Elliptical slice sampling

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.

Uplift resistance of buried pipelines at low cover-diameter ratios

Reliable estimates for the maximum available uplift resistance from the backfill soil are essential to prevent upheaval buckling of buried pipelines. The current design code DNV RP F110 does not offer guidance on how to predict the uplift resistance when the cover:pipe diameter (H/D) ratio is less than 2. Hence the current industry practice is to discount the shear contribution from uplift resitance for design scenarios with H/D ratios less than 1. The necessity of this extra conservatism is assessed through a series of full-scale and centrifuge tests, 21 in total, at the Schofield Centre, University of Cambridge. Backfill types include saturated loose sand, saturated dense sand and dry gravel. Data revealed that the Vertical Slip Surface Model remains applicable for design scenarios in loose sand, dense sand and gravel with H/D ratios less than 1, and that there is no evidence that the contribution from shear should be ignored at these low H/D ratios. For uplift events in gravel, the shear component seems reliable if the cover is more than 1-2 times the average particle size (D50), and more research effort is currenty being carried out to verify this conclusion. Strain analysis from the Particle Image Velocimetry (PIV) technique proves that the Vertical Slip Surface Model is a good representation of the true uplift deformation mechanism in loose sand at H/D ratios between 0.5 and 3.5. At very low H/D ratios (H/D < 0.5), the deformation mechanism is more wedge-like, but the increased contribution from soil weight is likely to be compensated by the reduced shear contributions. Hence the design equation based on the Vertical Slip Surface Model still produces good estimates for the maximum available uplift resistance. The evolution of shear strain field from PIV analysis provides useful insight into how uplift resistance is mobilized as the uplift event progresses. Copyright 2010, Offshore Technology Conference.

Phase transitions in planar magnetic nanostructures

Using numerical micromagnetics we have studied the ground state magnetization distribution of square planar ferromagnetic elements ("nanostructures"). As the element size is reduced from 250 to 2 nm at constant thickness (2-35 nm), we find that the magnetization distribution undergoes up to three phase transitions involving as many as three different near single domain states. One of these phase transitions is analogous to the reorientation phase transition observed in continuous ultrathin magnetic films. © 1998 American Institute of Physics.

Magnetic switching and anisotropy in nanomagnets: the influence of size and shape

Arrays of nanomagnets were fabricated out of Ni80Fe14Mo5 in the lateral size range 500-30nm and the thickness range 3-20nm. Elliptical, triangular, square, pentagonal and circular geometries were all considered. The magnetic properties of these nanomagnets were probed rapidly and non-invasively using a high sensitivity magneto-optical method.

Controlling magnetic ordering in coupled nanomagnet arrays

We have fabricated using high-resolution electron beam lithography circular magnetic particles (nanomagnets) of diameter 60 nm and thickness 7 nm out of the common magnetic alloy supermalloy. The nanomagnets were arranged on rectangular lattices of different periods. A high-sensitivity magneto-optical method was used to measure the magnetic properties of each lattice. We show experimentally how the magnetic properties of a lattice of nanomagnets can be profoundly changed by the magnetostatic interactions between nanomagnets within the lattice. We find that simply reducing the lattice spacing in one direction from 180 nm down to 80 nm (leaving a gap of only 20 nm between edges) causes the lattice to change from a magnetically disordered state to an ordered state. The change in state is accompanied by a peak in the magnetic susceptibility. We show that this is analogous to the paramagnetic-ferromagnetic phase transition which occurs in conventional magnetic materials, although low-dimensionality and kinetic effects must also be considered.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.