Publications

2017

• A user-perception based approach to create smiling embodied conversational agents
  
  • Ochs Magalie
  • Mckeown Gary
  • Pelachaud Catherine
  ACM Transactions on Interactive Intelligent Systems, Association for Computing Machinery (ACM), 2017, 1. no abstract
• Raman-tailored photonic crystal fiber for telecom band photon-pair generation
  
  • Cordier Martin
  • Orieux Adeline
  • Gabet Renaud
  • Harlé Thibault
  • Dubreuil Nicolas
  • Diamanti Eleni
  • Delaye Philippe
  • Zaquine Isabelle
  Optics Letters, Optical Society of America - OSA Publishing, 2017, 42 (13), pp.2583-2586. We report on the experimental characterization of a novel nonlinear liquid-filled hollow-core photonic crystal fiber for the generation of photon pairs at a telecommuni- cation wavelength through spontaneous four-wave mixing (SFWM). We show that the optimization procedure in view of this application links the choice of the nonlinear liquid to the design parameters of the fiber, and we give an example of such an optimization at telecom wavelengths. Combining the modeling of the fiber and classical charac- terization techniques at these wavelengths, we identify for the chosen fiber and liquid combination SFWM phase- matching frequency ranges with no Raman scattering noise contamination. This is a first step toward obtaining a tele- com band fibered photon-pair source with a high signal-to- noise ratio. (10.1364/OL.42.002583)
  DOI : 10.1364/OL.42.002583
• More Results on the Complexity of Domination Problems in Graphs
  
  • Hudry Olivier
  • Lobstein Antoine
  International Journal of Information and Coding Theory, Inderscience, 2017, 4 (2/3), pp.129-144. Given a graph G = (V, E) and an integer r ≥ 1, we call 'r-dominating code' any subset C of V such that every vertex in V is at distance at most r from at least one vertex in C. We investigate and locate in the complexity classes of the polynomial hierarchy, several problems linked with domination in graphs, such as, given r and G, the existence of, or search for, optimal r-dominating codes in G, or optimal r-dominating codes in G containing a subset of vertices X ⊂ V . (10.1504/ijicot.2017.083829)
  DOI : 10.1504/ijicot.2017.083829
• Behavioural semantics for asynchronous components
  
  • Ameur-Boulifa Rabéa
  • Henrio Ludovic
  • Kulankhina Oleksandra
  • Madelaine Eric
  • Savu Alexandra
  Journal of Logical and Algebraic Methods in Programming, Elsevier, 2017, 89, pp.1 - 40. Software components are a valuable programming abstraction that enables a compositional design of complex applications. In distributed systems, components can also be used to provide an abstraction of locations: each component is a unit of deployment that can be placed on a different machine. In this article, we consider this kind of distributed components that are additionally loosely coupled and communicate by asynchronous invocations. Components also provide a convenient abstraction for verifying the correct behaviour of systems: they provide structuring entities easing the correctness verification. This article provides a formal background for the generation of behavioural semantics for asynchronous components. It expresses the semantics of hierarchical distributed components communicating asynchronously by requests, futures, and replies; this semantics is provided using the pNet intermediate language. This article both demonstrates the expressiveness of the pNet model and formally specifies the complete process of the generation of a behavioural model for a distributed component system. The purpose of our be-havioural semantics is to allow for verification both by finite instantiation and model-checking, and by techniques for infinite systems. (10.1016/j.jlamp.2017.02.003)
  DOI : 10.1016/j.jlamp.2017.02.003
• Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
  
