Reversible nanoscale switching of polytwin orientation in a ferroelectric thin film induced by a local electric field

The response to a local, tip-induced electric field of ferroelastic domains in thin polycrystalline lead zirconate titanate films with predominantly (110) orientation has been studied using Enhanced Piezoresponse Force Microscopy. Two types of reversible polytwin switching between well-defined orientations have been observed. When a-c domains are switched to other forms of a-c domains, the ferroelastic domain walls rotate in-plane by 109.5°, and when a-c domains are switched to c-c domains (or vice-versa), the walls rotate by 54.75°. © 2013 AIP Publishing LLC.

Reversible tuning of the wettability of carbon nanotube arrays: the effect of ultraviolet/ozone and vacuum pyrolysis treatments.

Among diverse types of synthetic materials, arrays of vertically aligned carbon nanotubes have attracted the most attention, mainly because of their exceptional mechanical, electrical, optical, and thermal properties. However, their wetting properties are yet to be understood. In this present study, oxygenated surface functional groups have been identified as a vital factor in controlling the wetting properties of carbon nanotube arrays. The results presented herein indeed show that a combination of ultraviolet/ozone and vacuum pyrolysis treatments can be used to vary the surface concentration of these functional groups such that the carbon nanotube array can be repeatedly switched between hydrophilic and hydrophobic.

Reversible CO2 absorption by the 6H perovskite Ba 4Sb2O9

A novel compound for carbon capture and storage (CCS) applications, the 6H perovskite Ba4Sb2O9, was found to be able to absorb CO2 through a chemical reaction at 873 K to form barium carbonate and BaSb2O6. This absorption was shown to be reversible through the regeneration of the original Ba4Sb 2O9 material upon heating above 1223 K accompanied by the release of CO2. A combined synchrotron X-ray diffraction, thermogravimetric, and microscopy study was carried out to characterize first the physical absorption properties and then to analyze the structural evolution and formation of phases in situ. Importantly, through subsequent carbonation and regeneration of the material over 100 times, it was shown that the combined absorption and regeneration reactions proceed without any significant reduction in the CO2 absorption capacity of the material. After 100 cycles the capacity of Ba4Sb2O9 was ∼0.1 g (CO 2)/g (sorbent), representing 73% of the total molar capacity. This is the first report of a perovskite-type material showing such good properties, opening the way for studies of new classes of inorganic oxide materials with stable and flexible chemical compositions and structures for applications in carbon capture. © 2013 American Chemical Society.

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.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

Models and algorithms for detection and tracking of coordinated groups

In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely using Markov Random Field (MRF) and repulsive forces. These can be combined together with a group structure transition model to create realistic evolving group models. We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time. ©2008 IEEE.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

Robust modelling of noisy ARMA signals

In this paper methods are developed for enhancement and analysis of autoregressive moving average (ARMA) signals observed in additive noise which can be represented as mixtures of heavy-tailed non-Gaussian sources and a Gaussian background component. Such models find application in systems such as atmospheric communications channels or early sound recordings which are prone to intermittent impulse noise. Markov Chain Monte Carlo (MCMC) simulation techniques are applied to the joint problem of signal extraction, model parameter estimation and detection of impulses within a fully Bayesian framework. The algorithms require only simple linear iterations for all of the unknowns, including the MA parameters, which is in contrast with existing MCMC methods for analysis of noise-free ARMA models. The methods are illustrated using synthetic data and noise-degraded sound recordings.

Bayesian learning of noisy Markov decision processes

This work addresses the problem of estimating the optimal value function in a Markov Decision Process from observed state-action pairs. We adopt a Bayesian approach to inference, which allows both the model to be estimated and predictions about actions to be made in a unified framework, providing a principled approach to mimicry of a controller on the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from theposterior distribution over the optimal value function. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.