Fourier active appearance models

Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.

Great Expectations : stability and change in English teaching : the teacher as chameleon

Curriculum is always in a state of flux and so often the moves to ‘reform’ it are political rather than pedagogical. So often in these days of accountability we focus on the learner. I want to focus on the teacher in this presentation. As English educators we have to ‘fit’ whatever new policy model comes our way. The Australian curriculum seems to have tried to please every stakeholder in its process and as such has been formed without a single, unifying coherent theoretical basis. How do we challenge this paper tiger? We have to find the pedagogical models within the current framework and see what still works in practice. At the chalk-face there are still teaching, learning and assessment practices in English surviving from the last few decades of pedagogical change; and there is also room for accommodating new practices. Embracing and adapting the old and the new may be the key to staying creative and passionately engaged with our subject area.

Influence of practice setting on contact lens prescribing in the United Kingdom

We sought to determine the impact of optometric practice setting on contact lens prescribing by analysing annual survey data of lens fits collected between 2009 and 2013 from independent and national group practices throughout the United Kingdom. Compared to national group practices, independent practices fit contact lenses to older patients and more females. Independent practices also undertake a lower proportion of soft lens fits overall (and thus a higher proportion of rigid lens fits), soft toric lens fits and daily disposable lens fits. There is a higher proportion of soft extended wear and multifocal lens fits in independent practices. We conclude that contact lens fitting behaviour is influenced by optometric practice setting.

The Craving Experience Questionnaire : a brief, theory-based measure of consummatory desire and craving

Background and Aims Research into craving is hampered by lack of theoretical specification and a plethora of substance-specific measures. This study aimed to develop a generic measure of craving based on elaborated intrusion (EI) theory. Confirmatory factor analysis (CFA) examined whether a generic measure replicated the three-factor structure of the Alcohol Craving Experience (ACE) scale over different consummatory targets and time-frames. Design Twelve studies were pooled for CFA. Targets included alcohol, cigarettes, chocolate and food. Focal periods varied from the present moment to the previous week. Separate analyses were conducted for strength and frequency forms. Setting Nine studies included university students, with single studies drawn from an internet survey, a community sample of smokers and alcohol-dependent out-patients. Participants A heterogeneous sample of 1230 participants. Measurements Adaptations of the ACE questionnaire. Findings Both craving strength [comparative fit indices (CFI = 0.974; root mean square error of approximation (RMSEA) = 0.039, 95% confidence interval (CI) = 0.035–0.044] and frequency (CFI = 0.971, RMSEA = 0.049, 95% CI = 0.044–0.055) gave an acceptable three-factor solution across desired targets that mapped onto the structure of the original ACE (intensity, imagery, intrusiveness), after removing an item, re-allocating another and taking intercorrelated error terms into account. Similar structures were obtained across time-frames and targets. Preliminary validity data on the resulting 10-item Craving Experience Questionnaire (CEQ) for cigarettes and alcohol were strong. Conclusions The Craving Experience Questionnaire (CEQ) is a brief, conceptually grounded and psychometrically sound measure of desires. It demonstrates a consistent factor structure across a range of consummatory targets in both laboratory and clinical contexts.

The OnTrack Diabetes web-based program for type 2 diabetes and dysphoria self-management: A randomized controlled trial protocol

