A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots. The framework proposed combines nonlinear dimensionality reduction, nonlinear regression under noise, and variational Bayesian learning to create consistent probabilistic representations of places from images. These generative models are learnt from a few images and used for multi-class place recognition where classification is computed from a set of feature-vectors. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions and blurring. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Young people’s aspirations for education, work, family and leisure

Young people are arguably facing more ‘complex and contested’ transitions to adulthood and an increasing array of ‘non-linear’ paths. Education and training have been extended, identity is increasingly shaped through leisure and consumerism and youth must navigate their life trajectories in highly individualised ways. The study utilises 819 short essays compiled by students aged 14–16 years from 19 schools in Australia. It examines how young people understand their own unique positions and the possibilities open to them through their aspirations and future orientations to employment and family life. These young people do not anticipate postponing work identities, but rather embrace post-school options such as gaining qualifications, work experience and achieving financial security. Boys expected a distant involvement in family life secondary to participation in paid work. In contrast, around half the girls simultaneously expected a future involving primary care-giving and an autonomous, independent career, suggesting attempts to remake gendered inequalities

Convexity, Classification, and Risk Bounds

Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 0–1 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of convexity. To study these issues, we provide a general quantitative relationship between the risk as assessed using the 0–1 loss and the risk as assessed using any nonnegative surrogate loss function. We show that this relationship gives nontrivial upper bounds on excess risk under the weakest possible condition on the loss function—that it satisfies a pointwise form of Fisher consistency for classification. The relationship is based on a simple variational transformation of the loss function that is easy to compute in many applications. We also present a refined version of this result in the case of low noise, and show that in this case, strictly convex loss functions lead to faster rates of convergence of the risk than would be implied by standard uniform convergence arguments. Finally, we present applications of our results to the estimation of convergence rates in function classes that are scaled convex hulls of a finite-dimensional base class, with a variety of commonly used loss functions.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Local Rademacher complexities

We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

Developing the atlas of cancer in Queensland : methodological issues

Background: Achieving health equity has been identified as a major challenge, both internationally and within Australia. Inequalities in cancer outcomes are well documented, and must be quantified before they can be addressed. One method of portraying geographical variation in data uses maps. Recently we have produced thematic maps showing the geographical variation in cancer incidence and survival across Queensland, Australia. This article documents the decisions and rationale used in producing these maps, with the aim to assist others in producing chronic disease atlases. Methods: Bayesian hierarchical models were used to produce the estimates. Justification for the cancers chosen, geographical areas used, modelling method, outcome measures mapped, production of the adjacency matrix, assessment of convergence, sensitivity analyses performed and determination of significant geographical variation is provided. Conclusions: Although careful consideration of many issues is required, chronic disease atlases are a useful tool for assessing and quantifying geographical inequalities. In addition they help focus research efforts to investigate why the observed inequalities exist, which in turn inform advocacy, policy, support and education programs designed to reduce these inequalities.

Aspiration levels and educational choices: An experimental study

The explanation of social inequalities in education is still a debated issue in economics. Recent empirical studies tend to downplay the potential role of credit constraint. This article tests a different potential explanation of social inequalities in education, specifically that social differences in aspiration level result in different educational choices. Having existed for a long time in the sociology of education, this explanation can be justified if aspiration levels are seen as reference points in a prospect theory framework. In order to test this explanation, this article applies the method of experimental economics to the issue of education choice and behaviour. One hundred and twenty-nine individuals participated in an experiment in which they had to perform a task over 15 stages grouped in three blocks or levels. In order to continue through the experiment, a minimum level of success was required at the end of each level. Rewards were dependent on the final level successfully reached. At the end of each level, participants could either choose to stop and take their reward or to pay a cost to continue further in order to possibly receive higher rewards. To test the impact of aspiration levels, outcomes were either presented as gains or losses relative to an initial sum. In accordance with the theoretical predictions, participants in the loss framing group choose to go further in the experiment. There was also a significant and interesting gender effect in the loss framing treatment, such that males performed better and reached higher levels.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.

An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.

