames. To ensure that the reconstructed posture resembles the accurate parts of the observed posture, we embed a set of joint reliability measurements into the optimization framework. A major drawback of Gaussian Process is its cubic learning complexity when dealing with a large database due to the inverse of a covariance matrix. To solve the problem, we propose a new method based on a local mixture of Gaussian Processes, in which Gaussian Processes are defined in local regions of the state space. Due to the significantly decreased sample size in each local Gaussian Process, the learning time is greatly reduced. At the same time, the prediction speed is enhanced as the weighted mean prediction for a given sample is determined by the nearby local models only. Our system also allows incrementally updating a specific local Gaussian Process in real time, which enhances the likelihood of adapting to run-time postures that are different from those in the database. Experimental results demonstrate that our system can generate high quality postures even under severe self-occlusion situations, which is beneficial for real-time applications such as motion-based gaming and sport training.
ADAPTIVE BACKGROUND DENGAN METODE GAUSSIAN MIXTURE MODELS UNTUK REAL-TIME TRACKING

Directory of Open Access Journals (Sweden)

Silvia Rostianingsih

2008-01-01

Full Text Available Nowadays, motion tracking application is widely used for many purposes, such as detecting traffic jam and counting how many people enter a supermarket or a mall. A method to separate background and the tracked object is required for motion tracking. It will not be hard to develop the application if the tracking is performed on a static background, but it will be difficult if the tracked object is at a place with a non-static background, because the changing part of the background can be recognized as a tracking area. In order to handle the problem an application can be made to separate background where that separation can adapt to change that occur. This application is made to produce adaptive background using Gaussian Mixture Models (GMM as its method. GMM method clustered the input pixel data with pixel color value as it’s basic. After the cluster formed, dominant distributions are choosen as background distributions. This application is made by using Microsoft Visual C 6.0. The result of this research shows that GMM algorithm could made adaptive background satisfactory. This proofed by the result of the tests that succeed at all condition given. This application can be developed so the tracking process integrated in adaptive background maker process. Abstract in Bahasa Indonesia : Saat ini, aplikasi motion tracking digunakan secara luas untuk banyak tujuan, seperti mendeteksi kemacetan dan menghitung berapa banyak orang yang masuk ke sebuah supermarket atau sebuah mall. Sebuah metode untuk memisahkan antara background dan obyek yang di-track dibutuhkan untuk melakukan motion tracking. Membuat aplikasi tracking pada background yang statis bukanlah hal yang sulit, namun apabila tracking dilakukan pada background yang tidak statis akan lebih sulit, dikarenakan perubahan background dapat dikenali sebagai area tracking. Untuk mengatasi masalah tersebut, dapat dibuat suatu aplikasi untuk memisahkan background dimana aplikasi tersebut dapat
Two-photon optics of Bessel-Gaussian modes

CSIR Research Space (South Africa)

McLaren, M

2013-09-01

Full Text Available In this paper we consider geometrical two-photon optics of Bessel-Gaussian modes generated in spontaneous parametric down-conversion of a Gaussian pump beam. We provide a general theoretical expression for the orbital angular momentum (OAM) spectrum...
Fixed-Point Algorithms for the Blind Separation of Arbitrary Complex-Valued Non-Gaussian Signal Mixtures

Directory of Open Access Journals (Sweden)

Douglas Scott C

2007-01-01

Full Text Available We derive new fixed-point algorithms for the blind separation of complex-valued mixtures of independent, noncircularly symmetric, and non-Gaussian source signals. Leveraging recently developed results on the separability of complex-valued signal mixtures, we systematically construct iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of the FastICA algorithm of Hyvarinen and Oja in the real-valued mixture case. Thus, our methods inherit the fast convergence properties, computational simplicity, and ease of use of the FastICA algorithm while at the same time extending this class of techniques to complex signal mixtures. For extracting multiple sources, symmetric and asymmetric signal deflation procedures can be employed. Simulations for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data-record lengths.

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

OpenAIRE

Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D.

2013-01-01

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture th...
Disentangling the developmental and neurobehavioural effects of perinatal exposure to a chemical mixture found in blood of Arctic populations: differential toxicity of mixture components

Energy Technology Data Exchange (ETDEWEB)

Bowers, W.; Nakai, J.; Yagminas, A.; Chu, I.; Moir, D. [Health Canada (Canada)

2004-09-15

The current study was designed to evaluate the neurobehavioral effects of perinatal exposure to a chemical mixture that is based on relative concentrations of persistent organic pollutants found in the blood of Canadian Arctic populations and contains 14 PCB congeners, 12 organochlorine pesticides and methyl mercury. This study compared the effects of the complete mixture with the effects of three major components of the mixture (the PCB component, the organochlorine pesticide component, and the methyl mercury component). By examining a range of neurobehavioural functions over development we also determine if specific neurobehavioural disturbances produced by the mixture can be attributed to components of the mixture and if neurobehavioural effects produced by components of the mixture are altered by concurrent exposure to other components in the mixture. Ninety-two nulliparious female Sprague-Dawley rats served as subjects.
Osmotic Suppression of Positional Fluctuation of a Trapped Particle in a Near-Critical Binary Fluid Mixture in the Regime of the Gaussian Model

Science.gov (United States)

Fujitani, Youhei

2017-11-01

Suppose a spherical colloidal particle surrounded by a near-critical binary fluid mixture in the homogeneous phase. The particle surface usually preferentially attracts one component of the mixture, and the resultant concentration gradient, which causes the osmotic pressure, becomes significant in the ambient near-criticality. The concentration profile is deformed by the particle motion, and can generate a nonzero force exerted on the moving particle. This link was previously shown to slightly suppress the positional equal-time correlation of a particle trapped by a harmonic potential. This previous study presupposed a small fluctuation amplitude of a particle much larger than the correlation length, a weak preferential attraction, and the Gaussian model for the free-energy functional of the mixture. In the present study, we calculate the equal-time correlation without assuming the weak preferential attraction and show that the suppression becomes much more distinct in some range of the trap stiffness because of the increased induced mass. This suggests the possible experimental usage of a trapped particle as a probe for local environments of a near-critical binary fluid mixture.
Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

Science.gov (United States)

Liu, Yongqi; Ye, Lei; Qin, Hui; Hong, Xiaofeng; Ye, Jiajun; Yin, Xingli

2018-06-01

Reliable streamflow forecasts can be highly valuable for water resources planning and management. In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting. The HMM is initialized using a kernelized K-medoids clustering method, and the Baum-Welch algorithm is then executed to learn the model parameters. GMR derives a conditional probability distribution for the predictand given covariate information, including the antecedent flow at a local station and two surrounding stations. The performance of HMM-GMR was verified based on the mean square error and continuous ranked probability score skill scores. The reliability of the forecasts was assessed by examining the uniformity of the probability integral transform values. The results show that HMM-GMR obtained reasonably high skill scores and the uncertainty spread was appropriate. Different HMM states were assumed to be different climate conditions, which would lead to different types of observed values. We demonstrated that the HMM-GMR approach can handle multimodal and heteroscedastic data.
Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

Science.gov (United States)

Zhang, Hong; Hou, Rui; Yi, Lei; Meng, Juan; Pan, Zhisong; Zhou, Yuhuan

2016-07-01

The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.
Clustering gene expression time series data using an infinite Gaussian process mixture model.

Science.gov (United States)

McDowell, Ian C; Manandhar, Dinesh; Vockley, Christopher M; Schmid, Amy K; Reddy, Timothy E; Engelhardt, Barbara E

2018-01-01

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.
Clustering gene expression time series data using an infinite Gaussian process mixture model.

Directory of Open Access Journals (Sweden)

Ian C McDowell

2018-01-01

Full Text Available Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP, which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.
Vehicle speed detection based on gaussian mixture model using sequential of images

Science.gov (United States)

Setiyono, Budi; Ratna Sulistyaningrum, Dwi; Soetrisno; Fajriyah, Farah; Wahyu Wicaksono, Danang

2017-09-01

Intelligent Transportation System is one of the important components in the development of smart cities. Detection of vehicle speed on the highway is supporting the management of traffic engineering. The purpose of this study is to detect the speed of the moving vehicles using digital image processing. Our approach is as follows: The inputs are a sequence of frames, frame rate (fps) and ROI. The steps are following: First we separate foreground and background using Gaussian Mixture Model (GMM) in each frames. Then in each frame, we calculate the location of object and its centroid. Next we determine the speed by computing the movement of centroid in sequence of frames. In the calculation of speed, we only consider frames when the centroid is inside the predefined region of interest (ROI). Finally we transform the pixel displacement into a time unit of km/hour. Validation of the system is done by comparing the speed calculated manually and obtained by the system. The results of software testing can detect the speed of vehicles with the highest accuracy is 97.52% and the lowest accuracy is 77.41%. And the detection results of testing by using real video footage on the road is included with real speed of the vehicle.
Estimation of Seismic Wavelets Based on the Multivariate Scale Mixture of Gaussians Model

Directory of Open Access Journals (Sweden)

Jing-Huai Gao

2009-12-01

Full Text Available This paper proposes a new method for estimating seismic wavelets. Suppose a seismic wavelet can be modeled by a formula with three free parameters (scale, frequency and phase. We can transform the estimation of the wavelet into determining these three parameters. The phase of the wavelet is estimated by constant-phase rotation to the seismic signal, while the other two parameters are obtained by the Higher-order Statistics (HOS (fourth-order cumulant matching method. In order to derive the estimator of the Higher-order Statistics (HOS, the multivariate scale mixture of Gaussians (MSMG model is applied to formulating the multivariate joint probability density function (PDF of the seismic signal. By this way, we can represent HOS as a polynomial function of second-order statistics to improve the anti-noise performance and accuracy. In addition, the proposed method can work well for short time series.
Convergence Results for the Gaussian Mixture Implementation of the Extended-Target PHD Filter and Its Extended Kalman Filtering Approximation

Directory of Open Access Journals (Sweden)

Feng Lian

2012-01-01

Full Text Available The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD filter and its extended Kalman (EK filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which the two filters satisfy uniform convergence.
Dirichlet Process Parsimonious Mixtures for clustering

OpenAIRE

Chamroukhi, Faicel; Bartcus, Marius; Glotin, Hervé

2015-01-01

The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametric Bayesian prospective. We propose new Dirichlet Process Parsimonious mixtures (DPPM) which represent a Bayesian nonparametric formulation of these parsimonious Gaussian mixtur...
Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations

Science.gov (United States)

Del Pozzo, W.; Berry, C. P. L.; Ghosh, A.; Haines, T. S. F.; Singer, L. P.; Vecchio, A.

2018-06-01

We reconstruct posterior distributions for the position (sky area and distance) of a simulated set of binary neutron-star gravitational-waves signals observed with Advanced LIGO and Advanced Virgo. We use a Dirichlet Process Gaussian-mixture model, a fully Bayesian non-parametric method that can be used to estimate probability density functions with a flexible set of assumptions. The ability to reliably reconstruct the source position is important for multimessenger astronomy, as recently demonstrated with GW170817. We show that for detector networks comparable to the early operation of Advanced LIGO and Advanced Virgo, typical localization volumes are ˜104-105 Mpc3 corresponding to ˜102-103 potential host galaxies. The localization volume is a strong function of the network signal-to-noise ratio, scaling roughly ∝ϱnet-6. Fractional localizations improve with the addition of further detectors to the network. Our Dirichlet Process Gaussian-mixture model can be adopted for localizing events detected during future gravitational-wave observing runs, and used to facilitate prompt multimessenger follow-up.
Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

Science.gov (United States)

Yao, Shoukui; Qin, Xiaojuan

2018-02-01

Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.
The separation of solid and liquid components of mixtures

International Nuclear Information System (INIS)

Hunter, W.M.

1980-01-01

An improved method of separating solid and liquid components of mixtures is described which is particularly suited for use in automated radioimmunoassay systems in the analysis of bound and free fractions. A second liquid, having a density intermediate between those of the solid and liquid components, is delivered to the solid/ liquid mixture to form a discrete layer below the mixture and the solid separates into this lower liquid layer assisted by centrifugal force. The second liquid of intermediate density is an aqueous solution of a highly hydrophilic and electrically non-polar solute, such as an aqueous sucrose solution. Further liquids of intermediate density and progressively higher density may be delivered to form further discrete layers below the initial layer of the second dense liquid. After separation of the solid and liquid components of the mixture, the supernatant liquid component of the original mixture is removed in a controlled and non-turbulent manner. The method is illustrated in radioimmunoassays for platelet β-thromboglobulin and human follicle stimulating hormone. (U.K.)
Combinatorial bounds on the α-divergence of univariate mixture models

KAUST Repository

Nielsen, Frank

2017-06-20

We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.
A Grasp-Pose Generation Method Based on Gaussian Mixture Models

Directory of Open Access Journals (Sweden)

Wenjia Wu

2015-11-01

Full Text Available A Gaussian Mixture Model (GMM-based grasp-pose generation method is proposed in this paper. Through offline training, the GMM is set up and used to depict the distribution of the robot's reachable orientations. By dividing the robot's workspace into small 3D voxels and training the GMM for each voxel, a look-up table covering all the workspace is built with the x, y and z positions as the index and the GMM as the entry. Through the definition of Task Space Regions (TSR, an object's feasible grasp poses are expressed as a continuous region. With the GMM, grasp poses can be preferentially sampled from regions with high reachability probabilities in the online grasp-planning stage. The GMM can also be used as a preliminary judgement of a grasp pose's reachability. Experiments on both a simulated and a real robot show the superiority of our method over the existing method.
A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

Science.gov (United States)

Shekofteh, Yasser; Jafari, Sajad; Sprott, Julien Clinton; Hashemi Golpayegani, S. Mohammad Reza; Almasganj, Farshad

2015-02-01

As we know, many biological systems such as neurons or the heart can exhibit chaotic behavior. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. In this paper, a novel cost function is proposed to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian mixture model (GMM) which is fitted to the observed attractor generated by the real system. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. We have applied the proposed method to the parameter estimation of two important biological systems, a neuron and a cardiac pacemaker, which show chaotic behavior. Some simulated experiments are given to verify the usefulness of the proposed approach in clean and noisy conditions. The results show the adequacy of the proposed cost function.
LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION

Directory of Open Access Journals (Sweden)

IMEN TRABELSI

2017-05-01

Full Text Available Speaker Identification (SI aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM and Learning vector quantization (LVQ for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers for test data using 36 MFCC features.
Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning.

Science.gov (United States)

Lin, Lanny; Goodrich, Michael A

2014-12-01

During unmanned aerial vehicle (UAV) search missions, efficient use of UAV flight time requires flight paths that maximize the probability of finding the desired subject. The probability of detecting the desired subject based on UAV sensor information can vary in different search areas due to environment elements like varying vegetation density or lighting conditions, making it likely that the UAV can only partially detect the subject. This adds another dimension of complexity to the already difficult (NP-Hard) problem of finding an optimal search path. We present a new class of algorithms that account for partial detection in the form of a task difficulty map and produce paths that approximate the payoff of optimal solutions. The algorithms use the mode goodness ratio heuristic that uses a Gaussian mixture model to prioritize search subregions. The algorithms search for effective paths through the parameter space at different levels of resolution. We compare the performance of the new algorithms against two published algorithms (Bourgault's algorithm and LHC-GW-CONV algorithm) in simulated searches with three real search and rescue scenarios, and show that the new algorithms outperform existing algorithms significantly and can yield efficient paths that yield payoffs near the optimal.
Identification of damage in composite structures using Gaussian mixture model-processed Lamb waves

Science.gov (United States)

Wang, Qiang; Ma, Shuxian; Yue, Dong

2018-04-01

Composite materials have comprehensively better properties than traditional materials, and therefore have been more and more widely used, especially because of its higher strength-weight ratio. However, the damage of composite structures is usually varied and complicated. In order to ensure the security of these structures, it is necessary to monitor and distinguish the structural damage in a timely manner. Lamb wave-based structural health monitoring (SHM) has been proved to be effective in online structural damage detection and evaluation; furthermore, the characteristic parameters of the multi-mode Lamb wave varies in response to different types of damage in the composite material. This paper studies the damage identification approach for composite structures using the Lamb wave and the Gaussian mixture model (GMM). The algorithm and principle of the GMM, and the parameter estimation, is introduced. Multi-statistical characteristic parameters of the excited Lamb waves are extracted, and the parameter space with reduced dimensions is adopted by principal component analysis (PCA). The damage identification system using the GMM is then established through training. Experiments on a glass fiber-reinforced epoxy composite laminate plate are conducted to verify the feasibility of the proposed approach in terms of damage classification. The experimental results show that different types of damage can be identified according to the value of the likelihood function of the GMM.

Recovering four-component solutions by the inverse transformation of the infinite-order two-component wave functions

International Nuclear Information System (INIS)

Barysz, Maria; Mentel, Lukasz; Leszczynski, Jerzy

2009-01-01

The two-component Hamiltonian of the infinite-order two-component (IOTC) theory is obtained by a unitary block-diagonalizing transformation of the Dirac-Hamiltonian. Once the IOTC spin orbitals are calculated, they can be back transformed into four-component solutions. The transformed four component solutions are then used to evaluate different moments of the electron density distribution. This formally exact method may, however, suffer from certain approximations involved in its numerical implementation. As shown by the present study, with sufficiently large basis set of Gaussian functions, the Dirac values of these moments are fully recovered in spite of using the approximate identity resolution into eigenvectors of the p 2 operator.
Inferring Trial-to-Trial Excitatory and Inhibitory Synaptic Inputs from Membrane Potential using Gaussian Mixture Kalman Filtering

Directory of Open Access Journals (Sweden)

Milad eLankarany

2013-09-01

Full Text Available Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during linear regression techniques. Here, we propose a novel recursive algorithm based on Gaussian mixture Kalman filtering for estimating time-varying excitatory and inhibitory synaptic inputs from single trials of noisy membrane potential in current clamp recordings. The Kalman filtering is followed by an expectation maximization algorithm to infer the statistical parameters (time-varying mean and variance of the synaptic inputs in a non-parametric manner. As our proposed algorithm is repeated recursively, the inferred parameters of the mixtures are used to initiate the next iteration. Unlike other recent algorithms, our algorithm does not assume an a priori distribution from which the synaptic inputs are generated. Instead, the algorithm recursively estimates such a distribution by fitting a Gaussian mixture model. The performance of the proposed algorithms is compared to a previously proposed PF-based algorithm (Paninski et al., 2012 with several illustrative examples, assuming that the distribution of synaptic input is unknown. If noise is small, the performance of our algorithms is similar to that of the previous one. However, if noise is large, they can significantly outperform the previous proposal. These promising results suggest that our algorithm is a robust and efficient technique for estimating time varying excitatory and inhibitory synaptic conductances from single trials of membrane potential recordings.
Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis.

Science.gov (United States)

Morisaki, Soichiro; Yokoyama, Jun'ichi; Eda, Kazunari; Itoh, Yousuke

2016-01-01

We introduce a new analysis method to deal with stationary non-Gaussian noises in gravitational wave detectors in terms of the independent component analysis. First, we consider the simplest case where the detector outputs are linear combinations of the inputs, consisting of signals and various noises, and show that this method may be helpful to increase the signal-to-noise ratio. Next, we take into account the time delay between the inputs and the outputs. Finally, we extend our method to nonlinearly correlated noises and show that our method can identify the coupling coefficients and remove non-Gaussian noises. Although we focus on gravitational wave data analysis, our methods are applicable to the detection of any signals under non-Gaussian noises.
Gaussian statistics of the cosmic microwave background: Correlation of temperature extrema in the COBE DMR two-year sky maps

Science.gov (United States)

Kogut, A.; Banday, A. J.; Bennett, C. L.; Hinshaw, G.; Lubin, P. M.; Smoot, G. F.

1995-01-01

We use the two-point correlation function of the extrema points (peaks and valleys) in the Cosmic Background Explorer (COBE) Differential Microwave Radiometers (DMR) 2 year sky maps as a test for non-Gaussian temperature distribution in the cosmic microwave background anisotropy. A maximum-likelihood analysis compares the DMR data to n = 1 toy models whose random-phase spherical harmonic components a(sub lm) are drawn from either Gaussian, chi-square, or log-normal parent populations. The likelihood of the 53 GHz (A+B)/2 data is greatest for the exact Gaussian model. There is less than 10% chance that the non-Gaussian models tested describe the DMR data, limited primarily by type II errors in the statistical inference. The extrema correlation function is a stronger test for this class of non-Gaussian models than topological statistics such as the genus.
Itinerant Ferromagnetism in a Polarized Two-Component Fermi Gas

DEFF Research Database (Denmark)

Massignan, Pietro; Yu, Zhenhua; Bruun, Georg

2013-01-01

We analyze when a repulsively interacting two-component Fermi gas becomes thermodynamically unstable against phase separation. We focus on the strongly polarized limit, where the free energy of the homogeneous mixture can be calculated accurately in terms of well-defined quasiparticles, the repul......We analyze when a repulsively interacting two-component Fermi gas becomes thermodynamically unstable against phase separation. We focus on the strongly polarized limit, where the free energy of the homogeneous mixture can be calculated accurately in terms of well-defined quasiparticles...
Gaussian mixture models for detection of autism spectrum disorders (ASD) in magnetic resonance imaging

Science.gov (United States)

Almeida, Javier; Velasco, Nelson; Alvarez, Charlens; Romero, Eduardo

2017-11-01

Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by a triad of signs: stereotyped behaviors, verbal and non-verbal communication problems. The scientific community has been interested on quantifying anatomical brain alterations of this disorder. Several studies have focused on measuring brain cortical and sub-cortical volumes. This article presents a fully automatic method which finds out differences among patients diagnosed with autism and control patients. After the usual pre-processing, a template (MNI152) is registered to an evaluated brain which becomes then a set of regions. Each of these regions is the represented by the normalized histogram of intensities which is approximated by mixture of Gaussian (GMM). The gray and white matter are separated to calculate the mean and standard deviation of each Gaussian. These features are then used to train, region per region, a binary SVM classifier. The method was evaluated in an adult population aged from 18 to 35 years, from the public database Autism Brain Imaging Data Exchange (ABIDE). Highest discrimination values were found for the Right Middle Temporal Gyrus, with an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) the curve of 0.72.
Estimating alarm thresholds and the number of components in mixture distributions

Energy Technology Data Exchange (ETDEWEB)

Burr, Tom, E-mail: tburr@lanl.gov [Los Alamos National Laboratory, Mail Stop F600, Los Alamos, NM 87545 (United States); Hamada, Michael S. [Los Alamos National Laboratory, Mail Stop F600, Los Alamos, NM 87545 (United States)

2012-09-01

Mixtures of probability distributions arise in many nuclear assay and forensic applications, including nuclear weapon detection, neutron multiplicity counting, and in solution monitoring (SM) for nuclear safeguards. SM data is increasingly used to enhance nuclear safeguards in aqueous reprocessing facilities having plutonium in solution form in many tanks. This paper provides background for mixture probability distributions and then focuses on mixtures arising in SM data. SM data can be analyzed by evaluating transfer-mode residuals defined as tank-to-tank transfer differences, and wait-mode residuals defined as changes during non-transfer modes. A previous paper investigated impacts on transfer-mode and wait-mode residuals of event marking errors which arise when the estimated start and/or stop times of tank events such as transfers are somewhat different from the true start and/or stop times. Event marking errors contribute to non-Gaussian behavior and larger variation than predicted on the basis of individual tank calibration studies. This paper illustrates evidence for mixture probability distributions arising from such event marking errors and from effects such as condensation or evaporation during non-transfer modes, and pump carryover during transfer modes. A quantitative assessment of the sample size required to adequately characterize a mixture probability distribution arising in any context is included.
Scaled unscented transform Gaussian sum filter: Theory and application

KAUST Repository

Luo, Xiaodong

2010-05-01

In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. We introduce a framework, called the scaled unscented transform Gaussian sum filter (SUT-GSF), which combines two ideas: the scaled unscented Kalman filter (SUKF) based on the concept of scaled unscented transform (SUT) (Julier and Uhlmann (2004) [16]), and the Gaussian mixture model (GMM). The SUT is used to approximate the mean and covariance of a Gaussian random variable which is transformed by a nonlinear function, while the GMM is adopted to approximate the probability density function (pdf) of a random variable through a set of Gaussian distributions. With these two tools, a framework can be set up to assimilate nonlinear systems in a recursive way. Within this framework, one can treat a nonlinear stochastic system as a mixture model of a set of sub-systems, each of which takes the form of a nonlinear system driven by a known Gaussian random process. Then, for each sub-system, one applies the SUKF to estimate the mean and covariance of the underlying Gaussian random variable transformed by the nonlinear governing equations of the sub-system. Incorporating the estimations of the sub-systems into the GMM gives an explicit (approximate) form of the pdf, which can be regarded as a "complete" solution to the state estimation problem, as all of the statistical information of interest can be obtained from the explicit form of the pdf (Arulampalam et al. (2002) [7]). In applications, a potential problem of a Gaussian sum filter is that the number of Gaussian distributions may increase very rapidly. To this end, we also propose an auxiliary algorithm to conduct pdf re-approximation so that the number of Gaussian distributions can be reduced. With the auxiliary algorithm, in principle the SUT-GSF can achieve almost the same computational speed as the SUKF if the SUT-GSF is implemented in parallel. As an example, we will use the SUT-GSF to assimilate a 40-dimensional system due to
An equiratio mixture model for non-additive components : a case study for aspartame/acesulfame-K mixtures

NARCIS (Netherlands)

Schifferstein, H.N.J.

1996-01-01

The Equiratio Mixture Model predicts the psychophysical function for an equiratio mixture type on the basis of the psychophysical functions for the unmixed components. The model reliably estimates the sweetness of mixtures of sugars and sugar-alchohols, but is unable to predict intensity for
A hybrid pareto mixture for conditional asymmetric fat-tailed distributions.

Science.gov (United States)

Carreau, Julie; Bengio, Yoshua

2009-07-01

In many cases, we observe some variables X that contain predictive information over a scalar variable of interest Y , with (X,Y) pairs observed in a training set. We can take advantage of this information to estimate the conditional density p(Y|X = x). In this paper, we propose a conditional mixture model with hybrid Pareto components to estimate p(Y|X = x). The hybrid Pareto is a Gaussian whose upper tail has been replaced by a generalized Pareto tail. A third parameter, in addition to the location and spread parameters of the Gaussian, controls the heaviness of the upper tail. Using the hybrid Pareto in a mixture model results in a nonparametric estimator that can adapt to multimodality, asymmetry, and heavy tails. A conditional density estimator is built by modeling the parameters of the mixture estimator as functions of X. We use a neural network to implement these functions. Such conditional density estimators have important applications in many domains such as finance and insurance. We show experimentally that this novel approach better models the conditional density in terms of likelihood, compared to competing algorithms: conditional mixture models with other types of components and a classical kernel-based nonparametric model.
A further component analysis for illicit drugs mixtures with THz-TDS

Science.gov (United States)

Xiong, Wei; Shen, Jingling; He, Ting; Pan, Rui

2009-07-01

A new method for quantitative analysis of mixtures of illicit drugs with THz time domain spectroscopy was proposed and verified experimentally. In traditional method we need fingerprints of all the pure chemical components. In practical as only the objective components in a mixture and their absorption features are known, it is necessary and important to present a more practical technique for the detection and identification. Our new method of quantitatively inspect of the mixtures of illicit drugs is developed by using derivative spectrum. In this method, the ratio of objective components in a mixture can be obtained on the assumption that all objective components in the mixture and their absorption features are known but the unknown components are not needed. Then methamphetamine and flour, a illicit drug and a common adulterant, were selected for our experiment. The experimental result verified the effectiveness of the method, which suggested that it could be an effective method for quantitative identification of illicit drugs. This THz spectroscopy technique is great significant in the real-world applications of illicit drugs quantitative analysis. It could be an effective method in the field of security and pharmaceuticals inspection.
Uniform phases in fluids of hard isosceles triangles: One-component fluid and binary mixtures

Science.gov (United States)

Martínez-Ratón, Yuri; Díaz-De Armas, Ariel; Velasco, Enrique

2018-05-01

We formulate the scaled particle theory for a general mixture of hard isosceles triangles and calculate different phase diagrams for the one-component fluid and for certain binary mixtures. The fluid of hard triangles exhibits a complex phase behavior: (i) the presence of a triatic phase with sixfold symmetry, (ii) the isotropic-uniaxial nematic transition is of first order for certain ranges of aspect ratios, and (iii) the one-component system exhibits nematic-nematic transitions ending in critical points. We found the triatic phase to be stable not only for equilateral triangles but also for triangles of similar aspect ratios. We focus the study of binary mixtures on the case of symmetric mixtures: equal particle areas with aspect ratios (κi) symmetric with respect to the equilateral one, κ1κ2=3 . For these mixtures we found, aside from first-order isotropic-nematic and nematic-nematic transitions (the latter ending in a critical point): (i) a region of triatic phase stability even for mixtures made of particles that do not form this phase at the one-component limit, and (ii) the presence of a Landau point at which two triatic-nematic first-order transitions and a nematic-nematic demixing transition coalesce. This phase behavior is analogous to that of a symmetric three-dimensional mixture of rods and plates.
Efficient and robust relaxation procedures for multi-component mixtures including phase transition

International Nuclear Information System (INIS)

Han, Ee; Hantke, Maren; Müller, Siegfried

2017-01-01

We consider a thermodynamic consistent multi-component model in multi-dimensions that is a generalization of the classical two-phase flow model of Baer and Nunziato. The exchange of mass, momentum and energy between the phases is described by additional source terms. Typically these terms are handled by relaxation procedures. Available relaxation procedures suffer from efficiency and robustness resulting in very costly computations that in general only allow for one-dimensional computations. Therefore we focus on the development of new efficient and robust numerical methods for relaxation processes. We derive exact procedures to determine mechanical and thermal equilibrium states. Further we introduce a novel iterative method to treat the mass transfer for a three component mixture. All new procedures can be extended to an arbitrary number of inert ideal gases. We prove existence, uniqueness and physical admissibility of the resulting states and convergence of our new procedures. Efficiency and robustness of the procedures are verified by means of numerical computations in one and two space dimensions. - Highlights: • We develop novel relaxation procedures for a generalized, thermodynamically consistent Baer–Nunziato type model. • Exact procedures for mechanical and thermal relaxation procedures avoid artificial parameters. • Existence, uniqueness and physical admissibility of the equilibrium states are proven for special mixtures. • A novel iterative method for mass transfer is introduced for a three component mixture providing a unique and admissible equilibrium state.
Interaction of Airy–Gaussian beams in saturable media

International Nuclear Information System (INIS)

Zhou Meiling; Peng Yulian; Chen Chidao; Chen Bo; Peng Xi; Deng Dongmei

2016-01-01

Based on the nonlinear Schrödinger equation, the interactions of the two Airy–Gaussian components in the incidence are analyzed in saturable media, under the circumstances of the same amplitude and different amplitudes, respectively. It is found that the interaction can be both attractive and repulsive depending on the relative phase. The smaller the interval between two Airy–Gaussian components in the incidence is, the stronger the intensity of the interaction. However, with the equal amplitude, the symmetry is shown and the change of quasi-breathers is opposite in the in-phase case and out-of-phase case. As the distribution factor is increased, the phenomena of the quasi-breather and the self-accelerating of the two Airy–Gaussian components are weakened. When the amplitude is not equal, the image does not have symmetry. The obvious phenomenon of the interaction always arises on the side of larger input power in the incidence. The maximum intensity image is also simulated. Many of the characteristics which are contained within other images can also be concluded in this figure. (paper)
Efficient implementation of one- and two-component analytical energy gradients in exact two-component theory

Science.gov (United States)

Franzke, Yannick J.; Middendorf, Nils; Weigend, Florian

2018-03-01

We present an efficient algorithm for one- and two-component analytical energy gradients with respect to nuclear displacements in the exact two-component decoupling approach to the one-electron Dirac equation (X2C). Our approach is a generalization of the spin-free ansatz by Cheng and Gauss [J. Chem. Phys. 135, 084114 (2011)], where the perturbed one-electron Hamiltonian is calculated by solving a first-order response equation. Computational costs are drastically reduced by applying the diagonal local approximation to the unitary decoupling transformation (DLU) [D. Peng and M. Reiher, J. Chem. Phys. 136, 244108 (2012)] to the X2C Hamiltonian. The introduced error is found to be almost negligible as the mean absolute error of the optimized structures amounts to only 0.01 pm. Our implementation in TURBOMOLE is also available within the finite nucleus model based on a Gaussian charge distribution. For a X2C/DLU gradient calculation, computational effort scales cubically with the molecular size, while storage increases quadratically. The efficiency is demonstrated in calculations of large silver clusters and organometallic iridium complexes.
Construction of a 21-Component Layered Mixture Experiment Design

International Nuclear Information System (INIS)

Piepel, Gregory F.; Cooley, Scott K.; Jones, Bradley

2004-01-01

This paper describes the solution to a unique and challenging mixture experiment design problem involving: (1) 19 and 21 components for two different parts of the design, (2) many single-component and multi-component constraints, (3) augmentation of existing data, (4) a layered design developed in stages, and (5) a no-candidate-point optimal design approach. The problem involved studying the liquidus temperature of spinel crystals as a function of nuclear waste glass composition. The statistical objective was to develop an experimental design by augmenting existing glasses with new nonradioactive and radioactive glasses chosen to cover the designated nonradioactive and radioactive experimental regions. The existing 144 glasses were expressed as 19-component nonradioactive compositions and then augmented with 40 new nonradioactive glasses. These included 8 glasses on the outer layer of the region, 27 glasses on an inner layer, 2 replicate glasses at the centroid, and one replicate each of three existing glasses. Then, the 144 + 40 = 184 glasses were expressed as 21-component radioactive compositions and augmented with 5 radioactive glasses. A D-optimal design algorithm was used to select the new outer layer, inner layer, and radioactive glasses. Several statistical software packages can generate D-optimal experimental designs, but nearly all require a set of candidate points (e.g., vertices) from which to select design points. The large number of components (19 or 21) and many constraints made it impossible to generate the huge number of vertices and other typical candidate points. JMP(R) was used to select design points without candidate points. JMP uses a coordinate-exchange algorithm modified for mixture experiments, which is discussed in the paper
Evaluation of the H-point standard additions method (HPSAM) and the generalized H-point standard additions method (GHPSAM) for the UV-analysis of two-component mixtures.

Science.gov (United States)

Hund, E; Massart, D L; Smeyers-Verbeke, J

1999-10-01

The H-point standard additions method (HPSAM) and two versions of the generalized H-point standard additions method (GHPSAM) are evaluated for the UV-analysis of two-component mixtures. Synthetic mixtures of anhydrous caffeine and phenazone as well as of atovaquone and proguanil hydrochloride were used. Furthermore, the method was applied to pharmaceutical formulations that contain these compounds as active drug substances. This paper shows both the difficulties that are related to the methods and the conditions by which acceptable results can be obtained.
A Monte Carlo simulation model for stationary non-Gaussian processes

DEFF Research Database (Denmark)

Grigoriu, M.; Ditlevsen, Ove Dalager; Arwade, S. R.

2003-01-01

includes translation processes and is useful for both Monte Carlo simulation and analytical studies. As for translation processes, the mixture of translation processes can have a wide range of marginal distributions and correlation functions. Moreover, these processes can match a broader range of second...... athe proposed Monte Carlo algorithm and compare features of translation processes and mixture of translation processes. Keywords: Monte Carlo simulation, non-Gaussian processes, sampling theorem, stochastic processes, translation processes......A class of stationary non-Gaussian processes, referred to as the class of mixtures of translation processes, is defined by their finite dimensional distributions consisting of mixtures of finite dimensional distributions of translation processes. The class of mixtures of translation processes...
Two-mode Gaussian density matrices and squeezing of photons

International Nuclear Information System (INIS)

Tucci, R.R.

1992-01-01

In this paper, the authors generalize to 2-mode states the 1-mode state results obtained in a previous paper. The authors study 2-mode Gaussian density matrices. The authors find a linear transformation which maps the two annihilation operators, one for each mode, into two new annihilation operators that are uncorrelated and unsqueezed. This allows the authors to express the density matrix as a product of two 1-mode density matrices. The authors find general conditions under which 2-mode Gaussian density matrices become pure states. Possible pure states include the 2-mode squeezed pure states commonly mentioned in the literature, plus other pure states never mentioned before. The authors discuss the entropy and thermodynamic laws (Second Law, Fundamental Equation, and Gibbs-Duhem Equation) for the 2-mode states being considered
Analysis of Minor Component Segregation in Ternary Powder Mixtures

Directory of Open Access Journals (Sweden)

Asachi Maryam

2017-01-01

Full Text Available In many powder handling operations, inhomogeneity in powder mixtures caused by segregation could have significant adverse impact on the quality as well as economics of the production. Segregation of a minor component of a highly active substance could have serious deleterious effects, an example is the segregation of enzyme granules in detergent powders. In this study, the effects of particle properties and bulk cohesion on the segregation tendency of minor component are analysed. The minor component is made sticky while not adversely affecting the flowability of samples. The segregation extent is evaluated using image processing of the photographic records taken from the front face of the heap after the pouring process. The optimum average sieve cut size of components for which segregation could be reduced is reported. It is also shown that the extent of segregation is significantly reduced by applying a thin layer of liquid to the surfaces of minor component, promoting an ordered mixture.

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

Science.gov (United States)

Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.
Sworn testimony of the model evidence: Gaussian Mixture Importance (GAME) sampling

Science.gov (United States)

Volpi, Elena; Schoups, Gerrit; Firmani, Giovanni; Vrugt, Jasper A.

2017-07-01

What is the "best" model? The answer to this question lies in part in the eyes of the beholder, nevertheless a good model must blend rigorous theory with redeeming qualities such as parsimony and quality of fit. Model selection is used to make inferences, via weighted averaging, from a set of K candidate models, Mk; k=>(1,…,K>), and help identify which model is most supported by the observed data, Y>˜=>(y˜1,…,y˜n>). Here, we introduce a new and robust estimator of the model evidence, p>(Y>˜|Mk>), which acts as normalizing constant in the denominator of Bayes' theorem and provides a single quantitative measure of relative support for each hypothesis that integrates model accuracy, uncertainty, and complexity. However, p>(Y>˜|Mk>) is analytically intractable for most practical modeling problems. Our method, coined GAussian Mixture importancE (GAME) sampling, uses bridge sampling of a mixture distribution fitted to samples of the posterior model parameter distribution derived from MCMC simulation. We benchmark the accuracy and reliability of GAME sampling by application to a diverse set of multivariate target distributions (up to 100 dimensions) with known values of p>(Y>˜|Mk>) and to hypothesis testing using numerical modeling of the rainfall-runoff transformation of the Leaf River watershed in Mississippi, USA. These case studies demonstrate that GAME sampling provides robust and unbiased estimates of the evidence at a relatively small computational cost outperforming commonly used estimators. The GAME sampler is implemented in the MATLAB package of DREAM and simplifies considerably scientific inquiry through hypothesis testing and model selection.
Construction of a 21-Component Layered Mixture Experiment Design Using a New Mixture Coordinate-Exchange Algorithm

International Nuclear Information System (INIS)

Piepel, Gregory F.; Cooley, Scott K.; Jones, Bradley

2005-01-01

This paper describes the solution to a unique and challenging mixture experiment design problem involving: (1) 19 and 21 components for two different parts of the design, (2) many single-component and multi-component constraints, (3) augmentation of existing data, (4) a layered design developed in stages, and (5) a no-candidate-point optimal design approach. The problem involved studying the liquidus temperature of spinel crystals as a function of nuclear waste glass composition. The statistical objective was to develop an experimental design by augmenting existing glasses with new nonradioactive and radioactive glasses chosen to cover the designated nonradioactive and radioactive experimental regions. The existing 144 glasses were expressed as 19-component nonradioactive compositions and then augmented with 40 new nonradioactive glasses. These included 8 glasses on the outer layer of the region, 27 glasses on an inner layer, 2 replicate glasses at the centroid, and one replicate each of three existing glasses. Then, the 144 + 40 = 184 glasses were expressed as 21-component radioactive compositions, and augmented with 5 radioactive glasses. A D-optimal design algorithm was used to select the new outer layer, inner layer, and radioactive glasses. Several statistical software packages can generate D-optimal experimental designs, but nearly all of them require a set of candidate points (e.g., vertices) from which to select design points. The large number of components (19 or 21) and many constraints made it impossible to generate the huge number of vertices and other typical candidate points. JMP was used to select design points without candidate points. JMP uses a coordinate-exchange algorithm modified for mixture experiments, which is discussed and illustrated in the paper
Encoding information using laguerre gaussian modes

CSIR Research Space (South Africa)

Trichili, A

2015-08-01

Full Text Available The authors experimentally demonstrate an information encoding protocol using the two degrees of freedom of Laguerre Gaussian modes having different radial and azimuthal components. A novel method, based on digital holography, for information...
Novel two-tiered approach of ecological risk assessment for pesticide mixtures based on joint effects.

Science.gov (United States)

Tian, Dayong; Mao, Haichen; Lv, Huichao; Zheng, Yong; Peng, Conghu; Hou, Shaogang

2018-02-01

Ecological risk assessments for mixtures have attracted considerable attention. In this study, 38 pesticides in the real environment were taken as objects and their toxicities to different organisms from three trophic levels were employed to assess the ecological risk of the mixture. The first tier assessment was based on the CA effect and the obtained sum of risk quotients (SRQ species-CA ) were 3.06-9.22. The second tier assessment was based on non-CA effects and the calculated SRQ species-TU are 5.37-9.29 using joint effects (TU sum ) as modified coefficients, which is higher than SRQ species-CA and indicates that ignoring joint effects might run the risk of underestimating the actual impact of pesticide mixtures. Due to the influences of synergistic and antagonistic effects, risk contribution of components to mixture risks based on non-CA effects are different from those based on the CA effect. Moreover, it was found that the top 8 dominating components explained 95.5%-99.8% of mixture risks in this study. The dominating components are similar in the two tiers for a given species. Accordingly, a novel two-tiered approach was proposed to assess the ecological risks of mixtures based on joint effects. This study provides new insights for ecological risk assessments with the consideration of joint effects of components in the pesticide mixtures. Copyright © 2017 Elsevier Ltd. All rights reserved.
A path integral methodology for obtaining thermodynamic properties of nonadiabatic systems using Gaussian mixture distributions

Science.gov (United States)

Raymond, Neil; Iouchtchenko, Dmitri; Roy, Pierre-Nicholas; Nooijen, Marcel

2018-05-01

We introduce a new path integral Monte Carlo method for investigating nonadiabatic systems in thermal equilibrium and demonstrate an approach to reducing stochastic error. We derive a general path integral expression for the partition function in a product basis of continuous nuclear and discrete electronic degrees of freedom without the use of any mapping schemes. We separate our Hamiltonian into a harmonic portion and a coupling portion; the partition function can then be calculated as the product of a Monte Carlo estimator (of the coupling contribution to the partition function) and a normalization factor (that is evaluated analytically). A Gaussian mixture model is used to evaluate the Monte Carlo estimator in a computationally efficient manner. Using two model systems, we demonstrate our approach to reduce the stochastic error associated with the Monte Carlo estimator. We show that the selection of the harmonic oscillators comprising the sampling distribution directly affects the efficiency of the method. Our results demonstrate that our path integral Monte Carlo method's deviation from exact Trotter calculations is dominated by the choice of the sampling distribution. By improving the sampling distribution, we can drastically reduce the stochastic error leading to lower computational cost.
Isolation of EPR spectra and estimation of spin-states in two-component mixtures of paramagnets.

Science.gov (United States)

Chabbra, Sonia; Smith, David M; Bode, Bela E

2018-04-26

The presence of multiple paramagnetic species can lead to overlapping electron paramagnetic resonance (EPR) signals. This complication can be a critical obstacle for the use of EPR to unravel mechanisms and aid the understanding of earth abundant metal catalysis. Furthermore, redox or spin-crossover processes can result in the simultaneous presence of metal centres in different oxidation or spin states. In this contribution, pulse EPR experiments on model systems containing discrete mixtures of Cr(i) and Cr(iii) or Cu(ii) and Mn(ii) complexes demonstrate the feasibility of the separation of the EPR spectra of these species by inversion recovery filters and the identification of the relevant spin states by transient nutation experiments. We demonstrate the isolation of component spectra and identification of spin states in a mixture of catalyst precursors. The usefulness of the approach is emphasised by monitoring the fate of the chromium species upon activation of an industrially used precatalyst system.
Speech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering

Directory of Open Access Journals (Sweden)

M. H. Savoji

2014-09-01

Full Text Available Gaussian Mixture Models (GMMs of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equations whose solutions lead to the first estimates of speech and noise power spectra. The noise source is also identified and the input SNR estimated in this first step. These first estimates are then refined using approximate but explicit MMSE and MAP estimation formulations. The refined estimates are then used in a Wiener filter to reduce noise and enhance the noisy speech. The proposed schemes show good results. Nevertheless, it is shown that the MAP explicit solution, introduced here for the first time, reduces the computation time to less than one third with a slight higher improvement in SNR and PESQ score and also less distortion in comparison to the MMSE solution.
Mixture component effects on the in vitro dermal absorption of pentachlorophenol

Energy Technology Data Exchange (ETDEWEB)

Riviere, J.E.; Qiao, G.; Baynes, R.E.; Brooks, J.D. [Coll. of Veterinary Medicine, North Carolina State Univ., Raleigh, NC (United States); Mumtaz, M. [Agency for Toxic Substances and Disease Registry (ATSDR), Atlanta, GA (United States)

2001-08-01

Interactions between chemicals in a mixture and interactions of mixture components with the skin can significantly alter the rate and extent of percutaneous absorption, as well as the cutaneous disposition of a topically applied chemical. The predictive ability of dermal absorption models, and consequently the dermal risk assessment process, would be greatly improved by the elucidation and characterization of these interactions. Pentachlorophenol (PCP), a compound known to penetrate the skin readily, was used as a marker compound to examine mixture component effects using in vitro porcine skin models. PCP was administered in ethanol or in a 40% ethanol/60% water mixture or a 40% ethanol/60% water mixture containing either the rubefacient methyl nicotinate (MNA) or the surfactant sodium lauryl sulfate (SLS), or both MNA and SLS. Experiments were also conducted with {sup 14}C-labelled 3,3',4,4'-tetrachlorobiphenyl (TCB) and 3,3',4,4',5-pentachlorobiphenyl (PCB). Maximal PCP absorption was 14.12% of the applied dose from the mixture containing SLS, MNA, ethanol and water. However, when PCP was administered in ethanol only, absorption was only 1.12% of the applied dose. There were also qualitative differences among the absorption profiles for the different PCP mixtures. In contrast with the PCP results, absorption of TCB or PCB was negligible in perfused porcine skin, with only 0.14% of the applied TCB dose and 0.05% of the applied PCB dose being maximally absorbed. The low absorption levels for the PCB congeners precluded the identification of mixture component effects. These results suggest that dermal absorption estimates from a single chemical exposure may not reflect absorption seen after exposure as a chemical mixture and that absorption of both TCB and PCB are minimal in this model system. (orig.)
Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks

Directory of Open Access Journals (Sweden)

Masaru Yokoe

2009-03-01

Full Text Available This paper proposes a method to quantitatively measure and evaluate finger tapping movements for the assessment of motor function using log-linearized Gaussian mixture networks (LLGMNs. First, finger tapping movements are measured using magnetic sensors, and eleven indices are computed for evaluation. After standardizing these indices based on those of normal subjects, they are input to LLGMNs to assess motor function. Then, motor ability is probabilistically discriminated to determine whether it is normal or not using a classifier combined with the output of multiple LLGMNs based on bagging and entropy. This paper reports on evaluation and discrimination experiments performed on finger tapping movements in 33 Parkinson’s disease (PD patients and 32 normal elderly subjects. The results showed that the patients could be classified correctly in terms of their impairment status with a high degree of accuracy (average rate: 93:1 § 3:69% using 12 LLGMNs, which was about 5% higher than the results obtained using a single LLGMN.
Fabricating off-diagonal components of frequency-dependent linear and nonlinear polarizabilities of doped quantum dots by Gaussian white noise

International Nuclear Information System (INIS)

Saha, Surajit; Ganguly, Jayanta; Ghosh, Manas

2015-01-01

We make a rigorous exploration of the profiles of off-diagonal components of frequency-dependent linear (α xy , α yx ), first nonlinear (β xyy , β yxx ), and second nonlinear (γ xxyy , γ yyxx ) polarizabilities of quantum dots driven by Gaussian white noise. The quantum dot is doped with repulsive Gaussian impurity. Noise has been applied additively and multiplicatively to the system. An external oscillatory electric field has also been applied to the system. Gradual variations of external frequency, dopant location, and noise strength give rise to interesting features of polarizability components. The observations reveal intricate interplay between noise strength and dopant location which designs the polarizability profiles. Moreover, the mode of application of noise also modulates the polarizability components. Interestingly, in case of additive noise the noise strength has no role on polarizabilities whereas multiplicative noise invites greater delicacy in them. The said interplay provides a rather involved framework to attain stable, enhanced, and often maximized output of linear and nonlinear polarizabilities. - Highlights: • Linear and nonlinear polarizabilities of quantum dot are studied. • The polarizability components are off-diagonal and frequency-dependent. • Quantum dot is doped with a repulsive impurity. • Doped system is subject to Gaussian white noise. • Mode of noise application affects polarizabilities
Generation of two-dimensional binary mixtures in complex plasmas

Science.gov (United States)

Wieben, Frank; Block, Dietmar

2016-10-01

Complex plasmas are an excellent model system for strong coupling phenomena. Under certain conditions the dust particles immersed into the plasma form crystals which can be analyzed in terms of structure and dynamics. Previous experiments focussed mostly on monodisperse particle systems whereas dusty plasmas in nature and technology are polydisperse. Thus, a first and important step towards experiments in polydisperse systems are binary mixtures. Recent experiments on binary mixtures under microgravity conditions observed a phase separation of particle species with different radii even for small size disparities. This contradicts several numerical studies of 2D binary mixtures. Therefore, dedicated experiments are required to gain more insight into the physics of polydisperse systems. In this contribution first ground based experiments on two-dimensional binary mixtures are presented. Particular attention is paid to the requirements for the generation of such systems which involve the consideration of the temporal evolution of the particle properties. Furthermore, the structure of these two-component crystals is analyzed and compared to simulations. This work was supported by the Deutsche Forschungsgemeinschaft DFG in the framework of the SFB TR24 Greifswald Kiel, Project A3b.
Operation of the multigap resistive plate chamber using a gas mixture free of flammable components

CERN Document Server

Akindinov, A; Antonioli, P; Arcelli, S; Basile, M; Cara Romeo, G; Cifarelli, Luisa; Cindolo, F; De Caro, A; De Pasquale, S; Di Bartolomeo, A; Fusco-Girard, M; Golovine, V; Guida, M; Hatzifotiadou, D; Kaidalov, A B; Kim, D H; Kim, D W; Kisselev, S M; Laurenti, G; Lee, K; Lee, S C; Lioublev, E; Luvisetto, M L; Margotti, A; Martemyanov, A N; Nania, R; Noferini, F; Otiougova, P; Pesci, A; Pinazza, O; Polozov, P A; Scapparone, E; Scioli, G; Sellitto, S B; Semeria, F; Smirnitsky, A V; Tchoumakov, M M; Usenko, E; Valenti, G; Voloshin, K G; Williams, M C S; Zagreev, B V; Zampolli, C; Zichichi, A

2004-01-01

We have investigated the operation of the multigap resistive plate chamber (MRPC) for the ALICE-TOF system with a gas mixture free of flammable components. Two different gas mixtures, with and without iso-C//4H//1//0 have been used to measure the performance of the MRPC. The efficiency, time resolution, total charge, and the fast to total charge ratio have been found to be comparable.
An Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream

Directory of Open Access Journals (Sweden)

Li Yao

2014-01-01

Full Text Available Modeling background and segmenting moving objects are significant techniques for computer vision applications. Mixture-of-Gaussians (MoG background model is commonly used in foreground extraction in video steam. However considering the case that the objects enter the scenery and stay for a while, the foreground extraction would fail as the objects stay still and gradually merge into the background. In this paper, we adopt a blob tracking method to cope with this situation. To construct the MoG model more quickly, we add frame difference method to the foreground extracted from MoG for very crowded situations. What is more, a new shadow removal method based on RGB color space is proposed.
AUTONOMOUS GAUSSIAN DECOMPOSITION

International Nuclear Information System (INIS)

Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John

2015-01-01

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes
AUTONOMOUS GAUSSIAN DECOMPOSITION

Energy Technology Data Exchange (ETDEWEB)

Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian [Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States); Heiles, Carl [Radio Astronomy Lab, UC Berkeley, 601 Campbell Hall, Berkeley, CA 94720 (United States); Hennebelle, Patrick [Laboratoire AIM, Paris-Saclay, CEA/IRFU/SAp-CNRS-Université Paris Diderot, F-91191 Gif-sur Yvette Cedex (France); Goss, W. M. [National Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville, Socorro, NM 87801 (United States); Dickey, John, E-mail: rlindner@astro.wisc.edu [University of Tasmania, School of Maths and Physics, Private Bag 37, Hobart, TAS 7001 (Australia)

2015-04-15

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.
Multi-atlas segmentation for abdominal organs with Gaussian mixture models

Science.gov (United States)

Burke, Ryan P.; Xu, Zhoubing; Lee, Christopher P.; Baucom, Rebeccah B.; Poulose, Benjamin K.; Abramson, Richard G.; Landman, Bennett A.

