Constraints in XML Schema Integration. This project will produce worldwide leading technologies for designing XML data integration system. With the technologies, the well designed integration systems will be able store data with rich semantics and thus provide accurate and understandable information to users. In this way, Australia and communities will be benefited both financially and informatively. The research of this project will also add to the research reputation of Australia in data integ ....Constraints in XML Schema Integration. This project will produce worldwide leading technologies for designing XML data integration system. With the technologies, the well designed integration systems will be able store data with rich semantics and thus provide accurate and understandable information to users. In this way, Australia and communities will be benefited both financially and informatively. The research of this project will also add to the research reputation of Australia in data integration areas. At the same time, the knowledge capacity of Australia on data integration will be enlarged which further improves frontier research activities in the area. Through the research of the project, PhD students will be trained.Read moreRead less
Deep visual understanding: learning to see in an unruly world. Deep Learning has achieved incredible success at an astonishing variety of Computer Vision tasks recently. This project aims to convey this success into the challenging domain of high-level image-based reasoning. It will extend deep learning to achieve flexible semantic reasoning about the content of images based on information gleaned from the huge volumes of data available on the Internet. The project expects to overcome one of the ....Deep visual understanding: learning to see in an unruly world. Deep Learning has achieved incredible success at an astonishing variety of Computer Vision tasks recently. This project aims to convey this success into the challenging domain of high-level image-based reasoning. It will extend deep learning to achieve flexible semantic reasoning about the content of images based on information gleaned from the huge volumes of data available on the Internet. The project expects to overcome one of the primary limitations of deep learning and will greatly increase its practical application to a range of industrial, cultural or health settings.Read moreRead less
Added depth: automated high level image interpretation. Humans are very good at understanding the world through imagery, but computers lack this fundamental capacity because they lack experience of what they might see. This project will provide this experience by combining the large volumes of imagery on the Internet with three dimensional information generated by humans for other purposes.
Folding polymers for high-performance energy storage. This project aims to address the current bottleneck of energy storage capability in polymers by developing new compact structures through programmed polymer folding. This project expects to understand how structures determine electrochemistry properties by creating densely packed redox-active polymers to break the limits of charge transfer rates and storage ability. Expected outcomes include deep insights into fundamental electrochemical reac ....Folding polymers for high-performance energy storage. This project aims to address the current bottleneck of energy storage capability in polymers by developing new compact structures through programmed polymer folding. This project expects to understand how structures determine electrochemistry properties by creating densely packed redox-active polymers to break the limits of charge transfer rates and storage ability. Expected outcomes include deep insights into fundamental electrochemical reaction mechanisms, laying a strong foundation for the applications of polymers from flexible electronic devices to micro-grid energy storage. This project should provide significant benefit in new knowledge and support advanced manufacturing using our high value-added materials.Read moreRead less
Targeted electrolyte design for high energy aqueous batteries. The Project aims to develop a new generation, high-energy aqueous battery. A range of new aqueous electrolytes with large working window at low concentration will be designed to replace traditional, flammable and toxic organic electrolytes, and; low-cost and multi-electron reaction materials will be developed as high-capacity electrodes to replace traditional intercalation-type materials. The Project will establish the structure-prop ....Targeted electrolyte design for high energy aqueous batteries. The Project aims to develop a new generation, high-energy aqueous battery. A range of new aqueous electrolytes with large working window at low concentration will be designed to replace traditional, flammable and toxic organic electrolytes, and; low-cost and multi-electron reaction materials will be developed as high-capacity electrodes to replace traditional intercalation-type materials. The Project will establish the structure-property relationship for electrolytes and interphases via advanced characterization(s) and computation. The new battery will be safe, energetic and sustainable for the billion-dollar energy storage market for electric vehicle, and smart-grid whilst addressing concurrently battery safety and boosted energy-density.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
Challenging econometric issues in nonlinear high-dimensional spatio-temporal prediction: theory and applications. This project will develop cutting-edge methodologies to break through challenging issues in nonlinear spatio-temporal econometric prediction. It will yield a new generation of prediction tools that enpower practitioners in Australia to produce more accurate forecasts, with more informed countermeasures to viarious economic and enviromental risks.
Low cost aqueous rechargeable zinc batteries for grid-scale energy storage. This project aims to advance energy storage technology by developing high energy aqueous rechargeable zinc batteries, which are the most promising choice for large-scale electrical energy storage, in particular for smart electric grids, owing to their low cost, high safety, and eco-friendly features. The success of this project will advance our fundamental understanding of aqueous rechargeable batteries, provide techniqu ....Low cost aqueous rechargeable zinc batteries for grid-scale energy storage. This project aims to advance energy storage technology by developing high energy aqueous rechargeable zinc batteries, which are the most promising choice for large-scale electrical energy storage, in particular for smart electric grids, owing to their low cost, high safety, and eco-friendly features. The success of this project will advance our fundamental understanding of aqueous rechargeable batteries, provide techniques for the development of a low-cost, high energy, and long life system for renewable energy storage, and benefit Australia's environment, economy, and sustainability.Read moreRead less
Carbon conundrum: Functional characterisation of organic matter-clay mineral interactions in relation to carbon sequestration. Carbon sequestration in soil has been recognised as one of the possible measures through which greenhouse gas emissions can be mitigated. The major processes involved in carbon sequestration in soil include chemical immobilisation of carbon with soil particles and physical protection in the pores of soil microaggregates. These two processes are mediated through the funct ....Carbon conundrum: Functional characterisation of organic matter-clay mineral interactions in relation to carbon sequestration. Carbon sequestration in soil has been recognised as one of the possible measures through which greenhouse gas emissions can be mitigated. The major processes involved in carbon sequestration in soil include chemical immobilisation of carbon with soil particles and physical protection in the pores of soil microaggregates. These two processes are mediated through the functional relationships of soil organic matter and clay mineral interactions in soils. This project investigates nanoscale organomineral association underlying microaggregate formation and stability, as well as the distribution and microbial decomposition of carbon within microaggregates using a suite of advanced spectroscopic, molecular and isotopic techniques.Read moreRead less
Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving ....Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving this problem. It proposes to develop a set of effective methods for privacy-preserving data publication through combining randomisation with anonymisation, and for classifying the published data through uncertainty leveraging by probabilistic reasoning and accuracy lifting by inter-flow correlation analysis and active learning.Read moreRead less