Publications

2022

• Convergence of quantum and classical communications
  
  • Aymeric Raphaël
  , 2022. Quantum key distribution (QKD) protocols harness fundamental quantum properties of the light to construct communication channels sensitive to eavesdropping. In order to develop the technology at large scale, one of the main challenges to overcome is the deployment cost of such systems. A significant step towards reducing deployment costs would be to use the existing optical fiber infrastructure to perform QKD, since this would relax the need to use dark (and expensive !) fiber. However this also means we must insure QKD protocols can coexist with classical communications, which can be challenging as quantum states are very sensitive to perturbations. Here, we focus particularly on continuous-variable (CV) QKD because their natural proximity to classical coherent communication systems indicates that they are good candidates for coexistence over the same fiber. Assuming CV-QKD is destined to be incorporated in classical communication links, an interesting question is whether the coexistence with classical channels will necessarily be detrimental to the CV-QKD protocol. We show that in some cases, coexistence can actually provide an advantage to the CV-QKD protocol. In a first project, we experimentally demonstrate that a classical channel can be used as a pilot signal for the quantum channel. Thus, the need for pilot-tones, mandatory in a typical CV-QKD protocol, can be relaxed. In a second project, we show that the noise generated by classical channels can be used to ”hide” the quantum signal. The quantum communication therefore can become covert thanks to the classical channels. Covert QKD protocols are interesting because they provide extreme security guarantees. We investigate the necessary conditions for covert CV-QKD as well as scenarios for its deployment in a practical setting
• Reflections and Considerations on Running Creative Visualization Learning Activities
  
  • Roberts Jonathan C
  • Bach Benjamin
  • Boucher Magdalena
  • Chevalier Fanny
  • Diehl Alexandra
  • Hinrichs Uta
  • Huron Samuel
  • Kirk Andy
  • Knudsen Søren
  • Meirelles Isabel
  • Noonan Rebecca
  • Pelchmann Laura
  • Rajabiyazdi Fateme
  • Stoiber Christina
  , 2022. This paper draws together nine strategies for creative visualization activities. Teaching visualization often involves running learning activities where students perform tasks that directly support one or more topics that the teacher wishes to address in the lesson. As a group of educators and researchers in visualization, we reflect on our learning experiences. Our activities and experiences range from dividing the tasks into smaller parts, considering different learning materials, to encouraging debate. With this paper, our hope is that we can encourage, inspire, and guide other educators with visualization activities. Our reflections provide an initial starting point of methods and strategies to craft creative visualisation learning activities, and provide a foundation for developing best practices in visualization education.
• A Patch-Based Algorithm for Diverse and High Fidelity Single Image Generation
  
  • Cherel Nicolas
  • Almansa Andrés
  • Gousseau Yann
  • Newson Alasdair
  , 2022. Image generation is the task of producing new samples from one or several example images. Until recently, this has been done using large image databases, in particular using Generative Adversarial Networks (GANs). However, Shaham et al. [1] recently proposed the SinGAN method, which achieves this generation using a single image example. At the same time, researchers are realizing that classical patchbased methods can replace certain neural networks, with no costly training. In this paper, we present a purely patch-based method, named Patches for Single image generation (PSin), which requires no training and generates samples in seconds. Our algorithm is based on the minimization of a global, patchbased energy functional, which ensures the visual fidelity of the result to the original image. We also ensure diversity of the results by carefully choosing the initialization of the algorithm. We propose two initialization variants. We compare our results to both the original SinGAN and another recent patch-based image generation approach, both qualitatively and quantitatively using multiple metrics.
• End-to-End Delivery of VVC Multicast Services over 5G Mobile Network
  
  • Biatek Thibaud
  • Burdinat Christophe
  • Raulet Mickaël
  • Wieckowski Adam
  • Bross Benjamin
  • Le Feuvre J.
  , 2022. This industrial demo showcases an end-to-end live video delivery chain leveraging Versatile Video Coding (VVC) and multicast-ROUTE over a 5G radio access network. The VVC encoder is provided by Ateme, achieving live encoding of a complete OTT ladder, from SD to 4K, packaged into CMAF using low-latency chunks, published on a local origin server. The multicast server, deployed prior to the base-station, is provided by GPAC and is performing ROUTE encapsulation of the CMAF services pushed by the encoder on the origin server. The multicast bitstreams are ingested by an Amarisoft Callbox providing 4G-Lte and 5G-NR core network and Radio Access Network (RAN) enabling to deliver LTE-Broadcast or unicast services to the smartphones. The playback of the services is achieved on a 5G smartphone running both a multicast client and a VVC-compatible player, in an interactive manner (dynamic quality selection). The multicast client is provided by GPAC and the VVC decoding library is Fraunhofer HHI VVdeC, optimized for ARM. The demonstration is highlighting how these emerging technologies can be deployed together to enable next-generation video services over 5G mobile network.
• Smart Learning of Click and Refine for Nuclei Segmentation on Histology Images
  
