Occlusive Components Analysis

We study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learnt from an unlabelled set of images in which objects occlude one another. Exact maximum-likelihood learning is intractable. However, we show that tractable approximations to Expectation Maximization (EM) can be found if the training images each contain only a small number of objects on average. In numerical experiments it is shown that these approximations recover the correct set of object parameters. Experiments on a novel version of the bars test using colored bars, and experiments on more realistic data, show that the algorithm performs well in extracting the generating causes. Experiments based on the standard bars benchmark test for object learning show that the algorithm performs well in comparison to other recent component extraction approaches. The model and the learning algorithm thus connect research on occlusion with the research field of multiple-causes component extraction methods.

First-principles energetics of water: a many-body analysis

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for solid, liquid and cluster forms of water. We use a many-body separation of the total energy into its 1-body, 2-body (2B) and beyond-2-body (B2B) components to analyze the deficiencies of two popular DFT approximations. We show how machine-learning methods make this analysis possible for ice structures as well as for water clusters. We find that the crucial energy balance between compact and extended geometries can be distorted by 2B and B2B errors, and that both types of first-principles error are important.

Machine learning for predictive condensed-phase simulation

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.

Gaussian Processes for Machine Learning

The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning. It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs.

Empathy for pain involves the affective but not sensory components of pain.

Our ability to have an experience of another's pain is characteristic of empathy. Using functional imaging, we assessed brain activity while volunteers experienced a painful stimulus and compared it to that elicited when they observed a signal indicating that their loved one--present in the same room--was receiving a similar pain stimulus. Bilateral anterior insula (AI), rostral anterior cingulate cortex (ACC), brainstem, and cerebellum were activated when subjects received pain and also by a signal that a loved one experienced pain. AI and ACC activation correlated with individual empathy scores. Activity in the posterior insula/secondary somatosensory cortex, the sensorimotor cortex (SI/MI), and the caudal ACC was specific to receiving pain. Thus, a neural response in AI and rostral ACC, activated in common for "self" and "other" conditions, suggests that the neural substrate for empathic experience does not involve the entire "pain matrix." We conclude that only that part of the pain network associated with its affective qualities, but not its sensory qualities, mediates empathy.

Facets of prior experience and the effectiveness of inclusive design

Research in inclusive design has shown the importance of prior experience for the usability of interactive products. Prior experience, however, is an ill-defined and inconsistently used construct. A number of different definitions and operationalisations of experience exist, but the differing power of these operationalisations to predict the usability of products for older users has rarely been investigated systematically. This study seeks to fill that gap. It is argued that the construct of experience has at least three components. It is proposed that two of these components, exposure and competence, are directly relevant for the current discussion about prior experience in inclusive design and that they can predict to different degrees the usability of a product for older users. In an empirical study, these facets of expertise are each operationalised on three levels of specificity and their impact on usability is assessed. The results show that measures of competence predict usability variables more strongly than measures of exposure and that levels of medium and high specificity are the best predictors. The application of inclusive design principles to a redesigned version of a ticket vending machine-although not resulting in a difference of overall usability-changed the impact of prior experience on usability measures implying an enhanced inclusiveness of the redesign with regard to prior experience. The implications of these findings for the effectiveness of inclusive design for older users are discussed. © 2013 Springer-Verlag Berlin Heidelberg.

Design and Performance Analysis of a 2.5 MW-Class HTS Synchronous Motor for Ship Propulsion

The development of cryogenic technology and high temperature superconducting (HTS) materials has seen continued interest worldwide in the development of HTS machines since the late 1980s. In this paper, the authors present a conceptual design of a 2.5 MW class synchronous motor. The structure of the motor is specified and the motor performance is analyzed via a three-dimensional model using the finite element method (FEM). Rotor optimization is carried out to decrease the harmonic components in the air gap field generated by HTS tapes. Based on the results of this 3D simulation, the determination of the operating conditions and load angle is discussed with consideration to the HTS material properties. The economic viability of air-core and iron-core designs is compared. The results show that this type of HTS machine has the potential to achieve an economic, efficient and effective machine design, which operates at a low load angle, and this design process provides a practical way to simulate and analyze the performance of such machines.

