Feature selection using expected attainable discrimination

We propose expected attainable discrimination (EAD) as a measure to select discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves obtained when a simple histogram probability density model is trained and tested on many random partitions of a data set. EAD can be incorporated into various stepwise search methods to determine promising subsets of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described.

Stochastic analysis of transit route segments' passenger load variation for capacity and quality of service assessment

This paper investigates stochastic analysis of transit segment hourly passenger load factor variation for transit capacity and quality of service (QoS) analysis using Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia. It compares stochastic analysis to traditional peak hour factor (PHF) analysis to gain further insight into variability of transit route segments’ passenger loading during a study hour. It demonstrates that hourly design load factor is a useful method of modeling a route segment’s capacity and QoS time history across the study weekday. This analysis method is readily adaptable to different passenger load standards by adjusting design percentile, reflecting either a more relaxed or more stringent condition. This paper also considers hourly coefficient of variation of load factor as a capacity and QoS assessment measure, in particular through its relationships with hourly average and design load factors. Smaller value reflects uniform passenger loading, which is generally indicative of well dispersed passenger boarding demands and good schedule maintenance. Conversely, higher value may be indicative of pulsed or uneven passenger boarding demands, poor schedule maintenance, and/or bus bunching. An assessment table based on hourly coefficient of variation of load factor is developed and applied to this case study. Inferences are drawn for a selection of study hours across the weekday studied.

Effect of mobile phone use and aggression on speed selection by young drivers : a driving simulator study

Aggressive driving has been associated with engagement in other risky driving behaviours, such as speeding; while drivers using their mobile phones have an increased crash risk, despite the tendency to reduce their speed. Research has amassed separately for mobile phone use and aggressive driving among younger drivers, however little is known about the extent to which these behaviours may function independently and in combination to influence speed selection behaviour. The main aim of the current study was to investigate the effect of driver aggression (measured by the Driving Anger Expression Inventory) and mobile phone use on speed selection by young drivers. The CARRS-Q advanced driving simulator was used to test the speed selection of drivers aged 18 to 26 years (N = 32) in a suburban (60kph zone) driving context. A 2 (level of driving anger expression: low, high) X 3 (mobile phone use condition: baseline, hands-free, hand-held) mixed factorial ANOVA was conducted with speed selection as the dependent variable. Results revealed a significant main effect for mobile phone use condition such that speed selection was lowest for the hand-held condition and highest for the baseline condition. Speed selection, however, was not significantly different across the levels of driving anger expression; nor was there a significant interaction effect between the mobile phone use and driving anger expression. As young drivers are over-represented in road crash statistics, future research should further investigate the combined impact of driver aggression and mobile phone use on speed selection.

Growth medium selection and its economic impact on plasmid DNA production

Current developments in gene medicine and vaccination studies are utilizing plasmid DNA (pDNA) as the vector. For this reason, there has been an increasing trend towards larger and larger doses of pDNA utilized in human trials: from 100-1000 μg in 2002 to 500-5000 μg in 2005. The increasing demand of pDNA has created the need to revolutionalize current production levels under optimum economy. In this work, different standard media (LB, TB and SOC) for culturing recombinant Escherichia coli DH5α harbouring pUC19 were compared to a medium optimised for pDNA production. Lab scale fermentations using the standard media showed that the highest pDNA volumetric and specific yields were for TB (11.4 μg/ml and 6.3 μg/mg dry cell mass respectively) and the lowest was for LB (2.8 μg/ml and 3.3 μg/mg dry cell mass respectively). A fourth medium, PDMR, designed by modifying a stoichiometrically-formulated medium with an optimised carbon source concentration and carbon to nitrogen ratio displayed pDNA volumetric and specific yields of 23.8 μg/ml and 11.2 μg/mg dry cell mass respectively. However, it is the economic advantages of the optimised medium that makes it so attractive. Keeping all variables constant except medium and using LB as a base scenario (100 medium cost [MC] units/mg pDNA), the optimised PDMR medium yielded pDNA at a cost of only 27 MC units/mg pDNA. These results show that greater amounts of pDNA can be obtained more economically with minimal extra effort simply by using a medium optimised for pDNA production.

Rethinking board composition: Balancing competence and compatibility in director selection

Board composition is critical to board effectiveness. Shaping an effective board begins with the selection of directors. While much attention has been paid to the skills and qualifications directors require, there has been less focus on the necessity for board members to interact and work well together. This exploratory study offers insights into what qualities directors look for when selecting new members and the approach adopted to identify and select them. The findings of 10 in-depth interviews with Australian directors suggest new members are selected both on competencies and compatibility. Yet not all selection approaches adequately assess candidates for these two criteria. As a result many appointments fail to realise the selection criteria reducing capacity to reach its full potential.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Automatic camera exposure control

It is commonplace to use digital video cameras in robotic applications. These cameras have built-in exposure control but they do not have any knowledge of the environment, the lens being used, the important areas of the image and do not always produce optimal image exposure. Therefore, it is desirable and often necessary to control the exposure off the camera. In this paper we present a scheme for exposure control which enables the user application to determine the area of interest. The proposed scheme introduces an intermediate transparent layer between the camera and the user application which combines the information from these for optimal exposure production. We present results from indoor and outdoor scenarios using directional and fish-eye lenses showing the performance and advantages of this framework.

Reflexive and automatic violence: A function of aberrant perceptual inhibition

It’s commonly assumed that psychiatric violence is motivated by delusions, but here the concept of a reversed impetus is explored, to understand whether delusions are formed as ad-hoc or post-hoc rationalizations of behaviour or in advance of the actus reus. The reflexive violence model proposes that perceptual stimuli has motivational power and this may trigger unwanted actions and hallucinations. The model is based on the theory of ecological perception, where opportunities enabled by an object are cues to act. As an apple triggers a desire to eat, a gun triggers a desire to shoot. These affordances (as they are called) are part of the perceptual apparatus, they allow the direct recognition of objects – and in emergencies they enable the fastest possible reactions. Even under normal circumstances, the presence of a weapon will trigger inhibited violent impulses. The presence of a victim will also, but under normal circumstances, these affordances don’t become violent because negative action impulses are totally inhibited, whereas in psychotic illness, negative action impulses are treated as emergencies and bypass frontal inhibitory circuits. What would have been object recognition becomes a blind automatic action. A range of mental illnesses can cause inhibition to be bypassed. At its most innocuous, this causes both simple hallucinations (where the motivational power of an object is misattributed). But ecological perception may have the power to trigger serious violence also –a kind that’s devoid of motives or planning and is often shrouded in amnesia or post-rational delusions.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.