Support for an auto-associative model of spoken cued recall: Evidence from fMRI

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103-128]. In brief, the model assumes that cues elicit a network of phonological short term memory (STM) and semantic long term memory (LTM) representations distributed throughout the neocortex as patterns of sparse activations. This information is transferred to the hippocampus which converges upon the item closest to a stored pattern and outputs a response. Word pairs were learned from a study list, with one member of the pair serving as the cue at test. Unstudied words were also intermingled at test in order to provide an analogue of yes/no recognition tasks. Compared to incorrectly rejected studied items (misses) and correctly rejected (CR) unstudied items, correctly recalled items (hits) elicited increased responses in the left hippocampus and neocortical regions including the left inferior prefrontal cortex (LIPC), left mid lateral temporal cortex and inferior parietal cortex, consistent with predictions from the model. This network was very similar to that observed in yes/no recognition studies, supporting proposals that cued recall and item recognition involve common rather than separate mechanisms.

The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.

Learned ultra-wideband RADAR sensor model for augmented LIDAR-based traversability mapping in vegetated environments

In vegetated environments, reliable obstacle detection remains a challenge for state-of-the-art methods, which are usually based on geometrical representations of the environment built from LIDAR and/or visual data. In many cases, in practice field robots could safely traverse through vegetation, thereby avoiding costly detours. However, it is often mistakenly interpreted as an obstacle. Classifying vegetation is insufficient since there might be an obstacle hidden behind or within it. Some Ultra-wide band (UWB) radars can penetrate through vegetation to help distinguish actual obstacles from obstacle-free vegetation. However, these sensors provide noisy and low-accuracy data. Therefore, in this work we address the problem of reliable traversability estimation in vegetation by augmenting LIDAR-based traversability mapping with UWB radar data. A sensor model is learned from experimental data using a support vector machine to convert the radar data into occupancy probabilities. These are then fused with LIDAR-based traversability data. The resulting augmented traversability maps capture the fine resolution of LIDAR-based maps but clear safely traversable foliage from being interpreted as obstacle. We validate the approach experimentally using sensors mounted on two different mobile robots, navigating in two different environments.

A configurable airport reference model for passenger facilitation

The air transport industry is a complex environment facing many challenges while coping with changing global imperatives. International airport passenger facilitation is a part of the socio-technical system where these challenges manifest, impacting businesses in terms of time, cost and quality. This research inductively develops an extensible configurable reference model by capturing and merging the cross-organisational facilitation process from five Australian airports. The reference model can be filtered according to the contextual needs of airport users to inform relevant and accurate business process design. The domain and methodological contributions constitute the first reported application of questionnaire-based configurability to airport processes.

Firms’ information system characteristics and management accounting adaptability

This study explores the relationship between the qualities of an information system and management accounting adaptability (MAA) and effectiveness in firms. Design/methodology/approach I develop and empirically test a model where the qualities of the information system and management accounting effectiveness are mediated by MAA. Findings Information system flexibility (ISF) and shared knowledge had a significant and positive correlation with MAA, which in turn had a positive and significant correlation with management accounting effectiveness. There was also a moderation effect of ISF on the correlation betweeen information system integration and MAA. Research implications Information system integration may not lead to management accounting stability, but the lack of flexibility of a system and a lack of cooperation between the stakeholders might lead to its stagnation. Practical implications Organizations are advised to implement solutions that are relatively flexible and modular, as well as encourage cooperation between stakeholders to fully leverage and improve the existing and future systems. Originality/value The study extends the discourse on the interaction between management accounting and information systems by exploring the role of a number of factors that drive MAA.

A Model to Estimate the Population Contributing to the Wastewater Using Samples Collected on Census Day

An important uncertainty when estimating per capita consumption of, for example, illicit drugs by means of wastewater analysis (sometimes referred to as “sewage epidemiology”) relates to the size and variability of the de facto population in the catchment of interest. In the absence of a day-specific direct population count any indirect surrogate model to estimate population size lacks a standard to assess associated uncertainties. Therefore, the objective of this study was to collect wastewater samples at a unique opportunity, that is, on a census day, as a basis for a model to estimate the number of people contributing to a given wastewater sample. Mass loads for a wide range of pharmaceuticals and personal care products were quantified in influents of ten sewage treatment plants (STP) serving populations ranging from approximately 3500 to 500 000 people. Separate linear models for population size were estimated with the mass loads of the different chemical as the explanatory variable: 14 chemicals showed good, linear relationships, with highest correlations for acesulfame and gabapentin. De facto population was then estimated through Bayesian inference, by updating the population size provided by STP staff (prior knowledge) with measured chemical mass loads. Cross validation showed that large populations can be estimated fairly accurately with a few chemical mass loads quantified from 24-h composite samples. In contrast, the prior knowledge for small population sizes cannot be improved substantially despite the information of multiple chemical mass loads. In the future, observations other than chemical mass loads may improve this deficit, since Bayesian inference allows including any kind of information relating to population size.

Designing informal learning experiences for early career academics using a knowledge ecosystem model

This article presents a ‘knowledge ecosystem’ model of how early career academics experience using information to learn while building their social networks for developmental purposes. Developed using grounded theory methodology, the model offers a way of conceptualising how to empower early career academics through 1) agency (individual and relational) and 2) facilitation of personalised informal learning (design of physical and virtual systems and environments) in spaces where developmental relationships are formed including programs, courses, events, community, home and social media. It is suggested that the knowledge ecosystem model is suitable for use in designing informal learning experiences for early career academics.

