Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins

Police and community cooperation in counter-terrorism: evidence and insights from Australia

Effectively engaging the Muslim community is a challenge for police given many Muslims feel unfairly targeted by counter-terrorism policies and laws because of their faith. This paper explores how such perceptions influence the willingness of Muslims to voluntarily cooperate in counter-terrorism efforts, drawing on data collected from Muslims living in Australia. We test whether procedural justice policing can help buffer this perception of being targeted as a security threat and whether it can enhance Muslim's willingness to cooperate with police. Efforts by the Australian Federal Police to engage Muslim communities in Australia are also examined. The implications of the results for community-based approaches to counter-terrorism are discussed

Detecting large-scale structure in the era of petabyte/gigaparsec Astronomy

In this thesis, we present a study of the identification of large-scale structure in optical astronomical surveys. This encompasses the detection of large connected structures of alaxies in spectroscopic datasets and of galaxy clusters in deep photometric surveys. Beginning with a survey featuring full 3D galaxy data, in chapter 2 we present a method to identify filamentary structure after accounting for the line-of-sight velocity distortions characteristic of the virialised systems we search for. We compare data from a real galaxy survey to a series of realistic mocks. Despite broad similarities between the two, we find models do not reproduce the argest observed structures. To evaluate the exploration of a multi-band survey lacking spectroscopy, we simulate the effects of photometric redshift uncertainties on galaxy redshifts. Our findings provide limits on the accuracy of photometric redshift estimators required to recover the same diverse range of structures detected in the original spectroscopic survey. As an alternative means of exploiting the deep multi-band photometric data common to wide-area observational campaigns, in chapter 3 we present a red sequence-based algorithm to detect galaxy clusters with Voronoi diagrams. This algorithm makes no prior assumptions about cluster properties other than the similarity in colour of their members, and an enhanced projected surface density. Testing the algorithm with mock galaxy survey data reveals a detection performance equalling or exceeding that of alternative detection algorithms. Chapter 4 describes the application of this algorithm to a $270{\rm deg^2}$ survey with deep SDSS photometry. The scientific exploitation of $4,000\ {\rm z}\ \leq\ 0.6$ cluster detections from this survey is ongoing, but work presented here shows: agreement with the red sequence slope evolution derived from semi-analytic galaxy models, evidence stellar age is not responsible for responsible for the sequence slope, and a well-defined colour-colour track of potential use in photometric cluster redshift stimation. We detail improvements made to the cluster algorithm in chapter 5. Through a series of case studies we verify our approach successfully identifies galaxy clusters in a diverse range of surveys, from volumes spanning $2h^{-3}{\rm Gpc}^{3}$ to deep near-IR detections at ${\rm z}\sim 1$. based on our findings, we expect the Pan-STARRS $3\pi$ large-area survey to identify over $10^{5}$ clusters and groups. In chapter 6, we explore the characteristics of randomly-distributed noise in Voronoi diagrams. We verify the model traditionally used to describe the distribution of Voronoi cell areas in Poisson data fails to describe the frequency of high-density random cells. Because high-density cells resemble those expected from a population of galaxy cluster members, using a large dataset generated in this study we propose an alternative model that better estimates the frequency of their areas. This new model may in the future be used to improve Voronoi-based recovery of clustered data in a diverse range of applications, both astronomical and otherwise

Baby walkers in Europe--time to consider a ban.

In countries where baby walkers are used there has long been controversy about their risks and benefits. Baby walkers are a non-essential nursery product made of a seat surrounded by a rigid frame which is set on wheels. Most walkers also have a tray with toys or rattles attached, and many can be compacted for portability. Parents give various reasons for using walkers – to keep the infant quiet and happy, to encourage mobility and provide exercise, and to hold the infant during feeding. In the United Kingdom, baby walkers are used by over 50% of infants

Event Indexing Systems for Efficient Selection and Analysis of HERA Data

The design and implementation of two software systems introduced to improve the efficiency of offline analysis of event data taken with the ZEUS Detector at the HERA electron-proton collider at DESY are presented. Two different approaches were made, one using a set of event directories and the other using a tag database based on a commercial object-oriented database management system. These are described and compared. Both systems provide quick direct access to individual collision events in a sequential data store of several terabytes, and they both considerably improve the event analysis efficiency. In particular the tag database provides a very flexible selection mechanism and can dramatically reduce the computing time needed to extract small subsamples from the total event sample. Gains as large as a factor 20 have been obtained.Comment: Accepted for publication in Computer Physics Communication

Social Media Evidence in Government Investigations and Criminal Proceedings: A Frontier of New Legal Issues

As the newest pillar of communication in today’s society, social media is revolutionizing how the world does business, discovers and shares news, and instantly engages with friends and family. Not surprisingly, because social media factors into the majority of cases in some respect, this exploding medium significantly affects government investigations and criminal litigation. Social media evidence includes, among other things, photographs, status updates, a person’s location at a certain time, and direct communications to or from a defendant’s social media account. This Article will examine the importance of social media in government investigations and criminal litigation, including access to and use of social media evidence, constitutional issues that social media evidence raises, the authentication and admissibility of such evidence, in addition to the impact of social media on jurors

Health human resources planning and the production of health: Development of an extended analytical framework for needs-based health human resources planning.

Traditional approaches to health human resources planning emphasize the role of demographic change on the needs for health human resources. Conceptual frameworks have been presented that recognize the limited role of demographic change and the broader determinants of health human resource requirements. Nevertheless, practical applications of health human resources planning continue to base plans on the size and demographic mix of the population applied to simple population-provider or population-utilization ratios. In this paper an analytical framework is developed based on the production of health care services and the multiple determinants of health human resource requirements. In this framework attention is focused on estimating the ‘flow’ of services required to meet the needs of the population that is then translated into the required ‘stock’ of providers to deliver this ‘flow’ of services. The requirements for human resources in the future is shown to depend on four elements: the size and demographic mix of the population (demography), the levels of risks to health and morbidity in the population (epidemiology), the services deemed appropriate to address the levels of risks to health and morbidity (standards of care), and the rate of service delivery by providers (productivity). Application of the framework is illustrated using hypothetical scenarios.health human resources planning, demography, epidemiology, standards of care, productivity