Is phase-specific, community-oriented treatment of early psychosis- an economically viable method of improving outcome?

Objective: The Early Psychosis Prevention and Intervention Centre (EPPIC) provides a comprehensive 'real-world' model of early intervention to young people experiencing an emerging psychotic disorder. A prospective study has already provided evidence of improved clinical outcome at 12 months after entry. The present study examined whether the service was also cost-effective.

Method: A cost-effectiveness analysis compared EPPIC with its immediate precursor service, from the perspective of the government funding agency. Only direct costs were included.

Results: EPPIC proved to be more cost-effective. The weighted average cost per patient for the first 12 months was cheaper (by äD 7110 per patient), while treatment outcomes were superior. The savings were due to the marked reduction in in-patient costs outweighing substantial increases in the costs of community care.

Conclusion: These results, while encouraging in terms of the further development of integrated, phase-specific intervention programmes for early psychosis, are not conclusive, and further research is required.

Young children’s use of a delayed video representation to solve a retrieval problem pertaining to self

The experiments reported here were concerned with the development of delayed self-recognition. Children were videotaped playing a game and were marked covertly with a sticker on their forehead while doing so. The findings, of both a cross-sectional sample and a prospective longitudinal one, revealed that 3- but not 2.5-year-old children reached to remove this sticker reliably during video playback only after they had been trained to use the video to guide their search for an object that was not directly visible to the unaided eye. It appears that by 3 years of age children understand that their briefly delayed self video-representation is related to their present self. In contrast, while 2.5-year-olds can use delayed vid of information to locate objects in space that cannot be seen by the unaided eye, they cannot use this type of information to locate an object that pertains to a part of self that is not directly visible, such as a sticker on one’s hair. The findings are discussed in terms of the emergence of an extended
sense of self.

Algorithms and applications of preference based ranking for information retrieval

In this thesis, the author designed three sets of preference based ranking algorithms for information retrieval and provided the corresponsive applications for the algorithms. The main goal is to retrieve recommended, high similar and valuable ranking results to users.

An autonomic management system for service oriented enterprise grid computing

Grid computing and service oriented architectures improve the way computational tasks are performed. Through this research a management system, utilising the autonomic characteristics of self discovery and negotiation, self configuration and self healing, was designed and implemented, ultimately removing the need for users to know the intricacies of these systems.

Prototyping real-time systems using Petri net and object-oriented models

The objective of the research for this thesis is to develop techniques in order to build an executable model of a real-time system. This model is to be used early in the development of the system not only to detect errors in the specification of the system but also to validate expectations of the developer as to the operation of the system. A graphical specification of a real-time system called the transformation schema was chosen to be used to build the model. Two executable models of a real-time system are described.

Spherical harmonics and distance transform for image representation and retrieval

In this paper, we have proposed a method for 2D image retrieval based on object shapes. The method relies on transforming the 2D images into 3D space based on distance transform. Spherical harmonics are obtained for the 3D data and used as descriptors for the underlying 2D images. The proposed method is compared against two existing methods which use spherical harmonics for shape based retrieval of images. MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the performance of the proposed descriptors is significantly better than other methods in the same category.

An parallel information retrieval method for e-commerce

An online transaction always retrieves a large amount of information before making decisions. Currently, the parallel methods for retrieving such information can only provide a similar performance to serial methods. In this paper we first perform an analysis to determine the factors that affect the performance of exiting methods, i.e., HQR and EHQR, and show that the several of these factors are not considered by these methods. Motivated by this, we propose a new dispatch scheme called AEHQR, which takes into account the features of parallel dispatching. In addition, we provide cost models that determine the optimal performance achievable by any parallel dispatching method. Using experimental comparison, we illustrate that the AEHQR is significantly outperforms the HQR and EHQR under all conditions.

Clustering based multi-label classification for image annotation and retrieval

This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.

Efficient dimensionality reduction and one-class classification for content-based image retrieval

The thesis investigates various machine learning approaches to reducing data dimensionality, and studies the impact of asymmetric data on learning in image retrieval. Efficient algorithms are proposed to reduce the data dimensionality. Integration strategies for one-class classification are designed to address asymmetric data issue and improve retrieval effectiveness.

Empirical study of multi-label classification methods for image annotation and retrieval

This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications. The study shows that triple random ensemble multi-label classification algorithm (TREMLC) outperforms among its counterparts, especially on scene image dataset. Multi-label k-nearest neighbor (ML-kNN) and binary relevance (BR) learning algorithms perform well on Corel image dataset. Based on the overall evaluation results, examples are given to show label prediction performance for the algorithms using selected image examples. This provides an indication of the suitability of different multi-label classification methods for automatic image annotation under different problem settings.

Service discovery for service-oriented content adaptation

Service-Oriented Content Adaptation (SOCA) has emerged as a potential solution to the content-device mismatch problem. One of the key problems with the SOCA scheme is that a content adaptation task can potentially be performed by multiple services. In this paper, we propose an approach to the service discovery problem for SOCA and it is demonstrated to perform well.

Retrieval

Retrieval was a site-specific performance installation which transformed five floors of the National Library of Australia in Nov-Dec 2010. Devised in collaboration with a team of 30 young performers, the production lead the audience on a quest deep into the library to retrieve priceless cultural memories before all was lost. Louise Morris working in collaboration with co-designer Matthew Aberline and the artistic team created the installed environments for the production. The production won numerous awards including Best New Project- Express Media and Best Original Work- CAT awards.