  • Habis Antoine
  • Meas-Yedid Vannary
  • Gonzalez Obando Daniel Felipe
  • Olivo-Marin Jean-Christophe
  • Angelini Elsa
  , 2022, pp.2281-2285. Deep learning has proven to be a very efficient tool to help pathologists analyze Whole Slide Images (WSI) toward automated classification or segmentation of detailed structures such as nuclei, glands or glomeruli. These objects are particularly relevant for disease diagnosis and staging. Many deep learning methods have shown impressive performance but are still imperfect, while manual segmentation has poor inter-rater agreement. In this paper, we propose a patch-level automated correction of a given baseline initial segmentation, based on deep-learning of segmentation errors and downstream local refinements. Results on the MoNuSeg and PanNuke test datasets show significant improvement of nuclei segmentation quality. (10.1109/ICIP46576.2022.9897496)
  DOI : 10.1109/ICIP46576.2022.9897496
• Minconvnets: a New Class of Multiplication-Less Neural Networks
  
  • Yang Xuecan
  • Chaudhuri Sumanta
  • Likforman-Sulem Laurence
  • Naviner Lirida
  , 2022, pp.881-885. In this article, MinConvNets where the multiplications in the forward propagation path of CNNs are approximated by minimum comparator operations are introduced. Hardware complexity of minimum operator is of the order of O(N), whereas for multiplication it is O(N 2 ). Firstly, a methodology to find approximate operations based on statistical correlation is presented. We show that it is possible to replace multipliers by minimum operations in the forward propagation under certain constraints, i.e. given similar mean and variances of the feature and the weight vectors. A modified training method which guarantees the above constraints is proposed. And it is shown that equivalent precision can be achieved during inference with MinConvNets by using transfer learning from well trained exact CNNs. (10.1109/ICIP46576.2022.9897286)
  DOI : 10.1109/ICIP46576.2022.9897286
• 80 GHz compact photonic microwave generation from a solitary distributed feedback laser on silicon
  
  • Grillot Frédéric
  • Callado G.
  • Ding Shihao
  • Verolet T.
  • Decobert J.
  • Jany C.
  • Hassan K.
  • Malhouitre S.
  • Make D.
  • Coquiard A.
  • Combrie S.
  • Shen A.
  • de Rossi A.
  , 2022, pp.1-2. Microwave generation at 80 GHz is achieved from a distributed feedback laser on silicon made with a harmonic photonic potential from which a coherent beating between confined modes occurs. These results pave the way for all-optical microwave generation using compact and energy efficient semiconductor devices. (10.23919/ISLC52947.2022.9943524)
  DOI : 10.23919/ISLC52947.2022.9943524
• IS THE U-NET DIRECTIONAL-RELATIONSHIP AWARE?
  
  • Riva Mateus
  • Gori Pietro
  • Yger Florian
  • Bloch Isabelle
  , 2022. CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field. However, the nature and limits of this capacity has never been explored in full. We explore a specific type of relationship-directional-using a standard U-Net trained to optimize a cross-entropy loss function for segmentation. We train this network on a pretext segmentation task requiring directional relation reasoning for success and state that, with enough data and a sufficiently large receptive field, it succeeds to learn the proposed task. We further explore what the network has learned by analysing scenarios where the directional relationships are perturbed, and show that the network has learned to reason using these relationships.
• Towards zero-latency video transmission through frame extrapolation
  
