View from a bridge : what makes a musical city

The attention paid by the British music press in 1976 to the release of The Saints first single “I’m Stranded” was the trigger for a commercial and academic interest in the Brisbane music scene which still has significant energy. In 2007, Brisbane was identifed by Billboard Magazine as a “hot spot” of independent music. A place to watch. Someone turned a torch on this town, had a quick look, moved on. But this town has always had music in it. Some of it made by me. So, I’m taking this connection of mine, and working it into a contextual historical analysis of the creative lives of Brisbane musicians. I will be interviewing a number of Brisbane musicians. These interviews have begun, and will continue to be be conducted in 2011/2012. I will ask questions and pursue memories that will encompass family, teenage years, siblings, the suburbs, the city, venues, television and radio; but then widen to welcome the river, the hills and mountains, foes and friends, beliefs and death. The wider research will be a contextual historical analysis of the creative lives of Brisbane musicians. It will explore the changing nature of their work practices over time and will consider the notion, among other factors, of ‘place’ in both their creative practice and their creative output. It will also examine how the presence of the practitioners and their work is seen to contribute to the cultural life of the city and the creative lives of its citizens into the future. This paper offers an analysis of this last notion: how does this city see its music-makers? In addition to the interviews, over 300 Brisbane musicians were surveyed in September 2009 as part of a QUT-initiated recorded music event (BIGJAM). Their responses will inform the production of this paper.

Brain-derived neurotrophic factor (BDNF) gene : no major impact on antidepressant treatment response

The brain-derived neurotrophic factor (BDNF) has been suggested to play a pivotal role in the aetiology of affective disorders. In order to further clarify the impact of BDNF gene variation on major depression as well as antidepressant treatment response, association of three BDNF polymorphisms [rs7103411, Val66Met (rs6265) and rs7124442] with major depression and antidepressant treatment response was investigated in an overall sample of 268 German patients with major depression and 424 healthy controls. False discovery rate (FDR) was applied to control for multiple testing. Additionally, ten markers in BDNF were tested for association with citalopram outcome in the STAR*D sample. While BDNF was not associated with major depression as a categorical diagnosis, the BDNF rs7124442 TT genotype was significantly related to worse treatment outcome over 6 wk in major depression (p=0.01) particularly in anxious depression (p=0.003) in the German sample. However, BDNF rs7103411 and rs6265 similarly predicted worse treatment response over 6 wk in clinical subtypes of depression such as melancholic depression only (rs7103411: TT

Sequential decision fusion for controlled detection errors

Information fusion in biometrics has received considerable attention. The architecture proposed here is based on the sequential integration of multi-instance and multi-sample fusion schemes. This method is analytically shown to improve the performance and allow a controlled trade-off between false alarms and false rejects when the classifier decisions are statistically independent. Equations developed for detection error rates are experimentally evaluated by considering the proposed architecture for text dependent speaker verification using HMM based digit dependent speaker models. The tuning of parameters, n classifiers and m attempts/samples, is investigated and the resultant detection error trade-off performance is evaluated on individual digits. Results show that performance improvement can be achieved even for weaker classifiers (FRR-19.6%, FAR-16.7%). The architectures investigated apply to speaker verification from spoken digit strings such as credit card numbers in telephone or VOIP or internet based applications.

Housing supply in Australia : the impact of the availability of development finance

Before the Global Financial Crisis many providers of finance had growth mandates and actively pursued development finance deals as a way of gaining higher returns on funds with regular capital turnover and re-investment possible. This was able to be achieved through high gearing and low presales in a strong market. As asset prices fell, loan covenants breached and memories of the 1990’s returned, banks rapidly adjusted their risk appetite via retraction of gearing and expansion of presale requirements. Early signs of loosening in bank credit policy are emerging, however parties seeking development finance are faced with a severely reduced number of institutions from which to source funding. The few institutions that are lending are filtering out only the best credit risks by way of constrictive credit conditions including: low loan to value ratios, the corresponding requirement to contribute high levels of equity, lack of support in non-prime locations and the requirement for only borrowers with well established track records. In this risk averse and capital constrained environment, the ability of developers to proceed with real estate developments is still being constrained by their inability to obtain project finance. This paper will examine the pre and post GFC development finance environment. It will identify the key lending criteria relevant to real estate development finance and will detail the related changes to credit policies over this period. The associated impact to real estate development projects will be presented, highlighting the significant constraint to supply that the inability to obtain finance poses.

Augmenting RatSLAM using FAB-MAP-based visual data association

This paper investigates the use of the FAB-MAP appearance-only SLAM algorithm as a method for performing visual data association for RatSLAM, a semi-metric full SLAM system. While both systems have shown the ability to map large (60-70km) outdoor locations of approximately the same scale, for either larger areas or across longer time periods both algorithms encounter difficulties with false positive matches. By combining these algorithms using a mapping between appearance and pose space, both false positives and false negatives generated by FAB-MAP are significantly reduced during outdoor mapping using a forward-facing camera. The hybrid FAB-MAP-RatSLAM system developed demonstrates the potential for successful SLAM over large periods of time.

Emotional and behavioural reaction to intrusive thoughts

A self-report measure of the emotional and behavioural reactions to intrusive thoughts was developed. The paper presents data that confirm the stability, reliability and validity of the new 7-item measure. Emotional and behavioural reactions to intrusions emerged as separate factors on the Emotional and Behavioural Reactions to Intrusions Questionnaire (EBRIQ), a finding confirmed by an independent stress study. Test retest reliability over 30-70 days was good. Expected relationships with other constructs were significant. Stronger negative responses to intrusions were associated with lower mindfulness scores and higher ratings of experiential avoidance, thought suppression and intensity and frequency of craving. The EBRIQ will help explore differences in reactions to intrusive thoughts in clinical and non clinical populations, and across different emotional and behavioural states. It will also be useful in assessing the effects of therapeutic approaches such as mindfulness.

