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International audience; -L'observation de la Terre a été largement facilitée depuis de nombreuses années grâce à l'utilisation des satellites imageurs radar à synthèse d'ouverture (SAR), qui offrent des capacités d'imagerie indépendantes des conditions météorologiques. Toutefois, l'interprétation de ces images SAR est complexe en raison de la présence de bruit inhérent à l'imagerie cohérente. En effet, des fluctuations apparaissent dans les images, notamment là où la réflectivité radar est élevée. Ainsi, de nombreuses méthodes ont été développées pour réduire le bruit présent dans ces images, notamment des méthodes neuronales particulièrement efficaces. Dans cet article, nous proposons d'étudier comment l'ajout d'une donnée auxiliaire comme une image optique peut améliorer la restauration de la réflectivité dans ce cadre.

International audience; The Coq Community Survey 2022 was an online public survey of users of the Coq proof assistant conducted during February 2022. Broadly, the survey asked about use of Coq features, user interfaces, libraries, plugins, and tools, views on renaming Coq and Coq improvements, and also demographic data such as education and experience with Coq and other proof assistants and programming languages. The survey received 466 submitted responses, making it the largest survey of users of an interactive theorem prover (ITP) so far. We present the design of the survey, a summary of key results, and analysis of answers relevant to ITP technology development and usage. In particular, we analyze user characteristics associated with adoption of tools and libraries and make comparisons to adjacent software communities. Notably, we find that experience has significant impact on Coq user behavior, including on usage of tools, libraries, and integrated development environments.

Following previous investigations by {\"U}st{\"u}nel [22] about the invertibility of some transformations on the Wiener space, we find some entropic conditions under which a random change of time is invertible on the Poisson space. As a consequence, we provide a new construction of Hawkes processes. We also establish a new variational representation of the entropy.

International audience; Block ciphers are protected against side-channel attacks by masking. On one hand, when the leakage model is unknown, second-order correlation attacks are typically used. On the other hand, when the leakage model can be profiled, template attacks are prescribed. But what if the profiled model does not exactly match that of the attacked device? One solution consists in regressing on-the-fly the scaling parameters from the model. In this paper, we leverage an Expectation-Maximization (EM) algorithm to implement such an attack. The resulting unprofiled EM attack, termed U-EM, is shown to be both efficient (in terms of number of traces) and effective (computationally speaking). Based on synthetic and real traces, we introduce variants of our U-EM attack to optimize its performance, depending on trade-offs between model complexity and epistemic noise. We show that the approach is flexible, in that it can easily be adapted to refinements such as different points of interest and number of parameters in the leakage model.

International audience; As the Information and Communication Technology (ICT) sector represents 1.8% to 3.9% of the global Green House Gas (GHG) emissions, it is of upmost importance to know how much energy is spent annually in mobile networks and how this consumption is evolving. It is quite likely that the huge energy efficiency gains achieved by technology evolution have at least been compensated by the surge in data traffic. Therefore, in this paper, we estimate the operational power consumption of cellular Base Stations (BSs) deployed in France from 2015 to 2022. However, unfortunately, the lack of openly available data hinders the estimation process. In order to work around this issue, we rely on a public dataset on radio electric installations, on widely adopted power consumption models and on a set of assumptions backed by the scientific literature. We demonstrate that, over the considered period, the numbers of BSs and transceivers have grown at a sustained Compound Annual Growth Rate (CAGR) of 7.55% and 18.27%, respectively. Within the same period, the average BS power consumption has increased at a CAGR of 9.89% while the total operational power consumption of BSs has grown at a CAGR of 18.18%. We further show that the introduction of 5G has accelerated this trend despite the recent decommissioning of 2G and 3G transceivers. These alarming figures advocate for proactive digital sobriety policies.

Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguation benchmarks and tools are limited to specific domains, and the size of prior benchmarks is rather small. To accelerate the research on acronym disambiguation, we construct a new benchmark named GLADIS with three components: (1) a much larger acronym dictionary with 1.5M acronyms and 6.4M long forms; (2) a pre-training corpus with 160 million sentences; (3) three datasets that cover the general, scientific, and biomedical domains. We then pre-train a language model, \emph{AcroBERT}, on our constructed corpus for general acronym disambiguation, and show the challenges and values of our new benchmark., Long paper at EACL 23

Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world systems constrain to smaller ensembles and lower-capacity networks, significantly deteriorating their performance and properties. We introduce Packed-Ensembles (PE), a strategy to design and train lightweight structured ensembles by carefully modulating the dimension of their encoding space. We leverage grouped convolutions to parallelize the ensemble into a single shared backbone and forward pass to improve training and inference speeds. PE is designed to operate within the memory limits of a standard neural network. Our extensive research indicates that PE accurately preserves the properties of DE, such as diversity, and performs equally well in terms of accuracy, calibration, out-of-distribution detection, and robustness to distribution shift. We make our code available at https://github.com/ENSTA-U2IS/torch-uncertainty., Comment: Published as a conference paper at ICLR 2023 (notable 25%)

Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased datasets, including real instances of biases in the wild., Accepted at ICLR 2023 (v3); Fix typo in Eq.19 (v4)

International audience

International audience; Mission-critical communications (MCC) involve all communications between people in charge of the safety of the civil society. MCC have unique requirements that include improved reliability, security and group communication support. In this paper, we propose secure and robust Multiple-Input-Multiple-Output (MIMO) transceivers, designed for multiple Base Stations (BS) supporting multicast MCC in presence of multiple eavesdroppers. We formulate minimization problems with the Sum-Mean-Square-Error (SMSE) at legitimate users as an objective function, and a lower bound for the MSE at eavesdroppers as a constraint. Security is achieved thanks to physical layer security mechanisms, namely MIMO beamforming and Artificial Noise (AN). Reliability is achieved by designing a system which is robust to two types of channel state information errors: stochastic and norm-bounded. We propose a coordinate descent-based algorithm and a worst-case iterative algorithm to solve these problems. Numerical results at physical layer and system level reveal the crucial role of robust designs for reliable MCC. We show the interest of both robust design and AN to improve the security gap. We also show that full BS cooperation in preferred for highly secured and reliable MCC but dynamic clustering allows to trade-off security and reliability against capacity. Index Terms-mission critical communication (MCC), physical layer security, robust transceiver design I. INTRODUCTION Mission critical communications (MCC) are all communications between people in charge of the security and the safety of the civil society. Employees of public safety services, like policemen, firemen, rescue teams and ambulance nurses, but also from large companies managing critical infrastructures in the energy or transportation sectors require MCC for their operations [1]. MCC are conveyed by dedicated Private Mobile Radio (PMR) networks [2] that offer a group (or multicast) communication service. This is a one-to-many or many-tomany communication [3], which is one of the most important features of PMR networks and is essential to manage teams of employees. In 5G New Radio, group communication will be supported for MCC from Release R17 onwards [4]. Due to the critical aspects of their missions, MCC users also inherently require highly reliable and secure communication. In particular, sensitive information should not leak to unintended receivers although the broadcast nature of the wireless channel makes the network vulnerable to malicious eavesdroppers.

Profiling side-channel attacks in which an adversary creates a “profile” of a sensitive device and uses such profile to model a target device with similar implementation has received the lion’s share of attention in the recent years. In particular, template attacks are known to be the most powerful profiling side-channel attacks from an information theoretic point of view. When launching such attack, the adversary first builds a model based on the leakage of the profiling (training) device in his disposal which is then exploited in the second phase of the attack (i.e., matching) to extract the key from the target device. Discrepancies between the device used for modeling and the target device affect the attack success. The effect of process variation and temperature misalignment between the profiling and target devices in the template attack’s success has been studied extensively in literature, while the impact of device aging on the template attack’s success is yet to be investigated thoroughly. This paper moves one step forward and studies the impact of device aging, mainly Bias temperature Instability (BTI) and Hot Carrier Injection (HCI), in the devices that have been protected against power analysis attacks via dual rail logics. In particular, we focus on the Wave Dynamic Differential Logic (WDDL) circuits, and via extensive transistor-level simulations we will show how device aging misalignments between the profiling and target devices can hinder template attacks for both unprotected and WDDL protected counterparts. We mounted several attacks on the PRESENT cipher, with and without WDDL protection, at different temperatures and aging times. Our results shows that the attack is more difficult if there is an aging-duration mismatch between the training and target devices, and the attack-efficiency decrease is especially significant for mismatches of few weeks.

International audience; We study the problem of binary agreement in a spiking neural network (SNN). We show that binary agreement on n inputs can be achieved with O(n) of auxiliary neurons. Our simulation results suggest that agreement can be achieved in our network in O(log n) time. We then describe a subclass of SNNs with a biologically plausible property, which we call size-independence. We prove that solving a class of problems, including agreement and Winner-Take-All, in this model requires O(n) auxiliary neurons, which makes our agreement network size-optimal.

