Understanding and manipulating stress physiology of eucalypt seedlings to improve survival and growth. Eucalyptus globulus and E. nitens are the major species being established to meet the 2020 Vision (Anon 1999) of trebling Australia's plantation estate. Seedling mortality and/or reduced growth after planting, linked to the transition from ideal growing conditions in the nursery to stressful conditions (high drought and browsing risk) at the planting sites, significantly increase the costs of ....Understanding and manipulating stress physiology of eucalypt seedlings to improve survival and growth. Eucalyptus globulus and E. nitens are the major species being established to meet the 2020 Vision (Anon 1999) of trebling Australia's plantation estate. Seedling mortality and/or reduced growth after planting, linked to the transition from ideal growing conditions in the nursery to stressful conditions (high drought and browsing risk) at the planting sites, significantly increase the costs of plantation production. By investigating physiological mechanisms of seedlings and their responses to drought and browsing stress during establishment in the field, methods for the production of seedlings acclimated to drought or browsing stress will be developed. The research outcomes will help maximise the financial return on over $28 M per annum being invested by the 8 project partners in seedling production and planting over 80, 000 ha per year for the next 19 years to meet the requirements of Vision 2020.Read moreRead less
Devising ecologically sustainable restoration programs for degraded rural landscapes by integrating landscape ecology, genetics and ecophysiology. Concern about tree decline in rural landscape is widespread, and disturbingly climate change is predicted to exacerbate this problem. Past ill-considered tree plantings have proven to be economically wasteful, achieved limited ecological resilience and negligible improvement of biodiversity values. Using Tasmania as a 'model system', we will advance t ....Devising ecologically sustainable restoration programs for degraded rural landscapes by integrating landscape ecology, genetics and ecophysiology. Concern about tree decline in rural landscape is widespread, and disturbingly climate change is predicted to exacerbate this problem. Past ill-considered tree plantings have proven to be economically wasteful, achieved limited ecological resilience and negligible improvement of biodiversity values. Using Tasmania as a 'model system', we will advance this problem by undertaking research to determine how seedling establishment, tree growth, carbon storage and water use are influenced by landscape setting, management history, climate change, species type and local varieties. This research will provide a much needed evidence to devise ecologically sustainable tree-plantings in southern Australia.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC150100004
Funder
Australian Research Council
Funding Amount
$3,630,239.00
Summary
ARC Training Centre for Forest Value. ARC Training Centre for Forest Value. This training centre aims to build capacity to transform the Australian forest products sector by providing advanced training and research. In particular, it aims to train a workforce capable of improving resource utilisation and creating value at all stages along the forest-to-building supply chain. This should allow the sector to exploit emerging markets in the use of renewable materials. The centre’s partners range fr ....ARC Training Centre for Forest Value. ARC Training Centre for Forest Value. This training centre aims to build capacity to transform the Australian forest products sector by providing advanced training and research. In particular, it aims to train a workforce capable of improving resource utilisation and creating value at all stages along the forest-to-building supply chain. This should allow the sector to exploit emerging markets in the use of renewable materials. The centre’s partners range from forest managers to architects and engineers, to ensure a flow of information from forest to design and manufacture. The centre’s research, and the industry-ready graduates produced, should increase industry productivity, profitability and sustainability, and enable increased returns from Australia’s forests.Read moreRead less
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
Ring constructions and algorithms for enhancing performance of BCH codes. BCH codes form a major class of codes used in modern communication systems. The aim of this project is to enhance the efficiency of this class of codes by combining them in constructions enabling correction of deletion and insertion errors, and develop efficient implementations of encoding and decoding algorithms incorporating soft decision methods for enhanced error correction. Significance of the project is explained by ....Ring constructions and algorithms for enhancing performance of BCH codes. BCH codes form a major class of codes used in modern communication systems. The aim of this project is to enhance the efficiency of this class of codes by combining them in constructions enabling correction of deletion and insertion errors, and develop efficient implementations of encoding and decoding algorithms incorporating soft decision methods for enhanced error correction. Significance of the project is explained by the role of fast, secure and reliable communications in modern information and communication technology. Expected outcomes include new efficient algorithms and commercial modules available for symbolic computation systems with applications in telecommunications industry.
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Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data ar ....Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data are needed. This project will design, implement and evaluate visualisation methods for massive multivariate network data sets. This research is expected to be used by Australian software development, biotechnology and security companies to exploit their data.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less
Machine Assisted, Multi-scale Spatial and Temporal Observation and Modeling of Marine Benthic Habitats. The Integrated Marine Observing System (IMOS) science plans include sampling campaigns reliant on Autonomous Underwater Vehicle (AUV) Facility data and designed to address the issues of marine biodiversity quantification and assurance. The proposed research will directly enhance the effectiveness of these programs by speeding labour-intensive analyses, aggregating the results, and searching f ....Machine Assisted, Multi-scale Spatial and Temporal Observation and Modeling of Marine Benthic Habitats. The Integrated Marine Observing System (IMOS) science plans include sampling campaigns reliant on Autonomous Underwater Vehicle (AUV) Facility data and designed to address the issues of marine biodiversity quantification and assurance. The proposed research will directly enhance the effectiveness of these programs by speeding labour-intensive analyses, aggregating the results, and searching for ecological patterns on a national scale that would be difficult to identify using traditional approaches tuned to process-scale studies. Australian society stands to benefit by virtue of improved large-scale models of ecosystem function and reduced cost for conducting marine ecosystem investigations.Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less