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DRfit : A Java tool for the analysis of discrete data from multi-well plate assays

- Hofmann, Andreas, Preston, Sarah, Cross, Megan, Herath, Dilrukshi, Simon, Anne, Gasser, Robin

  • Authors: Hofmann, Andreas , Preston, Sarah , Cross, Megan , Herath, Dilrukshi , Simon, Anne , Gasser, Robin
  • Date: 2019
  • Type: Text , Journal article
  • Relation: BMC Bioinformatics Vol. 20, no. (2019), p. 1-6
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  • Description: Background: Analyses of replicates in sets of discrete data, typically acquired in multi-well plate formats, is a recurring task in many contemporary areas in the Life Sciences. The availability of accessible cross-platform data analysis tools for such fundamental tasks in varied projects and environments is an important prerequisite to ensuring a reliable and timely turnaround as well as to provide practical analytical tools for student training. Results: We have developed an easy-to-use, interactive software tool for the analysis of multiple data sets comprising replicates of discrete bivariate data points. For each dataset, the software identifies the replicate data points from a defined matrix layout and calculates their means and standard errors. The averaged values are then automatically fitted using either a linear or a logistic dose response function. Conclusions: DRfit is a practical and convenient tool for the analysis of one or multiple sets of discrete data points acquired as replicates from multi-well plate assays. The design of the graphical user interface and the built-in analysis features make it a flexible and useful tool for a wide range of different assays.

Critique of the article "Preparation and application of 2,4,6-tribromo-[13C6]-anisole for the quantitative determination of 2,4,6-tribromoanisole in wine" by Giannikopoulos and Whitfield

- Varelis, Peter

Significance level of a query for enterprise data

- Thi Ngoc Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew, Das, Rajkumar

  • Authors: Thi Ngoc Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew , Das, Rajkumar
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4494-4504
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  • Description: To operate enterprise activities, a large number of queries need to be processed every day through an enterprise system. Consequently, such a system frequently faces hugely overloaded information and incurs high delay in producing query responses for big data. This is because, traditional queries are normally treated with equal importance. With the advent of big data and its use in enterprise systems and the growth of process complexity, the traditional approach of query processing is no more suitable as it does not consider semantic information and captures all data irrespective of their relevance to a business organization, which eventually increases the computational time in both big data collection and analysis. The significance level of a query can make a trade-off between query response delay and the extent of data collection and analysis. This motivates us to concentrate on determining the significance level of a query considering its importance to an enterprise system. To our knowledge, no such approach is available in the literature. To bridge this research gap, this paper, for the first time, proposes an approach to determine the significance level of a query to prioritize them with the relevance to a business organization. As business processes play key roles in any enterprise system and all business processes are not equally important, this is done by determining the semantic similarity between a query and the processes of a business organization and the importance of a business process to that organization. With a case study on an enterprise system of a retail company, the results produced by our proposed approach have shown that significance level is higher for more important queries compared to the less important ones.

Development and evaluation of optimization based data mining techniques analysis of brain data

- Zarei, Mahdi

  • Authors: Zarei, Mahdi
  • Date: 2015
  • Type: Text , Thesis , PhD
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  • Description: Neuroscience is an interdisciplinary science which deals with the study of structure and function of the brain and nervous system. Neuroscience encompasses disciplines such as computer science, mathematics, engineering, and linguistics. The structure of the healthy brain and representation of information by neural activity are among most challenging problems in neuroscience. Neuroscience is experiencing exponentially growing volumes of data obtained by using different technologies. The investigation of such data has tremendous impact on developing new and improving existing models of both healthy and diseased brains. Various techniques have been used for collecting brain data sets for addressing neuroscience problems. These data sets can be categorized into two main groups: resting-state and state-dependent data sets. Resting-state data is based on recording the brain activity when a subject does not think about any specific concept while state-dependent data is based on recording brain activity related to specific tasks. In general, brain data sets contain a large number of features (e.g. tens of thousands) and significantly fewer samples (e.g. several hundred). Such data sets are sparse and noisy. In addition to these problems, brain data sets have a few number of subjects. Brains are very complex systems and data about any brain activity reflects very complex relationship between neurons as well as different parts of the brain. Such relationships are highly nonlinear and general purpose data mining algorithms are not always efficient for their study. The development of machine learning techniques for brain data sets is an emerging research area in neuroscience. Over the last decade, various machine learning techniques have been developed for application to brain data sets. In the meantime, some well-known algorithms such as feature selection and supervised classification have been modified for analysis of brain data sets. Support vector machines, logistic regression, and Gaussian Naive Bayes classifiers are widely used for application to brain data sets. However, Support vector machines and logistic regression algorithms are not efficient for sparse and noisy data sets and Gaussian Naive Bayes classifiers do not give high accuracy. The aim of this study is to develop new and modify the existing data mining algorithms for the analysis brain data sets. Our contribution in this thesis can be listed as follow: 1. Development of new algorithms: 1.1. Development of new voxel (feature) selection algorithms for Functional magnetic resonance imaging (fMRI) data sets, and evaluation of these algorithms on the Haxby and Science 2008 data sets. 1.2. Development of new feature selection algorithm based on the catastrophe model for regression analysis problems. 2. Development and evaluation of different versions of the adaptive neuro-fuzzy model for the analysis of the spike-discharge as a function of other neuronal parameters. 3. Development and evaluation of the modified global k-means clustering algorithm for investigation of the structure of the healthy brain. 4. Development and evaluation of region of interest (ROI) method for analysis of brain functionalconnectivity in healthy subjects and schizophrenia patients.
  • Description: Doctor of Philosophy

