Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Estimation of parameters for macroparasite population evolution using approximate Bayesian computation

We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.

Factors influencing health promotion activities in midlife and older Australian women with a chronic disease

Background: Chronic diseases including type 2 diabetes are a leading cause of morbidity and mortality in midlife and older Australian women. There are a number of modifiable risk factors for type 2 diabetes and other chronic diseases including smoking, nutrition, physical activity and overweight and obesity. Little research has been conducted in the Australian context to explore the perceived barriers to health promotion activities in midlife and older Australian women with a chronic disease. Aims: The primary aim of this study was to explore women’s perceived barriers to health promotion activities to reduce modifiable risk factors, and the relationship of perceived barriers to smoking behaviour, fruit and vegetable intake, physical activity and body mass index. A secondary aim of this study was to investigate nurses’ perceptions of the barriers to action for women with a chronic disease, and to compare those perceptions with those of the women. Methods: The study was divided into two phases where Phase 1 was a cross sectional survey of women, aged over 45 years with type 2 diabetes who were attending Diabetes clinics in the Primary and Community Health Service of the Metro North Health Service District of Queensland Health (N = 22). The women were a subsample of women participating in a multi-model lifestyle intervention, the ‘Reducing Chronic Disease among Adult Australian Women’ project. Phase 2 of the study was a cross sectional online survey of nurses working in Primary and Community Health Service in the Metro North Health Service District of Queensland Health (N = 46). Pender’s health promotion model was used as the theoretical framework for this study. Results: Women in this study had an average total barriers score of 32.18 (SD = 9.52) which was similar to average scores reported in the literature for women with a range of physical disabilities and illnesses. The leading five barriers for this group of women were: concern about safety; too tired; not interested; lack of information about what to do; with lack of time and feeling I can’t do things correctly the equal fifth ranked barriers. In this study there was no statistically significant difference in average total barriers scores between women in the intervention group and those is the usual care group of the parent study. There was also no significant relationship between the women’s socio-demographic variables and lifestyle risk factors and their level of perceived barriers. Nurses in the study had an average total barriers score of 44.48 (SD = 6.24) which was higher than all other average scores reported in the literature. The leading five barriers that nurses perceived were an issue for women with a chronic disease were: lack of time and interferes with other responsibilities the leading barriers; embarrassment about appearance; lack of money; too tired and lack of support from family and friends. There was no significant relationship between the nurses’ sociodemographic and nursing variables and the level of perceived barriers. When comparing the results of women and nurses in the study there was a statistically significant difference in the median total barriers score between the groups (p < 0.001), where the nurses perceived the barriers to be higher (Md = 43) than the women (Md = 33). There was also a significant difference in the responses to the individual barriers items in fifteen of the eighteen items (p < 0.002). Conclusion: Although this study is limited by a small sample size, it contributes to understanding the perception of midlife and older women with a chronic disease and also the perception of nurses, about the barriers to healthy lifestyle activities that women face. The study provides some evidence that the perceptions of women and nurses may differ and argues that these differences may have significant implications for clinical practice. The study recommends a greater emphasis on assessing and managing perceived barriers to health promotion activities in health education and policy development and proposes a conceptual model for understanding perceived barriers to action.

Improved image set classification via joint sparse approximated nearest subspaces

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).

Concurrent multi-scale modeling of civil infrastructures for analyses on structural deteriorating—Part II : Model updating and verification

This paper is a continuation of the paper titled “Concurrent multi-scale modeling of civil infrastructure for analyses on structural deteriorating—Part I: Modeling methodology and strategy” with the emphasis on model updating and verification for the developed concurrent multi-scale model. The sensitivity-based parameter updating method was applied and some important issues such as selection of reference data and model parameters, and model updating procedures on the multi-scale model were investigated based on the sensitivity analysis of the selected model parameters. The experimental modal data as well as static response in terms of component nominal stresses and hot-spot stresses at the concerned locations were used for dynamic response- and static response-oriented model updating, respectively. The updated multi-scale model was further verified to act as the baseline model which is assumed to be finite-element model closest to the real situation of the structure available for the subsequent arbitrary numerical simulation. The comparison of dynamic and static responses between the calculated results by the final model and measured data indicated the updating and verification methods applied in this paper are reliable and accurate for the multi-scale model of frame-like structure. The general procedures of multi-scale model updating and verification were finally proposed for nonlinear physical-based modeling of large civil infrastructure, and it was applied to the model verification of a long-span bridge as an actual engineering practice of the proposed procedures.

Multi-vehicles interaction graph model for cooperative collision warning system

Cooperative collision warning system for road vehicles, enabled by recent advances in positioning systems and wireless communication technologies, can potentially reduce traffic accident significantly. To improve the system, we propose a graph model to represent interactions between multiple road vehicles in a specific region and at a specific time. Given a list of vehicles in vicinity, we can generate the interaction graph using several rules that consider vehicle's properties such as position, speed, heading, etc. Safety applications can use the model to improve emergency warning accuracy and optimize wireless channel usage. The model allows us to develop some congestion control strategies for an efficient multi-hop broadcast protocol.

Predicting active material utilisation in LiFePO4 electrodes using a multi-scale mathematical model

A mathematical model is developed to simulate the discharge of a LiFePO4 cathode. This model contains 3 size scales, which match with experimental observations present in the literature on the multi-scale nature of LiFePO4 material. A shrinking-core is used on the smallest scale to represent the phase-transition of LiFePO4 during discharge. The model is then validated against existing experimental data and this validated model is then used to investigate parameters that influence active material utilisation. Specifically the size and composition of agglomerates of LiFePO4 crystals is discussed, and we investigate and quantify the relative effects that the ionic and electronic conductivities within the oxide have on oxide utilisation. We find that agglomerates of crystals can be tolerated under low discharge rates. The role of the electrolyte in limiting (cathodic) discharge is also discussed, and we show that electrolyte transport does limit performance at high discharge rates, confirming the conclusions of recent literature.

Development of a multi-scale finite element model of the osteoporotic lumbar vertebral body for the investigation of apparent level vertebra mechanics and micro-level trabecular mechanics.

Osteoporotic spinal fractures are a major concern in ageing Western societies. This study develops a multi-scale finite element (FE) model of the osteoporotic lumbar vertebral body to study the mechanics of vertebral compression fracture at both the apparent (whole vertebral body) and micro-structural (internal trabecular bone core)levels. Model predictions were verified against experimental data, and found to provide a reasonably good representation of the mechanics of the osteoporotic vertebral body. This novel modelling methodology will allow detailed investigation of how trabecular bone loss in osteoporosis affects vertebral stiffness and strength in the lumbar spine.

A 3D visualisation model applied to a thick, multi-layered alluvial groundwater system under stress : Condamine Valley, QLD

The upper Condamine River in southern Queensland has formed extensive alluvial deposits which have been used for irrigation of cotton crops for over 40 years. Due to excessive use and long term drought conditions these groundwater resources are under substantial threat. This condition is now recognised by all stakeholders, and Qld Department of Environment and Resource Management (DERM) are currently undertaking a water planning process for the Central Condamine Alluvium with water users and other stakeholders. DERM aims to effectively demonstrate the character of the groundwater system and its current status, and notably the continued long-term drawdown of the watertable. It was agreed that 3D visualisation was an ideal tool to achieve this. The Groundwater Visualisation System (GVS) developed at QUT was utilised and the visualisation model developed in conjunction with DERM to achieve a planning-management tool for this particular application