Sublinear algorithms for visual analytics of extreme-scale networks. This project aims to design new sublinear algorithms for the visual analytics of extreme-scale networks, involving billions of nodes. Based on algorithmics for graph drawing, integrating sublinear algorithms and distributed algorithms, the project will introduce new quality metrics for good visualisation of extreme-scale networks, design new sublinear-time algorithms to compute good visualisation, implement them in a distribute ....Sublinear algorithms for visual analytics of extreme-scale networks. This project aims to design new sublinear algorithms for the visual analytics of extreme-scale networks, involving billions of nodes. Based on algorithmics for graph drawing, integrating sublinear algorithms and distributed algorithms, the project will introduce new quality metrics for good visualisation of extreme-scale networks, design new sublinear-time algorithms to compute good visualisation, implement them in a distributed computing environment, and evaluate with a real world social network and biological network data sets. The new algorithms produced by this project will be used in the next generation visual analytic tools for extreme-scale data to enable analysts develop new insights and new knowledge of extreme-scale data.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE140100121
Funder
Australian Research Council
Funding Amount
$720,000.00
Summary
Equipment for International Collaboration in Gravitational Wave Detection. Equipment for international collaboration in gravitational wave detection: This project will allow the Australian Consortium for Gravitational Astronomy to install optical equipment at its dedicated research facility, and to install data analysis pipelines on new iVEC Pawsey Centre GPU-enabled supercomputers. The equipment is required for research aimed at stabilising instabilities in the new international gravitational w ....Equipment for International Collaboration in Gravitational Wave Detection. Equipment for international collaboration in gravitational wave detection: This project will allow the Australian Consortium for Gravitational Astronomy to install optical equipment at its dedicated research facility, and to install data analysis pipelines on new iVEC Pawsey Centre GPU-enabled supercomputers. The equipment is required for research aimed at stabilising instabilities in the new international gravitational wave detectors currently being commissioned in the USA and Europe. Real time data from the new detectors will be analysed using innovative new techniques. Scientists across Australia will be able to rapidly localise potential gravitational wave sources to direct robotic telescope observations. This could enable the first detection of gravitational waves.Read moreRead less
An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving futur ....An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving future secure key distribution. Multi-agent interactions in higher dimensional spaces are considered intractable using traditional matrix methods and this project will build on our exciting new breakthrough showing that such interactions are tractable using geometric multivectors.Read moreRead less
Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will desig ....Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will design very efficient binary networks, enabling large-scale deployment of deep learning to mobile devices. Thus this project will overcome two primary limitations of deep learning generally, however, and will greatly increase its already impressive domain of practical application.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100415
Funder
Australian Research Council
Funding Amount
$432,483.00
Summary
Cross-layer Design for Ultra-reliable Low-latency Communications. This project aims to develop fundamental theories and practical technologies for ultra-reliable low-latency communications – one of the grand challenges in 5G cellular networks. Due to the dynamic nature of wireless networks, existing approaches dividing networks into multiple layers cannot guarantee a hard deadline with high reliability. The outcomes of the project will be cross-layer models for characterising the end-to-end perf ....Cross-layer Design for Ultra-reliable Low-latency Communications. This project aims to develop fundamental theories and practical technologies for ultra-reliable low-latency communications – one of the grand challenges in 5G cellular networks. Due to the dynamic nature of wireless networks, existing approaches dividing networks into multiple layers cannot guarantee a hard deadline with high reliability. The outcomes of the project will be cross-layer models for characterising the end-to-end performance, a prediction and communication co-design framework for improving the delay-reliability trade-off, and an online architecture for implementing model-based algorithms in real networks. They will underpin the development of remote control and advancing automation in manufacturing, transportation, mining, etc.Read moreRead less
Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental ....Evolutionary computation for expensive bilevel multiobjective problems. This project aims to develop an evolutionary computation framework to solve computationally expensive bilevel multiobjective problems. The research is fundamental in nature and will address key open challenges in solving such problems, including hierarchical decision-making, multiple performance criteria, uncertainties and computational expense. The proposed research has applications in diverse domains such as environmental policy formulation, network design, engineering, defence and cybersecurity; offering significant benefits to the researchers and practitioners in these fields. In addition to research outputs, it will strengthen international collaboration and build research capacity to put Australia at the forefront of this research.
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Inventiveness and the progress of product innovation. Quantitative models of inventiveness will be used to forecast the potential rate of improvement of a technology and to re-design products to improve more rapidly and steadily. By focusing on innovation in products and technologies in energy conversion, this research can guide development funding for low-carbon energy generation.
Evolutionary computation for robust multi-objective engineering design. This project aims to develop an evolutionary computation framework for robust multi-objective design, a critical pursuit in engineering industries. Such problems are characterised by multiple conflicting performance objectives
and constraints which are highly nonlinear, often black-box, and prone to unavoidable real-life uncertainties. The existing evolutionary algorithms are often computationally impractical and have a numb ....Evolutionary computation for robust multi-objective engineering design. This project aims to develop an evolutionary computation framework for robust multi-objective design, a critical pursuit in engineering industries. Such problems are characterised by multiple conflicting performance objectives
and constraints which are highly nonlinear, often black-box, and prone to unavoidable real-life uncertainties. The existing evolutionary algorithms are often computationally impractical and have a number of fundamental
shortcomings which restrict their use in real applications. This project aims to investigate and overcome the underlying key challenges to advance knowledge and contribute towards diverse domains such as energy, transport and space research, helping deliver high quality robust designs.
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Development of methods and algorithms to support multidisciplinary optimisation. This project will aim to develop a number of novel and computationally efficient schemes to deal with the key challenges facing multidisciplinary optimisation. These advancements will allow us to solve a number of challenging and intractable problems in science and engineering.
Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collabor ....Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.Read moreRead less