Quantum Information and Entanglement: a new framework for Science and Technology with quantum many-body systems. The expected outcome of the research program is a significant boost in the understanding of quantum many-body systems, which will reinforce Australia's competitiveness and international profile in aspects of breakthrough science and frontier technologies. By developing both the underpinning theory and innovative computational tools, and by applying them to problems of recognised impor ....Quantum Information and Entanglement: a new framework for Science and Technology with quantum many-body systems. The expected outcome of the research program is a significant boost in the understanding of quantum many-body systems, which will reinforce Australia's competitiveness and international profile in aspects of breakthrough science and frontier technologies. By developing both the underpinning theory and innovative computational tools, and by applying them to problems of recognised importance, this program will have direct implications in areas of condensed matter physics, quantum statistical mechanics, particle physics, complex systems, quantum information science and technology, quantum computation, engineered quantum systems and nanotechnology. Read moreRead less
Coarse Grained Parallel Algorithms. Various fields of research face barriers created by problems that are computationally hard and/or require processing of large amounts of data. For example, some computational biochemistry methods on protein or gene sequences can not be scaled up to data sets required for human health research because of performance problems. Parallel computing enables new research by increasing the size of solvable problems. In addition to fundamental parallel computing resear ....Coarse Grained Parallel Algorithms. Various fields of research face barriers created by problems that are computationally hard and/or require processing of large amounts of data. For example, some computational biochemistry methods on protein or gene sequences can not be scaled up to data sets required for human health research because of performance problems. Parallel computing enables new research by increasing the size of solvable problems. In addition to fundamental parallel computing research, this project studies parallel algorithms for structure-based drug design and protein-protein interaction prediction that will enable new biochemistry research, as well as parallel algorithms for data cubes that will help enable the next generation of very large data warehouses.Read moreRead less
Towards automated and intelligent processing of web-based information. The successful outcome of this project will enhance Australia's research reputation in an important, practical area of ICT, will contribute to emerging Web standards, will produce frontier technology that will eventually be of benefit to Australian industry, and will train several postgraduate students.
Rule-based reasoning systems for complex and dynamic ontologies. The successful outcome of this project will enhance Australia's research reputation in an important, practical area of ICT, will contribute to emerging Web technologies that will eventually be of benefit to Australian industry, and will train several postgraduate students.
Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discove ....Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discovery and validation of group structure in data mining applications will considerably enhance knowledge management and decision support in science, industry, and government.
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