2015-03-01

Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid / gray matter / white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms. In the context of variable abdominal anatomy and rich algorithms, it is difficult to assess the marginal contribution of GMM. Herein, we characterize the efficacy of an a posteriori framework that integrates GMM of organ-wise intensity likelihood with spatial priors from multiple target-specific registered labels. In our study, we first manually labeled 100 CT images. Then, we assigned 40 images to use as training data for constructing target-specific spatial priors and intensity likelihoods. The remaining 60 images were evaluated as test targets for segmenting 12 abdominal organs. The overlap between the true and the automatic segmentations was measured by Dice similarity coefficient (DSC). A median improvement of 145% was achieved by integrating the GMM intensity likelihood against the specific spatial prior. The proposed framework opens the opportunities for abdominal organ segmentation by efficiently using both the spatial and appearance information from the atlases, and creates a benchmark for large-scale automatic abdominal segmentation.
Enantiomer-specific analysis of multi-component mixtures by correlated electron imaging-ion mass spectrometry

NARCIS (Netherlands)

Rafiee Fanood, M.M.; Ram, N.B.; Lehmann, C.S.; Powis, I.; Janssen, M.H.M.

2015-01-01

Simultaneous, enantiomer-specific identification of chiral molecules in multi-component mixtures is extremely challenging. Many established techniques for single-component analysis fail to provide selectivity in multi-component mixtures and lack sensitivity for dilute samples. Here we show how
Linear velocity fields in non-Gaussian models for large-scale structure

Science.gov (United States)

Scherrer, Robert J.

1992-01-01

Linear velocity fields in two types of physically motivated non-Gaussian models are examined for large-scale structure: seed models, in which the density field is a convolution of a density profile with a distribution of points, and local non-Gaussian fields, derived from a local nonlinear transformation on a Gaussian field. The distribution of a single component of the velocity is derived for seed models with randomly distributed seeds, and these results are applied to the seeded hot dark matter model and the global texture model with cold dark matter. An expression for the distribution of a single component of the velocity in arbitrary local non-Gaussian models is given, and these results are applied to such fields with chi-squared and lognormal distributions. It is shown that all seed models with randomly distributed seeds and all local non-Guassian models have single-component velocity distributions with positive kurtosis.
Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar

Science.gov (United States)

Qin, Yong; Ma, Hong; Chen, Jinfeng; Cheng, Li

2015-12-01

Conventional multitarget tracking systems presume that each target can produce at most one measurement per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments. First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography

International Nuclear Information System (INIS)

Aristophanous, Michalis; Penney, Bill C.; Martel, Mary K.; Pelizzari, Charles A.

2007-01-01

The increased interest in 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in radiation treatment planning in the past five years necessitated the independent and accurate segmentation of gross tumor volume (GTV) from FDG-PET scans. In some studies the radiation oncologist contours the GTV based on a computed tomography scan, while incorporating pertinent data from the PET images. Alternatively, a simple threshold, typically 40% of the maximum intensity, has been employed to differentiate tumor from normal tissue, while other researchers have developed algorithms to aid the PET based GTV definition. None of these methods, however, results in reliable PET tumor segmentation that can be used for more sophisticated treatment plans. For this reason, we developed a Gaussian mixture model (GMM) based segmentation technique on selected PET tumor regions from non-small cell lung cancer patients. The purpose of this study was to investigate the feasibility of using a GMM-based tumor volume definition in a robust, reliable and reproducible way. A GMM relies on the idea that any distribution, in our case a distribution of image intensities, can be expressed as a mixture of Gaussian densities representing different classes. According to our implementation, each class belongs to one of three regions in the image; the background (B), the uncertain (U) and the target (T), and from these regions we can obtain the tumor volume. User interaction in the implementation is required, but is limited to the initialization of the model parameters and the selection of an ''analysis region'' to which the modeling is restricted. The segmentation was developed on three and tested on another four clinical cases to ensure robustness against differences observed in the clinic. It also compared favorably with thresholding at 40% of the maximum intensity and a threshold determination function based on tumor to background image intensities proposed in a recent paper. The parts of the
Frequentist and Bayesian inference for Gaussian-log-Gaussian wavelet trees and statistical signal processing applications

DEFF Research Database (Denmark)

Jacobsen, Christian Robert Dahl; Møller, Jesper

2017-01-01

We introduce new estimation methods for a subclass of the Gaussian scale mixture models for wavelet trees by Wainwright, Simoncelli and Willsky that rely on modern results for composite likelihoods and approximate Bayesian inference. Our methodology is illustrated for denoising and edge detection...
Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model

Science.gov (United States)

Liu, Sijia; Sa, Ruhan; Maguire, Orla; Minderman, Hans; Chaudhary, Vipin

2015-03-01

Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.
Fluorescence lifetime selectivity in excitation-emission matrices for qualitative analysis of a two-component system

International Nuclear Information System (INIS)

Millican, D.W.; McGown, L.B.

1989-01-01

Steady-state fluorescence excitation-emission matrices (EEMs), and phase-resolved EEMs (PREEMs) collected at modulation frequencies of 6, 18, and 30 MHz, were used for qualitative analysis of mixtures of benzo[k]fluoranthene (τ = 8 ns) and benzo[b]fluoranthene (τ = 29 ns) in ethanol. The EEMs of the individual components were extracted from mixture EEMs by means of wavelength component vector-gram (WCV) analysis. Phase resolution was found to be superior to steady-state measurements for extraction of the component spectra, for mixtures in which the intensity contributions from the two components are unequal
MixSim : An R Package for Simulating Data to Study Performance of Clustering Algorithms

Directory of Open Access Journals (Sweden)

Volodymyr Melnykov

2012-11-01

Full Text Available The R package MixSim is a new tool that allows simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim, there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. All features of the package are illustrated in great detail. The utility of the package is highlighted through a small comparison study of several popular clustering algorithms.
Entanglement and purity of two-mode Gaussian states in noisy channels

International Nuclear Information System (INIS)

Serafini, Alessio; Illuminati, Fabrizio; De Siena, Silvio; Paris, Matteo G.A.

2004-01-01

We study the evolution of purity, entanglement, and total correlations of general two-mode continuous variable Gaussian states in arbitrary uncorrelated Gaussian environments. The time evolution of purity, von Neumann entropy, logarithmic negativity, and mutual information is analyzed for a wide range of initial conditions. In general, we find that a local squeezing of the bath leads to a faster degradation of purity and entanglement, while it can help to preserve the mutual information between the modes
Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data

DEFF Research Database (Denmark)

Røge, Rasmus; Madsen, Kristoffer Hougaard; Schmidt, Mikkel Nørgaard

2017-01-01

spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain...... Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians......Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying...
Molecular Orientation in Two Component Vapor-Deposited Glasses: Effect of Substrate Temperature and Molecular Shape

Science.gov (United States)

Powell, Charles; Jiang, Jing; Walters, Diane; Ediger, Mark

Vapor-deposited glasses are widely investigated for use in organic electronics including the emitting layers of OLED devices. These materials, while macroscopically homogenous, have anisotropic packing and molecular orientation. By controlling this orientation, outcoupling efficiency can be increased by aligning the transition dipole moment of the light-emitting molecules parallel to the substrate. Light-emitting molecules are typically dispersed in a host matrix, as such, it is imperative to understand molecular orientation in two-component systems. In this study we examine two-component vapor-deposited films and the orientations of the constituent molecules using spectroscopic ellipsometry, UV-vis and IR spectroscopy. The role of temperature, composition and molecular shape as it effects molecular orientation is examined for mixtures of DSA-Ph in Alq3 and in TPD. Deposition temperature relative to the glass transition temperature of the two-component mixture is the primary controlling factor for molecular orientation. In mixtures of DSA-Ph in Alq3, the linear DSA-Ph has a horizontal orientation at low temperatures and slight vertical orientation maximized at 0.96Tg,mixture, analogous to one-component films.
Effect of the addition of mixture of plant components on the mechanical properties of wheat bread

Science.gov (United States)

Wójcik, Monika; Dziki, Dariusz; Biernacka, Beata; Różyło, Renata; Miś, Antoni; Hassoon, Waleed H.

2017-10-01

Instrumental methods of measuring the mechanical properties of bread can be used to determine changes in the properties of it during storage, as well as to determine the effect of various additives on the bread texture. The aim of this study was to investigate the effect of the mixture of plant components on the physical properties of wheat bread. In particular, the mechanical properties of the crumb and crust were studied. A sensory evaluation of the end product was also performed. The mixture of plant components included: carob fiber, milled grain red quinoa and black oat (1:2:2) - added at 0, 5, 10, 15, 20, 25 % - into wheat flour. The results showed that the increase of the addition of the proposed additive significantly increased the water absorption of flour mixtures. Moreover, the use of the mixture of plant components above 5% resulted in the increase of bread volume and decrease of crumb density. Furthermore, the addition of the mixture of plant components significantly affected the mechanical properties of bread crumb. The hardness of crumb also decreased as a result of the mixture of plant components addition. The highest cohesiveness was obtained for bread with 10% of additive and the lowest for bread with 25% of mixture of plant components. Most importantly, the enrichment of wheat flour with the mixture of plant components significantly reduced the crust failure force and crust failure work. The results of sensory evaluation showed that the addition of the mixture of plant components of up to 10% had little effect on bread quality.
A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families

KAUST Repository

Dutta, Subhajit

2014-07-28

Several fascinating examples of non-Gaussian bivariate distributions which have marginal distribution functions to be Gaussian have been proposed in the literature. These examples often clarify several properties associated with the normal distribution. In this paper, we generalize this result in the sense that we construct a pp-dimensional distribution for which any proper subset of its components has the Gaussian distribution. However, the jointpp-dimensional distribution is inconsistent with the distribution of these subsets because it is not Gaussian. We study the probabilistic properties of this non-Gaussian multivariate distribution in detail. Interestingly, several popular tests of multivariate normality fail to identify this pp-dimensional distribution as non-Gaussian. We further extend our construction to a class of elliptically contoured distributions as well as skewed distributions arising from selections, for instance the multivariate skew-normal distribution.
A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families

KAUST Repository

Dutta, Subhajit; Genton, Marc G.

2014-01-01

Several fascinating examples of non-Gaussian bivariate distributions which have marginal distribution functions to be Gaussian have been proposed in the literature. These examples often clarify several properties associated with the normal distribution. In this paper, we generalize this result in the sense that we construct a pp-dimensional distribution for which any proper subset of its components has the Gaussian distribution. However, the jointpp-dimensional distribution is inconsistent with the distribution of these subsets because it is not Gaussian. We study the probabilistic properties of this non-Gaussian multivariate distribution in detail. Interestingly, several popular tests of multivariate normality fail to identify this pp-dimensional distribution as non-Gaussian. We further extend our construction to a class of elliptically contoured distributions as well as skewed distributions arising from selections, for instance the multivariate skew-normal distribution.
Application of Gaussian cubature to model two-dimensional population balances

Directory of Open Access Journals (Sweden)

Bałdyga Jerzy

2017-09-01

Full Text Available In many systems of engineering interest the moment transformation of population balance is applied. One of the methods to solve the transformed population balance equations is the quadrature method of moments. It is based on the approximation of the density function in the source term by the Gaussian quadrature so that it preserves the moments of the original distribution. In this work we propose another method to be applied to the multivariate population problem in chemical engineering, namely a Gaussian cubature (GC technique that applies linear programming for the approximation of the multivariate distribution. Examples of the application of the Gaussian cubature (GC are presented for four processes typical for chemical engineering applications. The first and second ones are devoted to crystallization modeling with direction-dependent two-dimensional and three-dimensional growth rates, the third one represents drop dispersion accompanied by mass transfer in liquid-liquid dispersions and finally the fourth case regards the aggregation and sintering of particle populations.
A comparison on the propagation characteristics of focused Gaussian beam and fundamental Gaussian beam in vacuum

International Nuclear Information System (INIS)

Liu Shixiong; Guo Hong; Liu Mingwei; Wu Guohua

2004-01-01

Propagation characteristics of focused Gaussian beam (FoGB) and fundamental Gaussian beam (FuGB) propagating in vacuum are investigated. Based on the Fourier transform and the angular spectral analysis, the transverse component and the second-order approximate longitudinal component of the electric field are obtained in the paraxial approximation. The electric field components, the phase velocity and the group velocity of FoGB are compared with those of FuGB. The spot size of FoGB is also discussed
GGRaSP: A R-package for selecting representative genomes using Gaussian mixture models.

Science.gov (United States)

Clarke, Thomas H; Brinkac, Lauren M; Sutton, Granger; Fouts, Derrick E

2018-04-14

The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes. The GGRaSP R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian Mixture Model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness. GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/. tclarke@jcvi.org. Supplementary data are available at the GitHub site.
Investigating Einstein-Podolsky-Rosen steering of continuous-variable bipartite states by non-Gaussian pseudospin measurements

Science.gov (United States)

Xiang, Yu; Xu, Buqing; Mišta, Ladislav; Tufarelli, Tommaso; He, Qiongyi; Adesso, Gerardo

2017-10-01

Einstein-Podolsky-Rosen (EPR) steering is an asymmetric form of correlations which is intermediate between quantum entanglement and Bell nonlocality, and can be exploited as a resource for quantum communication with one untrusted party. In particular, steering of continuous-variable Gaussian states has been extensively studied theoretically and experimentally, as a fundamental manifestation of the EPR paradox. While most of these studies focused on quadrature measurements for steering detection, two recent works revealed that there exist Gaussian states which are only steerable by suitable non-Gaussian measurements. In this paper we perform a systematic investigation of EPR steering of bipartite Gaussian states by pseudospin measurements, complementing and extending previous findings. We first derive the density-matrix elements of two-mode squeezed thermal Gaussian states in the Fock basis, which may be of independent interest. We then use such a representation to investigate steering of these states as detected by a simple nonlinear criterion, based on second moments of the correlation matrix constructed from pseudospin operators. This analysis reveals previously unexplored regimes where non-Gaussian measurements are shown to be more effective than Gaussian ones to witness steering of Gaussian states in the presence of local noise. We further consider an alternative set of pseudospin observables, whose expectation value can be expressed more compactly in terms of Wigner functions for all two-mode Gaussian states. However, according to the adopted criterion, these observables are found to be always less sensitive than conventional Gaussian observables for steering detection. Finally, we investigate continuous-variable Werner states, which are non-Gaussian mixtures of Gaussian states, and find that pseudospin measurements are always more effective than Gaussian ones to reveal their steerability. Our results provide useful insights on the role of non-Gaussian
The Umov effect in application to an optically thin two-component cloud of cosmic dust

Science.gov (United States)

Zubko, Evgenij; Videen, Gorden; Zubko, Nataliya; Shkuratov, Yuriy

2018-04-01

The Umov effect is an inverse correlation between linear polarization of the sunlight scattered by an object and its geometric albedo. The Umov effect has been observed in particulate surfaces, such as planetary regoliths, and recently it also was found in single-scattering small dust particles. Using numerical modeling, we study the Umov effect in a two-component mixture of small irregularly shaped particles. Such a complex chemical composition is suggested in cometary comae and other types of optically thin clouds of cosmic dust. We find that the two-component mixtures of small particles also reveal the Umov effect regardless of the chemical composition of their end-member components. The interrelation between log(Pmax) and log(A) in a two-component mixture of small irregularly shaped particles appears either in a straight linear form or in a slightly curved form. This curvature tends to decrease while the index n in a power-law size distribution r-n grows; at n > 2.5, the log(Pmax)-log(A) diagrams are almost straight linear in appearance. The curvature also noticeably decreases with the packing density of constituent material in irregularly shaped particles forming the mixture. That such a relation exists suggest the Umov effect may also be observed in more complex mixtures.
Analysis of water hammer in two-component two-phase flows

International Nuclear Information System (INIS)

Warde, H.; Marzouk, E.; Ibrahim, S.

1989-01-01

The water hammer phenomena caused by a sudden valve closure in air-water two-phase flows must be clarified for the safety analysis of LOCA in reactors and further for the safety of boilers, chemical plants, pipe transport of fluids such as petroleum and natural gas. In the present work water hammer phenomena caused by sudden valve closure in two-component two-phase flows are investigated theoretically and experimentally. The phenomena are more complicated than in single phase-flows due to the fact of the presence of compressible component. Basic partial differential equations based on a one-dimensional homogeneous flow model are solved by the method of characteristic. The analysis is extended to include friction in a two-phase mixture depending on the local flow pattern. The profiles of the pressure transients, the propagation velocity of pressure waves and the effect of valve closure on the transient pressure are found. Different two-phase flow pattern and frictional pressure drop correlations were used including Baker, Chesholm and Beggs and Bril correlations. The effect of the flow pattern on the characteristic of wave propagation is discussed primarily to indicate the effect of void fraction on the velocity of wave propagation and on the attenuation of pressure waves. Transient pressure in the mixture were recorded at different air void fractions, rates of uniform valve closure and liquid flow velocities with the aid of pressure transducers, transient wave form recorders interfaced with an on-line pc computer. The results are compared with computation, and good agreement was obtained within experimental accuracy
Two-component thermosensitive hydrogels : Phase separation affecting rheological behavior

NARCIS (Netherlands)

Abbadessa, Anna; Landín, Mariana; Oude Blenke, Erik; Hennink, Wim E.; Vermonden, Tina

2017-01-01

Extracellular matrices are mainly composed of a mixture of different biopolymers and therefore the use of two or more building blocks for the development of tissue-mimicking hydrogels is nowadays an attractive strategy in tissue-engineering. Multi-component hydrogel systems may undergo phase
Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment

OpenAIRE

Li, Rui; Perneczky, Robert; Drzezga, Alexander; Kramer, Stefan; Initiative, for the Alzheimer's Disease Neuroimaging

2015-01-01

We present a method to discover interesting brain regions in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, showing also the benefits when PET scans are in combined use with non-imaging variables. The discriminative brain regions facilitate a better understanding of Alzheimer's disease (AD) progression, and they can also be used for predicting conversion from mild cognitive impairment (MCI) to AD. A survival analysis(Cox regression) and infinite Gaussian mixture model (IGM...
Mixture modeling of multi-component data sets with application to ion-probe zircon ages

Science.gov (United States)

Sambridge, M. S.; Compston, W.

1994-12-01

A method is presented for detecting multiple components in a population of analytical observations for zircon and other ages. The procedure uses an approach known as mixture modeling, in order to estimate the most likely ages, proportions and number of distinct components in a given data set. Particular attention is paid to estimating errors in the estimated ages and proportions. At each stage of the procedure several alternative numerical approaches are suggested, each having their own advantages in terms of efficency and accuracy. The methodology is tested on synthetic data sets simulating two or more mixed populations of zircon ages. In this case true ages and proportions of each population are known and compare well with the results of the new procedure. Two examples are presented of its use with sets of SHRIMP U-238 - Pb-206 zircon ages from Palaeozoic rocks. A published data set for altered zircons from bentonite at Meishucun, South China, previously treated as a single-component population after screening for gross alteration effects, can be resolved into two components by the new procedure and their ages, proportions and standard errors estimated. The older component, at 530 +/- 5 Ma (2 sigma), is our best current estimate for the age of the bentonite. Mixture modeling of a data set for unaltered zircons from a tonalite elsewhere defines the magmatic U-238 - Pb-206 age at high precision (2 sigma +/- 1.5 Ma), but one-quarter of the 41 analyses detect hidden and significantly older cores.

General immunity and superadditivity of two-way Gaussian quantum cryptography.

Science.gov (United States)

Ottaviani, Carlo; Pirandola, Stefano

2016-03-01

We consider two-way continuous-variable quantum key distribution, studying its security against general eavesdropping strategies. Assuming the asymptotic limit of many signals exchanged, we prove that two-way Gaussian protocols are immune to coherent attacks. More precisely we show the general superadditivity of the two-way security thresholds, which are proven to be higher than the corresponding one-way counterparts in all cases. We perform the security analysis first reducing the general eavesdropping to a two-mode coherent Gaussian attack, and then showing that the superadditivity is achieved by exploiting the random on/off switching of the two-way quantum communication. This allows the parties to choose the appropriate communication instances to prepare the key, accordingly to the tomography of the quantum channel. The random opening and closing of the circuit represents, in fact, an additional degree of freedom allowing the parties to convert, a posteriori, the two-mode correlations of the eavesdropping into noise. The eavesdropper is assumed to have no access to the on/off switching and, indeed, cannot adapt her attack. We explicitly prove that this mechanism enhances the security performance, no matter if the eavesdropper performs collective or coherent attacks.
Interaction of Airy-Gaussian beams in saturable media

Science.gov (United States)

Zhou, Meiling; Peng, Yulian; Chen, Chidao; Chen, Bo; Peng, Xi; Deng, Dongmei

2016-08-01

Based on the nonlinear Schrödinger equation, the interactions of the two Airy-Gaussian components in the incidence are analyzed in saturable media, under the circumstances of the same amplitude and different amplitudes, respectively. It is found that the interaction can be both attractive and repulsive depending on the relative phase. The smaller the interval between two Airy-Gaussian components in the incidence is, the stronger the intensity of the interaction. However, with the equal amplitude, the symmetry is shown and the change of quasi-breathers is opposite in the in-phase case and out-of-phase case. As the distribution factor is increased, the phenomena of the quasi-breather and the self-accelerating of the two Airy-Gaussian components are weakened. When the amplitude is not equal, the image does not have symmetry. The obvious phenomenon of the interaction always arises on the side of larger input power in the incidence. The maximum intensity image is also simulated. Many of the characteristics which are contained within other images can also be concluded in this figure. Project supported by the National Natural Science Foundation of China (Grant Nos. 11374108 and 10904041), the Foundation for the Author of Guangdong Province Excellent Doctoral Dissertation (Grant No. SYBZZXM201227), and the Foundation of Cultivating Outstanding Young Scholars (“Thousand, Hundred, Ten” Program) of Guangdong Province, China. CAS Key Laboratory of Geospace Environment, University of Science and Technology of China.
Statistically tuned Gaussian background subtraction technique for ...

Indian Academy of Sciences (India)

temporal median method and mixture of Gaussian model and performance evaluation ... to process the videos captured by unmanned aerial vehicle (UAV). ..... The output is obtained by simulation using MATLAB 2010 in a standalone PC with ...
Flow boiling heat transfer coefficients at cryogenic temperatures for multi-component refrigerant mixtures of nitrogen-hydrocarbons

Science.gov (United States)

Ardhapurkar, P. M.; Sridharan, Arunkumar; Atrey, M. D.

2014-01-01

The recuperative heat exchanger governs the overall performance of the mixed refrigerant Joule-Thomson cryocooler. In these heat exchangers, the non-azeotropic refrigerant mixture of nitrogen-hydrocarbons undergoes boiling and condensation simultaneously at cryogenic temperature. Hence, the design of such heat exchanger is crucial. However, due to lack of empirical correlations to predict two-phase heat transfer coefficients of multi-component mixtures at low temperature, the design of such heat exchanger is difficult.
Mixture estimation with state-space components and Markov model of switching

Czech Academy of Sciences Publication Activity Database

Nagy, Ivan; Suzdaleva, Evgenia

2013-01-01

Roč. 37, č. 24 (2013), s. 9970-9984 ISSN 0307-904X R&D Projects: GA TA ČR TA01030123 Institutional support: RVO:67985556 Keywords : probabilistic dynamic mixtures, * probability density function * state-space models * recursive mixture estimation * Bayesian dynamic decision making under uncertainty * Kerridge inaccuracy Subject RIV: BC - Control Systems Theory Impact factor: 2.158, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/nagy-mixture estimation with state-space components and markov model of switching.pdf
Gaussian vs non-Gaussian turbulence: impact on wind turbine loads

DEFF Research Database (Denmark)

Berg, Jacob; Natarajan, Anand; Mann, Jakob

2016-01-01

taking into account the safety factor for extreme moments. Other extreme load moments as well as the fatigue loads are not affected because of the use of non-Gaussian turbulent inflow. It is suggested that the turbine thus acts like a low-pass filter that averages out the non-Gaussian behaviour, which......From large-eddy simulations of atmospheric turbulence, a representation of Gaussian turbulence is constructed by randomizing the phases of the individual modes of variability. Time series of Gaussian turbulence are constructed and compared with its non-Gaussian counterpart. Time series from the two...
Superfluid drag in the two-component Bose-Hubbard model

Science.gov (United States)

Sellin, Karl; Babaev, Egor

2018-03-01

In multicomponent superfluids and superconductors, co- and counterflows of components have, in general, different properties. A. F. Andreev and E. P. Bashkin [Sov. Phys. JETP 42, 164 (1975)] discussed, in the context of He3/He4 superfluid mixtures, that interparticle interactions produce a dissipationless drag. The drag can be understood as a superflow of one component induced by phase gradients of the other component. Importantly, the drag can be both positive (entrainment) and negative (counterflow). The effect is known to have crucial importance for many properties of diverse physical systems ranging from the dynamics of neutron stars and rotational responses of Bose mixtures of ultracold atoms to magnetic responses of multicomponent superconductors. Although substantial literature exists that includes the drag interaction phenomenologically, only a few regimes are covered by quantitative studies of the microscopic origin of the drag and its dependence on microscopic parameters. Here we study the microscopic origin and strength of the drag interaction in a quantum system of two-component bosons on a lattice with short-range interaction. By performing quantum Monte Carlo simulations of a two-component Bose-Hubbard model we obtain dependencies of the drag strength on the boson-boson interactions and properties of the optical lattice. Of particular interest are the strongly correlated regimes where the ratio of coflow and counterflow superfluid stiffnesses can diverge, corresponding to the case of saturated drag.
Multidimensional profiling of components in complex mixtures of natural products for metabolic analysis, proof of concept: application to Quillaja saponins.

Science.gov (United States)

Bankefors, Johan; Nord, Lars I; Kenne, Lennart

2010-02-01

A method for separation and detection of major and minor components in complex mixtures has been developed, utilising two-dimensional high-performance liquid chromatography (2D-HPLC) combined with electrospray ionisation ion-trap multiple-stage mass spectrometry (ESI-ITMS(n)). Chromatographic conditions were matched with mass spectrometric detection to maximise the number of components that could be separated. The described procedure has proven useful to discern several hundreds of saponin components when applied to Quillaja saponaria Molina bark extracts. The discrimination of each saponin component relies on the fact that three coordinates (x, y, z) for each component can be derived from the retention time of the two chromatographic steps (x, y) and the m/z-values from the multiple-stage mass spectrometry (z(n), n=1, 2, ...). Thus an improved graphical representation was obtained by combining retention times from the two-stage separation with +MS(1) (z(1)) and the additional structural information from the second mass stage +MS(2) (z(2), z(3)) corresponding to the main fragment ions. By this approach three-dimensional plots can be made that reveal both the chromatographic and structural properties of a specific mixture which can be useful in fingerprinting of complex mixtures. 2009 Elsevier B.V. All rights reserved.
Sufficient condition for a quantum state to be genuinely quantum non-Gaussian

Science.gov (United States)

Happ, L.; Efremov, M. A.; Nha, H.; Schleich, W. P.

2018-02-01

We show that the expectation value of the operator \\hat{{ \\mathcal O }}\\equiv \\exp (-c{\\hat{x}}2)+\\exp (-c{\\hat{p}}2) defined by the position and momentum operators \\hat{x} and \\hat{p} with a positive parameter c can serve as a tool to identify quantum non-Gaussian states, that is states that cannot be represented as a mixture of Gaussian states. Our condition can be readily tested employing a highly efficient homodyne detection which unlike quantum-state tomography requires the measurements of only two orthogonal quadratures. We demonstrate that our method is even able to detect quantum non-Gaussian states with positive–definite Wigner functions. This situation cannot be addressed in terms of the negativity of the phase-space distribution. Moreover, we demonstrate that our condition can characterize quantum non-Gaussianity for the class of superposition states consisting of a vacuum and integer multiples of four photons under more than 50 % signal attenuation.
Two-Microphone Separation of Speech Mixtures

DEFF Research Database (Denmark)

Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan

2008-01-01

combined, independent component analysis (ICA) and binary time–frequency (T–F) masking. By estimating binary masks from the outputs of an ICA algorithm, it is possible in an iterative way to extract basis speech signals from a convolutive mixture. The basis signals are afterwards improved by grouping...
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

Science.gov (United States)

Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

2015-02-01

We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.
XeCl Excimer Laser with Three- and Four-Component Mixture of Active Gases

International Nuclear Information System (INIS)

Iwanejko, L.; Pokora, L.

1998-01-01

Selected results of investigations of a XeCl excimer laser employing a new type (four-component)of mixture of gases, He-Kr:Xe-HCl, are presented. The mixture includes, instead of Xe, a mixture of not-separated Kr and Xe gases, much less expensive than pure xenon. A comparison of durations and energies of pulses generated in the XeCl excimer laser using three- or four-component gaseous active medium (He-Xe-HCl or He-Kr:Xe-HCl) is made. The investigations have been carried out with the use of a laser system with UV preionization and self sustained pumping discharge. (author)
An algorithm for automatic crystal identification in pixelated scintillation detectors using thin plate splines and Gaussian mixture models.

Science.gov (United States)

Schellenberg, Graham; Stortz, Greg; Goertzen, Andrew L

2016-02-07

A typical positron emission tomography detector is comprised of a scintillator crystal array coupled to a photodetector array or other position sensitive detector. Such detectors using light sharing to read out crystal elements require the creation of a crystal lookup table (CLUT) that maps the detector response to the crystal of interaction based on the x-y position of the event calculated through Anger-type logic. It is vital for system performance that these CLUTs be accurate so that the location of events can be accurately identified and so that crystal-specific corrections, such as energy windowing or time alignment, can be applied. While using manual segmentation of the flood image to create the CLUT is a simple and reliable approach, it is both tedious and time consuming for systems with large numbers of crystal elements. In this work we describe the development of an automated algorithm for CLUT generation that uses a Gaussian mixture model paired with thin plate splines (TPS) to iteratively fit a crystal layout template that includes the crystal numbering pattern. Starting from a region of stability, Gaussians are individually fit to data corresponding to crystal locations while simultaneously updating a TPS for predicting future Gaussian locations at the edge of a region of interest that grows as individual Gaussians converge to crystal locations. The algorithm was tested with flood image data collected from 16 detector modules, each consisting of a 409 crystal dual-layer offset LYSO crystal array readout by a 32 pixel SiPM array. For these detector flood images, depending on user defined input parameters, the algorithm runtime ranged between 17.5-82.5 s per detector on a single core of an Intel i7 processor. The method maintained an accuracy above 99.8% across all tests, with the majority of errors being localized to error prone corner regions. This method can be easily extended for use with other detector types through adjustment of the initial
An algorithm for automatic crystal identification in pixelated scintillation detectors using thin plate splines and Gaussian mixture models

International Nuclear Information System (INIS)

Schellenberg, Graham; Goertzen, Andrew L; Stortz, Greg

2016-01-01

A typical positron emission tomography detector is comprised of a scintillator crystal array coupled to a photodetector array or other position sensitive detector. Such detectors using light sharing to read out crystal elements require the creation of a crystal lookup table (CLUT) that maps the detector response to the crystal of interaction based on the x–y position of the event calculated through Anger-type logic. It is vital for system performance that these CLUTs be accurate so that the location of events can be accurately identified and so that crystal-specific corrections, such as energy windowing or time alignment, can be applied. While using manual segmentation of the flood image to create the CLUT is a simple and reliable approach, it is both tedious and time consuming for systems with large numbers of crystal elements. In this work we describe the development of an automated algorithm for CLUT generation that uses a Gaussian mixture model paired with thin plate splines (TPS) to iteratively fit a crystal layout template that includes the crystal numbering pattern. Starting from a region of stability, Gaussians are individually fit to data corresponding to crystal locations while simultaneously updating a TPS for predicting future Gaussian locations at the edge of a region of interest that grows as individual Gaussians converge to crystal locations. The algorithm was tested with flood image data collected from 16 detector modules, each consisting of a 409 crystal dual-layer offset LYSO crystal array readout by a 32 pixel SiPM array. For these detector flood images, depending on user defined input parameters, the algorithm runtime ranged between 17.5–82.5 s per detector on a single core of an Intel i7 processor. The method maintained an accuracy above 99.8% across all tests, with the majority of errors being localized to error prone corner regions. This method can be easily extended for use with other detector types through adjustment of the initial
An algorithm for automatic crystal identification in pixelated scintillation detectors using thin plate splines and Gaussian mixture models

Science.gov (United States)

Schellenberg, Graham; Stortz, Greg; Goertzen, Andrew L.

2016-02-01

A typical positron emission tomography detector is comprised of a scintillator crystal array coupled to a photodetector array or other position sensitive detector. Such detectors using light sharing to read out crystal elements require the creation of a crystal lookup table (CLUT) that maps the detector response to the crystal of interaction based on the x-y position of the event calculated through Anger-type logic. It is vital for system performance that these CLUTs be accurate so that the location of events can be accurately identified and so that crystal-specific corrections, such as energy windowing or time alignment, can be applied. While using manual segmentation of the flood image to create the CLUT is a simple and reliable approach, it is both tedious and time consuming for systems with large numbers of crystal elements. In this work we describe the development of an automated algorithm for CLUT generation that uses a Gaussian mixture model paired with thin plate splines (TPS) to iteratively fit a crystal layout template that includes the crystal numbering pattern. Starting from a region of stability, Gaussians are individually fit to data corresponding to crystal locations while simultaneously updating a TPS for predicting future Gaussian locations at the edge of a region of interest that grows as individual Gaussians converge to crystal locations. The algorithm was tested with flood image data collected from 16 detector modules, each consisting of a 409 crystal dual-layer offset LYSO crystal array readout by a 32 pixel SiPM array. For these detector flood images, depending on user defined input parameters, the algorithm runtime ranged between 17.5-82.5 s per detector on a single core of an Intel i7 processor. The method maintained an accuracy above 99.8% across all tests, with the majority of errors being localized to error prone corner regions. This method can be easily extended for use with other detector types through adjustment of the initial
When non-Gaussian states are Gaussian: Generalization of nonseparability criterion for continuous variables

International Nuclear Information System (INIS)

McHugh, Derek; Buzek, Vladimir; Ziman, Mario

2006-01-01

We present a class of non-Gaussian two-mode continuous-variable states for which the separability criterion for Gaussian states can be employed to detect whether they are separable or not. These states reduce to the two-mode Gaussian states as a special case
[Study of Determination of Oil Mixture Components Content Based on Quasi-Monte Carlo Method].

Science.gov (United States)

Wang, Yu-tian; Xu, Jing; Liu, Xiao-fei; Chen, Meng-han; Wang, Shi-tao

2015-05-01

Gasoline, kerosene, diesel is processed by crude oil with different distillation range. The boiling range of gasoline is 35 ~205 °C. The boiling range of kerosene is 140~250 °C. And the boiling range of diesel is 180~370 °C. At the same time, the carbon chain length of differentmineral oil is different. The carbon chain-length of gasoline is within the scope of C7 to C11. The carbon chain length of kerosene is within the scope of C12 to C15. And the carbon chain length of diesel is within the scope of C15 to C18. The recognition and quantitative measurement of three kinds of mineral oil is based on different fluorescence spectrum formed in their different carbon number distribution characteristics. Mineral oil pollution occurs frequently, so monitoring mineral oil content in the ocean is very important. A new method of components content determination of spectra overlapping mineral oil mixture is proposed, with calculation of characteristic peak power integrationof three-dimensional fluorescence spectrum by using Quasi-Monte Carlo Method, combined with optimal algorithm solving optimum number of characteristic peak and range of integral region, solving nonlinear equations by using BFGS(a rank to two update method named after its inventor surname first letter, Boyden, Fletcher, Goldfarb and Shanno) method. Peak power accumulation of determined points in selected area is sensitive to small changes of fluorescence spectral line, so the measurement of small changes of component content is sensitive. At the same time, compared with the single point measurement, measurement sensitivity is improved by the decrease influence of random error due to the selection of points. Three-dimensional fluorescence spectra and fluorescence contour spectra of single mineral oil and the mixture are measured by taking kerosene, diesel and gasoline as research objects, with a single mineral oil regarded whole, not considered each mineral oil components. Six characteristic peaks are
FPGA Implementation of Gaussian Mixture Model Algorithm for 47 fps Segmentation of 1080p Video

Directory of Open Access Journals (Sweden)

Mariangela Genovese

2013-01-01

Full Text Available Circuits and systems able to process high quality video in real time are fundamental in nowadays imaging systems. The circuit proposed in the paper, aimed at the robust identification of the background in video streams, implements the improved formulation of the Gaussian Mixture Model (GMM algorithm that is included in the OpenCV library. An innovative, hardware oriented, formulation of the GMM equations, the use of truncated binary multipliers, and ROM compression techniques allow reduced hardware complexity and increased processing capability. The proposed circuit has been designed having commercial FPGA devices as target and provides speed and logic resources occupation that overcome previously proposed implementations. The circuit, when implemented on Virtex6 or StratixIV, processes more than 45 frame per second in 1080p format and uses few percent of FPGA logic resources.
Anisotropic properties of phase separation in two-component dipolar Bose-Einstein condensates

Science.gov (United States)

Wang, Wei; Li, Jinbin

2018-03-01

Using Crank-Nicolson method, we calculate ground state wave functions of two-component dipolar Bose-Einstein condensates (BECs) and show that, due to dipole-dipole interaction (DDI), the condensate mixture displays anisotropic phase separation. The effects of DDI, inter-component s-wave scattering, strength of trap potential and particle numbers on the density profiles are investigated. Three types of two-component profiles are present, first cigar, along z-axis and concentric torus, second pancake (or blood cell), in xy-plane, and two non-uniform ellipsoid, separated by the pancake and third two dumbbell shapes.
Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

Science.gov (United States)

Rafal Podlaski; Francis A. Roesch

2014-01-01

Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

Entanglement concentration for two-mode Gaussian states in non-inertial frames

International Nuclear Information System (INIS)

Di Noia, Maurizio; Giraldi, Filippo; Petruccione, Francesco

2017-01-01

Entanglement creation and concentration by means of a beam splitter (BS) is analysed for a generic two-mode bipartite Gaussian state in a relativistic framework. The total correlations, the purity and the entanglement in terms of logarithmic negativity are analytically studied for observers in an inertial state and in a non-inertial state of uniform acceleration. The dependence of entanglement on the BS transmissivity due to the Unruh effect is analysed in the case when one or both observers undergo uniform acceleration. Due to the Unruh effect, depending on the initial Gaussian state parameters and observed accelerations, the best condition for entanglement generation limited to the two modes of the observers in their regions is not always a balanced beam splitter, as it is for the inertial case. (paper)
Gaussian elimination methods for calculating classical periodic trajectories in two dimensions

International Nuclear Information System (INIS)

Davies, K.T.R.

1991-08-01

A Gaussian-elimination method for calculating classical periodic trajectories is formulated for a two-dimensional system. Two variants of the theory are obtained, one assuming that the period of the motion is fixed and the other assuming that the total energy is fixed. Comparisons are made between various approaches. 14 refs
First and second derivatives of two electron integrals over Cartesian Gaussians using Rys polynomials

International Nuclear Information System (INIS)

Schlegel, H.B.; Binkley, J.S.; Pople, J.A.

1984-01-01

Formulas are developed for the first and second derivatives of two electron integrals over Cartesian Gaussians. Integrals and integral derivatives are evaluated by the Rys polynomial method. Higher angular momentum functions are not used to calculate the integral derivatives; instead the integral formulas are differentiated directly to produce compact and efficient expressions for the integral derivatives. The use of this algorithm in the ab initio molecular orbital programs gaussIan 80 and gaussIan 82 is discussed. Representative timings for some small molecules with several basis sets are presented. This method is compared with previously published algorithms and its computational merits are discussed
Phase Equilibrium Calculations for Multi-Component Polar Fluid Mixtures with tPC-PSAFT

DEFF Research Database (Denmark)

Karakatsani, Eirini; Economou, Ioannis

2007-01-01

The truncated Perturbed-Chain Polar Statistical Associating Fluid Theory (tPC-PSAFT) is applied to a number of different mixtures, including binary, ternary and quaternary mixtures of components that differ substantially in terms of intermolecular interactions and molecular size. In contrast to m...
Background based Gaussian mixture model lesion segmentation in PET

Energy Technology Data Exchange (ETDEWEB)

Soffientini, Chiara Dolores, E-mail: chiaradolores.soffientini@polimi.it; Baselli, Giuseppe [DEIB, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133 (Italy); De Bernardi, Elisabetta [Department of Medicine and Surgery, Tecnomed Foundation, University of Milano—Bicocca, Monza 20900 (Italy); Zito, Felicia; Castellani, Massimo [Nuclear Medicine Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milan 20122 (Italy)

2016-05-15

Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was
Stochastic cluster algorithms for discrete Gaussian (SOS) models

International Nuclear Information System (INIS)

Evertz, H.G.; Hamburg Univ.; Hasenbusch, M.; Marcu, M.; Tel Aviv Univ.; Pinn, K.; Muenster Univ.; Solomon, S.

1990-10-01

We present new Monte Carlo cluster algorithms which eliminate critical slowing down in the simulation of solid-on-solid models. In this letter we focus on the two-dimensional discrete Gaussian model. The algorithms are based on reflecting the integer valued spin variables with respect to appropriately chosen reflection planes. The proper choice of the reflection plane turns out to be crucial in order to obtain a small dynamical exponent z. Actually, the successful versions of our algorithm are a mixture of two different procedures for choosing the reflection plane, one of them ergodic but slow, the other one non-ergodic and also slow when combined with a Metropolis algorithm. (orig.)
Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

Directory of Open Access Journals (Sweden)

Xiaobo Yan

2015-01-01

Full Text Available This paper addresses missing value imputation for the Internet of Things (IoT. Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL, model of missing value imputation based on binary search (MBS, and model of missing value imputation based on Gaussian mixture model (MGI. Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.
Non-Gaussian conductivity fluctuations in semiconductors

International Nuclear Information System (INIS)

Melkonyan, S.V.

2010-01-01

A theoretical study is presented on the statistical properties of conductivity fluctuations caused by concentration and mobility fluctuations of the current carriers. It is established that mobility fluctuations result from random deviations in the thermal equilibrium distribution of the carriers. It is shown that mobility fluctuations have generation-recombination and shot components which do not satisfy the requirements of the central limit theorem, in contrast to the current carrier's concentration fluctuation and intraband component of the mobility fluctuation. It is shown that in general the mobility fluctuation consist of thermal (or intraband) Gaussian and non-thermal (or generation-recombination, shot, etc.) non-Gaussian components. The analyses of theoretical results and experimental data from literature show that the statistical properties of mobility fluctuation and of 1/f-noise fully coincide. The deviation from Gaussian statistics of the mobility or 1/f fluctuations goes hand in hand with the magnitude of non-thermal noise (generation-recombination, shot, burst, pulse noises, etc.).
A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models

Directory of Open Access Journals (Sweden)

Elias D. Nino-Ruiz

2018-03-01

Full Text Available In this paper, we propose an efficient EnKF implementation for non-Gaussian data assimilation based on Gaussian Mixture Models and Markov-Chain-Monte-Carlo (MCMC methods. The proposed method works as follows: based on an ensemble of model realizations, prior errors are estimated via a Gaussian Mixture density whose parameters are approximated by means of an Expectation Maximization method. Then, by using an iterative method, observation operators are linearized about current solutions and posterior modes are estimated via a MCMC implementation. The acceptance/rejection criterion is similar to that of the Metropolis-Hastings rule. Experimental tests are performed on the Lorenz 96 model. The results show that the proposed method can decrease prior errors by several order of magnitudes in a root-mean-square-error sense for nearly sparse or dense observational networks.
On the Achievable Rate-Regions for the Gaussian Two-way Diamond Channels

Directory of Open Access Journals (Sweden)

F. Askarian

2015-12-01

Full Text Available In this channel,we study rate region of a Gaussian two-way diamond channel which operates in half-duplex mode. In this channel, two transceiver (TR nodes exchange their messages with the help of two relay nodes. We consider a special case of the Gaussian two-way diamond channels which is called Compute-and-Forward Multiple Access Channel (CF-MAC. In the CF-MAC, the TR nodes transmit their messages to the relay nodes which are followed by a simultaneous communication from the relay nodes to the TRs. Adopting rate splitting method in the terminal encoders and then using Compute-and-Forward (CF relaying and decoding the sum of messages at the relay nodes, an achievable rate region for this channel is obtained. To this end, we use a superposition coding based on lattice codes. Using numerical results, we show that our proposed scheme has better performance than other similar methods and achieves a tighter gap to the outer bound.
Non-Gaussianities in a two-field generalization of natural inflation

Science.gov (United States)

Riquelme M., Simon

2018-04-01

We describe a two-field model that generalizes natural inflation, in which the inflaton is the pseudo-Goldstone boson of an approximate symmetry that is spontaneously broken, and the radial mode is dynamical. We analyze how the dynamics fundamentally depends on the mass of the radial mode and calculate/estimate the non-Gaussianities arising from such a scenario.
Diffusion coefficients in 4-component mixture expressed explicitly in terms of binary diffusion coefficients and mole fractions

International Nuclear Information System (INIS)

Furuta, Hiroshi; Yamamoto, Ichiro

1996-01-01

Diffusion coefficients in 4-component mixture D ij (4) were expressed explicitly in terms of binary diffusion coefficients and mole fractions by solving a ratio of determinants defined by Hirschfelder et al. The explicit expressions of D ij (4) were divided into two terms, a term due to the i-j pairs of attention and a term common to all the pairs out of the 4 components. The two terms of D ij (4) had extended structures similar to corresponding those of D ij (3) respectively. (author)
Equivariant Gröbner bases and the Gaussian two-factor model

NARCIS (Netherlands)

Brouwer, A.E.; Draisma, J.

