Early users of fertility treatment with hormones and IVF : women who live in major cities and have private health insurance

Objective: To identify early users (women aged <34 years) of fertility treatment with hormones and in vitro fertilisation (IVF). Methods: A cross-sectional survey of infertile women from fertility clinics (n=59) and from the community (Australian Longitudinal Study on Women's Health participants) who had (n=121) or had not (n=110) used hormones/IVF as treatment for infertility. Associations between socio-demographic, reproductive and lifestyle factors, medical conditions and recurrent symptoms and using treatment (or not) were analysed using multivariable logistic regression. Results: Among infertile women who had used treatment (community vs clinic), women from clinics had lower odds of living outside major cities, using hormones only, i.e., not IVF, or recurrent headaches/migraines, severe tiredness, or stiff/painful joints; and higher odds of recent diagnoses of urinary tract infection or anxiety disorder. Compared to infertile women who had not used treatment, women from clinics had lower odds of living outside major cities, recurrent allergies or severe tiredness; and higher odds of having private health insurance for hospital or ancillary services, recent diagnosis of polycystic ovary syndrome or recurrent constipation. Conclusions: Compared to infertile women in the community, living in major cities and having private health insurance are associated with early use of treatment for infertility at specialist clinics by women aged <34 years. Implications: These results provided evidence of inequity of services for infertile women.

Automatic Audio Segmentation Using the Generalized Likelihood Ratio

This paper presents a novel technique for segmenting an audio stream into homogeneous regions according to speaker identities, background noise, music, environmental and channel conditions. Audio segmentation is useful in audio diarization systems, which aim to annotate an input audio stream with information that attributes temporal regions of the audio into their specific sources. The segmentation method introduced in this paper is performed using the Generalized Likelihood Ratio (GLR), computed between two adjacent sliding windows over preprocessed speech. This approach is inspired by the popular segmentation method proposed by the pioneering work of Chen and Gopalakrishnan, using the Bayesian Information Criterion (BIC) with an expanding search window. This paper will aim to identify and address the shortcomings associated with such an approach. The result obtained by the proposed segmentation strategy is evaluated on the 2002 Rich Transcription (RT-02) Evaluation dataset, and a miss rate of 19.47% and a false alarm rate of 16.94% is achieved at the optimal threshold.

The Yonder project : literacy and social competency development in primary school students through co-creation of live performance

Since 2007 Kite Arts Education Program (KITE), based at Queensland Performing Arts Centre (QPAC), has been engaged in delivering a series of theatre-based experiences for children in low socio-economic primary schools in Queensland. The artist in residence (AIR) project titled Yonder includes performances developed by the children with the support and leadership of teacher artists from KITE for their community and parents/carers,supported by a peak community cultural institution. In 2009,Queensland Performing Arts Centre partnered with Queensland University of Technology (QUT) Creative Industries Faculty (Drama) to conduct a three-year evaluation of the Yonder project to understand the operational dynamics, artistic outputs and the educational benefits of the project. This paper outlines the research findings for children engaged in the Yonder project in the interrelated areas of literacy development and social competencies. Findings are drawn from six iterations of the project in suburban locations on the edge of Brisbane city and in regional Queensland.

The extrusion of continuous lengths of YBa2Cu3O7-x wire using hydroxypropyl methylcellulose

Wires of YBa2Cu3O7-x were fabricated by extrusion using a hydroxypropyl methylcellulose (HPMC) binder. As little as 2 wt.% binder was added to an oxide prepared by a novel co-precipitation process, to produce a plastic mass which readily gave continuous extrusion of long lengths of wire in a reproducible fashion. Critical temperatures of 92K were obtained for wires given optimum high-temperature heat treatments. Critical current densities greater than 1000 A cm-1 were measured at 77.3K using heat treatments at around 910°C for 10h. These transport critical current densities, measured on centimeter-long wires, were obtained with microstructures showing a relatively dense and uniform distribution of randomly oriented, small YBa2Cu3O7-x grains. © 1993.

'Going live' : establishing the creative attributes of the live multi-camera television professional

In my capacity as a television professional and teacher specialising in multi-camera live television production for over 40 years, I was drawn to the conclusion that opaque or inadequately formed understandings of how creativity applies to the field of live television, have impeded the development of pedagogies suitable to the teaching of live television in universities. In the pursuit of this hypothesis, the thesis shows that television degrees were born out of film studies degrees, where intellectual creativity was aligned to single camera production, and the 'creative roles' of producers, directors and scriptwriters. At the same time, multi-camera live television production was subsumed under the 'mass communication' banner, leading to an understanding that roles other than producer and director are simply technical, and bereft of creative intent or acumen. The thesis goes on to show that this attitude to other television production personnel, for example, the vision mixer, videotape operator and camera operator, relegates their roles to that of 'button pusher'. This has resulted in university teaching models with inappropriate resources and unsuitable teaching practices. As a result, the industry is struggling to find people with the skills to fill the demands of the multi-camera live television sector. In specific terms the central hypothesis is pursued through the following sequenced approach. Firstly, the thesis sets out to outline the problems, and traces the origins of the misconceptions that hold with the notion that intellectual creativity does not exist in live multi-camera television. Secondly, this more adequately conceptualised rendition, of the origins particular to the misconceptions of live television and creativity, is then anchored to the field of examination by presentation of the foundations of the roles involved in making live television programs, using multicamera production techniques. Thirdly, this more nuanced rendition of the field sets the stage for a thorough analysis of education and training in the industry, and teaching models at Australian universities. The findings clearly establish that the pedagogical models are aimed at single camera production, a position that deemphasises the creative aspects of multi-camera live television production. Informed by an examination of theories of learning, qualitative interviews, professional reflective practice and observations, the roles of four multi-camera live production crewmembers (camera operator, vision mixer, EVS/videotape operator and director's assistant), demonstrate the existence of intellectual creativity during live production. Finally, supported by the theories of learning, and the development and explication of a successful teaching model, a new approach to teaching students how to work in live television is proposed and substantiated.