  • Vijayaratnam Melan
  • Cagnazzo Marco
  • Valenzise Giuseppe
  • Trioux Anthony
  • Kieffer Michel
  , 2022. In the past few years, several efforts have been devoted to reduce individual sources of latency in video delivery, including acquisition, coding and network transmission. The goal is to improve the quality of experience in applications requiring real-time interaction. Nevertheless, these efforts are fundamentally constrained by technological and physical limits. In this paper, we investigate a radically different approach that can arbitrarily reduce the overall latency by means of video extrapolation. We propose two latency compensation schemes where video extrapolation is performed either at the encoder or at the decoder side. Since a loss of fidelity is the price to pay for compensating latency arbitrarily, we study the latency-fidelity compromise using three recent video prediction schemes. Our preliminary results show that by accepting a quality loss, we can compensate a typical latency of 100 ms with a loss of 8 dB in PSNR with the best extrapolator. This approach is promising but also suggests that further work should be done in video prediction to pursue zero-latency video transmission. (10.1109/icip46576.2022.9897958)
  DOI : 10.1109/icip46576.2022.9897958
• [Invited tutorial] Soft Video Delivery: Getting seamless quality adaptation in mobile and latency-critical applications
  
  • Trioux Anthony
  • Coudoux François-Xavier
  • Cagnazzo Marco
  • Kieffer Michel
  , 2022. Conventional video coding and transmission systems are currently based on digital video compression (e.g., HEVC) on a suitable network protocol (802.11, 4G, or 5G) and rely on Shannon separation theorem. However, they suffer from some inherent limitations when the video content is transmitted over wireless error-prone networks. First, the coding choices (compression rate, channel coding rate) are decided a priori and at the transmitter and are the same for all the potential receivers. They could misfit with the actual channel conditions. Some user(s) with degraded channels may undergo digital cliff (glitches or freeze of the video) while other(s) may have a very good channel and yet not taking fully benefit of it since the design choices are based on more pessimistic hypotheses. Second, the traditional techniques require a permanent adaptation of the coding parameters by the transmitter relying on an estimate of the rate-distortion characteristic of the source and on an estimation of the channel characteristics, implying additional delay to perform this adaptation. Third, delay is introduced by the various buffers present at the encoder, within the network, and at the receiver. They are either required to smooth out variations of the encoding rate and of the channel characteristics, or due to the shared network infrastructure.Soft Video Delivery (SVD) architectures, pioneered by the SoftCast scheme, have demonstrated over the last decade a high potential to address/mitigate these issues. SVD architectures are joint source-channel video coding and transmission schemes that process pixels by successive linear operations (spatio-temporal decorrelation transform, power allocation, analog modulation) and directly transmitthe information without quantization or coding. SVD architectures deliver a single data stream that can be decoded by any receiver, even those experiencing bad channel quality. This data stream allows each receiver to decode a video quality commensurate with its channel quality, without requiring any feedback information, while avoiding the complex adaptation mechanisms of conventional schemes. Moreover, SVD architectures offer a relatively low and controlled latency that can be adjusted through the size of the temporal transform. This is a paradigm break with respect to traditional video transmission architectures, which has the potential of dramatically improving the quality of experience in wireless and latency-constrained scenarios.This tutorial will first introduce use cases where SVD architectures can make a difference compared to traditional schemes relying on conventional encoded video streams (e.g., HEVC) over a suitable network protocol (802.11, 4G, or 5G). Issues with conventional digital schemes will also be discussed (e.g., complex adaptation, cliff-effect, etc.), justifying the SVD approaches. Then, a block-by-block description of the components of the baseline SoftCast SVD scheme will be presented and visual examples provided to facilitate the understanding. A third part will be devoted to real implementations of SVD architectures, the dense modulation process and bandwidth computation will be detailed. Recent technical innovations and results from the literature will be presented and discussed. Finally, current research challenges related to the development of SVD architectures will be presented.
• Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation
  
  • Giraldo Jhony
  • Scarrica Vincenzo
  • Staiano Antonino
  • Camastra Francesco
  • Bouwmans Thierry
  , 2022, pp.16-20. Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these models require a lot of densely annotated images. To address this problem, we propose a new algorithm that uses Hy-perGraph Convolutional Networks for Weakly-supervised Semantic Segmentation (HyperGCN-WSS). Our algorithm constructs spatial and k-Nearest Neighbor (k-NN) graphs from the images in the dataset to generate the hypergraphs. Then, we train a specialized HyperGraph Convolutional Network (HyperGCN) architecture using some weak signals. The outputs of the HyperGCN are denominated pseudo-labels, which are later used to train a DeepLab model for semantic segmentation. HyperGCN-WSS is evaluated on the PASCAL VOC 2012 dataset for semantic segmentation, using scribbles or clicks as weak signals. Our algorithm shows competitive performance against previous methods. (10.1109/ICIP46576.2022.9897774)
  DOI : 10.1109/ICIP46576.2022.9897774
• A Hybrid Deep Animation Codec for Low-Bitrate Video Conferencing
  
