Quality based frame selection for face clustering in news video

Clustering identities in a broadcast video is a useful task to aid in video annotation and retrieval. Quality based frame selection is a crucial task in video face clustering, to both improve the clustering performance and reduce the computational cost. We present a frame work that selects the highest quality frames available in a video to cluster the face. This frame selection technique is based on low level and high level features (face symmetry, sharpness, contrast and brightness) to select the highest quality facial images available in a face sequence for clustering. We also consider the temporal distribution of the faces to ensure that selected faces are taken at times distributed throughout the sequence. Normalized feature scores are fused and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face clustering system. We present a news video database to evaluate the clustering system performance. Experiments on the newly created news database show that the proposed method selects the best quality face images in the video sequence, resulting in improved clustering performance.

Impact of climate variability on Plasmodium vivax and Plasmodium falciparum malaria in Yunnan Province, China

BACKGROUND Malaria remains a public health problem in the remote and poor area of Yunnan Province, China. Yunnan faces an increasing risk of imported malaria infections from Mekong river neighboring countries. This study aimed to identify the high risk area of malaria transmission in Yunnan Province, and to estimate the effects of climatic variability on the transmission of Plasmodium vivax and Plasmodium falciparum in the identified area. METHODS We identified spatial clusters of malaria cases using spatial cluster analysis at a county level in Yunnan Province, 2005-2010, and estimated the weekly effects of climatic factors on P. vivax and P. falciparum based on a dataset of daily malaria cases and climatic variables. A distributed lag nonlinear model was used to estimate the impact of temperature, relative humidity and rainfall up to 10-week lags on both types of malaria parasite after adjusting for seasonal and long-term effects. RESULTS The primary cluster area was identified along the China-Myanmar border in western Yunnan. A 1°C increase in minimum temperature was associated with a lag 4 to 9 weeks relative risk (RR), with the highest effect at lag 7 weeks for P. vivax (RR = 1.03; 95% CI, 1.01, 1.05) and 6 weeks for P. falciparum (RR = 1.07; 95% CI, 1.04, 1.11); a 10-mm increment in rainfall was associated with RRs of lags 2-4 weeks and 9-10 weeks, with the highest effect at 3 weeks for both P. vivax (RR = 1.03; 95% CI, 1.01, 1.04) and P. falciparum (RR = 1.04; 95% CI, 1.01, 1.06); and the RRs with a 10% rise in relative humidity were significant from lag 3 to 8 weeks with the highest RR of 1.24 (95% CI, 1.10, 1.41) for P. vivax at 5-week lag. CONCLUSIONS Our findings suggest that the China-Myanmar border is a high risk area for malaria transmission. Climatic factors appeared to be among major determinants of malaria transmission in this area. The estimated lag effects for the association between temperature and malaria are consistent with the life cycles of both mosquito vector and malaria parasite. These findings will be useful for malaria surveillance-response systems in the Mekong river region.

A finite volume method for solving the two-sided time-space fractional advection-dispersion equation

We present a finite volume method to solve the time-space two-sided fractional advection-dispersion equation on a one-dimensional domain. The spatial discretisation employs fractionally-shifted Grünwald formulas to discretise the Riemann-Liouville fractional derivatives at control volume faces in terms of function values at the nodes. We demonstrate how the finite volume formulation provides a natural, convenient and accurate means of discretising this equation in conservative form, compared to using a conventional finite difference approach. Results of numerical experiments are presented to demonstrate the effectiveness of the approach.

A comparison of finite difference and finite volume methods for solving the space-fractional advection-dispersion equation with variable coefficients

Transport processes within heterogeneous media may exhibit non- classical diffusion or dispersion which is not adequately described by the classical theory of Brownian motion and Fick’s law. We consider a space-fractional advection-dispersion equation based on a fractional Fick’s law. Zhang et al. [Water Resources Research, 43(5)(2007)] considered such an equation with variable coefficients, which they dis- cretised using the finite difference method proposed by Meerschaert and Tadjeran [Journal of Computational and Applied Mathematics, 172(1):65-77 (2004)]. For this method the presence of variable coef- ficients necessitates applying the product rule before discretising the Riemann–Liouville fractional derivatives using standard and shifted Gru ̈nwald formulas, depending on the fractional order. As an alternative, we propose using a finite volume method that deals directly with the equation in conservative form. Fractionally-shifted Gru ̈nwald formulas are used to discretise the Riemann–Liouville fractional derivatives at control volume faces, eliminating the need for product rule expansions. We compare the two methods for several case studies, highlighting the convenience of the finite volume approach.

