New strategies to transmit data: Coping with exponential growth in demand. The aim of this project is to provide new technologies to facilitate the exponential growth in demand for streaming of digital data. Based on novel techniques combining graph theory, information theory, and coding, this project aims to change the way we encode data, offering significant improvements to the efficiency of communication networks and providing a 10-100 fold increase in transmission speed. If successful this p ....New strategies to transmit data: Coping with exponential growth in demand. The aim of this project is to provide new technologies to facilitate the exponential growth in demand for streaming of digital data. Based on novel techniques combining graph theory, information theory, and coding, this project aims to change the way we encode data, offering significant improvements to the efficiency of communication networks and providing a 10-100 fold increase in transmission speed. If successful this project expects to bring digital transmission improvements which could impact on almost every sector of the economy from education to advanced healthcare. Possible applications include cloud storage for big data, high-definition video streaming, and wide-coverage high-speed mobile broadband.Read moreRead less
Compression of distributed data: bridging the gap between theory and practice. In bushfire and tsunami early warning systems, environmental monitoring and healthcare applications, distributed sensors collect and transmit correlated data. This project will design novel data compression algorithms that exploit this correlation to dramatically increase the performance of existing networks and enable new applications.
Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are compl ....Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.Read moreRead less
The paradox of choice: Unravelling complex superannuation decisions. Australia has been a world-leader in retirement savings policy, but there remains a pervasive lack of understanding about how best to communicate complex financial information to decision-makers, along with a push by government for clearer disclosure and greater financial literacy. This project will inform regulators and the superannuation industry on how choices are made and how to present clearer, better-designed information ....The paradox of choice: Unravelling complex superannuation decisions. Australia has been a world-leader in retirement savings policy, but there remains a pervasive lack of understanding about how best to communicate complex financial information to decision-makers, along with a push by government for clearer disclosure and greater financial literacy. This project will inform regulators and the superannuation industry on how choices are made and how to present clearer, better-designed information to be understood by ordinary participants, thus encouraging active, well-informed participation rather than passive 'default' decisions. More efficient investment and benefit choices will improve the economic welfare of retirees, reduce the burden on the working-age population and improve fiscal sustainability.Read moreRead less
Physical layer security techniques for multiuser wireless networks. This project will develop innovative new security techniques for wireless networks. The novel techniques we develop will exploit the natural variability of wireless communication channels in order to deliver much-enhanced data security to a whole range of applications over the mobile internet.
Distributional Consequences of Mass-Market Higher Education in Business. Increased access to tertiary education has not been evaluated for its effects on the full spectrum of individuals served by the tertiary sector. Using longitudinal data on entire student populations at university business faculties, this project will provide the first Australian evidence on the trade-offs amongst the educational success of students with different levels of preparation that occur when those with poorer prep ....Distributional Consequences of Mass-Market Higher Education in Business. Increased access to tertiary education has not been evaluated for its effects on the full spectrum of individuals served by the tertiary sector. Using longitudinal data on entire student populations at university business faculties, this project will provide the first Australian evidence on the trade-offs amongst the educational success of students with different levels of preparation that occur when those with poorer preparation are added to classrooms. Short-term performance and medium-term attrition, a recent educational policy focus, will be evaluated. Theoretically grounded recommendations will result for undergraduate program design to suit a student population with varying levels of university preparation.Read moreRead less
Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the bui ....Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the building of next generation of computer vision systems to work in open and dynamic environments. This should be able to produce solid benefits to the science, society, and economy of Australian via the application of these advanced intelligent systems.Read moreRead less
Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart g ....Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart grids and optimal charging and discharging tasks in a large network of electric vehicles, helping Australian power industry improve efficiency and security, as well as training the next generation scientists and engineers for Australia in this emerging field.Read moreRead less
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less