Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

Rescaling clustering trees using impact ratios for robust hierarchical speaker clustering

We present a novel method for improving hierarchical speaker clustering in the tasks of speaker diarization and speaker linking. In hierarchical clustering, a tree can be formed that demonstrates various levels of clustering. We propose a ratio that expresses the impact of each cluster on the formation of this tree and use this to rescale cluster scores. This provides score normalisation based on the impact of each cluster. We use a state-of-the-art speaker diarization and linking system across the SAIVT-BNEWS corpus to show that our proposed impact ratio can provide a relative improvement of 16% in diarization error rate (DER).

Predicting transactive memory systems in multidisciplinary teams : The interplay between team and professional identities

In explaining how communication quality predicts TMS in multidisciplinary teams, we drew on the social identity approach to investigate the mediating role of team identification and the moderating role of professional identification. Recognizing that professional identification could trigger intergroup biases among professional subgroups, or alternatively, could bring resources to the team, we explored the potential moderating role of professional identification in the relationship between team identification and TMS. Using data collected from 882 healthcare personnel working in 126 multidisciplinary hospital teams, results supported our hypothesis that perceived communication quality predicted TMS through team identification. Furthermore, findings provided support for a resource view of professional subgroup identities with results indicating that high levels of professional identification compensated for low levels of team identification in predicting TMS. We provide recommendations on how social identities may be used to promote TMS in multidisciplinary teams.

The monadic environment : evoking emotion and memory in participatory performance

This practice-led research project examined the audience experience of immersive environments in participatory performance. Drawing upon the work of artist Ilya Kabakov, Gaston Bachelard's Poetics of Space (1964) and Leibniz's theory of the monad, the study investigated how an immersive space can be constructed to evoke emotion and memory recall in participants. The research consisted of two cycles of creative experimentation resulting in the presentation of a final piece entitled Dulcet. The research contributes new terminology to the discourse surrounding the participant experience in immersive environments, specifically space-as-memory, the role of ambiguity in spatial design and the construct of the monadic environment.

Double-strand breaks and the concept of short- and long-term epigenetic memory

Double-strand breaks represent an extremely cytolethal form of DNA damage and thus pose a serious threat to the preservation of genetic and epigenetic information. Though it is well-known that double-strand breaks such as those generated by ionising radiation are among the principal causative factors behind mutations, chromosomal aberrations, genetic instability and carcino-genesis, significantly less is known about the epigenetic consequences of double-strand break formation and repair for carcinogenesis. Double-strand break repair is a highly coordinated process that requires the unravelling of the compacted chromatin structure to facilitate repair machinery access and then restoration of the original undamaged chromatin state. Recent experimental findings have pointed to a potential mechanism for double-strand break-induced epigenetic silencing. This review will discuss some of the key epigenetic regulatory processes involved in double-strand break (DSB) repair and how incomplete or incorrect restoration of chromatin structure can leave a DSB-induced epigenetic memory of damage with potentially pathological repercussions

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making

Memory management and parallelization of data intensive all-to-all comparison in shared-memory systems

This thesis presents a novel program parallelization technique incorporating with dynamic and static scheduling. It utilizes a problem specific pattern developed from the prior knowledge of the targeted problem abstraction. Suitable for solving complex parallelization problems such as data intensive all-to-all comparison constrained by memory, the technique delivers more robust and faster task scheduling compared to the state-of-the art techniques. Good performance is achieved from the technique in data intensive bioinformatics applications.

In-memory business process management

In-memory databases have become a mainstay of enterprise computing offering significant performance and scalability boosts for online analytical and (to a lesser extent) transactional processing as well as improved prospects for integration across different applications through an efficient shared database layer. Significant research and development has been undertaken over several years concerning data management considerations of in-memory databases. However, limited insights are available on the impacts of applications and their supportive middleware platforms and how they need to evolve to fully function through, and leverage, in-memory database capabilities. This paper provides a first, comprehensive exposition into how in-memory databases impact Business Pro- cess Management, as a mission-critical and exemplary model-driven integration and orchestration middleware. Through it, we argue that in-memory databases will render some prevalent uses of legacy BPM middleware obsolete, but also open up exciting possibilities for tighter application integration, better process automation performance and some entirely new BPM capabilities such as process-based application customization. To validate the feasibility of an in-memory BPM, we develop a surprisingly simple BPM runtime embedded into SAP HANA and providing for BPMN-based process automation capabilities.

The Scent of Ancestral Memory

Poetry expresses the physical and spiritual worlds that other kinds of writing cannot. Travelling open our minds and frees our spirit. Creative writings and meditations on life, spirit, grace and relationships, art and nature weave their way through these lyrical poems. Some poems are suitable for study in the Australian Curriculum.

