Publications

2022

• Lyrics segmentation via bimodal text–audio representation
  
  • Fell Michael
  • Nechaev Yaroslav
  • Meseguer-Brocal Gabriel
  • Cabrio Elena
  • Gandon Fabien
  • Peeters Geoffroy
  Natural Language Engineering, Cambridge University Press (CUP), 2022, 28 (3), pp.317 - 336. Song lyrics contain repeated patterns that have been proven to facilitate automated lyrics segmentation, with the final goal of detecting the building blocks (e.g., chorus, verse) of a song text. Our contribution in this article is twofold. First, we introduce a convolutional neural network (CNN)-based model that learns to segment the lyrics based on their repetitive text structure. We experiment with novel features to reveal different kinds of repetitions in the lyrics, for instance based on phonetical and syntactical properties. Second, using a novel corpus where the song text is synchronized to the audio of the song, we show that the text and audio modalities capture complementary structure of the lyrics and that combining both is beneficial for lyrics segmentation performance. For the purely text-based lyrics segmentation on a dataset of 103k lyrics, we achieve an F-score of 67.4%, improving on the state of the art (59.2% F-score). On the synchronized text–audio dataset of 4.8k songs, we show that the additional audio features improve segmentation performance to 75.3% F-score, significantly outperforming the purely text-based approaches. (10.1017/S1351324921000024)
  DOI : 10.1017/S1351324921000024
• Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
  
  • Kips Robin
  • Jiang Ruowei
  • Ba Sileye
  • Duke Brendan
  • Perrot Matthieu
  • Gori Pietro
  • Bloch Isabelle
  Computer Graphics Forum, Wiley, 2022. Augmented reality applications have rapidly spread across online platforms, allowing consumers to virtually try-on a variety of products, such as makeup, hair dying, or shoes. However, parametrizing a renderer to synthesize realistic images of a given product remains a challenging task that requires expert knowledge. While recent work has introduced neural rendering methods for virtual try-on from example images, current approaches are based on large generative models that cannot be used in real-time on mobile devices. This calls for a hybrid method that combines the advantages of computer graphics and neural rendering approaches. In this paper we propose a novel framework based on deep learning to build a real-time inverse graphics encoder that learns to map a single example image into the parameter space of a given augmented reality rendering engine. Our method leverages self-supervised learning and does not require labeled training data which makes it extendable to many virtual try-on applications. Furthermore, most augmented reality renderers are not differentiable in practice due to algorithmic choices or implementation constraints to reach real-time on portable devices. To relax the need for a graphics-based differentiable renderer in inverse graphics problems, we introduce a trainable imitator module. Our imitator is a generative network that learns to accurately reproduce the behavior of a given non-differentiable renderer. We propose a novel rendering sensitivity loss to train the imitator, which ensures that the network learns an accurate and continuous representation for each rendering parameter. Our framework enables novel applications where consumers can virtually try-on a novel unknown product from an inspirational reference image on social media. It can also be used by graphics artists to automatically create realistic rendering from a reference product image.
• A variant of Young’s method in voting theory providing the same winners as Copeland’s method
  
  • Hudry Olivier
  International Journal of Mathematics, Statistics and Operations Research, Academic Research Foundations, 2022, 1 (2), pp.101-107.
• Reasoning about Moving Target Defense in Attack Modeling Formalisms
  
  • Ballot Gabriel
  • Malvone Vadim
  • Leneutre Jean
  • Borde Etienne
  , 2022. Since 2009, Moving Target Defense (MTD) has become a new paradigm of defensive mechanism that frequently changes the state of the target system to confuse the attacker. This frequent change is costly and leads to a trade-off between misleading the attacker and disrupting the quality of service. Optimizing the MTD activation frequency is necessary to develop this defense mechanism when facing realistic, multi-step attack scenarios. Attack modeling formalisms based on DAG are prominently used to specify these scenarios. Our contribution is a new DAG-based formalism for MTDs and its translation into a Price Timed Markov Decision Process to find the best activation frequencies against the attacker's time/cost-optimal strategies. For the first time, MTD activation frequencies are analyzed in a state-of-the-art DAG-based representation. Moreover, this is the first paper that considers the specificity of MTDs in the automatic analysis of attack modeling formalisms. Finally, we present some experimental results using Uppaal Stratego to demonstrate its applicability and relevance.
• Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation
  
