Standing Swells Surveyed Showing Surprisingly Stable Solutions for the Lorenz '96 Model

The Lorenz '96 model is an adjustable dimension system of ODEs exhibiting chaotic behavior representative of dynamics observed in the Earth's atmosphere. In the present study, we characterize statistical properties of the chaotic dynamics while varying the degrees of freedom and the forcing. Tuning the dimensionality of the system, we find regions of parameter space with surprising stability in the form of standing waves traveling amongst the slow oscillators. The boundaries of these stable regions fluctuate regularly with the number of slow oscillators. These results demonstrate hidden order in the Lorenz '96 system, strengthening the evidence for its role as a hallmark representative of nonlinear dynamical behavior.Comment: 10 pages, 8 figure

Erratum to: Instagram photos reveal predictive markers of depression (EPJ Data Science, (2017), 6, 1, (15), 10.1140/epjds/s13688-017-0110-z)

Upon publication of the original article [1], it was noticed that Figure 2 contained an error. The horizontal bars for the likes row were incorrectly shown as blue. The horizontal bars for the ‘likes’ row should be orange. This has now been acknowledged and corrected in this erratum. The correct Figure 2 is shown below. In the section Method, subsection Improving data quality, the sentence ‘We also excluded participants with CES-D scores of 22 or higher. should read as We also excluded participants with CES-D scores of 21 or lower. This has now been acknowledged and corrected in this erratum. (Figure presented.)

Measuring the happiness of large-scale written expression: Songs, blogs, and presidents

The importance of quantifying the nature and intensity of emotional states at the level of populations is evident: we would like to know how, when, and why individuals feel as they do if we wish, for example, to better construct public policy, build more successful organizations, and, from a scientific perspective, more fully understand economic and social phenomena. Here, by incorporating direct human assessment of words, we quantify happiness levels on a continuous scale for a diverse set of large-scale texts: song titles and lyrics, weblogs, and State of the Union addresses. Our method is transparent, improvable, capable of rapidly processing Web-scale texts, and moves beyond approaches based on coarse categorization. Among a number of observations, we find that the happiness of song lyrics trends downward from the 1960s to the mid 1990s while remaining stable within genres, and that the happiness of blogs has steadily increased from 2005 to 2009, exhibiting a striking rise and fall with blogger age and distance from the Earth\u27s equator. © 2009 The Author(s)

Instagram photos reveal predictive markers of depression

Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness