Data for: "Investigations into the within-host genomic diversity and phenotypic variation of Plasmodium falciparum"

Quantitative data produced to support a PhD thesis on the within-host genomic diversity and phenotypic variation of Plasmodium falciparum

‘Let’s get sexting’: risk, power, sex and criminalisation in the moral domain

This article explores the criminalisation and governance of sexting among young people.While the focus is on Australian jurisdictions, the article places debates and anxieties about sexting and young people in a broader analysis around concerns about new technologies, child sexual abuse, and the risks associated with childhood sexuality. The article argues that these broader social, cultural and moral anxieties have created an environment where rational debate and policy making around teen sexting has been rendered almost impossible. Not only has the voice of young people themselves been silenced in the public, political and media discourse about sexting, but any understanding about the differing behaviours and subsequent harms that constitute teen sexting has been lost. All the while, sexting has been rendered a pleasurable if somewhat risky pastime in an adult cultural context lending weight to the argument that teen sexting is often a subterranean expression of activities that are broadly accepted.The article concludes that the current approaches to regulating teen sexting, along with the emergence of sexting as a legitimate adult activity, may have had the perverse consequence of making teen sexting an even more attractive teenage risk taking activity.Authored by Murray Lee, Thomas Crofts, Michael Salter, Sanja Milivojevic and Alyce McGovern

Experiences and issues for environmental science sensor network deployments

Sensor network research is a large and growing area of academic effort, examining technological and deployment issues in the area of environmental monitoring. These technologies are used by environmental engineers and scientists to monitor a multiplicity of environments and services, and, specific to this paper, energy and water supplied to the built environment. Although the technology is developed by Computer Science specialists, the use and deployment is traditionally performed by environmental engineers. This paper examines deployment from the perspectives of environmental engineers and scientists and asks what computer scientists can do to improve the process. The paper uses a case study to demonstrate the agile operation of WSNs within the Cloud Computing infrastructure, and thus the demand-driven, collaboration-intense paradigm of Digital Ecosystems in Complex Environments

Experiences and issues for environmental engineering sensor network deployments

Sensor network research is a large and growing area of academic effort, examining technological and deployment issues in the area of environmental monitoring. These technologies are used by environmental engineers and scientists to monitor a multiplicity of environments and services, and, specific to this paper, energy and water supplied to the built environment. Although the technology is developed by Computer Science specialists, the use and deployment is traditionally performed by environmental engineers. This paper examines deployment from the perspectives of environmental engineers and scientists and asks what computer scientists can do to improve the process. The paper uses a case study to demonstrate the agile operation of WSNs within the Cloud Computing infrastructure, and thus the demand-driven, collaboration-intense paradigm of Digital Ecosystems in Complex Environments

Parallel resampling in the particle filter

Modern parallel computing devices, such as the graphics processing unit (GPU), have gained significant traction in scientific and statistical computing. They are particularly well-suited to data-parallel algorithms such as the particle filter, or more generally Sequential Monte Carlo (SMC), which are increasingly used in statistical inference. SMC methods carry a set of weighted particles through repeated propagation, weighting and resampling steps. The propagation and weighting steps are straightforward to parallelise, as they require only independent operations on each particle. The resampling step is more difficult, as standard schemes require a collective operation, such as a sum, across particle weights. Focusing on this resampling step, we analyse two alternative schemes that do not involve a collective operation (Metropolis and rejection resamplers), and compare them to standard schemes (multinomial, stratified and systematic resamplers). We find that, in certain circumstances, the alternative resamplers can perform significantly faster on a GPU, and to a lesser extent on a CPU, than the standard approaches. Moreover, in single precision, the standard approaches are numerically biased for upwards of hundreds of thousands of particles, while the alternatives are not. This is particularly important given greater single- than double-precision throughput on modern devices, and the consequent temptation to use single precision with a greater number of particles. Finally, we provide auxiliary functions useful for implementation, such as for the permutation of ancestry vectors to enable in-place propagation.Comment: 21 pages, 6 figure

Extending sensor networks into the cloud using Amazon web services

Sensor networks provide a method of collecting environmental data for use in a variety of distributed applications. However, to date, limited support has been provided for the development of integrated environmental monitoring and modeling applications. Specifically, environmental dynamism makes it difficult to provide computational resources that are sufficient to deal with changing environmental conditions. This paper argues that the Cloud Computing model is a good fit with the dynamic computational requirements of environmental monitoring and modeling. We demonstrate that Amazon EC2 can meet the dynamic computational needs of environmental applications. We also demonstrate that EC2 can be integrated with existing sensor network technologies to offer an end-to-end environmental monitoring and modeling solution