  • Fontaine Mathieu
  • Sekiguchi Kouhei
  • Nugraha Aditya
  • Bando Yoshiaki
  • Yoshii Kazuyoshi
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2022, pp.1-1. This paper describes heavy-tailed extensions of a state-of-the-art versatile blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) from a unified point of view. The common way of deriving such an extension is to replace the multivariate complex Gaussian distribution in the likelihood function with its heavy-tailed generalization, e.g., the multivariate complex Student's t and leptokurtic generalized Gaussian distributions, and tailor-make the corresponding parameter optimization algorithm. Using a wider class of heavy-tailed distributions called a Gaussian scale mixture (GSM), i.e., a mixture of Gaussian distributions whose variances are perturbed by positive random scalars called impulse variables, we propose GSM-FastMNMF and develop an expectationmaximization algorithm that works even when the probability density function of the impulse variables have no analytical expressions. We show that existing heavy-tailed FastMNMF extensions are instances of GSM-FastMNMF and derive a new instance based on the generalized hyperbolic distribution that include the normal-inverse Gaussian, Student's t, and Gaussian distributions as the special cases. Our experiments show that the normalinverse Gaussian FastMNMF outperforms the state-of-the-art FastMNMF extensions and ILRMA model in speech enhancement and separation in terms of the signal-to-distortion ratio. (10.1109/TASLP.2022.3172631)
  DOI : 10.1109/TASLP.2022.3172631
• Uniform Reliability of Self-Join-Free Conjunctive Queries
  
  • Amarilli Antoine
  • Kimelfeld Benny
  Logical Methods in Computer Science, Logical Methods in Computer Science Association, 2022. The reliability of a Boolean Conjunctive Query (CQ) over a tuple-independent probabilistic database is the probability that the CQ is satisfied when the tuples of the database are sampled one by one, independently, with their associated probability. For queries without self-joins (repeated relation symbols), the data complexity of this problem is fully characterized by a known dichotomy: reliability can be computed in polynomial time for hierarchical queries, and is #P-hard for non-hierarchical queries. Inspired by this dichotomy, we investigate a fundamental counting problem for CQs without self-joins: how many sets of facts from the input database satisfy the query? This is equivalent to the uniform case of the query reliability problem, where the probability of every tuple is required to be 1/2. Of course, for hierarchical queries, uniform reliability is solvable in polynomial time, like the reliability problem. We show that being hierarchical is also necessary for this tractability (under conventional complexity assumptions). In fact, we establish a generalization of the dichotomy that covers every restricted case of reliability in which the probabilities of tuples are determined by their relation. (10.46298/lmcs-18(4:3)2022)
  DOI : 10.46298/lmcs-18(4:3)2022
• Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs
  
  • Mallik Mohammed
  • Tesfay Angesom Ataklity
  • Allaert Benjamin
  • Kassi Rédha
  • Egea-Lopez Esteban
  • Molina-Garcia-Pardo Jose-Maria
  • Wiart Joe
  • Gaillot Davy
  • Clavier Laurent
  Sensors, MDPI, 2022, 22 (24), pp.9643. With the ongoing fifth-generation cellular network (5G) deployment, electromagnetic field exposure has become a critical concern. However, measurements are scarce, and accurate electromagnetic field reconstruction in a geographic region remains challenging. This work proposes a conditional generative adversarial network to address this issue. The main objective is to reconstruct the electromagnetic field exposure map accurately according to the environment’s topology from a few sensors located in an outdoor urban environment. The model is trained to learn and estimate the propagation characteristics of the electromagnetic field according to the topology of a given environment. In addition, the conditional generative adversarial network-based electromagnetic field mapping is compared with simple kriging. Results show that the proposed method produces accurate estimates and is a promising solution for exposure map reconstruction. (10.3390/s22249643)
  DOI : 10.3390/s22249643
• Survey of Exposure to RF Electromagnetic Fields in the Connected Car
  