International audience; The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic AT L, hence AT L∗ , under perfect recall and imperfect information is known to be undecidable. To tackle this problem, in this paper we investigate a notion of bounded recall under incomplete information. We present a novel three-valued semantics for AT L∗ in this setting and analyse the corresponding model checking problem. We show that the three-valued semantics here introduced is an approximation of the classic two-valued semantics, then give a sound, albeit partial, algorithm for model checking two-valued perfect recall via its approximation as three-valued bounded recall. Finally, we extend MCMAS, an open-source model checker for AT L and other agent specifications, to incorporate bounded recall; we illustrate its use and present experimental results.

International audience; Full Duplex (FD) in cellular networks is expected to increase the cell spectral efficiency. However, while the downlink (DL) spectral efficiency (SE) increases with FD, the uplink (UL) SE decreases because of the Base Station to Base Station (BS) interference. In this paper, assuming a three-node model, we propose a method based on Successive Interference Cancellation (SIC) to reduce the BS-to-BS interference present in FD cellular networks. The approach consists in coordinating BSs to enable the decoding and the suppression of undesired signals that impair uplink transmissions. We analyze both distributed and Centralized Radio Access Networks (CRAN) architectures. Stochastic geometry is used to derive the coverage probability and mean data rate of the proposed scheme. In the distributed scenario, the FD UL average data rate is increased by 25% with our solution compared to a classical FD network, while our FD scheme still outperforms Half-Duplex (HD) on the DL. In the centralized scenario, our solution outperforms HD by 10% and classical FD by 78% on the UL, while preserving classical FD gains on the DL.

Synthetic aperture radar (SAR) images are widely used for Earth observation to complement optical imaging. By combining information on the polarization and the phase shift of the radar echos, SAR images offer high sensitivity to the geometry and materials that compose a scene. This information richness comes with a drawback inherent to all coherent imaging modalities: a strong signal-dependent noise called "speckle". This paper addresses the mathematical issues of performing speckle reduction in a transformed domain: the matrix-log domain. Rather than directly estimating noiseless covariance matrices, recasting the denoising problem in terms of the matrix-log of the covariance matrices stabilizes noise fluctuations and makes it possible to apply off-the-shelf denoising algorithms. We refine the method MuLoG by replacing heuristic procedures with exact expressions and improving the estimation strategy. This corrects a bias of the original method and should facilitate and encourage the adaptation of general-purpose processing methods to SAR imaging.

In social interactions between humans and Embodied Conversational Agents (ECAs) conversational interruptions may occur. ECAs should be prepared to detect, manage and react to such interruptions in order to keep the interaction smooth, natural and believable. In this paper, we examined nonverbal reactions exhibited by an interruptee during conversational interruptions and we propose a novel technique driven by an evolutionary algorithm to build a computational model for ECAs to manage user's interruptions. We propose a taxonomy of conversational interruptions adapted from social psychology, an annotation schema for semi-automatic detection of user's interruptions and a corpus-based observational analysis of human nonverbal reactions to interruptions. Then we present a methodology for building an ECA behavioral model including the design and realization of an interactive study driven by an evolutionary algorithm, where participants interactively built the most appropriate set of multimodal reactive behaviours for an ECA to display interpersonal attitudes (friendly/hostile) through nonverbal reactions to a conversational interruption.

In this paper we consider functional data with heterogeneity in time and in population. We propose a mixture model with segmentation of time to represent this heterogeneity while keeping the functional structure. Maximum likelihood estimator is considered, proved to be identifiable and consistent. In practice, an EM algorithm is used, combined with dynamic programming for the maximization step, to approximate the maximum likelihood estimator. The method is illustrated on a simulated dataset, and used on a real dataset of electricity consumption., Comment: 38 pages, 8 figures

International audience; We investigate the verification of Multi-Agent Systems against strategic properties expressed in Alternating-time Temporal Logic under the assumptions of imperfect information and perfect recall. To this end, we develop a three-valued semantics for concurrent game structures upon which we define an abstraction method. We prove that concurrent game structures with imperfect information admit perfect information abstractions that preserve three-valued satisfaction. Furthermore, to deal with cases in which the value of a specification is undefined, we develop a novel automata-theoretic technique for the linear-time logic (LTL), then apply it to finding “failure” states. The latter can then be fed into a refinement procedure, thus providing a sound, albeit incomplete, verification method. We illustrate the overall procedure in a variant of the Train Gate Controller scenario and a simple voting protocol under imperfect information and perfect recall. We also present an implementation of our procedure and provide preliminary experimental results.

Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings. In this context, it has been demonstrated that larger self-supervised feature extractors are crucial for achieving lower downstream ASR error rates. Thus, better performance might be sanctioned with longer inferences. This article explores different approaches that may be deployed during the fine-tuning to reduce the computations needed in the SSL encoder, leading to faster inferences. We adapt a number of existing techniques to common ASR settings and benchmark them, displaying performance drops and gains in inference times. Interestingly, we found that given enough downstream data, a simple downsampling of the input sequences outperforms the other methods with both low performance drops and high computational savings, reducing computations by 61.3% with an WER increase of only 0.81. Finally, we analyze the robustness of the comparison to changes in dataset conditions, revealing sensitivity to dataset size., Comment: Submitted to ICASSP "Self-supervision in Audio, Speech and Beyond" workshop

International audience; The kissing number of a code is the average number of pairs of codewords at minimum distance from each other. It has fundamental applications in determining codes performances. Besides, a recent interest has arisen from the field of side- channel analysis of algorithms handling sensitive information (e.g., cryptographic keys). Namely, when code-base masking protections are applied, their performance in terms of attacker’s signal-to-noise ratio or mutual information is proportional to the kissing number of the masking code. Therefore the kissing number is also a security metric for a given minimum distance in side-channel protected implementation, as it is in codes performance evaluation. It is known exactly for some classical families of codes. To estimate it in general, two types of bounds are given. Linear programming, either numerically or by the polynomial method is the most versatile and the more precise. Spectral graph theory provides bounds on the multiplicity of the subdominant eigenvalue that are easier to state.

International audience; The Module Learning With Errors (M-LWE) problem is a core computational assumption of lattice-based cryptography which offers an interesting trade-off between guaranteed security and concrete efficiency. The problem is parameterized by a secret distribution as well as an error distribution. There is a gap between the choices of those distributions for theoretical hardness results (standard formulation of M-LWE, i.e., uniform secret modulo $q$ and Gaussian error) and practical schemes (small bounded secret and error). In this work, we make progress towards narrowing this gap. More precisely, we prove that M-LWE with uniform $\eta$-bounded secret for any $1 \leq \eta \ll q$ and Gaussian error, in both its search and decision variants, is at least as hard as the standard formulation of M-LWE, provided that the module rank $d$ is at least logarithmic in the ring degree $n$. We also prove that the search version of M-LWE with large uniform secret and uniform $\eta$-bounded error is at least as hard as the standard M-LWE problem, if the number of samples $m$ is close to the module rank $d$ and with further restrictions on $\eta$. The latter result can be extended to provide the hardness of search M-LWE with uniform η-bounded secret and error under specific parameter conditions. Overall, the results apply to all cyclotomic fields, but most of the intermediate results are proven in more general number fields.

Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a large number of elements backscatter the radar signal within each resolution cell. To reconstruct the vertical reflectivity profile, state-of-the-art techniques perform a regularized inversion implemented in the form of iterative minimization algorithms. We show that light-weight neural networks can be trained to perform the tomographic inversion with a single feed-forward pass, leading to fast reconstructions that could better scale to the amount of data provided by the future BIOMASS mission. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data., Comment: Submitted to IEEE Geoscience and Remote Sensing Letters, January 2023

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., Comment: Accepted at ICASSP 2020 (publication and oral presentation)

International audience; Older adults often need some level of assistance in performing daily living activities. Even though these activities are common to the vast majority of individuals (e.g., eating, bathing, dressing), the way they are performed varies across individuals. Supporting older people in performing their everyday activities is a major avenue of research in smart homes. However, because of its early stage, this line of work has paid little attention on customizing assistive computing support with respect to the specific needs of each older adult towards improving its effectiveness and acceptability. We propose a tool-based approach to allowing caregivers to define services in the area of home daily living, leveraging their knowledge and expertise on the older adult they care for. This approach consists of two stages: 1) a wizard allows caregivers to define an assistive service, which supports aspects of a daily activity that are specific to an older adult; 2) the wizard-generated service is uploaded in an existing smart home platform and interpreted by a dedicated component, carrying out the caregiver-defined service. Our approach has been implemented. Our wizard has been successfully used to define existing manually-programmed, activity-supporting services. The resulting services have been deployed and executed by an existing assisted living platform deployed in the home of community-dwelling individuals. They have been shown to be equivalent to their manually-programmed counterparts. We also conducted an ergonomics study involving five occupational therapists, who tested our wizard with clinical vignettes describing fictitious patients. Participants were able to successfully define services while revealing an ease of use of our wizard.

