Jérôme Fehrenbach

Traffic instabilities in self-organized pedestrian crowds

In human crowds as well as in many animal societies, local interactions among individuals often give rise to self-organized collective organizations that offer functional benefits to the group. For instance, flows of pedestrians moving in opposite directions spontaneously segregate into lanes of uniform walking directions. This phenomenon is often referred to as a smart collective pattern, as it increases the traffic efficiency with no need of external control. However, the functional benefits of this emergent organization have never been experimentally measured, and the underlying behavioral mechanisms are poorly understood. In this work, we have studied this phenomenon under controlled laboratory conditions. We found that the traffic segregation exhibits structural instabilities characterized by the alternation of organized and disorganized states, where the lifetime of well-organized clusters of pedestrians follow a stretched exponential relaxation process. Further analysis show that the inter-pedestrian variability of comfortable walking speeds is a key variable at the origin of the observed traffic perturbations. We show that the collective benefit of the emerging pattern is maximized when all pedestrians walk at the average speed of the group. In practice, however, local interactions between slow- and fast-walking pedestrians trigger global breakdowns of organization, which reduce the collective and the individual payoff provided by the traffic segregation. This work is a step ahead toward the understanding of traffic self-organization in crowds, which turns out to be modulated by complex behavioral mechanisms that do not always maximize the groups benefits. The quantitative understanding of crowd behaviors opens the way for designing bottom-up management strategies bound to promote the emergence of efficient collective behaviors in crowds.

Realistic following behaviors for crowd simulation

While walking through a crowd, a pedestrian experiences a large number of interactions with his neighbors. The nature of these interactions is varied, and it has been observed that macroscopic phenomena emerge from the combination of these local interactions. Crowd models have hitherto considered collision avoidance as the unique type of interactions between individuals, few have considered walking in groups. By contrast, our paper focuses on interactions due to the following behaviors of pedestrians. Following is frequently observed when people walk in corridors or when they queue. Typical macroscopic stop‐and‐go waves emerge under such traffic conditions. Our contributions are, first, an experimental study on following behaviors, second, a numerical model for simulating such interactions, and third, its calibration, evaluation and applications. Through an experimental approach, we elaborate and calibrate a model from microscopic analysis of real kinematics data collected during experiments. We carefully evaluate our model both at the microscopic and the macroscopic levels. We also demonstrate our approach on applications where following interactions are prominent.

Imaging by Modification: Numerical Reconstruction of Local Conductivities from Corresponding Power Density Measurements

We discuss the reconstruction of the impedance from the local power density. This study is motivated by a new imaging principle which allows us to recover interior measurements of the energy density by a noninvasive method. We discuss the theoretical feasibility in two dimensions, and propose numerical algorithms to recover the conductivity in two and three dimensions. The efficiency of this approach is documented by several numerical simulations.

Variational Algorithms to Remove Stationary Noise: Applications to Microscopy Imaging

A framework and an algorithm are presented in order to remove stationary noise from images. This algorithm is called variational stationary noise remover. It can be interpreted both as a restoration method in a Bayesian framework and as a cartoon+texture decomposition method. In numerous denoising applications, the white noise assumption fails. For example, structured patterns such as stripes appear in the images. The model described here addresses these cases. Applications are presented with images acquired using different modalities: scanning electron microscope, FIB-nanotomography, and an emerging fluorescence microscopy technique called selective plane illumination microscopy.

Live cell division dynamics monitoring in 3D large spheroid tumor models using light sheet microscopy

BackgroundMulticellular tumor spheroids are models of increasing interest for cancer and cell biology studies. They allow considering cellular interactions in exploring cell cycle and cell division mechanisms. However, 3D imaging of cell division in living spheroids is technically challenging and has never been reported.ResultsHere, we report a major breakthrough based on the engineering of multicellular tumor spheroids expressing an histone H2B fluorescent nuclear reporter protein, and specifically designed sample holders to monitor live cell division dynamics in 3D large spheroids using an home-made selective-plane illumination microscope.ConclusionsAs illustrated using the antimitotic drug, paclitaxel, this technological advance paves the way for studies of the dynamics of cell divion processes in 3D and more generally for the investigation of tumor cell population biology in integrated system as the spheroid model.

