Groups, graphs and mutually unbiased bases

A new characteristic free approach to constructing large sets of mutually unbiased bases in Hilbert space is developed. We associate with a seed set of bases a finite subgroup of which defines a strongly regular graph. Large sets of mutually unbiased bases are obtained as the cliques of the graph.

The influence of leisure preference, life priority and making time on regular participation in leisure time physical activity

Using an attitude-behaviour theory approach this study examined the direct and indirect influence of preference, life priority and time allocation on regular participation in leisure-time physical activity (LTPA). The crosssectional study used self-report questionnaires to collect data from a random sample of 250 people aged 19 to 87 years living in an Australian city. The findings suggest that people’s regular participation in LTPA is not directly influenced by their preference for it. Rather, making LTPA a high life priority and allocating time for LTPA are intervening factors that explain the relationship. The outcomes emphasise the importance of encouraging the formation of a preference for physical activity in young children. They suggest all levels of government and the leisure profession emphasise work/life balance by prioritising LTPA, educating people about time management and helping them to develop time management skills.

Body mass index, sexual difficulties and sexual satisfaction among people in regular heterosexual relationships : a population-based study

Background/Aims:  The aims of this study were to clarify the relationship between body mass index (BMI) and sexual difficulties and to investigate if BMI influenced sexual satisfaction, over and above the effects of sexual difficulties.

Methods:  Cross-sectional analyses of a nationally representative computer-assisted telephone interview. Eight thousand, six hundred and fifty-six respondents were recruited by random digit dialling in 2004–2005. Only those in a sexually active, heterosexual relationship were included in the current analyses.

Results:  After adjustments for demographic factors, both overweight and obese male and female participants were more likely to report worrying during sex about whether their body was unattractive. Among women, associations were also found between higher BMI and lack of interest in sex. No other significant associations between BMI and sexual difficulties were evident. There was an association between BMI and extreme physical pleasure for women but not men over and above the effects of sexual difficulties, with obese women being more likely than normal weight women to report extreme physical pleasure. No associations were found for either men or women between BMI and whether or not they reported extreme emotional or sexual satisfaction with their relationship.

Conclusions:  With the exception of body image difficulties, there is little association between BMI and self-reported sexual difficulties. Furthermore, extreme sexual and emotional satisfaction appeared to be associated with the presence or absence of sexual difficulties and not overly influenced by BMI. Overall, clinicians and patients should be aware that being overweight is not necessarily detrimental to sexual functioning.

An embedding scheme for detecting anomalous block structured graphs

Graph-based anomaly detection plays a vital role in various application domains such as network intrusion detection, social network analysis and road traffic monitoring. Although these evolving networks impose a curse of dimensionality on the learning models, they usually contain structural properties that anomaly detection schemes can exploit. The major challenge is finding a feature extraction technique that preserves graph structure while balancing the accuracy of the model against its scalability. We propose the use of a scalable technique known as random projection as a method for structure aware embedding, which extracts relational properties of the network, and present an analytical proof of this claim. We also analyze the effect of embedding on the accuracy of one-class support vector machines for anomaly detection on real and synthetic datasets. We demonstrate that the embedding can be effective in terms of scalability without detrimental influence on the accuracy of the learned model.