The cultural origins of human cognition

Malicious accounts

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The Matching Law: Papers In Psychology And Economics

Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for user/consumer's attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks. This strong correlation between iPhone tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data.

/pdf/predicting-iphone-sales-from-iphone-tweets-22xlq1aiea.pdf

Predicting iPhone Sales from iPhone Tweets

This paper describes a fault location method which uses sparsely located wide-area synchronized phasor measurements. The main contribution is the way fault location is reformulated as a sparse estimation problem. Faulted system is modeled by equivalent terminal bus injections which would cause the same changes in bus voltages with the fault current drawn at any point along the faulted line. Once these injections are estimated, the fact that the ratio between equivalent injections at the two terminal buses depends only on the ratio of serial impedances on each side of the fault point can be used to locate the fault. It is shown that this formulation applies to both two terminal lines as well as teed lines regardless of fault type or resistance. Assuming availability of an accurate three-phase network model and a sufficient number of phasor measurements over the entire network, an underdetermined set of linear equations can be formed and then solved for the sparse equivalent bus injections. This is accomplished by applying two $L_{1}$ norm regularization optimization methods namely Lasso and overlapping group Lasso. Effectiveness of these methods are verified by extensive simulations.

https://repository.library.northeastern.edu/files/neu:cj82k734s/fulltext.pdf

Fault Location Using Wide-Area Measurements and Sparse Estimation

Finding the sparse solution of an underdetermined system of linear equations (the so called sparse recovery problem) has been extensively studied in the last decade because of its applications in many different areas. So, there are now many sparse recovery algorithms (and program codes) available. However, most of these algorithms have been developed for real-valued systems. This paper discusses an approach for using available real-valued algorithms (or program codes) to solve complex-valued problems, too. The basic idea is to convert the complex-valued problem to an equivalent real-valued problem and solve this new real-valued problem using any real-valued sparse recovery algorithm. Theoretical guarantees for the success of this approach will be discussed, too. On the other hand, a widely used sparse recovery idea is finding the minimum l1 norm solution. For real-valued systems, this idea requires to solve a linear programming (LP) problem, but for complex-valued systems it needs to solve a second-order cone programming (SOCP) problem, which demands more computational load. However, based on the approach of this paper, the complex case can also be solved by linear programming, although the theoretical guarantee for finding the sparse solution is more limited.

/pdf/how-to-use-real-valued-sparse-recovery-algorithms-for-126tr0rvfl.pdf

How to use real-valued sparse recovery algorithms for complex-valued sparse recovery?

We use data extracted from many weblogs to identify the underlying relations of a set of companies in the Standard and Poor (S&P) 500 index. We define a pairwise similarity measure for the companies based on the weblog articles and then apply a graph clustering procedure. We show that it is possible to capture some interesting relations between companies using this method. As an application of this clustering procedure we propose a cluster-based portfolio selection method which combines information from the weblog data and historical stock prices. Through simulation experiments, we show that our method performs better (in terms of risk measures) than cluster-based portfolio strategies based on company sectors or historical stock prices. This suggests that the methodology has the potential to identify groups of companies whose stock prices are more likely to be correlated in the future.

https://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/viewFile/4649/5040

Weblog Analysis for Predicting Correlations in Stock Price Evolutions

Bayesian topic model approaches to online and time-dependent clustering

Specialized hybrid learners resolve Rogers' paradox about the adaptive value of social learning

http://networks.ece.mcgill.ca/sites/default/files/NeuralNetBayes.pdf

Milad Kharratzadeh

Papers

How to use real-valued sparse recovery algorithms for complex-valued sparse recovery?

Weblog Analysis for Predicting Correlations in Stock Price Evolutions

Bayesian topic model approaches to online and time-dependent clustering

Specialized hybrid learners resolve Rogers' paradox about the adaptive value of social learning

Neural-network Modelling of Bayesian Learning and Inference.