It remains an open question whether statistical mechanics approaches apply to random packings of athermal particles. Although a jamming phase diagram has recently been proposed for hard spheres with varying friction, here we use a frictionless emulsion system in the presence of depletion forces to sample the available phase space of packing configurations. Using confocal microscopy, we access their packing microstructure and test the theoretical assumptions. As a function of attraction, our packing protocol under gravity leads to well-defined jammed structures in which global density initially increases above random close packing and subsequently decreases monotonically. Microscopically, the fluctuations in parameters describing each particle, such as the coordination number, number of neighbors, and local packing fraction, are for all attractions in excellent agreement with a local stochastic model, indicating that long-range correlations are not important. Furthermore, the distributions of local cell volumes can be collapsed onto a universal curve using the predicted k-gamma distribution, in which the shape parameter k is fixed by the polydispersity while the effect of attraction is captured by rescaling the average cell volume. Within the Edwards statistical mechanics framework, this result measures the decrease in compactivity with global density, which represents a direct experimental test of a jamming phase diagram in athermal systems. The success of these theoretical tools in describing yet another class of materials gives support to the much-debated statistical physics of jammed granular matter.T he random packing of particles is a problem of great practical and theoretical importance with applications to systems as diverse as granular materials, emulsions, and colloids. How athermal particles pack is affected by many parameters, including the packing protocol, the shape and roughness of the particles, as well as their polydispersity. Nevertheless, experiments and numerical simulations have suggested the existence of reproducible disordered packing structures, which can be achieved by sedimenting and shaking monodisperse (1, 2) and polydisperse (3) spheres, ellipsoids (4), and tetrahedra (5). For frictional particles, there emerges a reversible packing density curve from random loose packing (RLP) to random close packing (RCP) as a function of the applied tapping protocol (6). The dependence of density of jammed states on the friction coefficient has led to an equation of state and a prediction of a jamming phase diagram to describe the behavior of these systems in numerical simulations (7). Moreover, experiments on compressible two-dimensional packings of photoelastic beads (8) and foams (9) have probed configurations at different packing densities by applying an external load. The reproducibility of all these jammed states for a given set of experimental conditions suggests that a statistical mechanics framework may apply to these inherently out-of-equilibrium systems (10-12). Other studies have argued...

We report on the penetration of cylindrical projectiles dropped from rest into a dry, noncohesive granular medium. The cylinder length, diameter, density, and tip shape are all explicitly varied. For deep penetrations, as compared to the cylinder diameter, the data collapse onto a single scaling law that varies as the 1/3 power of the total drop distance, the 1/2 power of cylinder length, and the 1/6 power of cylinder diameter. For shallow penetrations, the projectile shape plays a crucial role with sharper objects penetrating deeper.Comment: 3 pages, 3 figures; experimen

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Perfect spike-to-spike synchrony is studied in all-to-all coupled networks of identical excitatory, current-based, integrate-and-fire neurons with delta-impulse coupling currents and Poisson spike-train external drive. This synchrony is induced by repeated cascading "total firing events," during which all neurons fire at once. In this regime, the network exhibits nearly periodic dynamics, switching between an effectively uncoupled state and a cascade-coupled total firing state. The probability of cascading total firing events occurring in the network is computed through a combinatorial analysis conditioned upon the random time when the first neuron fires and using the probability distribution of the subthreshold membrane potentials for the remaining neurons in the network. The probability distribution of the former is found from a first-passage-time problem described by a Fokker-Planck equation, which is solved analytically via an eigenfunction expansion. The latter is found using a central limit argument via a calculation of the cumulants of a single neuronal voltage. The influence of additional physiological effects that hinder or eliminate cascade-induced synchrony are also investigated. Conditions for the validity of the approximations made in the analytical derivations are discussed and verified via direct numerical simulations.

Abstract. Driving nanomagnets by spin-polarized currents offers exciting prospects in magnetoelectronics, but the response of the magnets to such currents remains poorly understood. We show that an averaged equation describing the diffusion of energy on a graph captures the low-damping dynamics of these systems. From this equation we obtain the bifurcation diagram of the magnets, including the critical currents to induce stable precessional states and magnetization switching, as well as the mean times of thermally assisted magnetization reversal in situations where the standard reaction rate theory of Kramers is no longer valid. These results agree with experimental observations and give a theoretical basis for a Néel-Brown-type formula with an effective energy barrier for the reversal times.Manipulating thin-film magnetic elements with spin-polarized currents besides external magnetic fields [12] has generated a lot of recent interest in applications to magnetoelectronic devices that offer low power memory storage without the use of moving parts [2]. Understanding the response of the magnet to such currents is nontrivial, however, because they apply a nonconservative force, called spin-transfer torque (STT), on the system. Like other nongradient systems with no Lyapunov function, the phase portrait of nanomagnets in the presence of STT can be quite complex, and include limit cycles or chaotic trajectories besides fixed points. When the applied fields and/or currents are nonstationary, or in the presence of thermal noise, the situation is even worse. In particular, Kramers' reaction rate theory [14,10] is no longer applicable and the Néel-Brown formula [5] for the mean magnetization reversal time is not valid since there is no well-defined energy associated with STT.Nanomagnets typically operate in a regime where the nonconservative parts of the dynamics, including the effects of damping, STT, and thermal noise, act on time-scales that are much longer than that of the energy-conserving Hamiltonian part. Trajectories remain close to periodic Hamiltonian orbits for a long time, and slowly drift from one orbit to another due to damping, STT, and thermal noise. This separation of time scales can be exploited, using averaging techniques developed by Freidlin and Wentzell [9,8] (see also [20,4]), to reduce the dynamics to that of an energy diffusing on a graph. We show here that this reduced description permits to explain the features of nanomagnets subject to STT that are observed experimentally. Specifically, we obtain the full bifurcation diagram of the system at zero temperature and determine the critical spin-polarized currents needed to induce stable precessional states [15,3] and magnetization switching [15,19]. At finite temperature, we calculate the mean times of thermally assisted magnetization reversals [15,18], and give expressions for the effective energy barriers conjectured to exist [15,18,1,16,17]. These