A reversible infinite HMM using normalised random measures

Copyright 2014 by the author(s). We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.

Is there a preferred classifier for operational thematic mapping?

The importance of properly exploiting a classifier's inherent geometric characteristics when developing a classification methodology is emphasized as a prerequisite to achieving near optimal performance when carrying out thematic mapping. When used properly, it is argued that the long-standing maximum likelihood approach and the more recent support vector machine can perform comparably. Both contain the flexibility to segment the spectral domain in such a manner as to match inherent class separations in the data, as do most reasonable classifiers. The choice of which classifier to use in practice is determined largely by preference and related considerations, such as ease of training, multiclass capabilities, and classification cost. © 1980-2012 IEEE.

Hybrids of carbon nanotube forests and gold nanoparticles for improved surface plasmon manipulation.

We report the fabrication and characterization of hybrids of vertically-aligned carbon nanotube forests and gold nanoparticles for improved manipulation of their plasmonic properties. Raman spectroscopy of nanotube forests performed at the separation area of nanotube-nanoparticles shows a scattering enhancement factor of the order of 1 × 10(6). The enhancement is related to the plasmonic coupling of the nanoparticles and is potentially applicable in high-resolution scanning near-field optical microscopy, plasmonics, and photovoltaics.

A derivation of the asymptotic random-coding prefactor

This paper studies the subexponential prefactor to the random-coding bound for a given rate. Using a refinement of Gallager's bounding techniques, an alternative proof of a recent result by Altuǧ and Wagner is given, and the result is extended to the setting of mismatched decoding. © 2013 IEEE.

Carbon nanotube forests growth using catalysts from atomic layer deposition

We have grown carbon nanotubes using Fe and Ni catalyst films deposited by atomic layer deposition. Both metals lead to catalytically active nanoparticles for growing vertically aligned nanotube forests or carbon fibres, depending on the growth conditions and whether the substrate is alumina or silica. The resulting nanotubes have narrow diameter and wall number distributions that are as narrow as those grown from sputtered catalysts. The state of the catalyst is studied by in-situ and ex-situ X-ray photoemission spectroscopy. We demonstrate multi-directional nanotube growth on a porous alumina foam coated with Fe prepared by atomic layer deposition. This deposition technique can be useful for nanotube applications in microelectronics, filter technology, and energy storage. © 2014 AIP Publishing LLC.