Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Simultaneous measurement for liquid level and refractive index based on all-fiber modal interferometer

A simple fiber sensor capable of simultaneous measurement of liquid level and refractive index (RI) is proposed and experimentally demonstrated. The sensing head is an all-fiber modal interferometer manufactured by splicing an uncoated single-mode fiber with two short sections of multimode fiber. The interference pattern experiences blue shift along with an increase of axial strain and surrounding RI. Owing to the participation of multiple cladding modes with different sensitivities, the height and RI of the liquid could be simultaneously measured by monitoring two dips of the transmission spectrum. Experimental results show that the liquid level and RI sensitivities of the two dips are 245.7 pm/mm, -38 nm/RI unit (RIU), and 223.7 pm/mm, -62 nm/RIU, respectively. The approach has distinctive advantages of easy fabrication, low cost, and high sensitivity for liquid level detection with the capability of distinguishing the RI variation simultaneously. © 2013 Copyright Taylor and Francis Group, LLC.

Inference of helicopter airframe condition

The goal of this paper is to model normal airframe conditions for helicopters in order to detect changes. This is done by inferring the flying state using a selection of sensors and frequency bands that are best for discriminating between different states. We used non-linear state-space models (NLSSM) for modelling flight conditions based on short-time frequency analysis of the vibration data and embedded the models in a switching framework to detect transitions between states. We then created a density model (using a Gaussian mixture model) for the NLSSM innovations: this provides a model for normal operation. To validate our approach, we used data with added synthetic abnormalities which was detected as low-probability periods. The model of normality gave good indications of faults during the flight, in the form of low probabilities under the model, with high accuracy (>92 %). © 2013 IEEE.

Impact of high microwave power on hydrogen impurity trapping in nanocrystalline diamond films grown with simultaneous nitrogen and oxygen addition into methane/hydrogen plasma

In this work, we study for the first time the influence of microwave power higher than 2.0 kW on bonded hydrogen impurity incorporation (form and content) in nanocrystalline diamond (NCD) films grown in a 5 kW MPCVD reactor. The NCD samples of different thickness ranging from 25 to 205 μm were obtained through a small amount of simultaneous nitrogen and oxygen addition into conventional about 4% methane in hydrogen reactants by keeping the other operating parameters in the same range as that typically used for the growth of large-grained polycrystalline diamond films. Specific hydrogen point defect in the NCD films is analyzed by using Fourier-transform infrared (FTIR) spectroscopy. When the other operating parameters are kept constant (mainly the input gases), with increasing of microwave power from 2.0 to 3.2 kW (the pressure was increased slightly in order to stabilize the plasma ball of the same size), which simultaneously resulting in the rise of substrate temperature more than 100 °C, the growth rate of the NCD films increases one order of magnitude from 0.3 to 3.0 μm/h, while the content of hydrogen impurity trapped in the NCD films during the growth process decreases with power. It has also been found that a new H related infrared absorption peak appears at 2834 cm-1 in the NCD films grown with a small amount of nitrogen and oxygen addition at power higher than 2.0 kW and increases with power higher than 3.0 kW. According to these new experimental results, the role of high microwave power on diamond growth and hydrogen impurity incorporation is discussed based on the standard growth mechanism of CVD diamonds using CH4/H2 gas mixtures. Our current experimental findings shed light into the incorporation mechanism of hydrogen impurity in NCD films grown with a small amount of nitrogen and oxygen addition into methane/hydrogen plasma.

Simultaneous optimization of colloidal stability and interfacial charge transfer efficiency in photocatalytic Pt/CdS nanocrystals

Colloidal stability and efficient interfacial charge transfer in semiconductor nanocrystals are of great importance for photocatalytic applications in aqueous solution since they provide long-term functionality and high photocatalytic activity, respectively. However, colloidal stability and interfacial charge transfer efficiency are difficult to optimize simultaneously since the ligand layer often acts as both a shell stabilizing the nanocrystals in colloidal suspension and a barrier reducing the efficiency of interfacial charge transfer. Here, we show that, for cysteine-coated, Pt-decorated CdS nanocrystals and Na2SO3 as hole scavenger, triethanolamine (TEOA) replaces the original cysteine ligands in situ and prolongs the highly efficient and steady H2 evolution period by more than a factor of 10. It is shown that Na2SO3 is consumed during H2 generation while TEOA makes no significant contribution to the H2 generation. An apparent quantum yield of 31.5%, a turnover frequency of 0.11 H2/Pt/s, and an interfacial charge transfer rate faster than 0.3 ps were achieved in the TEOA stabilized system. The short length, branched structure and weak binding of TEOA to CdS as well as sufficient free TEOA in the solution are the keys to enhancing colloidal stability and maintaining efficient interfacial charge transfer at the same time. Additionally, TEOA is commercially available and cheap, and we anticipate that this approach can be widely applied in many photocatalytic applications involving colloidal nanocrystals.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

Improved inference in the evaluation of mutual fund performance using bootstrap methods

Two new methodologies are introduced to improve inference in the evaluation of mutual fund performance against benchmarks. First, the benchmark models are estimated using panel methods with both fund and time effects. Second, the non-normality of individual mutual fund returns is accounted for by using panel bootstrap methods. We also augment the standard benchmark factors with fund-specific characteristics, such as fund size. Using a dataset of UK equity mutual fund returns, we find that fund size has a negative effect on the average fund manager’s benchmark-adjusted performance. Further, when we allow for time effects and the non-normality of fund returns, we find that there is no evidence that even the best performing fund managers can significantly out-perform the augmented benchmarks after fund management charges are taken into account.