Complementarity and the uncertainty principle as aesthetic principles : the practice and performance of The Physics Project

Using the generative processes developed over two stages of creative development and the performance of The Physics Project at the Loft at the Creative Industries Precinct at the Queensland University of Technology (QUT) from 5th – 8th April 2006 as a case study, this exegesis considers how the principles of contemporary physics can be reframed as aesthetic principles in the creation of contemporary performance. The Physics Project is an original performance work that melds live performance, video and web-casting and overlaps an exploration of personal identity with the physics of space, time, light and complementarity. It considers the acts of translation between the language of physics and the language of contemporary performance that occur via process and form. This exegesis also examines the devices in contemporary performance making and contemporary performance that extend the reach of the performance, including the integration of the live and the mediated and the use of metanarratives.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

The effect of dialect mismatch on likelihood-maximising speech enhancement for noise-robust speech recognition

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but these approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks are an alternative that optimise parameters of enhancement algorithms based on state sequences generated for utterances with known transcriptions. Previous reports of LIMA frameworks have shown significant promise for improving speech recognition accuracies under additive background noise for a range of speech enhancement techniques. In this paper we discuss the drawbacks of the LIMA approach when multiple layers of acoustic mismatch are present – namely background noise and speaker accent. Experimentation using LIMA-based Mel-filterbank noise subtraction on American and Australian English in-car speech databases supports this discussion, demonstrating that inferior speech recognition performance occurs when a second layer of mismatch is seen during evaluation.

The application of phonetic distribution normalisation to likelihood-maximising speech enhancement for robust ASR

Traditional speech enhancement methods optimise signal-level criteria such as signal-to-noise ratio, but such approaches are sub-optimal for noise-robust speech recognition. Likelihood-maximising (LIMA) frameworks on the other hand, optimise the parameters of speech enhancement algorithms based on state sequences generated by a speech recogniser for utterances of known transcriptions. Previous applications of LIMA frameworks have generated a set of global enhancement parameters for all model states without taking in account the distribution of model occurrence, making optimisation susceptible to favouring frequently occurring models, in particular silence. In this paper, we demonstrate the existence of highly disproportionate phonetic distributions on two corpora with distinct speech tasks, and propose to normalise the influence of each phone based on a priori occurrence probabilities. Likelihood analysis and speech recognition experiments verify this approach for improving ASR performance in noisy environments.

Defending the sanctity of life principle : a reply to John Keown

This article is a response to Professor Keown’s criticism of my paper “Finding a Way Through the Ethical and Legal Maze: Withdrawal of Medical Treatment and Euthanasia” (2005) 13 (3) Medical Law Review 357. The article takes up and responds to a number of criticisms raised by Keown in an attempt to further the debate concerning the moral and legal status of withdrawing life-sustaining measures, its distinction from euthanasia, and the implications of the lawfulness of withdrawal for the principle of the sanctity of life.

Critical gap estimation by numerical and statistical highest likelihood search

Many traffic situations require drivers to cross or merge into a stream having higher priority. Gap acceptance theory enables us to model such processes to analyse traffic operation. This discussion demonstrated that numerical search fine tuned by statistical analysis can be used to determine the most likely critical gap for a sample of drivers, based on their largest rejected gap and accepted gap. This method shares some common features with the Maximum Likelihood Estimation technique (Troutbeck 1992) but lends itself well to contemporary analysis tools such as spreadsheet and is particularly analytically transparent. This method is considered not to bias estimation of critical gap due to very small rejected gaps or very large rejected gaps. However, it requires a sufficiently large sample that there is reasonable representation of largest rejected gap/accepted gap pairs within a fairly narrow highest likelihood search band.

Cross likelihood ratio based speaker clustering using eigenvoice models

This paper proposes the use of eigenvoice modeling techniques with the Cross Likelihood Ratio (CLR) as a criterion for speaker clustering within a speaker diarization system. The CLR has previously been shown to be a robust decision criterion for speaker clustering using Gaussian Mixture Models. Recently, eigenvoice modeling techniques have become increasingly popular, due to its ability to adequately represent a speaker based on sparse training data, as well as an improved capture of differences in speaker characteristics. This paper hence proposes that it would be beneficial to capitalize on the advantages of eigenvoice modeling in a CLR framework. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 35.1% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Letter to the editor : likelihood of tetraplegia after CT clearance of the cervical spine in obtunded blunt trauma patients

...the probabilistic computer simulation study by Dunham and colleagues evaluating the impact of different cervical spine management (CSM) strategies on tetraplegia and brain injury outcomes.1 Based on literature findings, expert opinion and with use of advances programming techniques the authors conclude that early collar removal without cervical spine magnetic resonance imaging (MRI) is a preferable CSM strategy for comatose, blunt trauma patients with extremity movement and a negative cervical spine computed tomography(CT) scan. Although we do not have the required expertise to comment on the applied statistical approach, we would like to comment on one of the medical assumptions raised by the authors, namely the likelihood of tetraplegia in this specific population....

Free software as a democratic principle

Software forms an important part of the interface between citizens and their government. An increasing amount of government functions are being performed, controlled, or delivered electronically. This software, like all language, is never value-neutral, but must, to some extent, reflect the values of the coder and proprietor. The move that many governments are making towards e-governance, and the increasing reliance that is being placed upon software in government, necessitates a rethinking of the relationships of power and control that are embodied in software.

A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals

Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.

The perceived importance of mall retail categories and its effect on likelihood of purchase during a recession

Recessions impact the retail sector and as such research into consumer decision making during such times is imperative. In response to this, our study takes an innovative approach to examining how the perceived importance of retail store categories in a shopping mall influences the relationship between consumers' shopping attitudes and likelihood of purchasing in those categories during a recession. The overall findings show the importance of a product category to a consumer, which is often overlooked, has a strong explanatory influence on consumer purchase intentions for that specific retail store categories in a shopping mall under recession conditions. Findings also show that for consumers’ who have altered their shopping behaviour the perceived importance of a retail store category fully mediates the relationship for: Majors, Leisure, Food Catered and Mini Majors categories, and partial mediation for Apparel. Importance has no mediating effect for: Food Retail, General Retail, Mobile Phone Services, Home wares, and Retail Services. Our study makes a key contribution to the retail management literature with the findings suggesting that redefining and articulating the importance of the value offering for specific retail store categories can help reduce the impact of changes in consumers' recessionary shopping intentions across the mall tenant mix. Such actions can then help preserve the image of the shopping mall in the minds of the consumers when the economic recovery begins.