"Show me, how does it look now" : remote help-giving in collaborative design

This paper examines the role of visual information in a remote help-giving situation involving the collaborative physical task of designing a prototype remote control. We analyze a set of video recordings captured within an experimental setting. Our analysis shows that using gestures and relevant artefacts and by projecting activities on the camera, participants were able to discuss several design-related issues. The results indicate that with a limited camera view (mainly faces and shoulders), participants' conversations were centered at the physical prototype that they were designing. The socially organized use of our experimental setting provides some key implications for designing future remote collaborative systems.

Promoting Statistical Literacy Through Data Modelling in the Early School Years

This chapter addresses data modelling as a means of promoting statistical literacy in the early grades. Consideration is first given to the importance of increasing young children’s exposure to statistical reasoning experiences and how data modelling can be a rich means of doing so. Selected components of data modelling are then reviewed, followed by a report on some findings from the third-year of a three-year longitudinal study across grades one through three.

Evaluating NEXUS: A suicide prevention program for young people from refugee backgrounds (2007 – 2009)

Although suicide deaths in Australia continue to decline since the peak of 2,720 suicide deaths in 1997, youth suicide and self-harm are a major health issue. In 2006, in the 20 to 24 year age group, suicide accounted for approximately 21% of all male deaths and 14% of all female deaths. There is a lack of solid data on the rates of suicide and self-harm among young people from refugee backgrounds. However, this population faces a significant number of post-resettlement stressors that may add to their vulnerability and increase their risk of suicide and self-harm. The NEXUS program is an innovative strategy developed by the Queensland Program of Assistance to Survivors of Torture and Trauma (QPASTT) that aims to reduce risk factors for suicide and self-harm and to promote protective factors among youth from refugee backgrounds living in Brisbane and Toowoomba. QPASTT is a non-government organisation that provides culturally appropriate support services to refugee and humanitarian entrants to Australia. QPASTT’s primary function is to provide counselling, advocacy support and community development activities for survivors of torture and trauma at an individual, family and community level. Since 2002 the NEXUS program has been developed and implemented by QPASTT. Since then, this multi-component program has been funded by the Commonwealth Department of Health and Ageing through the National Suicide Prevention Strategy (NSPS). NSPS funding of local community suicide prevention activities will contribute to the outcomes specified in the strategic framework: Living is for Everyone (LIFE): A framework for prevention of suicide and self-harm in Australia. The focus of this report is the evaluation of the NEXUS program conducted by QPASTT between August 2007 and May 2009.

Embedding nature into cities – a case study of economic lessons from Singapore

Built environment design around the world faces a number of 21st Century challenges such as rising urban heat island effect and rising pollution, which are further worsened by consequences of climate change and increasing urban populations. Such challenges have caused cities around the globe to investigate options that can help to significantly reduce the environmental pressures from current and future development, requiring new areas of innovation. One such area is ‘Biophilic Urbanism’, which refers to the use of natural elements as design features in urban centres to assist efforts to address climate change issues in rapidly growing economies. Singapore is an illustration of a thriving economy that exemplifies the value of embedding nature into its built environment. The significance of urban green space has been recognised in Singapore as early as the 1960s when Lee Kuan Yew embarked on the ‘Garden City’ concept. 50 years later, Singapore has achieved its Garden City goal and is now entering a new era of sustainability, to create a ‘City in a Garden’. Although the economics of such efforts is not entirely understood, the city of Singapore has continued to pursue visions of becoming a biophilic city. Indeed, there appears to be important lessons to be learned from a city that has challenged the preconceived notion that protecting vegetation in a city is not economically viable. Hence, this paper will discuss the case study of Singapore to highlight the drivers, along with the economic considerations identified along the way. The conclusions have implications for expanding the notion of biophilic urbanism, particularly in the Australian context by discussing the lessons learned from this city. The research is part of Sustainable Built Environment National Research Centre, and has been developed in collaboration with the Curtin University Sustainability Policy Institute.

Productivity differences, technology adoption and economic growth : the case of India

When the acronym of ëBRICí was coined in 2001 by Jim OíNeill of Goldman Sachs, it was expected that economic growth rates in India, Brazil and Russia would eventually catch up with that of China. However, China has continued to outperform the other economies in the group, even after it was renamed ëBRICSí to reflect the inclusion of South Africa in 2010. The focus of this chapter is on one of the BRICS economies, namely India. Its aim is to examine from an economic perspective, why Indiaís performance has not lived up to expectations, and comment on the key challenges it faces in meeting them. We begin with some descriptive statistics regarding the progress of the Indian economy since 1990. While it has been growing at a rapid rate since the reforms it introduced in the1990s, there has been a slowdown in its overall GDP growth rates since 2008. The rate of growth experienced in the period 2003ñ07 was an average of 10.5 per cent. However, since the recession following the Global Financial Crisis (GFC) of 2008, the growth rate has fallen. From the period 2008ñ12 it has only registered an average growth rate of 6.5 per cent (World Bank, 2013). This chapter suggests that one of the major factors underpinning this slowdown is the performance of Indiaís agricultural sector. The importance of the agricultural sector is highlighted by the following stylized facts.

