Publications

2022

• Reflection sensitivity of InAs/GaAs epitaxial quantum dot lasers under direct modulation
  
  • Ding Shihao
  • Dong Bozhang
  • Huang Heming
  • Bowers John E
  • Grillot Frédéric
  Electronics Letters, IET, 2022, 58 (9), pp.363-365. This paper reports on the reflection sensitivity under direct modulation operation of a 1.3 μm InAs/GaAs quantum dot laser that is epitaxially grown on silicon. The quantum dot laser exhibits a high tolerance to back reflections with low error transmission at 6 Gbps. This study paves the way for developing directly modulated isolator-free photonic integrated circuits based on quantum dot lasers. (10.1049/ell2.12440)
  DOI : 10.1049/ell2.12440
• Pulling, Pressing, and Sensing with In-Flat: Transparent Touch Overlay for Smartphones
  
  • Zhang Zhuoming
  • Alvina Jessalyn
  • Détienne Françoise
  • Lecolinet Eric
  , 2022, pp.1-9. (10.1145/3531073.3531111)
  DOI : 10.1145/3531073.3531111
• Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
  
  • Pethick Thomas
  • Latafat Puya
  • Patrinos Panagiotis
  • Fercoq Olivier
  • Cevher Volkan
  , 2022. This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems. It is well-known that finding a local solution for general minimax problems is computationally intractable. This observation has recently motivated the study of structures sufficient for convergence of first order methods in the more general setting of variational inequalities when the so-called weak Minty variational inequality (MVI) holds. This problem class captures non-trivial structures as we demonstrate with examples, for which a large family of existing algorithms provably converge to limit cycles. Our results require a less restrictive parameter range in the weak MVI compared to what is previously known, thus extending the applicability of our scheme. The proposed algorithm is applicable to constrained and regularized problems, and involves an adaptive stepsize allowing for potentially larger stepsizes. Our scheme also converges globally even in settings where the underlying operator exhibits limit cycles. Moreover, a variant with stochastic oracles is proposed-making it directly relevant for training of generative adversarial networks. For the stochastic algorithm only one of the stepsizes is required to be diminishing while the other may remain constant, making it interesting even in the monotone setting.
• Autoregressive Moving Average Jointly-Diagonalizable Spatial Covariance Analysis for Joint Source Separation and Dereverberation
  
  • Sekiguchi Kouhei
  • Bando Yoshiaki
  • Nugraha Aditya Arie
  • Fontaine Mathieu
  • Yoshii Kazuyoshi
  • Kawahara Tatsuya
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2022, 30, pp.2368 - 2382. This article describes a computationally-efficient statistical approach to joint (semi-)blind source separation and dereverberation for multichannel noisy reverberant mixture signals. A standard approach to source separation is to formulate a generative model of a multichannel mixture spectrogram that consists of source and spatial models representing the time-frequency power spectral densities (PSDs) and spatial covariance matrices (SCMs) of source images, respectively, and find the maximum-likelihood estimates of these parameters. A state-of-the-art blind source separation method in this thread of research is fast multichannel nonnegative matrix factorization (FastMNMF) based on the lowrank PSDs and jointly-diagonalizable full-rank SCMs. To perform mutually-dependent separation and dereverberation jointly, in this paper we integrate both moving average (MA) and autoregressive (AR) models that represent the early reflections and late reverberations of sources, respectively, into the FastMNMF formalism. Using a pretrained deep generative model of speech PSDs as a source model, we realize semi-blind joint speech separation and dereverberation. We derive an iterative optimization algorithm based on iterative projection or iterative source steering for jointly and efficiently updating the AR parameters and the SCMs. Our experimental results showed the superiority of the proposed ARMA extension over its AR-or MA-ablated version in a speech separation and/or dereverberation task. (10.1109/taslp.2022.3190734)
  DOI : 10.1109/taslp.2022.3190734
• Le pillage de la communauté des logiciels libres
  
  • O'Neil Mathieu
  • Muselli Laure
  • Pailler Fred
  • Zacchiroli Stefano
  Le Monde Diplomatique, Le Monde, 2022, pp.20-21.
• High resolution neural texture synthesis with long range constraints
  
  • Gonthier Nicolas
  • Gousseau Yann
  • Ladjal Saïd
  International Journal of Mathematical Imaging and Vision(JMIV), 2022, 64, pp.478-492. The field of texture synthesis has witnessed important progresses over the last years, most notably through the use of Convolutional Neural Networks. However, neural synthesis methods still struggle to reproduce large scale structures, especially with high resolution textures. To address this issue, we first introduce a simple multi-resolution framework that efficiently accounts for long-range dependency. Then, we show that additional statistical constraints further improve the reproduction of textures with strong regularity. This can be achieved by constraining both the Gram matrices of a neural network and the power spectrum of the image. Alternatively one may constrain only the autocorrelation of the features of the network and drop the Gram matrices constraints. In an experimental part, the proposed methods are then extensively tested and compared to alternative approaches, both in an unsupervised way and through a user study. Experiments show the interest of the multi-scale scheme for high resolution textures and the interest of combining it with additional constraints for regular textures.