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.

Integrating treatment for mental and physical disorders and substance misuse in Indigenous primary care settings

Objective: Australian Indigenous peoples in remote and rural settings continue to have limited access to treatment for mental illness. Comorbid disorders complicate presentations in primary care where Indigenous youths and perinatal women are at particular risk. Despite this high comorbidity there are few examples of successful models of integrated treatment. This paper outlines these challenges and provides recommendations for practice that derive from recent developments in the Northern Territory. Conclusions: There is a strong need to develop evidence for the effectiveness of integrated and culturally informed individual and service level interventions. We describe the Best practice in Early intervention Assessment and Treatment of depression and substance misuse study which seeks to address this need.

Small animal bone healing models : standards, tips, and pitfalls results of a consensus meeting

Small animal fracture models have gained increasing interest in fracture healing studies. To achieve standardized and defined study conditions, various variables must be carefully controlled when designing fracture healing experiments in mice or rats. The strain, age and sex of the animals may influence the process of fracture healing. Furthermore, the choice of the fracture fixation technique depends on the questions addressed, whereby intra- and extramedullary implants as well as open and closed surgical approaches may be considered. During the last few years, a variety of different, highly sophisticated implants for fracture fixation in small animals have been developed. Rigid fixation with locking plates or external fixators results in predominantly intramembranous healing in both mice and rats. Locking plates, external fixators, intramedullary screws, the locking nail and the pin-clip device allow different degrees of stability resulting in various amounts of endochondral and intramembranous healing. The use of common pins that do not provide rotational and axial stability during fracture stabilization should be discouraged in the future. Analyses should include at least biomechanical and histological evaluations, even if the focus of the study is directed towards the elucidation of molecular mechanisms of fracture healing using the largely available spectrum of antibodies and gene-targeted animals to study molecular mechanisms of fracture healing. This review discusses distinct requirements for the experimental setups as well as the advantages and pitfalls of the different fixation techniques in rats and mice.

Higher-Order spectra of nonlinear polynomial models for Chua's circuit

Polynomial models are shown to simulate accurately the quadratic and cubic nonlinear interactions (e.g. higher-order spectra) of time series of voltages measured in Chua's circuit. For circuit parameters resulting in a spiral attractor, bispectra and trispectra of the polynomial model are similar to those from the measured time series, suggesting that the individual interactions between triads and quartets of Fourier components that govern the process dynamics are modeled accurately. For parameters that produce the double-scroll attractor, both measured and modeled time series have small bispectra, but nonzero trispectra, consistent with higher-than-second order nonlinearities dominating the chaos.

Modelling word meaning using efficient tensor representations

Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these models has been raised as a weakness when performing cognitive tasks. This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information.

A proposed role for models and simulations in management research

Overall, computer models and simulations have a rather disappointing record within the management sciences as a tool for predicting the future. Social and market environments can be influenced by an overwhelming number of variables, and it is therefore difficult to use computer models to make forecasts or to test hypotheses concerning the relationship between individual behaviours and macroscopic outcomes. At the same time, however, advocates of computer models argue that they can be used to overcome the human mind's inability to cope with several complex variables simultaneously or to understand concepts that are highly counterintuitive. This paper seeks to bridge the gap between these two perspectives by suggesting that management research can indeed benefit from computer models by using them to formulate fruitful hypotheses.

An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.

Bayesian models for the determination of resonant frequencies in a DI diesel engine

A time series method for the determination of combustion chamber resonant frequencies is outlined. This technique employs the use of Markov-chain Monte Carlo (MCMC) to infer parameters in a chosen model of the data. The development of the model is included and the resonant frequency is characterised as a function of time. Potential applications for cycle-by-cycle analysis are discussed and the bulk temperature of the gas and the trapped mass in the combustion chamber are evaluated as a function of time from resonant frequency information.

The use of semantic priming as a paradigm for the detection of faking on self-report psychological tests

It is recognised that individuals do not always respond honestly when completing psychological tests. One of the foremost issues for research in this area is the inability to detect individuals attempting to fake. While a number of strategies have been identified in faking, a commonality of these strategies is the latent role of long term memory. Seven studies were conducted in order to examine whether it is possible to detect the activation of faking related cognitions using a lexical decision task. Study 1 found that engagement with experiential processing styles predicted the ability to fake successfully, confirming the role of associative processing styles in faking. After identifying appropriate stimuli for the lexical decision task (Studies 2A and 2B), Studies 3 to 5 examined whether a cognitive state of faking could be primed and subsequently identified, using a lexical decision task. Throughout the course of these studies, the experimental methodology was increasingly refined in an attempt to successfully identify the relevant priming mechanisms. The results were consistent and robust throughout the three priming studies: faking good on a personality test primed positive faking related words in the lexical decision tasks. Faking bad, however, did not result in reliable priming of negative faking related cognitions. To more completely address potential issues with the stimuli and the possible role of affective priming, two additional studies were conducted. Studies 6A and 6B revealed that negative faking related words were more arousing than positive faking related words, and that positive faking related words were more abstract than negative faking related words and neutral words. Study 7 examined whether the priming effects evident in the lexical decision tasks occurred as a result of an unintentional mood induction while faking the psychological tests. Results were equivocal in this regard. This program of research aligned the fields of psychological assessment and cognition to inform the preliminary development and validation of a new tool to detect faking. Consequently, an implicit technique to identify attempts to fake good on a psychological test has been identified, using long established and robust cognitive theories in a novel and innovative way. This approach represents a new paradigm for the detection of individuals responding strategically to psychological testing. With continuing development and validation, this technique may have immense utility in the field of psychological assessment.