2009-01-01

We show that the kernel I of the ring homomorphism R[yij | I, j ¿ N, i > j] ¿ R[si, ti | i ¿ N] determined by yij ¿ sisj +titj is generated by two types of polynomials: off-diagonal 3 x 3-minors and pentads. This confirms a conjecture by Drton, Sturmfels, and Sullivant on the Gaussian two-factor
METHODS OF ANALYSIS AND CLASSIFICATION OF THE COMPONENTS OF GRAIN MIXTURES BASED ON MEASURING THE REFLECTION AND TRANSMISSION SPECTRA

Directory of Open Access Journals (Sweden)

Artem O. Donskikh*

2017-10-01

Full Text Available The paper considers methods of classification of grain mixture components based on spectral analysis in visible and near-infrared wavelength ranges using various measurement approaches - reflection, transmission and combined spectrum methods. It also describes the experimental measuring units used and suggests the prototype of a multispectral grain mixture analyzer. The results of the spectral measurement were processed using neural network based classification algorithms. The probabilities of incorrect recognition for various numbers of spectral parts and combinations of spectral methods were estimated. The paper demonstrates that combined usage of two spectral analysis methods leads to higher classification accuracy and allows for reducing the number of the analyzed spectral parts. A detailed description of the proposed measurement device for high-performance real-time multispectral analysis of the components of grain mixtures is given.
Mode-field half-widths of Gaussian approximation for the fundamental mode of two kinds of optical waveguides

International Nuclear Information System (INIS)

Lian-Huang, Li; Fu-Yuan, Guo

2009-01-01

This paper analyzes the characteristic of matching efficiency between the fundamental mode of two kinds of optical waveguides and its Gaussian approximate field. Then, it presents a new method where the mode-field half-width of Gaussian approximation for the fundamental mode should be defined according to the maximal matching efficiency method. The relationship between the mode-field half-width of the Gaussian approximate field obtained from the maximal matching efficiency and normalized frequency is studied; furthermore, two formulas of mode-field half-widths as a function of normalized frequency are proposed
Evaluation of the non-Gaussianity of two-mode entangled states over a bosonic memory channel via cumulant theory and quadrature detection

Science.gov (United States)

Xiang, Shao-Hua; Wen, Wei; Zhao, Yu-Jing; Song, Ke-Hui

2018-04-01

We study the properties of the cumulants of multimode boson operators and introduce the phase-averaged quadrature cumulants as the measure of the non-Gaussianity of multimode quantum states. Using this measure, we investigate the non-Gaussianity of two classes of two-mode non-Gaussian states: photon-number entangled states and entangled coherent states traveling in a bosonic memory quantum channel. We show that such a channel can skew the distribution of two-mode quadrature variables, giving rise to a strongly non-Gaussian correlation. In addition, we provide a criterion to determine whether the distributions of these states are super- or sub-Gaussian.
Kinetic behavior of Fe(o,o-EDDHA)-humic substance mixtures in several soil components and in calcareous soils.

Science.gov (United States)

Cerdán, Mar; Alcañiz, Sara; Juárez, Margarita; Jordá, Juana D; Bermúdez, Dolores

2007-10-31

Ferric ethylenediamine- N, N'-bis-(o-hydroxyphenylacetic)acid chelate (Fe(o, o-EDDHA)) is one of the most effective Fe fertilizers in calcareous soils. However, humic substances are occasionally combined with iron chelates in drip irrigation systems in order to lower costs. The reactivity of iron chelate-humic substance mixtures in several soil components and in calcareous soils was investigated through interaction tests, and their behavior was compared to the application of iron chelates and humic substances separately. Two commercial humic substances and two Fe(o, o-EDDHA) chelates (one synthesized in the laboratory and one commercial) were used to prepare iron chelate-humic substance mixtures at 50% (w/w). Various soil components (calcium carbonate, gibbsite, amorphous iron oxide, hematite, tenorite, zincite, amorphous Mn oxide, and peat) and three calcareous soils were shaken for 15 days with the mixtures and with iron chelate and humic substance solutions. The kinetic behavior of Fe(o, o-EDDHA) and Fe non-(o,o-EDDHA) (Fe bonded to (o,p-EDDHA) and other polycondensated ligands) and of the different nutrients solubilized after the interaction assay was determined. The results showed that the mixtures did not significantly reduce the retention of Fe(o, o-EDDHA) and Fe non-(o,o-EDDHA) in the soil components and the calcareous soils compared to the iron chelate solutions, but they did produce changes in the retention rate. Moreover, the competition between humic substances and synthetic chelating agents for complexing metal cations limited the effectiveness of the mixtures to mobilize nutrients from the substrates. The presence of Fe(o, p-EDDHA) and other byproducts in the commercial iron chelate had an important effect on the evolution of Fe(o, o-EDDHA) and the nutrient solubilization process.
On the nonequilibrium segregation state of a two-phase mixture in a porous column

DEFF Research Database (Denmark)

Shapiro, Alexander; Stenby, Erling Halfdan

1996-01-01

The problem of segregation of a two-phase multicomponent mixture under the action of thermal gradient, gravity and capillary forces is studied with respect to component distribution in a thick oil-gas-condensate reservoir. Governing equations are derived on the basis of nonequilibrium...
Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution

KAUST Repository

Ahmed, Qasim Zeeshan

2015-04-01

Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.
Overlay Spectrum Sharing using Improper Gaussian Signaling

KAUST Repository

Amin, Osama

2016-11-30

Improper Gaussian signaling (IGS) scheme has been recently shown to provide performance improvements in interference limited networks as opposed to the conventional proper Gaussian signaling (PGS) scheme. In this paper, we implement the IGS scheme in overlay cognitive radio system, where the secondary transmitter broadcasts a mixture of two different signals. The first signal is selected from the PGS scheme to match the primary message transmission. On the other hand, the second signal is chosen to be from the IGS scheme in order to reduce the interference effect on the primary receiver. We then optimally design the overlay cognitive radio to maximize the secondary link achievable rate while satisfying the primary network quality of service requirements. In particular, we consider full and partial channel knowledge scenarios and derive the feasibility conditions of operating the overlay cognitive radio systems. Moreover, we derive the superiority conditions of the IGS schemes over the PGS schemes supported with closed form expressions for the corresponding power distribution and the circularity coefficient and parameters. Simulation results are provided to support our theoretical derivations.

Negativity of asymmetric two-mode Gaussian states: An explicit analytic formula and physical interpretation

International Nuclear Information System (INIS)

Poon, Phoenix S. Y.; Law, C. K.

2007-01-01

We show that the negativity of a general two-mode Gaussian state can be explicitly expressed in terms of an optimal uncertainty product in position-momentum space. Such an uncertainty product is shown to have the greatest violation of a separability criterion based on positive partial transposition. Our analytic formula indicates the observables determining the negativity. For asymmetric Gaussian states, we show that the negativity is controlled by an asymmetric parameter which sets an upper bound for the negativity
Bayesian estimation of mixtures with dynamic transitions and known component parameters

Czech Academy of Sciences Publication Activity Database

Nagy, I.; Suzdaleva, Evgenia; Kárný, Miroslav

2011-01-01

Roč. 47, č. 4 (2011), s. 572-594 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA TA ČR TA01030123; GA ČR GA102/08/0567 Grant - others:Skoda Auto(CZ) ENS/2009/UTIA Institutional research plan: CEZ:AV0Z10750506 Keywords : mixture model * Bayesian estimation * approximation * clustering * classification Subject RIV: BC - Control Systems Theory Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/nagy-bayesian estimation of mixtures with dynamic transitions and known component parameters.pdf
Non-Gaussianity in two-field inflation beyond the slow-roll approximation

Energy Technology Data Exchange (ETDEWEB)

Jung, Gabriel; Tent, Bartjan van, E-mail: gabriel.jung@th.u-psud.fr, E-mail: bartjan.van-tent@th.u-psud.fr [Laboratoire de Physique Théorique (UMR 8627), CNRS, Univ. Paris-Sud, Université Paris-Saclay, Bâtiment 210, 91405 Orsay Cedex (France)

2017-05-01

We use the long-wavelength formalism to investigate the level of bispectral non-Gaussianity produced in two-field inflation models with standard kinetic terms. Even though the Planck satellite has so far not detected any primordial non-Gaussianity, it has tightened the constraints significantly, and it is important to better understand what regions of inflation model space have been ruled out, as well as prepare for the next generation of experiments that might reach the important milestone of Δ f {sub NL}{sup local}=1. We derive an alternative formulation of the previously derived integral expression for f {sub NL}, which makes it easier to physically interpret the result and see which types of potentials can produce large non-Gaussianity. We apply this to the case of a sum potential and show that it is very difficult to satisfy simultaneously the conditions for a large f {sub NL} and the observational constraints on the spectral index n {sub s} . In the case of the sum of two monomial potentials and a constant we explicitly show in which small region of parameter space this is possible, and we show how to construct such a model. Finally, the new general expression for f {sub NL} also allows us to prove that for the sum potential the explicit expressions derived within the slow-roll approximation remain valid even when the slow-roll approximation is broken during the turn of the field trajectory (as long as only the ε slow-roll parameter remains small).
Frequency Characteristics of Surface Wave Generated by Single-Line Pulsed Laser Beam with Two Kinds of Spatial Energy Profile Models: Gaussian and Square-Like

Energy Technology Data Exchange (ETDEWEB)

Seo, Ho Geon; Kim, Myung Hwan; Choi, Sung Ho; Kim, Chung Seok; Jhang, Kyung Young [Hanyang University, Seoul (Korea, Republic of)

2012-08-15

Using a single-line pulsed laser beam is well known as a useful noncontact method to generate a directional surface acoustic wave. In this method, different laser beam energy profiles produce different waveforms and frequency characteristics. In this paper, we considered two typical kinds of laser beam energy profiles, Gaussian and square-like, to find out a difference in the frequency characteristics. To achieve this, mathematical models were proposed first for Gaussian laser beam profile and square-like respectively, both of which depended on the laser beam width. To verify the theoretical models, experimental setups with a cylindrical lens and a line-slit mask were respectively designed to produce a line laser beam with Gaussian spatial energy profile and square-like. The frequency responses of the theoretical models showed good agreement with experimental results in terms of the existence of harmonic frequency components and the shift of the first peak frequencies to low.
Polarization coupling of vector Bessel–Gaussian beams

International Nuclear Information System (INIS)

Takeuchi, Ryushi; Kozawa, Yuichi; Sato, Shunichi

2013-01-01

We report polarization coupling of radial and azimuthal electric field components of a vector light beam as predicted by the fact that the vector Helmholtz equation is expressed as coupled differential equations in cylindrical coordinates. To clearly observe the polarization variation of a beam as it propagates, higher order transverse modes of a vector Bessel–Gaussian beam were generated by a gain distribution modulation technique, which created a narrow ring-shaped gain region in a Nd:YVO 4 crystal. The polarization coupling was confirmed by the observation that the major polarization component of a vector Bessel–Gaussian beam alternates between radial and azimuthal components along with the propagation. (paper)
Adaptive wiener filter based on Gaussian mixture distribution model for denoising chest X-ray CT image

International Nuclear Information System (INIS)

Tabuchi, Motohiro; Yamane, Nobumoto; Morikawa, Yoshitaka

2008-01-01

In recent decades, X-ray CT imaging has become more important as a result of its high-resolution performance. However, it is well known that the X-ray dose is insufficient in the techniques that use low-dose imaging in health screening or thin-slice imaging in work-up. Therefore, the degradation of CT images caused by the streak artifact frequently becomes problematic. In this study, we applied a Wiener filter (WF) using the universal Gaussian mixture distribution model (UNI-GMM) as a statistical model to remove streak artifact. In designing the WF, it is necessary to estimate the statistical model and the precise co-variances of the original image. In the proposed method, we obtained a variety of chest X-ray CT images using a phantom simulating a chest organ, and we estimated the statistical information using the images for training. The results of simulation showed that it is possible to fit the UNI-GMM to the chest X-ray CT images and reduce the specific noise. (author)
Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes.

Science.gov (United States)

Alaa, Ahmed M; Yoon, Jinsung; Hu, Scott; van der Schaar, Mihaela

2018-01-01

In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit admissions for clinically deteriorating patients. The risk scoring system is based on the idea of sequential hypothesis testing under an uncertain time horizon. The system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patient's latent subtype and her static admission information (e.g., age, gender, transfer status, ICD-9 codes, etc). Experiments conducted on data from a heterogeneous cohort of 6321 patients admitted to Ronald Reagan UCLA medical center show that our score significantly outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE, and SOFA scores, in terms of timeliness, true positive rate, and positive predictive value. Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients' heterogeneity. The proposed risk scoring methodology can confer huge clinical and social benefits on a massive number of critically ill inpatients who exhibit adverse outcomes including, but not limited to, cardiac arrests, respiratory arrests, and septic shocks.
Flexible Mixture-Amount Models for Business and Industry Using Gaussian Processes

NARCIS (Netherlands)

A. Ruseckaite (Aiste); D. Fok (Dennis); P.P. Goos (Peter)

2016-01-01

markdownabstractMany products and services can be described as mixtures of ingredients whose proportions sum to one. Specialized models have been developed for linking the mixture proportions to outcome variables, such as preference, quality and liking. In many scenarios, only the mixture
Gaussian entanglement revisited

Science.gov (United States)

Lami, Ludovico; Serafini, Alessio; Adesso, Gerardo

2018-02-01

We present a novel approach to the separability problem for Gaussian quantum states of bosonic continuous variable systems. We derive a simplified necessary and sufficient separability criterion for arbitrary Gaussian states of m versus n modes, which relies on convex optimisation over marginal covariance matrices on one subsystem only. We further revisit the currently known results stating the equivalence between separability and positive partial transposition (PPT) for specific classes of Gaussian states. Using techniques based on matrix analysis, such as Schur complements and matrix means, we then provide a unified treatment and compact proofs of all these results. In particular, we recover the PPT-separability equivalence for: (i) Gaussian states of 1 versus n modes; and (ii) isotropic Gaussian states. In passing, we also retrieve (iii) the recently established equivalence between separability of a Gaussian state and and its complete Gaussian extendability. Our techniques are then applied to progress beyond the state of the art. We prove that: (iv) Gaussian states that are invariant under partial transposition are necessarily separable; (v) the PPT criterion is necessary and sufficient for separability for Gaussian states of m versus n modes that are symmetric under the exchange of any two modes belonging to one of the parties; and (vi) Gaussian states which remain PPT under passive optical operations can not be entangled by them either. This is not a foregone conclusion per se (since Gaussian bound entangled states do exist) and settles a question that had been left unanswered in the existing literature on the subject. This paper, enjoyable by both the quantum optics and the matrix analysis communities, overall delivers technical and conceptual advances which are likely to be useful for further applications in continuous variable quantum information theory, beyond the separability problem.
PCA: Principal Component Analysis for spectra modeling

Science.gov (United States)

Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas

2012-07-01

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.
Probabilistic mixture-based image modelling

Czech Academy of Sciences Publication Activity Database

Haindl, Michal; Havlíček, Vojtěch; Grim, Jiří

2011-01-01

Roč. 47, č. 3 (2011), s. 482-500 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593 Grant - others:CESNET(CZ) 387/2010; GA MŠk(CZ) 2C06019; GA ČR(CZ) GA103/11/0335 Institutional research plan: CEZ:AV0Z10750506 Keywords : BTF texture modelling * discrete distribution mixtures * Bernoulli mixture * Gaussian mixture * multi-spectral texture modelling Subject RIV: BD - Theory of Information Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/RO/haindl-0360244.pdf
Radial dependence of surface streamer-channel luminosity: experimental evidence of Gaussian radiative profiles in Ar and N2

International Nuclear Information System (INIS)

Šimek, M; Ambrico, P F

2012-01-01

Radial distributions of electronically excited species produced during surface streamer propagation were obtained by applying the Abel inverse transform to projected luminosities of single streamers. The streamers were generated in an argon and nitrogen surface coplanar dielectric barrier discharge at atmospheric pressure and their magnified microscopic images were registered with high time resolution. Selected regions of the projected luminosities were processed by the Abel inverse transform procedure based on the Hankel–Fourier method assuming cylindrical symmetry of the streamer channel. Projected as well as Abel-inverted profiles were fitted by Gaussian functions. It is shown that the projected profiles, in addition to the Abel-inverted ones, can be well approximated by the sum of two coaxial Gaussians with two different half-widths and weights. The sharper Gaussian component with higher weight characterizes the radial dependence of the primary cathode-directed streamer-channel luminosity. The second (broader) Gaussian component probably originates either from the pre-breakdown Townsend phase or from the second wave propagating towards the anode. (paper)
Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities

KAUST Repository

Nielsen, Frank

2016-12-09

Information-theoreticmeasures, such as the entropy, the cross-entropy and the Kullback-Leibler divergence between two mixture models, are core primitives in many signal processing tasks. Since the Kullback-Leibler divergence of mixtures provably does not admit a closed-form formula, it is in practice either estimated using costly Monte Carlo stochastic integration, approximated or bounded using various techniques. We present a fast and generic method that builds algorithmically closed-form lower and upper bounds on the entropy, the cross-entropy, the Kullback-Leibler and the α-divergences of mixtures. We illustrate the versatile method by reporting our experiments for approximating the Kullback-Leibler and the α-divergences between univariate exponential mixtures, Gaussian mixtures, Rayleigh mixtures and Gamma mixtures.
Processor core for real time background identification of HD video based on OpenCV Gaussian mixture model algorithm

Science.gov (United States)

Genovese, Mariangela; Napoli, Ettore

2013-05-01

The identification of moving objects is a fundamental step in computer vision processing chains. The development of low cost and lightweight smart cameras steadily increases the request of efficient and high performance circuits able to process high definition video in real time. The paper proposes two processor cores aimed to perform the real time background identification on High Definition (HD, 1920 1080 pixel) video streams. The implemented algorithm is the OpenCV version of the Gaussian Mixture Model (GMM), an high performance probabilistic algorithm for the segmentation of the background that is however computationally intensive and impossible to implement on general purpose CPU with the constraint of real time processing. In the proposed paper, the equations of the OpenCV GMM algorithm are optimized in such a way that a lightweight and low power implementation of the algorithm is obtained. The reported performances are also the result of the use of state of the art truncated binary multipliers and ROM compression techniques for the implementation of the non-linear functions. The first circuit has commercial FPGA devices as a target and provides speed and logic resource occupation that overcome previously proposed implementations. The second circuit is oriented to an ASIC (UMC-90nm) standard cell implementation. Both implementations are able to process more than 60 frames per second in 1080p format, a frame rate compatible with HD television.
Competitive adsorption of a two-component gas on a deformable adsorbent

International Nuclear Information System (INIS)

Usenko, A S

2014-01-01

We investigate the competitive adsorption of a two-component gas on the surface of an adsorbent whose adsorption properties vary due to the adsorbent deformation. The essential difference of adsorption isotherms for a deformable adsorbent both from the classical Langmuir adsorption isotherms of a two-component gas and from the adsorption isotherms of a one-component gas is obtained, taking into account variations in the adsorption properties of the adsorbent in adsorption. We establish bistability and tristability of the system caused by variations in adsorption properties of the adsorbent in competitive adsorption of gas particles on it. We derive conditions under which adsorption isotherms of a binary gas mixture have two stable asymptotes. It is shown that the specific features of the behavior of the system under study can be described in terms of a potential of the known explicit form. (paper)
Resource theory of non-Gaussian operations

Science.gov (United States)

Zhuang, Quntao; Shor, Peter W.; Shapiro, Jeffrey H.

2018-05-01

Non-Gaussian states and operations are crucial for various continuous-variable quantum information processing tasks. To quantitatively understand non-Gaussianity beyond states, we establish a resource theory for non-Gaussian operations. In our framework, we consider Gaussian operations as free operations, and non-Gaussian operations as resources. We define entanglement-assisted non-Gaussianity generating power and show that it is a monotone that is nonincreasing under the set of free superoperations, i.e., concatenation and tensoring with Gaussian channels. For conditional unitary maps, this monotone can be analytically calculated. As examples, we show that the non-Gaussianity of ideal photon-number subtraction and photon-number addition equal the non-Gaussianity of the single-photon Fock state. Based on our non-Gaussianity monotone, we divide non-Gaussian operations into two classes: (i) the finite non-Gaussianity class, e.g., photon-number subtraction, photon-number addition, and all Gaussian-dilatable non-Gaussian channels; and (ii) the diverging non-Gaussianity class, e.g., the binary phase-shift channel and the Kerr nonlinearity. This classification also implies that not all non-Gaussian channels are exactly Gaussian dilatable. Our resource theory enables a quantitative characterization and a first classification of non-Gaussian operations, paving the way towards the full understanding of non-Gaussianity.
A Two-Stage Layered Mixture Experiment Design for a Nuclear Waste Glass Application-Part 2

International Nuclear Information System (INIS)

Cooley, Scott K.; Piepel, Gregory F.; Gan, Hao; Kot, Wing; Pegg, Ian L.

2003-01-01

Part 1 (Cooley and Piepel, 2003a) describes the first stage of a two-stage experimental design to support property-composition modeling for high-level waste (HLW) glass to be produced at the Hanford Site in Washington state. Each stage used a layered design having an outer layer, an inner layer, a center point, and some replicates. However, the design variables and constraints defining the layers of the experimental glass composition region (EGCR) were defined differently for the second stage than for the first. The first-stage initial design involved 15 components, all treated as mixture variables. The second-stage augmentation design involved 19 components, with 14 treated as mixture variables and 5 treated as non-mixture variables. For each second-stage layer, vertices were generated and optimal design software was used to select alternative subsets of vertices for the design and calculate design optimality measures. A model containing 29 partial quadratic mixture terms plus 5 linear terms for the non-mixture variables was the basis for the optimal design calculations. Predicted property values were plotted for the alternative subsets of second-stage vertices and the first-stage design points. Based on the optimality measures and the predicted property distributions, a ''best'' subset of vertices was selected for each layer of the second-stage to augment the first-stage design
Negative refraction imaging of solid acoustic waves by two-dimensional three-component phononic crystal

International Nuclear Information System (INIS)

Li Jing; Liu Zhengyou; Qiu Chunyin

2008-01-01

By using of the multiple scattering methods, we study the negative refraction imaging effect of solid acoustic waves by two-dimensional three-component phononic crystals composed of coated solid inclusions placed in solid matrix. We show that localized resonance mechanism brings on a group of flat single-mode bands in low-frequency region, which provides two equivalent frequency surfaces (EFS) close to circular. The two constant frequency surfaces correspond to two Bloch modes, a right-handed and a left-handed, whose leading mode are respectively transverse (T) and longitudinal (L) modes. The negative refraction behaviors of the two kinds of modes have been demonstrated by simulation of a Gaussian beam through a finite system. High-quality far-field imaging by a planar lens for transverse or longitudinal waves has been realized separately. This three-component phononic crystal may thus serve as a mode selector in negative refraction imaging of solid acoustic waves
Quantum entanglement and nonlocality properties of two-mode Gaussian squeezed states

International Nuclear Information System (INIS)

Shao-Hua, Xiang; Bin, Shao; Ke-Hui, Song

2009-01-01

Quantum entanglement and nonlocality properties of a family of two-mode Gaussian pure states have been investigated. The results show that the entanglement of these states is determined by both the two-mode squeezing parameter and the difference of the two single-mode squeezing parameters. For the same two-mode squeezing parameter, these states show larger entanglement than the usual two-mode squeezed vacuum state. The violation of Bell inequality depends strongly on all the squeezing parameters of these states and disappears completely in the limit of large squeezing. In particular, these states can exhibit much stronger violation of local realism than two-mode squeezed vacuum state in the range of experimentally available squeezing values. (general)
Beyond GLMs: a generative mixture modeling approach to neural system identification.

Directory of Open Access Journals (Sweden)

Lucas Theis

Full Text Available Generalized linear models (GLMs represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM-a linear and a quadratic model-by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.

Interpolation on the manifold of K component GMMs.

Science.gov (United States)

Kim, Hyunwoo J; Adluru, Nagesh; Banerjee, Monami; Vemuri, Baba C; Singh, Vikas

2015-12-01

Probability density functions (PDFs) are fundamental objects in mathematics with numerous applications in computer vision, machine learning and medical imaging. The feasibility of basic operations such as computing the distance between two PDFs and estimating a mean of a set of PDFs is a direct function of the representation we choose to work with. In this paper, we study the Gaussian mixture model (GMM) representation of the PDFs motivated by its numerous attractive features. (1) GMMs are arguably more interpretable than, say, square root parameterizations (2) the model complexity can be explicitly controlled by the number of components and (3) they are already widely used in many applications. The main contributions of this paper are numerical algorithms to enable basic operations on such objects that strictly respect their underlying geometry. For instance, when operating with a set of K component GMMs, a first order expectation is that the result of simple operations like interpolation and averaging should provide an object that is also a K component GMM. The literature provides very little guidance on enforcing such requirements systematically. It turns out that these tasks are important internal modules for analysis and processing of a field of ensemble average propagators (EAPs), common in diffusion weighted magnetic resonance imaging. We provide proof of principle experiments showing how the proposed algorithms for interpolation can facilitate statistical analysis of such data, essential to many neuroimaging studies. Separately, we also derive interesting connections of our algorithm with functional spaces of Gaussians, that may be of independent interest.
The Gaussian cell two-point 'energy-like' equation : application to large-scale galaxy redshift and peculiar motion surveys

NARCIS (Netherlands)

Zaroubi, S; Branchini, E

2005-01-01

We introduce a simple linear equation relating the line-of-sight peculiar-velocity and density contrast correlation functions. The relation, which we call the Gaussian cell two-point 'energy-like' equation, is valid at the distant-observer limit and requires Gaussian smoothed fields. In the variance
Evaluation of solution stability for two-component polydisperse systems by small-angle scattering

Science.gov (United States)

Kryukova, A. E.; Konarev, P. V.; Volkov, V. V.

2017-12-01

The article is devoted to the modelling of small-angle scattering data using the program MIXTURE designed for the study of polydisperse multicomponent mixtures. In this work we present the results of solution stability studies for theoretical small-angle scattering data sets from two-component models. It was demonstrated that the addition of the noise to the data influences the stability range of the restored structural parameters. The recommendations for the optimal minimization schemes that permit to restore the volume size distributions for polydisperse systems are suggested.
A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation

Science.gov (United States)

Li, Zheng; Jiang, Yi-han; Duan, Lian; Zhu, Chao-zhe

2017-08-01

Objective. Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC). Approach. GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers. Main results. Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.
The chiral Gaussian two-matrix ensemble of real asymmetric matrices

International Nuclear Information System (INIS)

Akemann, G; Phillips, M J; Sommers, H-J

2010-01-01

We solve a family of Gaussian two-matrix models with rectangular N x (N + ν) matrices, having real asymmetric matrix elements and depending on a non-Hermiticity parameter μ. Our model can be thought of as the chiral extension of the real Ginibre ensemble, relevant for Dirac operators in the same symmetry class. It has the property that its eigenvalues are either real, purely imaginary or come in complex conjugate eigenvalue pairs. The eigenvalue joint probability distribution for our model is explicitly computed, leading to a non-Gaussian distribution including K-Bessel functions. All n-point density correlation functions are expressed for finite N in terms of a Pfaffian form. This contains a kernel involving Laguerre polynomials in the complex plane as a building block which was previously computed by the authors. This kernel can be expressed in terms of the kernel for complex non-Hermitian matrices, generalizing the known relation among ensembles of Hermitian random matrices. Compact expressions are given for the density at finite N as an example, as well as its microscopic large-N limits at the origin for fixed ν at strong and weak non-Hermiticity.
Gaussian mixture modeling of hemispheric lateralization for language in a large sample of healthy individuals balanced for handedness.

Science.gov (United States)

Mazoyer, Bernard; Zago, Laure; Jobard, Gaël; Crivello, Fabrice; Joliot, Marc; Perchey, Guy; Mellet, Emmanuel; Petit, Laurent; Tzourio-Mazoyer, Nathalie

2014-01-01

Hemispheric lateralization for language production and its relationships with manual preference and manual preference strength were studied in a sample of 297 subjects, including 153 left-handers (LH). A hemispheric functional lateralization index (HFLI) for language was derived from fMRI acquired during a covert sentence generation task as compared with a covert word list recitation. The multimodal HFLI distribution was optimally modeled using a mixture of 3 and 4 Gaussian functions in right-handers (RH) and LH, respectively. Gaussian function parameters helped to define 3 types of language hemispheric lateralization, namely "Typical" (left hemisphere dominance with clear positive HFLI values, 88% of RH, 78% of LH), "Ambilateral" (no dominant hemisphere with HFLI values close to 0, 12% of RH, 15% of LH) and "Strongly-atypical" (right-hemisphere dominance with clear negative HFLI values, 7% of LH). Concordance between dominant hemispheres for hand and for language did not exceed chance level, and most of the association between handedness and language lateralization was explained by the fact that all Strongly-atypical individuals were left-handed. Similarly, most of the relationship between language lateralization and manual preference strength was explained by the fact that Strongly-atypical individuals exhibited a strong preference for their left hand. These results indicate that concordance of hemispheric dominance for hand and for language occurs barely above the chance level, except in a group of rare individuals (less than 1% in the general population) who exhibit strong right hemisphere dominance for both language and their preferred hand. They call for a revisit of models hypothesizing common determinants for handedness and for language dominance.
Gaussian mixture modeling of hemispheric lateralization for language in a large sample of healthy individuals balanced for handedness.

Directory of Open Access Journals (Sweden)

Bernard Mazoyer

Full Text Available Hemispheric lateralization for language production and its relationships with manual preference and manual preference strength were studied in a sample of 297 subjects, including 153 left-handers (LH. A hemispheric functional lateralization index (HFLI for language was derived from fMRI acquired during a covert sentence generation task as compared with a covert word list recitation. The multimodal HFLI distribution was optimally modeled using a mixture of 3 and 4 Gaussian functions in right-handers (RH and LH, respectively. Gaussian function parameters helped to define 3 types of language hemispheric lateralization, namely "Typical" (left hemisphere dominance with clear positive HFLI values, 88% of RH, 78% of LH, "Ambilateral" (no dominant hemisphere with HFLI values close to 0, 12% of RH, 15% of LH and "Strongly-atypical" (right-hemisphere dominance with clear negative HFLI values, 7% of LH. Concordance between dominant hemispheres for hand and for language did not exceed chance level, and most of the association between handedness and language lateralization was explained by the fact that all Strongly-atypical individuals were left-handed. Similarly, most of the relationship between language lateralization and manual preference strength was explained by the fact that Strongly-atypical individuals exhibited a strong preference for their left hand. These results indicate that concordance of hemispheric dominance for hand and for language occurs barely above the chance level, except in a group of rare individuals (less than 1% in the general population who exhibit strong right hemisphere dominance for both language and their preferred hand. They call for a revisit of models hypothesizing common determinants for handedness and for language dominance.
Nonclassicality by Local Gaussian Unitary Operations for Gaussian States

Directory of Open Access Journals (Sweden)

Yangyang Wang

2018-04-01

Full Text Available A measure of nonclassicality N in terms of local Gaussian unitary operations for bipartite Gaussian states is introduced. N is a faithful quantum correlation measure for Gaussian states as product states have no such correlation and every non product Gaussian state contains it. For any bipartite Gaussian state ρ A B , we always have 0 ≤ N ( ρ A B < 1 , where the upper bound 1 is sharp. An explicit formula of N for ( 1 + 1 -mode Gaussian states and an estimate of N for ( n + m -mode Gaussian states are presented. A criterion of entanglement is established in terms of this correlation. The quantum correlation N is also compared with entanglement, Gaussian discord and Gaussian geometric discord.
On-line component ratio measurement of oil/gas/water mixtures using an admittance sensor

Energy Technology Data Exchange (ETDEWEB)

Andersen, J A

1984-01-01

The operator of a production platform is primarily interested in which types of fluids a well is producing and how quickly these different components are being produced. The component ratio and production rate of a well vary during the life of a field. To optimize production, measurement of each well's output is thus desirable. Current designs for subsea production systems lack means of continuously measuring three-component flows. A new method of component ratio measurement is described. The fraction of oil, gas and water flowing between two insulated electrode plates is determined by measuring both the electrical conductance and suseptance across the sensor. A preliminary evaluation of the new measurement system has been performed using a process oil/ water/air mixture. The method is not limited to small pipe diameters. The only possible limitation is that for low velocities in very large pipe diameters an in-line mixer may be required. Advantages of this new system are that real-time measurement of void fraction and water content is possible if a non-intrusive rugged sensor is used, and there are no range limitations, as each component may be measured for any given concentration. 4 references.
Reconstruction of electrons with the Gaussian-sum filter in the CMS tracker at the LHC

International Nuclear Information System (INIS)

Adam, W; Fruehwirth, R; Strandlie, A; Todorov, T

2005-01-01

The bremsstrahlung energy loss distribution of electrons propagating in matter is highly non-Gaussian. Because the Kalman filter relies solely on Gaussian probability density functions, it is not necessarily the optimal reconstruction algorithm for electron tracks. A Gaussian-sum filter (GSF) algorithm for electron reconstruction in the CMS tracker has therefore been developed and implemented. The basic idea is to model the bremsstrahlung energy loss distribution by a Gaussian mixture rather than by a single Gaussian. It is shown that the GSF is able to improve the momentum resolution of electrons compared to the standard Kalman filter. The momentum resolution and the quality of the error estimate are studied both with a fast simulation, modelling the radiative energy loss in a simplified detector, and the full CMS tracker simulation. (research note from collaboration)
Reconstruction of Electrons with the Gaussian-Sum Filter in the CMS Tracker at the LHC

CERN Document Server

Adam, Wolfgang; Strandlie, Are; Todor, T

2005-01-01

The bremsstrahlung energy loss distribution of electrons propagating in matter is highly non-Gaussian. Because the Kalman filter relies solely on Gaussian probability density functions, it is not necessarily the optimal reconstruction algorithm for electron tracks. A Gaussian-sum filter (GSF) algorithm for electron reconstruction in the CMS tracker has therefore been developed and implemented. The basic idea is to model the bremsstrahlung energy loss distribution by a Gaussian mixture rather than by a single Gaussian. It is shown that the GSF is able to improve the momentum resolution of electrons compared to the standard Kalman filter. The momentum resolution and the quality of he error estimate are studied both with a fast simulation, modelling the radiative energy loss in a simplified detector, and the full CMS tracker simulation.
Determination of two-dimensional correlation lengths in an anisotropic two-component flow

International Nuclear Information System (INIS)

Thomson, O.

1994-05-01

Former studies have shown that correlation methods can be used for determination of various two-component flow parameters, among these the correlation length. In cases where the flow can be described as a mixture, in which the minority component forms spatially limited perturbations within the majority component, this parameter gives a good indication of the maximum extension of these perturbations. In the former studies, spherical symmetry of the perturbations has been assumed, and the correlation length has been measured in the direction of the flow (axially) only. However, if the flow structure is anisotropic, the correlation length will be different in different directions. In the present study, the method has been developed further, allowing also measurements perpendicular to the flow direction (radially). The measurements were carried out using laser beams and the two-component flows consisted of either glass beads and air or air and water. In order to make local measurements of both the axial and radial correlation length simultaneously, it is necessary to use 3 laser beams and to form the triple cross-covariance. This lead to some unforeseen complications, due to the character of this function. The experimental results are generally positive and size determinations with an accuracy of better than 10% have been achieved in most cases. Less accurate results appeared only for difficult conditions (symmetrical signals), when 3 beams were used. 5 refs, 13 figs, 3 tabs
A Comprehensive Mixture of Tobacco Smoke Components Retards Orthodontic Tooth Movement via the Inhibition of Osteoclastogenesis in a Rat Model

Directory of Open Access Journals (Sweden)

Maya Nagaie

2014-10-01

Full Text Available Tobacco smoke is a complex mixture of numerous components. Nevertheless, most experiments have examined the effects of individual chemicals in tobacco smoke. The comprehensive effects of components on tooth movement and bone resorption remain unexplored. Here, we have shown that a comprehensive mixture of tobacco smoke components (TSCs attenuated bone resorption through osteoclastogenesis inhibition, thereby retarding experimental tooth movement in a rat model. An elastic power chain (PC inserted between the first and second maxillary molars robustly yielded experimental tooth movement within 10 days. TSC administration effectively retarded tooth movement since day 4. Histological evaluation disclosed that tooth movement induced bone resorption at two sites: in the bone marrow and the peripheral bone near the root. TSC administration significantly reduced the number of tartrate-resistant acid phosphatase (TRAP-positive osteoclastic cells in the bone marrow cavity of the PC-treated dentition. An in vitro study indicated that the inhibitory effects of TSCs on osteoclastogenesis seemed directed more toward preosteoclasts than osteoblasts. These results indicate that the comprehensive mixture of TSCs might be a useful tool for detailed verification of the adverse effects of tobacco smoke, possibly contributing to the development of reliable treatments in various fields associated with bone resorption.
Persistence Probabilities of Two-Sided (Integrated) Sums of Correlated Stationary Gaussian Sequences

Science.gov (United States)

Aurzada, Frank; Buck, Micha

2018-02-01

We study the persistence probability for some two-sided, discrete-time Gaussian sequences that are discrete-time analogues of fractional Brownian motion and integrated fractional Brownian motion, respectively. Our results extend the corresponding ones in continuous time in Molchan (Commun Math Phys 205(1):97-111, 1999) and Molchan (J Stat Phys 167(6):1546-1554, 2017) to a wide class of discrete-time processes.
Quantifying the non-Gaussianity in the EoR 21-cm signal through bispectrum

Science.gov (United States)

Majumdar, Suman; Pritchard, Jonathan R.; Mondal, Rajesh; Watkinson, Catherine A.; Bharadwaj, Somnath; Mellema, Garrelt

2018-05-01

The epoch of reionization (EoR) 21-cm signal is expected to be highly non-Gaussian in nature and this non-Gaussianity is also expected to evolve with the progressing state of reionization. Therefore the signal will be correlated between different Fourier modes (k). The power spectrum will not be able capture this correlation in the signal. We use a higher order estimator - the bispectrum - to quantify this evolving non-Gaussianity. We study the bispectrum using an ensemble of simulated 21-cm signal and with a large variety of k triangles. We observe two competing sources driving the non-Gaussianity in the signal: fluctuations in the neutral fraction (x_{H I}) field and fluctuations in the matter density field. We find that the non-Gaussian contribution from these two sources varies, depending on the stage of reionization and on which k modes are being studied. We show that the sign of the bispectrum works as a unique marker to identify which among these two components is driving the non-Gaussianity. We propose that the sign change in the bispectrum, when plotted as a function of triangle configuration cos θ and at a certain stage of the EoR can be used as a confirmative test for the detection of the 21-cm signal. We also propose a new consolidated way to visualize the signal evolution (with evolving \\bar{x}_{H I} or redshift), through the trajectories of the signal in a power spectrum and equilateral bispectrum i.e. P(k) - B(k, k, k) space.
Heat transfer from a high temperature condensable mixture

International Nuclear Information System (INIS)

Chan, S.H.; Cho, D.H.; Condiff, D.W.

1980-01-01

Bulk condensation and heat transfer in a very hot gaseous mixture that contains a vapor component condensable at high temperature are investigated. A general formulation of the problem is presented in various forms. Analytical solutions for three specific cases involving both one- and two-component two-phase mixtures are obtained. It is shown that a detached fog formation is induced by rapid radiative cooling from the mixture. The formation of radiatively induced fog is found to be an interesting and important phenomenon as it not only exhibits unique features different from the conventional diffusion induced fog, but also greatly enhances heat transfer from the mixture to the boundary. (author)
An approximate fractional Gaussian noise model with computational cost

KAUST Repository

Sørbye, Sigrunn H.

2017-09-18

Fractional Gaussian noise (fGn) is a stationary time series model with long memory properties applied in various fields like econometrics, hydrology and climatology. The computational cost in fitting an fGn model of length $n$ using a likelihood-based approach is ${\\\\mathcal O}(n^{2})$, exploiting the Toeplitz structure of the covariance matrix. In most realistic cases, we do not observe the fGn process directly but only through indirect Gaussian observations, so the Toeplitz structure is easily lost and the computational cost increases to ${\\\\mathcal O}(n^{3})$. This paper presents an approximate fGn model of ${\\\\mathcal O}(n)$ computational cost, both with direct or indirect Gaussian observations, with or without conditioning. This is achieved by approximating fGn with a weighted sum of independent first-order autoregressive processes, fitting the parameters of the approximation to match the autocorrelation function of the fGn model. The resulting approximation is stationary despite being Markov and gives a remarkably accurate fit using only four components. The performance of the approximate fGn model is demonstrated in simulations and two real data examples.
Algorithms and programs of dynamic mixture estimation unified approach to different types of components

CERN Document Server

Nagy, Ivan

2017-01-01

This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.
Impact of Improper Gaussian Signaling on the Achievable Rate of Overlay Cognitive Radio

KAUST Repository

Amin, Osama

2017-05-12

Improper Gaussian signaling (IGS) has been recently shown to provide performance improvements in underlay cognitive radio systems as opposed to the conventional proper Gaussian signaling (PGS) scheme. For the first time, this paper implements IGS scheme in overlay cognitive radio system, where the secondary transmitter broadcasts a mixture of two different signals. The first signal is selected from the PGS scheme to support the primary message transmission. On the other hand, the second signal is chosen to be from the IGS scheme in order to reduce the interference effect on the primary receiver. We then optimally design the overlay cognitive radio that employs IGS to maximize the secondary link achievable rate while satisfying the minimum rate requirement of the primary network. In particular, we derive closed form expressions for the circularity coefficient used in the IGS scheme and the power distribution parameters. Simulation results are provided to support our theoretical derivations.
Impact of Improper Gaussian Signaling on the Achievable Rate of Overlay Cognitive Radio

KAUST Repository

Amin, Osama; Abediseid, Walid; Alouini, Mohamed-Slim

2017-01-01

Improper Gaussian signaling (IGS) has been recently shown to provide performance improvements in underlay cognitive radio systems as opposed to the conventional proper Gaussian signaling (PGS) scheme. For the first time, this paper implements IGS scheme in overlay cognitive radio system, where the secondary transmitter broadcasts a mixture of two different signals. The first signal is selected from the PGS scheme to support the primary message transmission. On the other hand, the second signal is chosen to be from the IGS scheme in order to reduce the interference effect on the primary receiver. We then optimally design the overlay cognitive radio that employs IGS to maximize the secondary link achievable rate while satisfying the minimum rate requirement of the primary network. In particular, we derive closed form expressions for the circularity coefficient used in the IGS scheme and the power distribution parameters. Simulation results are provided to support our theoretical derivations.

Two-component scattering model and the electron density spectrum

Science.gov (United States)

Zhou, A. Z.; Tan, J. Y.; Esamdin, A.; Wu, X. J.

2010-02-01

In this paper, we discuss a rigorous treatment of the refractive scintillation caused by a two-component interstellar scattering medium and a Kolmogorov form of density spectrum. It is assumed that the interstellar scattering medium is composed of a thin-screen interstellar medium (ISM) and an extended interstellar medium. We consider the case that the scattering of the thin screen concentrates in a thin layer represented by a δ function distribution and that the scattering density of the extended irregular medium satisfies the Gaussian distribution. We investigate and develop equations for the flux density structure function corresponding to this two-component ISM geometry in the scattering density distribution and compare our result with the observations. We conclude that the refractive scintillation caused by this two-component ISM scattering gives a more satisfactory explanation for the observed flux density variation than does the single extended medium model. The level of refractive scintillation is strongly sensitive to the distribution of scattering material along the line of sight (LOS). The theoretical modulation indices are comparatively less sensitive to the scattering strength of the thin-screen medium, but they critically depend on the distance from the observer to the thin screen. The logarithmic slope of the structure function is sensitive to the scattering strength of the thin-screen medium, but is relatively insensitive to the thin-screen location. Therefore, the proposed model can be applied to interpret the structure functions of flux density observed in pulsar PSR B2111 + 46 and PSR B0136 + 57. The result suggests that the medium consists of a discontinuous distribution of plasma turbulence embedded in the interstellar medium. Thus our work provides some insight into the distribution of the scattering along the LOS to the pulsar PSR B2111 + 46 and PSR B0136 + 57.
Photonic generation of FCC-compliant UWB pulses based on modified Gaussian quadruplet and incoherent wavelength-to-time conversion

Science.gov (United States)

Mu, Hongqian; Wang, Muguang; Tang, Yu; Zhang, Jing; Jian, Shuisheng

2018-03-01

A novel scheme for the generation of FCC-compliant UWB pulse is proposed based on modified Gaussian quadruplet and incoherent wavelength-to-time conversion. The modified Gaussian quadruplet is synthesized based on linear sum of a broad Gaussian pulse and two narrow Gaussian pulses with the same pulse-width and amplitude peak. Within specific parameter range, FCC-compliant UWB with spectral power efficiency of higher than 39.9% can be achieved. In order to realize the designed waveform, a UWB generator based on spectral shaping and incoherent wavelength-to-time mapping is proposed. The spectral shaper is composed of a Gaussian filter and a programmable filter. Single-mode fiber functions as both dispersion device and transmission medium. Balanced photodetection is employed to combine linearly the broad Gaussian pulse and two narrow Gaussian pulses, and at same time to suppress pulse pedestals that result in low-frequency components. The proposed UWB generator can be reconfigured for UWB doublet by operating the programmable filter as a single-band Gaussian filter. The feasibility of proposed UWB generator is demonstrated experimentally. Measured UWB pulses match well with simulation results. FCC-compliant quadruplet with 10-dB bandwidth of 6.88-GHz, fractional bandwidth of 106.8% and power efficiency of 51% is achieved.
Gaussian entanglement distribution via satellite

Science.gov (United States)

Hosseinidehaj, Nedasadat; Malaney, Robert

2015-02-01

In this work we analyze three quantum communication schemes for the generation of Gaussian entanglement between two ground stations. Communication occurs via a satellite over two independent atmospheric fading channels dominated by turbulence-induced beam wander. In our first scheme, the engineering complexity remains largely on the ground transceivers, with the satellite acting simply as a reflector. Although the channel state information of the two atmospheric channels remains unknown in this scheme, the Gaussian entanglement generation between the ground stations can still be determined. On the ground, distillation and Gaussification procedures can be applied, leading to a refined Gaussian entanglement generation rate between the ground stations. We compare the rates produced by this first scheme with two competing schemes in which quantum complexity is added to the satellite, thereby illustrating the tradeoff between space-based engineering complexity and the rate of ground-station entanglement generation.
PRECISION MEASUREMENTS OF THE CLUSTER RED SEQUENCE USING AN ERROR-CORRECTED GAUSSIAN MIXTURE MODEL

International Nuclear Information System (INIS)

Hao Jiangang; Annis, James; Koester, Benjamin P.; Mckay, Timothy A.; Evrard, August; Gerdes, David; Rykoff, Eli S.; Rozo, Eduardo; Becker, Matthew; Busha, Michael; Wechsler, Risa H.; Johnston, David E.; Sheldon, Erin

2009-01-01

The red sequence is an important feature of galaxy clusters and plays a crucial role in optical cluster detection. Measurement of the slope and scatter of the red sequence are affected both by selection of red sequence galaxies and measurement errors. In this paper, we describe a new error-corrected Gaussian Mixture Model for red sequence galaxy identification. Using this technique, we can remove the effects of measurement error and extract unbiased information about the intrinsic properties of the red sequence. We use this method to select red sequence galaxies in each of the 13,823 clusters in the maxBCG catalog, and measure the red sequence ridgeline location and scatter of each. These measurements provide precise constraints on the variation of the average red galaxy populations in the observed frame with redshift. We find that the scatter of the red sequence ridgeline increases mildly with redshift, and that the slope decreases with redshift. We also observe that the slope does not strongly depend on cluster richness. Using similar methods, we show that this behavior is mirrored in a spectroscopic sample of field galaxies, further emphasizing that ridgeline properties are independent of environment. These precise measurements serve as an important observational check on simulations and mock galaxy catalogs. The observed trends in the slope and scatter of the red sequence ridgeline with redshift are clues to possible intrinsic evolution of the cluster red sequence itself. Most importantly, the methods presented in this work lay the groundwork for further improvements in optically based cluster cosmology.
Precision Measurements of the Cluster Red Sequence using an Error Corrected Gaussian Mixture Model

Energy Technology Data Exchange (ETDEWEB)

Hao, Jiangang; /Fermilab /Michigan U.; Koester, Benjamin P.; /Chicago U.; Mckay, Timothy A.; /Michigan U.; Rykoff, Eli S.; /UC, Santa Barbara; Rozo, Eduardo; /Ohio State U.; Evrard, August; /Michigan U.; Annis, James; /Fermilab; Becker, Matthew; /Chicago U.; Busha, Michael; /KIPAC, Menlo Park /SLAC; Gerdes, David; /Michigan U.; Johnston, David E.; /Northwestern U. /Brookhaven

2009-07-01

The red sequence is an important feature of galaxy clusters and plays a crucial role in optical cluster detection. Measurement of the slope and scatter of the red sequence are affected both by selection of red sequence galaxies and measurement errors. In this paper, we describe a new error corrected Gaussian Mixture Model for red sequence galaxy identification. Using this technique, we can remove the effects of measurement error and extract unbiased information about the intrinsic properties of the red sequence. We use this method to select red sequence galaxies in each of the 13,823 clusters in the maxBCG catalog, and measure the red sequence ridgeline location and scatter of each. These measurements provide precise constraints on the variation of the average red galaxy populations in the observed frame with redshift. We find that the scatter of the red sequence ridgeline increases mildly with redshift, and that the slope decreases with redshift. We also observe that the slope does not strongly depend on cluster richness. Using similar methods, we show that this behavior is mirrored in a spectroscopic sample of field galaxies, further emphasizing that ridgeline properties are independent of environment. These precise measurements serve as an important observational check on simulations and mock galaxy catalogs. The observed trends in the slope and scatter of the red sequence ridgeline with redshift are clues to possible intrinsic evolution of the cluster red-sequence itself. Most importantly, the methods presented in this work lay the groundwork for further improvements in optically-based cluster cosmology.
Improved gas mixtures for gas-filled particle detectors

Science.gov (United States)

Christophorou, L.G.; McCorkle, D.L.; Maxey, D.V.; Carter, J.G.

