Curtin University

About: Curtin University is a education organization based out in Perth, Western Australia, Australia. It is known for research contribution in the topics: Population & Zircon. The organization has 14257 authors who have published 48997 publications receiving 1336531 citations. The organization is also known as: WAIT & Western Australian Institute of Technology.

The dispersion–brightness relation for fast radio bursts from a wide-field survey

Abstract: Despite considerable efforts over the past decade, only 34 fast radio bursts—intense bursts of radio emission from beyond our Galaxy—have been reported1,2. Attempts to understand the population as a whole have been hindered by the highly heterogeneous nature of the searches, which have been conducted with telescopes of different sensitivities, at a range of radio frequencies, and in environments corrupted by different levels of radio-frequency interference from human activity. Searches have been further complicated by uncertain burst positions and brightnesses—a consequence of the transient nature of the sources and the poor angular resolution of the detecting instruments. The discovery of repeating bursts from one source3, and its subsequent localization4 to a dwarf galaxy at a distance of 3.7 billion light years, confirmed that the population of fast radio bursts is located at cosmological distances. However, the nature of the emission remains elusive. Here we report a well controlled, wide-field radio survey for these bursts. We found 20, none of which repeated during follow-up observations between 185–1,097 hours after the initial detections. The sample includes both the nearest and the most energetic bursts detected so far. The survey demonstrates that there is a relationship between burst dispersion and brightness and that the high-fluence bursts are the nearby analogues of the more distant events found in higher-sensitivity, narrower-field surveys5.

The development of a two-tier multiple-choice diagnostic instrument for evaluating secondary school students’ ability to describe and explain chemical reactions using multiple levels of representation

Abstract: A 15-item two-tier multiple-choice diagnostic instrument was developed to evaluate secondary students’ ability to describe and explain seven types of chemical reactions using macroscopic, submicroscopic and symbolic representations. A mixed qualitative and quantitative case study was conducted over four years involving 787 Years 9 and 10 students (15 to 16 years old). The instrument was administered to sixty-five Year 9 students after nine months of instruction to evaluate their use of multiple levels of representation. Analysis of the students’ responses demonstrated acceptable reliability of the instrument, a wide range of difficulty indices and acceptable discrimination indices for 12 of the items. The teaching program proved to be successful in that in most instances students were able to describe and explain the observed changes in terms of the atoms, molecules and ions that were involved in the chemical reactions using appropriate symbols, formulas, and chemical and ionic equations. Nevertheless, despite the emphasis on multiple levels of representation during instruction, 14 conceptions were identified that indicated confusion between macroscopic and submicroscopic representations, a tendency to extrapolate bulk macroscopic properties of substances to the submicroscopic level, and limited understanding of the symbolic representational system. [Chem. Educ. Res. Pract., 2007, 8 (3), 293-307.]

Joint learning and dictionary construction for pattern recognition

Abstract: We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.