Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.Read moreRead less
Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.
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Cohort discovery and activity mining for policy impact prediction. Cohort discovery and activity mining for policy impact prediction. This project aims to develop an intelligent systematic framework to predict policy impacts on Australian patients, by discovering inherent patient cohorts and assessing the impact of the policies on these cohorts. The proposed methods lay the theoretical foundations for building intelligent automated tools for policy assessment. Expected outcomes are data-driven p ....Cohort discovery and activity mining for policy impact prediction. Cohort discovery and activity mining for policy impact prediction. This project aims to develop an intelligent systematic framework to predict policy impacts on Australian patients, by discovering inherent patient cohorts and assessing the impact of the policies on these cohorts. The proposed methods lay the theoretical foundations for building intelligent automated tools for policy assessment. Expected outcomes are data-driven patient group discovery, which could more precisely identify the patient cohorts most likely to benefit from a specific policy; and a model to predict the efficacy of policy options, which could increase the sustainability of the national health system by enabling smarter, more efficient policy decision-making.Read moreRead less
Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less
Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
ARC Communications Research Network. Building on a strong platform of existing research excellence, the Aim of the Network is to facilitate nation-wide collaborative research, promoting four intersecting research Themes: Mobile and Wireless Communications, Rural Communications, Broadband and Optical Networks, and Fundamentals of Emerging Media. Each Theme is formulated to drive multidisciplinary, innovative research as well as inspire new collaborative initiatives. Four Programs encapsulate the ....ARC Communications Research Network. Building on a strong platform of existing research excellence, the Aim of the Network is to facilitate nation-wide collaborative research, promoting four intersecting research Themes: Mobile and Wireless Communications, Rural Communications, Broadband and Optical Networks, and Fundamentals of Emerging Media. Each Theme is formulated to drive multidisciplinary, innovative research as well as inspire new collaborative initiatives. Four Programs encapsulate the core activities of the Network: Researcher Mobility, Workshops and Conferences, Postgraduate Education, and Knowledge Management Systems. The Network is expected to add significant value to pre-existing investments and raise the profile of Australian telecommunications research.Read moreRead less
Asymptotic Geometric Analysis and Machine Learning. Phenomena in large dimensions appear in a number of domains of Mathematics and adjacent domains of science (e.g. Computer Science), dealing with functions of infinitely growing number of parameters. Here, we focus on several questions naturally linked to Asymptotic Geometric Analysis which have natural applications to Statistical Learning Theory. We intend to use geometric, probabilistic and combinatorial methods to investigate these problems, ....Asymptotic Geometric Analysis and Machine Learning. Phenomena in large dimensions appear in a number of domains of Mathematics and adjacent domains of science (e.g. Computer Science), dealing with functions of infinitely growing number of parameters. Here, we focus on several questions naturally linked to Asymptotic Geometric Analysis which have natural applications to Statistical Learning Theory. We intend to use geometric, probabilistic and combinatorial methods to investigate these problems, with an emphasis on modern tools in Empirical Processes Theory and the theory of Random Matrices.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0238631
Funder
Australian Research Council
Funding Amount
$200,000.00
Summary
Ultra High Resolution Electron Recycling Spectrometer. Electron collisions with atoms and molecules provide enormous versatility through the transfer of large amounts of angular momentum and the ability to excite dipole forbidden states. However a lack of energy resolution severely restricts the processes that can be studied.
We propose to construct a unique, ultra-high resolution, electron recycling spectrometer using a radical new design. State-of-the-art spectroscopic studies of ato ....Ultra High Resolution Electron Recycling Spectrometer. Electron collisions with atoms and molecules provide enormous versatility through the transfer of large amounts of angular momentum and the ability to excite dipole forbidden states. However a lack of energy resolution severely restricts the processes that can be studied.
We propose to construct a unique, ultra-high resolution, electron recycling spectrometer using a radical new design. State-of-the-art spectroscopic studies of atoms and molecules will be enabled, including the dynamics of near-threshold processes, the formation of transient states and the examination of processes such as rotational excitation and dissociative attachment in molecules - important in a number of gas-discharge based devices.
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Investigating Near-Threshold Atomic and Molecular Collision Processes with Multiparameter Detection Techniques. We are proposing to perform state-of-the-art, electron impact excitation and ionization measurements on a range of atoms and molecules. The combination of new detector technology and innovative experimental design will enable measurements of near-threshold excitation and ionization in a number of important atomic and molecular systems. The measurements will have implications for the ....Investigating Near-Threshold Atomic and Molecular Collision Processes with Multiparameter Detection Techniques. We are proposing to perform state-of-the-art, electron impact excitation and ionization measurements on a range of atoms and molecules. The combination of new detector technology and innovative experimental design will enable measurements of near-threshold excitation and ionization in a number of important atomic and molecular systems. The measurements will have implications for the further development of atomic scattering theory, particularly the role of electron-electron correlations, and provide much needed absolute scattering information on the excitation of molecules which are of relevance to our atmosphere and various technological devices.Read moreRead less
Positronic Atoms - A Search for Positron Bound States. An experimental verification of positron bound states will solve a long standing problem in positron physics. A clear understanding of positron binding and the underlying mechanisms will open a new era in low-energy positron-atom/molecular physics, leading the way for breakthrough sciences. For instance, a positron bound state should enhance the annihilation rate between the positron and target valence electron. Positron annihilation, to pro ....Positronic Atoms - A Search for Positron Bound States. An experimental verification of positron bound states will solve a long standing problem in positron physics. A clear understanding of positron binding and the underlying mechanisms will open a new era in low-energy positron-atom/molecular physics, leading the way for breakthrough sciences. For instance, a positron bound state should enhance the annihilation rate between the positron and target valence electron. Positron annihilation, to produce two gamma rays, is a key process in both materials research (e.g. as already heavily employed in defect detection) and bio-medical treatments (e.g. the Positron Emission Tomography, or PET).Read moreRead less