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Color object detection using spatial-color t probability functions. Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object.
Consequently, it typically requires a large of exemplars for rigid objects or a large amount of human intuition for nonrigid objects to develop a robust algorithm. We present a robust algorithm deed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color t probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope i.
Experimental demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely Visiting ft smithnsa algorithm based on color co-occurrence histograms by a decisive margin.
Exact t density-current probability function for the asymmetric exclusion process. We study the asymmetric simple exclusion process with open boundaries and derive the exact form of the t probability function for the occupation and the current through the system.
We further consider the thermodynamic limit, showing that the resulting distribution is non-Gaussian and that the density fluctuations have a discontinuity at the continuous phase transition, while the current fluctuations are continuous. The derivations are performed by using the standard operator algebraic approach and by the introduction of new operators satisfying a modified version of the original algebra.
Copyright Visiting ft smithnsa American Physical Society. Evaluation of t probability density function models for turbulent nonpremixed combustion with complex chemistry. Two types of mixing sub-models are evaluated in connection with a t -scalar probability density function method for turbulent nonpremixed combustion. Model calculations are made and compared to simulation for homogeneously distributed methane-air reaction zones mixing and reacting in decaying turbulence within a two-dimensional enclosed domain.
The comparison is arranged to ensure that both the simulation and model calculations a make use of exactly the same chemical mechanism, b do not involve non-unity Lewis transport of species, and c are free from radiation loss. The modified Curl mixing sub-model was found to provide superior predictive accuracy over the simple relaxation-to-mean submodel in the case studied.
Both mixing submodels were found to produce non-physical mixing behavior for mixture fractions removed from the immediate reaction zone. A suggestion for a further modified Curl mixing sub-model is made in connection with earlier work done in the field.
t probabilities and quantum cognition. In this paper we discuss the existence of t probability distributions for quantumlike response computations in the brain. We do so by focusing on a contextual neural-oscillator model shown to reproduce the main features of behavioral stimulus-response theory. We then exhibit a simple example of contextual random variables not having a t probability distribution, and describe how such variables can Visiting ft smithnsa obtained from neural oscillators, but not from a quantum observable algebra.
This fully automated PDF regulated continuum fitting method models the unknown quasar continuum with a linear principal component analysis PCA basis, with the PCA coefficients treated as nuisance parameters. Given the of principal component spectra, this is comparable to the underlying accuracy of the PCA model itself. Applying this method to real quasar spectra and comparing to a more realistic IGM model from hydrodynamical simulations would enable precise measurements of the thermal and cosmological parameters governing the IGM, albeit with somewhat larger uncertainties, given the increased flexibility of the model.
Excluding t probabilities from quantum theory. Quantum theory does not provide a unique definition for the t probability of two noncommuting observables, which is the next important question after the Born's probability for a single observable. Instead, various definitions were suggested, e. After reviewing open issues of the t probabilitywe relate it to quantum imprecise probabilitieswhich are noncontextual and are consistent with all constraints expected from a quantum probability.
We study two noncommuting observables in a two-dimensional Hilbert space and show that there is no precise t probability that applies for any quantum state and is consistent with imprecise probabilities. This contrasts with theorems by Bell and Kochen-Specker that exclude t probabilities for more than two noncommuting observables, in Hilbert space with dimension larger than two.
If measurement contexts are included into the definition, t probabilities are not excluded anymore, but they are still constrained by imprecise probabilities. The non-Gaussian t probability density function of slope and elevation for a nonlinear gravity wave field. On the basis of the mapping method developed by Huang et al. Various conditional and marginal density functions are also obtained through the t density function. The analytic are compared with a series of carefully controlled laboratory observations, and good agreement is noted. Furthermore, the laboratory wind wave field observations indicate that the capillary or capillary-gravity waves may not be the dominant components in determining the total roughness of the wave Visiting ft smithnsa.
Thus, the analyticthough derived specifically for the gravity waves, may have more general applications. Comment on "constructing quantum games from nonfactorizable t probabilities ". In the paper [Phys. We are going to prove, however, that the scheme does not generalize the games studied in the commented paper. Moreover, it allows the players to obtain nonclassical even if the factorizable t probabilities are used.
t genome-wide prediction in several populations ing for randomness of genotypes: A hierarchical Bayes approach. I: Multivariate Gaussian priors for marker effects and derivation of the t probability mass function of genotypes. It is important to consider heterogeneity of marker effects and allelic frequencies in across population genome-wide prediction studies. Moreover, all regression models used in genome-wide prediction overlook randomness of genotypes.
In this study, a family of hierarchical Bayesian models to perform across population genome-wide prediction modeling genotypes as random variables and allowing population-specific effects for each marker was developed. Models shared a common structure and differed in the priors used and the assumption about residual variances homogeneous or heterogeneous.
