An approach to statistical lip modelling for speaker identification via chromatic feature extraction

This paper presents a novel technique for the tracking of moving lips for the purpose of speaker identification. In our system, a model of the lip contour is formed directly from chromatic information in the lip region. Iterative refinement of contour point estimates is not required. Colour features are extracted from the lips via concatenated profiles taken around the lip contour. Reduction of order in lip features is obtained via principal component analysis (PCA) followed by linear discriminant analysis (LDA). Statistical speaker models are built from the lip features based on the Gaussian mixture model (GMM). Identification experiments performed on the M2VTS¹ database, show encouraging results

Time-dependent fractional advection–diffusion equations by an implicit MLS meshless method

Recently, many new applications in engineering and science are governed by a series of fractional partial differential equations (FPDEs). Unlike the normal partial differential equations (PDEs), the differential order in a FPDE is with a fractional order, which will lead to new challenges for numerical simulation, because most existing numerical simulation techniques are developed for the PDE with an integer differential order. The current dominant numerical method for FPDEs is Finite Difference Method (FDM), which is usually difficult to handle a complex problem domain, and also hard to use irregular nodal distribution. This paper aims to develop an implicit meshless approach based on the moving least squares (MLS) approximation for numerical simulation of fractional advection-diffusion equations (FADE), which is a typical FPDE. The discrete system of equations is obtained by using the MLS meshless shape functions and the meshless strong-forms. The stability and convergence related to the time discretization of this approach are then discussed and theoretically proven. Several numerical examples with different problem domains and different nodal distributions are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling and simulation of the FADE.

Evaluating multivariate volatility forecasts : how effective are statistical and economic loss functions?

Multivariate volatility forecasts are an important input in many financial applications, in particular portfolio optimisation problems. Given the number of models available and the range of loss functions to discriminate between them, it is obvious that selecting the optimal forecasting model is challenging. The aim of this thesis is to thoroughly investigate how effective many commonly used statistical (MSE and QLIKE) and economic (portfolio variance and portfolio utility) loss functions are at discriminating between competing multivariate volatility forecasts. An analytical investigation of the loss functions is performed to determine whether they identify the correct forecast as the best forecast. This is followed by an extensive simulation study examines the ability of the loss functions to consistently rank forecasts, and their statistical power within tests of predictive ability. For the tests of predictive ability, the model confidence set (MCS) approach of Hansen, Lunde and Nason (2003, 2011) is employed. As well, an empirical study investigates whether simulation findings hold in a realistic setting. In light of these earlier studies, a major empirical study seeks to identify the set of superior multivariate volatility forecasting models from 43 models that use either daily squared returns or realised volatility to generate forecasts. This study also assesses how the choice of volatility proxy affects the ability of the statistical loss functions to discriminate between forecasts. Analysis of the loss functions shows that QLIKE, MSE and portfolio variance can discriminate between multivariate volatility forecasts, while portfolio utility cannot. An examination of the effective loss functions shows that they all can identify the correct forecast at a point in time, however, their ability to discriminate between competing forecasts does vary. That is, QLIKE is identified as the most effective loss function, followed by portfolio variance which is then followed by MSE. The major empirical analysis reports that the optimal set of multivariate volatility forecasting models includes forecasts generated from daily squared returns and realised volatility. Furthermore, it finds that the volatility proxy affects the statistical loss functions’ ability to discriminate between forecasts in tests of predictive ability. These findings deepen our understanding of how to choose between competing multivariate volatility forecasts.

