939 resultados para Non-gaussian Random Functions
Resumo:
Optical tweezers are widely used for the manipulation of cells and their internal structures. However, the degree of manipulation possible is limited by poor control over the orientation of the trapped cells. We show that it is possible to controllably align or rotate disc-shaped cells-chloroplasts of Spinacia oleracea-in a plane-polarized Gaussian beam trap, using optical torques resulting predominantly from circular polarization induced in the transmitted beam by the non-spherical shape of the cells.
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A central feature in the Hilbert space formulation of classical mechanics is the quantisation of classical Lionville densities, leading to what may be termed Groenewold operators. We investigate the spectra of the Groenewold operators that correspond to Gaussian and to certain uniform Lionville densities. We show that when the classical coordinate-momentum uncertainty product falls below Heisenberg's limit, the Groenewold operators in the Gaussian case develop negative eigenvalues and eigenvalues larger than 1. However, in the uniform case, negative eigenvalues are shown to persist for arbitrarily large values of the classical uncertainty product.
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This paper investigates the non-linear bending behaviour of functionally graded plates that are bonded with piezoelectric actuator layers and subjected to transverse loads and a temperature gradient based on Reddy's higher-order shear deformation plate theory. The von Karman-type geometric non-linearity, piezoelectric and thermal effects are included in mathematical formulations. The temperature change is due to a steady-state heat conduction through the plate thickness. The material properties are assumed to be graded in the thickness direction according to a power-law distribution in terms of the volume fractions of the constituents. The plate is clamped at two opposite edges, while the remaining edges can be free, simply supported or clamped. Differential quadrature approximation in the X-axis is employed to convert the partial differential governing equations and the associated boundary conditions into a set of ordinary differential equations. By choosing the appropriate functions as the displacement and stress functions on each nodal line and then applying the Galerkin procedure, a system of non-linear algebraic equations is obtained, from which the non-linear bending response of the plate is determined through a Picard iteration scheme. Numerical results for zirconia/aluminium rectangular plates are given in dimensionless graphical form. The effects of the applied actuator voltage, the volume fraction exponent, the temperature gradient, as well as the characteristics of the boundary conditions are also studied in detail. Copyright (C) 2004 John Wiley Sons, Ltd.
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To investigate the effects of different management strategies for non-localized prostate cancer on men's quality of life and cognitive functioning. Men with prostate cancer were randomly assigned to one of four treatment arms: leuprorelin, goserelin, cyproterone acetate (CPA), or close clinical monitoring. In a repeated-measures design, men were assessed before treatment (baseline) and after 6 and 12 months of treatment. A community comparison group of men of the same age with no prostate cancer participated for the same length of time. The men were recruited from public and private urology departments from university teaching hospitals. All those with prostate cancer who were eligible for hormonal therapy had no symptoms requiring immediate therapy. In all, 82 patients were randomized and 62 completed the 1-year study, and of the 20 community participants, 15 completed the study. The main outcome measures were obtained from questionnaires on emotional distress, existential satisfaction, physical function and symptoms, social and role function, subjective cognitive function, and sexual function, combined with standard neuropsychological tests of memory, attention, and executive functions. Sexual dysfunction increased for patients on androgen-suppressing therapies, and emotional distress increased in those assigned to CPA or close clinical monitoring. Compared with before treatment there was evidence of an adverse effect of leuprorelin, goserelin, and CPA on cognitive function. In deciding the timing of androgen suppression therapy for prostate cancer, consideration should be given to potential adverse effects on quality of life and cognitive function.
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It is unclear whether a random plasma cortisol measurement and the corticotropin (ACTH) test adequately reflect glucocorticoid secretory capacity in critical illness. This study aimed to determine whether these tests provide information representative of the 24 hour period. Plasma cortisol was measured hourly for 24 hours in 21 critically ill septic patients followed by a corticotropin test with 1 μ g dose administered intravenously. Serum and urine were analysed for ACTH and free cortisol respectively. Marked hourly variability in plasma cortisol was evident (coefficient of variation 8-30%) with no demonstrable circadian rhythm. The individual mean plasma cortisol concentrations ranged from 286 59 nmol/l to 796 &PLUSMN; 83 nmol/l. The 24 hour mean plasma cortisol was strongly correlated with both random plasma cortisol (r(2) 0.9, P< 0.0001) and the cortisol response to corticotropin (r(2) 0.72, P< 0.001). Only nine percent of patients increased their plasma cortisol by 250 nmol/l after corticotropin (euadrenal response). However, 35% of non-responders had spontaneous hourly rises > 250 nmol/l thus highlighting the limitations of a single point corticotropin test. Urinary free cortisol was elevated (865&PLUSMN; 937 nmol) in both corticotropin responders and non-responders suggesting elevated plasma free cortisol. No significant relationship was demonstrable between plasma cortisol and ACTH. We conclude that although random cortisol measurements and the low dose corticotropin tests reliably reflect the 24 hour mean cortisol in critical illness, they do not take into account the pulsatile nature of cortisol secretion. Consequently, there is the potential for erroneous conclusions about adrenal function based on a single measurement. We suggest that caution be exercised when drawing conclusions on the adequacy of adrenal function based on a single random plasma cortisol or the corticotropin test.