  • Ndiaye Eugene
  • Fercoq Olivier
  • Gramfort Alexandre
  • Leclère Vincent
  • Salmon Joseph
  Journal of Physics: Conference Series, IOP Science, 2017, J. Phys.: Conf. Ser. 904 012006. In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider 1 penalty to enforce spar-sity in such scenarios. Sparsity enforcing methods, the Lasso being a canonical example, are popular candidates to address high dimension. For efficiency, they rely on tuning a parameter trading data fitting versus sparsity. For the Lasso theory to hold this tuning parameter should be proportional to the noise level, yet the latter is often unknown in practice. A possible remedy is to jointly optimize over the regression parameter as well as over the noise level. This has been considered under several names in the literature: Scaled-Lasso, Square-root Lasso, Concomitant Lasso estimation for instance, and could be of interest for confidence sets or uncertainty quantification. In this work, after illustrating numerical difficulties for the Smoothed Concomitant Lasso formulation, we propose a modification we coined Smoothed Concomitant Lasso, aimed at increasing numerical stability. We propose an efficient and accurate solver leading to a computational cost no more expansive than the one for the Lasso. We leverage on standard ingredients behind the success of fast Lasso solvers: a coordinate descent algorithm, combined with safe screening rules to achieve speed efficiency, by eliminating early irrelevant features. (10.1088/1742-6596/904/1/012006)
  DOI : 10.1088/1742-6596/904/1/012006
• Fast algebraic immunity of Boolean functions
  
  • Mesnager Sihem
  • Cohen Gérard
  Advances in Mathematics of Communications, AIMS, 2017, 11 (2), pp.373-377. (10.3934/amc.2017031)
  DOI : 10.3934/amc.2017031
• Quadratic Extension Field Codes for Free Space Optical Intensity Communications
  
  • Mroueh L.
  • Belfiore Jean-Claude
  IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2017, 65 (2), pp.751 - 763.
• Predicting Completeness in Knowledge Bases
  
  • Galárraga Luis
  • Razniewski Simon
  • Amarilli Antoine
  • Suchanek Fabian M.
  , 2017. no abstract
• A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
  
  • Aguerrebere Cecilia
  • Almansa Andrés
  • Delon Julie
  • Gousseau Yann
  • Musé Pablo
  IEEE Transactions on Computational Imaging, IEEE, 2017. Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme. (10.1109/TCI.2017.2704439)
  DOI : 10.1109/TCI.2017.2704439
• Synchronization in MPEG-4 Systems
  
  • Le Feuvre J.
  • Concolato Cyril
  , 2017, 18, pp.451-473. (10.1007/978-3-319-65840-7)
  DOI : 10.1007/978-3-319-65840-7
• Fast and privacy preserving distributed low-rank regression
  
  • Wai Hoi-To
  • Lafond Jean
  • Scaglione Anna
  • Moulines Éric
  , 2017.
• MCMC design-based non-parametric regression for rare event. Application to nested risk computation.
  
  • Fort Gersende
  • Gobet Emmanuel
  • Moulines Éric
  Monte Carlo Methods and Applications, De Gruyter, 2017.
• Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
  
  • Brosse Nicolas
  • Durmus Alain
  • Moulines Éric
  • Pereyra Marcelo
  Proceedings of Machine Learning Research, PMLR, 2017, 65, pp.319-342. This paper presents a detailed theoretical analysis of the Langevin Monte Carlo sampling algorithm recently introduced in [DMP16] when applied to log-concave probability distributions that are restricted to a convex body K. This method relies on a regularisation procedure involving the Moreau-Yosida envelope of the indicator function associated with K. Explicit convergence bounds in total variation norm and in Wasserstein distance of order 1 are established. In particular, we show that the complexity of this algorithm given a first order oracle is polynomial in the dimension of the state space. Finally, some numerical experiments are presented to compare our method with competing MCMC approaches from the literature.
• White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning
  
  • Xu Yongchao
  • Géraud Thierry
  • Puybareau Elodie
  • Bloch Isabelle
  • Chazalon Joseph
  , 2017, LNCS. In this paper, we propose a fast automatic method that seg- ments white matter hyperintensities (WMH) in 3D brain MR images, using a fully convolutional network (FCN) and transfer learning. This FCN is VGG, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI WMH Chal- lenge. We consider three images for each slice of volume to segment: the i-th T1 slice, the i-th FLAIR slice, and the residue of a morphological operator that emphasizes small bright structures. These three 2D images are assembled to form a 2D color image, that inputs the FCN to obtain the 2D segmentation of the i-th slice. We process all slices, and stack the results to form the 3D output segmentation. With such a technique, the segmentation of WMH on a 3D brain volume takes about 10 seconds. Our technique was ranked 6-th over 20 participants at the MICCAI WMH Challenge.