Additive Gaussian processes

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.

Analysis of defect-related localized emission processes in InGaN/GaN-based LEDs

This paper reports an extensive analysis of the defect-related localized emission processes occurring in InGaN/GaN-based light-emitting diodes (LEDs) at low reverse- and forward-bias conditions. The analysis is based on combined electrical characterization and spectrally and spatially resolved electroluminescence (EL) measurements. Results of this analysis show that: (i) under reverse bias, LEDs can emit a weak luminescence signal, which is directly proportional to the injected reverse current. Reverse-bias emission is localized in submicrometer-size spots; the intensity of the signal is strongly correlated to the threading dislocation (TD) density, since TDs are preferential paths for leakage current conduction. (ii) Under low forward-bias conditions, the intensity of the EL signal is not uniform over the device area. Spectrally resolved EL analysis of green LEDs identifies the presence of localized spots emitting at 600 nm (i.e., in the yellow spectral region), whose origin is ascribed to localized tunneling occurring between the quantum wells and the barrier layers of the diodes, with subsequent defect-assisted radiative recombination. The role of defects in determining yellow luminescence is confirmed by the high activation energy of the thermal quenching of yellow emission (Ea =0.64&eV). © 2012 IEEE.

Paradoxical evidence integration in rapid decision processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions. © 2012 Rüter et al.

State-space inference and learning with Gaussian processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors.

Robust filtering and smoothing with gaussian processes

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail. © 2011 IEEE.

Partially Observable Markov Decision Processes with continuous observations for dialogue management

This work shows how a dialogue model can be represented as a Partially Observable Markov Decision Process (POMDP) with observations composed of a discrete and continuous component. The continuous component enables the model to directly incorporate a confidence score for automated planning. Using a testbed simulated dialogue management problem, we show how recent optimization techniques are able to find a policy for this continuous POMDP which outperforms a traditional MDP approach. Further, we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring, including confidence score information. Experiments on the testbed system show significant improvements for several example handcrafted dialogue managers across a range of operating conditions.

Bayesian correlated clustering to integrate multiple datasets.

MOTIVATION: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. RESULTS: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI's performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques-as well as to non-integrative approaches-demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.

A high performance oxygen storage material for chemical looping processes with CO2 capture

Chemical looping combustion (CLC) is a novel combustion technology that involves cyclic reduction and oxidation of oxygen storage materials to provide oxygen for the combustion of fuels to CO2 and H2O, whilst giving a pure stream of CO2 suitable for sequestration or utilisation. Here, we report a method for preparing of oxygen storage materials from layered double hydroxides (LDHs) precursors and demonstrate their applications in the CLC process. The LDHs precursor enables homogeneous mixing of elements at the molecular level, giving a high degree of dispersion and high-loading of active metal oxide in the support after calcination. Using a Cu-Al LDH precursor as a prototype, we demonstrate that rational design of oxygen storage materials by material chemistry significantly improved the reactivity and stability in the high temperature redox cycles. We discovered that the presence of sodium-containing species were effective in inhibiting the formation of copper aluminates (CuAl2O4 or CuAlO 2) and stabilising the copper phase in an amorphous support over multiple redox cycles. A representative nanostructured Cu-based oxygen storage material derived from the LDH precursor showed stable gaseous O2 release capacity (∼5 wt%), stable oxygen storage capacity (∼12 wt%), and stable reaction rates during reversible phase changes between CuO-Cu 2O-Cu at high temperatures (800-1000 °C). We anticipate that the strategy can be extended to manufacture a variety of metal oxide composites for applications in novel high temperature looping cycles for clean energy production and CO2 capture. © The Royal Society of Chemistry 2013.

Bayesian non-parametrics and the probabilistic approach to modelling.

Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.

Er3+ excitation and up-conversion processes in y 2-xErxSi2O7 films for planar optical amplifiers

Structural and optical properties of Y2-xErxSi 2O7 thin films have been studied. For higher Er content mechanisms related to Er-Er interactions increase optical efficiency. Moreover the influence of up-conversion has been estimated. ©2009 IEEE.