Background: The prevalence of type 2 diabetes is rising with the majority of patients practicing inadequate disease self-management. Depression, anxiety, and diabetes-specific distress present motivational challenges to adequate self-care. Health systems globally struggle to deliver routine services that are accessible to the entire population, in particular in rural areas. Web-based diabetes self-management interventions can provide frequent, accessible support regardless of time and location Objective: This paper describes the protocol of an Australian national randomized controlled trial (RCT) of the OnTrack Diabetes program, an automated, interactive, self-guided Web program aimed to improve glycemic control, diabetes self-care, and dysphoria symptoms in type 2 diabetes patients. Methods: A small pilot trial is conducted that primarily tests program functionality, efficacy, and user acceptability and satisfaction. This is followed by the main RCT, which compares 3 treatments: (1) delayed program access: usual diabetes care for 3 months postbaseline followed by access to the full OnTrack Diabetes program; (2) immediate program: full access to the self-guided program from baseline onward; and (3) immediate program plus therapist support via Functional Imagery Training (FIT). Measures are administered at baseline and at 3, 6, and 12 months postbaseline. Primary outcomes are diabetes self-care behaviors (physical activity participation, diet, medication adherence, and blood glucose monitoring), glycated hemoglobin A1c (HbA1c) level, and diabetes-specific distress. Secondary outcomes are depression, anxiety, self-efficacy and adherence, and quality of life. Exposure data in terms of program uptake, use, time on each page, and program completion, as well as implementation feasibility will be conducted. Results: This trial is currently underway with funding support from the Wesley Research Institute in Brisbane, Australia. Conclusions: This is the first known trial of an automated, self-guided, Web-based support program that uses a holistic approach in targeting both type 2 diabetes self-management and dysphoria. Findings will inform the feasibility of implementing such a program on an ongoing basis, including in rural and regional locations.

Job embeddedness and the importance of retaining small firm employees

Small firms identify retention of staff as a significant problem. Voluntary turnover of talented staff can be costly, especially in small firms where there are few slack resources. However, there is scant research on retention in small firms. We use the concept of Job Embeddedness to understand why small firm employees stay. The concept refers to the totality of forces that embed employees in their jobs and it consists of three dimensions: fit, links, and sacrifice. Seven propositions are outlined comparing the ways fit, links and sacrifice might play out for small and large firm employees. Through testing these propositions small firm owner-managers may have a better understanding of what can be done to retain employees and maintain firm performance.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Search-phase echolocation calls of the New Zealand lesser short-tailed bat (Mystacina tuberculata) and long-tailed bat (Chalinolobus tuberculatus).

This paper describes the search-phase echolocation calls of lesser short-tailed bats (Mystacina tuberculata) and long-tailed bats (Chalinolobus tuberculatus). Calls were recorded from all three subspecies of short-tailed bat and seven populations of long-tailed bat, three in Northland, two in the central North Island, and two in the lower South Island. The calls were recorded in the field and digitised, then three spectral components and one temporal component of the calls were measured. Calls of the lesser short-tailed bat could be loosely classified into subspecies by means of multivariate discriminant function analysis. Similarly, long-tailed bat calls showed regional variation, and discriminant function analysis was able to fit calls to regional groups with a high rate of success. The significance of the results presented is discussed in terms of the conservation of New Zealand bats and the unique ecology of the lesser short-tailed bat.

EUTOPIA 75+ : exploratory futures scenarios for baby boomers’ preferred living spaces

Australia is experiencing the global phenomenon of an ageing population with the baby boomer generation starting to reach retirement age in large numbers. As a result, there is a growing need for appropriate accommodation and this will continue to grow for the foreseeable future. However, the needs of the fit, mobile and techno savvy baby boomers are likely to be far different from those of previous generations of older people, but are as yet unknown and unanticipated. This paper reports on the findings of a Futuring research project to explore the preferred housing futures for the baby boomer generation in the city of Brisbane, an aspiring creative city in South East Queensland (SEQ), Australia. Their future home design and service needs are predicted by firstly employing a global environmental scan of related and associated ageing futures issues. This was followed by a micro-Futuring workshop, based on Inayatullah’s Futures Triangle Analysis, to identify a range of scenarios. The key aspects of the workshop culminated in the development of a Transformational Scenario – EUTOPIA 75+. From this, a suite of six design recommendations for seniors’ housing design and smart services provision are synthesised to give a sense of direction of preferred living styles, especially in terms of physical housing spaces, with a view to identifying new house design opportunities for the allied industries and research organisations. The issues identified are also of concern for aged care service providers, retirement living developers, and for academics involved in the social and physical design of living spaces for older people.

