The link between cratonic roots, redox state, and mantle geodynamics. This project aims to understand the role of Earth's redox state on the geodynamic evolution of continental cratonic roots. Cratonic roots form strong, buoyant rafts upon which Australia's oldest crust and mineral deposits survived. Cratons preserve a record of planetary-scale chemical shifts, including the rise of surface oxygen, but it is unclear how these redox shifts themselves affected lithospheric processes. This project ....The link between cratonic roots, redox state, and mantle geodynamics. This project aims to understand the role of Earth's redox state on the geodynamic evolution of continental cratonic roots. Cratonic roots form strong, buoyant rafts upon which Australia's oldest crust and mineral deposits survived. Cratons preserve a record of planetary-scale chemical shifts, including the rise of surface oxygen, but it is unclear how these redox shifts themselves affected lithospheric processes. This project integrates new developments in geochemistry, geophysics, and geodynamics, to map the geochemical state and structure of cratonic roots, aiding mineral exploration, and also shedding light on the processes that modify, mineralise, and sometimes destroy cratonic roots.Read moreRead less
Reconceiving Machine Learning. The proposed research will develop a new way to consider problems to which machine learning can be applied. Machine learning is crucial enabler of the digital economy. The research will provide better opportunities for Australian industry to gain a competitive advantage with machine learning technology. The framework developed will enable better opportunities for collaborative research and will build and strengthen international linkages.
What lies beneath: unveiling the fine-scale 3D compositional and thermal structure of the sub continental lithosphere and upper mantle. We will produce the first high-resolution images of the thermal and mineralogical structure of the earth's mantle beneath Australia, western USA, and South Africa. This information represents the key to our understanding of society-relevant activities such as ore and energy exploration and natural hazard assessment.
Impact of hot gas on volcanic rocks and ore-forming processes. High temperature gases move from Earth's interior to the atmosphere at volcanoes, but little is known about how they react. Recent work shows that exceptionally rapid reactions occur between hot gases and the surfaces of solids. These reactions are instrumental in forming ore deposits. The proposed work aims to apply state-of-the-art chemical analysis of natural samples and investigate gas-solid reactions experimentally to determine ....Impact of hot gas on volcanic rocks and ore-forming processes. High temperature gases move from Earth's interior to the atmosphere at volcanoes, but little is known about how they react. Recent work shows that exceptionally rapid reactions occur between hot gases and the surfaces of solids. These reactions are instrumental in forming ore deposits. The proposed work aims to apply state-of-the-art chemical analysis of natural samples and investigate gas-solid reactions experimentally to determine how chemical elements, including metals, are distributed in these reactions. The study seeks to create robust geochenmical models for understanding geochemical and ore-forming processes. Improved understanding of ore deposition will enhance the long-term viability of Australia's metals sector.Read moreRead less
Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algori ....Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algorithms with the aim of designing algorithms better able to exploit prior knowledge, and to extend existing algorithms to new problem domains thus offering well principled and well understood algorithms for solving a variety of novel online problems.Read moreRead less
Down under down under: using multi-scale seismic tomography to image beneath Australia's Great Artesian Basin. Seismic arrays will be deployed in the Great Artesian Basin to image the crust and mantle using distant earthquake and ambient noise sources. This will answer fundamental questions about the tectonic evolution of eastern Australia and elucidate the structure of a region containing significant deep Earth resources.
Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected ....Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected outcomes include the
development of software that allows the solution of hitherto unsolved machine
learning problems, and the ability to solve problems larger than those solvable
by the current generation of machine learning tools.Read moreRead less
Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance witho ....Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance without accessing expensive test labels and improvements to system generalisation. This should provide significant benefits for computer vision applications that currently require expensive labelling, and commercial and economic benefits across sectors such as transportation, security and manufacturing.Read moreRead less
Artificial intelligence meets wireless sensor networks: filling the gaps between sensors using spatial reasoning. Monitoring potential disaster regions and integrating available information with expert knowledge can prevent disasters and save many lives. The outcome of our project is one of the key components for intelligent systems that can autonomously monitor the environment, make the correct inferences and issue appropriate warnings and recommendations.
Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vecto ....Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vector conversion methods. It is expected to develop a framework where semantic labels and hyperlinks can be embedded in visual data automatically. It hopes to pioneer the creation of a web of images where the links are on image/video regions. New image simplification, stylisation, and non-photorealistic rendering methods are expected to be provided.Read moreRead less