Tuning parallel applications on software-defined supercomputers. Supercomputers are used by many Australian industries and laboratories to make better products and perform critical predictions, and it is essential that codes operate efficiently. This project aims to assist programmers in identifying performance bottlenecks in their code quickly and easily. The project expects to supersede the current methods, which are often complex and time-consuming, by developing innovative software tools and ....Tuning parallel applications on software-defined supercomputers. Supercomputers are used by many Australian industries and laboratories to make better products and perform critical predictions, and it is essential that codes operate efficiently. This project aims to assist programmers in identifying performance bottlenecks in their code quickly and easily. The project expects to supersede the current methods, which are often complex and time-consuming, by developing innovative software tools and techniques. The expected outcomes include novel software, verified by industry partners in real world case studies, ranging from life sciences to hypersonic transport. This should provide significant benefits, including the capacity for Australian industries to access world-class supercomputing technology.Read moreRead less
The Material Science of Biomimetic Soft Network Composites. Nature combines stiff and strong collagen fibres intertwined within a weak polymer matrix of proteoglycans into soft tissues with outstanding mechanical durability and biological properties. We converge a biomimetic design strategy inspired in the architecture of natural soft tissues and a novel additive manufacturing technology termed melt electrowriting (MEW) to manufacture advanced biomimetic soft network composites (BSNC). The SNCs ....The Material Science of Biomimetic Soft Network Composites. Nature combines stiff and strong collagen fibres intertwined within a weak polymer matrix of proteoglycans into soft tissues with outstanding mechanical durability and biological properties. We converge a biomimetic design strategy inspired in the architecture of natural soft tissues and a novel additive manufacturing technology termed melt electrowriting (MEW) to manufacture advanced biomimetic soft network composites (BSNC). The SNCs are composed of a weak polymer matrix and a MEW reinforcing fibrous phase printed at the nanometre scale, containing patterns mimicking the natural tissue architectures. Advanced computational tools are applied for the rational design of the SNC while reducing costs and times associated to experimental work.Read moreRead less
Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture ....Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture Masking Interferometer. This yields new physics for actively growing protoplanets, carved rings and gaps in disks, and gravitationally sculpted patterns of leftover cometary debris. Confronting state-of-the-art models with these data will immediately yield profound insights into planetary system formation, including our own.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100816
Funder
Australian Research Council
Funding Amount
$444,000.00
Summary
Probing dark energy with the largest 3D Map of the Universe. Dark Energy is one of the most profound mysteries of modern physics. It makes up about 70 percent of the Universe, but no compelling theory can explain its nature. This project aims to measure the properties of Dark Energy with unprecedented accuracy: an order of magnitude better than the state of the art. It aims to accomplish this by extracting information from the largest 3D map of the cosmos, built with the optical spectra of 35 mi ....Probing dark energy with the largest 3D Map of the Universe. Dark Energy is one of the most profound mysteries of modern physics. It makes up about 70 percent of the Universe, but no compelling theory can explain its nature. This project aims to measure the properties of Dark Energy with unprecedented accuracy: an order of magnitude better than the state of the art. It aims to accomplish this by extracting information from the largest 3D map of the cosmos, built with the optical spectra of 35 million galaxies, observed by the Dark Energy Spectroscopic Instrument. This project will foster Australia's historic leadership and investments in galaxy surveys via unique international partnerships, and produce cutting-edge tools for big data analyses with important applications in a wide range of industries.Read moreRead less
RNA structure prediction by deep learning and evolution-derived restraints. This project addresses the long-standing structure-folding problem of Ribonucleic acids (RNA) whose solution is essential for elucidating the roles of noncoding RNAs in living organisms. The proposed approach will detect hidden homologous sequences and enhance evolutionary covariation signals by developing new algorithms for search and smarter neural networks for deep learning. The project expects to generate new tools ....RNA structure prediction by deep learning and evolution-derived restraints. This project addresses the long-standing structure-folding problem of Ribonucleic acids (RNA) whose solution is essential for elucidating the roles of noncoding RNAs in living organisms. The proposed approach will detect hidden homologous sequences and enhance evolutionary covariation signals by developing new algorithms for search and smarter neural networks for deep learning. The project expects to generate new tools for structure-based probing of RNA evolutional and functional mechanisms. The outcomes should provide significant benefits by high-accuracy computational modelling of RNA structures that are difficult and costly to solve by current structural biology techniques but important for enabling biotech and clinical applications.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcom ....Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcomes include the ability to ingest multiple video feeds into a dense and dynamic 3D reconstruction for knowledge representation and discovery, and analysis of events and behaviour through new spatio-temporal analytic approaches. This will offer significant benefits for video forensic analysis, policing, and emergency response.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101840
Funder
Australian Research Council
Funding Amount
$426,696.00
Summary
Fantastic companions of giant planets and where to find them. The gas giants in the Solar System are hypothesized to have played important roles in the formation and habitability of the Earth. This project aims to put the Solar System in a broader context of the exoplanet demography. The expected outcomes of this project include: (1) a uniform sample of small planets discovered with the public Transiting Exoplanet Survey Satellite data; (2) detection of additional gas giants and in-depth charact ....Fantastic companions of giant planets and where to find them. The gas giants in the Solar System are hypothesized to have played important roles in the formation and habitability of the Earth. This project aims to put the Solar System in a broader context of the exoplanet demography. The expected outcomes of this project include: (1) a uniform sample of small planets discovered with the public Transiting Exoplanet Survey Satellite data; (2) detection of additional gas giants and in-depth characterisation of the best planetary systems; (3) occurrence rate of planetary systems cohosting both gas giant and small planets. This study will provide significant benefits for theoretically understanding the uniqueness of the Solar System, as well as the formation and evolution of planetary systems in general.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less