  • Konuko Goluck
  • Lathuilière Stéphane
  • Valenzise Giuseppe
  , 2022. Deep generative models, and particularly facial animation schemes, can be used in video conferencing applications to efficiently compress a video through a sparse set of keypoints, without the need to transmit dense motion vectors. While these schemes bring significant coding gains over conventional video codecs at low bitrates, their performance saturates quickly when the available bandwidth increases. In this paper, we propose a layered, hybrid coding scheme to overcome this limitation. Specifically, we extend a codec based on facial animation by adding an auxiliary stream consisting of a very low bitrate version of the video, obtained through a conventional video codec (e.g., HEVC). The animated and auxiliary videos are combined through a novel fusion module. Our results show consistent average BD-Rate gains in excess of-30% on a large dataset of video conferencing sequences, extending the operational range of bitrates of a facial animation codec alone.
• THE RISE OF THE LOTTERY HEROES: WHY ZERO-SHOT PRUNING IS HARD
  
  • Tartaglione Enzo
  , 2022. Recent advances in deep learning optimization showed that just a subset of parameters are really necessary to successfully train a model. Potentially, such a discovery has broad impact from the theory to application; however, it is known that finding these trainable sub-network is a typically costly process. This inhibits practical applications: can the learned sub-graph structures in deep learning models be found at training time? In this work we explore such a possibility, observing and motivating why common approaches typically fail in the extreme scenarios of interest, and proposing an approach which potentially enables training with reduced computational effort. The experiments on either challenging architectures and datasets suggest the algorithmic accessibility over such a computational gain, and in particular a trade-off between accuracy achieved and training complexity deployed emerges.
• VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern mining
  
  • Mordvanyuk Natalia
  • Bifet Albert
  • López Beatriz
  Expert Systems with Applications, Elsevier, 2022, 204, pp.117517. Sequential pattern mining (SPM) discovers, from event transactions recorded along time, patterns of events fulfilling a sequential order. In this work, we introduce a new efficient sequential pattern mining algorithm called VEPRECO. VEPRECO proposes three main contributions that fasten the mining process: a vertical representation of patterns, pre-pruning strategies to avoid checking infrequent patterns, and common candidate selection policies that reduce the number of iterations performed by the algorithm. An experimental evaluation was performed with synthetic and real-world datasets, and the results have been compared with the most time and memory-efficient sequential pattern mining algorithm in the literature, the CM-SPAM algorithm, which we have taken as a baseline. We analysed separately how each of the proposed contributions affects time and memory usage and found that the one that reduced the most time and memory was the representation of the proposed patterns. Pre-pruning strategies and common candidate selection policies reduce runtime in datasets with many sequences and similar lengths of transactions and sequences. (10.1016/J.ESWA.2022.117517)
  DOI : 10.1016/J.ESWA.2022.117517
• Operational Fairness for Facial Authentication Systems
  
  • Gornet Mélanie
  • Kirchner Claude
  • Tessier Catherine
  ERCIM News, ERCIM, 2022. How to design a facial authentication system taking into account both performance and fairness? We consider the choices that a developer makes when coding such a system, such as the training parameters, the architecture of the neural network or the authentication threshold. We evaluate their impact on the global fairness of the system showing that fairness is not only affected by the training data but also by the multiple choices that are made when coding the model.
• A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams
  