Applying POVM to model non-orthogonality in quantum cognition

Much of the work currently occurring in the field of Quantum Interaction (QI) relies upon Projective Measurement. This is perhaps not optimal, cognitive states are not nearly as well behaved as standard quantum mechanical systems; they exhibit violations of repeatability, and the operators that we use to describe measurements do not appear to be naturally orthogonal in cognitive systems. Here we attempt to map the formalism of Positive Operator Valued Measure (POVM) theory into the domain of semantic memory, showing how it might be used to construct Bell-type inequalities.

Improving factory performance using an integrated sugar factory model

A whole of factory model of a raw sugar factory was developed in SysCAD software to assess and improve factory operations. The integrated sugar factory model ‘Sugar-SysCAD’ includes individual models for milling, heating and clarification, evaporation, crystallisation, steam cycle, sugar dryer and process and injection water circuits. These individual unit operation models can be either used as standalone models to optimise the unit operation or in the integrated mode to provide more accurate prediction of the effects of changes in any part of the process on the outputs of the whole factory process. Using the integrated sugar factory model, the effect of specific process operations can be understood and practical solutions can be determined to address process problems. The paper presents two factory scenarios to show the capabilities of the whole of factory model.

Enhancing information about sustainability features for sustainable housing delivery

Sustainability practices in government regulations and within the society influence the delivery of sustainable housing. The actual delivery rate of Australian sustain-able housing is not as high as other countries. There is an absence of engagement by stakeholders in adopting sustainable housing practices. This may be due, in the current Australian property market, to confusion as to what sustainability features should be considered, given the large range of environmental, economic and social sustainability options possible. One of the main problems appears to be that information demanders, especially real estate agents, valuers, insurance agents and mortgage lenders do not include sustainability perspectives in their advice or in their decision processes. Information distribution in the Australian property market is flawed, resulting in a lack of return-on-investment value of ‘green’ features implemented by some stakeholders. This paper reviewed the global sustainable development concept and Australian sustainable assessment methods. This review identified the possibility of a research project which aimed at identifying and integrating different perceptions and priority needs of the information demanders, for developing a model for the potential implementation of sustainability features distribution in the property industry. This research will reduce confusion on the sustainability-related information which can influence the decision making of stakeholders in the supply and demand of sustainable housing.

Informed learning in the undergraduate classroom: The role of information experiences in shaping outcomes

This research aimed to inform the design of effective information literacy lessons in higher education. Phenomenography, a research approach designed to study human experience, was used to explore the experiences of a teacher and undergraduate students using information to learn about language and gender issues. The findings show that the way learners use information influences content-focused learning outcomes, and reveal an instructional pattern for enabling students to use information while becoming aware of the topic they are investigating. Based on the findings, a design model is offered in which learning outcomes are realized through targeted information literacy activities.

Leveraging of software as a service by small and medium enterprises: Information systems capabilities and organisational complementarities

This research explored how small and medium enterprises can achieve success with software as a service (SaaS) applications from cloud. Based upon an empirical investigation of six growth oriented and early technology adopting small and medium enterprises, this study proposes a SaaS for small and medium enterprise success model with two approaches: one for basic and one for advanced benefits. The basic model explains the effective use of SaaS for achieving informational and transactional benefits. The advanced model explains the enhanced use of software as a service for achieving strategic and transformational benefits. Both models explicate the information systems capabilities and organizational complementarities needed for achieving success with SaaS.

Delegation of access in an Information Accountability Framework for eHealth

Shared eHealth records systems offer promising benefits for improving healthcare through high availability of information and improved decision making; however, their uptake has been hindered by concerns over the privacy of patient information. To address these privacy concerns while balancing the requirements of healthcare professionals to have access to the information they need to provide appropriate care, the use of an Information Accountability Framework (IAF) has been proposed. For the IAF and so called Accountable-eHealth systems to become a reality, the framework must provide for a diverse range of users and use cases. The initial IAF model did not provide for more diverse use cases including the need for certain users to delegate access to another user in the system to act on their behalf while maintaining accountability. In this paper, we define the requirements for delegation of access in the IAF, how such access policies would be represented in the Framework, and implement and validate an expanded IAF model.

Improving spoken term detection using complementary information

This research has made contributions to the area of spoken term detection (STD), defined as the process of finding all occurrences of a specified search term in a large collection of speech segments. The use of visual information in the form of lip movements of the speaker in addition to audio and the use of topic of the speech segments, and the expected frequency of words in the target speech domain, are proposed. By using these complementary information, improvement in the performance of STD has been achieved which enables efficient search of key words in large collection of multimedia documents.

MRI-based strain and strain rate analysis of left ventricle: A modified hierarchical transformation model

Background Different from other indicators of cardiac function, such as ejection fraction and transmitral early diastolic velocity, myocardial strain is promising to capture subtle alterations that result from early diseases of the myocardium. In order to extract the left ventricle (LV) myocardial strain and strain rate from cardiac cine-MRI, a modified hierarchical transformation model was proposed. Methods A hierarchical transformation model including the global and local LV deformations was employed to analyze the strain and strain rate of the left ventricle by cine-MRI image registration. The endocardial and epicardial contour information was introduced to enhance the registration accuracy by combining the original hierarchical algorithm with an Iterative Closest Points using Invariant Features algorithm. The hierarchical model was validated by a normal volunteer first and then applied to two clinical cases (i.e., the normal volunteer and a diabetic patient) to evaluate their respective function. Results Based on the two clinical cases, by comparing the displacement fields of two selected landmarks in the normal volunteer, the proposed method showed a better performance than the original or unmodified model. Meanwhile, the comparison of the radial strain between the volunteer and patient demonstrated their apparent functional difference. Conclusions The present method could be used to estimate the LV myocardial strain and strain rate during a cardiac cycle and thus to quantify the analysis of the LV motion function.