Publications

2020

• Epitaxial quantum dot lasers on silicon with high thermal stability and strong resistance to optical feedback
  
  • Huang H.
  • Duan J.
  • Dong B.
  • Norman J.
  • Jung D.
  • Bowers J. E
  • Grillot F.
  APL Photonics, AIP Publishing LLC, 2020, 5 (1), pp.016103. (10.1063/1.5120029)
  DOI : 10.1063/1.5120029
• An Indirect Determination of the Polarization Anisotropy in a Quantum Cascade Laser Under Strong Cross-Polarization Feedback
  
  • Spitz O
  • Herdt A
  • Carras M
  • Maisons G
  • Elsässer W
  • Grillot F
  , 2020. This work demonstrates that a non TM-polarized wave can be generated by a quantum cascade laser subjected to strong cross-polarization optical feedback. This finding is used to determine the anisotropy between the two existing polarizations Acknowledgments: this work is supported by the French Defense Agency (DGA), the French ANR program under grant ANR-17-ASMA-0006
• Optical noise of dual-state lasing quantum dot lasers
  
  • Zhou Yueguang
  • Duan Jianan
  • Grillot Frederic
  • Wang Cheng
  IEEE Journal of Quantum Electronics, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. (10.1109/JQE.2020.3026090)
  DOI : 10.1109/JQE.2020.3026090
• Constructions of optimal locally recoverable codes via Dickson polynomials.
  
  • Liu J.
  • Mesnager Sihem
  • Tang D.
  Journal of Designs, Codes, and Cryptography, 2020.
• An Experimental Study of State-of-the-Art Entity Alignment Approaches
  
  • Zhao Xiang
  • Zeng Weixin
  • Tang Jiuyang
  • Wang​ Wei
  • Suchanek Fabian
  IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2020. Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research. (10.1109/TKDE.2020.3018741)
  DOI : 10.1109/TKDE.2020.3018741
• Constructions of self-orthogonal codes from hulls of BCH codes and their parameters
  
  • Du Z.
  • Li C.
  • Mesnager Sihem
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020.
• Recent results and problems on constructions of linear codes from cryptographic functions
  
  • Li N.
  • Mesnager Sihem
  Journal of Cryptography and Communications- Discrete Structures, Boolean Functions, and Sequences, 2020.
• Processing Simple Geometric Attributes with Autoencoders
  
  • Newson Alasdair
  • Almansa Andrés
  • Gousseau Yann
  • Ladjal Saïd
  Journal of Mathematical Imaging and Vision, Springer Verlag, 2020, 62 (3), pp.293-312. Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and Generative Adversarial networks produce spectacular results on highly complex data, such as images of faces or landscapes. While these results open up a wide range of new, advanced synthesis applications, there is also a severe lack of theoretical understanding of how these networks work. This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems. In this paper, we propose to analyse the ability of the simplest generative network, the autoencoder, to encode and decode two simple geometric attributes : size and position. We believe that, in order to understand more complicated tasks, it is necessary to first understand how these networks process simple attributes. For the first property, we analyse the case of images of centred disks with variable radii. We explain how the autoencoder projects these images to and from a latent space of smallest possible dimension, a scalar. In particular, we describe a closed-form solution to the decoding training problem in a network without biases, and show that during training, the network indeed finds this solution. We then investigate the best regularisation approaches which yield networks that generalise well. For the second property, position, we look at the encoding and decoding of Dirac delta functions, also known as `one-hot' vectors. We describe a hand-crafted filter that achieves encoding perfectly, and show that the network naturally finds this filter during training. We also show experimentally that the decoding can be achieved if the dataset is sampled in an appropriate manner. (10.1007/s10851-019-00924-w)
  DOI : 10.1007/s10851-019-00924-w
• Raman-free fibered photon-pair source
  
  • Cordier Martin
  • Delaye Philippe
  • Gérôme Frédéric
  • Benabid Fetah
  • Zaquine Isabelle
  Scientific Reports, Nature Publishing Group, 2020, 10, pp.1650. Raman-scattering noise in silica has been the key obstacle toward the realisation of high quality fiber-based photon-pair sources. Here, we experimentally demonstrate how to get past this limitation by dispersion tailoring a xenon-filled hollow-core photonic crystal fiber. The source operates at room temperature, and is designed to generate Raman-free photon-pairs at useful wavelength ranges, with idler in the telecom, and signal in the visible range. We achieve a coincidence-to-accidentals ratio as high as 2740 combined with an ultra low heralded second order coherence g(2)H(0)=0.002, indicating a very high signal to noise ratio and a negligible multi-photon emission probability. Moreover, by gas-pressure tuning, we demonstrate the control of photon frequencies over a range as large as 13 THz, covering S-C and L telecom band for the idler photon. This work demonstrates that hollow-core photonic crystal fiber is an excellent platform to design high quality photon-pair sources, and could play a driving role in the emerging quantum technology. (10.1038/s41598-020-58229-7)
  DOI : 10.1038/s41598-020-58229-7
• On the decoding of Barnes-Wall lattices
  
