Relative entropy rate based model selection for linear hybrid system filters of uncertain nonlinear systems

Hybrid system representations have been exploited in a number of challenging modelling situations, including situations where the original nonlinear dynamics are too complex (or too imprecisely known) to be directly filtered. Unfortunately, the question of how to best design suitable hybrid system models has not yet been fully addressed, particularly in the situations involving model uncertainty. This paper proposes a novel joint state-measurement relative entropy rate based approach for design of hybrid system filters in the presence of (parameterised) model uncertainty. We also present a design approach suitable for suboptimal hybrid system filters. The benefits of our proposed approaches are illustrated through design examples and simulation studies.

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

A toolbox for animal call recognition

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems.

Contextualising learning for a real-world university: how an inverted curriculum in the first year can help better student retention

An identified issue within higher education is the high rates of student attrition after the first year, especially in the STEM disciplines. To address this issue, it is essential to reexamine and redesign the first year curriculum to engage and retain the students' interests while also scaffolding their learning experience. This session reports on an initiative based on the principles of the “inverted curriculum” within the Bachelor of Technology (BIT) course at the Queensland University of Technology (QUT) that began in 2009 and has resulted in a reduction in first-year attrition rates from 18% in 2008 to 10% in 2009 and 2010 despite a growth in student intake of 15% to 40% in the past two years. We present the process and methods that helped achieve this and initiate a discussion on the innovations that are possible within this concept of inverted curriculum and how it can be implemented.

Cultural impact on e-service use in Saudi Arabia : results from focus groups

This paper reports the results of focus groups obtained as part of a full study that uses a mixed method approach to answer the following question: what are the cultural values that impact on e-service use in Saudi Arabia? In order to answer this question we reviewed culture theories, dimensions, and models that have been identified in the literature. Four focus groups interviewing experts and general users (customers) of online services in Saudi Arabia have been completed aiming at the end to identify the uncovered elements of Saudi culture in the literature, which hopefully will result in developing a framework of cultural values that affect e-service use in Saudi context. This paper will firstly, introduce the importance of culture and define the aspects of Saudi culture. It will then describe the method used, and finally discussing the findings of the focus groups. Findings revealed four factors that have not been covered in the literature and need to be investigated namely: nepotism, the lack of human interaction, services oriented culture, and the career path.

Isolation and functional characterisation of banana phytoene synthase genes as potential cisgenes

Carotenoids occur in all photosynthetic organisms where they protect photosystems from auto-oxidation, participate in photosynthetic energy-transfer and are secondary metabolites. Of the more than 600 known plant carotenoids, few can be converted into vitamin A by humans and so these pro-vitamin A carotenoids (pVAC) are important in human nutrition. Phytoene synthase (PSY) is a key enzyme in the biosynthetic pathway of pVACs and plays a central role in regulating pVAC accumulation in the edible portion of crop plants. Bananas are a major commercial crop and serve as a staple crop for more than 30 million people. There is natural variation in fruit pVAC content across different banana cultivars, but this is not well understood. Therefore, we isolated PSY genes from banana cultivars with relatively high (cv. Asupina) and low (cv. Cavendish) pVAC content. We provide evidence that PSY in banana is encoded by two paralogs (PSY1 and PSY2), each with a similar gene structure to homologous genes in other monocots. Further, we demonstrate that PSY2 is more highly expressed in fruit pulp compared to leaf. Functional analysis of PSY1 and PSY2 in rice callus and E. coli demonstrate that both genes encode functional enzymes, and that Asupina PSYs have approximately twice the enzymatic activity of the corresponding Cavendish PSYs. These results suggest that differences in PSY enzyme activity contribute significantly to the differences in Asupina and Cavendish fruit pVAC content. Importantly, Asupina PSY genes could potentially be used to generate new cisgenic or intragenic banana cultivars with enhanced pVAC content.

Creative industries and innovation in a knowledge economy

The cultural and creative industries contribute to the knowledge economy by their role in reproducing cultural knowledge and through provision of entertainment, experience and leisure goods with cultural content, for which they are widely acknowledged as suffering serious market failure problems (Baumol and Bowen, 1966; Throsby and Withers, 1979). But they also contribute to the innovation process, an aspect that has only recently been appreciated. Specifically, the creative industries are a driver of the knowledge economy by their contribution to the innovation process on the demand side of consumer uptake of new ideas and by their facilitation of consumer-producer interaction. The creative industries are, in this respect, a legitimate part of the innovation system of a knowledge economy.