A transcriptomic analysis of striped catfish (Pangasianodon hypophthalmus) in response to salinity adaptation: De novo assembly, gene annotation and marker discovery

The striped catfish (Pangasianodon hypophthalmus) culture industry in the Mekong Delta in Vietnam has developed rapidly over the past decade. The culture industry now however, faces some significant challenges, especially related to climate change impacts notably from predicted extensive saltwater intrusion into many low topographical coastal provinces across the Mekong Delta. This problem highlights a need for development of culture stocks that can tolerate more saline culture environments as a response to expansion of saline water-intruded land. While a traditional artificial selection program can potentially address this need, understanding the genomic basis of salinity tolerance can assist development of more productive culture lines. The current study applied a transcriptomic approach using Ion PGM technology to generate expressed sequence tag (EST) resources from the intestine and swim bladder from striped catfish reared at a salinity level of 9 ppt which showed best growth performance. Total sequence data generated was 467.8 Mbp, consisting of 4,116,424 reads with an average length of 112 bp. De novo assembly was employed that generated 51,188 contigs, and allowed identification of 16,116 putative genes based on the GenBank non-redundant database. GO annotation, KEGG pathway mapping, and functional annotation of the EST sequences recovered with a wide diversity of biological functions and processes. In addition, more than 11,600 simple sequence repeats were also detected. This is the first comprehensive analysis of a striped catfish transcriptome, and provides a valuable genomic resource for future selective breeding programs and functional or evolutionary studies of genes that influence salinity tolerance in this important culture species.

Systems modelling of mine water and energy tradeoffs

In the coming decades, the mining industry faces the dual challenge of lowering both its water and energy use. This presents a difficulty since technological advances that decrease the use of one can increase the use of the other. Historically, energy and water use have been modelled independently, making it difficult to evaluate the true costs and benefits from water and energy improvements. This paper presents a hierarchical systems model that is able to represent interconnected water and energy use at a whole of site scale. In order to explore the links between water and energy four technologies advancements have been modelled: use of dust suppression additives, the adoption of thickened tailings, the transition to dry processing and the incorporation of a treatment plant. The results show a synergy between decreased water and energy use for dust suppression additives, but a trade-off for the others.

Regional trade-offs between mine water and energy use : a water treatment case study

The mining industry faces concurrent pressures of reducing water use, energy consumption and greenhouse gas (GHG) emissions in coming years. However, the interactions between water and energy use, as well as GHG e missions have largely been neglected in modelling studies to date. In addition, investigations tend to focus on the unit operation scale, with little consideration of whole-of-site or regional scale effects. This paper presents an application of a hierarchical systems model (HSM) developed to represent water, energy and GHG emissions fluxes at scales ranging from the unit operation, to the site level, to the regional level. The model allows for the linkages between water use, energy use and GHG emissions to be examined in a fl exible and intuitive way, so that mine sites can predict energy and emissions impacts of water use reduction schemes and vice versa. This paper examines whether this approach can also be applied to the regional scale with multiple mine sites. The model is used to conduct a case study of several coal mines in the Bowen Basin, Australia, to compare the utility of centralised and decentralised mine water treatment schemes. The case study takes into account geographical factors (such as water pumping distances and elevations), economic factors (such as capital and operating cost curves for desalination treatment plants) and regional factors (such as regionally varying climates and associated variance in mine water volumes and quality). The case study results indicate that treatment of saline mine water incurs a trade-off between water and energy use in all cases. However, significant cost differences between centralised and decentralised schemes can be observed in a simple economic analysis. Further research will examine the possibility for deriving model up-scaling algorithms to reduce computational requirements.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Convergent Media Policy

This chapter considers the implications of convergence for media policy from three perspectives. First, it discusses what have been the traditional concerns of media policy, and the challenges it faces, from the perspectives of public interest theories, economic capture theories, and capitalist state theories. Second, it looks at what media convergence involves, and some of the dilemmas arising from convergent media policy including: (1) determining who is a media company; (2) regulatory parity between ‘old’ and ‘new’ media; (3) treatment of similar media content across different platforms; (4) distinguishing ‘big media’ from user-created content; and (5) maintaining a distinction between media regulation and censorship of personal communication. Finally, it discusses attempts to reform media policy in light of these changes, including Australian media policy reports from 2011-12 including the Convergence Review, the Finkelstein Review of News Media, and the Australian Law Reform Commission’s National Classification Scheme Review. It concludes by arguing that ‘public interest’ approaches to media policy continue to have validity, even as they grapple with the complex question of how to understand the concept of influence in a convergent media environment.