  • Corlay Vincent
  • Boutros Joseph
  • Ciblat Philippe
  • Brunel Loïc
  , 2020. We present new efficient recursive decoders for the Barnes-Wall lattices based on their squaring construction. The analysis of the new decoders reveals a quasi-quadratic complexity in the lattice dimension. The error rate is shown to be close to the universal lower bound in dimensions 64 and 128.
• Power efficient all-fiberized 12-core erbium/ytterbium doped optical amplifier
  
  • Mélin Gilles
  • Kerampran Romain
  • Monteville Achille
  • Bordais Sylvain
  • Robin Thierry
  • Landais David
  • Lebreton Aurélien
  • Jaouën Yves
  • Taunay Thierry
  , 2020, pp.M4C.2. (10.1364/OFC.2020.M4C.2)
  DOI : 10.1364/OFC.2020.M4C.2
• Introduction of 3D Modeling and Peripheral Nerve Tractography in the Management of Pelvic Tumors
  
  • Goulin Jeanne
  • Meignan Pierre
  • Blanc Thomas
  • Delmonte Alessandro
  • Peyrot Quoc
  • Berteloot Laureline
  • Boddaert Nathalie
  • Bloch Isabelle
  • Sarnacki Sabine
  , 2020.
• Conveying Emotions Through Device-Initiated Touch
  
  • Teyssier Marc
  • Bailly Gilles
  • Pelachaud Catherine I
  • Lecolinet Eric
  IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. Humans have the ability to convey an array of emotions through complex and rich touch gestures. However, it is not clear how these touch gestures can be reproduced through interactive systems and devices in a remote mediated communication context. In this paper, we explore the design space of device-initiated touch for conveying emotions with an interactive system reproducing a collection of human touch characteristics. For this purpose, we control a robotic arm to touch the forearm of participants with different force, velocity and amplitude characteristics to simulate human touch. In view of adding touch as an emotional modality in human-machine interaction, we have conducted two studies. After designing the touch device, we explore touch in a context-free setup and then in a controlled context defined by textual scenarios and emotional facial expressions of a virtual agent. Our results suggest that certain combinations of touch characteristics are associated with the perception of different degrees of valence and of arousal. Moreover, in the case of non-congruent mixed signals (touch, facial expression, textual scenario) not conveying a priori the same emotion, the message conveyed by touch seems to prevail over the ones displayed by the visual and textual signals. (10.1109/TAFFC.2020.3008693)
  DOI : 10.1109/TAFFC.2020.3008693
• Spiking Neural Networks and online learning: An overview and perspectives
  
  • Lobo Jesus
  • del Ser Javier
  • Bifet Albert
  • Kasabov Nikola
  Neural Networks, Elsevier, 2020, 121, pp.88-100. Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts. (10.1016/j.neunet.2019.09.004)
  DOI : 10.1016/j.neunet.2019.09.004
• Tunable All-Optical Modulation and Building Blocks for Optical Neurons at Mid-Infrared Wavelength
  
  • Spitz O
  • Wu J
  • Herdt A
  • Carras M
  • Maisons G
  • Elsässer W
  • Wong C.-W
  • Grillot F
  , 2020. Quantum cascade lasers (QCLs) under optical feedback can output several non-linear dynamics whose properties depend on the reinjected light polarization. We demonstrate all-optical modulation, thresholding and excitability in QCLs, to experimentally build basic optical neurons.
• Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling
  
  • Clémençon Stéphan
  • Vogel Robin
  • Achab Mastane
  • Tillier Charles
  , 2020. We consider statistical learning problems, when the distribution P of the training observations Z 1 ,. .. , Z n differs from the distribution P involved in the risk one seeks to minimize (referred to as the test distribution) but is still defined on the same measurable space as P and dominates it. In the unrealistic case where the likelihood ratio Φ(z) = dP/dP (z) is known, one may straightforwardly extends the Empirical Risk Minimization (ERM) approach to this specific transfer learning setup using the same idea as that behind Importance Sampling, by minimizing a weighted version of the empirical risk functional computed from the 'biased' training data Z i with weights Φ(Z i). Although the importance function Φ(z) is generally unknown in practice, we show that, in various situations frequently encountered in practice, it takes a simple form and can be directly estimated from the Z i 's and some auxiliary information on the statistical population P. By means of linearization techniques, we then prove that the generalization capacity of the approach aforementioned is preserved when plugging the resulting estimates of the Φ(Z i)'s into the weighted empirical risk. Beyond these theoretical guarantees, numerical results provide strong empirical evidence of the relevance of the approach promoted in this article.
• Advanced Optical Communications and Networking
  
  • Gallion Philippe
  Proceeding of International Conference on Optical & Wireless Technologies , OWT 2019, 2020, Éditeurs : Ghanshyam Singh, Manish Tiwari, Tawfik Ismail, Vijay Janyani.
• Explicit Regularisation in Gaussian Noise Injections
  