An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration

Several biomedical applications require accurate image registration that can cope effectively with complex organ deformations. This paper addresses this problem by introducing a generic deformable registration algorithm with a new regularization scheme, which is performed through bilateral filtering of the deformation field. The proposed approach is primarily designed to handle smooth deformations both between and within body structures, and also more challenging deformation discontinuities exhibited by sliding organs. The conventional Gaussian smoothing of deformation fields is replaced by a bilateral filtering procedure, which compromises between the spatial smoothness and local intensity similarity kernels, and is further supported by a deformation field similarity kernel. Moreover, the presented framework does not require any explicit prior knowledge about the organ motion properties (e.g. segmentation) and therefore forms a fully automated registration technique. Validation was performed using synthetic phantom data and publicly available clinical 4D CT lung data sets. In both cases, the quantitative analysis shows improved accuracy when compared to conventional Gaussian smoothing. In addition, we provide experimental evidence that masking the lungs in order to avoid the problem of sliding motion during registration performs similarly in terms of the target registration error when compared to the proposed approach, however it requires accurate lung segmentation. Finally, quantification of the level and location of detected sliding motion yields visually plausible results by demonstrating noticeable sliding at the pleural cavity boundaries.

Sparse Non-negative Stencils for Anisotropic Diffusion

We introduce a new discretization scheme for Anisotropic Diffusion, AD-LBR, on two and three dimensional Cartesian grids. The main features of this scheme is that it is non-negative and has sparse stencils, of cardinality bounded by 6 in 2D, by 12 in 3D, despite allowing diffusion tensors of arbitrary anisotropy. The radius of these stencils is not a-priori bounded however, and can be quite large for pronounced anisotropies. Our scheme also has good spectral properties, which permits larger time steps and avoids e.g. chessboard artifacts.AD-LBR relies on Lattice Basis Reduction, a tool from discrete mathematics which has recently shown its relevance for the discretization on grids of strongly anisotropic Partial Differential Equations (Mirebeau in Preprint, 2012). We prove that AD-LBR is in 2D asymptotically equivalent to a finite element discretization on an anisotropic Delaunay triangulation, a procedure more involved and computationally expensive. Our scheme thus benefits from the theoretical guarantees of this procedure, for a fraction of its cost. Numerical experiments in 2D and 3D illustrate our results.

Detection of small inclusions by elastography

The problem of parameter identification for elastostatics equilibrium equations in two-dimensional inhomogeneous domains is considered. Elastic properties of a linear isotropic material depend on two parameters: Youngs modulus E and Poisson coefficient ?, and our objective is to determine the values and the spatial distribution of E in a plane domain ?, where ? is assumed to be constant. It is assumed that the input data are directional displacements in ? under a small quasistatic compression; this is consistent with an existing imaging modality called elastography. We prove that this problem involves a compact operator. A method is proposed here to identify the spatial distribution of E up to a multiplicative factor. It is based on an implementation of the Gauss?Newton method that is obtained without the calculation of the Jacobian matrix. It is performed by the use of conjugate gradient, through a combination of reverse (adjoint) and forward (direct) differentiation that makes sense in the case of compact operators. Our method is validated both with numerical and experimental results.

Influence of Poisson's ratio on elastographic direct and inverse problems

We consider the displacement of an elastic material under an external compression (axial or almost axial stress). We assume that only one component of the displacement is observed, in the direction of compression (axial displacement), or alternatively, that two components are observed in a plane. These hypotheses are in accordance with an imaging modality, namely ultrasonic elastography. In the case of a homogeneous medium we show that any value of Poissons ratio allows us to predict the observed value of the axial displacement. When two components of the displacement are measured in a plane, the Poissons ratio of the plane strain model that predicts the observed displacement is not the same as the tri-dimensional material. These facts are illustrated by numerical experiments in the case of an inhomogeneous medium. We also present results on experimental phantom data, where the inverse problem of reconstructing the Youngs modulus is solved assuming different values for Poissons ratio.

Oxygen Partial Pressure Is a Rate-Limiting Parameter for Cell Proliferation in 3D Spheroids Grown in Physioxic Culture Condition.

The in situ oxygen partial pressure in normal and tumor tissues is in the range of a few percent. Therefore, when studying cell growth in 3D culture systems, it is essential to consider how the physiological oxygen concentration, rather than the one in the ambient air, influences the proliferation parameters. Here, we investigated the effect of reducing oxygen partial pressure from 21% to 5% on cell proliferation rate and regionalization in a 3D tumor spheroid model. We found that 5% oxygen concentration strongly inhibited spheroid growth, changed the proliferation gradient and reduced the 50% In Depth Proliferation index (IDP50), compared with culture at 21% oxygen. We then modeled the oxygen partial pressure profiles using the experimental data generated by culturing spheroids in physioxic and normoxic conditions. Although hypoxia occurred at similar depth in spheroids grown in the two conditions, oxygen partial pressure was a major rate-limiting factor with a critical effect on cell proliferation rate and regionalization only in spheroids grown in physioxic condition and not in spheroids grown at atmospheric normoxia. Our findings strengthen the need to consider conducting experiment in physioxic conditions (i.e., tissue normoxia) for proper understanding of cancer cell biology and the evaluation of anticancer drugs in 3D culture systems.