Importance of hydrodynamic shielding for the dynamic behavior of short polyelectrolyte chains

The dynamic behavior of polyelectrolyte chains in the oligomer range is investigated with coarse-grained molecular dynamics simulation and compared to data obtained by two different experimental methods, namely capillary electrophoresis and electrophoresis NMR. We find excellent agreement of experiments and simulations when hydrodynamic interactions are accounted for in the simulations. We show that the electrophoretic mobility exhibits a maximum in the oligomer range and for the first time illustrate that this maximum is due to the hydrodynamical shielding between the chain monomers. Our findings demonstrate convincingly that it is possible to model dynamic behavior of polyelectrolytes using coarse grained models for both, the polyelectrolyte chains and the solvent induced hydrodynamic interactions.Comment: 5 pages, 3 figures -> published versio

Efficiency of purchasing and selling agents in markets with quality uncertainty: The case of illicit drug transactions

illicit drug markets, quality uncertainty, efficient transactions

Bad smells in design and design patterns

International audienceTo give a consistent and more valuable property on models, model-driven processes should be able to reuse the expert knowledge generally expressed in terms of patterns. We focus our work on the design stage and on the systematically use of design patterns. Choose a good design pattern and ensure the correct integration of the chosen pattern are non trivial for a designer who wants to use them. To help designers, we propose design inspection in order to detect “bad smells in design” and models reworking through use of design patterns. The automatic detection and the explanation of the misconceptions are performed thanks to spoiled patterns. A “spoiled pattern” is a pattern which allows to instantiate inadequate solutions for a given problem: requirements are respected, but architecture is improvable

Active learning for medical image segmentation with stochastic batches

The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On the one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches (SB) during sampling in AL. Stochastic batch querying is a simple and effective add-on that can be used on top of any uncertainty-based metric. Extensive experiments on two medical image segmentation datasets show that our strategy consistently improves conventional uncertainty-based sampling methods. Our method can hence act as a strong baseline for medical image segmentation. The code is available on: https://github.com/Minimel/StochasticBatchAL.git.Comment: Accepted to Medical Image Analysis, 17 page

Haptic Rendering of Hyperelastic Models with Friction

International audience— This paper presents an original method for inter-actions' haptic rendering when treating hyperelastic materials. Such simulations are known to be difficult due to the non-linear behavior of hyperelastic bodies; furthermore, haptic constraints enjoin contact forces to be refreshed at least at 1000 updates per second. To enforce the stability of simulations of generic objects of any range of stiffness, this method relies on implicit time integration. Soft tissues dynamics is simulated in real time (20 to 100 Hz) using the Multiplicative Jacobian Energy Decomposition (MJED) method. An asynchronous preconditioner, updated at low rates (1 to 10 Hz), is used to obtain a close approximation of the mechanical coupling of interactions. Finally, the contact problem is linearized and, using a specific-loop, it is updated at typical haptic rates (around 1000 Hz) allowing this way new simulations of prompt stiff-contacts and providing a continuous haptic feedback as well