Improved binary and tertiary gas mixture for gas-filled particle detectors are provided. The components are chosen on the basis of the principle that the first component is one gas or mixture of two gases having a large electron scattering cross section at energies of about 0.5 eV and higher, and the second component is a gas (Ar) having a very small cross section at and below about 0.5 eV; whereby fast electrons in the gaseous mixture are slowed into the energy range of about 0.5 eV where the cross section for the mixture is small and hence the electron mean free path is large. The reduction in both the cross section and the electron energy results in an increase in the drift velocity of the electrons in the gas mixtures over that for the separate components for a range of E/P (pressure-reduced electron field) values. Several gas mixtures are provided that provide faster response in gas-filled detectors for convenient E/P ranges as compared with conventional gas mixtures.
Simultaneous biodegradation of volatile and toxic contaminant mixtures by solid–liquid two-phase partitioning bioreactors

Energy Technology Data Exchange (ETDEWEB)

Poleo, Eduardo E.; Daugulis, Andrew J., E-mail: andrew.daugulis@chee.queensu.ca

2013-06-15

Highlights: • We investigate the simultaneous biodegradation of phenol and butyl acetate. • We identify an effective polymer mixture to selectively absorb each of the substrates and decrease their initial concentration. •The polymer mixture is used to overcome the high phenol cytotoxicity and reduce the abiotic losses of butyl acetate associated with volatility. • The solid–liquid Two Phase Partitioning Bioreactor (TPPB) outperforms the liquid–liquid TPPB and the single phase systems. -- Abstract: Microbial inhibition and stripping of volatile compounds are two common problems encountered in the biotreatment of contaminated wastewaters. Both can be addressed by the addition of a hydrophobic auxiliary phase that can absorb and subsequently re-release the substrates, lowering their initial aqueous concentrations. Such systems have been described as Two Phase Partitioning Bioreactors (TPPBs). In the current work the performances of a solid–liquid TPPB, a liquid–liquid TPPB and a single phase reactor for the simultaneous degradation of butyl acetate (the volatile component) and phenol (the toxic component) have been compared. The auxiliary phase used in the solid–liquid TPPB was a 50:50 polymer mixture of styrene–butadiene rubber and Hytrel{sup ®} 8206, with high affinities for butyl acetate and phenol, respectively. The liquid–liquid TPPB employed silicone oil which has fixed physical properties, and had no capacity to absorb the toxic contaminant (phenol). Butyl acetate degradation was enhanced in both TPPBs relative to the single phase, arising from its sequestration into the auxiliary phase, thereby reducing volatilization losses. The solid–liquid TPPB additionally showed a substantial increase in the phenol degradation rate, relative to the silicone oil system, demonstrating the superiority and versatility of polymer based systems.
Entanglement of two-mode Gaussian states: characterization and experimental production and manipulation

Energy Technology Data Exchange (ETDEWEB)

Laurat, Julien [Laboratoire Kastler Brossel, Case 74, Universite Pierre et Marie curie, 4 Place Jussieu, 75252 Paris cedex 05 (France); Keller, Gaelle [Laboratoire Kastler Brossel, Case 74, Universite Pierre et Marie curie, 4 Place Jussieu, 75252 Paris cedex 05 (France); Oliveira-Huguenin, Jose Augusto [Laboratoire Kastler Brossel, Case 74, Universite Pierre et Marie curie, 4 Place Jussieu, 75252 Paris cedex 05 (France); Fabre, Claude [Laboratoire Kastler Brossel, Case 74, Universite Pierre et Marie curie, 4 Place Jussieu, 75252 Paris cedex 05 (France); Coudreau, Thomas [Laboratoire Kastler Brossel, Case 74, Universite Pierre et Marie curie, 4 Place Jussieu, 75252 Paris cedex 05 (France); Laboratoire Materiaux et Phenomenes Quantiques, Case 7021, Universite Denis Diderot, 2 Place Jussieu, 75251 Paris cedex 05 (France); Serafini, Alessio [Dipartimento di Fisica ' E R Caianiello' , Universita di Salerno (Italy); CNR-Coherentia, Gruppo di Salerno (Italy); and INFN Sezione di Napoli-Gruppo Collegato di Salerno, Via S Allende, 84081 Baronissi (Saudi Arabia) (Italy); Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); Adesso, Gerardo [Dipartimento di Fisica ' E R Caianiello' , Universita di Salerno (Italy); CNR-Coherentia, Gruppo di Salerno (Italy); and INFN Sezione di Napoli-Gruppo Collegato di Salerno, Via S Allende, 84081 Baronissi (Saudi Arabia) (Italy); Illuminati, Fabrizio [Dipartimento di Fisica ' E R Caianiello' , Universita di Salerno (Italy) and CNR-Coherentia, Gruppo di Salerno (Italy) and INFN Sezione di Napoli-Gruppo Collegato di Salerno, Via S Allende, 84081 Baronissi (SA) (Italy)

2005-12-01

A powerful theoretical structure has emerged in recent years on the characterization and quantification of entanglement in continuous-variable systems. After reviewing this framework, we will illustrate it with an original set-up based on a type-II OPO (optical parametric oscillator) with adjustable mode coupling. Experimental results allow a direct verification of many theoretical predictions and provide a sharp insight into the general properties of two-mode Gaussian states and entanglement resource manipulation.
Entanglement of two-mode Gaussian states: characterization and experimental production and manipulation

International Nuclear Information System (INIS)

Laurat, Julien; Keller, Gaelle; Oliveira-Huguenin, Jose Augusto; Fabre, Claude; Coudreau, Thomas; Serafini, Alessio; Adesso, Gerardo; Illuminati, Fabrizio

2005-01-01

A powerful theoretical structure has emerged in recent years on the characterization and quantification of entanglement in continuous-variable systems. After reviewing this framework, we will illustrate it with an original set-up based on a type-II OPO (optical parametric oscillator) with adjustable mode coupling. Experimental results allow a direct verification of many theoretical predictions and provide a sharp insight into the general properties of two-mode Gaussian states and entanglement resource manipulation
A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA.

Science.gov (United States)

Kim, Seongho; Ouyang, Ming; Jeong, Jaesik; Shen, Changyu; Zhang, Xiang

2014-06-01

We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models.
A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA*

Science.gov (United States)

Kim, Seongho; Ouyang, Ming; Jeong, Jaesik; Shen, Changyu; Zhang, Xiang

2014-01-01

We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models. PMID:25264474
Interconversion of pure Gaussian states requiring non-Gaussian operations

Science.gov (United States)

Jabbour, Michael G.; García-Patrón, Raúl; Cerf, Nicolas J.

2015-01-01

We analyze the conditions under which local operations and classical communication enable entanglement transformations between bipartite pure Gaussian states. A set of necessary and sufficient conditions had been found [G. Giedke et al., Quant. Inf. Comput. 3, 211 (2003)] for the interconversion between such states that is restricted to Gaussian local operations and classical communication. Here, we exploit majorization theory in order to derive more general (sufficient) conditions for the interconversion between bipartite pure Gaussian states that goes beyond Gaussian local operations. While our technique is applicable to an arbitrary number of modes for each party, it allows us to exhibit surprisingly simple examples of 2 ×2 Gaussian states that necessarily require non-Gaussian local operations to be transformed into each other.
Gaussian operations and privacy

International Nuclear Information System (INIS)

Navascues, Miguel; Acin, Antonio

2005-01-01

We consider the possibilities offered by Gaussian states and operations for two honest parties, Alice and Bob, to obtain privacy against a third eavesdropping party, Eve. We first extend the security analysis of the protocol proposed in [Navascues et al. Phys. Rev. Lett. 94, 010502 (2005)]. Then, we prove that a generalized version of this protocol does not allow one to distill a secret key out of bound entangled Gaussian states
Quantum particle-number fluctuations in a two-component Bose gas in a double-well potential

International Nuclear Information System (INIS)

Zin, Pawel; Oles, Bartlomiej; Sacha, Krzysztof

2011-01-01

A two-component Bose gas in a double-well potential with repulsive interactions may undergo a phase separation transition if the interspecies interactions outweigh the intraspecies ones. We analyze the transition in the strong interaction limit within the two-mode approximation. Numbers of particles in each potential well are equal and constant. However, at the transition point, the ground state of the system reveals huge fluctuations of numbers of particles belonging to the different gas components; that is, the probability for observation of any mixture of particles in each potential well becomes uniform.
Doppler spectroscopy of hydrogen Balmer lines in a hollow cathode glow discharge in ammonia and argon-ammonia mixture

International Nuclear Information System (INIS)

Sisovic, N. M.; Konjevic, N.

2008-01-01

The results of Doppler spectroscopy of hydrogen Balmer lines from a stainless steel (SS) and copper (Cu) hollow cathode (HC) glow discharge in ammonia and argon-ammonia mixture are reported. The experimental profiles in ammonia discharge are fitted well by superposing three Gaussian profiles. The half widths, in energy units, of narrow and medium Gaussians are in the ranges 0.3-0.4 eV and 3-4 eV, respectively, for both hollow cathodes what is expected on the basis of earlier electron beam→NH 3 experiments. The half widths of the largest Gaussian in ammonia are 46 and 55 eV for SS and Cu HC, respectively. In argon-ammonia discharge, three Gaussians are also required to fit experimental profiles. While half widths of narrow and medium Gaussians are similar to those in ammonia, the half widths of the largest Gaussians are 35 and 42 eV for SS and Cu HC, respectively. The half widths of the largest Gaussians in ammonia and in argon-ammonia mixture indicate the presence of excessive Doppler broadening.
Quantum steering of multimode Gaussian states by Gaussian measurements: monogamy relations and the Peres conjecture

International Nuclear Information System (INIS)

Ji, Se-Wan; Nha, Hyunchul; Kim, M S

2015-01-01

It is a topic of fundamental and practical importance how a quantum correlated state can be reliably distributed through a noisy channel for quantum information processing. The concept of quantum steering recently defined in a rigorous manner is relevant to study it under certain circumstances and here we address quantum steerability of Gaussian states to this aim. In particular, we attempt to reformulate the criterion for Gaussian steering in terms of local and global purities and show that it is sufficient and necessary for the case of steering a 1-mode system by an N-mode system. It subsequently enables us to reinforce a strong monogamy relation under which only one party can steer a local system of 1-mode. Moreover, we show that only a negative partial-transpose state can manifest quantum steerability by Gaussian measurements in relation to the Peres conjecture. We also discuss our formulation for the case of distributing a two-mode squeezed state via one-way quantum channels making dissipation and amplification effects, respectively. Finally, we extend our approach to include non-Gaussian measurements, more precisely, all orders of higher-order squeezing measurements, and find that this broad set of non-Gaussian measurements is not useful to demonstrate steering for Gaussian states beyond Gaussian measurements. (paper)
Self-consistent field theory of protein adsorption in a non-Gaussian polyelectrolyte brush

NARCIS (Netherlands)

Biesheuvel, P.M.; Leermakers, F.A.M.; Stuart, M.A.C.

2006-01-01

To describe adsorption of globular protein molecules in a polyelectrolyte brush we use the strong-stretching approximation of the Edwards self-consistent field equation, combined with corrections for a non-Gaussian brush. To describe chemical potentials in this mixture of (globular) species of
New operator-ordering identities and associative integration formulas of two-variable Hermite polynomials for constructing non-Gaussian states

International Nuclear Information System (INIS)

Fan Hong-Yi; Wang Zhen

2014-01-01

For directly normalizing the photon non-Gaussian states (e.g., photon added and subtracted squeezed states), we use the method of integration within an ordered product (IWOP) of operators to derive some new bosonic operator-ordering identities. We also derive some new integration transformation formulas about one- and two-variable Hermite polynomials in complex function space. These operator identities and associative integration formulas provide much convenience for constructing non-Gaussian states in quantum engineering. (general)
Structural properties of dendrimer-colloid mixtures

International Nuclear Information System (INIS)

Lenz, Dominic A; Blaak, Ronald; Likos, Christos N

2012-01-01

We consider binary mixtures of colloidal particles and amphiphilic dendrimers of the second generation by means of Monte Carlo simulations. By using the effective interactions between monomer-resolved dendrimers and colloids, we compare the results of simulations of mixtures stemming from a full monomer-resolved description with the effective two-component description at different densities, composition ratios, colloid diameters and interaction strengths. Additionally, we map the two-component system onto an effective one-component model for the colloids in the presence of the dendrimers. Simulations based on the resulting depletion potentials allow us to extend the comparison to yet another level of coarse graining and to examine under which conditions this two-step approach is valid. In addition, a preliminary outlook into the phase behavior of this system is given. (paper)
XeBr excilamp based on a non-toxic component mixture

Energy Technology Data Exchange (ETDEWEB)

Kelman, V A; Shpenik, Yu O; Zhmenyak, Yu V, E-mail: mironkle@rambler.ru [Institute of Electron Physics, National Academy of Sciences of Ukraine, Universitetska 21, 88017 Uzhgorod (Ukraine)

2011-06-29

This paper presents the results of experimental studies on obtaining UV luminescence of XeBr* molecules at the excitation of a non-toxic Xe-CsBr gas-vapour mixture by a longitudinal pulse-periodic discharge. Effective UV emission yield of the exciplex XeBr* molecules (spectral maximum at 282 nm) is observed within a wide range of excitation conditions. The spectral distribution in the UV emission under the optimal excitation conditions does not differ essentially from that in other XeBr excilamps based on toxic components. The emission of the B {yields} X band of the XeBr* molecules provides the main contribution to the total power of the discharge UV emission. The determined average power of the UV emission for the experimental discharge tube is 12 W at an efficiency of 1%. Spectral, power-related and time-dependent parameters of the laboratory excilamp are presented for a wide range of excitation parameters. A new mechanism of exciplex molecule formation at the excitation of a rare gas/alkali halide vapour mixture is discussed.

XeBr excilamp based on a non-toxic component mixture

International Nuclear Information System (INIS)

Kelman, V A; Shpenik, Yu O; Zhmenyak, Yu V

2011-01-01

This paper presents the results of experimental studies on obtaining UV luminescence of XeBr* molecules at the excitation of a non-toxic Xe-CsBr gas-vapour mixture by a longitudinal pulse-periodic discharge. Effective UV emission yield of the exciplex XeBr* molecules (spectral maximum at 282 nm) is observed within a wide range of excitation conditions. The spectral distribution in the UV emission under the optimal excitation conditions does not differ essentially from that in other XeBr excilamps based on toxic components. The emission of the B → X band of the XeBr* molecules provides the main contribution to the total power of the discharge UV emission. The determined average power of the UV emission for the experimental discharge tube is 12 W at an efficiency of 1%. Spectral, power-related and time-dependent parameters of the laboratory excilamp are presented for a wide range of excitation parameters. A new mechanism of exciplex molecule formation at the excitation of a rare gas/alkali halide vapour mixture is discussed.
Tight focusing of a radially polarized Laguerre–Bessel–Gaussian beam and its application to manipulation of two types of particles

International Nuclear Information System (INIS)

Nie, Zhongquan; Shi, Guang; Li, Dongyu; Zhang, Xueru; Wang, Yuxiao; Song, Yinglin

2015-01-01

The intensity distributions near the focus for radially polarized Laguerre–Bessel–Gaussian beams by a high numerical aperture objective in the immersion liquid are computed based on the vector diffraction theory. We compare the focusing properties of the radially polarized Laguerre–Bessel–Gaussian beams with those of Laguerre–Gaussian and Bessel–Gaussian modes. Furthermore, the effects of the optimally designed concentric three-zone phase filters on the intensity profiles in the focal region are examined. We further analyze the radiation forces on Rayleigh particles produced by the highly focused radially polarized Laguerre–Bessel–Gaussian beams using the specially engineered three-zone phase filters. - Highlights: • The tightly focusing of radially polarized LBG beams is examined. • The focusing performances of LBG beams are preferable over that of LG and BG modes. • A bright spot and an optical cage can be formed by special phase modulation. • These special focusing patterns can stably manipulate two types of particles
Independent component analysis: recent advances

OpenAIRE

Hyv?rinen, Aapo

2013-01-01

Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in th...
Investigations of an excimer laser working with a four-component gaseous mixture He-Kr:Xe-HCl

Science.gov (United States)

Iwanejko, Leszek; Pokora, Ludwik J.

1991-08-01

The paper presnts working conditions of an XCI excimer laser untypical gas mixture based on KrzXe instead of pure Xe. Such a choice was influenced by the necessity of Findin9 the way to replace imported and expensive Xe by gaseous components accesible in Poland. Determining the range of changes of laser extrnal parameters which enables its proper work with the new gas mixture was the aim of same investigations results of which are presented in this paper. The laser pulse output energy and the pulse duration as a Function of supply voltage and the mixture composition are presented. The range of proper conditions for the laser working with the new mixture He-Kr:Xe--HC1 was determined. The analysis of experimental results showed that using the new mixture ensures value of energy and pulse duration comparable with the ones obtained for the mixture He-''Xe--HCl. Spectral investigations showed the lack of influence of Kr presence in the mixture on the generation spectrum of the laser. L.
Non-gaussian turbulence

Energy Technology Data Exchange (ETDEWEB)

Hoejstrup, J [NEG Micon Project Development A/S, Randers (Denmark); Hansen, K S [Denmarks Technical Univ., Dept. of Energy Engineering, Lyngby (Denmark); Pedersen, B J [VESTAS Wind Systems A/S, Lem (Denmark); Nielsen, M [Risoe National Lab., Wind Energy and Atmospheric Physics, Roskilde (Denmark)

1999-03-01

The pdf`s of atmospheric turbulence have somewhat wider tails than a Gaussian, especially regarding accelerations, whereas velocities are close to Gaussian. This behaviour is being investigated using data from a large WEB-database in order to quantify the amount of non-Gaussianity. Models for non-Gaussian turbulence have been developed, by which artificial turbulence can be generated with specified distributions, spectra and cross-correlations. The artificial time series will then be used in load models and the resulting loads in the Gaussian and the non-Gaussian cases will be compared. (au)
Controllable gaussian-qubit interface for extremal quantum state engineering.

Science.gov (United States)

Adesso, Gerardo; Campbell, Steve; Illuminati, Fabrizio; Paternostro, Mauro

2010-06-18

We study state engineering through bilinear interactions between two remote qubits and two-mode gaussian light fields. The attainable two-qubit states span the entire physically allowed region in the entanglement-versus-global-purity plane. Two-mode gaussian states with maximal entanglement at fixed global and marginal entropies produce maximally entangled two-qubit states in the corresponding entropic diagram. We show that a small set of parameters characterizing extremally entangled two-mode gaussian states is sufficient to control the engineering of extremally entangled two-qubit states, which can be realized in realistic matter-light scenarios.
Digital simulation of two-dimensional random fields with arbitrary power spectra and non-Gaussian probability distribution functions.

Science.gov (United States)

Yura, Harold T; Hanson, Steen G

2012-04-01

Methods for simulation of two-dimensional signals with arbitrary power spectral densities and signal amplitude probability density functions are disclosed. The method relies on initially transforming a white noise sample set of random Gaussian distributed numbers into a corresponding set with the desired spectral distribution, after which this colored Gaussian probability distribution is transformed via an inverse transform into the desired probability distribution. In most cases the method provides satisfactory results and can thus be considered an engineering approach. Several illustrative examples with relevance for optics are given.
Gaussian statistics for palaeomagnetic vectors

Science.gov (United States)

Love, J.J.; Constable, C.G.

2003-01-01

With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimoda) non-zero mean, spherically-symmetrical (isotropic) Gaussian function. For palaeomagnetic data of mixed polarities, we consider a bimodal distribution consisting of a pair of such symmetrical Gaussian functions, with equal, but opposite, means and equal variances. For both the Gaussian and bi-Gaussian distributions, and in the spherical three-space of intensity, inclination, and declination, we obtain analytical expressions for the marginal density functions, the cumulative distributions, and the expected values and variances for each spherical coordinate (including the angle with respect to the axis of symmetry of the distributions). The mathematical expressions for the intensity and off-axis angle are closed-form and especially manageable, with the intensity distribution being Rayleigh-Rician. In the limit of small relative vectorial dispersion, the Gaussian (bi-Gaussian) directional distribution approaches a Fisher (Bingham) distribution and the intensity distribution approaches a normal distribution. In the opposite limit of large relative vectorial dispersion, the directional distributions approach a spherically-uniform distribution and the intensity distribution approaches a Maxwell distribution. We quantify biases in estimating the properties of the vector field resulting from the use of simple arithmetic averages, such as estimates of the intensity or the inclination of the mean vector, or the variances of these quantities. With the statistical framework developed here and using the maximum-likelihood method, which gives unbiased estimates in the limit of large data numbers, we demonstrate how to
Gaussian statistics for palaeomagnetic vectors

Science.gov (United States)

Love, J. J.; Constable, C. G.

2003-03-01

With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimodal) non-zero mean, spherically-symmetrical (isotropic) Gaussian function. For palaeomagnetic data of mixed polarities, we consider a bimodal distribution consisting of a pair of such symmetrical Gaussian functions, with equal, but opposite, means and equal variances. For both the Gaussian and bi-Gaussian distributions, and in the spherical three-space of intensity, inclination, and declination, we obtain analytical expressions for the marginal density functions, the cumulative distributions, and the expected values and variances for each spherical coordinate (including the angle with respect to the axis of symmetry of the distributions). The mathematical expressions for the intensity and off-axis angle are closed-form and especially manageable, with the intensity distribution being Rayleigh-Rician. In the limit of small relative vectorial dispersion, the Gaussian (bi-Gaussian) directional distribution approaches a Fisher (Bingham) distribution and the intensity distribution approaches a normal distribution. In the opposite limit of large relative vectorial dispersion, the directional distributions approach a spherically-uniform distribution and the intensity distribution approaches a Maxwell distribution. We quantify biases in estimating the properties of the vector field resulting from the use of simple arithmetic averages, such as estimates of the intensity or the inclination of the mean vector, or the variances of these quantities. With the statistical framework developed here and using the maximum-likelihood method, which gives unbiased estimates in the limit of large data numbers, we demonstrate how to
Shedding new light on Gaussian harmonic analysis

NARCIS (Netherlands)

Teuwen, J.J.B.

2016-01-01

This dissertation consists out of two rather disjoint parts. One part concerns some results on Gaussian harmonic analysis and the other on an optimization problem in optics. In the first part we study the Ornstein–Uhlenbeck process with respect to the Gaussian measure. We focus on two areas. One is
Entanglement in Gaussian matrix-product states

International Nuclear Information System (INIS)

Adesso, Gerardo; Ericsson, Marie

2006-01-01

Gaussian matrix-product states are obtained as the outputs of projection operations from an ancillary space of M infinitely entangled bonds connecting neighboring sites, applied at each of N sites of a harmonic chain. Replacing the projections by associated Gaussian states, the building blocks, we show that the entanglement range in translationally invariant Gaussian matrix-product states depends on how entangled the building blocks are. In particular, infinite entanglement in the building blocks produces fully symmetric Gaussian states with maximum entanglement range. From their peculiar properties of entanglement sharing, a basic difference with spin chains is revealed: Gaussian matrix-product states can possess unlimited, long-range entanglement even with minimum number of ancillary bonds (M=1). Finally we discuss how these states can be experimentally engineered from N copies of a three-mode building block and N two-mode finitely squeezed states
Segregation of granular binary mixtures by a ratchet mechanism.

Science.gov (United States)

Farkas, Zénó; Szalai, Ferenc; Wolf, Dietrich E; Vicsek, Tamás

2002-02-01

We report on a segregation scheme for granular binary mixtures, where the segregation is performed by a ratchet mechanism realized by a vertically shaken asymmetric sawtooth-shaped base in a quasi-two-dimensional box. We have studied this system by computer simulations and found that most binary mixtures can be segregated using an appropriately chosen ratchet, even when the particles in the two components have the same size and differ only in their normal restitution coefficient or friction coefficient. These results suggest that the components of otherwise nonsegregating granular mixtures may be separated using our method.
A robust and coherent network statistic for detecting gravitational waves from inspiralling compact binaries in non-Gaussian noise

CERN Document Server

Bose, S

2002-01-01

The robust statistic proposed by Creighton (Creighton J D E 1999 Phys. Rev. D 60 021101) and Allen et al (Allen et al 2001 Preprint gr-gc/010500) for the detection of stationary non-Gaussian noise is briefly reviewed. We compute the robust statistic for generic weak gravitational-wave signals in the mixture-Gaussian noise model to an accuracy higher than in those analyses, and reinterpret its role. Specifically, we obtain the coherent statistic for detecting gravitational-wave signals from inspiralling compact binaries with an arbitrary network of earth-based interferometers. Finally, we show that excess computational costs incurred owing to non-Gaussianity is negligible compared to the cost of detection in Gaussian noise.
Bilocal current densities and mean trajectories in a Young interferometer with two Gaussian slits and two detectors

Energy Technology Data Exchange (ETDEWEB)

Withers, L. P., E-mail: lpwithers@mitre.org [School of Physics, Astronomy, and Computational Science, George Mason University, Fairfax, Virginia 22030-4444 (United States); Narducci, F. A., E-mail: francesco.narducci@navy.mil [Naval Air Systems Command, Patuxent River, Maryland 20670 (United States)

2015-06-15

The recent single-photon double-slit experiment of Steinberg et al., based on a weak measurement method proposed by Wiseman, showed that, by encoding the photon’s transverse momentum behind the slits into its polarization state, the momentum profile can subsequently be measured on average, from a difference of the separated fringe intensities for the two circular polarization components. They then integrated the measured average velocity field, to obtain the average trajectories of the photons enroute to the detector array. In this paper, we propose a modification of their experiment, to demonstrate that the average particle velocities and trajectories change when the mode of detection changes. The proposed experiment replaces a single detector by a pair of detectors with a given spacing between them. The pair of detectors is configured so that it is impossible to distinguish which detector received the particle. The pair of detectors is then analogous to the simple pair of slits, in that it is impossible to distinguish which slit the particle passed through. To establish the paradoxical outcome of the modified experiment, the theory and explicit three-dimensional formulas are developed for the bilocal probability and current densities, and for the average velocity field and trajectories as the particle wavefunction propagates in the volume of space behind the Gaussian slits. Examples of these predicted results are plotted. Implementation details of the proposed experiment are discussed.
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies.

Science.gov (United States)

Savitsky, Terrance; Vannucci, Marina; Sha, Naijun

2011-02-01

This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data. Our specific interest is in the analysis of data sets with predictors that have an a priori unknown form of possibly nonlinear associations to the response. The modeling approach we describe incorporates Gaussian processes in a generalized linear model framework to obtain a class of nonparametric regression models where the covariance matrix depends on the predictors. We consider, in particular, continuous, categorical and count responses. We also look into models that account for survival outcomes. We explore alternative covariance formulations for the Gaussian process prior and demonstrate the flexibility of the construction. Next, we focus on the important problem of selecting variables from the set of possible predictors and describe a general framework that employs mixture priors. We compare alternative MCMC strategies for posterior inference and achieve a computationally efficient and practical approach. We demonstrate performances on simulated and benchmark data sets.
Nonlinear Bayesian Estimation of BOLD Signal under Non-Gaussian Noise

Directory of Open Access Journals (Sweden)

Ali Fahim Khan

2015-01-01

Full Text Available Modeling the blood oxygenation level dependent (BOLD signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the e-mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF.
Interplay of nonclassicality and entanglement of two-mode Gaussian fields generated in optical parametric processes

Czech Academy of Sciences Publication Activity Database

Arkhipov, Ie.I.; Peřina, Jan; Peřina, J.; Miranowicz, A.

2016-01-01

Roč. 94, č. 1 (2016), 1-15, č. článku 013807. ISSN 2469-9926 R&D Projects: GA ČR GAP205/12/0382 Institutional support: RVO:68378271 Keywords : two-mode Gaussian fields * optical parametric processes Subject RIV: BH - Optics, Masers, Lasers Impact factor: 2.925, year: 2016
Statistical-mechanics analysis of Gaussian labeled-unlabeled classification problems

International Nuclear Information System (INIS)

Tanaka, Toshiyuki

2013-01-01

The labeled-unlabeled classification problem in semi-supervised learning is studied via statistical-mechanics approach. We analytically investigate performance of a learner with an equal-weight mixture of two symmetrically-located Gaussians, performing posterior mean estimation of the parameter vector on the basis of a dataset consisting of labeled and unlabeled data generated from the same probability model as that assumed by the learner. Under the assumption of replica symmetry, we have analytically obtained a set of saddle-point equations, which allows us to numerically evaluate performance of the learner. On the basis of the analytical result we have observed interesting phenomena, in particular the coexistence of good and bad solutions, which may happen when the number of unlabeled data is relatively large compared with that of labeled data
How Many Separable Sources? Model Selection In Independent Components Analysis

DEFF Research Database (Denmark)

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian....
Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise.

Science.gov (United States)

Zhang, Jiachao; Hirakawa, Keigo

2017-04-01

This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. A quantile analysis in pixel, wavelet transform, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed to correct the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we propose a mixture of Poisson denoising method to remove the denoising artifacts without affecting image details, such as edge and textures. Experiments with real sensor data verify that denoising for real image sensor data is indeed improved by this new technique.

A Two-Stage Layered Mixture Experiment Design for a Nuclear Waste Glass Application-Part 1

International Nuclear Information System (INIS)

Cooley, Scott K.; Piepel, Gregory F.; Gan, Hao; Kot, Wing; Pegg, Ian L.

2003-01-01

A layered experimental design involving mixture variables was generated to support developing property-composition models for high-level waste (HLW) glasses. The design was generated in two stages, each having unique characteristics. Each stage used a layered design having an outer layer, an inner layer, a center point, and some replicates. The layers were defined by single- and multi-variable constraints. The first stage involved 15 glass components treated as mixture variables. For each layer, vertices were generated and optimal design software was used to select alternative subsets of vertices and calculate design optimality measures. Two partial quadratic mixture models, containing 25 terms for the outer layer and 30 terms for the inner layer, were the basis for the optimal design calculations. Distributions of predicted glass property values were plotted and evaluated for the alternative subsets of vertices. Based on the optimality measures and the predicted property distributions, a ''best'' subset of vertices was selected for each layer to form a layered design for the first stage. The design for the second stage was selected to augment the first-stage design. The discussion of the second-stage design begins in this Part 1 and is continued in Part 2 (Cooley and Piepel, 2003b)
Gaussian discriminating strength

Science.gov (United States)

Rigovacca, L.; Farace, A.; De Pasquale, A.; Giovannetti, V.

2015-10-01

We present a quantifier of nonclassical correlations for bipartite, multimode Gaussian states. It is derived from the Discriminating Strength measure, introduced for finite dimensional systems in Farace et al., [New J. Phys. 16, 073010 (2014), 10.1088/1367-2630/16/7/073010]. As the latter the new measure exploits the quantum Chernoff bound to gauge the susceptibility of the composite system with respect to local perturbations induced by unitary gates extracted from a suitable set of allowed transformations (the latter being identified by posing some general requirements). Closed expressions are provided for the case of two-mode Gaussian states obtained by squeezing or by linearly mixing via a beam splitter a factorized two-mode thermal state. For these density matrices, we study how nonclassical correlations are related with the entanglement present in the system and with its total photon number.
Identification and estimation of non-Gaussian structural vector autoregressions

DEFF Research Database (Denmark)

Lanne, Markku; Meitz, Mika; Saikkonen, Pentti

-Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence......, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions....
Composite optical vortices in noncollinear Laguerre–Gaussian beams and their propagation in free space

International Nuclear Information System (INIS)

Chen Ke; Liu Pusheng; Lü Baida

2008-01-01

Taking two Laguerre—Gaussian beams with topological charge l = ± 1 as an example, this paper studies the composite optical vortices formed by two noncollinear Laguerre—Gaussian beams with different phases, amplitudes, waist widths, off-axis distances, and their propagation in free space. It is shown by detailed numerical illustrative examples that the number and location of composite vortices at the waist plane are variable by varying the relative phase β, amplitude ratio η, waist width ratio ζ, or off-axis distance ratio μ. The net topological charge l net is not always equal to the sum l sum of charges of the two component beams. The motion, creation and annihilation of composite vortices take place in the free-space propagation, and the net charge during the propagation remains unchanged and equals to the net charge at the waist plane
Topology in two dimensions. IV - CDM models with non-Gaussian initial conditions

Science.gov (United States)

Coles, Peter; Moscardini, Lauro; Plionis, Manolis; Lucchin, Francesco; Matarrese, Sabino; Messina, Antonio

1993-02-01

The results of N-body simulations with both Gaussian and non-Gaussian initial conditions are used here to generate projected galaxy catalogs with the same selection criteria as the Shane-Wirtanen counts of galaxies. The Euler-Poincare characteristic is used to compare the statistical nature of the projected galaxy clustering in these simulated data sets with that of the observed galaxy catalog. All the models produce a topology dominated by a meatball shift when normalized to the known small-scale clustering properties of galaxies. Models characterized by a positive skewness of the distribution of primordial density perturbations are inconsistent with the Lick data, suggesting problems in reconciling models based on cosmic textures with observations. Gaussian CDM models fit the distribution of cell counts only if they have a rather high normalization but possess too low a coherence length compared with the Lick counts. This suggests that a CDM model with extra large scale power would probably fit the available data.
Environmental risk assessment of biocidal products: identification of relevant components and reliability of a component-based mixture assessment.

Science.gov (United States)

Coors, Anja; Vollmar, Pia; Heim, Jennifer; Sacher, Frank; Kehrer, Anja

2018-01-01

Biocidal products are mixtures of one or more active substances (a.s.) and a broad range of formulation additives. There is regulatory guidance currently under development that will specify how the combined effects of the a.s. and any relevant formulation additives shall be considered in the environmental risk assessment of biocidal products. The default option is a component-based approach (CBA) by which the toxicity of the product is predicted from the toxicity of 'relevant' components using concentration addition. Hence, unequivocal and practicable criteria are required for identifying the 'relevant' components to ensure protectiveness of the CBA, while avoiding unnecessary workload resulting from including by default components that do not significantly contribute to the product toxicity. The present study evaluated a set of different criteria for identifying 'relevant' components using confidential information on the composition of 21 wood preservative products. Theoretical approaches were complemented by experimentally testing the aquatic toxicity of seven selected products. For three of the seven tested products, the toxicity was underestimated for the most sensitive endpoint (green algae) by more than factor 2 if only the a.s. were considered in the CBA. This illustrated the necessity of including at least some additives along with the a.s. Considering additives that were deemed 'relevant' by the tentatively established criteria reduced the underestimation of toxicity for two of the three products. A lack of data for one specific additive was identified as the most likely reason for the remaining toxicity underestimation of the third product. In three other products, toxicity was overestimated by more than factor 2, while prediction and observation fitted well for the seventh product. Considering all additives in the prediction increased only the degree of overestimation. Supported by theoretical calculations and experimental verifications, the present
Morphology-tunable and photoresponsive properties in a self-assembled two-component gel system.

Science.gov (United States)

Zhou, Yifeng; Xu, Miao; Yi, Tao; Xiao, Shuzhang; Zhou, Zhiguo; Li, Fuyou; Huang, Chunhui

2007-01-02

Photoresponsive C3-symmetrical trisurea self-assembling building blocks containing three azobenzene groups (LC10 and LC4) at the rim were designed and synthesized. By introducing a trisamide gelator (G18), which can self-aggregate through hydrogen bonds of acylamino moieties to form a fibrous network, the mixture of LC10 (or LC4) and G18 forms an organogel with coral-like supramolecular structure from 1,4-dioxane. The cooperation of hydrogen bonding and the hydrophobic diversity between these components are the main contributions to the specific superstructure. The two-component gel exhibits reversible photoisomerization from trans to cis transition without breakage of the gel state.
Shear viscosity of liquid mixtures: Mass dependence

International Nuclear Information System (INIS)

Kaushal, Rohan; Tankeshwar, K.

2002-06-01

Expressions for zeroth, second, and fourth sum rules of transverse stress autocorrelation function of two component fluid have been derived. These sum rules and Mori's memory function formalism have been used to study shear viscosity of Ar-Kr and isotopic mixtures. It has been found that theoretical result is in good agreement with the computer simulation result for the Ar-Kr mixture. The mass dependence of shear viscosity for different mole fraction shows that deviation from ideal linear model comes even from mass difference in two species of fluid mixture. At higher mass ratio shear viscosity of mixture is not explained by any of the emperical model. (author)
Shear viscosity of liquid mixtures Mass dependence

CERN Document Server

Kaushal, R

2002-01-01

Expressions for zeroth, second, and fourth sum rules of transverse stress autocorrelation function of two component fluid have been derived. These sum rules and Mori's memory function formalism have been used to study shear viscosity of Ar-Kr and isotopic mixtures. It has been found that theoretical result is in good agreement with the computer simulation result for the Ar-Kr mixture. The mass dependence of shear viscosity for different mole fraction shows that deviation from ideal linear model comes even from mass difference in two species of fluid mixture. At higher mass ratio shear viscosity of mixture is not explained by any of the emperical model.
Assessment of competition and yield advantage in addition series of barley variety mixtures

Directory of Open Access Journals (Sweden)

Kari Jokinen

1991-09-01

Full Text Available In an addition series experiment the competition between three barley varieties (Agneta, Arra and Porno and the yield performance of mixtures were evaluated. Also two levels of nitrogen fertilization (50 and 100 kgN/ha were applied. Two approaches (the replacement series and the linear regression equation were used to analyse the competitive relationship based on grain yields in two-component mixtures. In three component mixtures the replacement series approach was applied. Both methods showed a similar dominance order of the varieties with Arra always being dominant and Agneta subordinate. The relationship between varieties was independent of the number of varieties in the mixture. Increase in available nitrogen strengthened the competitiveness of Arra especially in the dense, two-variety mixtures. Some mixtures over yielded but the differences were not statistically significant. The yield advantage based on relative yield total or on the ratio of actual and expected yield was greatest when the density and nitrogen fertilization were low and especially when one component in the mixture was a rather low yielding variety (Agneta. The land equivalent ratios (LER (the reference pure culture yield was the maximum yield of each variety were close to one, suggesting that under optimal growing conditions the yield advantage of barley varietal mixtures is marginal.
Optimal cloning of mixed Gaussian states

International Nuclear Information System (INIS)

Guta, Madalin; Matsumoto, Keiji

2006-01-01

We construct the optimal one to two cloning transformation for the family of displaced thermal equilibrium states of a harmonic oscillator, with a fixed and known temperature. The transformation is Gaussian and it is optimal with respect to the figure of merit based on the joint output state and norm distance. The proof of the result is based on the equivalence between the optimal cloning problem and that of optimal amplification of Gaussian states which is then reduced to an optimization problem for diagonal states of a quantum oscillator. A key concept in finding the optimum is that of stochastic ordering which plays a similar role in the purely classical problem of Gaussian cloning. The result is then extended to the case of n to m cloning of mixed Gaussian states
Maximum likelihood estimation of semiparametric mixture component models for competing risks data.

Science.gov (United States)

Choi, Sangbum; Huang, Xuelin

2014-09-01

In the analysis of competing risks data, the cumulative incidence function is a useful quantity to characterize the crude risk of failure from a specific event type. In this article, we consider an efficient semiparametric analysis of mixture component models on cumulative incidence functions. Under the proposed mixture model, latency survival regressions given the event type are performed through a class of semiparametric models that encompasses the proportional hazards model and the proportional odds model, allowing for time-dependent covariates. The marginal proportions of the occurrences of cause-specific events are assessed by a multinomial logistic model. Our mixture modeling approach is advantageous in that it makes a joint estimation of model parameters associated with all competing risks under consideration, satisfying the constraint that the cumulative probability of failing from any cause adds up to one given any covariates. We develop a novel maximum likelihood scheme based on semiparametric regression analysis that facilitates efficient and reliable estimation. Statistical inferences can be conveniently made from the inverse of the observed information matrix. We establish the consistency and asymptotic normality of the proposed estimators. We validate small sample properties with simulations and demonstrate the methodology with a data set from a study of follicular lymphoma. © 2014, The International Biometric Society.
Self-selection of two diet components by Tennebrio molitor (Coleoptera: Tenebrionidae) larvae and its impact on fitness

Science.gov (United States)

We studied the ability of Tenebrio molitor L. (Coleoptera: Tenebrionidae) to self-select optimal ratios of two dietary components to approach nutritional balance and maximum fitness. Life table analysis was used to determine the fitness of T. molitor developing in diet mixtures comprised of four dif...
Use of Mixture Designs to Investigate Contribution of Minor Sex Pheromone Components to Trap Catch of the Carpenterworm Moth, Chilecomadia valdiviana.

Science.gov (United States)

Lapointe, Stephen L; Barros-Parada, Wilson; Fuentes-Contreras, Eduardo; Herrera, Heidy; Kinsho, Takeshi; Miyake, Yuki; Niedz, Randall P; Bergmann, Jan

2017-12-01

Field experiments were carried out to study responses of male moths of the carpenterworm, Chilecomadia valdiviana (Lepidoptera: Cossidae), a pest of tree and fruit crops in Chile, to five compounds previously identified from the pheromone glands of females. Previously, attraction of males to the major component, (7Z,10Z)-7,10-hexadecadienal, was clearly demonstrated while the role of the minor components was uncertain due to the use of an experimental design that left large portions of the design space unexplored. We used mixture designs to study the potential contributions to trap catch of the four minor pheromone components produced by C. valdiviana. After systematically exploring the design space described by the five pheromone components, we concluded that the major pheromone component alone is responsible for attraction of male moths in this species. The need for appropriate experimental designs to address the problem of assessing responses to mixtures of semiochemicals in chemical ecology is described. We present an analysis of mixture designs and response surface modeling and an explanation of why this approach is superior to commonly used, but statistically inappropriate, designs.
Comparison of Gaussian and non-Gaussian Atmospheric Profile Retrievals from Satellite Microwave Data

Science.gov (United States)

Kliewer, A.; Forsythe, J. M.; Fletcher, S. J.; Jones, A. S.

2017-12-01

The Cooperative Institute for Research in the Atmosphere at Colorado State University has recently developed two different versions of a mixed-distribution (lognormal combined with a Gaussian) based microwave temperature and mixing ratio retrieval system as well as the original Gaussian-based approach. These retrieval systems are based upon 1DVAR theory but have been adapted to use different descriptive statistics of the lognormal distribution to minimize the background errors. The input radiance data is from the AMSU-A and MHS instruments on the NOAA series of spacecraft. To help illustrate how the three retrievals are affected by the change in the distribution we are in the process of creating a new website to show the output from the different retrievals. Here we present initial results from different dynamical situations to show how the tool could be used by forecasters as well as for educators. However, as the new retrieved values are from a non-Gaussian based 1DVAR then they will display non-Gaussian behaviors that need to pass a quality control measure that is consistent with this distribution, and these new measures are presented here along with initial results for checking the retrievals.
A fast Gaussian filtering algorithm for three-dimensional surface roughness measurements

International Nuclear Information System (INIS)

Yuan, Y B; Piao, W Y; Xu, J B

2007-01-01

The two-dimensional (2-D) Gaussian filter can be separated into two one-dimensional (1-D) Gaussian filters. The 1-D Gaussian filter can be implemented approximately by the cascaded Butterworth filters. The approximation accuracy will be improved with the increase of the number of the cascaded filters. A recursive algorithm for Gaussian filtering requires a relatively small number of simple mathematical operations such as addition, subtraction, multiplication, or division, so that it has considerable computational efficiency and it is very useful for three-dimensional (3-D) surface roughness measurements. The zero-phase-filtering technique is used in this algorithm, so there is no phase distortion in the Gaussian filtered mean surface. High-order approximation Gaussian filters are proposed for practical use to assure high accuracy of Gaussian filtering of 3-D surface roughness measurements
A fast Gaussian filtering algorithm for three-dimensional surface roughness measurements

Science.gov (United States)

Yuan, Y. B.; Piao, W. Y.; Xu, J. B.

2007-07-01

The two-dimensional (2-D) Gaussian filter can be separated into two one-dimensional (1-D) Gaussian filters. The 1-D Gaussian filter can be implemented approximately by the cascaded Butterworth filters. The approximation accuracy will be improved with the increase of the number of the cascaded filters. A recursive algorithm for Gaussian filtering requires a relatively small number of simple mathematical operations such as addition, subtraction, multiplication, or division, so that it has considerable computational efficiency and it is very useful for three-dimensional (3-D) surface roughness measurements. The zero-phase-filtering technique is used in this algorithm, so there is no phase distortion in the Gaussian filtered mean surface. High-order approximation Gaussian filters are proposed for practical use to assure high accuracy of Gaussian filtering of 3-D surface roughness measurements.
Radiation-energy partition among mixture components: current ideas on an old question

International Nuclear Information System (INIS)

Swallow, A.J.

1988-01-01

We review the basis of the familiar idea that the energy partition among mixture components in the initial stage would be governed by the total electron fraction. For considerations of many problems in radiation chemistry, it is better to use the valence-electron fraction. We also point out recent developments in more detailed treatments, which indicate limitations of the very concept of the energy partition for the determination of the yields of initial molecular species that appear under irradiation. (author)
DCMDN: Deep Convolutional Mixture Density Network

Science.gov (United States)

D'Isanto, Antonio; Polsterer, Kai Lars

2017-09-01

Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.
Multi-brid inflation and non-gaussianity

International Nuclear Information System (INIS)

Sasaki, Misao

2008-01-01

We consider a class of multi-component hybrid inflation models whose evolution may be analytically solved under the slow-roll approximation. We call it multi-brid inflation (or n-brid inflation where n stands for the number of inflaton fields). As an explicit example, we consider a two-brid inflation model, in which the inflaton potentials are of exponential type and a waterfall field that terminates inflation has the standard quartic potential with two minima. Using the δN formalism, we derive an expression for the curvature perturbation valid to full nonlinear order. Then we give an explicit expression for the curvature perturbation to second order in the inflaton perturbation. We find that the final from of the curvature perturbation depends crucially on how the inflation ends. Using this expression, we present closed analytical expressions for the spectrum of the curvature perturbation Ps(k), the spectral index n s , the tensor to scalar ratio r, and the non-Gaussian parameter f NL local , in terms of the model parameters. We find that a wide range of the parameter space (n s , r, f NL local ) can be covered by varying the model parameters within a physically reasonable range. In particular, for plausible values of the model parameters, we may have a large non-Gaussianity f NL local ∼10-100. This is in sharp contrast to the case of single-field hybrid inflation in which these parameters are tightly constrained. (author)

Einstein-Podolsky-Rosen-steering swapping between two Gaussian multipartite entangled states

Science.gov (United States)

Wang, Meihong; Qin, Zhongzhong; Wang, Yu; Su, Xiaolong

2017-08-01

Multipartite Einstein-Podolsky-Rosen (EPR) steering is a useful quantum resource for quantum communication in quantum networks. It has potential applications in secure quantum communication, such as one-sided device-independent quantum key distribution and quantum secret sharing. By distributing optical modes of a multipartite entangled state to space-separated quantum nodes, a local quantum network can be established. Based on the existing multipartite EPR steering in a local quantum network, secure quantum communication protocol can be accomplished. In this manuscript, we present swapping schemes for EPR steering between two space-separated Gaussian multipartite entangled states, which can be used to connect two space-separated quantum networks. Two swapping schemes, including the swapping between a tripartite Greenberger-Horne-Zeilinger (GHZ) entangled state and an EPR entangled state and that between two tripartite GHZ entangled states, are analyzed. Various types of EPR steering are presented after the swapping of two space-separated independent multipartite entanglement states without direct interaction, which can be used to implement quantum communication between two quantum networks. The presented schemes provide technical reference for more complicated quantum networks with EPR steering.
Unsupervised Two-Way Clustering of Metagenomic Sequences

Directory of Open Access Journals (Sweden)

Shruthi Prabhakara

2012-01-01

Full Text Available A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. In this paper, we formulate an unsupervised naive Bayes multispecies, multidimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and is robust to varying abundances, divergences and read lengths.
Robust non-rigid point set registration using student's-t mixture model.