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The estimated parameters of output distance functions frequently violate the monotonicity, quasi-convexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis-Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models-a fixed effects model and a random effects model-are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed. (c) 2004 Elsevier B.V. All rights reserved.
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The present paper investigates the characteristics of short-term interest rates in several countries. We examine the importance of nonlinearities in the mean reversion and volatility of short-term interest rates. We examine various models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate.We find that different markets require different models. In particular, we find evidence of nonlinear mean reversion in some of the countries that we examine, linear mean reversion in others and no mean reversion in some countries. For all countries we examine, there is strong evidence of the need for the volatility of interest rate changes to be highly sensitive to the level of the short-term interest rate. Out-of-sample forecasting performance of one-factor short rate models is poor, stemming from the inability of the models to accommodate jumps and discontinuities in the time series data.
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A new approach to identify multivariable Hammerstein systems is proposed in this paper. By using cardinal cubic spline functions to model the static nonlinearities, the proposed method is effective in modelling processes with hard and/or coupled nonlinearities. With an appropriate transformation, the nonlinear models are parameterized such that the nonlinear identification problem is converted into a linear one. The persistently exciting condition for the transformed input is derived to ensure the estimates are consistent with the true system. A simulation study is performed to demonstrate the effectiveness of the proposed method compared with the existing approaches based on polynomials. (C) 2006 Elsevier Ltd. All rights reserved.
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The random switching of measurement bases is commonly assumed to be a necessary step of quantum key distribution protocols. In this paper we present a no-switching protocol and show that switching is not required for coherent-state continuous-variable quantum key distribution. Further, this protocol achieves higher information rates and a simpler experimental setup compared to previous protocols that rely on switching. We propose an optimal eavesdropping attack against this protocol, assuming individual Gaussian attacks. Finally, we investigate and compare the no-switching protocol applied to the original Bennett-Brassard 1984 scheme.
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The hypothesis that lipid rafts exist in plasma membranes and have crucial biological functions remains controversial. The lateral heterogeneity of proteins in the plasma membrane is undisputed, but the contribution of cholesterol-dependent lipid assemblies to this complex, non-random organization promotes vigorous debate. In the light of recent studies with model membranes, computational modelling and innovative cell biology, I propose an updated model of lipid rafts that readily accommodates diverse views on plasma-membrane micro-organization.
Resumo:
The term non-coding RNA (ncRNA) is commonly employed for RNA that does not encode a protein, but this does not mean that such RNAs do not contain information nor have function. Although it has been generally assumed that most genetic information is transacted by proteins, recent evidence suggests that the majority of the genomes of mammals and other complex organisms is in fact transcribed into ncRNAs, many of which are alternatively spliced and/or processed into smaller products. These ncRNAs include microRNAs and snoRNAs (many if not most of which remain to be identified), as well as likely other classes of yet-to-be-discovered small regulatory RNAs, and tens of thousands of longer transcripts (including complex patterns of interlacing and overlapping sense and antisense transcripts), most of whose functions are unknown. These RNAs (including those derived from introns) appear to comprise a hidden layer of internal signals that control various levels of gene expression in physiology and development, including chromatin architecture/epigenetic memory, transcription, RNA splicing, editing, translation and turnover. RNA regulatory networks may determine most of our complex characteristics, play a significant role in disease and constitute an unexplored world of genetic variation both within and between species.
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The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out exactly using matrix operations. The method has been tested on two challenging problems and has produced excellent results.
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The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.
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The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems.
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This report seeks to make concrete some of the ideas we have been discussing about sensible priors for winds over the ocean. In particular, random field models are reviewed, as are permissible covariance functions. The criteria which these covariance functions must satisfy in order that vorticity and divergence exist and are continuous are defined. The use of Helmholtz theorem is discussed, and possible choices for the covariances are suggested.