Quantifying the development of user-generated art during 2001-2010.

One of the main questions in the humanities is how cultures and artistic expressions change over time. While a number of researchers have used quantitative computational methods to study historical changes in literature, music, and cinema, our paper offers the first quantitative analysis of historical changes in visual art created by users of a social online network. We propose a number of computational methods for the analysis of temporal development of art images. We then apply these methods to a sample of 270,000 artworks created between 2001 and 2010 by users of the largest social network for art-DeviantArt (www.deviantart.com). We investigate changes in subjects, techniques, sizes, proportions and also selected visual characteristics of images. Because these artworks are classified by their creators into two general categories-Traditional Art and Digital Art-we are also able to investigate if the use of digital tools has had a significant effect on the content and form of artworks. Our analysis reveals a number of gradual and systematic changes over a ten-year period in artworks belonging to both categories.

A simple control policy for achieving minimum jerk trajectories.

Point-to-point fast hand movements, often referred to as ballistic movements, are a class of movements characterized by straight paths and bell-shaped velocity profiles. In this paper we propose a bang-bang optimal control policy that can achieve such movements. This optimal control policy is accomplished by minimizing the L∞ norm of the jerk profile of ballistic movements with known initial position, final position, and duration of movement. We compare the results of this control policy with human motion data recorded with a manipulandum. We propose that such bang-bang control policies are inherently simple for the central nervous system to implement and also minimize wear and tear on the bio-mechanical system. Physiological experiments support the possibility that some parts of the central nervous system use bang-bang control policies. Furthermore, while many computational neural models of movement control have used a bang-bang control policy without justification, our study shows that the use of such policies is not only convenient, but optimal.

The association of smoking with components of the metabolic syndrome in non-diabetic patients.

INTRODUCTION: There is limited evidence about the association between smoking and metabolic syndrome (MS). The aim of this study was to assess the association of smoking with MS components. MATERIALS AND METHODS: As part of the baseline survey of a community-based study, we studied 5,573 non-diabetic men. All participants were interviewed and underwent physical examinations and blood collection. RESULTS: The study participants comprised 1,625 smokers and 3,948 non-smokers, with a mean age of 38.07 +/- 14.85 years. Serum low-density lipoprotein-cholesterol (LDL-C) and triglycerides (TG) were higher in smokers than in non-smokers (LDLC: 115.34 +/- 39.03 vs 112.65 +/- 40.94 mg/dL, respectively, P = 0.015 and TG: 175.13 +/- 102.05 vs 172.32 +/- 116.83 mg/dL, respectively, P = 0.005). Body mass index, waist circumference and waist-hip ratio were lower in smokers than in non-smokers. Mean systolic and diastolic blood pressures were significantly lower in smokers than in non-smokers (systolic: 112.06 +/- 15.888 vs 117.25 +/- 17.745 mmHg, respectively, P = 0.000; diastolic: 73.66 +/- 10.084 vs 76.23 +/- 10.458 mmHg, respectively, P = 0.000). The percentage of individuals with 2 MS components was higher in smokers than in non-smokers (39.64% vs 33.00%, respectively, P = 0.000). However, the percentage of non-smokers with 3 MS components was higher than in smokers (49.62 % vs 43.82%, respectively, P = 0.000). CONCLUSIONS: Our findings support the hypothesis that lifestyle factors such as smoking can adversely affect MS components. However, we should acknowledge that these differences may have resulted from the large sample sizes studied and may not be clinically significant. The lower prevalence of some MS components in smokers than in nonsmokers might be because of their lower anthropometric measures.