Speaker
Mason A. Porter

Description We study the social structure of Facebook "friendship" networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes - gender, class year, major, high school, and residence - at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on the user characteristics. We thereby compare the relative importances of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.

Speaker
Oriol Pont

Description Complex systems in nature tend to selforganize in "scale invariant" fashion, both in their structure and dynamics. Although this property is widely known, in most systems it is little exploited and rarely incorporated into models directly. We will show that with appropriate signal-analysis methods --namely "singularity analysis" and "optimal wavelet" decomposition-- we can obtain the key parameters to characterize, reconstruct and forecast these signals under a universality-class like framework. Despite the characteristic particularities of each kind of signal, the data-inference methods presented can be applied to problems as diverse as ocean circulation or hearbeat dynamics.

Speaker
Duane Nykamp

Description Evidence is emerging that local connectivity among neurons contains a microstructure that deviates from standard random network models. I develop a simple network model that captures key correlations among network edges and investigate its consequences on the dynamics of populations of interacting excitatory and inhibitory neurons.

Speaker
Carme Calderer

Description In hormone therapies, the cyclic delivery of the drug to match the hormonal natural cycle is very relevant to the effectiveness of the therapy. We model a prototype device made of a polyelectrolyte gel and powered by glucose, following the experimental work carried out by Siegel. Polyelectrolyte gels in a ionic environment may experience phase transitions of the first type between the swollen and the collapsed states of the gel. The hydrogen ions produced in the enzymatic reaction of the glucose combined with the hysteresis of the phase transition yield a cyclic process, for suitable parameter ranges of the system. We propose and analyze a system of partial differential equations modeling the coupled mechanical and electrochemical processes that take place in the experiment. The periodic nature of the solutions results from the competition between the elastic forces of the polymer network, the hydrophobic-hydrophilic interaction between the gel network and fluid, the ionic and Coulomb forces of interaction between in-fluid ions and the charged-side chain groups of the polymer network, and the external pH.

Speaker
S. De

Description We present a multiscale computational method to simulate flow of fluid systems with a large spatial extent. We simulate small parts of the system using molecular dynamics, and only occasionally pass information among these parts based on a continuum fluid mechanics framework. This method does not rely on any constitutive equation, and hence is well-suited for a fluid the flow of which suffers from memory-effects. In addition, the simulation scheme can be parallelized in the sense that the molecular dynamics simulations of the different parts of the system can be performed on several individual computers.