Directory of Open Access Journals (Sweden)

Zhiyong Zhou

Full Text Available The Student's-t mixture model, which is heavily tailed and more robust than the Gaussian mixture model, has recently received great attention on image processing. In this paper, we propose a robust non-rigid point set registration algorithm using the Student's-t mixture model. Specifically, first, we consider the alignment of two point sets as a probability density estimation problem and treat one point set as Student's-t mixture model centroids. Then, we fit the Student's-t mixture model centroids to the other point set which is treated as data. Finally, we get the closed-form solutions of registration parameters, leading to a computationally efficient registration algorithm. The proposed algorithm is especially effective for addressing the non-rigid point set registration problem when significant amounts of noise and outliers are present. Moreover, less registration parameters have to be set manually for our algorithm compared to the popular coherent points drift (CPD algorithm. We have compared our algorithm with other state-of-the-art registration algorithms on both 2D and 3D data with noise and outliers, where our non-rigid registration algorithm showed accurate results and outperformed the other algorithms.
Phase statistics in non-Gaussian scattering

International Nuclear Information System (INIS)

Watson, Stephen M; Jakeman, Eric; Ridley, Kevin D

2006-01-01

Amplitude weighting can improve the accuracy of frequency measurements in signals corrupted by multiplicative speckle noise. When the speckle field constitutes a circular complex Gaussian process, the optimal function of amplitude weighting is provided by the field intensity, corresponding to the intensity-weighted phase derivative statistic. In this paper, we investigate the phase derivative and intensity-weighted phase derivative returned from a two-dimensional random walk, which constitutes a generic scattering model capable of producing both Gaussian and non-Gaussian fluctuations. Analytical results are developed for the correlation properties of the intensity-weighted phase derivative, as well as limiting probability densities of the scattered field. Numerical simulation is used to generate further probability densities and determine optimal weighting criteria from non-Gaussian fields. The results are relevant to frequency retrieval in radiation scattered from random media
Relative entropy as a measure of entanglement for Gaussian states

Institute of Scientific and Technical Information of China (English)

Lu Huai-Xin; Zhao Bo

2006-01-01

In this paper, we derive an explicit analytic expression of the relative entropy between two general Gaussian states. In the restriction of the set for Gaussian states and with the help of relative entropy formula and Peres-Simon separability criterion, one can conveniently obtain the relative entropy entanglement for Gaussian states. As an example,the relative entanglement for a two-mode squeezed thermal state has been obtained.
Conversion of cresols and naphthalene in the hydroprocessing of three-component model mixtures simulating fast pyrolysis tars

Energy Technology Data Exchange (ETDEWEB)

Wandas, R.; Surygala, J.; Sliwka, E. [Technical University of Wroclaw, Wroclaw (Poland). Inst. of Chemistry and Technology of Petroleum and Coal

1996-05-01

The hydroconversion of o-, m- and p-cresols in three-component model mixtures with naphthalene and n-hexadecane was investigated over a CoMo/Al{sub 2}O{sub 3} catalyst at 360{degree}C, a hydrogen pressure of 7 MPa and a reaction time of 60 min. The results were compared with those obtained for cresols and naphthalene as single model compounds. A lower efficiency of cresol hydrodeoxygenation as well as naphthalene hydrogenation in the mixtures was found than in the conversion of the single compounds. Conversion mechanisms of cresols in the mixtures with naphthalene are considerably more complex than for individual components. Beside typical catalytic reactions, they include radical reactions in which tetralin, formed by naphthalene hydrogenation, participates as a labile-hydrogen source. The cresol reaction products in such systems include phenol, xylenols, xylenes and dimethycyclohexanes, i.e. compounds essentially absent in hydroconversion of cresols as single substances. Under the experimental conditions, the hydrodeoxygenation efficiency of the cresol isomers decreases in the sequence: para {gt} metal {gt} ortho. 22 refs., 3 figs., 3 tabs.
Solid-Liquid Equilibria for Many-component Mixtures Using Cubic-Plus-Association (CPA) equation of state

DEFF Research Database (Denmark)

Fettouhi, André; Thomsen, Kaj

2010-01-01

In the creation of liquefied natural gas the formation of solids play a substantial role, hence detailed knowledge is needed about solid-liquid equilibria (SLE). In this abstract we shortly summarize the work we have carried out at CERE over the past year with SLE for many-component mixtures usin...... the Cubic-Plus-Association (CPA) equation of state. Components used in this work are highly relevant to the oil and gas industry and include light and heavy hydrocarbons, alcohols, water and carbon dioxide....
Human toxicology of chemical mixtures toxic consequences beyond the impact of one-component product and environmental exposures

CERN Document Server

Zeliger, Harold I

2011-01-01

In this important reference work, Zeliger catalogs the known effects of chemical mixtures on the human body and also proposes a framework for understanding and predicting their actions in terms of lipophile (fat soluble)/hydrophile (water soluble) interactions. The author's focus is on illnesses that ensue following exposures to mixtures of chemicals that cannot be attributed to any one component of the mixture. In the first part the mechanisms of chemical absorption at a molecular and macromolecular level are explained, as well as the body's methods of defending itself against xenobiotic intrusion. Part II examines the sources of the chemicals discussed, looking at air and water pollution, food additives, pharmaceuticals, etc. Part III, which includes numerous case studies, examines specific effects of particular mixtures on particular body systems and organs and presents a theoretical framework for predicting what the effects of uncharacterized mixtures might be. Part IV covers regulatory requirements and t...
Realistic continuous-variable quantum teleportation with non-Gaussian resources

International Nuclear Information System (INIS)

Dell'Anno, F.; De Siena, S.; Illuminati, F.

2010-01-01

We present a comprehensive investigation of nonideal continuous-variable quantum teleportation implemented with entangled non-Gaussian resources. We discuss in a unified framework the main decoherence mechanisms, including imperfect Bell measurements and propagation of optical fields in lossy fibers, applying the formalism of the characteristic function. By exploiting appropriate displacement strategies, we compute analytically the success probability of teleportation for input coherent states and two classes of non-Gaussian entangled resources: two-mode squeezed Bell-like states (that include as particular cases photon-added and photon-subtracted de-Gaussified states), and two-mode squeezed catlike states. We discuss the optimization procedure on the free parameters of the non-Gaussian resources at fixed values of the squeezing and of the experimental quantities determining the inefficiencies of the nonideal protocol. It is found that non-Gaussian resources enhance significantly the efficiency of teleportation and are more robust against decoherence than the corresponding Gaussian ones. Partial information on the alphabet of input states allows further significant improvement in the performance of the nonideal teleportation protocol.
Application of approximations for joint cumulative k-distributions for mixtures to FSK radiation heat transfer in multi-component high temperature non-LTE plasmas

International Nuclear Information System (INIS)

Maurente, André; França, Francis H.R.; Miki, Kenji; Howell, John R.

2012-01-01

Approximations for joint cumulative k-distribution for mixtures are efficient for full spectrum k-distribution (FSK) computations. These approximations provide reduction of the database that is necessary to perform FSK computation when compared to the direct approach, which uses cumulative k-distributions computed from the spectrum of the mixture, and also less computational expensive when compared to techniques in which RTE's are required to be solved for each component of the mixture. The aim of the present paper is to extend the approximations for joint cumulative k-distributions for non-LTE media. For doing that, a FSK to non-LTE media formulation well-suited to be applied along with approximations for joint cumulative k-distributions is presented. The application of the proposed methodology is demonstrated by solving the radiation heat transfer in non-LTE high temperature plasmas composed of N, O, N 2 , NO, N 2 + and mixtures of these species. The two more efficient approximations, that is, the superposition and multiplication are employed and analyzed.
Searching for non-Gaussianity in the WMAP data

International Nuclear Information System (INIS)

Bernui, A.; Reboucas, M. J.

2009-01-01

Some analyses of recent cosmic microwave background (CMB) data have provided hints that there are deviations from Gaussianity in the WMAP CMB temperature fluctuations. Given the far-reaching consequences of such a non-Gaussianity for our understanding of the physics of the early universe, it is important to employ alternative indicators in order to determine whether the reported non-Gaussianity is of cosmological origin, and/or extract further information that may be helpful for identifying its causes. We propose two new non-Gaussianity indicators, based on skewness and kurtosis of large-angle patches of CMB maps, which provide a measure of departure from Gaussianity on large angular scales. A distinctive feature of these indicators is that they provide sky maps of non-Gaussianity of the CMB temperature data, thus allowing a possible additional window into their origins. Using these indicators, we find no significant deviation from Gaussianity in the three and five-year WMAP Internal Linear Combination (ILC) map with KQ75 mask, while the ILC unmasked map exhibits deviation from Gaussianity, quantifying therefore the WMAP team recommendation to employ the new mask KQ75 for tests of Gaussianity. We also use our indicators to test for Gaussianity the single frequency foreground unremoved WMAP three and five-year maps, and show that the K and Ka maps exhibit a clear indication of deviation from Gaussianity even with the KQ75 mask. We show that our findings are robust with respect to the details of the method.
Adaptive Laguerre-Gaussian variant of the Gaussian beam expansion method.

Science.gov (United States)

Cagniot, Emmanuel; Fromager, Michael; Ait-Ameur, Kamel

2009-11-01

A variant of the Gaussian beam expansion method consists in expanding the Bessel function J0 appearing in the Fresnel-Kirchhoff integral into a finite sum of complex Gaussian functions to derive an analytical expression for a Laguerre-Gaussian beam diffracted through a hard-edge aperture. However, the validity range of the approximation depends on the number of expansion coefficients that are obtained by optimization-computation directly. We propose another solution consisting in expanding J0 onto a set of collimated Laguerre-Gaussian functions whose waist depends on their number and then, depending on its argument, predicting the suitable number of expansion functions to calculate the integral recursively.
Toward the detection of gravitational waves under non-Gaussian noises I. Locally optimal statistic.

Science.gov (United States)

Yokoyama, Jun'ichi

2014-01-01

After reviewing the standard hypothesis test and the matched filter technique to identify gravitational waves under Gaussian noises, we introduce two methods to deal with non-Gaussian stationary noises. We formulate the likelihood ratio function under weakly non-Gaussian noises through the Edgeworth expansion and strongly non-Gaussian noises in terms of a new method we call Gaussian mapping where the observed marginal distribution and the two-body correlation function are fully taken into account. We then apply these two approaches to Student's t-distribution which has a larger tails than Gaussian. It is shown that while both methods work well in the case the non-Gaussianity is small, only the latter method works well for highly non-Gaussian case.
Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models

Science.gov (United States)

Wen, Xian-Huan; Gómez-Hernández, J. Jaime

1998-03-01

The macrodispersion of an inert solute in a 2-D heterogeneous porous media is estimated numerically in a series of fields of varying heterogeneity. Four different random function (RF) models are used to model log-transmissivity (ln T) spatial variability, and for each of these models, ln T variance is varied from 0.1 to 2.0. The four RF models share the same univariate Gaussian histogram and the same isotropic covariance, but differ from one another in terms of the spatial connectivity patterns at extreme transmissivity values. More specifically, model A is a multivariate Gaussian model for which, by definition, extreme values (both high and low) are spatially uncorrelated. The other three models are non-multi-Gaussian: model B with high connectivity of high extreme values, model C with high connectivity of low extreme values, and model D with high connectivities of both high and low extreme values. Residence time distributions (RTDs) and macrodispersivities (longitudinal and transverse) are computed on ln T fields corresponding to the different RF models, for two different flow directions and at several scales. They are compared with each other, as well as with predicted values based on first-order analytical results. Numerically derived RTDs and macrodispersivities for the multi-Gaussian model are in good agreement with analytically derived values using first-order theories for log-transmissivity variance up to 2.0. The results from the non-multi-Gaussian models differ from each other and deviate largely from the multi-Gaussian results even when ln T variance is small. RTDs in non-multi-Gaussian realizations with high connectivity at high extreme values display earlier breakthrough than in multi-Gaussian realizations, whereas later breakthrough and longer tails are observed for RTDs from non-multi-Gaussian realizations with high connectivity at low extreme values. Longitudinal macrodispersivities in the non-multi-Gaussian realizations are, in general, larger than
New Results On the Sum of Two Generalized Gaussian Random Variables

KAUST Repository

Soury, Hamza

2015-01-01

We propose in this paper a new method to compute the characteristic function (CF) of generalized Gaussian (GG) random variable in terms of the Fox H function. The CF of the sum of two independent GG random variables is then deduced. Based on this results, the probability density function (PDF) and the cumulative distribution function (CDF) of the sum distribution are obtained. These functions are expressed in terms of the bivariate Fox H function. Next, the statistics of the distribution of the sum, such as the moments, the cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of bivariate Fox H function, a solution to reduce such complexity is to approximate the sum of two independent GG random variables by one GG random variable with suitable shape factor. The approximation method depends on the utility of the system so three methods of estimate the shape factor are studied and presented.
New Results on the Sum of Two Generalized Gaussian Random Variables

KAUST Repository

Soury, Hamza

2016-01-06

We propose in this paper a new method to compute the characteristic function (CF) of generalized Gaussian (GG) random variable in terms of the Fox H function. The CF of the sum of two independent GG random variables is then deduced. Based on this results, the probability density function (PDF) and the cumulative distribution function (CDF) of the sum distribution are obtained. These functions are expressed in terms of the bivariate Fox H function. Next, the statistics of the distribution of the sum, such as the moments, the cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of bivariate Fox H function, a solution to reduce such complexity is to approximate the sum of two independent GG random variables by one GG random variable with suitable shape factor. The approximation method depends on the utility of the system so three methods of estimate the shape factor are studied and presented [1].
New Results on the Sum of Two Generalized Gaussian Random Variables

KAUST Repository

Soury, Hamza; Alouini, Mohamed-Slim

2016-01-01

We propose in this paper a new method to compute the characteristic function (CF) of generalized Gaussian (GG) random variable in terms of the Fox H function. The CF of the sum of two independent GG random variables is then deduced. Based on this results, the probability density function (PDF) and the cumulative distribution function (CDF) of the sum distribution are obtained. These functions are expressed in terms of the bivariate Fox H function. Next, the statistics of the distribution of the sum, such as the moments, the cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of bivariate Fox H function, a solution to reduce such complexity is to approximate the sum of two independent GG random variables by one GG random variable with suitable shape factor. The approximation method depends on the utility of the system so three methods of estimate the shape factor are studied and presented [1].
Continuous-variable quantum teleportation with non-Gaussian resources

International Nuclear Information System (INIS)

Dell'Anno, F.; De Siena, S.; Albano, L.; Illuminati, F.

2007-01-01

We investigate continuous variable quantum teleportation using non-Gaussian states of the radiation field as entangled resources. We compare the performance of different classes of degaussified resources, including two-mode photon-added and two-mode photon-subtracted squeezed states. We then introduce a class of two-mode squeezed Bell-like states with one-parameter dependence for optimization. These states interpolate between and include as subcases different classes of degaussified resources. We show that optimized squeezed Bell-like resources yield a remarkable improvement in the fidelity of teleportation both for coherent and nonclassical input states. The investigation reveals that the optimal non-Gaussian resources for continuous variable teleportation are those that most closely realize the simultaneous maximization of the content of entanglement, the degree of affinity with the two-mode squeezed vacuum, and the, suitably measured, amount of non-Gaussianity
Scattering and radiative properties of semi-external versus external mixtures of different aerosol types

International Nuclear Information System (INIS)

Mishchenko, Michael I.; Liu Li; Travis, Larry D.; Lacis, Andrew A.

2004-01-01

The superposition T-matrix method is used to compute the scattering of unpolarized light by semi-external aerosol mixtures in the form of polydisperse, randomly oriented two-particle clusters with touching components. The results are compared with those for composition-equivalent external aerosol mixtures, in which the components are widely separated and scatter light in isolation from each other. It is concluded that aggregation is likely to have a relatively weak effect on scattering and radiative properties of two-component tropospheric aerosols and can be replaced by the much simpler external-mixture model in remote sensing studies and atmospheric radiation balance computations
Bayesian Kernel Mixtures for Counts.

Science.gov (United States)

Canale, Antonio; Dunson, David B

2011-12-01

Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.

Detecting periodicities with Gaussian processes

Directory of Open Access Journals (Sweden)

Nicolas Durrande

2016-04-01

Full Text Available We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples. Our approach is based on Gaussian process regression, which provides a flexible non-parametric framework for modelling periodic data. We introduce a novel decomposition of the covariance function as the sum of periodic and aperiodic kernels. This decomposition allows for the creation of sub-models which capture the periodic nature of the signal and its complement. To quantify the periodicity of the signal, we derive a periodicity ratio which reflects the uncertainty in the fitted sub-models. Although the method can be applied to many kernels, we give a special emphasis to the Matérn family, from the expression of the reproducing kernel Hilbert space inner product to the implementation of the associated periodic kernels in a Gaussian process toolkit. The proposed method is illustrated by considering the detection of periodically expressed genes in the arabidopsis genome.
Meta-analysis of Gaussian individual patient data: Two-stage or not two-stage?

Science.gov (United States)

Morris, Tim P; Fisher, David J; Kenward, Michael G; Carpenter, James R

2018-04-30

Quantitative evidence synthesis through meta-analysis is central to evidence-based medicine. For well-documented reasons, the meta-analysis of individual patient data is held in higher regard than aggregate data. With access to individual patient data, the analysis is not restricted to a "two-stage" approach (combining estimates and standard errors) but can estimate parameters of interest by fitting a single model to all of the data, a so-called "one-stage" analysis. There has been debate about the merits of one- and two-stage analysis. Arguments for one-stage analysis have typically noted that a wider range of models can be fitted and overall estimates may be more precise. The two-stage side has emphasised that the models that can be fitted in two stages are sufficient to answer the relevant questions, with less scope for mistakes because there are fewer modelling choices to be made in the two-stage approach. For Gaussian data, we consider the statistical arguments for flexibility and precision in small-sample settings. Regarding flexibility, several of the models that can be fitted only in one stage may not be of serious interest to most meta-analysis practitioners. Regarding precision, we consider fixed- and random-effects meta-analysis and see that, for a model making certain assumptions, the number of stages used to fit this model is irrelevant; the precision will be approximately equal. Meta-analysts should choose modelling assumptions carefully. Sometimes relevant models can only be fitted in one stage. Otherwise, meta-analysts are free to use whichever procedure is most convenient to fit the identified model. © 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Phase equilibria for mixtures containing very many components. development and application of continuous thermodynamics for chemical process design

International Nuclear Information System (INIS)

Cotterman, R.L.; Bender, R.; Prausnitz, J.M.

1984-01-01

For some multicomponent mixtures, where detailed chemical analysis is not feasible, the compositio of the mixture may be described by a continuous distribution function of some convenient macroscopic property suc as normal boiling point or molecular weight. To attain a quantitative description of phase equilibria for such mixtures, this work has developed thermodynamic procedures for continuous systems; that procedure is called continuous thermodynamics. To illustrate, continuous thermodynamics is used to calculate dew points for natural-gas mixtures, solvent loss in a high-pressure absorber, and liquid-liquid phase equilibria in a polymer fractionation process. Continuous thermodynamics provides a rational method for calculating phase equilibria for those mixtures where complete chemical analysis is not available but where composition can be given by some statistical description. While continuous thermodynamics is only the logical limit of the well-known pseudo-component method, it is more efficient than that method because it is less arbitrary and it often requires less computer time
Fractional Gaussian noise: Prior specification and model comparison

KAUST Repository

Sørbye, Sigrunn Holbek

2017-07-07

Fractional Gaussian noise (fGn) is a stationary stochastic process used to model antipersistent or persistent dependency structures in observed time series. Properties of the autocovariance function of fGn are characterised by the Hurst exponent (H), which, in Bayesian contexts, typically has been assigned a uniform prior on the unit interval. This paper argues why a uniform prior is unreasonable and introduces the use of a penalised complexity (PC) prior for H. The PC prior is computed to penalise divergence from the special case of white noise and is invariant to reparameterisations. An immediate advantage is that the exact same prior can be used for the autocorrelation coefficient ϕ(symbol) of a first-order autoregressive process AR(1), as this model also reflects a flexible version of white noise. Within the general setting of latent Gaussian models, this allows us to compare an fGn model component with AR(1) using Bayes factors, avoiding the confounding effects of prior choices for the two hyperparameters H and ϕ(symbol). Among others, this is useful in climate regression models where inference for underlying linear or smooth trends depends heavily on the assumed noise model.
Fractional Gaussian noise: Prior specification and model comparison

KAUST Repository

Sø rbye, Sigrunn Holbek; Rue, Haavard

2017-01-01

Fractional Gaussian noise (fGn) is a stationary stochastic process used to model antipersistent or persistent dependency structures in observed time series. Properties of the autocovariance function of fGn are characterised by the Hurst exponent (H), which, in Bayesian contexts, typically has been assigned a uniform prior on the unit interval. This paper argues why a uniform prior is unreasonable and introduces the use of a penalised complexity (PC) prior for H. The PC prior is computed to penalise divergence from the special case of white noise and is invariant to reparameterisations. An immediate advantage is that the exact same prior can be used for the autocorrelation coefficient ϕ(symbol) of a first-order autoregressive process AR(1), as this model also reflects a flexible version of white noise. Within the general setting of latent Gaussian models, this allows us to compare an fGn model component with AR(1) using Bayes factors, avoiding the confounding effects of prior choices for the two hyperparameters H and ϕ(symbol). Among others, this is useful in climate regression models where inference for underlying linear or smooth trends depends heavily on the assumed noise model.
Bonding and structure in dense multi-component molecular mixtures.

Science.gov (United States)

Meyer, Edmund R; Ticknor, Christopher; Bethkenhagen, Mandy; Hamel, Sebastien; Redmer, Ronald; Kress, Joel D; Collins, Lee A

2015-10-28

We have performed finite-temperature density functional theory molecular dynamics simulations on dense methane, ammonia, and water mixtures (CH4:NH3:H2O) for various compositions and temperatures (2000 K ≤ T ≤ 10,000 K) that span a set of possible conditions in the interiors of ice-giant exoplanets. The equation-of-state, pair distribution functions, and bond autocorrelation functions (BACF) were used to probe the structure and dynamics of these complex fluids. In particular, an improvement to the choice of the cutoff in the BACF was developed that allowed analysis refinements for density and temperature effects. We note the relative changes in the nature of these systems engendered by variations in the concentration ratios. A basic tenet emerges from all these comparisons that varying the relative amounts of the three heavy components (C,N,O) can effect considerable changes in the nature of the fluid and may in turn have ramifications for the structure and composition of various planetary layers.
Mixtures of Two Bile Alcohol Sulfates Function as a Proximity Pheromone in Sea Lamprey.

Directory of Open Access Journals (Sweden)

Cory O Brant

Full Text Available Unique mixtures of pheromone components are commonly identified in insects, and have been shown to increase attractiveness towards conspecifics when reconstructed at the natural ratio released by the signaler. In previous field studies of pheromones that attract female sea lamprey (Petromyzon marinus, L., putative components of the male-released mating pheromone included the newly described bile alcohol 3,12-diketo-4,6-petromyzonene-24-sulfate (DkPES and the well characterized 3-keto petromyzonol sulfate (3kPZS. Here, we show chemical evidence that unequivocally confirms the elucidated structure of DkPES, electrophysiological evidence that each component is independently detected by the olfactory epithelium, and behavioral evidence that mature female sea lamprey prefer artificial nests activated with a mixture that reconstructs the male-released component ratio of 30:1 (3kPZS:DkPES, molar:molar. In addition, we characterize search behavior (sinuosity of swim paths of females approaching ratio treatment sources. These results suggest unique pheromone ratios may underlie reproductive isolating mechanisms in vertebrates, as well as provide utility in pheromone-integrated control of invasive sea lamprey in the Great Lakes.
Non-gaussianity versus nonlinearity of cosmological perturbations.

Science.gov (United States)

Verde, L

2001-06-01

Following the discovery of the cosmic microwave background, the hot big-bang model has become the standard cosmological model. In this theory, small primordial fluctuations are subsequently amplified by gravity to form the large-scale structure seen today. Different theories for unified models of particle physics, lead to different predictions for the statistical properties of the primordial fluctuations, that can be divided in two classes: gaussian and non-gaussian. Convincing evidence against or for gaussian initial conditions would rule out many scenarios and point us toward a physical theory for the origin of structures. The statistical distribution of cosmological perturbations, as we observe them, can deviate from the gaussian distribution in several different ways. Even if perturbations start off gaussian, nonlinear gravitational evolution can introduce non-gaussian features. Additionally, our knowledge of the Universe comes principally from the study of luminous material such as galaxies, but galaxies might not be faithful tracers of the underlying mass distribution. The relationship between fluctuations in the mass and in the galaxies distribution (bias), is often assumed to be local, but could well be nonlinear. Moreover, galaxy catalogues use the redshift as third spatial coordinate: the resulting redshift-space map of the galaxy distribution is nonlinearly distorted by peculiar velocities. Nonlinear gravitational evolution, biasing, and redshift-space distortion introduce non-gaussianity, even in an initially gaussian fluctuation field. I investigate the statistical tools that allow us, in principle, to disentangle the above different effects, and the observational datasets we require to do so in practice.
Linking the Value Assessment of Oil and Gas Firms to Ambidexterity Theory Using a Mixture of Normal Distributions

Directory of Open Access Journals (Sweden)

Casault Sébastien

2016-05-01

Full Text Available Oil and gas exploration and production firms have return profiles that are not easily explained by current financial theory – the variation in their market returns is non-Gaussian. In this paper, the nature and underlying reason for these significant deviations from expected behavior are considered. Understanding these differences in financial market behavior is important for a wide range of reasons, including: assessing investments, investor relations, decisions to raise capital, assessment of firm and management performance. We show that using a “thicker tailed” mixture of two normal distributions offers a significantly more accurate model than the traditionally Gaussian approach in describing the behavior of the value of oil and gas firms. This mixture of normal distribution is also more effective in bridging the gap between management theory and practice without the need to introduce complex time-sensitive GARCH and/or jump diffusion dynamics. The mixture distribution is consistent with ambidexterity theory that suggests firms operate in two distinct states driven by the primary focus of the firm: an exploration state with high uncertainty and, an exploitation (or production state with lower uncertainty. The findings have direct implications on improving the accuracy of real option pricing techniques and futures analysis of risk management. Traditional options pricing models assume that commercial returns from these assets are described by a normal random walk. However, a normal random walk model discounts the possibility of large changes to the marketplace from events such as the discovery of important reserves or the introduction of new technology. The mixture distribution proves to be well suited to inherently describe the unusually large risks and opportunities associated with oil and gas production and exploration. A significance testing study of 554 oil and gas exploration and production firms empirically supports using a mixture
Collective modes across the soliton-droplet crossover in binary Bose mixtures

Science.gov (United States)

Cappellaro, Alberto; Macrı, Tommaso; Salasnich, Luca

2018-05-01

We study the collective modes of a binary Bose mixture across the soliton to droplet crossover in a quasi-one-dimensional waveguide with a beyond-mean-field equation of state and a variational Gaussian ansatz for the scalar bosonic field of the corresponding effective action. We observe a sharp difference in the collective modes in the two regimes. Within the soliton regime, modes vary smoothly upon the variation of particle number or interaction strength. On the droplet side, collective modes are inhibited by the emission of particles. This mechanism turns out to be dominant for a wide range of particle numbers and interactions. In a small window of particle number range and for intermediate interactions, we find that monopole frequency is likely to be observed. We focus on the spin-dipole modes for the case of equal intraspecies interactions and equal equilibrium particle numbers in the presence of a weak longitudinal confinement. We find that such modes might be unobservable in the real-time dynamics close to the equilibrium as their frequency is higher than the particle emission spectrum by at least one order of magnitude in the droplet phase. Our results are relevant for experiments with two-component Bose-Einstein condensates for which we provide realistic parameters.
Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

Science.gov (United States)

Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

2012-01-01

Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.
Mixture-mixture design for the fingerprint optimization of chromatographic mobile phases and extraction solutions for Camellia sinensis.

Science.gov (United States)

Borges, Cleber N; Bruns, Roy E; Almeida, Aline A; Scarminio, Ieda S

2007-07-09

A composite simplex centroid-simplex centroid mixture design is proposed for simultaneously optimizing two mixture systems. The complementary model is formed by multiplying special cubic models for the two systems. The design was applied to the simultaneous optimization of both mobile phase chromatographic mixtures and extraction mixtures for the Camellia sinensis Chinese tea plant. The extraction mixtures investigated contained varying proportions of ethyl acetate, ethanol and dichloromethane while the mobile phase was made up of varying proportions of methanol, acetonitrile and a methanol-acetonitrile-water (MAW) 15%:15%:70% mixture. The experiments were block randomized corresponding to a split-plot error structure to minimize laboratory work and reduce environmental impact. Coefficients of an initial saturated model were obtained using Scheffe-type equations. A cumulative probability graph was used to determine an approximate reduced model. The split-plot error structure was then introduced into the reduced model by applying generalized least square equations with variance components calculated using the restricted maximum likelihood approach. A model was developed to calculate the number of peaks observed with the chromatographic detector at 210 nm. A 20-term model contained essentially all the statistical information of the initial model and had a root mean square calibration error of 1.38. The model was used to predict the number of peaks eluted in chromatograms obtained from extraction solutions that correspond to axial points of the simplex centroid design. The significant model coefficients are interpreted in terms of interacting linear, quadratic and cubic effects of the mobile phase and extraction solution components.
Two-Phase Iteration for Value Function Approximation and Hyperparameter Optimization in Gaussian-Kernel-Based Adaptive Critic Design

Directory of Open Access Journals (Sweden)

Xin Chen

2015-01-01

Full Text Available Adaptive Dynamic Programming (ADP with critic-actor architecture is an effective way to perform online learning control. To avoid the subjectivity in the design of a neural network that serves as a critic network, kernel-based adaptive critic design (ACD was developed recently. There are two essential issues for a static kernel-based model: how to determine proper hyperparameters in advance and how to select right samples to describe the value function. They all rely on the assessment of sample values. Based on the theoretical analysis, this paper presents a two-phase simultaneous learning method for a Gaussian-kernel-based critic network. It is able to estimate the values of samples without infinitively revisiting them. And the hyperparameters of the kernel model are optimized simultaneously. Based on the estimated sample values, the sample set can be refined by adding alternatives or deleting redundances. Combining this critic design with actor network, we present a Gaussian-kernel-based Adaptive Dynamic Programming (GK-ADP approach. Simulations are used to verify its feasibility, particularly the necessity of two-phase learning, the convergence characteristics, and the improvement of the system performance by using a varying sample set.
Gaussian cloning of coherent states with known phases

International Nuclear Information System (INIS)

Alexanian, Moorad

2006-01-01

The fidelity for cloning coherent states is improved over that provided by optimal Gaussian and non-Gaussian cloners for the subset of coherent states that are prepared with known phases. Gaussian quantum cloning duplicates all coherent states with an optimal fidelity of 2/3. Non-Gaussian cloners give optimal single-clone fidelity for a symmetric 1-to-2 cloner of 0.6826. Coherent states that have known phases can be cloned with a fidelity of 4/5. The latter is realized by a combination of two beam splitters and a four-wave mixer operated in the nonlinear regime, all of which are realized by interaction Hamiltonians that are quadratic in the photon operators. Therefore, the known Gaussian devices for cloning coherent states are extended when cloning coherent states with known phases by considering a nonbalanced beam splitter at the input side of the amplifier
Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Analogies between random matrix ensembles and the one-component plasma in two-dimensions

Directory of Open Access Journals (Sweden)

Peter J. Forrester

2016-03-01

Full Text Available The eigenvalue PDF for some well known classes of non-Hermitian random matrices — the complex Ginibre ensemble for example — can be interpreted as the Boltzmann factor for one-component plasma systems in two-dimensional domains. We address this theme in a systematic fashion, identifying the plasma system for the Ginibre ensemble of non-Hermitian Gaussian random matrices G, the spherical ensemble of the product of an inverse Ginibre matrix and a Ginibre matrix G1−1G2, and the ensemble formed by truncating unitary matrices, as well as for products of such matrices. We do this when each has either real, complex or real quaternion elements. One consequence of this analogy is that the leading form of the eigenvalue density follows as a corollary. Another is that the eigenvalue correlations must obey sum rules known to characterise the plasma system, and this leads us to an exhibit of an integral identity satisfied by the two-particle correlation for real quaternion matrices in the neighbourhood of the real axis. Further random matrix ensembles investigated from this viewpoint are self dual non-Hermitian matrices, in which a previous study has related to the one-component plasma system in a disk at inverse temperature β=4, and the ensemble formed by the single row and column of quaternion elements from a member of the circular symplectic ensemble.
On the Performance Analysis of Digital Communication Systems Perturbed by Non-Gaussian Noise and Interference

KAUST Repository

Soury, Hamza

2016-06-29

The Gaussian distribution is typically used to model the additive noise affecting communication systems. However, in many cases the noise cannot be modeled by a Gaussian distribution. In this thesis, we investigate the performance of different communication systems perturbed by non-Gaussian noise. Three families of noise are considered in this work, namely the generalized Gaussian noise, the Laplace noise/interference, and the impulsive noise that is modeled by an α-stable distribution. More specifically, in the first part of this thesis, the impact of an additive generalized Gaussian noise is studied by computing the average symbol error rate (SER) of one dimensional and two dimensional constellations in fading environment. We begin by the simple case of two symbols, i.e. binary phase shift keying (BPSK) constellation. From the results of this constellation, we extended the work to the average SER of an M pulse amplitude modulation (PAM). The first 2 − D constellation is the M quadrature amplitude modulation (QAM) (studied for two geometric shapes, namely square and rectangular), which is the combination of two orthogonal PAM signals (in-phase and quadrature phase PAM). In the second part, the system performance of a circular constellation, namely M phase shift keying (MPSK) is studied in conjunction with a Laplace noise with independent noise components. A closed form and an asymptotic expansion of the SER are derived for two detectors, maximum likelihood and minimum distance detectors. Next, we look at the intra cell interference of a full duplex cellular network which is shown to follow a Laplacian distribution with dependent, but uncorrelated, complex components. The densities of that interference are expressed in a closed form in order to obtain the SER of several communication systems (BPSK, PAM, QAM, and MPSK). Finally, we study the statistics of the α-stable distribution. Those statistics are expressed in closed form in terms of the Fox H function and
Method for separating gaseous mixtures of isotopes

International Nuclear Information System (INIS)

Neimann, H.J.; Schuster, E.; Kersting, A.

1976-01-01

A gaseous mixture of isotopes is separated by laser excitation of the isotope mixture with a narrow band of wavelengths, molecularly exciting mainly the isotope to be separated and thereby promoting its reaction with its chemical partner which is excited in a separate chamber. The excited isotopes and the chemical partner are mixed, perhaps in a reaction chamber to which the two excited components are conducted by very short conduits. The improvement of this method is the physical separation of the isotope mixture and its partner during excitation. The reaction between HCl and the mixture of 238 UF 6 and 235 UF 6 is discussed
Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model

Science.gov (United States)

Skakun, Sergii; Franch, Belen; Vermote, Eric; Roger, Jean-Claude; Becker-Reshef, Inbal; Justice, Christopher; Kussul, Nataliia

2017-01-01

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.

Stochastic response of van der Pol oscillator with two kinds of fractional derivatives under Gaussian white noise excitation

International Nuclear Information System (INIS)

Yang Yong-Ge; Xu Wei; Sun Ya-Hui; Gu Xu-Dong

2016-01-01

This paper aims to investigate the stochastic response of the van der Pol (VDP) oscillator with two kinds of fractional derivatives under Gaussian white noise excitation. First, the fractional VDP oscillator is replaced by an equivalent VDP oscillator without fractional derivative terms by using the generalized harmonic balance technique. Then, the stochastic averaging method is applied to the equivalent VDP oscillator to obtain the analytical solution. Finally, the analytical solutions are validated by numerical results from the Monte Carlo simulation of the original fractional VDP oscillator. The numerical results not only demonstrate the accuracy of the proposed approach but also show that the fractional order, the fractional coefficient and the intensity of Gaussian white noise play important roles in the responses of the fractional VDP oscillator. An interesting phenomenon we found is that the effects of the fractional order of two kinds of fractional derivative items on the fractional stochastic systems are totally contrary. (paper)
Quantum information with Gaussian states

International Nuclear Information System (INIS)

Wang Xiangbin; Hiroshima, Tohya; Tomita, Akihisa; Hayashi, Masahito

2007-01-01

Quantum optical Gaussian states are a type of important robust quantum states which are manipulatable by the existing technologies. So far, most of the important quantum information experiments are done with such states, including bright Gaussian light and weak Gaussian light. Extending the existing results of quantum information with discrete quantum states to the case of continuous variable quantum states is an interesting theoretical job. The quantum Gaussian states play a central role in such a case. We review the properties and applications of Gaussian states in quantum information with emphasis on the fundamental concepts, the calculation techniques and the effects of imperfections of the real-life experimental setups. Topics here include the elementary properties of Gaussian states and relevant quantum information device, entanglement-based quantum tasks such as quantum teleportation, quantum cryptography with weak and strong Gaussian states and the quantum channel capacity, mathematical theory of quantum entanglement and state estimation for Gaussian states
Beam shape coefficients of the most general focused Gaussian laser beam for light scattering applications

International Nuclear Information System (INIS)

Lock, James A.

2013-01-01

The vector wave equation for electromagnetic waves, when subject to a number of constraints corresponding to propagation of a monochromatic beam, reduces to a pair of inhomogeneous differential equations describing the transverse electric and transverse magnetic polarized beam components. These differential equations are solved analytically to obtain the most general focused Gaussian beam to order s 4 , where s is the beam confinement parameter, and various properties of the most general Gaussian beam are then discussed. The radial fields of the most general Gaussian beam are integrated to obtain the on-axis beam shape coefficients of the generalized Lorenz–Mie theory formalism of light scattering. The beam shape coefficients are then compared with those of the localized Gaussian beam model and the Davis–Barton fifth-order symmetrized beam. -- Highlights: ► Derive the differential equation for the most general Gaussian beam. ► Solve the differential equation for the most general Gaussian beam. ► Determine the properties of the most general Gaussian beam. ► Determine the beam shape coefficients of the most general Gaussian beam
The ground state magnetic moment and susceptibility of a two electron Gaussian quantum dot

Science.gov (United States)

Boda, Aalu; Chatterjee, Ashok

2018-04-01

The problem of two interacting electrons moving in a two-dimensional semiconductor quantum dot with Gaussian confinement under the influence of an external magnetic field is studied by using a method of numerical diagonalization of the Hamiltonian matrix with in the effective-mass approximation. The energy spectrum is calculated as a function of the magnetic field. We find the ground state magnetic moment and the magnetic susceptibility show zero temperature diamagnetic peaks due to exchange induced singlet-triplet oscillations. The position and the number of these peaks depend on the size of the quantum dot and also strength of the electro-electron interaction. The theory is applied to a GaAs quantum dot.
Volatility of components of saturated vapours of UCl4-CsCl and UCl4-LiCl molten mixtures

International Nuclear Information System (INIS)

Smirnov, M.V.; Kudyakov, V.Ya.; Salyulev, A.B.; Komarov, V.E.; Posokhin, Yu.V.; Afonichkin, V.K.

1979-01-01

The flow method has been used for measuring the volatility of the components from UCl 4 -CsCl and UCl 4 -LiCl melted mixtures containing 2.0, 5.0, 12.0, 25.0, 33.0, 50.0, 67.0, and 83.0 mol.% of UCl 4 within the temperature ranges of 903-1188 K and 740-1200 K, respectively. The chemical composition of saturated vapours above the melted salts has been determined. The melted mixtures in question exhibit negative deviation from ideal behaviour. Made was the conclusion about the presence in a vapour phase, along with monomeric UCl 4 , LiCl, CsCl and Li 2 Cl 2 , Cs 2 Cl 2 dimers of double compounds of the MeUCl 5 most probable composition. Their absolute contribution into a total pressure above the UCl 4 -CsCl melted mixtures is considerably smaller than above the UCl 4 -LiCl mixtures
Global existence and blow-up phenomena for two-component Degasperis-Procesi system and two-component b-family system

OpenAIRE

Liu, Jingjing; Yin, Zhaoyang

2014-01-01

This paper is concerned with global existence and blow-up phenomena for two-component Degasperis-Procesi system and two-component b-family system. The strategy relies on our observation on new conservative quantities of these systems. Several new global existence results and a new blowup result of strong solutions to the two-component Degasperis- Procesi system and the two-component b-family system are presented by using these new conservative quantities.
Two-dimensional unwrapped phase inversion with damping and a Gaussian filter

KAUST Repository

Choi, Yun Seok; Alkhalifah, Tariq Ali

2014-01-01

Phase wrapping is one of main causes of the local minima problem in waveform inversion. However, the unwrapping process for 2D phase maps that includes singular points (residues) is complicated and does not guarantee unique solutions. We employ an exponential damping to eliminate the residues in the 2D phase maps, which makes the 2D phase unwrapping process easy and produce a unique solution. A recursive inversion process using the damped unwrapped phase provides an opportunity to invert for smooth background updates first, and higher resolution updates later as we reduce the damping. We also apply a Gaussian filter to the gradient to mitigate the edge artifacts resulting from the narrow shape of the sensitivity kernels at high damping. Numerical examples demonstrate that our unwrapped phase inversion with damping and a Gaussian filter produces good convergent results even for a 3Hz single frequency of Marmousi dataset and with a starting model far from the true model.
Component separation in harmonically trapped boson-fermion mixtures

DEFF Research Database (Denmark)

Nygaard, Nicolai; Mølmer, Klaus

1999-01-01

We present a numerical study of mixed boson-fermion systems at zero temperature in isotropic and anise tropic harmonic traps. We investigate the phenomenon of component separation as a function of the strength ut the interparticle interaction. While solving a Gross-Pitaevskii mean-field equation ...... for the boson distribution in the trap, we utilize two different methods to extract the density profile of the fermion component; a semiclassical Thomas-Fermi approximation and a quantum-mechanical Slater determinant Schrodinger equation....
Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

Directory of Open Access Journals (Sweden)

Georgios C Manikis

Full Text Available The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer.Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2 at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG and non-Gaussian (MNG and BNG were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE. To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC and F-ratio.All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.
Bounded Gaussian process regression

DEFF Research Database (Denmark)

Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan

2013-01-01

We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
Planck 2013 Results. XXIV. Constraints on primordial non-Gaussianity

CERN Document Server

Ade, P.A.R.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bock, J.J.; Bonaldi, A.; Bonavera, L.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Bridges, M.; Bucher, M.; Burigana, C.; Butler, R.C.; Cardoso, J.F.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, L.Y.; Chiang, H.C.; Christensen, P.R.; Church, S.; Clements, D.L.; Colombi, S.; Colombo, L.P.L.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.M.; Desert, F.X.; Diego, J.M.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Ducout, A.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Ensslin, T.A.; Eriksen, H.K.; Fergusson, J.; Finelli, F.; Forni, O.; Frailis, M.; Franceschi, E.; Galeotta, S.; Ganga, K.; Giard, M.; Giraud-Heraud, Y.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Hansen, F.K.; Hanson, D.; Harrison, D.; Heavens, A.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Hobson, M.; Holmes, W.A.; Hornstrup, A.; Hovest, W.; Huffenberger, K.M.; Jaffe, T.R.; Jaffe, A.H.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kisner, T.S.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lacasa, F.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Laureijs, R.J.; Lawrence, C.R.; Leahy, J.P.; Leonardi, R.; Lesgourgues, J.; Lewis, A.; Liguori, M.; Lilje, P.B.; Linden-Vornle, M.; Lopez-Caniego, M.; Lubin, P.M.; Macias-Perez, J.F.; Maffei, B.; Maino, D.; Mandolesi, N.; Mangilli, A.; Marinucci, D.; Maris, M.; Marshall, D.J.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Meinhold, P.R.; Melchiorri, A.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Miville-Deschenes, M.A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Naselsky, P.; Natoli, P.; Netterfield, C.B.; Norgaard-Nielsen, H.U.; Noviello, F.; Novikov, D.; Novikov, I.; Osborne, S.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Pasian, F.; Patanchon, G.; Peiris, H.V.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G.W.; Prezeau, G.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Racine, B.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ricciardi, S.; Riller, T.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Roudier, G.; Rubino-Martin, J.A.; Rusholme, B.; Sandri, M.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M.D.; Shellard, E.P.S.; Smith, K.; Spencer, L.D.; Starck, J.L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sunyaev, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Varis, J.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L.A.; Wandelt, B.D.; White, M.; White, S.D.M.; Yvon, D.; Zacchei, A.; Zonca, A.

2014-01-01

The Planck nominal mission cosmic microwave background (CMB) maps yield unprecedented constraints on primordial non-Gaussianity (NG). Using three optimal bispectrum estimators, separable template-fitting (KSW), binned, and modal, we obtain consistent values for the primordial local, equilateral, and orthogonal bispectrum amplitudes, quoting as our final result fNL^local= 2.7+/-5.8, fNL^equil= -42+/-75, and fNL^ortho= -25+\\-39 (68% CL statistical). NG is detected in the data; using skew-C_l statistics we find a nonzero bispectrum from residual point sources, and the ISW-lensing bispectrum at a level expected in the LambdaCDM scenario. The results are based on comprehensive cross-validation of these estimators on Gaussian and non-Gaussian simulations, are stable across component separation techniques, pass an extensive suite of tests, and are confirmed by skew-C_l, wavelet bispectrum and Minkowski functional estimators. Beyond estimates of individual shape amplitudes, we present model-independent, 3-dimensional...
Overview of online two-dimensional liquid chromatography based on cell membrane chromatography for screening target components from traditional Chinese medicines.

Science.gov (United States)

Muhammad, Saqib; Han, Shengli; Xie, Xiaoyu; Wang, Sicen; Aziz, Muhammad Majid

2017-01-01

Cell membrane chromatography is a simple, specific, and time-saving technique for studying drug-receptor interactions, screening of active components from complex mixtures, and quality control of traditional Chinese medicines. However, the short column life, low sensitivity, low column efficiency (so cannot resolve satisfactorily mixture of compounds), low peak capacity, and inefficient in structure identification were bottleneck in its application. Combinations of cell membrane chromatography with multidimensional chromatography such as two-dimensional liquid chromatography and high sensitivity detectors like mass have significantly reduced many of the above-mentioned shortcomings. This paper provides an overview of the current advances in online two-dimensional-based cell membrane chromatography for screening target components from traditional Chinese medicines with particular emphasis on the instrumentation, preparation of cell membrane stationary phase, advantages, and disadvantages compared to alternative approaches. The last section of the review summarizes the applications of the online two-dimensional high-performance liquid chromatography based cell membrane chromatography reported since its emergence to date (2010-June 2016). © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
CO2 Removal from Multi-component Gas Mixtures Utilizing Spiral-Wound Asymmetric Membranes

International Nuclear Information System (INIS)

Said, W.B.; Fahmy, M.F.M.; Gad, F.K.; EI-Aleem, G.A.

2004-01-01

A systematic procedure and a computer program have been developed for simulating the performance of a spiral-wound gas permeate for the CO 2 removal from natural gas and other hydrocarbon streams. The simulation program is based on the approximate multi-component model derived by Qi and Henson(l), in addition to the membrane parameters achieved from the binary simulation program(2) (permeability and selectivity). Applying the multi-component program on the same data used by Qi and Henson to evaluate the deviation of the approximate model from the basic transport model, showing results more accurate than those of the approximate model, and are very close to those of the basic transport model, while requiring significantly less than 1 % of the computation time. The program was successfully applied on the data of Salam gas plant membrane unit at Khalda Petroleum Company, Egypt, for the separation of CO 2 from hydrocarbons in an eight-component mixture to estimate the stage cut, residue, and permeate compositions, and gave results matched with the actual Gas Chromatography Analysis measured by the lab
Legendre Duality of Spherical and Gaussian Spin Glasses

Energy Technology Data Exchange (ETDEWEB)

Genovese, Giuseppe, E-mail: giuseppe.genovese@math.uzh.ch [Universität Zürich, Institut für Mathematik (Switzerland); Tantari, Daniele, E-mail: daniele.tantari@sns.it [Scuola Normale Superiore di Pisa, Centro Ennio de Giorgi (Italy)

2015-12-15

The classical result of concentration of the Gaussian measure on the sphere in the limit of large dimension induces a natural duality between Gaussian and spherical models of spin glass. We analyse the Legendre variational structure linking the free energies of these two systems, in the spirit of the equivalence of ensembles of statistical mechanics. Our analysis, combined with the previous work (Barra et al., J. Phys. A: Math. Theor. 47, 155002, 2014), shows that such models are replica symmetric. Lastly, we briefly discuss an application of our result to the study of the Gaussian Hopfield model.
Legendre Duality of Spherical and Gaussian Spin Glasses

International Nuclear Information System (INIS)

Genovese, Giuseppe; Tantari, Daniele

2015-01-01

The classical result of concentration of the Gaussian measure on the sphere in the limit of large dimension induces a natural duality between Gaussian and spherical models of spin glass. We analyse the Legendre variational structure linking the free energies of these two systems, in the spirit of the equivalence of ensembles of statistical mechanics. Our analysis, combined with the previous work (Barra et al., J. Phys. A: Math. Theor. 47, 155002, 2014), shows that such models are replica symmetric. Lastly, we briefly discuss an application of our result to the study of the Gaussian Hopfield model
The Matter Bispectrum in N-body Simulations with non-Gaussian Initial Conditions

OpenAIRE

Sefusatti, Emiliano; Crocce, Martin; Desjacques, Vincent

2010-01-01

We present measurements of the dark matter bispectrum in N-body simulations with non-Gaussian initial conditions of the local kind for a large variety of triangular configurations and compare them with predictions from Eulerian perturbation theory up to one-loop corrections. We find that the effects of primordial non-Gaussianity at large scales, when compared to perturbation theory, are well described by the initial component of the matter bispectrum, linearly extrapolated at the redshift of ...
Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah

Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address t...... of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.......Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...... the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows...
Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality

Directory of Open Access Journals (Sweden)

Carlos A. L. Pires

2017-01-01

Full Text Available We propose an expansion of multivariate time-series data into maximally independent source subspaces. The search is made among rotations of prewhitened data which maximize non-Gaussianity of candidate sources. We use a tensorial invariant approximation of the multivariate negentropy in terms of a linear combination of squared coskewness and cokurtosis. By solving a high-order singular value decomposition problem, we extract the axes associated with most non-Gaussianity. Moreover, an estimate of the Gaussian subspace is provided by the trailing singular vectors. The independent subspaces are obtained through the search of “quasi-independent” components within the estimated non-Gaussian subspace, followed by the identification of groups with significant joint negentropies. Sources result essentially from the coherency of extremes of the data components. The method is then applied to the global sea surface temperature anomalies, equatorward of 65°, after being tested with non-Gaussian surrogates consistent with the data anomalies. The main emerging independent components and subspaces, supposedly generated by independent forcing, include different variability modes, namely, The East-Pacific, the Central Pacific, and the Atlantic Niños, the Atlantic Multidecadal Oscillation, along with the subtropical dipoles in the Indian, South Pacific, and South-Atlantic oceans. Benefits and usefulness of independent subspaces are then discussed.
Analysis of Influence of Foaming Mixture Components on Structure and Properties of Foam Glass

Science.gov (United States)

Karandashova, N. S.; Goltsman, B. M.; Yatsenko, E. A.