  • Tognola Gabriella
  • Bonato Marta
  • Benini Martina
  • Aerts Sam
  • Gallucci Silvia
  • Chiaramello Emma
  • Fiocchi Serena
  • Parazzini Marta
  • Masini Barbara
  • Joseph Wout
  • Wiart Joe
  • Ravazzani Paolo
  IEEE Access, IEEE, 2022, 10, pp.47764-47781. Future vehicles will be increasingly connected to enable new applications and improve safety, traffic efficiency and comfort, through the use of several wireless access technologies, ranging from vehicle-to-everything (V2X) connectivity to automotive radar sensing and Internet of Things (IoT) technologies for intra-car wireless sensor networks. These technologies span the radiofrequency (RF) range, from a few hundred MHz as in intra-car network of sensors to hundreds of GHz as in automotive radars used for in-vehicle occupant detection and advanced driver assistance systems. Vehicle occupants and road users in the vicinity of the connected vehicle are thus daily immersed in a multi-source and multi-band electromagnetic field (EMF) generated by such technologies. This paper is the first comprehensive and specific survey about EMF exposure generated by the whole ensemble of connectivity technologies in cars. For each technology we describe the main characteristics, relevant standards, the application domain, and the typical deployment in modern cars. We then extensively describe the EMF exposure scenarios resulting from such technologies by resuming and comparing the outcomes from past studies on the exposure in the car. Results from past studies suggested that in no case EMF exposure was above the safe limits for the general population. Finally, open challenges for a more realistic characterization of the EMF exposure scenario in the connected car are discussed. (10.1109/ACCESS.2022.3170035)
  DOI : 10.1109/ACCESS.2022.3170035
• Video-to-Music Recommendation using Temporal Alignment of Segments
  
  • Prétet Laure
  • Richard Gael
  • Souchier Clément
  • Peeters Geoffroy
  IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2022.
• A User Centric Blockage Model for Wireless Networks
  
  • Baccelli François
  • Liu Bin
  • Decreusefond Laurent
  • Song Rongfang
  IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers, 2022, 21 (10), pp.10 p.. This paper proposes a cascade blockage model for analyzing the vision that a user has of a wireless network. This model, inspired by the classical multiplicative cascade models, has a radial structure meant to analyze blockages seen by the receiver at the origin in different angular sectors. The main novelty is that it is based on the geometry of obstacles and takes the joint blockage phenomenon into account. We show on a couple of simple instances that the Laplace transforms of total interference satisfies a functional equation that can be solved efficiently by an iterative scheme. This is used to analyze the coverage probability of the receiver and the effect of blockage correlation and penetration loss in both dense and sparse blockage environments. Furthermore, this model is used to investigate the effect of blockage correlation on user beamforming techniques. Another functional equation and its associated iterative algorithm are proposed to derive the coverage performance of the best beam selection in this context. In addition, the conditional coverage probability is also derived to evaluate the effect of beam switching. The results not only show that beam selection is quite efficient for multi-beam terminals, but also show how the correlation brought by blockages can be leveraged to accelerate beam sweeping and pairing. (10.1109/TWC.2022.3166211)
  DOI : 10.1109/TWC.2022.3166211
• Unifying conditional and unconditional semantic image synthesis with OCO-GAN
  
  • Careil Marlène
  • Lathuilière Stéphane
  • Couprie Camille
  • Verbeek Jakob
  , 2022. Generative image models have been extensively studied in recent years. In the unconditional setting, they model the marginal distribution from unlabelled images. To allow for more control, image synthesis can be conditioned on semantic segmentation maps that instruct the generator the position of objects in the image. While these two tasks are intimately related, they are generally studied in isolation. We propose OCO-GAN, for Optionally COnditioned GAN, which addresses both tasks in a unified manner, with a shared image synthesis network that can be conditioned either on semantic maps or directly on latents. Trained adversarially in an end-to-end approach with a shared discriminator, we are able to leverage the synergy between both tasks. We experiment with Cityscapes, COCO-Stuff, ADE20K datasets in a limited data, semi-supervised and full data regime and obtain excellent performance, improving over existing hybrid models that can generate both with and without conditioning in all settings. Moreover, our results are competitive or better than state-of-the art specialised unconditional and conditional models.
• Approximate Bayesian computation with the sliced-Wasserstein distance
  
