Strong Dynamical Heterogeneity and Universal Scaling in Driven Granular Fluids

Large-scale simulations of two-dimensional bidisperse granular fluids allow us to determine spatial correlations of slow particles via the four-point structure factor S-4 (q, t). Both cases, elastic (epsilon = 1) and inelastic (epsilon < 1) collisions, are studied. As the fluid approaches structural arrest, i.e., for packing fractions in the range 0.6 <= phi <= 0.805, scaling is shown to hold: S-4 (q, t)/chi(4)(t) = s(q xi(t)). Both the dynamic susceptibility chi(4)(tau(alpha)) and the dynamic correlation length xi(tau(alpha)) evaluated at the alpha relaxation time tau(alpha) can be fitted to a power law divergence at a critical packing fraction. The measured xi(tau(alpha)) widely exceeds the largest one previously observed for three-dimensional (3d) hard sphere fluids. The number of particles in a slow cluster and the correlation length are related by a robust power law, chi(4)(tau(alpha)) approximate to xi(d-p) (tau(alpha)), with an exponent d - p approximate to 1.6. This scaling is remarkably independent of epsilon, even though the strength of the dynamical heterogeneity at constant volume fraction depends strongly on epsilon.

“All Data is Credit Data,” or, On Close Reading as a Reciprocal Process in Digital Knowledge Environments

The new knowledge environments of the digital age are oen described as places where we are all closely read, with our buying habits, location, and identities available to advertisers, online merchants, the government, and others through our use of the Internet. This is represented as a loss of privacy in which these entities learn about our activities and desires, using means that were unavailable in the pre-digital era. This article argues that the reciprocal nature of digital networks means 1) that the privacy issues that we face online are not radically different from those of the pre-Internet era, and 2) that we need to reconceive of close reading as an activity of which both humans and computer algorithms are capable.