On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.

A theory of slow feature analysis for transformation-based input signals with an application to complex cells

We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as the orientation and frequency tuning, are in good agreement with previous simulations of complex cells in primary visual cortex (Berkes and Wiskott, 2005). The theory suggests that side and end stopping can be interpreted as a weak breaking of translation invariance. Direction selectivity is also discussed. © 2011 Massachusetts Institute of Technology.

Angle-of-attack effects on counter-rotating propellers at take-off

Due to their potential for significant fuel consumption savings, Counter-Rotating Open Rotors (CRORs) are currently being considered as an alternative to high-bypass turbofans. When CRORs are mounted on an aircraft, several 'installation effects' arise which are not present when the engine is operated in isolation. This paper investigates how flow features arising from one such effect - The angle-of-attack of the engine centre-line relative to the oncoming flow - can influence the design of CROR engines. Three-dimensional full-annulus unsteady CFD simulations are used to predict the time-varying flow field experienced by each rotor and emphasis is put on the interaction of the frontrotor wake and tip vortex with the rear-rotor. A parametric study is presented that quantifies the rotorrotor interaction as a function of the angle-of-attack. It is shown that angle-of-attack operation significantly changes the flow field and the unsteady lift on both rotors. In particular, a frequency analysis shows that the unsteady lift exhibits sidebands around the rotor-rotor interaction frequencies. Further, a non-linear increase in the total rear-rotor tip unsteadiness is observed for moderate and high angles-of-attack. The results presented in this paper demonstrate that common techniques used to mitigate CROR noise, such as modifying the rotor-rotor axial spacing and rear-rotor crop, can not be applied correctly unless angle-of-attack effects are taken into account. Copyright © 2012 by ASME.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.

Mode-locking using right-angle waveguide, based nanotube saturable absorber

We report passive mode-locking of an Er-doped fiber laser using carbon nanotubes deposited on the facet of a right-angle optical waveguide. © 2013 IEEE.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.

Aerodynamic design of high end wall angle turbine stages-part I: Methodology development

A design methodology is presented for turbines in an annulus with high end wall angles. Such stages occur where large radial offsets between the stage inlet and stage outlet are required, for example in the first stage of modern low pressure turbines, and are becoming more prevalent as bypass ratios increase. The turbine vanes operate within s-shaped ducts which result in meridional curvature being of a similar magnitude to the bladeto-blade curvature. Through a systematic series of idealized computational cases, the importance of two aspects of vane design are shown. First, the region of peak end wall meridional curvature is best located within the vane row. Second, the vane should be leant so as to minimize spanwise variations in surface pressure-this condition is termed "ideal lean." This design philosophy is applied to the first stage of a low pressure turbine with high end wall angles. © 2014 by ASME.

Aerodynamic design of high end wall angle turbine stages- part II: Experimental verification

An experimental investigation of a turbine stage featuring very high end wall angles is presented. The initial turbine design did not achieve a satisfactory performance and the difference between the design predictions and the test results was traced to a large separated region on the rear suction-surface. To improve the agreement between computational fluid dynamics (CFD) and experiment, it was found necessary to modify the turbulence modeling employed. The modified CFD code was then used to redesign the vane, and the changes made are described. When tested, the performance of the redesigned vane was found to have much closer agreement with the predictions than the initial vane. Finally, the flowfield and performance of the redesigned stage are compared to a similar turbine, designed to perform the same duty, which lies in an annulus of moderate end wall angles. A reduction in stage efficiency of at least 2.4% was estimated for the very high end wall angle design. © 2014 by ASME.

Tandem system adaptation using multiple linear feature transforms

Adaptation to speaker and environment changes is an essential part of current automatic speech recognition (ASR) systems. In recent years the use of multi-layer percpetrons (MLPs) has become increasingly common in ASR systems. A standard approach to handling speaker differences when using MLPs is to apply a global speaker-specific constrained MLLR (CMLLR) transform to the features prior to training or using the MLP. This paper considers the situation when there are both speaker and channel, communication link, differences in the data. A more powerful transform, front-end CMLLR (FE-CMLLR), is applied to the inputs to the MLP to represent the channel differences. Though global, these FE-CMLLR transforms vary from time-instance to time-instance. Experiments on a channel distorted dialect Arabic conversational speech recognition task indicates the usefulness of adapting MLP features using both CMLLR and FE-CMLLR transforms. © 2013 IEEE.

Numerical study on hydrate bearing sediments during gas production

The fully coupled methane hydrate model developed in Cambridge was adopted in this numerical study on gas production trial at the Eastern Nankai Trough, Japan 2013. Based on the latest experimental data of hydrate soil core samples, the clay parameters at Eastern Nankai site were successfully calibrated. With updated clay parameters and site geometry, a 50 days gas production trail was numerically simulated in FLAC2D. The geomechanical behaviour of hydrate bearing sediments under 3 different depressurization strategies were explored and discussed. The results from both axisymmetrical and plane-strain models suggest, the slope of the seabed only affects mechanical properties while no significant impact on the dissociation, temperature and pore pressure. For mechanical deformation after PT recovery, there are large settlements above the perforation zone and small uplift underneath the production zone. To validate the fully coupled model, numerical simulation with finer mesh in the hydrate production zone was carried out. The simulation results suggest good agreement between our model and JOE's results on history matching of gas and water production during trial. Parameter sensitivity of gas production is also investigated and concluded the sea water salinity is a dominant factor for gas production.