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.

Free-space optoelectronic switching cores with MPLS for SANs over WDM ring networks

With increasing demands on storage devices in the modern communication environment, the storage area network (SAN) has evolved to provide a direct connection allowing these storage devices to be accessed efficiently. To optimize the performance of a SAN, a three-stage hybrid electronic/optical switching node architecture based on the concept of a MPLS label switching mechanism, aimed at serving as a multi-protocol label switching (MPLS) ingress label edge router (LER) for a SAN-enabled application, has been designed. New shutter-based free-space multi-channel optical switching cores are employed as the core switch fabric to solve the packet contention and switching path conflict problems. The system-level node architecture design constraints are evaluated through self-similar traffic sourced from real gigabit Ethernet network traces and storage systems. The extension performance of a SAN over a proposed WDM ring network, aimed at serving as an MPLS-enabled transport network, is also presented and demonstrated. © 2012 OSA.

Transcription of multi-genre media archives using out-of-domain data

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15% over a PLP baseline, 9% over in-domain tandem features and 8% over the best out-of-domain tandem features. © 2012 IEEE.

FEC-free 50 m 1.5 Gb/s plastic optical fibre link using CAP modulation for home networks

LED-based carrierless amplitude and phase modulation is investigated for a multi-gigabit plastic optical fibre link. An FPGA-based 1.5 Gbit/s error free transmission over 50 m standard SI-POF using CAP64 is achieved, providing 2.9 dB power margin without forward error correction. © 2012 OSA.

FEC-free 50 m 1.5 Gb/s plastic optical fibre link using CAP modulation for home networks

LED-based carrierless amplitude and phase modulation is investigated for a multi-gigabit plastic optical fibre link. An FPGA-based 1.5 Gbit/s error free transmission over 50 m standard SI-POF using CAP64 is achieved, providing 2.9 dB power margin without forward error correction. © 2012 Optical Society of America.

Multi-walled carbon nanotube conductivity enhancement and band gap widening via rapid pulsed thermal annealing

Herein we report on the transport characteristics of rapid pulsed vacuum-arc thermally annealed, individual and network multi-walled carbon nanotubes. Substantially reduced defect densities (by at least an order of magnitude), measured by micro-Raman spectroscopy, and were achieved by partial reconstruction of the bamboo-type defects during thermal pulsing compared with more traditional single-pulse thermal annealing. Rapid pulsed annealed processed networks and individual multi-walled nanotubes showed a consistent increase in conductivity (of over a factor of five at room temperature), attributed to the reduced number density of resistive axial interfaces and, in the case of network samples, the possible formation of structural bonds between crossed nanotubes. Compared to the highly defective as-grown nanotubes, the pulsed annealed samples exhibited reduced temperature sensitivity in their transport characteristics signifying the dominance of scattering events from structural defects. Transport measurements in the annealed multi-walled nanotubes deviated from linear Ohmic, typically metallic, behavior to an increasingly semiconducting-like behavior attributed to thermally induced axial strains. Rapid pulsed annealed networks had an estimated band gap of 11.26 meV (as-grown; 6.17 meV), and this observed band gap enhancement was inherently more pronounced for individual nanotubes compared with the networks most likely attributed to mechanical pinning effect of the probing electrodes which possibly amplifies the strain induced band gap. In all instances the estimated room temperature band gaps increased by a factor of two. The gating performance of back-gated thin-film transistor structures verified that the observed weak semiconductivity (p-type) inferred from the transport characteristic at room temperature. © 2014 Copyright Taylor & Francis Group, LLC.