  • Halstead Ben
  • Koh Yun Sing
  • Riddle Patricia
  • Pechenizkiy Mykola
  • Bifet Albert
  , 2022, pp.1--10. The distribution of streaming data often changes over time as conditions change, a phenomenon known as concept drift. Only a subset of previous experience, collected in similar conditions, is relevant to learning an accurate classifier for current data. Learning from irrelevant experience describing a different concept can degrade performance. A system learning from streaming data must identify which recent experience is irrelevant when conditions change and which past experience is relevant when concepts reoccur, e.g., when weather events or financial patterns repeat. Existing streaming approaches either do not consider experience to change in relevance over time and thus cannot handle concept drift, or only consider the recency of experience and thus cannot handle recurring concepts, or only sparsely evaluate relevance and thus fail when concept drift is missed. To enable learning in changing conditions, we propose SELeCT, a probabilistic method for continuously evaluating the relevance of past experience. SELeCT maintains a distinct internal state for each concept, representing relevant experience with a unique classifier. We propose a Bayesian algorithm for estimating state relevance, combining the likelihood of drawing recent observations from a given state with a transition pattern prior based on the system’s current state. The current state is continuously maintained using a Hoeffding bound based algorithm, which unlike existing methods, guarantees that every observation is classified using the state estimated as the most relevant, while also maintaining temporal stability. We find SELeCT is able to choose experience relevant to ground truth concepts with recall and precision above 0.9, significantly outperforming existing methods and close to a theoretical optimum, leading to significantly higher accuracy and enabling new opportunities for learning in complex changing conditions. (10.1109/DSAA54385.2022.10032368)
  DOI : 10.1109/DSAA54385.2022.10032368
• AutoAD: an Automated Framework for Unsupervised Anomaly Detection
  
  • Putina Andrian
  • Bahri Maroua
  • Salutari Flavia
  • Sozio Mauro
  , 2022. Over the last decade, we witnessed the prolifera-tion of several machine learning algorithms capable of solving different tasks for the most diverse applications. Often, for an algorithm to be effective, significant human effort is required, in particular for hyper-parameter tuning and data cleaning. Recently, there have been increasing efforts to alleviate such a burden and make machine learning algorithms easier to use for researchers with varying levels of expertise. Nevertheless, the question of whether an efficient and fully generalizable automated Machine Learning (autoML) framework is possible remains unanswered. In this paper, we present autoAD, the first autoML framework for unsupervised anomaly detection. By leveraging a pool of different anomaly detection algorithms, each one coming with its own hyper-parameter search space, our framework automatically selects the best performing ap-proach, while determining an optimal configuration for its hyper-parameters on a given dataset. Our extensive experimental evaluation, conducted on a rich collection of datasets, shows the substantial gains that can be achieved with autoAD compared to state-of-the-art methods for unsupervised anomaly detection.
• Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation
  
  • Chhun Cyril
  • Colombo Pierre
  • Suchanek Fabian M
  • Clavel Chloé
  , 2022. Research on Automatic Story Generation (ASG) relies heavily on human and automatic evaluation. However, there is no consensus on which human evaluation criteria to use, and no analysis of how well automatic criteria correlate with them. In this paper, we propose to re-evaluate ASG evaluation. We introduce a set of 6 orthogonal and comprehensive human criteria, carefully motivated by the social sciences literature. We also present HANNA, an annotated dataset of 1,056 stories produced by 10 different ASG systems. HANNA allows us to quantitatively evaluate the correlations of 72 automatic metrics with human criteria. Our analysis highlights the weaknesses of current metrics for ASG and allows us to formulate practical recommendations for ASG evaluation.
• One Word, Two Sides: Traces of Stance in Contextualized Word Representations
  
  • Soler Aina Garí
  • Labeau Matthieu
  • Clavel Chloé
  , 2022. The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of "animal" different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.
• Adaptive Neural Networks for Online Domain Incremental Continual Learning
  
  • Gunasekara Nuwan
  • Gomes Heitor Murilo
  • Bifet Albert
  • Pfahringer Bernhard
  , 2022, 13601, pp.89--103. Continual Learning (CL) poses a significant challenge to Neural Network (NN)s, where the data distribution changes from one task to another. In Online domain incremental continual learning (OD-ICL), this distribution change happens in the input space without affecting the label distribution. In order to adapt to such changes, the model being trained risks forgetting previously learned knowledge (stability). On the other hand, enforcing that the model preserves past knowledge will cause it to fail to learn new concepts (plasticity). We propose Online Domain Incremental Networks (ODIN), a novel method to alleviate catastrophic forgetting by automatically detecting the end of a task using concept drift detection. As a consequence, ODIN does not require the specification of task ids. ODIN maintains a pool of NNs, each trained on a single task and frozen for further updates. A Task Predictor (TP) is trained to select the most suitable NN from the frozen pool for prediction. We compare ODIN against popular regularization and replay methods. It outperforms regularization methods and achieves comparable predictive performance to replay methods. (10.1007/978-3-031-18840-4_7)
  DOI : 10.1007/978-3-031-18840-4_7
• Complexity reduction over bi-RNN-based Kerr nonlinearity equalization in dual-polarization fiber-optic communications via a CRNN-based approach
  