  • Nadjahi Kimia
  • de Bortoli Valentin
  • Durmus Alain
  • Badeau Roland
  • Şimşekli Umut
  , 2022. Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which the simulated data are close to the observations in terms of summary statistics. These statistics are defined beforehand and might induce a loss of information, which has been shown to deteriorate the quality of the approximation. To overcome this problem, Wasserstein-ABC has been recently proposed, and compares the datasets via the Wasserstein distance between their empirical distributions, but does not scale well to the dimension or the number of samples. We propose a new ABC technique, called Sliced-Wasserstein ABC and based on the Sliced-Wasserstein distance, which has better computational and statistical properties. We derive two theoretical results showing the asymptotical consistency of our approach, and we illustrate its advantages on synthetic data and an image denoising task. (10.1109/icassp40776.2020.9054735)
  DOI : 10.1109/icassp40776.2020.9054735
• Custom Structure Preservation in Face Aging
  
  • Gomez-Trenado Guillermo
  • Lathuilière Stéphane
  • Mesejo Pablo
  • Cordón Óscar
  , 2022. In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style and content representations of the input image while conditioning the output on the target age. We go beyond existing aging methods allowing users to adjust the degree of structure preservation in the input image during inference. To this purpose, we introduce a masking mechanism, the CUstom Structure Preservation module, that distinguishes relevant regions in the input image from those that should be discarded. CUSP requires no additional supervision. Finally, our quantitative and qualitative analysis which include a user study, show that our method outperforms prior art and demonstrates the effectiveness of our strategy regarding image editing and adjustable structure preservation. Code and pretrained models are available at https://github.com/guillermogotre/CUSP.
• Dominating, Locating-Dominating and Identifying Codes in the q-ary Lee Hypercube
  
  • Hudry Olivier
  • Charon Irene
  • Lobstein Antoine
  , 2022.
• Negative Sampling Strategies for Contrastive Self-Supervised Learning of Graph Representations
  
  • Hafidi Hakim
  • Ghogho Mounir
  • Ciblat Philippe
  • Swami Ananthram
  Signal Processing, Elsevier, 2022, 190 (4). Contrastive learning has become a successful approach for learning powerful text and image representations in a self-supervised manner. Contrastive frameworks learn to distinguish between representations coming from augmentations of the same data point (positive pairs) and those of other (negative) examples. Recent studies aim at extending methods from contrastive learning to graph data. In this work, we propose a general framework for learning node representations in a self supervised manner called Graph Constrastive Learning (GraphCL). It learns node embeddings by maximizing the similarity between the nodes representations of two randomly perturbed versions of the same graph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. We investigate different standard and new negative sampling strategies as well as a comparison without negative sampling approach. We demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks in both transductive and inductive learning setups.
• A general sample complexity analysis of vanilla policy gradient
  
  • Yuan Rui
  • Gower Robert M
  • Lazaric Alessandro
  , 2022. We adapt recent tools developed for the analysis of Stochastic Gradient Descent (SGD) in non-convex optimization to obtain convergence and sample complexity guarantees for the vanilla policy gradient (PG). Our only assumptions are that the expected return is smooth w.r.t. the policy parameters, that its H-step truncated gradient is close to the exact gradient, and a certain ABC assumption. This assumption requires the second moment of the estimated gradient to be bounded by A ≥ 0 times the suboptimality gap, B ≥ 0 times the norm of the full batch gradient and an additive constant C ≥ 0, or any combination of aforementioned. We show that the ABC assumption is more general than the commonly used assumptions on the policy space to prove convergence to a stationary point. We provide a single convergence theorem that recovers the O(−4) sample complexity of PG. Our results also affords greater flexibility in the choice of hyper parameters such as the step size and places no restriction on the batch size m, including the single trajectory case (i.e., m = 1). We then instantiate our theorem in different settings, where we both recover existing results and obtained improved sample complexity, e.g., for convergence to the global optimum for Fisher-nondegenerated parameterized policies.
• FAST STRATEGIES FOR MULTI-TEMPORAL SPECKLE REDUCTION OF SENTINEL-1 GRD IMAGES
  
  • Meraoumia Inès
  • Dalsasso Emanuele
  • Denis Loïc
  • Tupin Florence
  , 2022. Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data. Nowadays, deep learning approaches produce state of the art results in single-image SAR restoration. Nevertheless, huge multi-temporal stacks are now often available and could be efficiently exploited to further improve image quality. This paper explores two fast strategies employing a singleimage despeckling algorithm, namely SAR2SAR [1], in a multi-temporal framework. The first one is based on Quegan filter [2] and replaces the local reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress speckle from a ratio image encoding the multi-temporal information under the form of a "super-image", i.e. the temporal arithmetic mean of a time series. Experimental results on Sentinel-1 GRD data show that these two multi-temporal strategies provide improved filtering results while adding a limited computational cost. (10.1109/IGARSS46834.2022.9883448)
  DOI : 10.1109/IGARSS46834.2022.9883448
• Comparing Deep Models and Evaluation Strategies for Multi-Pitch Estimation in Music Recordings
  