Social and emotional determinants of health

- describe what is meant by socioeconomic differences in health, and the social and emotional determinants of health - understand how health inequalities are affected by the social and economic circumstances that people experience throughout their lives - discuss how factors such as living and working conditions, income, place and education can impact on health - identify actions for public health policy-makers that have the potential to make a difference in improving health outcomes within populations - appreciate the concept of social cohesion and social capital, and their role as potential protective factors in health - understand conceptual models that can assist in analysing these issues.

Socio-economic differences in takeaway food consumption among adults

To examine socioeconomic differences in the frequency and types of takeaway foods consumed. Cross-sectional postal survey. Participants were asked about their usual consumption of overall takeaway food (< four times a month, or ≥ four times a month) and 22 specific takeaway food items (< once a month, or ≥ once a month): these latter foods were grouped into “healthy” and “less healthy” choices. Socioeconomic position was measured using education and equivalised household income and differences in takeaway food consumption were assessed by calculating prevalence ratios using log binomial regression. Adults aged 25–64 years from Brisbane, Australia were randomly selected from the electoral roll (N = 903, 63.7% response rate). Compared with their more educated counterparts, the least educated were more regular consumers of overall takeaway food, fruit/vegetable juice, and less regular consumers of sushi. For the “less healthy” items, the least educated more regularly consumed potato chips, savoury pies, fried chicken, and non-diet soft drinks; however, the least educated were less likely to consume curry. Household income was not associated with overall takeaway consumption. The lowest income group were more regular consumers of fruit/vegetable juice compared with the highest income group. Among the “less healthy” items, the lowest income group were more regular consumers of fried fish, ice-cream, and milk shakes, while curry was consumed less regularly. The frequency and types of takeaway foods consumed by socioeconomically disadvantaged groups may contribute to inequalities in overweight/obesity and chronic disease.

Organizational rhetoric in the prospectuses of elite private schools: unpacking strategies of persuasion

The way in which private schools use rhetoric in their communications offers important insights into how these organizational sites persuade audiences and leverage marketplace advantage in the context of contemporary educational platforms. Through systemic analysis of rhetorical strategies employed in 65 ‘elite’ school prospectuses in Australia, this paper contributes to understandings of the ways schools’ communications draw on broader cultural politics in order to shape meanings and interactions among organizational actors. We identify six strategies consistently used by schools to this end: identification, juxtapositioning, bolstering or self-promotion, partial reporting, self-expansion, and reframing or reversal. We argue that, in the context of marketization and privatization discourses in twenty-first-century western education, these strategies attempt to subvert potentially threatening discourses, in the process actively reproducing broader economic and social privilege and inequalities.

Mapping Services to Support a Patient's Journey through Evidence-Based Care Pathways After a Cardiac Event

Background: There are inequalities in geographical access and delivery of health care services in Australia, particularly for cardiovascular disease (CVD), Australia's major cause of death. Analyses and models that can inform and positively influence strategies to augment services and preventative measures are needed. The Cardiac-ARIA project is using geographical spatial technology (GIS) to develop a national index for each of Australia's 13,000 population centres. The index will describe the spatial distribution of CVD health care services available to support populations at risk, in a timely manner, after a major cardiac event. Methods: In the initial phase of the project, an expert panel of cardiologists and an emergency physician have identified key elements of national and international guidelines for management of acute coronary syndromes, cardiac arrest, life-threatening arrhythmias and acute heart failure, from the time of onset (potentially dial 000) to return from the hospital to the community (cardiac rehabilitation). Results: A systematic search has been undertaken to identify the geographical location of, and type of, cardiac services currently available. This has enabled derivation of a master dataset of necessary services, e.g. telephone networks, ambulance, RFDS, helicopter retrieval services, road networks, hospitals, general practitioners, medical community centres, pathology services, CCUs, catheterisation laboratories, cardio-thoracic surgery units and cardiac rehabilitation services. Conclusion: This unique and innovative project has the potential to deliver a powerful tool to both highlight and combat the burden of disease of CVD in urban and regional Australia.

Using mobile social media and GIS in health and place research

This paper presents new research methods that combine the use of location-based, social media on mobile phones with geographic information systems (GIS) to explore connections between people, place and health. It discusses the feasibility, limitations, and benefits of using these methods, which enable real-time, location-based, quantitative data to be collected on the recreation, consumption, and physical activity patterns of urban residents in Brisbane, Queensland. The study employs mechanisms already inherent in popular mobile social media applications (Facebook, Twitter and Foursquare) to collect this data. The research methods presented in this paper are innovative and potentially applicable to an increasing number of academic research areas, as well as to a growing range of service providers that benefit from monitoring consumer behaviour, and responding to emerging changes in these patterns and trends. The ability to both collect and map objective, real-time data about the consumption, leisure, recreation, and physical activity patterns amongst urban communities has direct implications for a range of research disciplines including media studies, advertising, health promotion, social marketing, public health inequalities, and urban design.

What explains the association between socioeconomic status and depression among Vietnamese adults?

Background Depression is a major public health problem worldwide and is currently ranked second to heart disease for years lost due to disability. For many decades, international research has found that depressive symptoms occur more frequently among low socioeconomic (SES) individuals than their more-advantaged peers. However, the reasons as to why those of low socioeconomic groups suffer more depressive symptoms are not well understood. Studies investigating the prevalence of depression and its association with SES emanate largely from developed countries, with little research among developing countries. In particular, there is a serious dearth of research on depression and no investigation of its association with SES in Vietnam. The aims of the research presented in this Thesis are to: estimate the prevalence of depressive symptoms among Vietnamese adults, examine the nature and extent of the association between SES and depression and to elucidate causal pathways linking SES to depressive symptoms Methods The research was conducted between September 2008 and November 2009 in Hue city in central Vietnam and used a combination of qualitative (in-depth interviews) and quantitative (survey) data collection methods. The qualitative study contributed to the development of the theoretical model and to the refinement of culturally-appropriate data collection instruments for the quantitative study. The main survey comprised a cross-sectional population–based survey with randomised cluster sampling. A sample of 1976 respondents aged between 25-55 years from ten randomly-selected residential zones (quarters) of Hue city completed the questionnaire (response rate 95.5%). Measures SES was classified using three indicators: education, occupation and income. The Center for Epidemiologic Studies-Depression (CES-D) scale was used to measure depressive symptoms (range0-51, mean=11.0, SD=8.5). Three cut-off points for the CES-D scores were applied: ‘at risk for clinical depression’ (16 or above), ‘depressive symptoms’ (above 21) and ‘depression’ (above 25). Six psychosocial indicators: life time trauma, chronic stress, recent life events, social support, self esteem, and mastery were hypothesized to mediate the association between SES and depressive symptoms. Analyses The prevalence of depressive symptoms were analysed using bivariate analyses. The multivariable analytic phase comprised of ordinary least squares regression, in accordance with Baron and Kenny’s three-step framework for mediation modeling. All analyses were adjusted for a range of confounders, including age, marital status, smoking, drinking and chronic diseases and the mediation models were stratified by gender. Results Among these Vietnamese adults, 24.3% were at or above the cut-off for being ‘at risk for clinical depression’, 11.9% were classified as having depressive symptoms and 6.8% were categorised as having depression. SES was inversely related to depressive symptoms: the least educated those with low occupational status or with the lowest incomes reported more depressive symptoms. Socioeconomicallydisadvantaged individuals were more likely to report experiencing stress (life time trauma, chronic stress or recent life events), perceived less social support and reported fewer personal resources (self esteem and mastery) than their moreadvantaged counterparts. These psychosocial resources were all significantly associated with depressive symptoms independent of SES. Each psychosocial factor showed a significant mediating effect on the association between SES and depressive symptoms. This was found for all measures of SES, and for males and females. In particular, personal resources (mastery, self esteem) and chronic stress accounted for a substantial proportion of the variation in depressive symptoms between socioeconomic groups. Social support and recent life events contributed modestly to socioeconomic differences in depressive symptoms, whereas lifetime trauma contributed the least to these inequalities. Conclusion This is the first known study in Vietnam or any developing country to systematically examine the extent to which psychosocial factors mediate the relationship between SES and depression. The study contributes new evidence regarding the burden of depression in Vietnam. The findings have practical relevance for advocacy, for mental health promotion and health-care services, and point to the need for programs that focus on building a sense of personal mastery and self esteem. More broadly, the work presented in this Thesis contributes to the international scientific literature on the social determinants of depression.