2017-11-01

It is recommended to use high-quality thermal insulation materials to increase the energy efficiency of buildings. One of the best thermal insulation materials is foam glass - durable, porous material that is resistant to almost any effect of substance. Glass foaming is a complex process depending on the foaming mode and the initial mixture composition. This paper discusses the influence of all components of the mixture - glass powder, foaming agent, enveloping material and water - on the foam glass structure. It was determined that glass powder is the basis of the future material. A foaming agent forms a gas phase in the process of thermal decomposition. This aforementioned gas foams the viscous glass mass. The unreacted residue thus changes a colour of the material. The enveloping agent slows the foaming agent decomposition preventing its premature burning out and, in addition, helps to accelerate the sintering of glass particles. The introduction of water reduces the viscosity of the foaming mixture making it evenly distributed and also promotes the formation of water gas that additionally foams the glass mass. The optimal composition for producing the foam glass with the density of 150 kg/m3 is defined according to the results of the research.
Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

Directory of Open Access Journals (Sweden)

Javier Amezcua

2014-09-01

Full Text Available The analysis step of the (ensemble Kalman filter is optimal when (1 the distribution of the background is Gaussian, (2 state variables and observations are related via a linear operator, and (3 the observational error is of additive nature and has Gaussian distribution. When these conditions are largely violated, a pre-processing step known as Gaussian anamorphosis (GA can be applied. The objective of this procedure is to obtain state variables and observations that better fulfil the Gaussianity conditions in some sense. In this work we analyse GA from a joint perspective, paying attention to the effects of transformations in the joint state-variable/observation space. First, we study transformations for state variables and observations that are independent from each other. Then, we introduce a targeted joint transformation with the objective to obtain joint Gaussianity in the transformed space. We focus primarily in the univariate case, and briefly comment on the multivariate one. A key point of this paper is that, when (1–(3 are violated, using the analysis step of the EnKF will not recover the exact posterior density in spite of any transformations one may perform. These transformations, however, provide approximations of different quality to the Bayesian solution of the problem. Using an example in which the Bayesian posterior can be analytically computed, we assess the quality of the analysis distributions generated after applying the EnKF analysis step in conjunction with different GA options. The value of the targeted joint transformation is particularly clear for the case when the prior is Gaussian, the marginal density for the observations is close to Gaussian, and the likelihood is a Gaussian mixture.

Ignition of alkane-rich FACE gasoline fuels and their surrogate mixtures

KAUST Repository

Sarathy, Mani

2015-01-01

Petroleum derived gasoline is the most used transportation fuel for light-duty vehicles. In order to better understand gasoline combustion, this study investigated the ignition propensity of two alkane-rich FACE (Fuels for Advanced Combustion Engines) gasoline test fuels and their corresponding PRF (primary reference fuel) blend in fundamental combustion experiments. Shock tube ignition delay times were measured in two separate facilities at pressures of 10, 20, and 40 bar, temperatures from 715 to 1500 K, and two equivalence ratios. Rapid compression machine ignition delay times were measured for fuel/air mixtures at pressures of 20 and 40 bar, temperatures from 632 to 745 K, and two equivalence ratios. Detailed hydrocarbon analysis was also performed on the FACE gasoline fuels, and the results were used to formulate multi-component gasoline surrogate mixtures. Detailed chemical kinetic modeling results are presented herein to provide insights into the relevance of utilizing PRF and multi-component surrogate mixtures to reproduce the ignition behavior of the alkane-rich FACE gasoline fuels. The two FACE gasoline fuels and their corresponding PRF mixture displayed similar ignition behavior at intermediate and high temperatures, but differences were observed at low temperatures. These trends were mimicked by corresponding surrogate mixture models, except for the amount of heat release in the first stage of a two-stage ignition events, when observed. © 2014 The Combustion Institute.
How Gaussian can our Universe be?

Energy Technology Data Exchange (ETDEWEB)

Cabass, G. [Physics Department and INFN, Università di Roma ' ' La Sapienza' ' , P.le Aldo Moro 2, 00185, Rome (Italy); Pajer, E. [Institute for Theoretical Physics and Center for Extreme Matter and Emergent Phenomena, Utrecht University, Princetonplein 5, 3584 CC Utrecht (Netherlands); Schmidt, F., E-mail: giovanni.cabass@roma1.infn.it, E-mail: e.pajer@uu.nl, E-mail: fabians@mpa-garching.mpg.de [Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85741 Garching (Germany)

2017-01-01

Gravity is a non-linear theory, and hence, barring cancellations, the initial super-horizon perturbations produced by inflation must contain some minimum amount of mode coupling, or primordial non-Gaussianity. In single-field slow-roll models, where this lower bound is saturated, non-Gaussianity is controlled by two observables: the tensor-to-scalar ratio, which is uncertain by more than fifty orders of magnitude; and the scalar spectral index, or tilt, which is relatively well measured. It is well known that to leading and next-to-leading order in derivatives, the contributions proportional to the tilt disappear from any local observable, and suspicion has been raised that this might happen to all orders, allowing for an arbitrarily low amount of primordial non-Gaussianity. Employing Conformal Fermi Coordinates, we show explicitly that this is not the case. Instead, a contribution of order the tilt appears in local observables. In summary, the floor of physical primordial non-Gaussianity in our Universe has a squeezed-limit scaling of k {sub ℓ}{sup 2}/ k {sub s} {sup 2}, similar to equilateral and orthogonal shapes, and a dimensionless amplitude of order 0.1 × ( n {sub s}−1).
How Gaussian can our Universe be?

Science.gov (United States)

Cabass, G.; Pajer, E.; Schmidt, F.

2017-01-01

Gravity is a non-linear theory, and hence, barring cancellations, the initial super-horizon perturbations produced by inflation must contain some minimum amount of mode coupling, or primordial non-Gaussianity. In single-field slow-roll models, where this lower bound is saturated, non-Gaussianity is controlled by two observables: the tensor-to-scalar ratio, which is uncertain by more than fifty orders of magnitude; and the scalar spectral index, or tilt, which is relatively well measured. It is well known that to leading and next-to-leading order in derivatives, the contributions proportional to the tilt disappear from any local observable, and suspicion has been raised that this might happen to all orders, allowing for an arbitrarily low amount of primordial non-Gaussianity. Employing Conformal Fermi Coordinates, we show explicitly that this is not the case. Instead, a contribution of order the tilt appears in local observables. In summary, the floor of physical primordial non-Gaussianity in our Universe has a squeezed-limit scaling of kl2/ks2, similar to equilateral and orthogonal shapes, and a dimensionless amplitude of order 0.1 × (ns-1).
Deciding which chemical mixtures risk assessment methods work best for what mixtures

International Nuclear Information System (INIS)

Teuschler, Linda K.

2007-01-01

The most commonly used chemical mixtures risk assessment methods involve simple notions of additivity and toxicological similarity. Newer methods are emerging in response to the complexities of chemical mixture exposures and effects. Factors based on both science and policy drive decisions regarding whether to conduct a chemical mixtures risk assessment and, if so, which methods to employ. Scientific considerations are based on positive evidence of joint toxic action, elevated human exposure conditions or the potential for significant impacts on human health. Policy issues include legislative drivers that may mandate action even though adequate toxicity data on a specific mixture may not be available and risk assessment goals that impact the choice of risk assessment method to obtain the amount of health protection desired. This paper discusses three important concepts used to choose among available approaches for conducting a chemical mixtures risk assessment: (1) additive joint toxic action of mixture components; (2) toxicological interactions of mixture components; and (3) chemical composition of complex mixtures. It is proposed that scientific support for basic assumptions used in chemical mixtures risk assessment should be developed by expert panels, risk assessment methods experts, and laboratory toxicologists. This is imperative to further develop and refine quantitative methods and provide guidance on their appropriate applications. Risk assessors need scientific support for chemical mixtures risk assessment methods in the form of toxicological data on joint toxic action for high priority mixtures, statistical methods for analyzing dose-response for mixtures, and toxicological and statistical criteria for determining sufficient similarity of complex mixtures
[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].

Science.gov (United States)

Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong

2015-11-01

With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level.
On the Shaker Simulation of Wind-Induced Non-Gaussian Random Vibration

Directory of Open Access Journals (Sweden)

Fei Xu

2016-01-01

Full Text Available Gaussian signal is produced by ordinary random vibration controllers to test the products in the laboratory, while the field data is usually non-Gaussian. Two methodologies are presented in this paper for shaker simulation of wind-induced non-Gaussian vibration. The first methodology synthesizes the non-Gaussian signal offline and replicates it on the shaker in the Time Waveform Replication (TWR mode. A new synthesis method is used to model the non-Gaussian signal as a Gaussian signal multiplied by an amplitude modulation function (AMF. A case study is presented to show that the synthesized non-Gaussian signal has the same power spectral density (PSD, probability density function (PDF, and loading cycle distribution (LCD as the field data. The second methodology derives a damage equivalent Gaussian signal from the non-Gaussian signal based on the fatigue damage spectrum (FDS and the extreme response spectrum (ERS and reproduces it on the shaker in the closed-loop frequency domain control mode. The PSD level and the duration time of the derived Gaussian signal can be manipulated for accelerated testing purpose. A case study is presented to show that the derived PSD matches the damage potential of the non-Gaussian environment for both fatigue and peak response.
Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

International Nuclear Information System (INIS)

Jung, Young Mee

2003-01-01

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra
Influence of Gaussian white noise on the frequency-dependent linear polarizability of doped quantum dot

International Nuclear Information System (INIS)

Ganguly, Jayanta; Ghosh, Manas

2014-01-01

Highlights: • Linear polarizability of quantum dot has been studied. • Quantum dot is doped with a repulsive impurity. • The polarizabilities are frequency-dependent. • Influence of Gaussian white noise has been monitored. • Noise exploited is of additive and multiplicative nature. - Abstract: We investigate the profiles of diagonal components of frequency-dependent linear (α xx and α yy ) optical response of repulsive impurity doped quantum dots. The dopant impurity potential chosen assumes Gaussian form. The study principally puts emphasis on investigating the role of noise on the polarizability components. In view of this we have exploited Gaussian white noise containing additive and multiplicative characteristics (in Stratonovich sense). The frequency-dependent polarizabilities are studied by exposing the doped dot to a periodically oscillating external electric field of given intensity. The oscillation frequency, confinement potentials, dopant location, and above all, the noise characteristics tune the linear polarizability components in a subtle manner. Whereas the additive noise fails to have any impact on the polarizabilities, the multiplicative noise influences them delicately and gives rise to additional interesting features
Mode entanglement of Gaussian fermionic states

Science.gov (United States)

Spee, C.; Schwaiger, K.; Giedke, G.; Kraus, B.

2018-04-01

We investigate the entanglement of n -mode n -partite Gaussian fermionic states (GFS). First, we identify a reasonable definition of separability for GFS and derive a standard form for mixed states, to which any state can be mapped via Gaussian local unitaries (GLU). As the standard form is unique, two GFS are equivalent under GLU if and only if their standard forms coincide. Then, we investigate the important class of local operations assisted by classical communication (LOCC). These are central in entanglement theory as they allow one to partially order the entanglement contained in states. We show, however, that there are no nontrivial Gaussian LOCC (GLOCC) among pure n -partite (fully entangled) states. That is, any such GLOCC transformation can also be accomplished via GLU. To obtain further insight into the entanglement properties of such GFS, we investigate the richer class of Gaussian stochastic local operations assisted by classical communication (SLOCC). We characterize Gaussian SLOCC classes of pure n -mode n -partite states and derive them explicitly for few-mode states. Furthermore, we consider certain fermionic LOCC and show how to identify the maximally entangled set of pure n -mode n -partite GFS, i.e., the minimal set of states having the property that any other state can be obtained from one state inside this set via fermionic LOCC. We generalize these findings also to the pure m -mode n -partite (for m >n ) case.
Numerical analysis of mass transfer with graphite oxidation in a laminar flow of multi-component gas mixture through a circular tube

International Nuclear Information System (INIS)

Ogawa, Masuro

1992-10-01

In the present paper, mass transfer has been numerically studied in a laminar flow through a circular graphite tube to evaluate graphite corrosion rate and generation rate of carbon monoxide during a pipe rupture accident in a high temperature gas cooled reactor. In the analysis, heterogeneous (graphite oxidation and graphite/carbon dioxide reaction) and homogeneous (carbon monoxide combustion) chemical reactions were dealt in the multi-component gas mixture; helium, oxygen, carbon monoxide and carbon dioxide. Multi-component diffusion coefficients were used in a diffusion term. Mass conservation equations of each gas component, mass conservation equation and momentum conservation equations of the gas mixture were solved by using SIMPLE algorism. Chemical reactions between graphite and oxygen, graphite and carbon dioxide, and carbon monoxide combustion were taken into account in the present numerical analysis. An energy equation for the gas mixture was not solved and temperature was held to be constant in order to understand basic mass transfer characteristics without heat transfer. But, an energy conservation equation for single component gas was added to know heat transfer characteristics without mass transfer. The effects of these chemical reactions on the mass transfer coefficients were quantitatively and qualitatively clarified in the range of 50 to 1000 of inlet Reynolds numbers, 0 to 0.5 of inlet oxygen mass fraction and 800 to 1600degC of temperature. (author)
Coincidence Imaging and interference with coherent Gaussian beams

Institute of Scientific and Technical Information of China (English)

CAI Yang-jian; ZHU Shi-yao

2006-01-01

we present a theoretical study of coincidence imaging and interference with coherent Gaussian beams The equations for the coincidence image formation and interference fringes are derived,from which it is clear that the imaging is due to the corresponding focusing in the two paths .The quality and visibility of the images and fringes can be high simultaneously.The nature of the coincidence imaging and interference between quantum entangled photon pairs and coherent Gaussian beams are different .The coincidence image with coherent Gaussian beams is due to intensity-intensity correspondence,a classical nature,while that with entangled photon pairs is due to the amplitude correlation a quantum nature.
Exact Partial Information Decompositions for Gaussian Systems Based on Dependency Constraints

Directory of Open Access Journals (Sweden)

Jim W. Kay

2018-03-01

Full Text Available The Partial Information Decomposition, introduced by Williams P. L. et al. (2010, provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method ( I dep has recently been proposed by James R. G. et al. (2017 for computing a two-predictor partial information decomposition over discrete spaces. A lattice of maximum entropy probability models is constructed based on marginal dependency constraints, and the unique information that a particular predictor has about the target is defined as the minimum increase in joint predictor-target mutual information when that particular predictor-target marginal dependency is constrained. Here, we apply the I dep approach to Gaussian systems, for which the marginally constrained maximum entropy models are Gaussian graphical models. Closed form solutions for the I dep PID are derived for both univariate and multivariate Gaussian systems. Numerical and graphical illustrations are provided, together with practical and theoretical comparisons of the I dep PID with the minimum mutual information partial information decomposition ( I mmi , which was discussed by Barrett A. B. (2015. The results obtained using I dep appear to be more intuitive than those given with other methods, such as I mmi , in which the redundant and unique information components are constrained to depend only on the predictor-target marginal distributions. In particular, it is proved that the I mmi method generally produces larger estimates of redundancy and synergy than does the I dep method. In discussion of the practical examples, the PIDs are complemented by the use of tests of deviance for the comparison of Gaussian graphical models.
Quantum Teamwork for Unconditional Multiparty Communication with Gaussian States

Science.gov (United States)

Zhang, Jing; Adesso, Gerardo; Xie, Changde; Peng, Kunchi

2009-08-01

We demonstrate the capability of continuous variable Gaussian states to communicate multipartite quantum information. A quantum teamwork protocol is presented according to which an arbitrary possibly entangled multimode state can be faithfully teleported between two teams each comprising many cooperative users. We prove that N-mode Gaussian weighted graph states exist for arbitrary N that enable unconditional quantum teamwork implementations for any arrangement of the teams. These perfect continuous variable maximally multipartite entangled resources are typical among pure Gaussian states and are unaffected by the entanglement frustration occurring in multiqubit states.
A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise

Directory of Open Access Journals (Sweden)

Han Pan

2017-01-01

Full Text Available The removal of mixed Gaussian-impulse noise plays an important role in many areas, such as remote sensing. However, traditional methods may be unaware of promoting the degree of the sparsity adaptively after decomposing into low rank component and sparse component. In this paper, a new problem formulation with regular spectral k-support norm and regular k-support l1 norm is proposed. A unified framework is developed to capture the intrinsic sparsity structure of all two components. To address the resulting problem, an efficient minimization scheme within the framework of accelerated proximal gradient is proposed. This scheme is achieved by alternating regular k-shrinkage thresholding operator. Experimental comparison with the other state-of-the-art methods demonstrates the efficacy of the proposed method.
Noise-level determination for discrete spectra with Gaussian or Lorentzian probability density functions

International Nuclear Information System (INIS)

Moriya, Netzer

2010-01-01

A method, based on binomial filtering, to estimate the noise level of an arbitrary, smoothed pure signal, contaminated with an additive, uncorrelated noise component is presented. If the noise characteristics of the experimental spectrum are known, as for instance the type of the corresponding probability density function (e.g., Gaussian), the noise properties can be extracted. In such cases, both the noise level, as may arbitrarily be defined, and a simulated white noise component can be generated, such that the simulated noise component is statistically indistinguishable from the true noise component present in the original signal. In this paper we present a detailed analysis of the noise level extraction when the additive noise is Gaussian or Lorentzian. We show that the statistical parameters in these cases (mainly the variance and the half width at half maximum, respectively) can directly be obtained from the experimental spectrum even when the pure signal is erratic. Further discussion is given for cases where the noise probability density function is initially unknown.
Improved Expectation Maximization Algorithm for Gaussian Mixed Model Using the Kernel Method

Directory of Open Access Journals (Sweden)

Mohd Izhan Mohd Yusoff

2013-01-01

Full Text Available Fraud activities have contributed to heavy losses suffered by telecommunication companies. In this paper, we attempt to use Gaussian mixed model, which is a probabilistic model normally used in speech recognition to identify fraud calls in the telecommunication industry. We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. Firstly, we look at a mechanism for the determination of the initial number of Gaussian components and the choice of the initial values of the algorithm using the kernel method. We show via simulation that the technique improves the performance of the algorithm. Secondly, we developed a procedure for determining the order of the Gaussian mixed model using the log-likelihood function and the Akaike information criteria. Finally, for illustration, we apply the improved algorithm to real telecommunication data. The modified method will pave the way to introduce a comprehensive method for detecting fraud calls in future work.
Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering

OpenAIRE

Xiang, Sijia; Yao, Weixin

2017-01-01

In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models a...
Rotating quantum Gaussian packets

International Nuclear Information System (INIS)

Dodonov, V V

2015-01-01

We study two-dimensional quantum Gaussian packets with a fixed value of mean angular momentum. This value is the sum of two independent parts: the ‘external’ momentum related to the motion of the packet center and the ‘internal’ momentum due to quantum fluctuations. The packets minimizing the mean energy of an isotropic oscillator with the fixed mean angular momentum are found. They exist for ‘co-rotating’ external and internal motions, and they have nonzero correlation coefficients between coordinates and momenta, together with some (moderate) amount of quadrature squeezing. Variances of angular momentum and energy are calculated, too. Differences in the behavior of ‘co-rotating’ and ‘anti-rotating’ packets are shown. The time evolution of rotating Gaussian packets is analyzed, including the cases of a charge in a homogeneous magnetic field and a free particle. In the latter case, the effect of initial shrinking of packets with big enough coordinate-momentum correlation coefficients (followed by the well known expansion) is discovered. This happens due to a competition of ‘focusing’ and ‘de-focusing’ in the orthogonal directions. (paper)
Revisiting non-Gaussianity from non-attractor inflation models

Science.gov (United States)

Cai, Yi-Fu; Chen, Xingang; Namjoo, Mohammad Hossein; Sasaki, Misao; Wang, Dong-Gang; Wang, Ziwei

2018-05-01

Non-attractor inflation is known as the only single field inflationary scenario that can violate non-Gaussianity consistency relation with the Bunch-Davies vacuum state and generate large local non-Gaussianity. However, it is also known that the non-attractor inflation by itself is incomplete and should be followed by a phase of slow-roll attractor. Moreover, there is a transition process between these two phases. In the past literature, this transition was approximated as instant and the evolution of non-Gaussianity in this phase was not fully studied. In this paper, we follow the detailed evolution of the non-Gaussianity through the transition phase into the slow-roll attractor phase, considering different types of transition. We find that the transition process has important effect on the size of the local non-Gaussianity. We first compute the net contribution of the non-Gaussianities at the end of inflation in canonical non-attractor models. If the curvature perturbations keep evolving during the transition—such as in the case of smooth transition or some sharp transition scenarios—the Script O(1) local non-Gaussianity generated in the non-attractor phase can be completely erased by the subsequent evolution, although the consistency relation remains violated. In extremal cases of sharp transition where the super-horizon modes freeze immediately right after the end of the non-attractor phase, the original non-attractor result can be recovered. We also study models with non-canonical kinetic terms, and find that the transition can typically contribute a suppression factor in the squeezed bispectrum, but the final local non-Gaussianity can still be made parametrically large.
Finite mixture models for sub-pixel coastal land cover classification

CSIR Research Space (South Africa)

Ritchie, Michaela C

2017-05-01

Full Text Available Models for Sub- pixel Coastal Land Cover Classification M. Ritchie Dr. M. Lück-Vogel Dr. P. Debba Dr. V. Goodall ISRSE - 37 Tshwane, South Africa 10 May 2017 2Study Area Africa South Africa FALSE BAY 3Strand Gordon’s Bay Study Area WorldView-2 Image.../Urban 1 10 10 Herbaceous Vegetation 1 5 5 Shadow 1 8 8 Sparse Vegetation 1 3 3 Water 1 10 10 Woody Vegetation 1 5 5 11 Maximum Likelihood Classification (MLC) 12 Gaussian Mixture Discriminant Analysis (GMDA) 13 A B C t-distribution Mixture Discriminant...

Improved predictive model for n-decane kinetics across species, as a component of hydrocarbon mixtures.

Science.gov (United States)

Merrill, E A; Gearhart, J M; Sterner, T R; Robinson, P J

2008-07-01

n-Decane is considered a major component of various fuels and industrial solvents. These hydrocarbon products are complex mixtures of hundreds of components, including straight-chain alkanes, branched chain alkanes, cycloalkanes, diaromatics, and naphthalenes. Human exposures to the jet fuel, JP-8, or to industrial solvents in vapor, aerosol, and liquid forms all have the potential to produce health effects, including immune suppression and/or neurological deficits. A physiologically based pharmacokinetic (PBPK) model has previously been developed for n-decane, in which partition coefficients (PC), fitted to 4-h exposure kinetic data, were used in preference to measured values. The greatest discrepancy between fitted and measured values was for fat, where PC values were changed from 250-328 (measured) to 25 (fitted). Such a large change in a critical parameter, without any physiological basis, greatly impedes the model's extrapolative abilities, as well as its applicability for assessing the interactions of n-decane or similar alkanes with other compounds in a mixture model. Due to these limitations, the model was revised. Our approach emphasized the use of experimentally determined PCs because many tissues had not approached steady-state concentrations by the end of the 4-h exposures. Diffusion limitation was used to describe n-decane kinetics for the brain, perirenal fat, skin, and liver. Flow limitation was used to describe the remaining rapidly and slowly perfused tissues. As expected from the high lipophilicity of this semivolatile compound (log K(ow) = 5.25), sensitivity analyses showed that parameters describing fat uptake were next to blood:air partitioning and pulmonary ventilation as critical in determining overall systemic circulation and uptake in other tissues. In our revised model, partitioning into fat took multiple days to reach steady state, which differed considerably from the previous model that assumed steady-state conditions in fat at 4 h post
Feasibility study on the least square method for fitting non-Gaussian noise data

Science.gov (United States)

Xu, Wei; Chen, Wen; Liang, Yingjie

2018-02-01

This study is to investigate the feasibility of least square method in fitting non-Gaussian noise data. We add different levels of the two typical non-Gaussian noises, Lévy and stretched Gaussian noises, to exact value of the selected functions including linear equations, polynomial and exponential equations, and the maximum absolute and the mean square errors are calculated for the different cases. Lévy and stretched Gaussian distributions have many applications in fractional and fractal calculus. It is observed that the non-Gaussian noises are less accurately fitted than the Gaussian noise, but the stretched Gaussian cases appear to perform better than the Lévy noise cases. It is stressed that the least-squares method is inapplicable to the non-Gaussian noise cases when the noise level is larger than 5%.
Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data

Directory of Open Access Journals (Sweden)

Ji Hoon Ryoo

2017-08-01

Full Text Available As in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including analytic techniques to estimate parameters in both fixed and random effects models. However, as yet growth modeling with non-Gaussian data is somewhat limited when considering the transformed expectation of the response via a linear predictor as a functional form of explanatory variables. In this study, we introduce a fractional polynomial model (FPM that can be applied to model non-linear growth with non-Gaussian longitudinal data and demonstrate its use by fitting two empirical binary and count data models. The results clearly show the efficiency and flexibility of the FPM for such applications.
Consistency relations for sharp inflationary non-Gaussian features

Energy Technology Data Exchange (ETDEWEB)

Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris [Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Blanco Encalada 2008, Santiago (Chile); Soto, Alex, E-mail: sander.mooij@ing.uchile.cl, E-mail: gpalmaquilod@ing.uchile.cl, E-mail: gpanotop@ing.uchile.cl, E-mail: gatogeno@gmail.com [Departamento de Física, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Ñuñoa, Santiago (Chile)

2016-09-01

If cosmic inflation suffered tiny time-dependent deviations from the slow-roll regime, these would induce the existence of small scale-dependent features imprinted in the primordial spectra, with their shapes and sizes revealing information about the physics that produced them. Small sharp features could be suppressed at the level of the two-point correlation function, making them undetectable in the power spectrum, but could be amplified at the level of the three-point correlation function, offering us a window of opportunity to uncover them in the non-Gaussian bispectrum. In this article, we show that sharp features may be analyzed using only data coming from the three point correlation function parametrizing primordial non-Gaussianity. More precisely, we show that if features appear in a particular non-Gaussian triangle configuration (e.g. equilateral, folded, squeezed), these must reappear in every other configuration according to a specific relation allowing us to correlate features across the non-Gaussian bispectrum. As a result, we offer a method to study scale-dependent features generated during inflation that depends only on data coming from measurements of non-Gaussianity, allowing us to omit data from the power spectrum.
Consistency relations for sharp inflationary non-Gaussian features

International Nuclear Information System (INIS)

Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris; Soto, Alex

2016-01-01

If cosmic inflation suffered tiny time-dependent deviations from the slow-roll regime, these would induce the existence of small scale-dependent features imprinted in the primordial spectra, with their shapes and sizes revealing information about the physics that produced them. Small sharp features could be suppressed at the level of the two-point correlation function, making them undetectable in the power spectrum, but could be amplified at the level of the three-point correlation function, offering us a window of opportunity to uncover them in the non-Gaussian bispectrum. In this article, we show that sharp features may be analyzed using only data coming from the three point correlation function parametrizing primordial non-Gaussianity. More precisely, we show that if features appear in a particular non-Gaussian triangle configuration (e.g. equilateral, folded, squeezed), these must reappear in every other configuration according to a specific relation allowing us to correlate features across the non-Gaussian bispectrum. As a result, we offer a method to study scale-dependent features generated during inflation that depends only on data coming from measurements of non-Gaussianity, allowing us to omit data from the power spectrum.
Generation of singular optical beams from fundamental Gaussian beam using Sagnac interferometer

Science.gov (United States)

Naik, Dinesh N.; Viswanathan, Nirmal K.

2016-09-01

We propose a simple free-space optics recipe for the controlled generation of optical vortex beams with a vortex dipole or a single charge vortex, using an inherently stable Sagnac interferometer. We investigate the role played by the amplitude and phase differences in generating higher-order Gaussian beams from the fundamental Gaussian mode. Our simulation results reveal how important the control of both the amplitude and the phase difference between superposing beams is to achieving optical vortex beams. The creation of a vortex dipole from null interference is unveiled through the introduction of a lateral shear and a radial phase difference between two out-of-phase Gaussian beams. A stable and high quality optical vortex beam, equivalent to the first-order Laguerre-Gaussian beam, is synthesized by coupling lateral shear with linear phase difference, introduced orthogonal to the shear between two out-of-phase Gaussian beams.
A numerical study of blood flow using mixture theory

OpenAIRE

Wu, Wei-Tao; Aubry, Nadine; Massoudi, Mehrdad; Kim, Jeongho; Antaki, James F.

2014-01-01

In this paper, we consider the two dimensional flow of blood in a rectangular microfluidic channel. We use Mixture Theory to treat this problem as a two-component system: One component is the red blood cells (RBCs) modeled as a generalized Reiner–Rivlin type fluid, which considers the effects of volume fraction (hematocrit) and influence of shear rate upon viscosity. The other component, plasma, is assumed to behave as a linear viscous fluid. A CFD solver based on OpenFOAM® was developed and ...
Introducing Students to Gas Chromatography-Mass Spectrometry Analysis and Determination of Kerosene Components in a Complex Mixture

Science.gov (United States)

Pacot, Giselle Mae M.; Lee, Lyn May; Chin, Sung-Tong; Marriott, Philip J.

2016-01-01

Gas chromatography-mass spectrometry (GC-MS) and GC-tandem MS (GC-MS/MS) are useful in many separation and characterization procedures. GC-MS is now a common tool in industry and research, and increasingly, GC-MS/MS is applied to the measurement of trace components in complex mixtures. This report describes an upper-level undergraduate experiment…
Vortices in Gaussian beams

CSIR Research Space (South Africa)

Roux, FS

2009-01-01

Full Text Available , t0)} = P(du, dv) {FR{g(u, v, t0)}} Replacement: u→ du = t− t0 i2 ∂ ∂u′ v → dv = t− t0 i2 ∂ ∂v′ CSIR National Laser Centre – p.13/30 Differentiation i.s.o integration Evaluate the integral over the Gaussian beam (once and for all). Then, instead... . Gaussian beams with vortex dipoles CSIR National Laser Centre – p.2/30 Gaussian beam notation Gaussian beam in normalised coordinates: g(u, v, t) = exp ( −u 2 + v2 1− it ) u = xω0 v = yω0 t = zρ ρ = piω20 λ ω0 — 1/e2 beam waist radius; ρ— Rayleigh range ω ω...
Prediction of two-phase mixture density using artificial neural networks

International Nuclear Information System (INIS)

Lombardi, C.; Mazzola, A.

1997-01-01

In nuclear power plants, the density of boiling mixtures has a significant relevance due to its influence on the neutronic balance, the power distribution and the reactor dynamics. Since the determination of the two-phase mixture density on a purely analytical basis is in fact impractical in many situations of interest, heuristic relationships have been developed based on the parameters describing the two-phase system. However, the best or even a good structure for the correlation cannot be determined in advance, also considering that it is usually desired to represent the experimental data with the most compact equation. A possible alternative to empirical correlations is the use of artificial neural networks, which allow one to model complex systems without requiring the explicit formulation of the relationships existing among the variables. In this work, the neural network methodology was applied to predict the density data of two-phase mixtures up-flowing in adiabatic channels under different experimental conditions. The trained network predicts the density data with a root-mean-square error of 5.33%, being ∼ 93% of the data points predicted within 10%. When compared with those of two conventional well-proven correlations, i.e. the Zuber-Findlay and the CISE correlations, the neural network performances are significantly better. In spite of the good accuracy of the neural network predictions, the 'black-box' characteristic of the neural model does not allow an easy physical interpretation of the knowledge integrated in the network weights. Therefore, the neural network methodology has the advantage of not requiring a formal correlation structure and of giving very accurate results, but at the expense of a loss of model transparency. (author)
Biofiltration of paint solvent mixtures in two reactor types: overloading by polar components.

Science.gov (United States)

Paca, Jan; Halecky, Martin; Misiaczek, Ondrej; Kozliak, Evguenii I; Jones, Kim

2012-01-01

Steady-state performances of a trickle bed reactor (TBR) and a biofilter (BF) in loading experiments with increasing inlet concentrations of polar solvents, acetone, methyl ethyl ketone, methyl isobutyl ketone and n-butyl acetate, were investigated, along with the system's dynamic responses. Throughout the entire experimentation time, a constant loading rate of aromatic components of 4 g(c)·m(-3)·h(-1) was maintained to observe the interactions between the polar substrates and aromatic hydrocarbons. Under low combined substrate loadings, the BF outperformed TBR not only in the removal of aromatic hydrocarbons but also in the removal of polar substrates. However, increasing the loading rate of polar components above the threshold value of 31-36 g(c)·m(-3)·h(-1) resulted in a steep and significant drop in the removal efficiencies of both polar (except for butyl acetate) and hydrophobic components, which was more pronounced in the BF; so the relative TBR/BF efficiency became reversed under such overloading conditions. A step-drop of the overall OL(POLAR) (combined loading by polar air pollutants) from overloading values to 7 g(c)·m(-3)·h(-1) resulted in an increase of all pollutant removal efficiencies, although in TBR the recovery was preceded by lag periods lasting between 5 min (methyl ethyl ketone) to 3.7 h (acetone). The occurrence of lag periods in the TBR recovery was, in part, due to the saturation of mineral medium with water-soluble polar solvents, particularly, acetone. The observed bioreactor behavior was consistent with the biological steps being rate-limiting.
Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

KAUST Repository

Yan, Yuan; Genton, Marc G.

2017-01-01

Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.
Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

KAUST Repository

Yan, Yuan

2017-07-13

Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.
A study of the Gaussian overlap approach in the two-center shell model

International Nuclear Information System (INIS)

Reinhard, P.-G.

1976-01-01

The Gaussian overlap approach (GOA) to the generator coordinate method (GCM) is carried through up to fourth order in the derivatives. By diagonalizing the norm overlap, a collective Schroedinger equation is obtained. The potential therein contains the usual potential energy surface (PES) plus correction terms, which subtract the zero-point energies (ZPE) is the PES. The formalism is applied to BCS states obtained from a two-center shell model (TCSM). To understand the crucial role of the pairing contributions in the GOA a schematic picture, the multi-level model, is constructed. An explicit numerical study of the convergence of the GOA is given for the TCSM, with the result that the GOA seems to be justified for medium and heavy nuclei but critical for light nuclei. (Auth.)
Evaluation of drug-carrier interactions in quaternary powder mixtures containing perindopril tert-butylamine and indapamide.

Science.gov (United States)

Voelkel, Adam; Milczewska, Kasylda; Teżyk, Michał; Milanowski, Bartłomiej; Lulek, Janina

2016-04-30

Interactions occurring between components in the quaternary powder mixtures consisting of perindopril tert-butylamine, indapamide (active pharmaceutical ingredients), carrier substance and hydrophobic colloidal silica were examined. Two grades of lactose monohydrate: Spherolac(®) 100 and Granulac(®) 200 and two types of microcrystalline cellulose: M101D+ and Vivapur(®) 102 were used as carriers. We determined the size distribution (laser diffraction method), morphology (scanning electron microscopy) and a specific surface area of the powder particles (by nitrogen adsorption-desorption). For the determination of the surface energy of powder mixtures the method of inverse gas chromatography was applied. Investigated mixtures were characterized by surface parameters (dispersive component of surface energy, specific interactions parameters, specific surface area), work of adhesion and cohesion as well as Flory-Huggins parameter χ23('). Results obtained for all quaternary powder mixtures indicate existence of interactions between components. The strongest interactions occur for both blends with different types of microcrystalline cellulose (PM-1 and PM-4) while much weaker ones for powder mixtures with various types of lactose (PM-2 and PM-3). Published by Elsevier B.V.
Detecting the presence of a magnetic field under Gaussian and non-Gaussian noise by adaptive measurement

Energy Technology Data Exchange (ETDEWEB)

Wang, Yuan-Mei; Li, Jun-Gang, E-mail: jungl@bit.edu.cn; Zou, Jian

2017-06-15

Highlights: • Adaptive measurement strategy is used to detect the presence of a magnetic field. • Gaussian Ornstein–Uhlenbeck noise and non-Gaussian noise have been considered. • Weaker magnetic fields may be more easily detected than some stronger ones. - Abstract: By using the adaptive measurement method we study how to detect whether a weak magnetic field is actually present or not under Gaussian noise and non-Gaussian noise. We find that the adaptive measurement method can effectively improve the detection accuracy. For the case of Gaussian noise, we find the stronger the magnetic field strength, the easier for us to detect the magnetic field. Counterintuitively, for non-Gaussian noise, some weaker magnetic fields are more likely to be detected rather than some stronger ones. Finally, we give a reasonable physical interpretation.
Heat of combustion, sound speed and component fluctuations in natural gas

International Nuclear Information System (INIS)

Burstein, L.; Ingman, D.

1998-01-01

The heat of combustion and sound speed of natural gas were studied as a function of random fluctuation of the gas fractions. A method of sound speed determination was developed and used for over 50,000 possible variants of component concentrations in four- and five- component mixtures. A test on binary (methane-ethane) and multicomponent (Gulf Coast) gas mixtures under standard pressure and moderate temperatures shows satisfactory predictability of sound speed on the basis of the binary virial coefficients, sound speeds and heat capacities of the pure components. Uncertainty in the obtained values does not exceed that of the pure component data. The results of comparison between two natural gas mixtures - with and without nonflammable components - are reported
Non-Gaussian signatures arising from warm inflation driven by geometric tachyon

International Nuclear Information System (INIS)

Bhattacharjee, Anindita; Deshamukhya, Atri

2014-01-01

In a warm inflationary scenario, the initial seeds of density perturbation arise from thermal fluctuations of the inflaton field. These fluctuations in principle have Gaussian distribution. In a Gaussian distribution the density perturbation can be expressed as the two point correlation function. Thus if in an inflationary model the density perturbation is expressed as correlation function of order higher than two, these fluctuations are non-Gaussian in nature. A simple inflationary model containing single scalar field, slow roll, canonical kinetic term and vacuum initial state can produce a tiny amount of non-Gaussianity which are very small to be detected by any experiment. Non-Gaussianity can also arise in inflationary models containing multiple scalar fields. For an inflationary scenario with single scalar field, non-Gaussianity can be expressed in terms of bi-spectrum however for multi field Inflation, it is expressed in terms of trispectrum etc. In this piece of work, the warm inflationary scenario, driven by a D3 brane due to the presence of a stack of k coincident NS 5 branes is considered and the non-Gaussian effects in such an inflationary scenario has been analysed by measuring the bispectrum of the gravitational field fluctuations generated during the warm inflation in strong dissipative regime. The bi-spectrum of the Inflation is expressed in terms of the parameter f NL and it is seen that the value of f NL parameter lies well within the limit observed by WMAP7
Efficient testing of the homogeneity, scale parameters and number of components in the Rayleigh mixture

International Nuclear Information System (INIS)

Stehlik, M.; Ososkov, G.A.

2003-01-01

The statistical problem to expand the experimental distribution of transverse momenta into Rayleigh distribution is considered. A high-efficient testing procedure for testing the hypothesis of the homogeneity of the observed measurements which is optimal in the sense of Bahadur is constructed. The exact likelihood ratio (LR) test of the scale parameter of the Rayleigh distribution is proposed for cases when the hypothesis of homogeneity holds. Otherwise the efficient procedure for testing the number of components in the mixture is also proposed
Geometry of Gaussian quantum states

International Nuclear Information System (INIS)

Link, Valentin; Strunz, Walter T

2015-01-01

We study the Hilbert–Schmidt measure on the manifold of mixed Gaussian states in multi-mode continuous variable quantum systems. An analytical expression for the Hilbert–Schmidt volume element is derived. Its corresponding probability measure can be used to study typical properties of Gaussian states. It turns out that although the manifold of Gaussian states is unbounded, an ensemble of Gaussian states distributed according to this measure still has a normalizable distribution of symplectic eigenvalues, from which unitarily invariant properties can be obtained. By contrast, we find that for an ensemble of one-mode Gaussian states based on the Bures measure the corresponding distribution cannot be normalized. As important applications, we determine the distribution and the mean value of von Neumann entropy and purity for the Hilbert–Schmidt measure. (paper)

The stability and stratification of a quantum liquid mixture

International Nuclear Information System (INIS)

Yukalov, V.I.

1980-01-01

A mixture of quantum liquids was investigated microscopically. The spectrum of collective excitations at finite temperature was determined. The form of the spectrum demonstrates whether there is a stability or stratification of the mixture. The influence of a relative motion of liquids on the spectrum was considered. It was demonstrated that beginning with some finite momentun, the spectrum of each component of the solution splits into two branches, one of which continues the spectrum into the single-particle region. The dynamic susceptibility, the dynamic form-factor, the coefficients of compressibility and the structure factor for the mixture of two Bose liquids were obtained. The integral relations that generalize some rules concerning the binary Bose solution was established. (author)
Effective leaf area index retrieving from terrestrial point cloud data: coupling computational geometry application and Gaussian mixture model clustering

Science.gov (United States)

Jin, S.; Tamura, M.; Susaki, J.

2014-09-01

Leaf area index (LAI) is one of the most important structural parameters of forestry studies which manifests the ability of the green vegetation interacted with the solar illumination. Classic understanding about LAI is to consider the green canopy as integration of horizontal leaf layers. Since multi-angle remote sensing technique developed, LAI obliged to be deliberated according to the observation geometry. Effective LAI could formulate the leaf-light interaction virtually and precisely. To retrieve the LAI/effective LAI from remotely sensed data therefore becomes a challenge during the past decades. Laser scanning technique can provide accurate surface echoed coordinates with densely scanned intervals. To utilize the density based statistical algorithm for analyzing the voluminous amount of the 3-D points data is one of the subjects of the laser scanning applications. Computational geometry also provides some mature applications for point cloud data (PCD) processing and analysing. In this paper, authors investigated the feasibility of a new application for retrieving the effective LAI of an isolated broad leaf tree. Simplified curvature was calculated for each point in order to remove those non-photosynthetic tissues. Then PCD were discretized into voxel, and clustered by using Gaussian mixture model. Subsequently the area of each cluster was calculated by employing the computational geometry applications. In order to validate our application, we chose an indoor plant to estimate the leaf area, the correlation coefficient between calculation and measurement was 98.28 %. We finally calculated the effective LAI of the tree with 6 × 6 assumed observation directions.
Effect of Asymmetric Potential and Gaussian Colored Noise on Stochastic Resonance

International Nuclear Information System (INIS)

Han Yinxia; Li Jinghui; Chen Shigang

2005-01-01

The phenomenon of stochastic resonance (SR) in a bistable nonlinear system is studied when the system is driven by the asymmetric potential and additive Gaussian colored noise. Using the unified colored noise approximation method, the additive Gaussian colored noise can be simplified to additive Gaussian white noise. The signal-to-noise ratio (SNR) is calculated according to the generalized two-state theory (shown in [H.S. Wio and S. Bouzat, Brazilian J. Phys. 29 (1999) 136]). We find that the SNR increases with the proximity of a to zero. In addition, the correlation time τ between the additive Gaussian colored noise is also an ingredient to improve SR. The shorter the correlation time τ between the Gaussian additive colored noise is, the higher of the peak value of SNR.
On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio

Directory of Open Access Journals (Sweden)

Tatjana Miljkovic

2018-05-01

Full Text Available We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM and mixture-based clustering for an ordered stereotype model (OSM. The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.
Measurement of damping and temperature: Precision bounds in Gaussian dissipative channels

International Nuclear Information System (INIS)

Monras, Alex; Illuminati, Fabrizio

2011-01-01

We present a comprehensive analysis of the performance of different classes of Gaussian states in the estimation of Gaussian phase-insensitive dissipative channels. In particular, we investigate the optimal estimation of the damping constant and reservoir temperature. We show that, for two-mode squeezed vacuum probe states, the quantum-limited accuracy of both parameters can be achieved simultaneously. Moreover, we show that for both parameters two-mode squeezed vacuum states are more efficient than coherent, thermal, or single-mode squeezed states. This suggests that at high-energy regimes, two-mode squeezed vacuum states are optimal within the Gaussian setup. This optimality result indicates a stronger form of compatibility for the estimation of the two parameters. Indeed, not only the minimum variance can be achieved at fixed probe states, but also the optimal state is common to both parameters. Additionally, we explore numerically the performance of non-Gaussian states for particular parameter values to find that maximally entangled states within d-dimensional cutoff subspaces (d≤6) perform better than any randomly sampled states with similar energy. However, we also find that states with very similar performance and energy exist with much less entanglement than the maximally entangled ones.
Innovative aspects of protein stability in ionic liquid mixtures.

Science.gov (United States)

Kumar, Awanish; Venkatesu, Pannuru

2018-06-01

Mixtures of ionic liquids (ILs) have attracted our attention because of their extraordinary performances in extraction technologies and in absorbing large amount of CO 2 gas. It has been observed that when two or more ILs are mixed in different proportions, a new solvent is obtained which is much better than that of each component of ILs from which the mixture is obtained. Within a mixture of ILs, several unidentified interactions occur among several ions which give rise to unique solvent properties to the mixture. Herein, in this review, we have highlighted the utilization of the advantageous properties of the IL mixtures in protein stability studies. This approach is exceptional and opens new directions to the use of ILs in biotechnology.
Ostwald ripening in two-phase mixtures

International Nuclear Information System (INIS)

Voorhees, P.W.

1982-01-01

Experimental measurements of the temperature of a rapidly solidified solid-liquid mixture have been made over a range of volume fractions solid 0.23 to 0.95. These experiments demonstrate the viability of measuring the change in interfacial curvature with time via precision thermometry. The experimental measurements also indicate that there is no radical change in interface morphology over a wide range of volume fractions solid. A solution to the multi-particle diffusion problem (MDP) has been constructed through the use of potential theory. The solution to the MDP was used to describe the diffusion field within a coarsening two-phase mixture consisting of dispersed spherical second-phase particles. Since this theory is based upon the MDP, interparticle diffusional interactions are specifically included in the treatment. As a result, the theory yields, for the first time, insights into the influence of the local distribution of curvature on a particle's coarsening rate. The effect of interparticle interactions on the collective behavior of an ensemble of coarsening particles was also investigated. It was found that any arbitrary distribution of particle radii will tend to a specific time independent distribution when the particle radii are scaled by the average particle radius. Furthermore, it was determined that with increasing volume fraction of coarsening phase, these time independent distributions become broader and more symmetric. It was also found that the ripening kinetics, as measured by the growth rate of the average particle size, increases by a factor of five upon increasing the volume fraction of coarsening phase from zero to 0.5
Convective boiling heat transfer of mixture of immiscible two-liquids

International Nuclear Information System (INIS)

Hijikata, K.; Ito, H.; Mori, Y.

1987-01-01

Thermal energy conversion of low or middle temperature difference to electric power is conventionally made by the Rankine cycle using the organic compound as a working fluid. However, the energy conversion efficiency from thermal energy to electric power is limited by the pinch point temperature difference in the high temperature side heat exchanging. In order to avoid the efficiency ceiling due to the pinch point temperature difference, utilization of mixture of miscible two liquids as the working fluid of the Rankine cycle has been proposed and its cycle efficiency has been calculated. However, in the miscible mixture, mutual diffusion process is considered to greatly affect the thermo-fluid characteristics, but has not been clarified yet because of its complexity
A wavelet-based Gaussian method for energy dispersive X-ray fluorescence spectrum

Directory of Open Access Journals (Sweden)

Pan Liu

2017-05-01

Full Text Available This paper presents a wavelet-based Gaussian method (WGM for the peak intensity estimation of energy dispersive X-ray fluorescence (EDXRF. The relationship between the parameters of Gaussian curve and the wavelet coefficients of Gaussian peak point is firstly established based on the Mexican hat wavelet. It is found that the Gaussian parameters can be accurately calculated by any two wavelet coefficients at the peak point which has to be known. This fact leads to a local Gaussian estimation method for spectral peaks, which estimates the Gaussian parameters based on the detail wavelet coefficients of Gaussian peak point. The proposed method is tested via simulated and measured spectra from an energy X-ray spectrometer, and compared with some existing methods. The results prove that the proposed method can directly estimate the peak intensity of EDXRF free from the background information, and also effectively distinguish overlap peaks in EDXRF spectrum.
The predatory mite Phytoseiulus persimilis does not perceive odor mixtures as strictly elemental objects.