  • Weis Christof
  • Peeters Geoffroy
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2022, 30, pp.2814-2827. (10.1109/TASLP.2022.3200547)
  DOI : 10.1109/TASLP.2022.3200547
• M A T R I X Transpositions. Spécial Prod : de l’autre côté du miroir
  
  • Zayana Karim
  • Boyer Ivan
  • Massard Marc-Aurèle
  • Rabiet Victor
  CultureMath, ENS, 2022.
• SELF ATTENTION DEEP GRAPH CNN CLASSIFICATION OF TIMES SERIES IMAGES FOR LAND COVER MONITORING
  
  • Chaabane Ferdaous
  • Réjichi Safa
  • Tupin Florence
  , 2022. Time Series of Satellite Imagery (SITS) acquired by recent Earth observation systems represent an important source of information that supports several remote sensing applications related to monitoring the dynamics of the Earth's surface over large areas. A major challenge then is to design new deep learning models that can take into account intelligently the complementarity between temporal and spatial contexts that characterize these data structures. In this work, we propose to use an adapted self-attention convolutional neural network for spatio-temporal graphs classification that exploits both spatial and temporal dimensions. The graphs will be generated from a series of temporal images that are segmented into different regions. Those graphs are then classified using the Self-Attention Deep Graph CNN (DGCNN) model to highlight the temporal evolution of land cover areas through the construction of a spatio-temporal Map.
• Conditional independence testing via weighted partial copulas and nearest neighbors
  
  • Bianchi Pascal
  • Elgui Kevin
  • Portier François
  Journal of Multivariate Analysis, Elsevier, 2022, 30 (3), pp.1117-1147. This paper introduces the \textit{weighted partial copula} function for testing conditional independence. The proposed test procedure results from these two ingredients: (i) the test statistic is an explicit Cramer-von Mises transformation of the \textit{weighted partial copula}, (ii) the regions of rejection are computed using a bootstrap procedure which mimics conditional independence by generating samples from the product measure of the estimated conditional marginals. Under conditional independence, the weak convergence of the \textit{weighted partial copula proces}s is established when the marginals are estimated using a smoothed local linear estimator. Finally, an experimental section demonstrates that the proposed test has competitive power compared to recent state-of-the-art methods such as kernel-based test.
• Delaunay Painting: Perceptual image coloring from raster contours with gaps
  
  • Parakkat Amal Dev
  • Memari Pooran
  • Cani Marie-Paule
  Computer Graphics Forum, Wiley, 2022. We introduce Delaunay Painting, a novel and easy-to-use method to flat-color contour-sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colors, thanks to the dynamic update of flow values calculated from color hints. Aesthetic finish is then achieved, through energy minimisation of contour curves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our color diffusion framework which automatically extends coloring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any coloring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the coloring strategy for quick segmentation of contours images, that we illustrate with an application to medical imaging.
• A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation
  
  • Mina Rayan
  • Jabbour Chadi
  • Sakr George E
  Electronics, MDPI, 2022, 11 (3), pp.435. Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective. (10.3390/electronics11030435)
  DOI : 10.3390/electronics11030435
• Codes in the q-ary Lee Hypercube
  
  • Hudry Olivier
  • Charon Irène
  • Lobstein Antoine
  WSEAS Transactions on Mathematics, World Scientific and Engineering Academy and Society (WSEAS), 2022, 21, pp.173-186. (10.37394/23206.2022.21.24)
  DOI : 10.37394/23206.2022.21.24
• Curves on Frobenius classical surfaces in $\mathbb{P}^{3}$ over finite fields
  
  • Berardini Elena
  • Nardi Jade
  Acta Arithmetica, Instytut Matematyczny PAN, 2022, 205 (4), pp.323-340. (10.4064/aa211118-12-9)
  DOI : 10.4064/aa211118-12-9