Thinking with the body: Mind and movement in the work of Wayne McGregor | Random Dance

Thinking with the Body was an exhibition at London's Wellcome Collection, offering a glimpse into Wayne McGregor | Random Dance's interdisciplinary research and the impact it has in the rehearsal studio. Staged in the run-up to the first performances of Atomos at Sadler's Wells (Oct 2013), the exhibition featured the results of over a decade of interdisciplinary research into choreographic creativity which has been applied in the studio, in dance education, and to increase public understanding.

Wellcome Collection is a free visitor destination exploring the connections between medicine, life and art in the past, present and future. Wellcome Collection is part of the Wellcome Trust, a global charitable foundation dedicated to achieving improvements in human and animal health.

The exhibition finished on 27 October 2013, but the film exhibits are still available to view online.

Salt intake assessed by 24 h urinary sodium excretion in a random and opportunistic sample in Australia

The gold standard method for measuring population sodium intake is based on a 24 h urine collection carried out in a random population sample. However, because participant burden is high, response rates are typically low with less than one in four agreeing to provide specimens. At this low level of response it is possible that simply asking for volunteers would produce the same results.

Incidence semirings of graphs and visible bases

We consider the incidence semirings of graphs and prove that every incidence semiring has convenient visible bases for its right ideals and for its left ideals, and that these visible bases can be used to determine the weights of all right ideals that have maximum weight and all left ideals that have maximum weight. ©2013 Australian Mathematical Publishing Association Inc..

Detection on application layer DDoS using random walk model

Application Layer Distributed Denial of Service (ALDDoS) attacks have been increasing rapidly with the growth of Botnets and Ubiquitous computing. Differentiate to the former DDoS attacks, ALDDoS attacks cannot be efficiently detected, as attackers always adopt legitimate requests with real IP address, and the traffic has high similarity to legitimate traffic. In spite of that, we think, the attackers' browsing behavior will have great disparity from that of the legitimate users'. In this paper, we put forward a novel user behavior-based method to detect the application layer asymmetric DDoS attack. We introduce an extended random walk model to describe user browsing behavior and establish the legitimate pattern of browsing sequences. For each incoming browser, we observe his page request sequence and predict subsequent page request sequence based on random walk model. The similarity between the predicted and the observed page request sequence is used as a criterion to measure the legality of the user, and then attacker would be detected based on it. Evaluation results based on real collected data set has demonstrated that our method is very effective in detecting asymmetric ALDDoS attacks. © 2014 IEEE.

The prevalence of vitamin B12 deficiency in a random sample from the Australian population

 Objective: Vitamin B12 deficiency is common in older adults, and may increase the risk of cognitive impairment. The distribution of vitamin B12 insufficiency in younger age groups is less studied. This study aims to assess the prevalence of vitamin B12 deficiency (<156ρmol/L) and subclinical low-normal levels (156-250ρmol/L) in a large, random sample of the Australian population across the adult life span.
Methods: We examined serum vitamin B12 levels in a random sample of 1,085 men and 1,125 women aged 20-97 years between 1994 and 2006; in the Barwon Statistical Division, a regional area in south eastern Australia that is representative of the socioeconomic status of the Australian population.
Results: The age-standardised prevalence of vitamin B12 deficiency in this cohort of men and women was 3.6%. Subclinical low-normal vitamin B12 levels (156-250ρmol/L) were found in 26%. Serum vitamin B12 levels declined with age among men (p-value <0.001) and were lower in men than women (p-value <0.001). Vitamin B12 levels were higher among supplement users (8.0% of the cohort).
Conclusions: Vitamin B12 levels decline with age, and have been associated with neurodegenerative diseases and cognitive decline. Early intervention by diet education or supplement use to address this age-associated decline in vitamin levels may be an effective strategy to prevent decline in a significant segment of the population. Such intervention may need to start in mid-life (from 50-years of age) before the onset age-related decline in vitamin B12 levels.