Antiretroviral therapy (ART) adherence among people living with HIV/AIDS (PLHIV) in the north of Vietnam : a multi-method approach

The antiretroviral therapy (ART) program for People Living with HIV/AIDS (PLHIV) in Vietnam has been scaled up rapidly in recent years (from 50 clients in 2003 to almost 38,000 in 2009). ART success is highly dependent on the ability of the patients to fully adhere to the prescribed treatment regimen. Despite the remarkable extension of ART programs in Vietnam, HIV/AIDS program managers still have little reliable data on levels of ART adherence and factors that might promote or reduce adherence. Several previous studies in Vietnam estimated extremely high levels of ART adherence among their samples, although there are reasons to question the veracity of the conclusion that adherence is nearly perfect. Further, no study has quantitatively assessed the factors influencing ART adherence. In order to reduce these gaps, this study was designed to include several phases and used a multi-method approach to examine levels of ART non-adherence and its relationship to a range of demographic, clinical, social and psychological factors. The study began with an exploratory qualitative phase employing four focus group discussions and 30 in-depth interviews with PLHIV, peer educators, carers and health care providers (HCPs). Survey interviews were completed with 615 PLHIV in five rural and urban out-patient clinics in northern Vietnam using an Audio Computer Assisted Self-Interview (ACASI) and clinical records extraction. The survey instrument was carefully developed through a systematic procedure to ensure its reliability and validity. Cultural appropriateness was considered in the design and implementation of both the qualitative study and the cross sectional survey. The qualitative study uncovered several contrary perceptions between health care providers and HIV/AIDS patients regarding the true levels of ART adherence. Health care providers often stated that most of their patients closely adhered to their regimens, while PLHIV and their peers reported that “it is not easy” to do so. The quantitative survey findings supported the PLHIV and their peers’ point of view in the qualitative study, because non-adherence to ART was relatively common among the study sample. Using the ACASI technique, the estimated prevalence of onemonth non-adherence measured by the Visual Analogue Scale (VAS) was 24.9% and the prevalence of four-day not-on-time-adherence using the modified Adult AIDS Clinical Trials Group (AACTG) instrument was 29%. Observed agreement between the two measures was 84% and kappa coefficient was 0.60 (SE=0.04 and p<0.0001). The good agreement between the two measures in the current study is consistent with those found in previous research and provides evidence of cross-validation of the estimated adherence levels. The qualitative study was also valuable in suggesting important variables for the survey conceptual framework and instrument development. The survey confirmed significant correlations between two measures of ART adherence (i.e. dose adherence and time adherence) and many factors identified in the qualitative study, but failed to find evidence of significant correlations of some other factors and ART adherence. Non-adherence to ART was significantly associated with untreated depression, heavy alcohol use, illicit drug use, experiences with medication side-effects, chance health locus of control, low quality of information from HCPs, low satisfaction with received support and poor social connectedness. No multivariate association was observed between ART adherence and age, gender, education, duration of ART, the use of adherence aids, disclosure of ART, patients’ ability to initiate communication with HCPs or distance between clinic and patients’ residence. This is the largest study yet reported in Asia to examine non-adherence to ART and its possible determinants. The evidence strongly supports recent calls from other developing nations for HIV/AIDS services to provide screening, counseling and treatment for patients with depressive symptoms, heavy use of alcohol and substance use. Counseling should also address fatalistic beliefs about chance or luck determining health outcomes. The data suggest that adherence could be enhanced by regularly providing information on ART and assisting patients to maintain social connectedness with their family and the community. This study highlights the benefits of using a multi-method approach in examining complex barriers and facilitators of medication adherence. It also demonstrated the utility of the ACASI interview method to enhance open disclosure by people living with HIV/AIDS and thus, increase the veracity of self-reported data.

Quantification of the accuracy of MRI generated 3D models of long bones compared to CT generated 3D models

Orthopaedic fracture fixation implants are increasingly being designed using accurate 3D models of long bones based on computer tomography (CT). Unlike CT, magnetic resonance imaging (MRI) does not involve ionising radiation and is therefore a desirable alternative to CT. This study aims to quantify the accuracy of MRI-based 3D models compared to CT-based 3D models of long bones. The femora of five intact cadaver ovine limbs were scanned using a 1.5T MRI and a CT scanner. Image segmentation of CT and MRI data was performed using a multi-threshold segmentation method. Reference models were generated by digitising the bone surfaces free of soft tissue with a mechanical contact scanner. The MRI- and CT-derived models were validated against the reference models. The results demonstrated that the CT-based models contained an average error of 0.15mm while the MRI-based models contained an average error of 0.23mm. Statistical validation shows that there are no significant differences between 3D models based on CT and MRI data. These results indicate that the geometric accuracy of MRI based 3D models was comparable to that of CT-based models and therefore MRI is a potential alternative to CT for generation of 3D models with high geometric accuracy.

