Gaussian etched single transverse mode vertical-cavity surface-emitting laser

A continuous Gaussian profile matched to the fundamental mode was etched onto the aperture of a vertical cavity surface emitting laser (VCSEL). Single Gaussian spot emission was achieved over the entire operating current range.

Friction of sliding surfaces carrying adsorbed lubricant layers

In concentrated contacts the behaviour of lubricants is much modified by the high local pressures: changes can arise both from molecular ordering within the very thin film lubricant layers present at the interface as well as from the deposition on the component surfaces of more solid-like polymeric boundary layers. These 'third bodies' separating the solid surfaces may have rheological or mechanical properties very different from those observed in the bulk. Classical elasto-hydrodynamic theory considers the entrapped lubricant to exhibit a piezo-viscous behaviour while the conventional picture of more solid boundary lubricant layers views their shear strength r as being linearly dependent on local pressure p, so that T = TO + ap where TO and a are constants. If TO is relatively small, then the coefficient of friction \i = T Ip ~ a and so Amonton's laws are recovered. However, the properties of adsorbed or deposited surface films, or indeed other third bodies such as debris layers, may be more complex than this. A preliminary study has looked quantitatively at the influence of the pressure dependence of the shear strength of any surface layer on the overall friction coefficient of a contact which is made up of an array of asperities whose height varies in a Gaussian manner. Individual contact points may be elastic or plastic. The analysis results in plots of coefficient of friction versus the service or load parameter PIH&NRa where P is the nominal pressure on the contact, HS the hardness of the deforming surface, N the asperity density, R the mean radius of curvature of the asperities, and a is the standard deviation of their height distribution. In principle, any variation oft withp can be incorporated into the model; however, in this initial study we have used data on colloidal suspensions from the group at the Ecole Centrale de Lyon as well as examining the effect of functional relationships of somewhat greater complexity than a simple linear form. Results of the analysis indicate that variations in fj. are possible as the load is varied which depend on the statistical spread of behaviour at individual asperity contacts. The value of this analysis is that it attempts to combine the behaviour of films on the molecular scale with the topography of real engineering surfaces and so give an indication of the effects at the full-size or macro-scale that can be achieved by chemical or molecular surface engineering.

Gaussian process change point models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.