Matthew T. McMahon

Human language reveals a universal positivity bias

Significance The most commonly used words of 24 corpora across 10 diverse human languages exhibit a clear positive bias, a big data confirmation of the Pollyanna hypothesis. The study’s findings are based on 5 million individual human scores and pave the way for the development of powerful language-based tools for measuring emotion. Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i) the words of natural human language possess a universal positivity bias, (ii) the estimated emotional content of words is consistent between languages under translation, and (iii) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts.

Goal-Directed Grid-Enabled Computing for Legacy Simulations

We describe a middleware framework conceived to enhance the effectiveness and efficiency of existing simulation applications by providing three capabilities: (1) access to grid-based and cloud-based execution, (2) access to advanced Design of Experiments (DOE) methodologies such as simulation-based optimization, and (3) access to robust data processing and visualization. The framework has been applied to a variety of simulations in both commercial and open source programming languages employing both discrete and continuous modeling formalisms. A key design objective is to minimize the workload necessary to adapt a simulation for use with the framework. User experience to date reveals that the learning curve for the framework is reasonable, but further automation of key tasks would enhance the frameworks utility.

An approach for the effective utilization of GP-GPUs in parallel combined simulation

A major challenge in the field of Modeling & Simulation is providing efficient parallel computation for a variety of algorithms. Algorithms that are described easily and computed efficiently for continuous simulation, may be complex to describe and/or efficiently execute in a discrete event context, and vice-versa. Real-world models often employ multiple algorithms that are optimally defined in one approach or the other. Parallel combined simulation addresses this problem by allowing models to define algorithmic components across multiple paradigms. In this paper, we illustrate the performance of parallel combined simulation, where the continuous component is executed across multiple graphical processing units (GPU) and the discrete event component is executed across multiple central processing units (CPU).

Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristics.

The concerns expressed by Garcia et al. (1) are misplaced due to a range of misconceptions about word usage frequency, word rank, and expert-constructed word lists such as LIWC (Linguist Inquiry and Word Count) (2). We provide a complete response in our papers online appendices (3). Garcia et al. (1) suggest that the set of function words in the LIWC dataset (2) show a wide spectrum of average happiness with positive skew (figure 1A in ref. 1) when, according to their interpretation, these words should exhibit a Dirac δ function located at neutral (havg = 5 on a 1–9 scale). However, many words tagged as function words in the LIWC dataset readily elicit an emotional response in raters as exemplified by “greatest” (havg = 7.26), “best” (havg = 7.26), “negative” (havg = 2.42), and “worst” (havg = 2.10). In our study (3), basic function words that are expected to be neutral, such as “the” (havg = 4.98) and “to” (havg = 4.98), were appropriately scored as such. Moreover, no meaningful statement about biases can be made for sets of words chosen without frequency of use properly incorporated.

Optimization of Heterogeneous Simulations of Complex Enterprise Systems

Attempting to optimize the design of a complex enterprise system is extremely difficult at best. The interconnections and human elements inherent in these systems make determining the impact of design elements and changes to these elements challenging. We suggest that a combination of sophisticated modeling and simulation techniques, including the use of agent- based models, and biologically inspired optimization techniques can be used to address the aforementioned difficulties. Finally, we discuss visualization techniques for these types of systems that enhance the ability of subject matter experts and decision-makers to understand the results.