  • Shahkarami Abtin
  , 2022. The impairments arising from the Kerr nonlinearity in optical fibers limit the achievable information rates in fiber-optic communication. Unlike linear effects, such as chromatic dispersion and polarization-mode dispersion, which can be compensated via relatively simple linear equalization at the receiver, the computational complexity of the conventional nonlinearity mitigation techniques, such as the digital backpropagation, can be substantial. Neural networks have recently attracted attention, in this context, for low-complexity nonlinearity mitigation in fiber-optic communications. This Ph.D. dissertation deals with investigating the recurrent neural networks to efficiently compensate for the nonlinear channel impairments in dual-polarization long-haul fiber-optic transmission. We present a hybrid convolutional recurrent neural network (CRNN) architecture, comprising a convolutional neural network (CNN) -based encoder followed by a recurrent layer working in tandem. The CNN-based encoder represents the shortterm channel memory arising from the chromatic dispersion efficiently, while transitioning the signal to a latent space with fewer relevant features. The subsequent recurrent layer is implemented in the form of a unidirectional vanilla RNN, responsible for capturing the long-range interactions neglected by the CNN encoder. We demonstrate that the proposed CRNN achieves the performance of the state-of-theart equalizers in optical fiber communication, with significantly lower computational complexity depending on the system model. Finally, the performance complexity trade-off is established for a number of models, including multi-layer fully-connected neural networks, CNNs, bidirectional recurrent neural networks, bidirectional long short-term memory (bi-LSTM), bidirectional gated recurrent units, convolutional bi-LSTM models, and the suggested hybrid model.
• Innovative Secure Element Grids For Internet Of Secure Elements Servers
  
  • Urien Pascal
  , 2022, pp.466-469. This paper presents an innovative open physical and logical design for secure element grids. There is need for online services providing secure storage and tamper resistant computing resources. Secure Elements, typically under smartcard form factors, are widely used in bank cards or SIM modules The Internet Of Secure Elements (IOSE) project attempts to deploy on-line secure element grids, providing remote application uploads and TLS user interfaces. We introduce secure element processors (SEP) that perform bridge between ISO7816 and I2C protocol, and which support non blocking operations. We present a protocol enabling efficient support of secure element grid in multi tasks environment. Finally we present some experimental results (10.1109/WiMob55322.2022.9941674)
  DOI : 10.1109/WiMob55322.2022.9941674
• Wave mixing efficiency in InAs/GaAs semiconductor quantum dot optical amplifiers and lasers
  
  • Renaud Thibaut
  • Huang Heming
  • Grillot Frédéric
  • Bimberg Dieter
  Laser Physics Letters, IOP Publishing, 2022, 19 (11), pp.1-6. The nonlinear features of both semiconductor optical amplifiers (SOAs) and semiconductor lasers, which are made from the same InAs/GaAs quantum dot (QD) wafers, are investigated in detail. By employing pump-probe driven four-wave mixing as an experimental tool, the wave conversion process shows notably different profiles for the two types of devices. Due to the contributions of ultrafast, sub-picosecond mechanisms, such as carrier heating and spectral hole burning, the pump-probe frequency can be easily tuned to the THz range. SOAs generally benefit more from sub-picosecond carrier dynamics, hence exhibiting a higher conversion efficiency (CE) in the THz range, compared to their laser diode counterparts. The discrepancy even exceeds 10 dB. In addition, laser experiments yield some differences from the amplifier ones, hence leading to a higher nonlinear CE at small detuning ranges. These results strongly improve our insight into the fundamental nonlinear properties of InAs/GaAs QD material, and contribute to the conception of novel devices for future on-chip applications in all-optical communication networks, such as signal wavelength conversion, mode-locking, and optical frequency comb generation. (10.1088/1612-202x/ac9595)
  DOI : 10.1088/1612-202x/ac9595
• Éthique et intelligence artificielle en pédiatrie
  
  • Bloch Isabelle
  , 2022.
• Solving X 2 3 n + 2 2 n + 2 n − 1 + ( X + 1 ) 2 3 n + 2 2 n + 2 n − 1 = b in F 2 4 n and an alternative proof of a conjecture on the differential spectrum of the related monomial functions
  
  • Kim Kwang Ho
  • Mesnager Sihem
  Finite Fields and Their Applications, Elsevier, 2022, 83, pp.102086. (10.1016/j.ffa.2022.102086)
  DOI : 10.1016/j.ffa.2022.102086