Users segmentations for recommendation

Traditional recommendation methods provide recommendations equally to all users. In this paper, a segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs in order to offer a specific recommendation strategy to each segment. Experiment is conducted using a live online dating network data.

A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation

This paper presents an alternative approach to image segmentation by using the spatial distribution of edge pixels as opposed to pixel intensities. The segmentation is achieved by a multi-layered approach and is intended to find suitable landing areas for an aircraft emergency landing. We combine standard techniques (edge detectors) with novel developed algorithms (line expansion and geometry test) to design an original segmentation algorithm. Our approach removes the dependency on environmental factors that traditionally influence lighting conditions, which in turn have negative impact on pixel-based segmentation techniques. We present test outcomes on realistic visual data collected from an aircraft, reporting on preliminary feedback about the performance of the detection. We demonstrate consistent performances over 97% detection rate.

Automatic object segmentation of unstructured scenes using colour and depth maps

This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.

GrabCutSFM : how 3D information improves unsupervised object segmentation

In this paper, we present an unsupervised graph cut based object segmentation method using 3D information provided by Structure from Motion (SFM), called Grab- CutSFM. Rather than focusing on the segmentation problem using a trained model or human intervention, our approach aims to achieve meaningful segmentation autonomously with direct application to vision based robotics. Generally, object (foreground) and background have certain discriminative geometric information in 3D space. By exploring the 3D information from multiple views, our proposed method can segment potential objects correctly and automatically compared to conventional unsupervised segmentation using only 2D visual cues. Experiments with real video data collected from indoor and outdoor environments verify the proposed approach.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

On the use of Bluetooth MAC Scanners for live reporting of the transport network

Recently there has been significant interest of researchers and practitioners on the use of Bluetooth as a complementary transport data. However, literature is limited with the understanding of the Bluetooth MAC Scanner (BMS) based data acquisition process and the properties of the data being collected. This paper first provides an insight on the BMS data acquisition process. Thereafter, it discovers the interesting facts from analysis of the real BMS data from both motorway and arterial networks of Brisbane, Australia. The knowledge gained is helpful for researchers and practitioners to understand the BMS data being collected which is vital to the development of management and control algorithms using the data.

Making the train : an examination of five perspectives of rail access and how they resonate with existing and potential passengers

Rail operators recognize a need to increase ridership in order to improve the economic viability of rail service, and to magnify the role that rail travel plays in making cities feel liveable. This study extends previous research that used cluster analysis with a small sample of rail passengers to identify five salient perspectives of rail access (Zuniga et al, 2013). In this project stage, we used correlation techniques to determine how those perspectives would resonate with two larger study populations, including a relatively homogeneous sample of university students in Brisbane, Australia and a diverse sample of rail passengers in Melbourne, Australia. Findings from Zuniga et al. (2013) described a complex typology of current passengers that was based on respondents’ subjective attitudes and perceptions rather than socio-demographic or travel behaviour characteristics commonly used for segmentation analysis. The typology included five qualitative perspectives of rail travel. Based on the transport accessibility literature, we expected to find that perspectives from that study emphasizing physical access to rail stations would be shared by current and potential rail passengers who live further from rail stations. Other perspectives might be shared among respondents who live nearby, since the relevance of distance would be diminished. The population living nearby would thus represent an important target group for increasing ridership, since making rail travel accessible to them does not require expansion of costly infrastructure such as new lines or stations. By measuring the prevalence of each perspective in a larger respondent pool, results from this study provide insight into the typical socio-demographic and travel behaviour characteristics that correspond to each perspective of intra-urban rail travel. In several instances, our quantitative findings reinforced Zuniga et al.’s (2013) qualitative descriptions of passenger types, further validating the original research. This work may directly inform rail operators’ approach to increasing ridership through marketing and improvements to service quality and station experience. Operators in other parts of Australia and internationally may also choose to replicate the study locally, to fine-tune understanding of diverse customer bases. Developing regional and international collaboration would provide additional opportunities to evaluate and benchmark service and station amenities as they address the various access dimensions.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Exploiting Web 2.0 technologies to forge live connectedness in class

The economics of supporting learning has seen institutional encouragement of a wide range of blended learning initiatives in face to face and online teaching and learning. This has become one of the key drivers for the adoption of technology in teaching, in a manner occassionally guilty of putting the cart before the horse. Learning spaces are increasingly equipped with a dizzying array of technological options testifying to institutional and governmental investment and commitment in supporting face to face blended learning (QUT, 2011, C/4.2). Yet innovation within traditional learning and teaching models faces a number of challenges both at an institutional level and at the teaching coal face. Web 2.0 technologies present a vast array of opportunities to harness and capture the attention of students in engaging learning opportunitites. This presentation will explore technologies supportive of active learning pedagogies.