Detailed thermal modelling of a tubular linear machine for marine renewable generation

A detailed lumped-parameter thermal model is presented for a tubular linear machine that has been designed for use in a marine environment. The model has been developed for a static machine, the worst-case thermal scenario, and is used to establish a rating for the machine. The model has been validated against a large range of experimental tests and shows good correlation to both steady-state and transient experimental results. The model was constructed from a mostly theoretical basis with very little calibration, suggesting that the techniques used are applicable in a more general sense. © 2013 IEEE.

Autoignition of alcohols and ethers in a rapid compression machine

The autoignition characteristics of methanol, ethanol and MTBE (methyl tert-butyl ether) have been investigated in a rapid compression machine at pressures in the range 20-40 atm and temperatures within 750-1000 K. All three oxygenated fuels tested show higher autoignition temperatures than paraffins, a trend consistent with the high octane number of these fuels. The autoignition delay time for methanol was slightly lower than predicted values using reported reaction mechanisms. However, the experimental and measured values for the activation energy are in very good agreement around 44 kcal/mol. The measured activation energy for ethanol autoignition is in good agreement with previous shock tube results (31 kcal/mol), although ignition times predicted by the shock tube correlation are a factor of three lower than the measured values. The measured activation energy for MTBE, 41.4 kcal/mol, was significantly higher than the value previously observed in shock tubes (28.1 kcal/mol). The onset of preignition, characterized by a slow energy release prior to early ignition was observed in some instances. Possible reasons for these ocurrences are discussed. © Copyright 1993 Society of Automotive engineers, Inc.

N-gram posterior probability confidence measures for statistical machine translation: An empirical study

We report an empirical study of n-gram posterior probability confidence measures for statistical machine translation (SMT). We first describe an efficient and practical algorithm for rapidly computing n-gram posterior probabilities from large translation word lattices. These probabilities are shown to be a good predictor of whether or not the n-gram is found in human reference translations, motivating their use as a confidence measure for SMT. Comprehensive n-gram precision and word coverage measurements are presented for a variety of different language pairs, domains and conditions. We analyze the effect on reference precision of using single or multiple references, and compare the precision of posteriors computed from k-best lists to those computed over the full evidence space of the lattice. We also demonstrate improved confidence by combining multiple lattices in a multi-source translation framework. © 2012 The Author(s).

Generalized power method for sparse principal component analysis

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computationally intractable, we rewrite them into the form of an optimization program involving maximization of a convex function on a compact set. The dimension of the search space is decreased enormously if the data matrix has many more columns (variables) than rows. We then propose and analyze a simple gradient method suited for the task. It appears that our algorithm has best convergence properties in the case when either the objective function or the feasible set are strongly convex, which is the case with our single-unit formulations and can be enforced in the block case. Finally, we demonstrate numerically on a set of random and gene expression test problems that our approach outperforms existing algorithms both in quality of the obtained solution and in computational speed. © 2010 Michel Journée, Yurii Nesterov, Peter Richtárik and Rodolphe Sepulchre.

100 Gb/s uncooled WDM system using conventional WDM components and advanced receiver signal processing

We demonstrate an uncooled WDM system using standard WDM components and receiver signal processing, with a different number of receivers to transmitters, to allow wide temperature drift of the transmitter lasers. A 100 Gb/s 8-wavelength demonstrator has been developed, which proves the feasibility of the approach over 25 km of SMF. © 2012 OSA.

Sensorless commutation of a two-phase linear machine for marine renewable power generation

A sensorless scheme is presented for a two-phase permanent-magnet linear machine targeted for use in marine wave-power generation. This is a field where system reliability is a key concern. The scheme is able to extract the effective inductance and back-emf of the machine's phases simultaneously from measurements of the current ripple present on the power electronic converter. These measurements can then be used to estimate position. An enhancement to the scheme in the presence of spatially-varying mutual inductance between phases allows more accurate and reliable tracking from indutance-based measurements than would otherwise be expected. This scheme is able to operate at any speed including, critically, when stationary. Experimental results show promise for the scheme, although some work to reduce the level of noise would be desirable. © 2013 IEEE.