Randomness of genotypes was ed for by deriving the t probability mass function of marker genotypes conditional on allelic frequencies and pedigree information. As a consequence, these models incorporated kinship and genotypic information that not only permitted to for heterogeneity of allelic frequencies, but also to include individuals with missing genotypes at some or all loci without the Visiting ft smithnsa for imputation.
This was possible because the non-observed fraction of the de matrix was treated as an unknown model parameter. For each model, a simpler version ignoring population structure, but still ing for randomness of genotypes was proposed. Implementation of these models and computation of some criteria for model comparison were illustrated using two simulated datasets. Theoretical and computational issues along with possible applications, extensions and refinements were discussed.
Some features of the models developed in this study make them promising for genome-wide prediction, the use of information contained in the probability distribution of genotypes is perhaps the most appealing. Further studies to assess the performance of the models proposed here and also to compare them with conventional models used in genome-wide prediction are needed. All rights reserved.
Idealized models of the t probability distribution of wind speeds. The t probability distribution of wind speeds at two separate locations in space or points in time completely characterizes the statistical dependence of these two quantities, providing more information than linear measures such as correlation. In this study, we consider two models of the t distribution of wind speeds obtained from idealized models of the dependence structure of the horizontal wind velocity components.
The bivariate Rice distribution follows from assuming that the wind components have Gaussian and isotropic fluctuations. The bivariate Weibull distribution arises from power law transformations of wind speeds corresponding to vector components with Gaussian, isotropic, mean-zero variability.
Maximum likelihood estimates of these distributions are compared using wind speed data from the mid-troposphere, from different altitudes at the Cabauw tower in the Netherlands, and from scatterometer observations over the sea surface. While the bivariate Rice distribution is more flexible and can represent a broader class of dependence structures, the bivariate Weibull distribution is mathematically simpler and may be more convenient in many applications. The complexity of the mathematical expressions obtained for the t distributions suggests that the development of explicit functional forms for multivariate speed distributions from distributions of the components will not be practical for more complicated dependence structure or more than two speed variables.
The effects of the one-step replica symmetry breaking on the Sherrington-Kirkpatrick spin glass model in the presence of random field with a t Gaussian probability density function for the exchange interactions and random fields. The Sherrington-Kirkpatrick Ising spin glass model, in the presence of a random magnetic field, is investigated within the framework of the one-step replica symmetry breaking. The thermodynamic properties, the three different phase diagrams and system's parameters are computed with respect to the natural parameters of the t Gaussian probability density function at non-zero and zero temperatures.
The low temperature negative entropy controversy, a result of the replica symmetry approach, has been partly remedied in the current study, leading to a less negative result. In addition, the present system possesses two successive spin glass phase transitions with characteristic temperatures. Psychophysics of the probability weighting function. A probability weighting function w p for an objective probability p in decision under risk plays a pivotal role in Kahneman-Tversky prospect theory.
Although recent studies in econophysics and neuroeconomics widely utilized probability weighting functionspsychophysical foundations of the probability weighting functions have been unknown. The present study utilizes psychophysical theory to derive Prelec's probability weighting function from psychophysical laws of perceived waiting time in probabilistic choices.
Also, the relations between the parameters in the probability weighting function and the probability discounting function in behavioral psychology are derived. Future directions in the application of the psychophysical theory of the probability weighting function in econophysics and neuroeconomics are discussed. Computation of the Complex Probability Function. The complex probability Visiting ft smithnsa is important in many areas of physics and many techniques have been developed in an attempt to compute it for some z quickly and e ciently.
Most prominent are the methods that use Gauss-Hermite quadrature, which uses the roots of the n th degree Hermite polynomial and corresponding weights to approximate the complex probability function. This document serves as an overview and discussion of the use, shortcomings, and potential improvements on the Gauss-Hermite quadrature for the complex probability function. t probability of statistical success of multiple phase III trials. The probability of statistical success PoSS of the phase III trials based on data from earlier studies is an important factor in that decision-making process.
Instead of statistical power, the predictive power of a phase III trial, which takes into the uncertainty in the estimation of treatment effect from earlier studies, has been proposed to evaluate the PoSS of a single trial. However, regulatory authorities generally require statistical ificance in two or more trials for marketing licensure. We show that the predictive statistics of two future trials are statistically correlated through use of the common observed data from earlier studies. Thus, the t predictive power should not be evaluated as a simplistic product of the predictive powers of the Visiting ft smithnsa trials.
We develop the relevant formulae for the appropriate evaluation of the t predictive power and provide numerical examples. Investigation of estimators of probability density functions. Probability distribution functions in turbulent convection.
of an extensive investigation of probability distribution functions pdfs for Rayleigh-Benard convection, in hard turbulence regime, are presented. It is shown that the pdfs exhibit a high degree of Visiting ft smithnsa universality. In certain cases this universality is established within two Kolmogorov scales of a boundary.Visiting ft smithnsa
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