Adaptive and phase transition behavior in performance of discrete multi-articular actions by degenerate neurobiological systems

The identification of attractors is one of the key tasks in studies of neurobiological coordination from a dynamical systems perspective, with a considerable body of literature resulting from this task. However, with regards to typical movement models investigated, the overwhelming majority of actions studied previously belong to the class of continuous, rhythmical movements. In contrast, very few studies have investigated coordination of discrete movements, particularly multi-articular discrete movements. In the present study, we investigated phase transition behavior in a basketball throwing task where participants were instructed to shoot at the basket from different distances. Adopting the ubiquitous scaling paradigm, throwing distance was manipulated as a candidate control parameter. Using a cluster analysis approach, clear phase transitions between different movement patterns were observed in performance of only two of eight participants. The remaining participants used a single movement pattern and varied it according to throwing distance, thereby exhibiting hysteresis effects. Results suggested that, in movement models involving many biomechanical degrees of freedom in degenerate systems, greater movement variation across individuals is available for exploitation. This observation stands in contrast to movement variation typically observed in studies using more constrained bi-manual movement models. This degenerate system behavior provides new insights and poses fresh challenges to the dynamical systems theoretical approach, requiring further research beyond conventional movement models.

Up the junction? Exploiting knowledge-based development through supply chain and SME cluster interactions

Maximisation of Knowledge-Based Development (KBD) benefits requires effective dissemination and utilisation mechanisms to accompany the initial knowledge creation process. This work highlights the potential for interactions between Supply Chains (SCs) and Small and Medium sized Enterprise Clusters (SMECs), (including via ‘junction’ firms which are members of both networks), to facilitate such effective dissemination and utilisation of knowledge. In both these network types there are firms that readily utilise their relationships and ties for ongoing business success through innovation. The following chapter highlights the potential for such beneficial interactions between SCs and SMECs in key elements of KBD, particularly knowledge management, innovation and technology transfer. Because there has been little focus on the interactions between SCs and SMECs, particularly when firms simultaneously belong to both, this chapter examines the conduits through which information and knowledge can be transferred and utilised. It shows that each network type has its own distinct advantages in the types of information searched for and transferred amongst network member firms. Comparing and contrasting these advantages shows opportunities for both networks to leverage the knowledge sharing strengths of each other, through these ‘junctions’ to address their own weaknesses, allowing implications to be drawn concerning new ways of utilising relationships for mutual network gains.

User behaviour modelling in a multi-dimensional environment for personalization and recommendation

Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.

Algorithm clustering for multi-algorithm processor design

An Application Specific Instruction-set Processor (ASIP) is a specialized processor tailored to run a particular application/s efficiently. However, when there are multiple candidate applications in the application’s domain it is difficult and time consuming to find optimum set of applications to be implemented. Existing ASIP design approaches perform this selection manually based on a designer’s knowledge. We help in cutting down the number of candidate applications by devising a classification method to cluster similar applications based on the special-purpose operations they share. This provides a significant reduction in the comparison overhead while resulting in customized ASIP instruction sets which can benefit a whole family of related applications. Our method gives users the ability to quantify the degree of similarity between the sets of shared operations to control the size of clusters. A case study involving twelve algorithms confirms that our approach can successfully cluster similar algorithms together based on the similarity of their component operations.

Hybrid multi-channel cooperative spectrum sensing to satisfy channel target

Spectrum sensing of multiple primary user channels is a crucial function in cognitive radio networks. In this paper we propose an optimal, sensing resource allocation algorithm for multi-channel cooperative spectrum sensing. The channel target is implemented as an objective and constraint to ensure a pre-determined number of empty channels are detected for secondary user network operations. Based on primary user traffic parameters, we calculate the minimum number of primary user channels that must be sensed to satisfy the channel target. We implement a hybrid sensing structure by grouping secondary user nodes into clusters and assign each cluster to sense a different primary user channels. We then solve the resource allocation problem to find the optimal sensing configuration and node allocation to minimise sensing duration. Simulation results show that the proposed algorithm requires the shortest sensing duration to achieve the channel target compared to existing studies that require long sensing and cannot guarantee the target.

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

Evaluation of the relationship between Chlamydia pecorum sequence types and disease using a species-specific multi-locus sequence typing scheme (MLST)

