ORCID Profile
0000-0002-0704-4968
Current Organisation
Monash University
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Applied Statistics | Nanomaterials | Pattern Recognition and Data Mining | Nanotechnology not elsewhere classified | Interdisciplinary Engineering not elsewhere classified | Nanotechnology | Artificial Intelligence and Image Processing
Industrial Chemicals and Related Products not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences | Manufacturing not elsewhere classified | Expanding Knowledge in Technology |
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2014
Publisher: Elsevier BV
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Wiley
Date: 19-11-2009
DOI: 10.1002/SAM.10057
Abstract: Finding patterns that are interesting to a user in a certain application context is one of the central goals of data mining research. Regarding all patterns above a certain frequency threshold as interesting is one way of defining interestingness. In this paper, however, we argue that in many applications, a different notion of interestingness is required in order to be able to capture “long”, and thus particularly informative, patterns that are correspondingly of low frequency. To identify such patterns, our proposed measure of interestingness is based on the degree or strength of closedness of the patterns. We show that (i) indeed this definition selects long interesting patterns that are difficult to identify with frequency‐based approaches, and (ii) that it selects patterns that are robust against noise and/or dynamic changes. We prove that the family of interesting patterns proposed here forms a closure system and use the corresponding closure operator to design a mining algorithm listing these patterns in amortized quadratic time. In particular, for nonsparse datasets its time complexity is \\documentclass{article}\\usepackage{amsmath}\\usepackage{amssymb}\\usepackage{amsbsy}\\usepackage{amsfonts}\\pagestyle{empty}\\begin{document}$\\newcommand{\\bigo}[1]{O\\!\\left(#1\\right)} \\bigo{nm}$\\end{document} per pattern, where n denotes the number of items and m the size of the database. This is equal to the best known time bound for listing ordinary closed frequent sets, which is a special case of our problem. We also report empirical results with real‐world datasets. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 346‐360, 2009
Publisher: IEEE
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 28-06-2017
Publisher: Elsevier BV
Date: 10-2023
Publisher: ACM
Date: 11-08-2013
Publisher: IEEE
Date: 12-2013
Publisher: Elsevier BV
Date: 10-2023
Publisher: ACM
Date: 12-08-2012
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 04-09-2020
DOI: 10.1038/S41467-020-17112-9
Abstract: Although machine learning (ML) models promise to substantially accelerate the discovery of novel materials, their performance is often still insufficient to draw reliable conclusions. Improved ML models are therefore actively researched, but their design is currently guided mainly by monitoring the average model test error. This can render different models indistinguishable although their performance differs substantially across materials, or it can make a model appear generally insufficient while it actually works well in specific sub-domains. Here, we present a method, based on subgroup discovery, for detecting domains of applicability (DA) of models within a materials class. The utility of this approach is demonstrated by analyzing three state-of-the-art ML models for predicting the formation energy of transparent conducting oxides. We find that, despite having a mutually indistinguishable and unsatisfactory average error, the models have DAs with distinctive features and notably improved performance.
Publisher: Springer Science and Business Media LLC
Date: 19-05-2009
Publisher: Springer Science and Business Media LLC
Date: 14-05-2014
Publisher: ACM
Date: 21-08-2011
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/ICDM.2008.85
Publisher: American Chemical Society (ACS)
Date: 19-05-2023
Publisher: ACM
Date: 04-08-2017
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Cold Spring Harbor Laboratory
Date: 04-05-2022
DOI: 10.1101/2022.05.03.490402
Abstract: Neural mechanisms are complex and difficult to image. This paper presents a new space-time resolved whole-brain imaging framework, called Neurophysiological Mechanism Imaging (NMI), that identifies neurophysiological mechanisms within cerebral cortex at the macroscopic scale. By fitting neural mass models to electromagnetic source imaging data using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately imaged across the whole brain at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural mechanisms in different brain states. The whole-brain imaging framework can disclose the neurophysiological substrates of complicated brain functions in a spatiotemporal manner. Developed a semi-analytical Kalman filter to estimate neurophysiological variables in the nonlinear neural mass model efficiently and accurately from large-scale electromagnetic time-series. The semi-analytical Kalman filter is 7.5 times faster and 5% more accurate in estimating model parameters than the unscented Kalman filter. Provided several group-level statistical observations based on neurophysiological variables and visualised them in a whole-brain manner to show different perspectives of neurophysiological mechanisms. Applied the framework to study resting-state alpha oscillation and found novel relationships between local neurophysiological variables in specific brain regions and alpha power.
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 11-2019
Publisher: IEEE
Date: 11-2018
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IOP Publishing
Date: 25-01-2017
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.29
Start Date: 07-2021
End Date: 07-2024
Amount: $450,605.00
Funder: Australian Research Council
View Funded ActivityStart Date: 09-2022
End Date: 09-2027
Amount: $4,379,165.00
Funder: Australian Research Council
View Funded Activity