Factor analysis based VTS discriminative adaptive training

Vector Taylor Series (VTS) model based compensation is a powerful approach for noise robust speech recognition. An important extension to this approach is VTS adaptive training (VAT), which allows canonical models to be estimated on diverse noise-degraded training data. These canonical model can be estimated using EM-based approaches, allowing simple extensions to discriminative VAT (DVAT). However to ensure a diagonal corrupted speech covariance matrix the Jacobian (loading matrix) relating the noise and clean speech is diagonalised. In this work an approach for yielding optimal diagonal loading matrices based on minimising the expected KL-divergence between the diagonal loading matrix and "correct" distributions is proposed. The performance of DVAT using the standard and optimal diagonalisation was evaluated on both in-car collected data and the Aurora4 task. © 2012 IEEE.

Model reductions for inference: generality of pairwise, binary, and planar factor graphs.

We offer a solution to the problem of efficiently translating algorithms between different types of discrete statistical model. We investigate the expressive power of three classes of model-those with binary variables, with pairwise factors, and with planar topology-as well as their four intersections. We formalize a notion of "simple reduction" for the problem of inferring marginal probabilities and consider whether it is possible to "simply reduce" marginal inference from general discrete factor graphs to factor graphs in each of these seven subclasses. We characterize the reducibility of each class, showing in particular that the class of binary pairwise factor graphs is able to simply reduce only positive models. We also exhibit a continuous "spectral reduction" based on polynomial interpolation, which overcomes this limitation. Experiments assess the performance of standard approximate inference algorithms on the outputs of our reductions.

Measurement of filling-factor-dependent magnetophonon resonances in graphene using Raman spectroscopy

We perform polarization-resolved Raman spectroscopy on graphene in magnetic fields up to 45 T. This reveals a filling-factor-dependent, multicomponent anticrossing structure of the Raman G peak, resulting from magnetophonon resonances between magnetoexcitons and E2g phonons. This is explained with a model of Raman scattering taking into account the effects of spatially inhomogeneous carrier densities and strain. Random fluctuations of strain-induced pseudomagnetic fields lead to increased scattering intensity inside the anticrossing gap, consistent with the experiments. © 2013 American Physical Society.

2.1 GHz MOS mixer linearisation approach for IIP2 enhancement using multiple factor compensation

An integrated downconversion CMOS mixer incorporating a comprehensive compensation scheme is presented which aims to minimise second-order intermodulation distortion (IMD2). Unlike previously reported IMD2 calibration schemes which tune only one nonlinear factor at a time, the presented solution allows simultaneous adjustment of several different factors thus achieving a better compensation. The mixer has been implemented in UMC 0.18 μm CMOS to verify the proposed scheme and for comparison with alternative compensation methods. Measurements show that the solution described can improve the input intercept point (IIP2) by over 20 dB while maintaining good amplification and noise performance. IMD2 calibration results are presented and show useful advantages over other approaches. To the best of the authors' knowledge, this scheme for IMD2 calibration has not been previously reported. © The Institution of Engineering and Technology 2013.

Factor analysis and structural equation modelling of sustainable behaviour in contaminated land remediation

Contaminated land remediation has traditionally been viewed as sustainable practice because it reduces urban sprawl and mitigates risks to human being and the environment. However, in an emerging green and sustainable remediation (GSR) movement, remediation practitioners have increasingly recognized that remediation operations have their own environmental footprint. The GSR calls for sustainable behaviour in the remediation industry, for which a series of white papers and guidance documents have been published by various government agencies and professional organizations. However, the relationship between the adoption of such sustainable behaviour and its underlying driving forces has not been studied. This study aims to contribute to sustainability science by rendering a better understanding of what drives organizational behaviour in adopting sustainable practices. Factor analysis (FA) and structural equation modelling (SEM) were used to investigate the relationship between sustainable practices and key factors driving these behaviour changes in the remediation field. A conceptual model on sustainability in the environmental remediation industry was developed on the basis of stakeholder and institutional theories. The FA classified sustainability considerations, institutional promoting and impeding forces, and stakeholder's influence. Subsequently the SEM showed that institutional promoting forces had significant positive effects on adopting sustainability measures, and institutional impeding forces had significant negative effects. Stakeholder influences were found to have only marginal direct effect on the adoption of sustainability; however, they exert significant influence on institutional promoting forces, thus rendering high total effect (i.e. direct effect plus indirect effect) on the adoption of sustainability. This study suggests that sustainable remediation represents an advanced sustainable practice, which may only be fully endorsed by both internal and external stakeholders after its regulatory, normative and cognitive components are institutionalized. © 2014 Elsevier Ltd. All rights reserved.