The Soldier's Wife (LP)

Audio recording of 10 tracks funded by Legacy, The Australia Council, and Arts Queensland. The recordings explore the untold stories of soldiers' wives through song. It features the vocal and songwriting talents of Jackie Marshall, Bertie Page, Sahara Beck, Emma Bosworth, Roz Pappalardo, and Kristy Apps. Recorded, Mixed, Mastered, and Co-Produced by Phil Graham.

Queensland farmers rise up to take on the miners

Queensland’s Surat Basin has the third largest energy resource in the world — with vast coal seam gas and coal reserves — but farm groups are warning that mining areas, which are prime farm land, risk catastrophic environmental damage to food-producing areas. Mining development is moving very quickly with 36,000 wells due to be sunk in the next few years. A Senate inquiry into the impacts of mining in the Murray-Darling Basin heard evidence from farm and mining industry representatives in Oakey on the Darling Downs yesterday.

Conspiracy for good

"Tim Kring, Creator of the hit television show 'Heroes' tells how the big idea began, and where you can jump in. "A few years ago, I started thinking about an entirely new way to tell a story, far different from traditional TV. I didn't just want to talk about 'saving the world' in fiction, I wanted to create a narrative that spilled out into the streets. One that you could live inside of for a while. How cool would it be, I thought, to create a story that exists all around you all of the time? On your laptop, your mobile phone, on your sidewalks, as a secret message hidden in your favorite song or while standing at the bus stop on your way to work. And, taking it further, what if your participation over a few weeks or months actually impacts the story's development and creates positive change in the real world because a philanthropic mission is integrated into the narrative itself? The Conspiracy For Good is the culmination of this dream. This is the pilot project for a first-of-itskind interactive story that empowers its audience to take real-life action and create positive change in the world. Call it Social Benefit Storytelling. To achieve this, I need you to participate. Reality and fiction have to blur. Every story needs a villain and you will meet the villain in the STORY SO FAR section on this site. And every story needs a hero. That's where YOU come in. As part of The Conspiracy For Good you will join a collective of thinkers, artists, musicians, and causes, creating a unified voice to fight the forces of social and environmental injustice. This is our site, where together we can follow the story and build a community that focuses on changing the world for the better, one person and one action at a time. Welcome to the Conspiracy." Tim Kring"

Adaptive estimation of HMM transition probabilities

This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMM's) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the ...

On adaptive HMM state estimation

In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.

Faster parameter estimation using risk-sensitive filters

In this paper, we propose a risk-sensitive approach to parameter estimation for hidden Markov models (HMMs). The parameter estimation approach considered exploits estimation of various functions of the state, based on model estimates. We propose certain practical suboptimal risk-sensitive filters to estimate the various functions of the state during transients, rather than optimal risk-neutral filters as in earlier studies. The estimates are asymptotically optimal, if asymptotically risk neutral, and can give significantly improved transient performance, which is a very desirable objective for certain engineering applications. To demonstrate the improvement in estimation simulation studies are presented that compare parameter estimation based on risk-sensitive filters with estimation based on risk-neutral filters.

Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

Looming aircraft threats : shape-based passive ranging of aircraft from monocular vision

This paper proposes new techniques for aircraft shape estimation, passive ranging, and shape-adaptive hidden Markov model filtering which are suitable for a monocular vision-based non-cooperative collision avoidance system. Vision-based passive ranging is an important missing technology that could play a significant role in resolving the sense-and-avoid problem in un-manned aerial vehicles (UAVs); a barrier hindering the wider adoption of UAVs for civilian applications. The feasibility of the pro- posed shape estimation, passive ranging and shape-adaptive filtering techniques is evaluated on flight test data.

