Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

3D body scanning and healthcare applications

Developed largely for the clothing industry, 3D body-surface scanners are transforming our ability to accurately measure and visualize a person's body size, shape, and skin-surface area. Advancements in 3D whole-body scanning seem to offer even greater potential for healthcare applications

Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact into such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. To this purpose, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategies calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategies calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha

Asymptotically Optimal Algorithms for Pickup and Delivery Problems with Application to Large-Scale Transportation Systems

The Stacker Crane Problem is NP-Hard and the best known approximation algorithm only provides a 9/5 approximation ratio. The objective of this paper is threefold. First, by embedding the problem within a stochastic framework, we present a novel algorithm for the SCP that: (i) is asymptotically optimal, i.e., it produces, almost surely, a solution approaching the optimal one as the number of pickups/deliveries goes to infinity; and (ii) has computational complexity O(n^{2+\eps}), where $n$ is the number of pickup/delivery pairs and \eps is an arbitrarily small positive constant. Second, we asymptotically characterize the length of the optimal SCP tour. Finally, we study a dynamic version of the SCP, whereby pickup and delivery requests arrive according to a Poisson process, and which serves as a model for large-scale demand-responsive transport (DRT) systems. For such a dynamic counterpart of the SCP, we derive a necessary and sufficient condition for the existence of stable vehicle routing policies, which depends only on the workspace geometry, the stochastic distributions of pickup and delivery points, the arrival rate of requests, and the number of vehicles. Our results leverage a novel connection between the Euclidean Bipartite Matching Problem and the theory of random permutations, and, for the dynamic setting, exhibit novel features that are absent in traditional spatially-distributed queueing systems.Comment: 27 pages, plus Appendix, 7 figures, extended version of paper being submitted to IEEE Transactions of Automatic Contro

Anthropometry and body composition of 18 year old men according to duration of breast feeding: birth cohort study from Brazil

Objective: To assess the association between duration of breast feeding and measures of adiposity in adolescence. Design: Population based birth cohort study. Setting: Pelotas, a city of 320 000 inhabitants in a relatively developed area in southern Brazil. Participants: All newborn infants in the city's hospitals were enrolled in 1982; 78.8% (2250) of all male participants were located at age 18 years when enrolling in the national army. Main outcome measures: Weight, height, sitting height, subscapular and triceps skinfolds, and body composition (body fat, lean mass). Results: Neither the duration of total breast feeding nor that of predominant breast feeding (breast milk plus non-nutritive fluids) showed consistent associations with anthropometric or body composition indices. After adjustment for confounding factors, the only significant associations were a greater than 50% reduction in obesity among participants breast fed for three to five months compared with all other breastfeeding categories (P = 0.007) and a linear decreasing trend in obesity with increasing duration of predominant breast feeding (P = 0.03). Similar significant effects were not observed for other measures of adiposity. Borderline direct associations also occurred between total duration of breast feeding and adult height (P = 0.06). Conclusions: The significant reduction in obesity among children breast fed for three to five months is difficult to interpret, as no a priori hypothesis existed regarding a protective effect of intermediate duration of breast feeding. The findings indicate that, in this population, breast feeding has no marked protective effect against adolescent adiposity

Gender, Generation, and Jobs: Differences in Gender Role Ideologies by Age and Occupation

Gender inequality in the workplace remains a salient issue today; women continue to earn less than men, driven in part by occupational segregation and by general perceptions about socially constructed gender norms. Using the United States General Social Survey, I conduct multivariate Ordinary Least Squares regression analysis to explore the differences in gender role ideologies by generational cohort and occupation. The results highlight differences in gender role ideologies amongst occupations and suggest that while perceptions of gender influence occupational choices, so too do occupations impact our perceptions of gender roles. Individuals working in occupations atypical for their gender, those who challenge gender norms through their field of work, tend to hold more egalitarian attitudes towards gender roles. Such findings reinforce the importance of not only understanding the individual but also the structural factors that drive our attitudes towards gender and gender roles, which are of key importance for driving gender equality

Intolerance of Uncertainty and Emotion Regulation in Pregnant Women

Anxiety during pregnancy is common, with up to 25% of women reporting anxiety symptoms. This has implications for the mother, feotus and for birth outcomes. When anxiety is specifically related to the pregnancy, these risks increase. The current study aimed to investigate the relationship between Intolerance of Uncertainty (IU) and Emotion Regulation (ER) in pregnancy-related anxiety. A convenience sample of 40 pregnant women were randomised into an experimental induction of pregnancy specific versus general uncertainty. Participants completed a Vertical Arrow Technique (VAT) procedure to induce uncertainty. General Anxiety and Distress increased significantly in both groups, but Pregnancy-related anxiety was significantly higher in the pregnancy-specific group only. Neither trait IU nor trait ER moderated this relationship, suggesting that uncertainty related to pregnancy uniquely predicted pregnancy-related anxiety. Furthermore, pregnancy-specific uncertainty may lead to less adaptive ER strategies being used. Although further research is required to examine what is driving the relationship between uncertainty and pregnancy-related anxiety, this research has implications for antenatal services, as well as perinatal mental health assessments and interventions

Future Computers: Digital, Quantum, Biological

Quantum computers offer huge potential performance, and biological computers can revolutionize pharmaceuticals. However, simple “engineering” descriptions and standardized approaches are needed. We provide “layperson” descriptions of quantum and biological computer architectures, comparisons with digital computers, and discussions of industry-standard models