Temporal-based support vector machines for facial expression recognition

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

PIPP #1 - Pedestrian Interaction Patch Project

The Pedestrian Interaction Patch Project (PIPP) seeks to exert influence over and encourage abnormal pedestrian behavior. By placing an unadvertised (and non recording) interactive video manipulation system and projection source in a high traffic public area, the PIPP allows pedestrians to privately (and publically) re-engage with a previously inactive physical environment, like a commonly used walkway or corridor. This system, the results of which are projected in real time on the architectural surface, inadvertently provides pedestrians with questions around preconceived notions of self and space. In an attempt to re-activate our relationship with the physical surrounds we occupy each day the PIPP creates a new set of memories to be recalled as we re-enter known environments once PIPP has moved on and as such re-enlivens our relationship with the everyday architecture we stroll past everyday. The PIPP environment is controlled using the software program Isadora, devised by Mark Coniglio at Troika Ranch, and contains a series of video manipulation patches that are designed to not only grab the pedestrians attention but to also encourage a sense of play and interaction between the architecture, the digital environment, the initially unsuspecting participant(s) and the pedestrian audience. The PIPP was included as part of the planned walking tour for the “Playing in Urban Spaces” seminar day, and was an installation that ran for the length of the symposium in a reclaimed pedestrian space that was encountered by both the participants and general public during the course of the day long event. Ideally once discovered PIPP encouraged pedestrians to return through the course of the seminar day to see if the environmental patches had changed or altered, and changed their standard route to include the PIPP installation or to avoid it, either way, encouraging an active response to the pathways normally traveled or newly discovered each day.

Robust image hashing using higher order spectral features

Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These image hashes can be used for watermarking, image integrity authentication or image indexing for fast retrieval. This paper introduces a new method of generating image hashes based on extracting Higher Order Spectral features from the Radon projection of an input image. The feature extraction process is non-invertible, non-linear and different hashes can be produced from the same image through the use of random permutations of the input. We show that the transform is robust to typical image transformations such as JPEG compression, noise, scaling, rotation, smoothing and cropping. We evaluate our system using a verification-style framework based on calculating false match, false non-match likelihoods using the publicly available Uncompressed Colour Image database (UCID) of 1320 images. We also compare our results to Swaminathan’s Fourier-Mellin based hashing method with at least 1% EER improvement under noise, scaling and sharpening.

Dangerous places: A novel

Dangerous Places is a novel about the gap between mythological (or 'dreamed') constructions of reality and actual life. The story centres on V en, a married woman with two young children. Her love for her children is fiercely protective and encompassing, but she feels alienated from her husband and to a certain extent her society; so when her first love, Yanni, re-enters her life,she is strongly tempted to resume her affair with him. She is however seduced more by the memories she has 'mythologized' about him than by his physical reality; in the course of the novel she is forced to come to terms with her own delusions. The subplot of the novel involves other characters who are caught between illusion and reality as well, and who deal with 'truth' in differing ways. The themes of the book are explored using a number of structures which underlie and support the surface story. The Greek myths of Adonis/ Aphrodite and Hades/Persephone are framing agents for the plot, and the setting in contemporary Brisbane and North Stradbroke Island is symbolic.

Acoustic keyword spotting in speech with applications to data mining

Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.

Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks.

Rice Tungro Bacilliform

Many well-known specialists have contributed to this book which presents for the first time an in-depth look at the viruses, their satellites and the retrotransposons infecting (or occuring in) one plant family: the Poaceae (Gramineae). After molecular and biological descriptions of the viruses to species level, virus diseases are presented by crop: barley, maize, rice, rye, sorghum, sugarcane, triticales, wheats, forage, ornamental and lawn. A detailed index of the viruses and taxonomic lists will help readers in the search for information.

An improved modal strain energy method for damage assessment

The modal strain energy method, which depends on the vibration characteristics of the structure, has been reasonably successful in identifying and localising damage in the structure. However, existing strain energy methods require the first few modes to be measured to provide meaningful damage detection. Use of individual modes with existing strain energy methods may indicate false alarms or may not detect the damage at or near the nodal points. This paper proposes a new modal strain energy based damage index which can detect and localize the damage using any one of the modes measured and illustrates its application for beam structures. It becomes evident that the proposed strain energy based damage index also has potential for damage quantification.

Focus-score weighted super-resolution for uncooperative iris recognition at a distance and on the move

Uncooperative iris identification systems at a distance and on the move often suffer from poor resolution and poor focus of the captured iris images. The lack of pixel resolution and well-focused images significantly degrades the iris recognition performance. This paper proposes a new approach to incorporate the focus score into a reconstruction-based super-resolution process to generate a high resolution iris image from a low resolution and focus inconsistent video sequence of an eye. A reconstruction-based technique, which can incorporate middle and high frequency components from multiple low resolution frames into one desired super-resolved frame without introducing false high frequency components, is used. A new focus assessment approach is proposed for uncooperative iris at a distance and on the move to improve performance for variations in lighting, size and occlusion. A novel fusion scheme is then proposed to incorporate the proposed focus score into the super-resolution process. The experiments conducted on the The Multiple Biometric Grand Challenge portal database shows that our proposed approach achieves an EER of 2.1%, outperforming the existing state-of-the-art averaging signal-level fusion approach by 19.2% and the robust mean super-resolution approach by 8.7%.