Capturing affective experiences using the SMS experience sampling (SMS-ES) method

This paper reports the feasibility and methodological considerations of using the Short Message System Experience Sampling (SMS-ES) Method, which is an experience sampling research method developed to assist researchers to collect repeat measures of consumers’ affective experiences. The method combines SMS with web-based technology in a simple yet effective way. It is described using a practical implementation study that collected consumers’ emotions in response to using mobile phones in everyday situations. The method is further evaluated in terms of the quality of data collected in the study, as well as against the methodological considerations for experience sampling studies. These two evaluations suggest that the SMS-ES Method is both a valid and reliable approach for collecting consumers’ affective experiences. Moreover, the method can be applied across a range of for-profit and not-for-profit contexts where researchers want to capture repeated measures of consumers’ affective experiences occurring over a period of time. The benefits of the method are discussed to assist researchers who wish to apply the SMS-ES Method in their own research designs.

Look before you leap : a confidence-based method for selecting species criticality while avoiding negative populations in τ-leaping

The stochastic simulation algorithm was introduced by Gillespie and in a different form by Kurtz. There have been many attempts at accelerating the algorithm without deviating from the behavior of the simulated system. The crux of the explicit τ-leaping procedure is the use of Poisson random variables to approximate the number of occurrences of each type of reaction event during a carefully selected time period, τ. This method is acceptable providing the leap condition, that no propensity function changes “significantly” during any time-step, is met. Using this method there is a possibility that species numbers can, artificially, become negative. Several recent papers have demonstrated methods that avoid this situation. One such method classifies, as critical, those reactions in danger of sending species populations negative. At most, one of these critical reactions is allowed to occur in the next time-step. We argue that the criticality of a reactant species and its dependent reaction channels should be related to the probability of the species number becoming negative. This way only reactions that, if fired, produce a high probability of driving a reactant population negative are labeled critical. The number of firings of more reaction channels can be approximated using Poisson random variables thus speeding up the simulation while maintaining the accuracy. In implementing this revised method of criticality selection we make use of the probability distribution from which the random variable describing the change in species number is drawn. We give several numerical examples to demonstrate the effectiveness of our new method.

Stochastic regularization method for backward Cauchy problem in Banach spaces

We consider a stochastic regularization method for solving the backward Cauchy problem in Banach spaces. An order of convergence is obtained on sourcewise representative elements.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

Development of the rural statistical sustainability framework tool

It is important to promote a sustainable development approach to ensure that economic, environmental and social developments are maintained in balance. Sustainable development and its implications are not just a global concern, it also affects Australia. In particular, rural Australian communities are facing various economic, environmental and social challenges. Thus, the need for sustainable development in rural regions is becoming increasingly important. To promote sustainable development, proper frameworks along with the associated tools optimised for the specific regions, need to be developed. This will ensure that the decisions made for sustainable development are evidence based, instead of subjective opinions. To address these issues, Queensland University of Technology (QUT), through an Australian Research Council (ARC) linkage grant, has initiated research into the development of a Rural Statistical Sustainability Framework (RSSF) to aid sustainable decision making in rural Queensland. This particular branch of the research developed a decision support tool that will become the integrating component of the RSSF. This tool is developed on the web-based platform to allow easy dissemination, quick maintenance and to minimise compatibility issues. The tool is developed based on MapGuide Open Source and it follows the three-tier architecture: Client tier, Web tier and the Server tier. The developed tool is interactive and behaves similar to a familiar desktop-based application. It has the capability to handle and display vector-based spatial data and can give further visual outputs using charts and tables. The data used in this tool is obtained from the QUT research team. Overall the tool implements four tasks to help in the decision-making process. These are the Locality Classification, Trend Display, Impact Assessment and Data Entry and Update. The developed tool utilises open source and freely available software and accounts for easy extensibility and long-term sustainability.