  • Camuto Alexander
  • Willetts Matthew
  • Şimşekli Umut
  • Roberts Stephen
  • Holmes Chris
  , 2020. We study the regularisation induced in neural networks by Gaussian noise injections (GNIs). Though such injections have been extensively studied when applied to data, there have been few studies on understanding the regularising effect they induce when applied to network activations. Here we derive the explicit regulariser of GNIs, obtained by marginalising out the injected noise, and show that it penalises functions with high-frequency components in the Fourier domain; particularly in layers closer to a neural network's output. We show analytically and empirically that such regularisation produces calibrated classifiers with large classification margins.
• Resolution of a Routing and Wavelength Assignment Problem by Independent Sets in Conflict Graphs
  
  • Hudry Olivier
  , 2020.
• KClist++: A Simple Algorithm for Finding k-Clique Densest Subgraphs in Large Graphs
  
  • Sun Bintao
  • Danisch Maximilien
  • Chan T-H Hubert
  • Sozio Mauro
  Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2020. The problem of finding densest subgraphs has received increasing attention in recent years finding applications in biology , finance, as well as social network analysis. The k-clique densest subgraph problem is a generalization of the densest subgraph problem, where the objective is to find a subgraph maximizing the ratio between the number of k-cliques in the subgraph and its number of nodes. It includes as a special case the problem of finding subgraphs with largest average number of triangles (k = 3), which plays an important role in social network analysis. Moreover, algorithms that deal with larger values of k can effectively find quasi-cliques. The densest subgraph problem can be solved in polynomial time with algorithms based on maximum flow, linear programming or a recent approach based on convex optimization. In particular, the latter approach can scale to graphs containing tens of billions of edges. While finding a densest subgraph in large graphs is no longer a bottleneck , the k-clique densest subgraph remains challenging even when k = 3. Our work aims at developing near-optimal and exact algorithms for the k-clique densest subgraph problem on large real-world graphs. We give a surprisingly simple procedure that can be employed to find the maximal k-clique densest subgraph in large-real world graphs. By leveraging appealing properties of existing results, we combine it with a recent approach for listing all k-cliques in a graph and a sampling scheme, obtaining the state-of-the-art approaches for the aforementioned problem. Our theoretical results are complemented with an extensive experimental evaluation showing the effectiveness of our approach in large real-world graphs.
• A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments
  
  • Catanese Clara
  • Ayassi Reda
  • Pincemin Erwan
  • Jaouën Yves
  , 2020, pp.SpTh3I.1. (10.1364/SPPCOM.2020.SpTh3I.1)
  DOI : 10.1364/SPPCOM.2020.SpTh3I.1
• SENSITIVITY ANALYSIS FOR STOCHASTIC SIMULATORS USING DIFFERENTIAL ENTROPY
  
  • Azzi Soumaya
  • Sudret Bruno
  • Wiart Joe
  International Journal for Uncertainty Quantification, Begell House Publishers, 2020, 10 (1), pp.25-33. (10.1615/Int.J.UncertaintyQuantification.2020031610)
  DOI : 10.1615/Int.J.UncertaintyQuantification.2020031610
• A new parametrization for the Rician distribution
  
  • Nicolas Jean Marie
  • Tupin Florence
  IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2020. The Rician distribution is widely used in SAR imagery to model the backscattering of a strong target inside a resolution cell. Nevertheless the computation of the parameters of the Rice distribution remains a difficult task. In this paper, a new parametrization to model the Rice distribution is introduced. Thanks to the introduction of a new variable defined by the ratio of the target contribution to the speckle, the relationship between the coefficient of variation and this new parameter can be derived. An efficient numerical method is proposed to evaluate it from the coefficient of variation and a discussion on the variance of this estimator is led. A comparison with other methods of estimation showed that the proposed approach is a good compromise between the variance of the estimate and the computation time. At last, a link between permanent scatterers and Rice distributed targets is proposed through this new parametrization. (10.1109/LGRS.2019.2957240)
  DOI : 10.1109/LGRS.2019.2957240
• New Characterizations for the Multi-output Correlation- Immune Boolean Functions
  
  • Chai J.
  • Mesnager Sihem
  • Wang Z.
  Discrete Mathematics, Elsevier, 2020.
• Query Rewriting On Path Views Without Integrity Constraints
  
  • Romero Julien
  • Preda Nicoleta
  • Suchanek Fabian
  , 2020. A view with a binding pattern is a parameterised query on a database. Such views are used, e.g., to model Web services. To answer a query on such views, one has to orchestrate the views together in execution plans. The goal is usually to nd equivalent rewritings, which deliver precisely the same results as the query on all databases. However, such rewritings are usually possible only in the presence of integrity constraints and not all databases have such constraints. In this paper, we describe a class of plans that give practical guarantees about their result even if there are no integrity constraints. We provide a characterisation of such plans and a complete and correct algorithm to enumerate them. Finally, we show that our method can nd plans on real-world Web Services.