Generalised rational approximation and its application to improve deep learning classifiers

- Peiris, V, Sharon, Nir, Sukhorukova, Nadezda, Ugon, Julien

  • Authors: Peiris, V , Sharon, Nir , Sukhorukova, Nadezda , Ugon, Julien
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Applied Mathematics and Computation Vol. 389, no. (2021), p.
  • Relation: http://purl.org/au-research/grants/arc/DP180100602
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  • Description: A rational approximation (that is, approximation by a ratio of two polynomials) is a flexible alternative to polynomial approximation. In particular, rational functions exhibit accurate estimations to nonsmooth and non-Lipschitz functions, where polynomial approximations are not efficient. We prove that the optimisation problems appearing in the best uniform rational approximation and its generalisation to a ratio of linear combinations of basis functions are quasiconvex even when the basis functions are not restricted to monomials. Then we show how this fact can be used in the development of computational methods. This paper presents a theoretical study of the arising optimisation problems and provides results of several numerical experiments. We apply our approximation as a preprocessing step to deep learning classifiers and demonstrate that the classification accuracy is significantly improved compared to the classification of the raw signals. © 2020
  • Description: This research was supported by the Australian Research Council (ARC), Solving hard Chebyshev approximation problems through nonsmooth analysis (Discovery Project DP180100602 ).  This research was partially sponsored by Tel Aviv-Swinburne Research Collaboration Grant (2019).

Researching rural-regional (teacher) education in Australia

- Lock, Graeme, Reid, Joanne, Green, Bill, Hastings, Wendy, Cooper, Maxine, White, Simone

  • Authors: Lock, Graeme , Reid, Joanne , Green, Bill , Hastings, Wendy , Cooper, Maxine , White, Simone
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Education in Rural Australia Vol. 19, no. 2 (2009), p. 31-44
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  • Description: In 2007 a group of researchers from four Australian universities was awarded an ARC Discovery grant to undertake a longitudinal study into the nature of successful teacher education strategies aimed at making rural teaching an attractive long-term career option. This paper presents descriptive insights into how the research team, located in three Australian states (New South Wales, Victoria and Western Australia) is able to maintain a sustained cohesive approach to achieving the project's aim. The initial section of the paper introduces each team member prior to discussing the importance of taking a national perspective on rural education. The second section considers the research design and shows how the main objective of the investigation will be achieved. Emerging trends from the quantitative and qualitative data collected in 2008 are revealed in the third section. The discussion in the fourth section centres on how the trends emerging from the collected data requires a reconceptualisation of preparing pre-service teachers for non-metropolitan placements. In doing so, the project's emerging conceptual framework, which emphasizes that preparation of teachers for rural and regional appointments needs to be considered beyond the terms and forms of traditional professional practice, is explored. [Author abstract, ed]
  • Description: 1301 Education Systems

Classification through incremental max-min separability

- Bagirov, Adil, Ugon, Julien, Webb, Dean, Karasozen, Bulent

  • Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean , Karasozen, Bulent
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Pattern Analysis and Applications Vol. 14, no. 2 (2011), p. 165-174
  • Relation: http://purl.org/au-research/grants/arc/DP0666061
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  • Description: Piecewise linear functions can be used to approximate non-linear decision boundaries between pattern classes. Piecewise linear boundaries are known to provide efficient real-time classifiers. However, they require a long training time. Finding piecewise linear boundaries between sets is a difficult optimization problem. Most approaches use heuristics to avoid solving this problem, which may lead to suboptimal piecewise linear boundaries. In this paper, we propose an algorithm for globally training hyperplanes using an incremental approach. Such an approach allows one to find a near global minimizer of the classification error function and to compute as few hyperplanes as needed for separating sets. We apply this algorithm for solving supervised data classification problems and report the results of numerical experiments on real-world data sets. These results demonstrate that the new algorithm requires a reasonable training time and its test set accuracy is consistently good on most data sets compared with mainstream classifiers. © 2010 Springer-Verlag London Limited.

We're here to help: Agencies dealing with apprenticeships in Australia

- Smith, Erica

  • Authors: Smith, Erica
  • Date: 2010
  • Type: Text , Book chapter
  • Relation: Rediscovering apprenticeship p. 113-124
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  • Description: In Australia, approximately 3.5% of the working population is employed in apprenticeships and their newer counterparts, traineeships (both of these are combined under the title of 'Australian apprenticeships'). While apprenticeships were originally intended for young school leavers, they are now open to people of all ages and to part-time as well as full-time workers. The huge growth in numbers, over 300% since the mid-1990s, has been the result of very conscious planning and financial investment by the Australian Government. This paper, using data drawn from a series of research projects, analyses the different agencies that help to promote and manage the apprenticeship system. The paper points out both positive and negative effects of the large numbers of agencies involved.

The effect of simplification based on words and their role in approving or rejecting articles

- Mirzaei, Bahareh, Jafarabad, Mohammad, Karimi, Hossein, Khastavaneh, Mohammad, Parveh, Abdolkarim, Dehno, Shahin, Khandelwal, Manoj, Safari, Amirali

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