Response of a polymer network to the motion of a rigid sphere

In view of recent microrheology experiments we re-examine the problem of a rigid sphere oscillating inside a dilute polymer network. The network and its solvent are treated using the two-fluid model. We show that the dynamics of the medium can be decomposed into two independent incompressible flows. The first, dominant at large distances and obeying the Stokes equation, corresponds to the collective flow of the two components as a whole. The other, governing the dynamics over an intermediate range of distances and following the Brinkman equation, describes the flow of the network and solvent relative to one another. The crossover between these two regions occurs at a dynamic length scale which is much larger than the network's mesh size. The analysis focuses on the spatial structure of the medium's response and the role played by the dynamic crossover length. We examine different boundary conditions at the sphere surface. The large-distance collective flow is shown to be independent of boundary conditions and network compressibility, establishing the robustness of two-point microrheology at large separations. The boundary conditions that fit the experimental results for inert spheres in entangled F-actin networks are those of a free network, which does not interact directly with the sphere. Closed-form expressions and scaling relations are derived, allowing for the extraction of material parameters from a combination of one- and two-point microrheology. We discuss a basic deficiency of the two-fluid model and a way to bypass it when analyzing microrheological data.Comment: 11 page

Tension and solute depletion in multilamellar vesicles

We show that a metastable multilamellar vesicle (`onion'), in contact with excess solvent, can spontaneously deplete solute molecules from its interior through an unusual, entropy-driven mechanism. Fluctuation entropy is gained as the uneven partition of solute molecules helps the onion relieve tension in its lamellae. This mechanism accounts for recent experiments on the interaction between uncharged phospholipid onions and dissolved sugars.Comment: 5 pages, 2 figure

An Algorithmic Approach to Pick's Theorem

We give an algorithmic proof of Pick's theorem which calculates the area of a lattice-polygon in terms of the lattice-points

Transition to chaos in random neuronal networks

Firing patterns in the central nervous system often exhibit strong temporal irregularity and heterogeneity in their time averaged response properties. Previous studies suggested that these properties are outcome of an intrinsic chaotic dynamics. Indeed, simplified rate-based large neuronal networks with random synaptic connections are known to exhibit sharp transition from fixed point to chaotic dynamics when the synaptic gain is increased. However, the existence of a similar transition in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work we investigate rate based dynamics of neuronal circuits composed of several subpopulations and random connectivity. Nonzero connections are either positive-for excitatory neurons, or negative for inhibitory ones, while single neuron output is strictly positive; in line with known constraints in many biological systems. Using Dynamic Mean Field Theory, we find the phase diagram depicting the regimes of stable fixed point, unstable dynamic and chaotic rate fluctuations. We characterize the properties of systems near the chaotic transition and show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as a network with Gaussian connectivity. Interestingly, the critical properties near transition depend on the shape of the single- neuron input-output transfer function near firing threshold. Finally, we investigate network models with spiking dynamics. When synaptic time constants are slow relative to the mean inverse firing rates, the network undergoes a sharp transition from fast spiking fluctuations and static firing rates to a state with slow chaotic rate fluctuations. When the synaptic time constants are finite, the transition becomes smooth and obeys scaling properties, similar to crossover phenomena in statistical mechanicsComment: 28 Pages, 12 Figures, 5 Appendice

High-performance Kernel Machines with Implicit Distributed Optimization and Randomization

In order to fully utilize "big data", it is often required to use "big models". Such models tend to grow with the complexity and size of the training data, and do not make strong parametric assumptions upfront on the nature of the underlying statistical dependencies. Kernel methods fit this need well, as they constitute a versatile and principled statistical methodology for solving a wide range of non-parametric modelling problems. However, their high computational costs (in storage and time) pose a significant barrier to their widespread adoption in big data applications. We propose an algorithmic framework and high-performance implementation for massive-scale training of kernel-based statistical models, based on combining two key technical ingredients: (i) distributed general purpose convex optimization, and (ii) the use of randomization to improve the scalability of kernel methods. Our approach is based on a block-splitting variant of the Alternating Directions Method of Multipliers, carefully reconfigured to handle very large random feature matrices, while exploiting hybrid parallelism typically found in modern clusters of multicore machines. Our implementation supports a variety of statistical learning tasks by enabling several loss functions, regularization schemes, kernels, and layers of randomized approximations for both dense and sparse datasets, in a highly extensible framework. We evaluate the ability of our framework to learn models on data from applications, and provide a comparison against existing sequential and parallel libraries.Comment: Work presented at MMDS 2014 (June 2014) and JSM 201