Science.gov (United States)

van Wijk, Michiel; de Bruijn, Paulien J A; Sabelis, Maurice W

2010-11-01

Phytoseiulus persimilis is a predatory mite that in absence of vision relies on the detection of herbivore-induced plant odors to locate its prey, the two-spotted spider-mite Tetranychus urticae. This herbivorous prey is feeding on leaves of a wide variety of plant species in different families. The predatory mites respond to numerous structurally different compounds. However, typical spider-mite induced plant compounds do not attract more predatory mites than plant compounds not associated with prey. Because the mites are sensitive to many compounds, components of odor mixtures may affect each other's perception. Although the response to pure compounds has been well documented, little is known how interactions among compounds affect the response to odor mixtures. We assessed the relation between the mites' responses elicited by simple mixtures of two compounds and by the single components of these mixtures. The preference for the mixture was compared to predictions under three conceptual models, each based on one of the following assumptions: (1) the responses elicited by each of the individual components can be added to each other; (2) they can be averaged; or (3) one response overshadows the other. The observed response differed significantly from the response predicted under the additive response, average response, and overshadowing response model in 52, 36, and 32% of the experimental tests, respectively. Moreover, the behavioral responses elicited by individual compounds and their binary mixtures were determined as a function of the odor concentration. The relative contribution of each component to the behavioral response elicited by the mixture varied with the odor concentration, even though the ratio of both compounds in the mixture was kept constant. Our experiments revealed that compounds that elicited no response had an effect on the response elicited by binary mixtures that they were part of. The results are not consistent with the hypothesis that P
Palm distributions for log Gaussian Cox processes

DEFF Research Database (Denmark)

Coeurjolly, Jean-Francois; Møller, Jesper; Waagepetersen, Rasmus Plenge

2017-01-01

This paper establishes a remarkable result regarding Palm distributions for a log Gaussian Cox process: the reduced Palm distribution for a log Gaussian Cox process is itself a log Gaussian Cox process that only differs from the original log Gaussian Cox process in the intensity function. This new...... result is used to study functional summaries for log Gaussian Cox processes....
High-Order Local Pooling and Encoding Gaussians Over a Dictionary of Gaussians.

Science.gov (United States)

Li, Peihua; Zeng, Hui; Wang, Qilong; Shiu, Simon C K; Zhang, Lei

2017-07-01

Local pooling (LP) in configuration (feature) space proposed by Boureau et al. explicitly restricts similar features to be aggregated, which can preserve as much discriminative information as possible. At the time it appeared, this method combined with sparse coding achieved competitive classification results with only a small dictionary. However, its performance lags far behind the state-of-the-art results as only the zero-order information is exploited. Inspired by the success of high-order statistical information in existing advanced feature coding or pooling methods, we make an attempt to address the limitation of LP. To this end, we present a novel method called high-order LP (HO-LP) to leverage the information higher than the zero-order one. Our idea is intuitively simple: we compute the first- and second-order statistics per configuration bin and model them as a Gaussian. Accordingly, we employ a collection of Gaussians as visual words to represent the universal probability distribution of features from all classes. Our problem is naturally formulated as encoding Gaussians over a dictionary of Gaussians as visual words. This problem, however, is challenging since the space of Gaussians is not a Euclidean space but forms a Riemannian manifold. We address this challenge by mapping Gaussians into the Euclidean space, which enables us to perform coding with common Euclidean operations rather than complex and often expensive Riemannian operations. Our HO-LP preserves the advantages of the original LP: pooling only similar features and using a small dictionary. Meanwhile, it achieves very promising performance on standard benchmarks, with either conventional, hand-engineered features or deep learning-based features.
Intra-cavity metamorphosis of a Gaussian beam to flat-top distribution

CSIR Research Space (South Africa)

Naidoo, Darryl

2014-02-01

Full Text Available We explore an intra-cavity beam shaping approach to generate a Gaussian distribution by the metamorphosis of a Gaussian beam into a flat-top distribution on opposing mirrors. The concept is tested external to the cavity through the use of two...
Effect of a mixture of caffeine and nicotinamide on the solubility of vitamin (B2) in aqueous solution.

Science.gov (United States)

Evstigneev, M P; Evstigneev, V P; Santiago, A A Hernandez; Davies, D B

2006-05-01

The effect of caffeine (CAF) and nicotinamide (NMD) on the solubility of a vitamin B2 derivative (FMN) has been evaluated for mixtures containing either a single hydrotrope (CAF or NMD) or the two hydrotropes simultaneously. A model for analysis of ternary systems, which takes into account all possible complexes between the molecules, has been developed and tested with experimental NMR data on the three-component mixture FMN-CAF-NMD. The results indicate that special attention should be given to the concentration of a hydrotropic agent used to enhance the solubility of a particular drug. A decrease in the efficacy of solubility of the vitamin on addition of large amounts of hydrotropic agent is expected in the two-component systems due to the increased proportion of self-association of the hydrotrope. It is found that a mixture of two hydrotropic agents leads to an increase in the solubility of the vitamin in three-component compared to the two-component system. Rather than using just one hydrotropic agent, it is proposed that a strategy for optimising the solubility of aromatic drugs is to use a mixture of hydrotropic agents.
Unitarily localizable entanglement of Gaussian states

International Nuclear Information System (INIS)

Serafini, Alessio; Adesso, Gerardo; Illuminati, Fabrizio

2005-01-01

We consider generic (mxn)-mode bipartitions of continuous-variable systems, and study the associated bisymmetric multimode Gaussian states. They are defined as (m+n)-mode Gaussian states invariant under local mode permutations on the m-mode and n-mode subsystems. We prove that such states are equivalent, under local unitary transformations, to the tensor product of a two-mode state and of m+n-2 uncorrelated single-mode states. The entanglement between the m-mode and the n-mode blocks can then be completely concentrated on a single pair of modes by means of local unitary operations alone. This result allows us to prove that the PPT (positivity of the partial transpose) condition is necessary and sufficient for the separability of (m+n)-mode bisymmetric Gaussian states. We determine exactly their negativity and identify a subset of bisymmetric states whose multimode entanglement of formation can be computed analytically. We consider explicit examples of pure and mixed bisymmetric states and study their entanglement scaling with the number of modes
Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

Science.gov (United States)

Semper, Sean R.; Crassidis, John L.; George, Jemin; Mukherjee, Siddharth; Singla, Puneet

2015-01-01

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating
Depletion interactions in two-dimensional colloid-polymer mixtures: molecular dynamics simulations

International Nuclear Information System (INIS)

Kim, Soon-Chul; Seong, Baek-Seok; Suh, Soong-Hyuck

2009-01-01

The depletion interactions acting between two hard colloids immersed in a bath of polymers, in which the interaction potentials include the soft repulsion/attraction, are extensively studied by using the molecular dynamics simulations. The collision frequencies and collision angle distributions for both incidental and reflection conditions are computed to study the dynamic properties of the colloidal mixtures. The depletion effect induced by the polymer-polymer and colloid-polymer interactions are investigated as well as the size ratio of the colloid and polymer. The simulated results show that the strong depletion interaction between two hard colloids appears for the highly asymmetric hard-disc mixtures. The attractive depletion force at contact becomes deeper and the repulsive barrier becomes wider as the asymmetry in size ratio increases. The strong polymer-polymer attraction leads to the purely attractive depletion interaction between two hard colloids, whereas the purely repulsive depletion interaction is induced by the strong colloid-polymer attraction.
Volatility of components of saturated vapours of UCl/sub 4/-CsCl and UCl/sub 4/-LiCl molten mixtures

Energy Technology Data Exchange (ETDEWEB)

Smirnov, M V; Kudyakov, V Ya; Salyulev, A B; Komarov, V E; Posokhin, Yu V; Afonichkin, V K

1979-01-01

The flow method has been used for measuring the volatility of the components from UCl/sub 4/-CsCl and UCl/sub 4/-LiCl melted mixtures containing 2.0, 5.0, 12.0, 25.0, 33.0, 50.0, 67.0, and 83.0 mol.% of UCl/sub 4/ within the temperature ranges of 903-1188 K and 740-1200 K, respectively. The chemical composition of saturated vapours above the melted salts has been determined. The melted mixtures in question exhibit negative deviation from ideal behaviour. Made was the conclusion about the presence in a vapour phase, along with monomeric UCl/sub 4/, LiCl, CsCl and Li/sub 2/Cl/sub 2/, Cs/sub 2/Cl/sub 2/ dimers of double compounds of the MeUCl/sub 5/ most probable composition. Their absolute contribution into a total pressure above the UCl/sub 4/-CsCl melted mixtures is considerably smaller than above the UCl/sub 4/ -LiCl mixtures.
Partitioning detectability components in populations subject to within-season temporary emigration using binomial mixture models.

Science.gov (United States)

O'Donnell, Katherine M; Thompson, Frank R; Semlitsch, Raymond D

2015-01-01

Detectability of individual animals is highly variable and nearly always binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model's potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3-5 surveys each spring and fall 2010-2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability.
Breaking Gaussian incompatibility on continuous variable quantum systems

Energy Technology Data Exchange (ETDEWEB)

Heinosaari, Teiko, E-mail: teiko.heinosaari@utu.fi [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Kiukas, Jukka, E-mail: jukka.kiukas@aber.ac.uk [Department of Mathematics, Aberystwyth University, Penglais, Aberystwyth, SY23 3BZ (United Kingdom); Schultz, Jussi, E-mail: jussi.schultz@gmail.com [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy)

2015-08-15

We characterise Gaussian quantum channels that are Gaussian incompatibility breaking, that is, transform every set of Gaussian measurements into a set obtainable from a joint Gaussian observable via Gaussian postprocessing. Such channels represent local noise which renders measurements useless for Gaussian EPR-steering, providing the appropriate generalisation of entanglement breaking channels for this scenario. Understanding the structure of Gaussian incompatibility breaking channels contributes to the resource theory of noisy continuous variable quantum information protocols.

Scaling and crossover in a fermion-boson mixture

International Nuclear Information System (INIS)

Singh, K.K.

1987-01-01

Thermodynamic behaviour of a mixture of weakly interacting fermions and bosons is investigated in (4 - ε) dimensions by the renormalization group method with a view to study scaling and crossover properties of the system in the tricritical region. Conventional tricritical scaling, first found to breakdown for a classical infinite-component model, is seen to do so more spectacularly in the case of the mixture. Whereas in the infinite-component model, conventional scaling holds in the ordered and disordered phases separately (i.e. with different tricritical exponents), no such thing is possible in either of the phases of the mixture. The breakdown of scaling in the mixture is associated with the dimensionless strength v 6 of the 6-point interaction in the effective Hamiltonian which causes the parameters of the renormalized Hamiltonian to depend on two combinations of scaling fields rather than one. The strength v 6 is a quantum mechanical parameter being proportional in 3 dimensions to (b 3 /λ T 4 K F ) where λ T , K F and b denote, respectively, the boson thermal wavelength, the Fermi momentum of the fermion component and the scattering length associated with the fermion-boson interaction. The square root of this quantity agrees with the non-universality parameter which was found to characterize tricritical amplitude ratios in 3 dimensions in an earlier work. (author). 19 refs, 8 figs
Gaussian density matrices: Quantum analogs of classical states

International Nuclear Information System (INIS)

Mann, A.; Revzen, M.

1993-01-01

We study quantum analogs of clasical situations, i.e. quantum states possessing some specific classical attribute(s). These states seem quite generally, to have the form of gaussian density matrices. Such states can always be parametrized as thermal squeezed states (TSS). We consider the following specific cases: (a) Two beams that are built from initial beams which passed through a beam splitter cannot, classically, be distinguished from (appropriately prepared) two independent beams that did not go through a splitter. The only quantum states possessing this classical attribute are TSS. (b) The classical Cramer's theorem was shown to have a quantum version (Hegerfeldt). Again, the states here are Gaussian density matrices. (c) The special case in the study of the quantum version of Cramer's theorem, viz. when the state obtained after partial tracing is a pure state, leads to the conclusion that all states involved are zero temperature limit TSS. The classical analog here are gaussians of zero width, i.e. all distributions are δ functions in phase space. (orig.)
Gaussian processes and constructive scalar field theory

International Nuclear Information System (INIS)

Benfatto, G.; Nicolo, F.

1981-01-01

The last years have seen a very deep progress of constructive euclidean field theory, with many implications in the area of the random fields theory. The authors discuss an approach to super-renormalizable scalar field theories, which puts in particular evidence the connections with the theory of the Gaussian processes associated to the elliptic operators. The paper consists of two parts. Part I treats some problems in the theory of Gaussian processes which arise in the approach to the PHI 3 4 theory. Part II is devoted to the discussion of the ultraviolet stability in the PHI 3 4 theory. (Auth.)
Searching for primordial non-Gaussianity in Planck CMB maps using a combined estimator

Energy Technology Data Exchange (ETDEWEB)

Novaes, C.P.; Wuensche, C.A. [Divisão de Astrofísica, Instituto Nacional de Pesquisas Espaciais, Av. dos Astronautas 1758, São José dos Campos 12227-010, SP (Brazil); Bernui, A. [Observatório Nacional, Rua General José Cristino 77, São Cristóvão, 20921-400, Rio de Janeiro, RJ (Brazil); Ferreira, I.S., E-mail: camilapnovaes@gmail.com, E-mail: bernui@on.br, E-mail: ivan@fis.unb.br, E-mail: ca.wuensche@inpe.br [Instituto de Física, Universidade de Brasília, Campus Universitário Darcy Ribeiro, Asa Norte, 70919-970, Brasília, DF (Brazil)

2014-01-01

The extensive search for deviations from Gaussianity in cosmic microwave background radiation (CMB) data is very important due to the information about the very early moments of the universe encoded there. Recent analyses from Planck CMB data do not exclude the presence of non-Gaussianity of small amplitude, although they are consistent with the Gaussian hypothesis. The use of different techniques is essential to provide information about types and amplitudes of non-Gaussianities in the CMB data. In particular, we find interesting to construct an estimator based upon the combination of two powerful statistical tools that appears to be sensitive enough to detect tiny deviations from Gaussianity in CMB maps. This estimator combines the Minkowski functionals with a Neural Network, maximizing a tool widely used to study non-Gaussian signals with a reinforcement of another tool designed to identify patterns in a data set. We test our estimator by analyzing simulated CMB maps contaminated with different amounts of local primordial non-Gaussianity quantified by the dimensionless parameter f{sub NL}. We apply it to these sets of CMB maps and find ∼> 98% of chance of positive detection, even for small intensity local non-Gaussianity like f{sub NL} = 38±18, the current limit from Planck data for large angular scales. Additionally, we test the suitability to distinguish between primary and secondary non-Gaussianities: first we train the Neural Network with two sets, one of nearly Gaussian CMB maps (|f{sub NL}| ≤ 10) but contaminated with realistic inhomogeneous Planck noise (i.e., secondary non-Gaussianity) and the other of non-Gaussian CMB maps, that is, maps endowed with weak primordial non-Gaussianity (28 ≤ f{sub NL} ≤ 48); after that we test an ensemble composed of CMB maps either with one of these non-Gaussian contaminations, and find out that our method successfully classifies ∼ 95% of the tested maps as being CMB maps containing primordial or
A comparison of the Gaussian Plume models of Pasquill and Smith

International Nuclear Information System (INIS)

Barker, C.D.

1978-03-01

The Gaussian Plume models of Pasquill and Smith are compared over the full range of atmospheric stability for both short and continuous releases of material. For low level releases the two models compare well (to within a factor of approximately 2) except for very unstable conditions. The agreement between the two models for high level sources is not so good. It is concluded that the two Gaussian models are cheap and simple to use, but may require experimental verification in specific applications. (author)
VISCOSITY OF BINARY NON-ELECTROLYTE LIQUID MIXTURES: PREDICTION AND CORRELATION

Directory of Open Access Journals (Sweden)

Mirjana Lj. Kijevčanin

2008-11-01

Full Text Available The viscosity of 31 binary liquid mixtures containing diverse groups of organic compounds, determined at atmospheric pressure: alcohols, alkanes (cyclo and aliphatic, esters, aromatics, ketones etc., were calculated using two different approaches, correlative (with Teja-Rice and McAllister models and predictive by group contribution models (UNIFAC-VISCO, ASOG-VISCO and Grunberg-Nissan. The obtained results were analysed in terms of the applied approach and model, the structure of the investigated mixtures, the nature of components of the mixtures and the influence of alkyl chain length of the alcohol molecule.
Gaussian approximation to single particle correlations at and below the picosecond scale for Lennard-Jones and nanoparticle fluids

International Nuclear Information System (INIS)

Van Zon, Ramses; Ashwin, S S; Cohen, E G D

2008-01-01

To describe short time (picosecond) and small scale (nanometre) transport in fluids, a Green's function approach was recently developed. This approach relies on an expansion of the distribution of single particle displacements around a Gaussian function, yielding an infinite series of correction terms. Applying a recent theorem (van Zon and Cohen 2006 J. Stat. Phys. 123 1–37) shows that for sufficiently small times the terms in this series become successively smaller, so that truncating the series near or at the Gaussian level might provide a good approximation. In this paper, we derive a theoretical estimate for the time scale at which truncating the series at or near the Gaussian level could be supposed to be accurate for equilibrium nanoscale systems. In order to numerically estimate this time scale, the coefficients for the first few terms in the series are determined in computer simulations for a Lennard-Jones (LJ) fluid, an isotopic LJ mixture and a suspension of a LJ-based model of nanoparticles in a LJ fluid. The results suggest that for LJ fluids an expansion around a Gaussian is accurate at time scales up to a picosecond, while for nanoparticles in suspension (a nanofluid), the characteristic time scale up to which the Gaussian is accurate becomes of the order of 5–10 ps. (invited article)
Grouting mixture

Energy Technology Data Exchange (ETDEWEB)

Klyusov, A A; Bakshutov, V S; Kulyavtsev, V A

1980-10-23

A grouting mixture is proposed for low-temperature boreholes. The mixture contains cement, beta gypsum polyhydrate, and calcium chloride, so as to increase the water resistance and strength properties of expanding brick at conditions from 20 to -5/sup 0/ C, the components are in the following ratios: (by wt.-%): cement, 77.45-88.06; beta gypsum polyhydrate, 9.79-19.36; calcium chloride, 2.15-3.19. Grouting mortar for cold boreholes serves as the cement.
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process

OpenAIRE

Rios, Gonzalo; Tobar, Felipe

2018-01-01

Gaussian processes (GPs) are Bayesian nonparametric generative models that provide interpretability of hyperparameters, admit closed-form expressions for training and inference, and are able to accurately represent uncertainty. To model general non-Gaussian data with complex correlation structure, GPs can be paired with an expressive covariance kernel and then fed into a nonlinear transformation (or warping). However, overparametrising the kernel and the warping is known to, respectively, hin...
On Alternate Relaying with Improper Gaussian Signaling

KAUST Repository

Gaafar, Mohamed; Amin, Osama; Ikhlef, Aissa; Chaaban, Anas; Alouini, Mohamed-Slim

2016-01-01

In this letter, we investigate the potential benefits of adopting improper Gaussian signaling (IGS) in a two-hop alternate relaying (AR) system. Given the known benefits of using IGS in interference-limited networks, we propose to use IGS to relieve the inter-relay interference (IRI) impact on the AR system assuming no channel state information is available at the source. In this regard, we assume that the two relays use IGS and the source uses proper Gaussian signaling (PGS). Then, we optimize the degree of impropriety of the relays signal, measured by the circularity coefficient, to maximize the total achievable rate. Simulation results show that using IGS yields a significant performance improvement over PGS, especially when the first hop is a bottleneck due to weak source-relay channel gains and/or strong IRI.
On Alternate Relaying with Improper Gaussian Signaling

KAUST Repository

Gaafar, Mohamed

2016-06-06

In this letter, we investigate the potential benefits of adopting improper Gaussian signaling (IGS) in a two-hop alternate relaying (AR) system. Given the known benefits of using IGS in interference-limited networks, we propose to use IGS to relieve the inter-relay interference (IRI) impact on the AR system assuming no channel state information is available at the source. In this regard, we assume that the two relays use IGS and the source uses proper Gaussian signaling (PGS). Then, we optimize the degree of impropriety of the relays signal, measured by the circularity coefficient, to maximize the total achievable rate. Simulation results show that using IGS yields a significant performance improvement over PGS, especially when the first hop is a bottleneck due to weak source-relay channel gains and/or strong IRI.
Non-Gaussian halo assembly bias

International Nuclear Information System (INIS)

Reid, Beth A.; Verde, Licia; Dolag, Klaus; Matarrese, Sabino; Moscardini, Lauro

2010-01-01

The strong dependence of the large-scale dark matter halo bias on the (local) non-Gaussianity parameter, f NL , offers a promising avenue towards constraining primordial non-Gaussianity with large-scale structure surveys. In this paper, we present the first detection of the dependence of the non-Gaussian halo bias on halo formation history using N-body simulations. We also present an analytic derivation of the expected signal based on the extended Press-Schechter formalism. In excellent agreement with our analytic prediction, we find that the halo formation history-dependent contribution to the non-Gaussian halo bias (which we call non-Gaussian halo assembly bias) can be factorized in a form approximately independent of redshift and halo mass. The correction to the non-Gaussian halo bias due to the halo formation history can be as large as 100%, with a suppression of the signal for recently formed halos and enhancement for old halos. This could in principle be a problem for realistic galaxy surveys if observational selection effects were to pick galaxies occupying only recently formed halos. Current semi-analytic galaxy formation models, for example, imply an enhancement in the expected signal of ∼ 23% and ∼ 48% for galaxies at z = 1 selected by stellar mass and star formation rate, respectively
Estimation of value at risk and conditional value at risk using normal mixture distributions model

Science.gov (United States)

Kamaruzzaman, Zetty Ain; Isa, Zaidi

2013-04-01

Normal mixture distributions model has been successfully applied in financial time series analysis. In this paper, we estimate the return distribution, value at risk (VaR) and conditional value at risk (CVaR) for monthly and weekly rates of returns for FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) from July 1990 until July 2010 using the two component univariate normal mixture distributions model. First, we present the application of normal mixture distributions model in empirical finance where we fit our real data. Second, we present the application of normal mixture distributions model in risk analysis where we apply the normal mixture distributions model to evaluate the value at risk (VaR) and conditional value at risk (CVaR) with model validation for both risk measures. The empirical results provide evidence that using the two components normal mixture distributions model can fit the data well and can perform better in estimating value at risk (VaR) and conditional value at risk (CVaR) where it can capture the stylized facts of non-normality and leptokurtosis in returns distribution.
Gaussian-state entanglement in a quantum beat laser

International Nuclear Information System (INIS)

Tahira, Rabia; Ikram, Manzoor; Nha, Hyunchul; Zubairy, M. Suhail

2011-01-01

Recently quantum beat lasers have been considered as a source of entangled radiation [S. Qamar, F. Ghafoor, M. Hillery, and M. S. Zubairy, Phys. Rev. A 77, 062308 (2008)]. We investigate and quantify the entanglement of this system when the initial cavity modes are prepared in a Gaussian two-mode state, one being a nonclassical state and the other a thermal state. It is investigated how the output entanglement varies with the nonclassicality of the input Gaussian state, thermal noise, and the strength of the driving field.
Symplectic invariants, entropic measures and correlations of Gaussian states

Energy Technology Data Exchange (ETDEWEB)

Serafini, Alessio; Illuminati, Fabrizio; Siena, Silvio De [Dipartimento di Fisica ' E R Caianiello' , Universita di Salerno, INFM UdR Salerno, INFN Sezione di Napoli, Gruppo Collegato di Salerno, Via S Allende, 84081 Baronissi, SA (Italy)

2004-01-28

We present a derivation of the Von Neumann entropy and mutual information of arbitrary two-mode Gaussian states, based on the explicit determination of the symplectic eigenvalues of a generic covariance matrix. The key role of the symplectic invariants in such a determination is pointed out. We show that the Von Neumann entropy depends on two symplectic invariants, while the purity (or the linear entropy) is determined by only one invariant, so that the two quantities provide two different hierarchies of mixed Gaussian states. A comparison between mutual information and entanglement of formation for symmetric states is considered, taking note of the crucial role of the symplectic eigenvalues in qualifying and quantifying the correlations present in a generic state. (letter to the editor)
Symplectic invariants, entropic measures and correlations of Gaussian states

International Nuclear Information System (INIS)

Serafini, Alessio; Illuminati, Fabrizio; Siena, Silvio De

2004-01-01

We present a derivation of the Von Neumann entropy and mutual information of arbitrary two-mode Gaussian states, based on the explicit determination of the symplectic eigenvalues of a generic covariance matrix. The key role of the symplectic invariants in such a determination is pointed out. We show that the Von Neumann entropy depends on two symplectic invariants, while the purity (or the linear entropy) is determined by only one invariant, so that the two quantities provide two different hierarchies of mixed Gaussian states. A comparison between mutual information and entanglement of formation for symmetric states is considered, taking note of the crucial role of the symplectic eigenvalues in qualifying and quantifying the correlations present in a generic state. (letter to the editor)
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

KAUST Repository

Yan, Yuan

2017-11-20

When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

KAUST Repository

Yan, Yuan; Genton, Marc G.

2017-01-01

When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Assessment of an ultrasonic sensor and a capacitance probe for measurement of two-phase mixture level

International Nuclear Information System (INIS)

Kim, Chang Hyun; Lee, Dong Won; No, Hee Cheon

2004-01-01

We perform a comparison of two-phase mixture levels measured by an ultrasonic sensor and a two-wire type capacitance probe with visual data under the same experimental conditions. A series of experiments are performed with various combinations of airflow and initial water level using a test vessel with a height of 2m and an inner diameter of 0.3 m under atmospheric pressure and room temperature. The ultrasonic sensor measures the two-phase mixture level with a maximum error of 1.77% with respect to the visual data. The capacitance probe severely under-predicts the level data in the high void fraction region. The cause of the error is identified as the change of the dielectric constant as the void fraction changes when the probe is applied to the measurement of the two-phase mixture levels. A correction method for the capacitance probe is proposed by correcting the change of dielectric constant of the two-phase mixture. The correction method for the capacitance probe produces a r.m.s. error of 5.4%. (author)
Yield and competition in barley variety mixtures

Directory of Open Access Journals (Sweden)

Kari Jokinen

1991-09-01

Full Text Available Competition between spring barley varieties and yield performance of two-, three and four-variety mixtures were studied in two replacement series field experiments. In the first experiment, repeated in three successive years (1983 —85 the components were the six-row varieties Agneta, Arra, Hja-673 and Porno. In the second experiment (1984, including two nitrogen doses (50 and 100 kgN/ha, both six-row (Agneta, Pomo and two-row (Ida, Kustaa varieties were used. Arra in the first and Agneta in the second experiment were the most competitive varieties. The results suggested that the fast growth of Arra at the beginning promoted its competitive ability. Increase in available nitrogen usually strengthened the competitiveness of Agneta. The observed competitive differences between varieties were not related to the earliness of a variety, neither to the morphological characters (two- and six-row varieties nor to the grain yield of a variety grown alone. The competitive ability was not always a stable character, the dominant suppression relationship varying from one environment to another (e.g. growing season, nitrogen dose. The observed overyielding was not statistically significant. The ratio of actual to expected yield and the relative yield total of several mixtures exceeded slightly one. As a conclusion, the yield advantage of mixtures was marginal. As a rule, the mixtures were not more stable than monocultures as determined by the coefficient of variation. However, the yield of some mixtures varied less than the yield of the most stable monoculture.

Area of isodensity contours in Gaussian and non-Gaussian fields

International Nuclear Information System (INIS)

Ryden, B.S.

1988-01-01

The area of isodensity contours in a smoothed density field can be measured by the contour-crossing statistic N1, the number of times per unit length that a line drawn through the density field pierces an isodensity contour. The contour-crossing statistic distinguishes between Gaussian and non-Gaussian fields and provides a measure of the effective slope of the power spectrum. The statistic is easy to apply and can be used on pencil beams and slices as well as on a three-dimensional field. 10 references
Signed zeros of Gaussian vector fields - density, correlation functions and curvature

CERN Document Server

Foltin, G

2003-01-01

We calculate correlation functions of the (signed) density of zeros of Gaussian distributed vector fields. We are able to express correlation functions of arbitrary order through the curvature tensor of a certain abstract Riemann Cartan or Riemannian manifold. As an application, we discuss one- and two-point functions. The zeros of a two-dimensional Gaussian vector field model the distribution of topological defects in the high-temperature phase of two-dimensional systems with orientational degrees of freedom, such as superfluid films, thin superconductors and liquid crystals.
Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain

Science.gov (United States)

Goossens, Bart; Aelterman, Jan; Luong, Hiep; Pizurica, Aleksandra; Philips, Wilfried

2013-02-01

In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-processing techniques are required to resolve many of the artifacts. Color filter arrays (CFAs), which use alternating patterns of color filters, are very popular because of price and power consumption reasons. However, color filter arrays require the use of a post-processing technique such as demosaicing to recover full resolution RGB images. Recently, there has been some interest in techniques that jointly perform the demosaicing and denoising. This has the advantage that the demosaicing and denoising can be performed optimally (e.g. in the MSE sense) for the considered noise model, while avoiding artifacts introduced when using demosaicing and denoising sequentially. In this paper, we will continue the research line of the wavelet-based demosaicing techniques. These approaches are computationally simple and very suited for combination with denoising. Therefore, we will derive Bayesian Minimum Squared Error (MMSE) joint demosaicing and denoising rules in the complex wavelet packet domain, taking local adaptivity into account. As an image model, we will use Gaussian Scale Mixtures, thereby taking advantage of the directionality of the complex wavelets. Our results show that this technique is well capable of reconstructing fine details in the image, while removing all of the noise, at a relatively low computational cost. In particular, the complete reconstruction (including color correction, white balancing etc) of a 12 megapixel RAW image takes 3.5 sec on a recent mid-range GPU.
A numerical study of blood flow using mixture theory.

Science.gov (United States)

Wu, Wei-Tao; Aubry, Nadine; Massoudi, Mehrdad; Kim, Jeongho; Antaki, James F

2014-03-01

In this paper, we consider the two dimensional flow of blood in a rectangular microfluidic channel. We use Mixture Theory to treat this problem as a two-component system: One component is the red blood cells (RBCs) modeled as a generalized Reiner-Rivlin type fluid, which considers the effects of volume fraction (hematocrit) and influence of shear rate upon viscosity. The other component, plasma, is assumed to behave as a linear viscous fluid. A CFD solver based on OpenFOAM ® was developed and employed to simulate a specific problem, namely blood flow in a two dimensional micro-channel, is studied. Finally to better understand this two-component flow system and the effects of the different parameters, the equations are made dimensionless and a parametric study is performed.
Coulomb Final State Interactions for Gaussian Wave Packets

CERN Document Server

Wiedemann, Urs Achim; Heinz, Ulrich W

1999-01-01

Two-particle like-sign and unlike-sign correlations including Coulomb final state interactions are calculated for Gaussian wave packets emitted from a Gaussian source. We show that the width of the wave packets can be fully absorbed into the spatial and momentum space widths of an effective emission function for plane wave states, and that Coulomb final state interaction effects are sensitive only to the latter, but not to the wave packet width itself. Results from analytical and numerical calculations are compared with recently published work by other authors.
Particle-in-cell vs straight-line airflow Gaussian calculations of concentration and deposition of airborne emissions out to 70 km for two sites of differing meteorological and topographical character

International Nuclear Information System (INIS)

Lange, R.; Dickerson, M.A.; Peterson, K.R.; Sherman, C.A.; Sullivan, T.J.

1976-01-01

Two numerical models for the calculation of air concentration and ground deposition of airborne effluent releases are compared. The Particle-in-Cell (PIC) model and the Straight-Line Airflow Gaussian model were used for the simulation. Two sites were selected for comparison: the Hudson River Valley, New York, and the area around the Savannah River Plant, South Carolina. Input for the models was synthesized from meteorological data gathered in previous studies by various investigators. It was found that the PIC model more closely simulated the three-dimensional effects of the meteorology and topography. Overall, the Gaussian model calculated higher concentrations under stable conditions with better agreement between the two methods during neutral to unstable conditions. In addition, because of its consideration of exposure from the returning plume after flow reversal, the PIC model calculated air concentrations over larger areas than did the Gaussian model
Invariant measures on multimode quantum Gaussian states

Science.gov (United States)

Lupo, C.; Mancini, S.; De Pasquale, A.; Facchi, P.; Florio, G.; Pascazio, S.

2012-12-01

We derive the invariant measure on the manifold of multimode quantum Gaussian states, induced by the Haar measure on the group of Gaussian unitary transformations. To this end, by introducing a bipartition of the system in two disjoint subsystems, we use a parameterization highlighting the role of nonlocal degrees of freedom—the symplectic eigenvalues—which characterize quantum entanglement across the given bipartition. A finite measure is then obtained by imposing a physically motivated energy constraint. By averaging over the local degrees of freedom we finally derive the invariant distribution of the symplectic eigenvalues in some cases of particular interest for applications in quantum optics and quantum information.
Invariant measures on multimode quantum Gaussian states

International Nuclear Information System (INIS)

Lupo, C.; Mancini, S.; De Pasquale, A.; Facchi, P.; Florio, G.; Pascazio, S.

2012-01-01

We derive the invariant measure on the manifold of multimode quantum Gaussian states, induced by the Haar measure on the group of Gaussian unitary transformations. To this end, by introducing a bipartition of the system in two disjoint subsystems, we use a parameterization highlighting the role of nonlocal degrees of freedom—the symplectic eigenvalues—which characterize quantum entanglement across the given bipartition. A finite measure is then obtained by imposing a physically motivated energy constraint. By averaging over the local degrees of freedom we finally derive the invariant distribution of the symplectic eigenvalues in some cases of particular interest for applications in quantum optics and quantum information.
Invariant measures on multimode quantum Gaussian states

Energy Technology Data Exchange (ETDEWEB)

Lupo, C. [School of Science and Technology, Universita di Camerino, I-62032 Camerino (Italy); Mancini, S. [School of Science and Technology, Universita di Camerino, I-62032 Camerino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, I-06123 Perugia (Italy); De Pasquale, A. [NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, I-56126 Pisa (Italy); Facchi, P. [Dipartimento di Matematica and MECENAS, Universita di Bari, I-70125 Bari (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari (Italy); Florio, G. [Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari (Italy); Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Piazza del Viminale 1, I-00184 Roma (Italy); Dipartimento di Fisica and MECENAS, Universita di Bari, I-70126 Bari (Italy); Pascazio, S. [Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari (Italy); Dipartimento di Fisica and MECENAS, Universita di Bari, I-70126 Bari (Italy)

2012-12-15

We derive the invariant measure on the manifold of multimode quantum Gaussian states, induced by the Haar measure on the group of Gaussian unitary transformations. To this end, by introducing a bipartition of the system in two disjoint subsystems, we use a parameterization highlighting the role of nonlocal degrees of freedom-the symplectic eigenvalues-which characterize quantum entanglement across the given bipartition. A finite measure is then obtained by imposing a physically motivated energy constraint. By averaging over the local degrees of freedom we finally derive the invariant distribution of the symplectic eigenvalues in some cases of particular interest for applications in quantum optics and quantum information.
Generalization of two-phase model with topology microstructure of mixture to Lagrange-Euler methodology

International Nuclear Information System (INIS)

Vladimir V Chudanov; Alexei A Leonov

2005-01-01

Full text of publication follows: One of the mathematical models (hyperbolic type) for describing evolution of compressible two-phase mixtures was offered in [1] to deal with the following applications: interfaces between compressible materials; shock waves in multiphase mixtures; evolution of homogeneous two-phase flows; cavitation in liquids. The basic difficulties of this model was connected to discretization of the non-conservative equation terms. As result, the class of problems concerned with passage of shock waves through fields with a discontinuing profile of a volume fraction was not described by means of this model. A class of schemes that are able to converge to the correct solution of such problems was received in [2] due to a deeper analysis of two-phase model. The technique offered in [2] was implemented on a Eulerian grid via the Godunov scheme. In present paper the additional analysis of two-phase model in view of microstructure of an mixture topology is carried out in Lagrange mass coordinates. As result, the equations averaged over the set of all possible realizations for two-phase mixture are received. The numerical solution is carried out with use of PPM method [3] in two steps: at first - the equations averaged over mass variable are solved; on the second - the solution, found on the previous step, is re-mapped to a fixed Eulerian grid. Such approach allows to expand the proposed technique on two-dimensional (three-dimensional) case, as in the Lagrange variables the Euler equations system is split on two (three) identical subsystems, each of which describes evolution of considered medium in the given direction. The accuracy and robustness of the described procedure are demonstrated on a sequence of the numerical problems. References: (1). R. Saurel, R. Abgrall, A multiphase Godunov method for compressible multi-fluid and multiphase flows, J. Comput. Phys. 150 (1999) 425-467; (2). R. Saurel, R. Abgrall, Discrete equations for physical and
The Research of Indoor Positioning Based on Double-peak Gaussian Model

Directory of Open Access Journals (Sweden)

Lina Chen

2014-04-01

Full Text Available Location fingerprinting using Wi-Fi signals has been very popular and is a well accepted indoor positioning method. The key issue of the fingerprinting approach is generating the fingerprint radio map. Limited by the practical workload, only a few samples of the received signal strength are collected at each reference point. Unfortunately, fewer samples cannot accurately represent the actual distribution of the signal strength from each access point. This study finds most Wi- Fi signals have two peaks. According to the new finding, a double-peak Gaussian arithmetic is proposed to generate a fingerprint radio map. This approach requires little time to receive WiFi signals and it easy to estimate the parameters of the double-peak Gaussian function. Compared to the Gaussian function and histogram method to generate a fingerprint radio map, this method better approximates the occurrence signal distribution. This paper also compared the positioning accuracy using K-Nearest Neighbour theory for three radio maps, the test results show that the positioning distance error utilizing the double-peak Gaussian function is better than the other two methods.
Quantification of Multiple Components of Complex Aluminum-Based Adjuvant Mixtures by Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Modeling.

Science.gov (United States)

Dowling, Quinton M; Kramer, Ryan M

2017-01-01

Fourier transform infrared (FTIR) spectroscopy is widely used in the pharmaceutical industry for process monitoring, compositional quantification, and characterization of critical quality attributes in complex mixtures. Advantages over other spectroscopic measurements include ease of sample preparation, quantification of multiple components from a single measurement, and the ability to quantify optically opaque samples. This method describes the use of a multivariate model for quantifying a TLR4 agonist (GLA) adsorbed onto aluminum oxyhydroxide (Alhydrogel ® ) using FTIR spectroscopy that may be adapted to quantify other complex aluminum based adjuvant mixtures.
An investigation on two-phase mixture discharges: the effects of macroparticle sizes

Energy Technology Data Exchange (ETDEWEB)

Deng Heming; He Zhenghao; Xu Yuhang; Ma Jun; Liu Junxiang; Guo Runkai, E-mail: denghem@gmail.co [School of Environmental Science and Engineering, Huazhong University of Science and Technology, Hubei province Wuhan 430074 (China)

2010-06-30

A two-phase mixture (TPM) is a mixture of gas and macroparticles of high concentration, and there has been significant interest in many technical applications and natural phenomena concerning two-phase mixture discharges (TPMDs), but until now there has been no widely accepted analysis for the propagation of discharges in TPMs. In this paper, 21 kinds of different dielectric materials are used to investigate the effects on TPMD. The diameters of macroparticles in 21 kinds of TPMs are measured by microscope, laser particle size analyzer, etc, and the volume fractions are measured by a video camera and particle image velocimetry system. Based on a direct comparison of the breakdown voltages and the percentages of the discharge path in TPMs with those in air, this work reveals that whether TPMs promote the discharge development or not depends mainly on the macroparticle sizes. These macroparticles in TPMs distort the electric field, interact with ions, electrons or photons, and produce corresponding enhancements or decreases in ionization and excitation as the streamer front encounters them, but the details of alterations on the discharge development are highly correlated with the macroparticle sizes.
Bayesian Analysis of two Censored Shifted Gompertz Mixture Distributions using Informative and Noninformative Priors

Directory of Open Access Journals (Sweden)

Tabassum Naz Sindhu

2017-03-01

Full Text Available This study deals with Bayesian analysis of shifted Gompertz mixture model under type-I censored samples assuming both informative and noninformative priors. We have discussed the Bayesian estimation of parameters of shifted Gompertz mixture model under the uniform, and gamma priors assuming three loss functions. Further, some properties of the model with some graphs of the mixture density are discussed. These properties include Bayes estimators, posterior risks and reliability function under simulation scheme. Bayes estimates are obtained considering two cases: (a when the shape parameter is known and (b when all parameters are unknown. We analyzed some simulated sets in order to investigate the effect of prior belief, loss functions, and performance of the proposed set of estimators of the mixture model parameters.
Response moments of dynamic systems under non-Gaussian random excitation by the equivalent non-Gaussian excitation method

International Nuclear Information System (INIS)

Tsuchida, Takahiro; Kimura, Koji

2016-01-01

Equivalent non-Gaussian excitation method is proposed to obtain the response moments up to the 4th order of dynamic systems under non-Gaussian random excitation. The non-Gaussian excitation is prescribed by the probability density and the power spectrum, and is described by an Ito stochastic differential equation. Generally, moment equations for the response, which are derived from the governing equations for the excitation and the system, are not closed due to the nonlinearity of the diffusion coefficient in the equation for the excitation even though the system is linear. In the equivalent non-Gaussian excitation method, the diffusion coefficient is replaced with the equivalent diffusion coefficient approximately to obtain a closed set of the moment equations. The square of the equivalent diffusion coefficient is expressed by a quadratic polynomial. In numerical examples, a linear system subjected to nonGaussian excitations with bimodal and Rayleigh distributions is analyzed by using the present method. The results show that the method yields the variance, skewness and kurtosis of the response with high accuracy for non-Gaussian excitation with the widely different probability densities and bandwidth. The statistical moments of the equivalent non-Gaussian excitation are also investigated to describe the feature of the method. (paper)
Structure properties of the 3He-4He mixture at T = O K

International Nuclear Information System (INIS)

Boronat, J.; Polls, A.; Fabrocini, A.

1993-01-01

The spatial structure properties of 3 He- 4 He mixtures at T = O K are investigated using the hypernetted-chain formalism. The variational wave function used to describe the ground-state of the mixture is a simple generalization of the trial wave functions for pure phases and contains two- and three-body correlations. The elementary diagrams are taken into account by means of an extension of the scaling approximation to the mixtures. The two-body distribution (g (α,β) (r)) and the structure functions (S (α,β) (k)) together with the different spin-spin distribution functions for the 3 He component in the mixture are analyzed for several concentrations of 3 He. Two sum-rules, for the direct and the exchange part of the g (3,3) (r), are used to ascertain the importance of the full treatment of the Fermi statistics in the calculation. The statistical correlations are found responsible for the main differences between the several components of the distribution function. Due to its low concentration, 3 He behaves as a quasi-free Fermi gas, as far as the statistical correlations are concerned, although it is strongly correlated with the 4 He atoms through the interatomic potential
Spatial pattern formation induced by Gaussian white noise.

Science.gov (United States)

Scarsoglio, Stefania; Laio, Francesco; D'Odorico, Paolo; Ridolfi, Luca

2011-02-01

The ability of Gaussian noise to induce ordered states in dynamical systems is here presented in an overview of the main stochastic mechanisms able to generate spatial patterns. These mechanisms involve: (i) a deterministic local dynamics term, accounting for the local rate of variation of the field variable, (ii) a noise component (additive or multiplicative) accounting for the unavoidable environmental disturbances, and (iii) a linear spatial coupling component, which provides spatial coherence and takes into account diffusion mechanisms. We investigate these dynamics using analytical tools, such as mean-field theory, linear stability analysis and structure function analysis, and use numerical simulations to confirm these analytical results. Copyright © 2010 Elsevier Inc. All rights reserved.
Model structure selection in convolutive mixtures

DEFF Research Database (Denmark)

Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

2006-01-01

The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....
Analytic matrix elements with shifted correlated Gaussians

DEFF Research Database (Denmark)

Fedorov, D. V.

2017-01-01

Matrix elements between shifted correlated Gaussians of various potentials with several form-factors are calculated analytically. Analytic matrix elements are of importance for the correlated Gaussian method in quantum few-body physics.......Matrix elements between shifted correlated Gaussians of various potentials with several form-factors are calculated analytically. Analytic matrix elements are of importance for the correlated Gaussian method in quantum few-body physics....
Semiparametric inference on the fractal index of Gaussian and conditionally Gaussian time series data

DEFF Research Database (Denmark)

Bennedsen, Mikkel

Using theory on (conditionally) Gaussian processes with stationary increments developed in Barndorff-Nielsen et al. (2009, 2011), this paper presents a general semiparametric approach to conducting inference on the fractal index, α, of a time series. Our setup encompasses a large class of Gaussian...

Gravel-Sand-Clay Mixture Model for Predictions of Permeability and Velocity of Unconsolidated Sediments

Science.gov (United States)

Konishi, C.

2014-12-01

Gravel-sand-clay mixture model is proposed particularly for unconsolidated sediments to predict permeability and velocity from volume fractions of the three components (i.e. gravel, sand, and clay). A well-known sand-clay mixture model or bimodal mixture model treats clay contents as volume fraction of the small particle and the rest of the volume is considered as that of the large particle. This simple approach has been commonly accepted and has validated by many studies before. However, a collection of laboratory measurements of permeability and grain size distribution for unconsolidated samples show an impact of presence of another large particle; i.e. only a few percent of gravel particles increases the permeability of the sample significantly. This observation cannot be explained by the bimodal mixture model and it suggests the necessity of considering the gravel-sand-clay mixture model. In the proposed model, I consider the three volume fractions of each component instead of using only the clay contents. Sand becomes either larger or smaller particles in the three component mixture model, whereas it is always the large particle in the bimodal mixture model. The total porosity of the two cases, one is the case that the sand is smaller particle and the other is the case that the sand is larger particle, can be modeled independently from sand volume fraction by the same fashion in the bimodal model. However, the two cases can co-exist in one sample; thus, the total porosity of the mixed sample is calculated by weighted average of the two cases by the volume fractions of gravel and clay. The effective porosity is distinguished from the total porosity assuming that the porosity associated with clay is zero effective porosity. In addition, effective grain size can be computed from the volume fractions and representative grain sizes for each component. Using the effective porosity and the effective grain size, the permeability is predicted by Kozeny-Carman equation
Equivalent non-Gaussian excitation method for response moment calculation of systems under non-Gaussian random excitation

International Nuclear Information System (INIS)

Tsuchida, Takahiro; Kimura, Koji

2015-01-01

Equivalent non-Gaussian excitation method is proposed to obtain the moments up to the fourth order of the response of systems under non-Gaussian random excitation. The excitation is prescribed by the probability density and power spectrum. Moment equations for the response can be derived from the stochastic differential equations for the excitation and the system. However, the moment equations are not closed due to the nonlinearity of the diffusion coefficient in the equation for the excitation. In the proposed method, the diffusion coefficient is replaced with the equivalent diffusion coefficient approximately to obtain a closed set of the moment equations. The square of the equivalent diffusion coefficient is expressed by the second-order polynomial. In order to demonstrate the validity of the method, a linear system to non-Gaussian excitation with generalized Gaussian distribution is analyzed. The results show the method is applicable to non-Gaussian excitation with the widely different kurtosis and bandwidth. (author)
Einstein-Podolsky-Rosen-like separability indicators for two-mode Gaussian states

Science.gov (United States)

Marian, Paulina; Marian, Tudor A.

2018-02-01

We investigate the separability of the two-mode Gaussian states (TMGSs) by using the variances of a pair of Einstein-Podolsky-Rosen (EPR)-like observables. Our starting point is inspired by the general necessary condition of separability introduced by Duan et al (2000 Phys. Rev. Lett. 84 2722). We evaluate the minima of the normalized forms of both the product and sum of such variances, as well as that of a regularized sum. Making use of Simon’s separability criterion, which is based on the condition of positivity of the partial transpose (PPT) of the density matrix (Simon 2000 Phys. Rev. Lett. 84 2726), we prove that these minima are separability indicators in their own right. They appear to quantify the greatest amount of EPR-like correlations that can be created in a TMGS by means of local operations. Furthermore, we reconsider the EPR-like approach to the separability of TMGSs which was developed by Duan et al with no reference to the PPT condition. By optimizing the regularized form of their EPR-like uncertainty sum, we derive a separability indicator for any TMGS. We prove that the corresponding EPR-like condition of separability is manifestly equivalent to Simon’s PPT one. The consistency of these two distinct approaches (EPR-like and PPT) affords a better understanding of the examined separability problem, whose explicit solution found long ago by Simon covers all situations of interest.
A Bernoulli Gaussian Watermark for Detecting Integrity Attacks in Control Systems

Energy Technology Data Exchange (ETDEWEB)

Weerakkody, Sean [Carnegie Mellon Univ., Pittsburgh, PA (United States); Ozel, Omur [Carnegie Mellon Univ., Pittsburgh, PA (United States); Sinopoli, Bruno [Carnegie Mellon Univ., Pittsburgh, PA (United States)

2017-11-02

We examine the merit of Bernoulli packet drops in actively detecting integrity attacks on control systems. The aim is to detect an adversary who delivers fake sensor measurements to a system operator in order to conceal their effect on the plant. Physical watermarks, or noisy additive Gaussian inputs, have been previously used to detect several classes of integrity attacks in control systems. In this paper, we consider the analysis and design of Gaussian physical watermarks in the presence of packet drops at the control input. On one hand, this enables analysis in a more general network setting. On the other hand, we observe that in certain cases, Bernoulli packet drops can improve detection performance relative to a purely Gaussian watermark. This motivates the joint design of a Bernoulli-Gaussian watermark which incorporates both an additive Gaussian input and a Bernoulli drop process. We characterize the effect of such a watermark on system performance as well as attack detectability in two separate design scenarios. Here, we consider a correlation detector for attack recognition. We then propose efficiently solvable optimization problems to intelligently select parameters of the Gaussian input and the Bernoulli drop process while addressing security and performance trade-offs. Finally, we provide numerical results which illustrate that a watermark with packet drops can indeed outperform a Gaussian watermark.
Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain

Science.gov (United States)

Lee, Sangyoon; Lee, Min Seok; Kang, Moon Gi

2018-01-01

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods. PMID:29596335
Poisson-Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain.

Science.gov (United States)

Lee, Sangyoon; Lee, Min Seok; Kang, Moon Gi

2018-03-29

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson-Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson-Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.
Non-Gaussianity from isocurvature perturbations

Energy Technology Data Exchange (ETDEWEB)

Kawasaki, Masahiro; Nakayama, Kazunori; Sekiguchi, Toyokazu; Suyama, Teruaki [Institute for Cosmic Ray Research, University of Tokyo, Kashiwa 277-8582 (Japan); Takahashi, Fuminobu, E-mail: kawasaki@icrr.u-tokyo.ac.jp, E-mail: nakayama@icrr.u-tokyo.ac.jp, E-mail: sekiguti@icrr.u-tokyo.ac.jp, E-mail: suyama@icrr.u-tokyo.ac.jp, E-mail: fuminobu.takahashi@ipmu.jp [Institute for the Physics and Mathematics of the Universe, University of Tokyo, Kashiwa 277-8568 (Japan)

2008-11-15

We develop a formalism for studying non-Gaussianity in both curvature and isocurvature perturbations. It is shown that non-Gaussianity in the isocurvature perturbation between dark matter and photons leaves distinct signatures in the cosmic microwave background temperature fluctuations, which may be confirmed in future experiments, or possibly even in the currently available observational data. As an explicit example, we consider the quantum chromodynamics axion and show that it can actually induce sizable non-Gaussianity for the inflationary scale, H{sub inf} = O(10{sup 9}-10{sup 11}) GeV.
Handbook of Gaussian basis sets

International Nuclear Information System (INIS)

Poirier, R.; Kari, R.; Csizmadia, I.G.

1985-01-01

A collection of a large body of information is presented useful for chemists involved in molecular Gaussian computations. Every effort has been made by the authors to collect all available data for cartesian Gaussian as found in the literature up to July of 1984. The data in this text includes a large collection of polarization function exponents but in this case the collection is not complete. Exponents for Slater type orbitals (STO) were included for completeness. This text offers a collection of Gaussian exponents primarily without criticism. (Auth.)
Calculations of Sobol indices for the Gaussian process metamodel

Energy Technology Data Exchange (ETDEWEB)

Marrel, Amandine [CEA, DEN, DTN/SMTM/LMTE, F-13108 Saint Paul lez Durance (France)], E-mail: amandine.marrel@cea.fr; Iooss, Bertrand [CEA, DEN, DER/SESI/LCFR, F-13108 Saint Paul lez Durance (France); Laurent, Beatrice [Institut de Mathematiques, Universite de Toulouse (UMR 5219) (France); Roustant, Olivier [Ecole des Mines de Saint-Etienne (France)

2009-03-15

Global sensitivity analysis of complex numerical models can be performed by calculating variance-based importance measures of the input variables, such as the Sobol indices. However, these techniques, requiring a large number of model evaluations, are often unacceptable for time expensive computer codes. A well-known and widely used decision consists in replacing the computer code by a metamodel, predicting the model responses with a negligible computation time and rending straightforward the estimation of Sobol indices. In this paper, we discuss about the Gaussian process model which gives analytical expressions of Sobol indices. Two approaches are studied to compute the Sobol indices: the first based on the predictor of the Gaussian process model and the second based on the global stochastic process model. Comparisons between the two estimates, made on analytical examples, show the superiority of the second approach in terms of convergence and robustness. Moreover, the second approach allows to integrate the modeling error of the Gaussian process model by directly giving some confidence intervals on the Sobol indices. These techniques are finally applied to a real case of hydrogeological modeling.
Calculations of Sobol indices for the Gaussian process metamodel

International Nuclear Information System (INIS)

Marrel, Amandine; Iooss, Bertrand; Laurent, Beatrice; Roustant, Olivier

2009-01-01

Global sensitivity analysis of complex numerical models can be performed by calculating variance-based importance measures of the input variables, such as the Sobol indices. However, these techniques, requiring a large number of model evaluations, are often unacceptable for time expensive computer codes. A well-known and widely used decision consists in replacing the computer code by a metamodel, predicting the model responses with a negligible computation time and rending straightforward the estimation of Sobol indices. In this paper, we discuss about the Gaussian process model which gives analytical expressions of Sobol indices. Two approaches are studied to compute the Sobol indices: the first based on the predictor of the Gaussian process model and the second based on the global stochastic process model. Comparisons between the two estimates, made on analytical examples, show the superiority of the second approach in terms of convergence and robustness. Moreover, the second approach allows to integrate the modeling error of the Gaussian process model by directly giving some confidence intervals on the Sobol indices. These techniques are finally applied to a real case of hydrogeological modeling
Volatilization of multicomponent mixtures in soil vapor extraction applications

International Nuclear Information System (INIS)

Bass, D.H.

1995-01-01

In soil vapor extraction (SVE) applications involving multicomponent mixtures, prediction of mass removal by volatilization as a function remediation extent is required to estimate remediation time and to size offgas treatment equipment. SVE is a commonly used remediation technology which volatilizes and enhances aerobic biodegradation of contamination adsorbed to vadose zone soils. SVE is often applied at sites contaminated with petroleum products, which are usually mixtures of many different compounds with vapor pressures spanning several orders of magnitude. The most volatile components are removed first, so the vapor pressure of the remaining contaminant continually decreases over the course of the remediation. A method for assessing how vapor pressure, and hence the rate of volatilization, of a multicomponent mixture changes over the course of a vapor extraction remedy has been developed. Each component is listed, alone, with its mass fraction in the mixture, in decreasing order of pure component vapor pressure (where component analyses are unavailable, model compounds can be used), For most petroleum distillates, the vapor pressure for each component plotted against the cumulative mass fraction of the component in the mixture on semilog coordinates will produce a straight line with a high correlation coefficient. This regression can be integrated to produce an expression for vapor pressure of the overall mixture as a function of extent or remediation
Analytic Treatment of Deep Neural Networks Under Additive Gaussian Noise

KAUST Repository

Alfadly, Modar

2018-01-01

Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours. One puzzling behaviour is the reaction of DNNs to various noise attacks, where it has been shown that there exist small adversarial noise that can result in a severe degradation in the performance of DNNs. To rigorously treat this, we derive exact analytic expressions for the first and second moments (mean and variance) of a small piecewise linear (PL) network with a single rectified linear unit (ReLU) layer subject to general Gaussian input. We experimentally show that these expressions are tight under simple linearizations of deeper PL-DNNs, especially popular architectures in the literature (e.g. LeNet and AlexNet). Extensive experiments on image classification show that these expressions can be used to study the behaviour of the output mean of the logits for each class, the inter-class confusion and the pixel-level spatial noise sensitivity of the network. Moreover, we show how these expressions can be used to systematically construct targeted and non-targeted adversarial attacks. Then, we proposed a special estimator DNN, named mixture of linearizations (MoL), and derived the analytic expressions for its output mean and variance, as well. We employed these expressions to train the model to be particularly robust against Gaussian attacks without the need for data augmentation. Upon training this network on a loss that is consolidated with the derived output probabilistic moments, the network is not only robust under very high variance Gaussian attacks but is also as robust as networks that are trained with 20 fold data augmentation.
Analytic Treatment of Deep Neural Networks Under Additive Gaussian Noise

KAUST Repository

Alfadly, Modar M.

2018-04-12

Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours. One puzzling behaviour is the reaction of DNNs to various noise attacks, where it has been shown that there exist small adversarial noise that can result in a severe degradation in the performance of DNNs. To rigorously treat this, we derive exact analytic expressions for the first and second moments (mean and variance) of a small piecewise linear (PL) network with a single rectified linear unit (ReLU) layer subject to general Gaussian input. We experimentally show that these expressions are tight under simple linearizations of deeper PL-DNNs, especially popular architectures in the literature (e.g. LeNet and AlexNet). Extensive experiments on image classification show that these expressions can be used to study the behaviour of the output mean of the logits for each class, the inter-class confusion and the pixel-level spatial noise sensitivity of the network. Moreover, we show how these expressions can be used to systematically construct targeted and non-targeted adversarial attacks. Then, we proposed a special estimator DNN, named mixture of linearizations (MoL), and derived the analytic expressions for its output mean and variance, as well. We employed these expressions to train the model to be particularly robust against Gaussian attacks without the need for data augmentation. Upon training this network on a loss that is consolidated with the derived output probabilistic moments, the network is not only robust under very high variance Gaussian attacks but is also as robust as networks that are trained with 20 fold data augmentation.
Isotope enrichment effect of gaseous mixtures in standing sound vibration

International Nuclear Information System (INIS)

Knesebeck, R.L.

1984-01-01

When standing acoustic waves are excited in a tube containing a mixture of two gases, a partial zonal fractioning of the components arises as consequence of mass transport by diffusion, driven by the thermal and pressure gradients which are associeted with the standing waves. This effect is present in each zone corresponding to a quarter wavelength, with the heavier component becoming enriched at the nodes fo the standing waves and deplected at the crests. The magnitude of the enrichment in one of the components of a binary gas mixture is given by Δω=ap 2 /lambda [b + (1-bω)] 2 . Where ω is the mass concentration of the component in the mixture, a and b are parameters which are related to molecular proprieties of the gases, p is the relative pressure amplitude of the standing wave and lambda is its wavelength. For a natural mixture of uranium hexafluorate, with 0.715% of the uranium isotope 340 an enrichment of about 2 x 10 -6 % in the concentration of this isotope is theorecticaly attainable per stage consisting of a quarter wavelenght, when a standing acoustical wave of relative pressure amplitude of 0,2 and wavelenght of 20 cm is used. Since standing acoustical waves are easely excited in gas columns, an isotope enrichment plant made of a cascade of tubes in which standing waves are excited, is presumably feasible with relatively low investment and operation costs. (Author) [pt
Application of some geometrical and empirical models to excess molar volume for the multi-component mixtures at T = 298.15 K

International Nuclear Information System (INIS)

Iloukhani, H.; Khanlarzadeh, K.

2012-01-01

Highlights: ► Excess molar volume of quartenary mixtures of 1-chlorobutane, 2-chlorobutane, butylamine, and butylacetate was determined. ► The experimental data were correlated by some empirical and semi empirical models. ► A comparison with PFP theory has been successfully applied from binary data. - Abstract: Densities of the quaternary mixture consisting of {1-chlorobutane (1) + 2-chlorobutane (2) + butylamine (3) + butylacetate (4)} and related ternary mixtures of {1-chlorobutane (1) + 2-chlorobutane (2) + butylamine (3)}, {1-chlorobutane (1) + 2-chlorobutane (2) + butylacetate (4)}, {2-chlorobutane (2) + butylamine (3) + butylacetate (4)}, and binary systems of {1-chlorobutane (1) + 2-chlorobutane (2)}, {2-chlorobutane (2) + butylamine (3)}, were measured over the whole range of composition at T = 298.15 K and ambient pressure. Excess molar volumes, V m E , for the mixtures were derived and correlated as a function of mole fraction by using the Redlich–Kister and the Cibulka equations for binary and ternary mixtures, respectively. From the experimental data, partial molar volumes, V m,i and excess partial molar volumes, V m,i E were also calculated for binary systems. The experimental results of the constituted binary mixtures have been used to test the applicability of the Prigogine–Flory–Paterson (PFP) theory. A number of geometrical and empirical equations were also used to verify their ability to predict ternary and quaternary properties from their lower order mixtures. The experimental data were used to evaluate the nature and type of intermolecular interactions in multi-component mixtures.
Gaussian capacity of the quantum bosonic memory channel with additive correlated Gaussian noise

International Nuclear Information System (INIS)

Schaefer, Joachim; Karpov, Evgueni; Cerf, Nicolas J.

2011-01-01

We present an algorithm for calculation of the Gaussian classical capacity of a quantum bosonic memory channel with additive Gaussian noise. The algorithm, restricted to Gaussian input states, is applicable to all channels with noise correlations obeying certain conditions and works in the full input energy domain, beyond previous treatments of this problem. As an illustration, we study the optimal input states and capacity of a quantum memory channel with Gauss-Markov noise [J. Schaefer, Phys. Rev. A 80, 062313 (2009)]. We evaluate the enhancement of the transmission rate when using these optimal entangled input states by comparison with a product coherent-state encoding and find out that such a simple coherent-state encoding achieves not less than 90% of the capacity.
Polynomial approximation of non-Gaussian unitaries by counting one photon at a time

Science.gov (United States)

Arzani, Francesco; Treps, Nicolas; Ferrini, Giulia

2017-05-01

In quantum computation with continuous-variable systems, quantum advantage can only be achieved if some non-Gaussian resource is available. Yet, non-Gaussian unitary evolutions and measurements suited for computation are challenging to realize in the laboratory. We propose and analyze two methods to apply a polynomial approximation of any unitary operator diagonal in the amplitude quadrature representation, including non-Gaussian operators, to an unknown input state. Our protocols use as a primary non-Gaussian resource a single-photon counter. We use the fidelity of the transformation with the target one on Fock and coherent states to assess the quality of the approximate gate.
Bootstrapping realized volatility and realized beta under a local Gaussianity assumption

DEFF Research Database (Denmark)

Hounyo, Ulrich

The main contribution of this paper is to propose a new bootstrap method for statistics based on high frequency returns. The new method exploits the local Gaussianity and the local constancy of volatility of high frequency returns, two assumptions that can simplify inference in the high frequency...... context, as recently explained by Mykland and Zhang (2009). Our main contributions are as follows. First, we show that the local Gaussian bootstrap is firstorder consistent when used to estimate the distributions of realized volatility and ealized betas. Second, we show that the local Gaussian bootstrap...... matches accurately the first four cumulants of realized volatility, implying that this method provides third-order refinements. This is in contrast with the wild bootstrap of Gonçalves and Meddahi (2009), which is only second-order correct. Third, we show that the local Gaussian bootstrap is able...
Characterization of two-phase mixture (petroleum, salted water or gas) by gamma radiation transmission

International Nuclear Information System (INIS)

Eichlt, Jair Romeu

2003-01-01

A mathematical description was accomplished to determine the discrimination of a substance in a two-phase mixture, for one beam system, using the five energy lines (13.9, 17.8,26.35 and 59,54 keV) of the 241 Am source. The mathematical description was also accomplished to determine the discrimination of two substances in a three-phase mixture, for a double beam system.. he simulated mixtures for the one beam system were petroleum/salted water or gas. The materials considered in these simulations were: four oils types, denominated as A, B, Bell and Generic, one kind of natural gas and salted water with the following salinities: 35.5, 50, 100, 150, 200, 250 and 300 kg/m 3 of Na Cl. The simulation for the one beam system consisted of a box with acrylic walls and other situation with a box of epoxi walls reinforced with fiber of carbon. The epoxi with carbon fiber was used mainly due to the fact that this material offers little attenuation to the fotons and it resists great pressures. With the results of the simulations it was calculated tables of minimum discrimination for each possible two-phase mixture with petroleum, gas and salted water at several salinities. These discrimination tables are the theoretical forecasts for experimental measurements, since they supply the minimum mensurable percentage for each energy line, as well as the ideal energy for the measurement of each mixture, or situation. The simulated discrimination levels were tested employing experimental arrangements with conditions and materials similar to those of the simulations, for the case of box with epoxi wall reinforced with carbon fiber, at the energies of 20.8 and 59.54 keV. It was obtained good results. For example, for the mixture of salted water (35.5 kg/m 3 ) in paraffin (simulating the petroleum), it was obtained an experimental discrimination minimum of 10% of salted water for error statistics of 5% in I and I o , while the theoretical simulation foresaw the same discrimination level
Assessment of an ultrasonic sensor and a capacitance probe for measurement of two-phase mixture level

International Nuclear Information System (INIS)

Kim, Chang Hyun; Lee, Dong Won; No, Hee Cheon

2004-01-01

We performed a comparison of two-phase mixture levels measured by an ultrasonic sensor and a two-wire type capacitance probe with visual data under the same experimental conditions. A series of experiments are performed with various combinations of airflow and initial water level using a test vessel with a height of 2m and an inner diameter of 0.3m. The ultrasonic sensor measured the two-phase mixture level with a maximum error of 1.77% with respect to the visual data. The capacitance probe severely under-predicted the level data in the high void fraction region. The cause of the error was identified as the change of the dielectric constant as the void fraction changes when the probe is applied to the measurement of the two-phase mixture levels. A correction method for the capacitance probe is proposed by correcting the change of the dielectric constant of the two-phase mixture. The correction method for the capacitance probe produces a r.m.s. error of 5.4%. The present experimental data are compared with the existing pool void fraction correlations based on drift-flux model. The Kataoka-Ishii correlation has the best agreement with the present experimental data with an r.m.s error of 2.5%

Bayesian D-Optimal Choice Designs for Mixtures

NARCIS (Netherlands)

A. Ruseckaite (Aiste); P.P. Goos (Peter); D. Fok (Dennis)

2014-01-01

markdownabstract__Abstract__ Consumer products and services can often be described as mixtures of ingredients. Examples are the mixture of ingredients in a cocktail and the mixture of different components of waiting time (e.g., in-vehicle and out-of-vehicle travel time) in a transportation
Predictive Active Set Selection Methods for Gaussian Processes

DEFF Research Database (Denmark)

Henao, Ricardo; Winther, Ole

2012-01-01

We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists of a two step alternating procedure of active set update rules and hyperparameter optimization based upon marginal...... high impact to the classifier decision process while removing those that are less relevant. We introduce two active set rules based on different criteria, the first one prefers a model with interpretable active set parameters whereas the second puts computational complexity first, thus a model...... with active set parameters that directly control its complexity. We also provide both theoretical and empirical support for our active set selection strategy being a good approximation of a full Gaussian process classifier. Our extensive experiments show that our approach can compete with state...
Reproducing kernel Hilbert spaces of Gaussian priors

NARCIS (Netherlands)

Vaart, van der A.W.; Zanten, van J.H.; Clarke, B.; Ghosal, S.

2008-01-01

We review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors. The rate of contraction of posterior distributions based on Gaussian priors can be described
Crossover between the Gaussian orthogonal ensemble, the Gaussian unitary ensemble, and Poissonian statistics.

Science.gov (United States)

Schweiner, Frank; Laturner, Jeanine; Main, Jörg; Wunner, Günter

2017-11-01

Until now only for specific crossovers between Poissonian statistics (P), the statistics of a Gaussian orthogonal ensemble (GOE), or the statistics of a Gaussian unitary ensemble (GUE) have analytical formulas for the level spacing distribution function been derived within random matrix theory. We investigate arbitrary crossovers in the triangle between all three statistics. To this aim we propose an according formula for the level spacing distribution function depending on two parameters. Comparing the behavior of our formula for the special cases of P→GUE, P→GOE, and GOE→GUE with the results from random matrix theory, we prove that these crossovers are described reasonably. Recent investigations by F. Schweiner et al. [Phys. Rev. E 95, 062205 (2017)2470-004510.1103/PhysRevE.95.062205] have shown that the Hamiltonian of magnetoexcitons in cubic semiconductors can exhibit all three statistics in dependence on the system parameters. Evaluating the numerical results for magnetoexcitons in dependence on the excitation energy and on a parameter connected with the cubic valence band structure and comparing the results with the formula proposed allows us to distinguish between regular and chaotic behavior as well as between existent or broken antiunitary symmetries. Increasing one of the two parameters, transitions between different crossovers, e.g., from the P→GOE to the P→GUE crossover, are observed and discussed.
Dynamics of Gaussian Wigner functions derived from a time-dependent variational principle

Directory of Open Access Journals (Sweden)

Jens Aage Poulsen

2017-11-01

Full Text Available By using a time-dependent variational principle formulated for Wigner phase-space functions, we obtain the optimal time-evolution for two classes of Gaussian Wigner functions, namely those of either thawed real-valued or frozen but complex Gaussians. It is shown that tunneling effects are approximately included in both schemes.
Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Models for the computation of opacity of mixtures

International Nuclear Information System (INIS)

Klapisch, Marcel; Busquet, Michel

2013-01-01

We compare four models for the partial densities of the components of mixtures. These models yield different opacities as shown on polystyrene, acrylic and polyimide in local thermodynamical equilibrium (LTE). Two of these models, the ‘whole volume partial pressure’ model (M1) and its modification (M2) are not thermodynamically consistent (TC). The other two models are TC and minimize free energy. M3, the ‘partial volume equal pressure’ model, uses equality of chemical potential. M4 uses commonality of free electron density. The latter two give essentially identical results in LTE, but M4’s convergence is slower. M4 is easily generalized to non-LTE conditions. Non-LTE effects are shown by the variation of the Planck mean opacity of the mixtures with temperature and density. (paper)
Onsager Vortex Formation in Two-component Bose-Einstein Condensates

Science.gov (United States)

Han, Junsik; Tsubota, Makoto

2018-06-01

We numerically study the dynamics of quantized vortices in two-dimensional two-component Bose-Einstein condensates (BECs) trapped by a box potential. For one-component BECs in a box potential, it is known that quantized vortices form Onsager vortices, which are clusters of same-sign vortices. We confirm that the vortices of the two components spatially separate from each other — even for miscible two-component BECs — suppressing the formation of Onsager vortices. This phenomenon is caused by the repulsive interaction between vortices belonging to different components, hence, suggesting a new possibility for vortex phase separation.
Phase Diagram in a Random Mixture of Two Antiferromagnets with Competing Spin Anisotropies. I

Science.gov (United States)

Someya, Yoshiko

1981-12-01

The phase diagram of a random mixture of two antiferromagnets with competing spin anisotropies (A1-xBx) has been analyzed by extending the theory of Matsubara and Inawashiro, and Oguchi and Ishikawa. In the model assumed, the anisotropy energies are expressed by the anisotropic exchange interactions. According to this formulation, it has been shown that the concentration dependence of TN becomes a function of \\includegraphics{dummy.eps}, where P, Q=A, B; SP is a magnitude of P-spin, and JPQη is a η component of exchange integral between P- and Q-spin). Further, the phase boundary between an AF phase and an OAF (oblique antiferromagnetic) phase at T{=}0 K has been shown to be determined by α({\\equiv}SB/SA), if \\includegraphics{dummy.eps} are given. The obtained phase diagrams for Fe1-xCoxCl2, K2Mn1-xFexF4 and Fe1-xCoxCl2\\cdot2H2O are compared with the experimental ones.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.

2012-01-01

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina

2012-08-03

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
Electron scattering in dense He-Ar gas mixtures: A pressure shift study

International Nuclear Information System (INIS)

Asaf, U.; Felps, W.S.; McGlynn, S.P.

1989-01-01

The dependence of the energies of high-n Rydberg states of CH 3 I on the molar composition of helium-argon mixtures (in the number density range 1.3x10 20 --5.6x10 20 cm -3 ) is reported. The energy shifts, when normalized to a given density value, are found to vary linearly with the mole fraction of either component of the binary, rare-gas mixture. The observed change in sign of the energy shift is attributable to the different signs of the electron scattering lengths for the two rare-gas components. As a result, there exists a mixture composition, at a mole ratio [He]/[Ar]=2.0, at which the shift is null. The experimental results for the gas mixture agree with the Fermi formula, as modified to include the Alekseev-Sobel'man polarization term. Effective electron scattering lengths and cross sections, polarizabilities, and thermal velocities are used to characterize the effects of the binary gas perturber system
Spectral line profile analysis for evaluation of Gaussian and Lorentzian widths and collisional broadening coefficient

International Nuclear Information System (INIS)

Nakhate, S.G.; Ahmad, S.A.; Pushpa; Rao, M.; Saksena, G.D.

1991-01-01

Deconvolution of atomic line profiles, recorded on PC interfaced recording Fabry-Perot spectrometer, into its Lorentzian and Gaussian component has been carried out. Effect of various parameters of hollow cathode discharge lamp (light source) such as discharge current, bath temperature and gas pressure on Lorentzian and Gaussian width has been studied. The value of the self-broadening coefficient for neon-neon atomic interaction for the transition 2p 5 4P-2P 5 3s (λ=3472.571 A) has been determined. (author). 15 refs., 6 figs., 4 tabs
Propagation dynamics of super-Gaussian beams in fractional Schrödinger equation: from linear to nonlinear regimes.

Science.gov (United States)

Zhang, Lifu; Li, Chuxin; Zhong, Haizhe; Xu, Changwen; Lei, Dajun; Li, Ying; Fan, Dianyuan

2016-06-27

We have investigated the propagation dynamics of super-Gaussian optical beams in fractional Schrödinger equation. We have identified the difference between the propagation dynamics of super-Gaussian beams and that of Gaussian beams. We show that, the linear propagation dynamics of the super-Gaussian beams with order m > 1 undergo an initial compression phase before they split into two sub-beams. The sub-beams with saddle shape separate each other and their interval increases linearly with propagation distance. In the nonlinear regime, the super-Gaussian beams evolve to become a single soliton, breathing soliton or soliton pair depending on the order of super-Gaussian beams, nonlinearity, as well as the Lévy index. In two dimensions, the linear evolution of super-Gaussian beams is similar to that for one dimension case, but the initial compression of the input super-Gaussian beams and the diffraction of the splitting beams are much stronger than that for one dimension case. While the nonlinear propagation of the super-Gaussian beams becomes much more unstable compared with that for the case of one dimension. Our results show the nonlinear effects can be tuned by varying the Lévy index in the fractional Schrödinger equation for a fixed input power.
Equivalent water height extracted from GRACE gravity field model with robust independent component analysis

Science.gov (United States)

Guo, Jinyun; Mu, Dapeng; Liu, Xin; Yan, Haoming; Dai, Honglei

2014-08-01

The Level-2 monthly GRACE gravity field models issued by Center for Space Research (CSR), GeoForschungs Zentrum (GFZ), and Jet Propulsion Laboratory (JPL) are treated as observations used to extract the equivalent water height (EWH) with the robust independent component analysis (RICA). The smoothing radii of 300, 400, and 500 km are tested, respectively, in the Gaussian smoothing kernel function to reduce the observation Gaussianity. Three independent components are obtained by RICA in the spatial domain; the first component matches the geophysical signal, and the other two match the north-south strip and the other noises. The first mode is used to estimate EWHs of CSR, JPL, and GFZ, and compared with the classical empirical decorrelation method (EDM). The EWH STDs for 12 months in 2010 extracted by RICA and EDM show the obvious fluctuation. The results indicate that the sharp EWH changes in some areas have an important global effect, like in Amazon, Mekong, and Zambezi basins.
Teleportation of squeezing: Optimization using non-Gaussian resources

International Nuclear Information System (INIS)

Dell'Anno, Fabio; De Siena, Silvio; Illuminati, Fabrizio; Adesso, Gerardo

2010-01-01

We study the continuous-variable quantum teleportation of states, statistical moments of observables, and scale parameters such as squeezing. We investigate the problem both in ideal and imperfect Vaidman-Braunstein-Kimble protocol setups. We show how the teleportation fidelity is maximized and the difference between output and input variances is minimized by using suitably optimized entangled resources. Specifically, we consider the teleportation of coherent squeezed states, exploiting squeezed Bell states as entangled resources. This class of non-Gaussian states, introduced by Illuminati and co-workers [F. Dell'Anno, S. De Siena, L. Albano, and F. Illuminati, Phys. Rev. A 76, 022301 (2007); F. Dell'Anno, S. De Siena, and F. Illuminati, ibid. 81, 012333 (2010)], includes photon-added and photon-subtracted squeezed states as special cases. At variance with the case of entangled Gaussian resources, the use of entangled non-Gaussian squeezed Bell resources allows one to choose different optimization procedures that lead to inequivalent results. Performing two independent optimization procedures, one can either maximize the state teleportation fidelity, or minimize the difference between input and output quadrature variances. The two different procedures are compared depending on the degrees of displacement and squeezing of the input states and on the working conditions in ideal and nonideal setups.
Additivity properties of a Gaussian channel

International Nuclear Information System (INIS)

Giovannetti, Vittorio; Lloyd, Seth

2004-01-01

The Amosov-Holevo-Werner conjecture implies the additivity of the minimum Renyi entropies at the output of a channel. The conjecture is proven true for all Renyi entropies of integer order greater than two in a class of Gaussian bosonic channel where the input signal is randomly displaced or where it is coupled linearly to an external environment
Amplitude calculations for 3D Gaussian beam migration using complex-valued traveltimes

International Nuclear Information System (INIS)

Bleistein, Norman; Gray, Samuel H

2010-01-01

Gaussian beams are often used to represent Green's functions in three-dimensional Kirchhoff-type true-amplitude migrations because such migrations made using Gaussian beams yield superior images to similar migrations using classical ray-theoretic Green's functions. Typically, the integrand of a migration formula consists of two Green's functions—each describing propagation to the image point—one from the source and the other from the receiver position. The use of Gaussian beams to represent each of these Green's functions in 3D introduces two additional double integrals when compared to a Kirchhoff migration using ray-theoretic Green's functions, thereby adding a significant computational burden. Hill (2001 Geophysics 66 1240–50) proposed a method for reducing those four integrals to two, compromising slightly on the full potential quality of the Gaussian beam representations for the sake of more efficient computation. That approach requires a two-dimensional steepest descent analysis for the asymptotic evaluation of a double integral. The method requires evaluation of the complex traveltimes of the Gaussian beams as well as the amplitudes of the integrands at the determined saddle points. In addition, it is necessary to evaluate the determinant of a certain (Hessian) matrix of second derivatives. Hill (2001 Geophysics 66 1240–50) did not report on this last part; thus, his proposed migration formula is kinematically correct but lacks correct amplitude behavior. In this paper, we derive a formula for that Hessian matrix in terms of dynamic ray tracing quantities. We also show in a simple example how the integral that we analyze here arises in a true amplitude migration formula
Cold water injection into two-phase mixtures

International Nuclear Information System (INIS)

1989-07-01

This report presents the results of a review of the international literature regarding the dynamic loadings associated with the injection of cold water into two-phase mixtures. The review placed emphasis on waterhammer in nuclear power plants. Waterhammmer incidence data were reviewed for information related to thermalhydraulic conditions, underlying causes and consequential damage. Condensation induced waterhammer was found to be the most significant consequence of injecting cold water into a two-phase system. Several severe waterhammer incidents have been attributed to slug formation and steam bubble collapse under conditions of stratified steam and cold water flows. These phenomena are complex and not well understood. The current body of experimental and analytical knowledge is not large enough to establish maps of expected regimes of condensation induced waterhammer. The Electric Power Research Institute, in the United States, has undertaken a major research and development programme to develop the knowledge base for this area. The limited models and data currently available show that mechanical parameters are as important as thermodynamic conditions for the initiation of condensation induced waterhammer. Examples of bounds for avoiding two-phase waterhammer are given. These bounds are system specific and depend upon parameters such as pump capacity, pipe length and pipe orientation
Tridiagonal realization of the antisymmetric Gaussian β-ensemble

International Nuclear Information System (INIS)

Dumitriu, Ioana; Forrester, Peter J.

2010-01-01

The Householder reduction of a member of the antisymmetric Gaussian unitary ensemble gives an antisymmetric tridiagonal matrix with all independent elements. The random variables permit the introduction of a positive parameter β, and the eigenvalue probability density function of the corresponding random matrices can be computed explicitly, as can the distribution of (q i ), the first components of the eigenvectors. Three proofs are given. One involves an inductive construction based on bordering of a family of random matrices which are shown to have the same distributions as the antisymmetric tridiagonal matrices. This proof uses the Dixon-Anderson integral from Selberg integral theory. A second proof involves the explicit computation of the Jacobian for the change of variables between real antisymmetric tridiagonal matrices, its eigenvalues, and (q i ). The third proof maps matrices from the antisymmetric Gaussian β-ensemble to those realizing particular examples of the Laguerre β-ensemble. In addition to these proofs, we note some simple properties of the shooting eigenvector and associated Pruefer phases of the random matrices.

Geometry of perturbed Gaussian states and quantum estimation

International Nuclear Information System (INIS)

Genoni, Marco G; Giorda, Paolo; Paris, Matteo G A

2011-01-01

We address the non-Gaussianity (nG) of states obtained by weakly perturbing a Gaussian state and investigate the relationships with quantum estimation. For classical perturbations, i.e. perturbations to eigenvalues, we found that the nG of the perturbed state may be written as the quantum Fisher information (QFI) distance minus a term depending on the infinitesimal energy change, i.e. it provides a lower bound to statistical distinguishability. Upon moving on isoenergetic surfaces in a neighbourhood of a Gaussian state, nG thus coincides with a proper distance in the Hilbert space and exactly quantifies the statistical distinguishability of the perturbations. On the other hand, for perturbations leaving the covariance matrix unperturbed, we show that nG provides an upper bound to the QFI. Our results show that the geometry of non-Gaussian states in the neighbourhood of a Gaussian state is definitely not trivial and cannot be subsumed by a differential structure. Nevertheless, the analysis of perturbations to a Gaussian state reveals that nG may be a resource for quantum estimation. The nG of specific families of perturbed Gaussian states is analysed in some detail with the aim of finding the maximally non-Gaussian state obtainable from a given Gaussian one. (fast track communication)
Efficient Hardware Implementation For Fingerprint Image Enhancement Using Anisotropic Gaussian Filter.

Science.gov (United States)

Khan, Tariq Mahmood; Bailey, Donald G; Khan, Mohammad A U; Kong, Yinan

2017-05-01

A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is the expensive nature of their hardware implementations. To circumvent this, a modified anisotropic Gaussian filter is efficiently adopted in hardware by decomposing the filter into two orthogonal Gaussians and an oriented line Gaussian. An architecture is developed for dynamically controlling the orientation of the line Gaussian filter. To further improve the performance of the filter, the input image is homogenized by a local image normalization. In the proposed structure, for a middle-range reconfigurable FPGA, both parallel compute-intensive and real-time demands were achieved. We manage to efficiently speed up the image-processing time and improve the resource utilization of the FPGA. Test results show an improved speed for its hardware architecture while maintaining reasonable enhancement benchmarks.
Composition inversion in mixtures of binary colloids and polymer

Science.gov (United States)

Zhang, Isla; Pinchaipat, Rattachai; Wilding, Nigel B.; Faers, Malcolm A.; Bartlett, Paul; Evans, Robert; Royall, C. Patrick

2018-05-01

Understanding the phase behaviour of mixtures continues to pose challenges, even for systems that might be considered "simple." Here, we consider a very simple mixture of two colloidal and one non-adsorbing polymer species, which can be simplified even further to a size-asymmetrical binary mixture, in which the effective colloid-colloid interactions depend on the polymer concentration. We show that this basic system exhibits surprisingly rich phase behaviour. In particular, we enquire whether such a system features only a liquid-vapor phase separation (as in one-component colloid-polymer mixtures) or whether, additionally, liquid-liquid demixing of two colloidal phases can occur. Particle-resolved experiments show demixing-like behaviour, but when combined with bespoke Monte Carlo simulations, this proves illusory, and we reveal that only a single liquid-vapor transition occurs. Progressive migration of the small particles to the liquid phase as the polymer concentration increases gives rise to composition inversion—a maximum in the large particle concentration in the liquid phase. Close to criticality, the density fluctuations are found to be dominated by the larger colloids.
Phase behaviour of the symmetric binary mixture from thermodynamic perturbation theory.

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Dorsaz, N; Foffi, G

2010-03-17

We study the phase behaviour of symmetric binary mixtures of hard core Yukawa (HCY) particles via thermodynamic perturbation theory (TPT). We show that all the topologies of phase diagram reported for the symmetric binary mixtures are correctly reproduced within the TPT approach. In a second step we use the capability of TPT to be straightforwardly extended to mixtures that are nonsymmetric in size. Starting from mixtures that belong to the different topologies of symmetric binary mixtures we investigate the effect on the phase behaviour when an asymmetry in the diameters of the two components is introduced. Interestingly, when the energy of interaction between unlike particles is weaker than the interaction between like particles, the propensity for the solution to demix is found to increase strongly with size asymmetry.
Mixture Design and Its Application in Cement Solidification for Spent Resin

International Nuclear Information System (INIS)

Gan, Xueying; Lin, Meiqing; Chen, Hui

1994-01-01

The study is aimed to assess the usefulness of the mixture design for spent resin immobilization in cement. Although a considerable amount of research has been carried out to determine the limits for the composition of an acceptable resin-cement mixture, no efficient experimental strategy exists that explores the full properties of waste form against composition relationship. In order to gain an overall view, this report introduces the method of mixture design and mixture analysis, and describes the design of experiment of the 5-component mixture with the constraint conditions. The mathematic models of 28-day compressive strength varying with the ingredients are fitted, and the main effect and interaction effect of two ingredients are identified quantitatively along with the graphical interpretation using the response trace plot and contour plots
Probabilistic analysis and fatigue damage assessment of offshore mooring system due to non-Gaussian bimodal tension processes

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Chang, Anteng; Li, Huajun; Wang, Shuqing; Du, Junfeng

2017-08-01

Both wave-frequency (WF) and low-frequency (LF) components of mooring tension are in principle non-Gaussian due to nonlinearities in the dynamic system. This paper conducts a comprehensive investigation of applicable probability density functions (PDFs) of mooring tension amplitudes used to assess mooring-line fatigue damage via the spectral method. Short-term statistical characteristics of mooring-line tension responses are firstly investigated, in which the discrepancy arising from Gaussian approximation is revealed by comparing kurtosis and skewness coefficients. Several distribution functions based on present analytical spectral methods are selected to express the statistical distribution of the mooring-line tension amplitudes. Results indicate that the Gamma-type distribution and a linear combination of Dirlik and Tovo-Benasciutti formulas are suitable for separate WF and LF mooring tension components. A novel parametric method based on nonlinear transformations and stochastic optimization is then proposed to increase the effectiveness of mooring-line fatigue assessment due to non-Gaussian bimodal tension responses. Using time domain simulation as a benchmark, its accuracy is further validated using a numerical case study of a moored semi-submersible platform.
Maximum likelihood estimation of finite mixture model for economic data

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Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-06-01

Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

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Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.
Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

Directory of Open Access Journals (Sweden)

Wu Chi-Yeh

2010-01-01

Full Text Available Abstract Background MicroRNAs (miRNAs are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention. Results This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G2DE based classifier. The G2DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set. Conclusion Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G2DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G
Non-Gaussianity in island cosmology

International Nuclear Information System (INIS)

Piao Yunsong

2009-01-01

In this paper we fully calculate the non-Gaussianity of primordial curvature perturbation of the island universe by using the second order perturbation equation. We find that for the spectral index n s ≅0.96, which is favored by current observations, the non-Gaussianity level f NL seen in an island will generally lie between 30 and 60, which may be tested by the coming observations. In the landscape, the island universe is one of anthropically acceptable cosmological histories. Thus the results obtained in some sense mean the coming observations, especially the measurement of non-Gaussianity, will be significant to clarify how our position in the landscape is populated.
Monogamy inequality for distributed gaussian entanglement.

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Hiroshima, Tohya; Adesso, Gerardo; Illuminati, Fabrizio

2007-02-02

We show that for all n-mode Gaussian states of continuous variable systems, the entanglement shared among n parties exhibits the fundamental monogamy property. The monogamy inequality is proven by introducing the Gaussian tangle, an entanglement monotone under Gaussian local operations and classical communication, which is defined in terms of the squared negativity in complete analogy with the case of n-qubit systems. Our results elucidate the structure of quantum correlations in many-body harmonic lattice systems.
Degradation of chlorophenol mixtures in a fed-batch system by two ...

African Journals Online (AJOL)

This work was undertaken to investigate the effect of variations of the feed rate on a fed-batch set-up used to degrade xenobiotics. The mixture of substrates was composed of PCP, 2,4,6 TCP and 2,3,5,6 TeCP (pentachlorophenol, 2,4,6 trichlorophenol and 2,3,5,6 tetrachlorophenol respectively). Two acclimated bacteria ...
Analysis of overdispersed count data by mixtures of Poisson variables and Poisson processes.

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Hougaard, P; Lee, M L; Whitmore, G A

1997-12-01

Count data often show overdispersion compared to the Poisson distribution. Overdispersion is typically modeled by a random effect for the mean, based on the gamma distribution, leading to the negative binomial distribution for the count. This paper considers a larger family of mixture distributions, including the inverse Gaussian mixture distribution. It is demonstrated that it gives a significantly better fit for a data set on the frequency of epileptic seizures. The same approach can be used to generate counting processes from Poisson processes, where the rate or the time is random. A random rate corresponds to variation between patients, whereas a random time corresponds to variation within patients.
Influence of Gaussian white noise on the frequency-dependent first nonlinear polarizability of doped quantum dot

Energy Technology Data Exchange (ETDEWEB)

Ganguly, Jayanta [Department of Chemistry, Brahmankhanda Basapara High School, Basapara, Birbhum 731215, West Bengal (India); Ghosh, Manas, E-mail: pcmg77@rediffmail.com [Department of Chemistry, Physical Chemistry Section, Visva Bharati University, Santiniketan, Birbhum 731 235, West Bengal (India)

2014-05-07

We investigate the profiles of diagonal components of frequency-dependent first nonlinear (β{sub xxx} and β{sub yyy}) optical response of repulsive impurity doped quantum dots. We have assumed a Gaussian function to represent the dopant impurity potential. This study primarily addresses the role of noise on the polarizability components. We have invoked Gaussian white noise consisting of additive and multiplicative characteristics (in Stratonovich sense). The doped system has been subjected to an oscillating electric field of given intensity, and the frequency-dependent first nonlinear polarizabilities are computed. The noise characteristics are manifested in an interesting way in the nonlinear polarizability components. In case of additive noise, the noise strength remains practically ineffective in influencing the optical responses. The situation completely changes with the replacement of additive noise by its multiplicative analog. The replacement enhances the nonlinear optical response dramatically and also causes their maximization at some typical value of noise strength that depends on oscillation frequency.
Gaussianization for fast and accurate inference from cosmological data

Science.gov (United States)

Schuhmann, Robert L.; Joachimi, Benjamin; Peiris, Hiranya V.

2016-06-01

We present a method to transform multivariate unimodal non-Gaussian posterior probability densities into approximately Gaussian ones via non-linear mappings, such as Box-Cox transformations and generalizations thereof. This permits an analytical reconstruction of the posterior from a point sample, like a Markov chain, and simplifies the subsequent joint analysis with other experiments. This way, a multivariate posterior density can be reported efficiently, by compressing the information contained in Markov Chain Monte Carlo samples. Further, the model evidence integral (I.e. the marginal likelihood) can be computed analytically. This method is analogous to the search for normal parameters in the cosmic microwave background, but is more general. The search for the optimally Gaussianizing transformation is performed computationally through a maximum-likelihood formalism; its quality can be judged by how well the credible regions of the posterior are reproduced. We demonstrate that our method outperforms kernel density estimates in this objective. Further, we select marginal posterior samples from Planck data with several distinct strongly non-Gaussian features, and verify the reproduction of the marginal contours. To demonstrate evidence computation, we Gaussianize the joint distribution of data from weak lensing and baryon acoustic oscillations, for different cosmological models, and find a preference for flat Λcold dark matter. Comparing to values computed with the Savage-Dickey density ratio, and Population Monte Carlo, we find good agreement of our method within the spread of the other two.
Two-phase mixture level swell and liquid entrainment/off-take in a vessel during rapid depressurization

International Nuclear Information System (INIS)

Kim, Chang Hyun

2004-02-01

An experimental study has been performed to analyze the two-phase mixture level swell and the liquid entrainment/off-take through the break in a vessel, which are important phenomena to determine the bleed capacity of the Safety Depressurization System (SDS) of Korea Advanced Power Reactor 1400 (APR1400). Three separate experiments are performed in this study: (a) the depressurization and two-phase mixture level swell experiment: (b) the two-phase mixture level measurement experiment: (c) the liquid entrainment and off-take experiment. A series of experiments has been performed using a scaled pressurized vessel in various depressurization conditions to analyze the two-phase mixture level swell and the liquid entrainment/off-take phenomena from the two-phase mixture surface in the first experiment. The test parameters are the initial pressure (10 - 38.75bars), the initial water level (43.7% - 80.0% of full height), the orifice inner diameter (10mm, 17.5mm, and 20mm). The liquid off-take takes place in certain experimental conditions. The measured parameters in the present experiments are axial void fraction distributions, pressures, temperatures in the test vessel, and the mixture density and mass flowrate through the discharge pipe. An assessment of RELAP5/MOD3 code with the present experimental data has been performed. With appropriate nodalization and time step, RELAP5/MOD3 showed reasonable agreement with the present experimental data for the gradual depressurization without liquid off-take. In the case that the off-take takes place, however, RELAP5/MOD3 under-predicts the amount of liquid entrainment/off-take during depressurization. In the second experiment, an assessment of an ultrasonic sensor and a two-wire type capacitance probe for the two-phase mixture level measurement has been performed under the same experimental conditions to adopt an appropriate measurement method for the two-phase mixture level swell and to investigate pool void fraction by the
The two-component spin-fermion model for high-Tc cuprates: its applications in neutron scattering and ARPES experiments

International Nuclear Information System (INIS)

Bang, Yunkyu

2012-01-01

Motivated by neutron scattering experiments in high-T c cuprates, we propose the two-component spin-fermion model as a minimal phenomenological model, which has both local spins and itinerant fermions as independent degrees of freedom (d.o.f.). Our calculations of the dynamic spin correlation function provide a successful description of the puzzling neutron experiment data and show that: (i) the upward dispersion branch of magnetic excitations is mostly due to local spin excitations; (ii) the downward dispersion branch is from collective particle-hole excitations of fermions; and (iii) the resonance mode is a mixture of both d.o.f. Using the same model with the same set of parameters, we calculated the renormalized quasiparticle (q.p.) dispersion and successfully reproduced one of the key features of the angle-resolved photoemission spectroscopy (ARPES) experiments, namely the high-energy kink structure in the fermion q.p. dispersion, thus supporting the two-component spin-fermion phenomenology. (paper)
Primordial soup was edible: abiotically produced Miller-Urey mixture supports bacterial growth.

Science.gov (United States)

Xie, Xueshu; Backman, Daniel; Lebedev, Albert T; Artaev, Viatcheslav B; Jiang, Liying; Ilag, Leopold L; Zubarev, Roman A

2015-09-28

Sixty years after the seminal Miller-Urey experiment that abiotically produced a mixture of racemized amino acids, we provide a definite proof that this primordial soup, when properly cooked, was edible for primitive organisms. Direct admixture of even small amounts of Miller-Urey mixture strongly inhibits E. coli bacteria growth due to the toxicity of abundant components, such as cyanides. However, these toxic compounds are both volatile and extremely reactive, while bacteria are highly capable of adaptation. Consequently, after bacterial adaptation to a mixture of the two most abundant abiotic amino acids, glycine and racemized alanine, dried and reconstituted MU soup was found to support bacterial growth and even accelerate it compared to a simple mixture of the two amino acids. Therefore, primordial Miller-Urey soup was perfectly suitable as a growth media for early life forms.
On thermal conductivity of gas mixtures containing hydrogen

Science.gov (United States)

Zhukov, Victor P.; Pätz, Markus

2017-06-01

A brief review of formulas used for the thermal conductivity of gas mixtures in CFD simulations of rocket combustion chambers is carried out in the present work. In most cases, the transport properties of mixtures are calculated from the properties of individual components using special mixing rules. The analysis of different mixing rules starts from basic equations and ends by very complex semi-empirical expressions. The formulas for the thermal conductivity are taken for the analysis from the works on modelling of rocket combustion chambers. \\hbox {H}_2{-}\\hbox {O}_2 mixtures are chosen for the evaluation of the accuracy of the considered mixing rules. The analysis shows that two of them, of Mathur et al. (Mol Phys 12(6):569-579, 1967), and of Mason and Saxena (Phys Fluids 1(5):361-369, 1958), have better agreement with the experimental data than other equations for the thermal conductivity of multicomponent gas mixtures.
Non-Gaussianity from inflation: theory and observations

Science.gov (United States)

Bartolo, N.; Komatsu, E.; Matarrese, S.; Riotto, A.

2004-11-01

This is a review of models of inflation and of their predictions for the primordial non-Gaussianity in the density perturbations which are thought to be at the origin of structures in the Universe. Non-Gaussianity emerges as a key observable to discriminate among competing scenarios for the generation of cosmological perturbations and is one of the primary targets of present and future Cosmic Microwave Background satellite missions. We give a detailed presentation of the state-of-the-art of the subject of non-Gaussianity, both from the theoretical and the observational point of view, and provide all the tools necessary to compute at second order in perturbation theory the level of non-Gaussianity in any model of cosmological perturbations. We discuss the new wave of models of inflation, which are firmly rooted in modern particle physics theory and predict a significant amount of non-Gaussianity. The review is addressed to both astrophysicists and particle physicists and contains useful tables which summarize the theoretical and observational results regarding non-Gaussianity.

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