Louise Lightfoot : in search of India

 Louise Lightfoot, a trained architect by profession and an ardent balletomane, is best known for moving away from pure Western classical Ballet to a fusion of classical technique and romantic emotion in Australia through her First Australian Ballet group and school. During late 1920s, she was impressed by the performances of Anna Pavlova and Uday Shankar and to bring more appropriateness and authenticity to her own Indian classical dance style that she was trying to experiment with, virtually unknown and unseen in Australia till then in its original form, Lightfoot took a few weeks stopover in India. This short holiday eventually stretched to months and then eight years as she travelled to Tamil Nadu and Kerala’s Kalamandalam, where she began her study of the complex traditions of Kathakali and Bharata Natyam dance. Here she also became a Stage Manager cum Artistic and Publicity director for local troupes and artistes in residence. She was so thrilled by the whole experience of learning Kathakali – involving poetry, song, acting and dance – that soon she started appealing to the British in India to not only appreciate the Indian dance but also to Indian parents to allow their sons and daughters to dance. Lightfoot, as Dance Director of Shivaram, Janaki Devi, Priyagopal Singh and Lakshman Singh, supported by an ensemble of Australian dancers including Ruth Bergner, Moya Beaver, Leona Welch, Pat Martin and Betty Russell, successfully toured and promoted a range of Indian classical dance forms, like Kathakali, Manipuri, Bharatanatyam, throughout Australia, New Zealand, and Fiji. As an early image-maker, she also paved the way for many other noted Indian dancers and troupes. In spite of decades of hard work and dedication to Indian dancing and creating awareness about India in Australia her work and life is little known! Her journey is fascinating because of the workings of race relations not just in Australia but also India – existing prejudices against “Whites.” In this paper I try to chart out through Australian and Indian newspaper reports her search for India.

A hybrid cuckoo search and variable neighborhood descent for single and multiobjective scheduling problems

Cuckoo search (CS) is a relatively new meta-heuristic that has proven its strength in solving continuous optimization problems. This papers applies cuckoo search to the class of sequencing problems by hybridizing it with a variable neighborhood descent local search for enhancing the quality of the obtained solutions. The Lévy flight operator proposed in the original CS is modified to address the discrete nature of scheduling problems. Two well-known problems are used to demonstrate the effectiveness of the proposed hybrid CS approach. The first is the NP-hard single objective problem of minimizing the weighted total tardiness time (Formula presented.) and the second is the multiobjective problem of minimizing the flowtime ¯ and the maximum tardiness Tmaxfor single machine (Formula presented.). For the first problem, computational results show that the hybrid CS is able to find the optimal solutions for all benchmark test instances with 40, 50, and 100 jobs and for most instances with 150, 200, 250, and 300 jobs. For the second problem, the hybrid CS generated solutions on and very close to the exact Pareto fronts of test instances with 10, 20, 30, and 40 jobs. In general, the results reveal that the hybrid CS is an adequate and robust method for tackling single and multiobjective scheduling problems.

Facilitated communication and authorship: a systematic review

Facilitated Communication (FC) is a technique whereby individuals with disabilities and communication impairments allegedly select letters by typing on a keyboard while receiving physical support, emotional encouragement, and other communication supports from facilitators. The validity of FC stands or falls on the question of who is authoring the typed messages--the individual with a disability or the facilitator. The International Society for Augmentative and Alternative Communication (ISAAC) formed an Ad Hoc Committee on FC and charged this committee to synthesize the evidence base related to this question in order to develop a position statement. The purpose of this paper is to report this synthesis of the extant peer-reviewed literature on the question of authorship in FC. A multi-faceted search was conducted including electronic database searches, ancestry searches, and contacting selected authors. The authors considered synopses of systematic reviews, and systematic reviews, which were supplemented with individual studies not included in any prior reviews. Additionally, documents submitted by the membership were screened for inclusion. The evidence was classified into articles that provided (a) quantitative experimental data related to the authorship of messages, (b) quantitative descriptive data on the output generated through FC without testing of authorship, (c) qualitative descriptive data on the output generated via FC without testing of authorship, and (d) anecdotal reports in which writers shared their perspectives on FC. Only documents with quantitative experimental data were analyzed for authorship. Results indicated unequivocal evidence for facilitator control: messages generated through FC are authored by the facilitators rather than the individuals with disabilities. Hence, FC is a technique that has no validity.