The role of δ-opioid receptors in learning and memory underlying the development of addiction

Opioids are important endogenous ligands that exist in both invertebrates and vertebrates and signal by activation of opioid receptors to produce analgesia and reward or pleasure. The μ-opioid receptor is the best known of the opioid receptors and mediates the acute analgesic effects of opiates, while the δ-opioid receptor (DOR) has been less well studied and has been linked to effects that follow from chronic use of opiates such as stress, inflammation and anxiety. Recently, DORs have been shown to play an essential role in emotions and increasing evidence points to a role in learning actions and outcomes. The process of learning and memory in addiction has been proposed to involve strengthening of specific brain circuits when a drug is paired with a context or environment. The DOR is highly expressed in the hippocampus, amygdala, striatum and other basal ganglia structures known to participate in learning and memory. In this review, we will focus on the role of the DOR and its potential role in learning and memory underlying the development of addiction.

Memory and Epileptogenesis in Complex Biological and Simulated Systems

Oscillations of neural activity may bind widespread cortical areas into a neural representation that encodes disparate aspects of an event. In order to test this theory we have turned to data collected from complex partial epilepsy (CPE) patients with chronically implanted depth electrodes. Data from regions critical to word and face information processing was analyzed using spectral coherence measurements. Similar analyses of intracranial EEG (iEEG) during seizure episodes display HippoCampal Formation (HCF)—NeoCortical (NC) spectral coherence patterns that are characteristic of specific seizure stages (Klopp et al. 1996). We are now building a computational memory model to examine whether spatio-temporal patterns of human iEEG spectral coherence emerge in a computer simulation of HCF cellular distribution, membrane physiology and synaptic connectivity. Once the model is reasonably scaled it will be used as a tool to explore neural parameters that are critical to memory formation and epileptogenesis.

Mice behaviorally selected for high Pavlovian fear memory exhibit fear generalization, HPA-axis alterations and increased basal activity in cortico-limbic circuits

Biological factors underlying individual variability in fearfulness and anxiety have important implications for stress-related psychiatric illness including PTSD and major depression. Using an advanced intercross line (AIL) derived from C57BL/6 and DBA/2J mouse strains and behavioral selection over 3 generations, we established two lines exhibiting High or Low fear behavior after fear conditioning. Across the selection generations, the two lines showed clear differences in training and tests for contextual and conditioned fear. Before fear conditioning training, there were no differences between lines in baseline freezing to a novel context. However, after fear conditioning High line mice demonstrated pronounced freezing in a new context suggestive of poor context discrimination. Fear generalization was not restricted to contextual fear. High fear mice froze to a novel acoustic stimulus while freezing in the Low line did not increase over baseline. Enhanced fear learning and generalization are consistent with transgenic and pharmacological disruption of the hypothalamic-pituitary-adrenal axis (HPA-axis) (Brinks, 2009, Thompson, 2004, Kaouane, 2012). To determine whether there were differences in HPA-axis regulation between the lines, morning urine samples were collected to measure basal corticosterone. Levels of secreted corticosterone in the circadian trough were analyzed by corticosterone ELISA. High fear mice were found to have higher basal corticosterone levels than low line animals. Examination of hormonal stress response components by qPCR revealed increased expression of CRH mRNA and decreased mRNA for MR and CRHR1 in hypothalamus of high fear mice. These alterations may contribute to both the behavioral phenotype and higher basal corticosterone in High fear mice. To determine basal brain activity in vivo in High and Low fear mice we used manganese-enhanced magnetic resonance imaging (MEMRI). Analysis revealed a pattern of basal brain activity made up of amygdala, cortical and hippocampal circuits that was elevated in the High line. Ongoing studies also seek to determine the relative balance of excitatory and inhibitory tone in the amygdala and hippocampus and the neuronal structure of its neurons. While these heterogeneous lines are selected on fear memory expression, HPA-axis alterations and differences in hippocampal activity segregate with the behavioral phenotypes. These differences are detectable in a basal state strongly suggesting these are biological traits underlying the behavioral phenotype (Johnson et al, 2011).

Virtual business role-play : leveraging familiar environments to prime stakeholder memory during process elicitation

Business process models have traditionally been an effective way of examining business practices to identify areas for improvement. While common information gathering approaches are generally efficacious, they can be quite time consuming and have the risk of developing inaccuracies when information is forgotten or incorrectly interpreted by analysts. In this study, the potential of a role-playing approach for process elicitation and specification has been examined. This method allows stakeholders to enter a virtual world and role-play actions as they would in reality. As actions are completed, a model is automatically developed, removing the need for stakeholders to learn and understand a modelling grammar. Empirical data obtained in this study suggests that this approach may not only improve both the number of individual process task steps remembered and the correctness of task ordering, but also provide a reduction in the time required for stakeholders to model a process view.