Shear-thinning in dense colloidal suspensions and its effect on elastic instabilities: from the microscopic equations of motion to an approximation of the macroscopic rheology

In the vicinity of their glass transition, dense colloidal suspensions acquire elastic properties over experimental timescales. We investigate the possibility of a visco-elastic flow instability in curved geometry for such materials. To this end, we first present a general strategy extending a first-principles approach based on projections onto slow variables (so far restricted to strictly homogeneous flow) in order to handle inhomogeneities. In particular, we separate the advection of the microstructure by the flow, at the origin of a fluctuation advection term, from the intrinsic dynamics. On account of the complexity of the involved equations, we then opt for a drastic simplification of the theory, in order to establish its potential to describe instabilities. These very strong approximations lead to a constitutive equation of the White-Metzner class, whose parameters are fitted with experimental measurements of the macroscopic rheology of a glass-forming colloidal dispersion. The model properly accounts for the shear-thinning properties of the dispersions, but, owing to the approximations, the description is not fully quantitative. Finally, we perform a linear stability analysis of the flow in the experimentally relevant cylindrical (Taylor-Couette) geometry and provide evidence that shear-thinning strongly stabilises the flow, which can explain why visco-elastic instabilities are not observed in dense colloidal suspensions

Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing

In this paper, we present a new method for detecting road users in an urban environment which leads to an improvement in multiple object tracking. Our method takes as an input a foreground image and improves the object detection and segmentation. This new image can be used as an input to trackers that use foreground blobs from background subtraction. The first step is to create foreground images for all the frames in an urban video. Then, starting from the original blobs of the foreground image, we merge the blobs that are close to one another and that have similar optical flow. The next step is extracting the edges of the different objects to detect multiple objects that might be very close (and be merged in the same blob) and to adjust the size of the original blobs. At the same time, we use the optical flow to detect occlusion of objects that are moving in opposite directions. Finally, we make a decision on which information we keep in order to construct a new foreground image with blobs that can be used for tracking. The system is validated on four videos of an urban traffic dataset. Our method improves the recall and precision metrics for the object detection task compared to the vanilla background subtraction method and improves the CLEAR MOT metrics in the tracking tasks for most videos

A mesoscopic model for the rheology of soft amorphous solids, with application to mi- crochannel flows

We study a mesoscopic model for the flow of amorphous solids. The model is based on the key features identified at the microscopic level, namely peri- ods of elastic deformation interspersed with localised rearrangements of parti- cles that induce long-range elastic deformation. These long-range deformations are derived following a continuum mechanics approach, in the presence of solid boundaries, and are included in full in the model. Indeed, they mediate spatial cooperativity in the flow, whereby a localised rearrangement may lead a distant region to yield. In particular, we simulate a channel flow and find manifestations of spatial cooperativity that are consistent with published experimental obser- vations for concentrated emulsions in microchannels. Two categories of effects are distinguished. On the one hand, the coupling of regions subject to different shear rates, for instance,leads to finite shear rate fluctuations in the seemingly un- sheared "plug" in the centre of the channel. On the other hand, there is convinc- ing experimental evidence of a specific rheology near rough walls. We discuss diverse possible physical origins for this effect, and we suggest that it may be associated with the bumps of particles into surface asperities as they slide along the wall

Spatial cooperativity in microchannel flows of soft jammed materials: A mesoscopic approach

The flow of amorphous solids results from a combination of elastic deformation and local structural rearrangements, which induce non-local elastic deformations. These elements are incorporated into a mechanically-consistent mesoscopic model of interacting elastoplastic blocks. We investigate the specific case of channel flow with numerical simulations, paying particular attention to situations of strong confinement. We find that the simple picture of plastic events embedded in an elastic matrix successfully accounts for manifestations of spatial cooperativity. Shear rate fluctuations are observed in seemingly quiescent regions, and the velocity profiles in confined flows at high applied pressure deviate from those expected in the absence of non-local effects, in agreement with experimental data. However, we suggest a different physical origin for the large deviations observed when walls have rough surfaces, associated with "bumps" of the particles against the asperities of the walls.Comment: 5 figure

Statistical fluctuations in pedestrian evacuation times and the effect of social contagion

Mathematical models of pedestrian evacuation and the associated simulation software have become essential tools for the assessment of the safety of public facilities and buildings. While a variety of models are now available, their calibration and test against empirical data are generally restricted to global, averaged quantities, the statistics compiled from the time series of individual escapes (" microscopic " statistics) measured in recent experiments are thus overlooked. In the same spirit, much research has primarily focused on the average global evacuation time, whereas the whole distribution of evacuation times over some set of realizations should matter. In the present paper we propose and discuss the validity of a simple relation between this distribution and the " microscopic " statistics, which is theoretically valid in the absence of correlations. To this purpose, we develop a minimal cellular automaton, with novel features that afford a semi-quantitative reproduction of the experimental " microscopic " statistics. We then introduce a process of social contagion of impatient behavior in the model and show that the simple relation under test may dramatically fail at high contagion strengths, the latter being responsible for the emergence of strong correlations in the system. We conclude with comments on the potential practical relevance for safety science of calculations based on " microscopic " statistics

Computational Complexity versus Statistical Performance on Sparse Recovery Problems

We show that several classical quantities controlling compressed sensing performance directly match classical parameters controlling algorithmic complexity. We first describe linearly convergent restart schemes on first-order methods solving a broad range of compressed sensing problems, where sharpness at the optimum controls convergence speed. We show that for sparse recovery problems, this sharpness can be written as a condition number, given by the ratio between true signal sparsity and the largest signal size that can be recovered by the observation matrix. In a similar vein, Renegar's condition number is a data-driven complexity measure for convex programs, generalizing classical condition numbers for linear systems. We show that for a broad class of compressed sensing problems, the worst case value of this algorithmic complexity measure taken over all signals matches the restricted singular value of the observation matrix which controls robust recovery performance. Overall, this means in both cases that, in compressed sensing problems, a single parameter directly controls both computational complexity and recovery performance. Numerical experiments illustrate these points using several classical algorithms.Comment: Final version, to appear in information and Inferenc