Application of cuckoo search for design optimization of heat exchangers

A wide variety of evolutionary optimization algorithms have been used by researcher for optimal design of shell and tube heat exchangers (STHX). The purpose of optimization is to minimize capital and operational costs subject to efficiency constraints. This paper comprehensively examines performance of genetic algorithm (GA) and cuckoo search (CS) for solving STHX design optimization. While GA has been widely adopted in the last decade for STHX optimal design, there is no report on application of CS method for this purpose. Simulation results in this paper demonstrate that CS greatly outperforms GA in terms of finding admissible and optimal configurations for STHX. It is also found that CS method not only has a lower computational requirement, but also generates the most consistent results.

Ipoll: Automatic polling using online search

For years, opinion polls rely on data collected through telephone or person-to-person surveys. The process is costly, inconvenient, and slow. Recently online search data has emerged as potential proxies for the survey data. However considerable human involvement is still needed for the selection of search indices, a task that requires knowledge of both the target issue and how search terms are used by the online community. The robustness of such manually selected search indices can be questionable. In this paper, we propose an automatic polling system through a novel application of machine learning. In this system, the needs for examining, comparing, and selecting search indices have been eliminated through automatic generation of candidate search indices and intelligent combination of the indices. The results include a publicly accessible web application that provides real-time, robust, and accurate measurements of public opinions on several subjects of general interest.

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.

An analysis of hatchling resting metabolism: in search of ecological correlates that explain deviations from allometric relations

From data in the literature, an allometric equation is compiled for hatchling resting metabolic rate and an attempt is made to explain residual variation in terms of hatchling type, yolk and water content, embryonic and postnatal growth rate, and environmental circumstances (latitudinal distribution). The body mass exponent for resting metabolism in hatchlings was 0.86 and, thus, substantially different from the values compiled for adult birds (0.67-0.75). Relatively high hatchling metabolic rates were found for birds exhibiting high embryonic and postnatal growth rates, as well as for those species that hatched at high latitudes. A functional explanation is postulated for the correlations between hatchling metabolism and these three variables.

Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Traffic congestion in urban roads is one of the biggest challenges of 21 century. Despite a myriad of research work in the last two decades, optimization of traffic signals in network level is still an open research problem. This paper for the first time employs advanced cuckoo search optimization algorithm for optimally tuning parameters of intelligent controllers. Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study. For the sake of comparison, we also implement Q-learning and fixed-time controllers as benchmarks. Comprehensive simulation scenarios are designed and executed for a traffic network composed of nine four-way intersections. Obtained results for a few scenarios demonstrate the optimality of trained intelligent controllers using the cuckoo search method. The average performance of NN, ANFIS, and Q-learning controllers against the fixed-time controller are 44%, 39%, and 35%, respectively.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

Who am I? 52 Tuesdays, intimacy and the search for an authentic life

Recent research shows that young people list media entertainment as one of the sources where they find information about what they really want to know about sex and what is not taught through the school curriculum – namely, relationships and eroticism. This paper addresses the potential role that may be played by small independent alternative feature films such as 52 Tuesdays in the sexual education of young people. While 52 Tuesdays’ purpose was never explicitly pedagogic, the subject matter – family relationships, sexual experimentation, sexual identity and agency, and transgender experience – situates it firmly within the concerns of contemporary young people.

Web search activity data accurately predict population chronic disease risk in the USA

BACKGROUND: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. METHODS: Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. RESULTS: For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81; 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96; 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. CONCLUSIONS: The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.

Inflation tax in the lab: a theoretical and experimental study of competitive search equilibrium with inflation

In this paper we measure the effect of the inflation tax on economic activity and welfare within a controlled setting.To do so,we develop a model of price posting and monetary exchange with inflation and finite populations.The model,which provides a game–theoreticfoundation to Rocheteau and Wright(2005)'s competitive search monetary equilibrium, is used to derive theoretical propositions regarding the effects of inflation in thisenvironment, which we test with a laboratory experiment that closely implements the theoretical framework.We find that the inflation tax is harmful – with cash holdings, production and welfare all falling as inflation rises – and that its effect is relatively larger at low inflation rates than at higher rates.For instance,for inflation rates between 0%and5%, welfare in the two markets we consider (2[seller] 2[buyer] and 3 2) falls by roughly 1 percent for each percentage–point rise in inflation, compared with 0.4 percent over the range from 5% to 30%.Our findings lead us to conclude that the impact of the inflation tax should not be underestimated, even under low inflation.