In situ observations of the atomistic mechanisms of Ni catalyzed low temperature graphene growth.

The key atomistic mechanisms of graphene formation on Ni for technologically relevant hydrocarbon exposures below 600 °C are directly revealed via complementary in situ scanning tunneling microscopy and X-ray photoelectron spectroscopy. For clean Ni(111) below 500 °C, two different surface carbide (Ni2C) conversion mechanisms are dominant which both yield epitaxial graphene, whereas above 500 °C, graphene predominantly grows directly on Ni(111) via replacement mechanisms leading to embedded epitaxial and/or rotated graphene domains. Upon cooling, additional carbon structures form exclusively underneath rotated graphene domains. The dominant graphene growth mechanism also critically depends on the near-surface carbon concentration and hence is intimately linked to the full history of the catalyst and all possible sources of contamination. The detailed XPS fingerprinting of these processes allows a direct link to high pressure XPS measurements of a wide range of growth conditions, including polycrystalline Ni catalysts and recipes commonly used in industrial reactors for graphene and carbon nanotube CVD. This enables an unambiguous and consistent interpretation of prior literature and an assessment of how the quality/structure of as-grown carbon nanostructures relates to the growth modes.

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.