Analysis of rural activity spaces and transport disadvantage using a multi-method approach

Current knowledge about the relationship between transport disadvantage and activity space size is limited to urban areas, and as a result, very little is known about this link in a rural context. In addition, although research has identified transport disadvantaged groups based on their size of activity space, these studies have, however, not empirically explained such differences and the result is often a poor identification of the problems facing disadvantaged groups. Research has shown that transport disadvantage varies over time. The static nature of analysis using the activity space concept in previous research studies has lacked the ability to identify transport disadvantage in time. Activity space is a dynamic concept; and therefore possesses a great potential in capturing temporal variations in behaviour and access opportunities. This research derives measures of the size and fullness of activity spaces for 157 individuals for weekdays, weekends, and for a week using weekly activity-travel diary data from three case study areas located in rural Northern Ireland. Four focus groups were also conducted in order to triangulate quantitative findings and to explain the differences between different socio-spatial groups. The findings of this research show that despite having a smaller sized activity space, individuals were not disadvantaged because they were able to access their required activities locally. Car-ownership was found to be an important life line in rural areas. Temporal disaggregation of the data reveals that this is true only on weekends due to a lack of public transport services. In addition, despite activity spaces being at a similar size, the fullness of activity spaces of low-income individuals was found to be significantly lower compared to their high-income counterparts. Focus group data shows that financial constraint, poor connections both between public transport services and between transport routes and opportunities forced individuals to participate in activities located along the main transport corridors.

Robust multidisciplinary UAS design optimisation

There are many applications in aeronautical/aerospace engineering where some values of the design parameters states cannot be provided or determined accurately. These values can be related to the geometry(wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the flrst case considers robust multi-objective (single disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design.

Bayesian models for the determination of resonant frequencies in a DI diesel engine

A time series method for the determination of combustion chamber resonant frequencies is outlined. This technique employs the use of Markov-chain Monte Carlo (MCMC) to infer parameters in a chosen model of the data. The development of the model is included and the resonant frequency is characterised as a function of time. Potential applications for cycle-by-cycle analysis are discussed and the bulk temperature of the gas and the trapped mass in the combustion chamber are evaluated as a function of time from resonant frequency information.

Adaptive wing/aerofoil design optimisation using MOEA coupled to uncertainty design method

The use of adaptive wing/aerofoil designs is being considered as promising techniques in aeronautic/aerospace since they can reduce aircraft emissions, improve aerodynamic performance of manned or unmanned aircraft. The paper investigates the robust design and optimisation for one type of adaptive techniques; Active Flow Control (AFC) bump at transonic flow conditions on a Natural Laminar Flow (NLF) aerofoil designed to increase aerodynamic efficiency (especially high lift to drag ratio). The concept of using Shock Control Bump (SCB) is to control supersonic flow on the suction/pressure side of NLF aerofoil: RAE 5243 that leads to delaying shock occurrence or weakening its strength. Such AFC technique reduces total drag at transonic speeds due to reduction of wave drag. The location of Boundary Layer Transition (BLT) can influence the position the supersonic shock occurrence. The BLT position is an uncertainty in aerodynamic design due to the many factors, such as surface contamination or surface erosion. The paper studies the SCB shape design optimisation using robust Evolutionary Algorithms (EAs) with uncertainty in BLT positions. The optimisation method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Two test cases are conducted; the first test assumes the BLT is at 45% of chord from the leading edge and the second test considers robust design optimisation for SCB at the variability of BLT positions and lift coefficient. Numerical result shows that the optimisation method coupled to uncertainty design techniques produces Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction.