Connectivity-based shape descriptors

In this paper, we propose a method for indexing and retrieval of images based on shapes of objects. The concept of connectivity is introduced. 3D models are used to represent 2D images. 2D images are decomposed a priori using connectivity which is followed by 3D model construction. 3D model descriptors are obtained for 3D models and used to represent the underlying 2D shapes. We have used spherical harmonics descriptors as the 3D model descriptors. Difference between two images is computed as the Euclidean distance between their descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. The proposed method is compared with methods based on principal components analysis (PCA) and generic Fourier descriptors (GFD). It is found that the proposed method is effective. Item S8 within the MPEG-7 still images content set is used for performing experiments.

The Early Prevention of Obesity in CHildren (EPOCH) collaboration : an individual patient data prospective meta-analysis

Background
Efforts to prevent the development of overweight and obesity have increasingly focused early in the life course as we recognise that both metabolic and behavioural patterns are often established within the first few years of life. Randomised controlled trials (RCTs) of interventions are even more powerful when, with forethought, they are synthesised into an individual patient data (IPD) prospective meta-analysis (PMA). An IPD PMA is a unique research design where several trials are identified for inclusion in an analysis before any of the individual trial results become known and the data are provided for each randomised patient. This methodology minimises the publication and selection bias often associated with a retrospective meta-analysis by allowing hypotheses, analysis methods and selection criteria to be specified a priori.

Methods/Design
The Early Prevention of Obesity in CHildren (EPOCH) Collaboration was formed in 2009. The main objective of the EPOCH Collaboration is to determine if early intervention for childhood obesity impacts on body mass index (BMI) z scores at age 18-24 months. Additional research questions will focus on whether early intervention has an impact on children's dietary quality, TV viewing time, duration of breastfeeding and parenting styles. This protocol includes the hypotheses, inclusion criteria and outcome measures to be used in the IPD PMA. The sample size of the combined dataset at final outcome assessment (approximately 1800 infants) will allow greater precision when exploring differences in the effect of early intervention with respect to pre-specified participant- and intervention-level characteristics.

Discussion
Finalisation of the data collection procedures and analysis plans will be complete by the end of 2010. Data collection and analysis will occur during 2011-2012 and results should be available by 2013.

Power-cycle-library-based control strategy for plug-in hybrid electric vehicles

It has been demonstrated that considering the knowledge of drive cycle as a priori in the PHEV control strategy can improve its performance. The concept of power cycle instead of drive cycle is introduced to consider the effect of noise factors in the prediction of future drivetrain power demand. To minimize the effect of noise factors, a practical solution for developing a power-cycle library is introduced. A control strategy is developed using the predicted power cycle which inherently improves the optimal operation of engine and consequently improves the vehicle performance. Since the control strategy is formed exclusively for each PHEV rather than a preset strategy which is designed by OEM, the effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered for each PHEV. Simulation results show that the control strategy based on the driver library of power cycle would improve both vehicle performance and battery health.

Diet quality in bipolar disorder in a population-based sample of women

Background : Recent epidemiological evidence has indicated a role for diet quality in unipolar depressive illness. This study examined the association between diet quality and bipolar disorder (BD) in an epidemiological cohort of randomly selected, population-based women aged 20–93 years.

Methods : An a priori diet quality score was derived from food frequency questionnaire data, a factor analysis identified habitual dietary patterns and glycemic load was assessed. Mental health was assessed using the SCID-I/NP.

Results : BD was identified in 23 women and there were 691 participants with no history of psychopathology. Compared to those with no psychopathology, those with BD had a higher glycemic load (p = 0.06) and higher scores on a ‘western’ dietary factor (p = 0.03) and the ‘modern’ dietary factor (p = 0.02). For each standard deviation increase in a ‘western’ and ‘modern’ dietary pattern and glycemic load, the odds ratios for BD were increased (‘western’ OR = 1.88, 95% CI 1.33–2.65; ‘modern’ OR = 1.72, 95% CI 1.14–2.39; GL OR = 1.56, 95% CI 1.13–2.14). Conversely, a ‘traditional’ dietary pattern was associated with reduced odds for BD (OR = 0.53 95% CI 0.32–0.89) after adjustments for overall energy intake.

Limitations : The small sample size did not allow for multivariate analyses and the cross-sectional study design precludes any determinations regarding the direction of the relationships between diet quality and BD.

Conclusion : These data are largely concordant with results from dietary studies in unipolar depression. However, clinical recommendations cannot be made until the direction of the relationship between diet quality and BD is determined. Longitudinal studies are warranted.

Text document clustering with metric learning

One reason for semi-supervised clustering fail to deliver satisfactory performance in document clustering is that the transformed optimization problem could have many candidate solutions, but existing methods provide no mechanism to select a suitable one from all those candidates. This paper alleviates this problem by posing the same task as a soft-constrained optimization problem, and introduces the salient degree measure as an information guide to control the searching of an optimal solution. Experimental results show the effectiveness of the proposed method in the improvement of the performance, especially when the amount of priori domain knowledge is limited.

Educational and mental health implications of the multidimensional model of the self-concept for adolescent girls : comparison of clinical and non-clinical samples

The importance of a positive self-concept as an educational outcome and a facilitator of other desirable outcomes are well established within the education research field. Although the multidimensional and hierarchical model of the self-concept is widely accepted within the educational psychology, this perspective is not widely used within the mental health research. Hence, the purpose of the present investigation is to compare the psychometric properties of the short version of the Self-Description Questionnaire (SDQII-S) based on responses by a large sample of female adolescent high school students (N= 829) and a clinical sample of adolescent girls who have been diagnosed with anorexia nervosa (N= 75). The well-established psychometric properties of the longer version of the SDQII generalise well to both samples of adolescent girls, and analyses provided good support for the invariance of the factor structure across the two samples. Furthermore, analyses employing new structural equation modelling approaches to comparing the latent mean differences indicated that there were differences (although surprisingly small) between the two groups that were generally consistent with a priori predictions. The important educational and clinical implications of these results are discussed.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.
2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.
3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.
4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.
5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.
6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

A validity-driven approach to the understanding of the personal and societal burden of low back pain : development of a conceptual and measurement model

Introduction: While the importance and magnitude of the burden of low back pain upon the individual is well recognized, a systematic understanding of the impact of the condition on individuals is currently hampered by the lack of an organized understanding of what aspects of a person’s life are affected and the lack of comprehensive measures for these effects. The aim of the present study was to develop a conceptual and measurement model of the overall burden of low back pain from the individual’s perspective using a validity-driven approach.
Methods: To define the breadth of low back pain burden we conducted three concept-mapping workshops to generate an item pool. Two face-to-face workshops (Australia) were conducted with people with low back pain and clinicians and policy-makers, respectively. A third workshop (USA) was held with international multidisciplinary experts. Multidimensional scaling, cluster analysis, participant input and thematic analyses organized participants’ ideas into clusters of ideas that then informed the conceptual model.
Results: One hundred and ninety-nine statements were generated. Considerable overlap was observed between groups, and four major clusters were observed - Psychosocial, Physical, Treatment and Employment - each with between two and six subclusters. Content analysis revealed that elements of the Psychosocial cluster were sufficiently distinct to be split into Psychological and Social, and a further cluster of elements termed Positive Effects also emerged. Finally, a hypothesized structure was proposed with six domains and 16 subdomains. New domains not previously considered in the back pain field emerged for psychometric verification: loss of independence, worry about the future, and negative or discriminatory actions by others.
Conclusions: Using a grounded approach, an explicit a priori and testable model of the overall burden of low back pain has been proposed that captures the full breadth of the burden experienced by patients and observed by experts.

3D sparse feature model using short baseline stereo and multiple view registration

This paper outlines a methodology to generate a distinctive object representation offline, using short-baseline stereo fundamentals to triangulate highly descriptive object features in multiple pairs of stereo images. A group of sparse 2.5D perspective views are built and the multiple views are then fused into a single sparse 3D model using a common 3D shape registration technique. Having prior knowledge, such as the proposed sparse feature model, is useful when detecting an object and estimating its pose for real-time systems like augmented reality.

Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives

In this paper, the single machine job shop scheduling problem is studied with the objectives of minimizing the tardiness and the material cost of jobs. The simultaneous consideration of these objectives is the multi-criteria optimization problem under study. A metaheuristic procedure based on simulated annealing is proposed to find the approximate Pareto optimal (non-dominated) solutions. The two objectives are combined in one composite utility function based on the decision maker’s interest in having a schedule with weighted combination. In view of the unknown nature of the weights for the defined objectives, a priori approach is applied to search for the non-dominated set of solutions based on the Pareto dominance. The obtained solutions set is presented to the decision maker to choose the best solution according to his preferences. The performance of the algorithm is evaluated in terms of the number of non-dominated schedules generated and the proximity of the obtained non-dominated front to the true Pareto front. Results show that the produced solutions do not differ significantly from the optimal solutions.

Latent variable models of need for uniqueness

The theory of uniqueness has been invoked to explain attitudinal and behavioral nonconformity with respect to peer-group, social-cultural, and statistical norms, as well as the development of a distinctive view of self via seeking novelty goods, adopting new products, acquiring scarce commodities, and amassing material possessions. Present research endeavors in psychology and consumer behavior are inhibited by uncertainty regarding the psychometric properties of the Need for Uniqueness Scale, the primary instrument for measuring individual differences in uniqueness motivation. In an important step toward facilitating research on uniqueness motivation, we used confirmatory factor analysis to evaluate three a priori latent variable models of responses to the Need for Uniqueness Scale. Among the a priori models, an oblique three-factor model best accounted for commonality among items. Exploratory factor analysis followed by estimation of unrestricted three- and four-factor models revealed that a model with a complex pattern of loadings on four modestly correlated factors may best explain the latent structure of the Need for Uniqueness Scale. Additional analyses evaluated the associations among the three a priori factors and an array of individual differences. Results of those analyses indicated the need to distinguish among facets of the uniqueness motive in behavioral research.

A new design of sliding mode control systems

A new sliding mode control technique for a class of SISO dynamic systems is presented in this chapter. It is seen that the stability status of the closed-loop system is first checked, based on the approximation of the most recent information of the first-order derivative of the Lyapunov function of the closed-loop system, an intelligent sliding mode controller can then be designed with the following intelligent features: (i) If the closed-loop system is stable, the correction term in the controller will continuously adjust control signal to drive the closed-loop trajectory to reach the sliding mode surface in a finite time and the desired closed-loop dynamics with the zero-error convergence can then be achieved on the sliding mode surface. (ii) If, however, the closed-loop system is unstable, the correction term is capable of modifying the control signal to continuously reduce the value of the derivative of the Lyapunov function from the positive to the negative and then drives the closed-loop trajectory to reach the sliding mode surface and ensures that the desired closed-loop dynamics can be obtained on the sliding mode surface. The main advantages of this new sliding mode control technique over the conventional one are that no chattering occurs in the sliding mode control system because of the recursive learning control structure; the system uncertainties are embedded in the Lipschitz-like condition and thus, no priori information on the upper and/or the lower bounds of the unknown system parameters and uncertain system dynamics is required for the controller design; the zero-error convergence can be achieved after the closed-loop dynamics reaches the sliding mode surface and remains on it. The performance for controlling a third-order linear system is evaluated in the simulation section to show the effectiveness and efficiency of the new sliding mode control technique.

Effect of noise factors in energy management of series plug-in hybrid electric vehicles

It has been demonstrated that charge depletion (CD) energy management strategies are more efficient choices for energy management of plug-in hybrid electric vehicles (PHEVs). The knowledge of drive cycle as a priori can improve the performance of CD energy management in PHEVs. However, there are many noise factors which affect both drivetrain power demand and vehicle performance even in identical drive cycles. In this research, the effect of each noise factor is investigated by introducing the concept of power cycle instead of drive cycle for a journey. Based on the nature of the noise factors, a practical solution for developing a power-cycle library is introduced. Investigating the predicted power cycle, an energy management strategy is developed which considers the influence of temperature noise factor on engine performance. The effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered. Simulation results for a modelled series PHEV similar to GM Volt show that the suggested energy management strategy based on the driver power cycle library improves both vehicle fuel economy and battery health by reducing battery load and temperature.

An empirical study of neighbourhood decay in Kohonen's self organizing map

In this paper, empirical results are presented which suggest that size and rate of decay of region size plays a much more significant role in the learning, and especially the development, of topographic feature maps. Using these results as a basis, a scheme for decaying region size during SOM training is proposed. The proposed technique provides near optimal training time. This scheme avoids the need for sophisticated learning gain decay schemes, and precludes the need for a priori knowledge of likely training times. This scheme also has some potential uses for continuous learning.

An efficient least common subgraph algorithm for video indexing

Many tasks in computer vision can be expressed as graph problems. This allows the task to be solved using a well studied algorithm, however many of these algorithms are of exponential complexity. This is a disadvantage when considered in the context of searching a database of images or videos for similarity. Work by Mesaner and Bunke (1995) has suggested a new class of graph matching algorithms which uses a priori knowledge about a database of models to reduce the time taken during online classification. This paper presents a new algorithm which extends the earlier work to detection of the largest common subgraph.