Development of a log-quadratic model to describe microbial inactivation, illustrated by thermal inactivation of Clostridium botulinum

In the commercial food industry, demonstration of microbiological safety and thermal process equivalence often involves a mathematical framework that assumes log-linear inactivation kinetics and invokes concepts of decimal reduction time (DT), z values, and accumulated lethality. However, many microbes, particularly spores, exhibit inactivation kinetics that are not log linear. This has led to alternative modeling approaches, such as the biphasic and Weibull models, that relax strong log-linear assumptions. Using a statistical framework, we developed a novel log-quadratic model, which approximates the biphasic and Weibull models and provides additional physiological interpretability. As a statistical linear model, the log-quadratic model is relatively simple to fit and straightforwardly provides confidence intervals for its fitted values. It allows a DT-like value to be derived, even from data that exhibit obvious "tailing." We also showed how existing models of non-log-linear microbial inactivation, such as the Weibull model, can fit into a statistical linear model framework that dramatically simplifies their solution. We applied the log-quadratic model to thermal inactivation data for the spore-forming bacterium Clostridium botulinum and evaluated its merits compared with those of popular previously described approaches. The log-quadratic model was used as the basis of a secondary model that can capture the dependence of microbial inactivation kinetics on temperature. This model, in turn, was linked to models of spore inactivation of Sapru et al. and Rodriguez et al. that posit different physiological states for spores within a population. We believe that the log-quadratic model provides a useful framework in which to test vitalistic and mechanistic hypotheses of inactivation by thermal and other processes. Copyright © 2009, American Society for Microbiology. All Rights Reserved.

A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

A communication model and partitioning algorithm for streaming applications for an embedded MPSoC

Energy efficient embedded computing enables new application scenarios in mobile devices like software-defined radio and video processing. The hierarchical multiprocessor considered in this work may contain dozens or hundreds of resource efficient VLIW CPUs. Programming this number of CPU cores is a complex task requiring compiler support. The stream programming paradigm provides beneficial properties that help to support automatic partitioning. This work describes a compiler for streaming applications targeting the self-build hierarchical CoreVA-MPSoC multiprocessor platform. The compiler is supported by a programming model that is tailored to fit the streaming programming paradigm. We present a novel simulated-annealing (SA) based partitioning algorithm, called Smart SA. The overall speedup of Smart SA is 12.84 for an MPSoC with 16 CPU cores compared to a single CPU implementation. Comparison with a state of the art partitioning algorithm shows an average performance improvement of 34.07%.

Potentially inappropriate prescribing in older patients admitted to acute care hospitals and discharged to residential aged care facilities

Background The frequency of prescribing potentially inappropriate medications (PIMs) in older patients remains high regardless of the evidence of adverse outcomes from their use. This study aims to identify the prevalence and nature of PIMs at admission to acute care and at discharge to residential aged care facilities (RACFs) using the recently updated Beers’ Criteria. We also aim to identify if polypharmacy, age, gender and the frailty status of patients are independent risk factors for receiving a PIM. Methods This was a retrospective study of 206 patients discharged to RACFs from acute care. All patients were aged at least70 years and were admitted between July 2005 and May 2010; their admission and discharge medications were evaluated. Frailty status was measured as the Frailty Index (FI), adding each individual’s deficits and dividing by the total number of deficits considered, with FI 0.25 used as the cut-off between “fit” and “frail”. Results Mean patient age was 84.8 ± 6.7 years; the majority (57%) were older than 85 years and approximately 90% were frail. Patients were prescribed a mean of 7.2 regular medications at admission and 8.1 on discharge. At least one PIM was identified in 112 (54.4%) patients on admission and 102 (49.5%) patients on discharge. Of all medications prescribed at admission (1728), 10.8% were PIMs and at discharge of 1759 medications, 9.6% were PIMs. Of the total 187 PIMs on admission, 56 (30%) were stopped, and 131 were continued; 32 new PIMs were introduced. Commonly prescribed PIMs at both admission and discharge were central nervous system, cardiovascular and gastrointestinal drugs and analgesics. Of the potential risk factors, frailty status was the only significant predictor of PIMs at both admission and discharge (p = 0.016). Conclusion A high prevalence of unnecessary drug use was observed in frail older patients on admission to acute care hospitals and on discharge to RACFs. The only association with PIM use was the frailty status of patients. Further studies are needed to further evaluate this association.

The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.