Gambling participation and problem gambling severity in a stratified random survey : Findings from the second social and economic impact study of gambling in Tasmania

Demographic characteristics associated with gambling participation and problem gambling severity were investigated in a stratified random survey in Tasmania, Australia. Computer-assisted telephone interviews were conducted in March 2011 resulting in a representative sample of 4,303 Tasmanian residents aged 18 years or older. Overall, 64.8 % of Tasmanian adults reported participating in some form of gambling in the previous 12 months. The most common forms of gambling were lotteries (46.5 %), keno (24.3 %), instant scratch tickets (24.3 %), and electronic gaming machines (20.5 %). Gambling severity rates were estimated at non-gambling (34.8 %), non-problem gambling (57.4 %), low risk gambling (5.3 %), moderate risk (1.8 %), and problem gambling (.7 %). Compared to Tasmanian gamblers as a whole significantly higher annual participation rates were reported by couples with no children, those in full time paid employment, and people who did not complete secondary school. Compared to Tasmanian gamblers as a whole significantly higher gambling frequencies were reported by males, people aged 65 or older, and people who were on pensions or were unable to work. Compared to Tasmanian gamblers as a whole significantly higher gambling expenditure was reported by males. The highest average expenditure was for horse and greyhound racing ($AUD 1,556), double the next highest gambling activity electronic gaming machines ($AUD 767). Compared to Tasmanian gamblers as a whole problem gamblers were significantly younger, in paid employment, reported lower incomes, and were born in Australia. Although gambling participation rates appear to be falling, problem gambling severity rates remain stable. These changes appear to reflect a maturing gambling market and the need for population specific harm minimisation strategies. © 2014 Springer Science+Business Media New York.

Hydrologic landscape regionalisation using deductive classification and random forests

Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

Ordinal random fields for recommender systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

A random coefficient approach to the predictability of stock returns in panels

© The Author, 2014. Most studies of the predictability of returns are based on time series data, and whenever panel data are used, the testing is almost always conducted in an unrestricted unit-by-unit fashion, which makes for a very heavy parametrization of the model. On the other hand, the few panel tests that exist are too restrictive in the sense that they are based on homogeneity assumptions that might not be true. As a response to this, the current study proposes new predictability tests in the context of a random coefficient panel data model, in which the null of no predictability corresponds to the joint restriction that the predictive slope has zero mean and variance. The tests are applied to a large panel of stocks listed at the New York Stock Exchange. The results suggest that while the predictive slopes tend to average to zero, in case of book-to-market and cash flow-to-price the variance of the slopes is positive, which we take as evidence of predictability.

Stabilizing high-dimensional prediction models using feature graphs

We investigate feature stability in the context of clinical prognosis derived from high-dimensional electronic medical records. To reduce variance in the selected features that are predictive, we introduce Laplacian-based regularization into a regression model. The Laplacian is derived on a feature graph that captures both the temporal and hierarchic relations between hospital events, diseases, and interventions. Using a cohort of patients with heart failure, we demonstrate better feature stability and goodness-of-fit through feature graph stabilization.

Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Differentially private random forest with high utility

Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We propose a novel random forest algorithm under the framework of differential privacy. Unlike previous works that strictly follow differential privacy and keep the complete data distribution approximately invariant to change in one data instance, we only keep the necessary statistics (e.g. variance of the estimate) invariant. This relaxation results in significantly higher utility. To realize our approach, we propose a novel differentially private decision tree induction algorithm and use them to create an ensemble of decision trees. We also propose feasible adversary models to infer about the attribute and class label of unknown data in presence of the knowledge of all other data. Under these adversary models, we derive bounds on the maximum number of trees that are allowed in the ensemble while maintaining privacy. We focus on binary classification problem and demonstrate our approach on four real-world datasets. Compared to the existing privacy preserving approaches we achieve significantly higher utility.