Short-data recursive HMM parameter estimation for rapid vision-based aircraft heading estimation

Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that place an emphasis on the early availability of reasonable estimates (e.g. for change detection) rather than the provision of longer-term asymptotic properties (such as convergence, convergence rate, and consistency). In the context of vision- based aircraft (image-plane) heading estimation, this paper suggests and evaluates the short-data estimation properties of 3 recursive HMM parameter estimation techniques (a recursive maximum likelihood estimator, an online EM HMM estimator, and a relative entropy based estimator). On both simulated and real data, our studies illustrate the feasibility of rapid recursive heading estimation, but also demonstrate the need for careful step-size design of HMM recursive estimation techniques when these techniques are intended for use in applications where short-data behaviour is paramount.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Soluble NSF attachment protein receptor molecular mimicry by a Legionella pneumophila Dot/Icm effector

Upon infection, Legionella pneumophila uses the Dot/Icm type IV secretion system to translocate effector proteins from the Legionella-containing vacuole (LCV) into the host cell cytoplasm. The effectors target a wide array of host cellular processes that aid LCV biogenesis, including the manipulation of membrane trafficking. In this study, we used a hidden Markov model screen to identify two novel, non-eukaryotic soluble NSF attachment protein receptor (SNARE) homologs: the bacterial Legionella SNARE effector A (LseA) and viral SNARE homolog A proteins. We characterized LseA as a Dot/Icm effector of L. pneumophila, which has close homology to the Qc-SNARE subfamily. The lseA gene was present in multiple sequenced L. pneumophila strains including Corby and was well distributed among L. pneumophila clinical and environmental isolates. Employing a variety of biochemical, cell biological and microbiological techniques, we found that farnesylated LseA localized to membranes associated with the Golgi complex in mammalian cells and LseA interacted with a subset of Qa-, Qb- and R-SNAREs in host cells. Our results suggested that LseA acts as a SNARE protein and has the potential to regulate or mediate membrane fusion events in Golgi-associated pathways.

The concept of collaboration : a critical exploration of the care continuum

This thesis critically explored the concept of collaboration through an analysis of the experiences of midwives, child health nurses and women in the process of transition from hospital to community care and related policy documents. The research concluded that the concept serves an important social function in obscuring the complexity of social relations in healthcare. Rather than adopt an unquestioning attitude to what is represented as collaboration this thesis argues for a more critical examination of what is occurring, what is potentially hidden and how specific interests are served through its use.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Issues to consider when developing mandatory reporting legislation and policy to improve discovery of child abuse

In the United Kingdom, recent investigations into child sexual abuse occurring within schools, the Catholic Church and the British Broadcasting Corporation, have intensified debate on ways to improve the discovery of child sexual abuse, and child maltreatment generally. One approach adopted in other jurisdictions to better identify cases of severe child maltreatment is the introduction of some form of legislative mandatory reporting to require designated persons to report known and suspected cases. The debate in England has raised the prospect of whether adopting a strategy of some kind of mandatory reporting law is advisable. The purpose of this article is to add to this debate by identifying fundamental principles, issues and complexities underpinning policy and even legislative developments in the interests of children and society. The article will first highlight the data on the hidden nature of child maltreatment and the background to the debate. Secondly, it will identify some significant gaps in knowledge that need to be filled. Thirdly, the article will summarise the barriers to reporting abuse and neglect. Fourthly, we will identify a range of options for, and clarify the dilemmas in developing, legislative mandatory reporting, addressing two key issues: who should be mandated to report, and what types of child maltreatment should they be required to report? Finally, we draw attention to some inherently different goals and competing interests, both between and within the various institutions involved in the safeguarding of children and the criminal prosecution of some offenders. Based on this analysis we offer some concluding observations that we hope contribute to informed and careful debate about mandatory reporting.

Caught in the headlights: Designing for creative learning and teaching in higher education

This study was an exploration of the different ways that educators conceptualise and approach creative learning and teaching. The research revealed theoretical and practice-based insights, demonstrating that exemplary teachers adopt an ecological approach to designing for student creativity; this approach acknowledges and works with the complexity of the higher education environment and the dynamic relationships between students, peers and teachers. The inquiry confirmed the value of using learning design patterns to uncover hidden creative processes.