Chlamydia pecorum is globally associated with several ovine diseases including keratoconjunctivitis and polyarthritis. The exact relationship between the variety of C. pecorum strains reported and the diseases described in sheep remains unclear, challenging efforts to accurately diagnose and manage infected flocks. In the present study, we applied C. pecorum multi-locus sequence typing (MLST) to C. pecorum positive samples collected from sympatric flocks of Australian sheep presenting with conjunctivitis, conjunctivitis with polyarthritis, or polyarthritis only and with no clinical disease (NCD) in order to elucidate the exact relationships between the infecting strains and the range of diseases. Using Bayesian phylogenetic and cluster analyses on 62 C. pecorum positive ocular, vaginal and rectal swab samples from sheep presenting with a range of diseases and in a comparison to C. pecorum sequence types (STs) from other hosts, one ST (ST 23) was recognised as a globally distributed strain associated with ovine and bovine diseases such as polyarthritis and encephalomyelitis. A second ST (ST 69) presently only described in Australian animals, was detected in association with ovine as well as koala chlamydial infections. The majority of vaginal and rectal C. pecorum STs from animals with NCD and/or anatomical sites with no clinical signs of disease in diseased animals, clustered together in a separate group, by both analyses. Furthermore, 8/13 detected STs were novel. This study provides a platform for strain selection for further research into the pathogenic potential of C. pecorum in animals and highlights targets for potential strain-specific diagnostic test development.

Effect of an interactive therapeutic robotic animal on engagement, mood states, agitation and psychotropic drug use in people with dementia: A cluster-randomised controlled trial protocol

Introduction: Apathy, agitated behaviours, loneliness and depression are common consequences of dementia. This trial aims to evaluate the effect of a robotic animal on behavioural and psychological symptoms of dementia in people with dementia living in long-term aged care. Methods and analysis: A cluster-randomised controlled trial with three treatment groups: PARO (robotic animal), Plush-Toy (non-robotic PARO) or Usual Care (Control). The nursing home sites are Australian Government approved and accredited facilities of 60 or more beds. The sites are located in South-East Queensland, Australia. A sample of 380 adults with a diagnosis of dementia, aged 60 years or older living in one of the participating facilities will be recruited. The intervention consists of three individual 15 min non-facilitated sessions with PARO or Plush- Toy per week, for a period of 10 weeks. The primary outcomes of interest are improvement in agitation, mood states and engagement. Secondary outcomes include sleep duration, step count, change in psychotropic medication use, change in treatment costs, and staff and family perceptions of PARO or Plush-Toy. Video data will be analysed using Noldus XT Pocket Observer; descriptive statistics will be used for participants’ demographics and outcome measures; cluster and individual level analyses to test all hypotheses and Generalised Linear Models for cluster level and Generalised Estimation Equations and/or Multi-level Modeling for individual level data. Ethics and dissemination: The study participants or their proxy will provide written informed consent. The Griffith University Human Research Ethics Committee has approved the study (NRS/03/14/HREC). The results of the study will provide evidence of the efficacy of a robotic animal as a psychosocial treatment for the behavioural and psychological symptoms of dementia. Findings will be presented at local and international conference meetings and published in peer-reviewed journals.

Cluster randomised-control trial for an Australian child protection education program: Study protocol for the Learn to be safe with Emmy and friends™

Background Child maltreatment has severe short-and long-term consequences for children’s health, development, and wellbeing. Despite the provision of child protection education programs in many countries, few have been rigorously evaluated to determine their effectiveness. We describe the design of a multi-site gold standard evaluation of an Australian school-based child protection education program. The intervention has been developed by a not-for-profit agency and comprises 5 1-h sessions delivered to first grade students (aged 5–6 years) in their regular classrooms. It incorporates common attributes of effective programs identified in the literature, and aligns with the Australian education curriculum. Methods/Design A three-site cluster randomised controlled trial (RCT) of Learn to be safe with Emmy and friends™ will be conducted with children in approximately 72 first grade classrooms in 24 Queensland primary (elementary) schools from three state regions, over a period of 2 years. Entire schools will be randomised, using a computer generated list of random numbers, to intervention and wait-list control conditions, to prevent contamination effects across students and classes. Data will be collected at baseline (pre-assessment), immediately after the intervention (post-assessment), and at 6-, 12-, and 18-months (follow-up assessments). Outcome assessors will be blinded to group membership. Primary outcomes assessed are children’s knowledge of program concepts; intentions to use program knowledge, skills, and help-seeking strategies; actual use of program material in a simulated situation; and anxiety arising from program participation. Secondary outcomes include a parent discussion monitor, parent observations of their children’s use of program materials, satisfaction with the program, and parental stress. A process evaluation will be conducted concurrently to assess program performance. Discussion This RCT addresses shortcomings in previous studies and methodologically extends research in this area by randomising at school-level to prevent cross-learning between conditions; providing longer-term outcome assessment than any previous study; examining the degree to which parents/guardians discuss intervention content with children at home; assessing potential moderating/mediating effects of family and child demographic variables; testing an in-vivo measure to assess children’s ability to discriminate safe/unsafe situations and disclose to trusted adults; and testing enhancements to existing measures to establish greater internal consistency.

Experiences with an Object-Oriented, Multi-Stage Language

Metaphor is a multi-stage programming language extension to an imperative, object-oriented language in the style of C# or Java. This paper discusses some issues we faced when applying multi-stage language design concepts to an imperative base language and run-time environment. The issues range from dealing with pervasive references and open code to garbage collection and implementing cross-stage persistence.