Emergency health services : demand and service delivery models. Monograph 1: literature review and activity trends

Emergency Health Services (EHS), encompassing hospital-based Emergency Departments (ED) and pre-hospital ambulance services, are a significant and high profile component of Australia’s health care system and congestion of these, evidenced by physical overcrowding and prolonged waiting times, is causing considerable community and professional concern. This concern relates not only to Australia’s capacity to manage daily health emergencies but also the ability to respond to major incidents and disasters. EHS congestion is a result of the combined effects of increased demand for emergency care, increased complexity of acute health care, and blocked access to ongoing care (e.g. inpatient beds). Despite this conceptual understanding there is a lack of robust evidence to explain the factors driving increased demand, or how demand contributes to congestion, and therefore public policy responses have relied upon limited or unsound information. The Emergency Health Services Queensland (EHSQ) research program proposes to determine the factors influencing the growing demand for emergency health care and to establish options for alternative service provision that may safely meet patient’s needs. The EHSQ study is funded by the Australian Research Council (ARC) through its Linkage Program and is supported financially by the Queensland Ambulance Service (QAS). This monograph is part of a suite of publications based on the research findings that examines the existing literature, and current operational context. Literature was sourced using standard search approaches and a range of databases as well as a selection of articles cited in the reviewed literature. Public sources including the Australian Institute of Health and Welfare (AIHW), the Council of Ambulance Authorities (CAA) Annual Reports, Australian Bureau of Statistics (ABS) and Department of Health and Ageing (DoHA) were examined for trend data across Australia.

Mediators of the relationship between person-organisation fit and individual outcomes

To date, research on P-O fit has focused heavily on the effect of P-O fit on individual and organisational outcomes. Few studies have attempted to explain how or why P-O fit leads to these outcomes. Meglino, Ravlin, and Adkins (1989) and Schein (1985) identified several intervening mechanisms for explaining fit-outcome relationships but only few of these explanations have been tested empirically (Cable & Edwards, 2004; Edwards & Cable, 2009; Kalliath, Bluedorn, & Strube, 1999). This thesis investigates role conflict, cognitive style and organisational justice as three potential mediating mechanisms in the relationship between P-O fit (defined as fit between personal and organisational values – value congruence or value fit) and outcomes including job satisfaction, job performance, service performance, affective commitment and continuance commitment. The study operationalised P-O fit using three measures: subjective fit, perceived fit and objective fit. The mediation model of subjective fit was tested using a Mplus analytical technique, while the mediation models of both perceived and objective fit were tested by modeling the difference between two scores (that is, between personal values and organisational values) using a polynomial regression and response surface analysis (Edwards, 1993). A survey of 558 mid-level managers from seven Brunei public sector organisations provided the data. Our results showed that the relationship between P-O fit and outcomes was partially mediated by organisational justice and cognitive style - for all the three measures of fit, while role conflict had no mediating effects. The findings from this research therefore have both theoretical and practical implications. This research contributes to the literature by combining these theoretical explanations for value congruence effects into one integrated model, and by providing evidence on the partial mediating effects of organisational justice and cognitive style. Future research needs to address and investigate other potential mechanisms by which value congruence affects individual and organisational outcomes. In addition, the study is considered to be the first to test these mediating roles for a value fit-outcomes relationship using three different measures of fit in a non-Western context.

Query expansion using term relationships in language models for information retrieval

Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been proposed to incorporate term relationships into LM, so that links can be created between words in the same sentence, and term relationships (e.g. synonymy) can be used to expand the document model. In this study, we further extend this family of dependence models in the following two ways: (1) Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented; (2) We exploit more sophisticated inferential relationships extracted with Information Flow (IF). Information flow relationships are not simply pairwise term relationships as those used in previous studies, but are between a set of terms and another term. They allow for context-dependent query expansion. Our experiments conducted on TREC collections show that we can obtain large and significant improvements with our approach. This study shows that LM is an appropriate framework to implement effective query expansion.

Using Bayesian methods for the estimation of uncertainty in complex statistical models

The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

Velocity-jump models with crowding effects

Velocity jump processes are discrete random walk models that have many applications including the study of biological and ecological collective motion. In particular, velocity jump models are often used to represent a type of persistent motion, known as a “run and tumble”, which is exhibited by some isolated bacteria cells. All previous velocity jump processes are non-interacting, which means that crowding effects and agent-to-agent interactions are neglected. By neglecting these agent-to-agent interactions, traditional velocity jump models are only applicable to very dilute systems. Our work is motivated by the fact that many applications in cell biology, such as wound healing, cancer invasion and development, often involve tissues that are densely packed with cells where cell-to-cell contact and crowding effects can be important. To describe these kinds of high cell density problems using a velocity jump process we introduce three different classes of crowding interactions into a one-dimensional model. Simulation data and averaging arguments lead to a suite of continuum descriptions of the interacting velocity jump processes. We show that the resulting systems of hyperbolic partial differential equations predict the mean behavior of the stochastic simulations very well.