We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by gradient descent. Our formulation scales efficiently beyond the first order and is agnostic of the quantization scheme. The network can then be trained to minimize the entropy of the quantized parameters, so that they can be optimally compressed via entropy coding. We experiment with our entropy formulation at quantizing and compressing well-known network architectures over multiple datasets. Our approach compares favorably over similar methods, enjoying the benefits of higher order entropy estimate, showing flexibility towards non-uniform quantization (we use Lloyd-max quantization), scalability towards any entropy order to be minimized and efficiency in terms of compression. We show that HEMP is able to work in synergy with other approaches aiming at pruning or quantizing the model itself, delivering significant benefits in terms of storage size compressibility without harming the model’s performance.

International audience; We present STREAMRHF, an unsupervised anomaly detection algorithm for data streams. Our algorithm builds on some of the ideas of Random Histogram Forest (RHF), a state-of-the-art algorithm for batch unsupervised anomaly detection. STREAMRHF constructs a forest of decision trees, where feature splits are determined according to the kurtosis score of every feature. It irrevocably assigns an anomaly score to data points, as soon as they arrive, by means of an incremental computation of its random trees and the kurtosis scores of the features. This allows efficient online scoring and concept drift detection altogether. Our approach is tree-based which boasts several appealing properties, such as explainability of the results. We conduct an extensive experimental evaluation on multiple datasets from different real-world applications. Our evaluation shows that our streaming algorithm achieves comparable average precision to RHF while outperforming state-of-the-art streaming approaches for unsupervised anomaly detection with furthermore limited computational complexity.

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.

International audience; Specification part 14 of the Open Platform Communication Unified Architecture (OPC UA) standard provides five different profiles to implement the publish-subscribe messaging pattern. The specification is also called OPC UA PubSub, and its profiles are called PubSub profiles. Two devices deployed with the same PubSub profile can exchange and collaborate; however, two devices deployed with two different PubSub profiles are unable to communicate. It is a limit for the Industry Internet of Things, a complex environment where there would be heterogeneous devices and networks. One approach to overcoming this issue is to use a bridge for the devices deployed with different PubSub profiles. In this sense, this paper provides a unified method to design bridges for OPC UA PubSub networks. The proof-ofconcept experiment, also presented in this paper, is a use case of bridging PubSub broker-less and broker-based networks.

International audience; 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.

Ce document a pour objectif de rassembler les connaissances actuelles liées aux impacts environnementaux de la 5G.Il s’articule autour de questions liées à ces impacts. Pour cela, il est organisé en trois partie. La première présentée icidonne les bases nécessaires à la compréhension de la technologie elle-même. La deuxième détaillera les services renduspossibles par la 5G. La dernière se penchera enfin sur les impacts environnementaux du déploiement de la 5G.

Semiconductor nanostructures with low dimensionality like quantum dots and quantum dashes are one of the best attractive and heuristic solutions for achieving high performance photonic devices. When one or more spatial dimensions of the nanocrystal approach the de Broglie wavelength, nanoscale size effects create a spatial quantization of carriers leading to a complete discretization of energy levels along with additional quantum phenomena like entangled-photon generation or squeezed states of light among others. This article reviews our recent findings and prospects on nanostructure based light emitters where active region is made with quantum-dot and quantum-dash nanostructures. Many applications ranging from silicon-based integrated technologies to quantum information systems rely on the utilization of such laser sources. Here, we link the material and fundamental properties with the device physics. For this purpose, spectral linewidth, polarization anisotropy, optical nonlinearities as well as microwave, dynamic and nonlinear properties are closely examined. The paper focuses on photonic devices grown on native substrates (InP and GaAs) as well as those heterogeneously and epitaxially grown on silicon substrate. This research pipelines the most exciting recent innovation developed around light emitters using nanostructures as gain media and highlights the importance of nanotechnologies on industry and society especially for shaping the future information and communication society., Quantum dot are one of the best practical examples of nanotechnologies. Owing to the discrete energy levels, quantum dot lasers output unique features like thermal stability, feedback insensitivity and spectral purity.

With the development of modern Intelligent Transportation System (ITS), reliable and efficient transportation information sharing becomes more and more important. Although there are promising wireless communication schemes such as Vehicle-to-Everything (V2X) communication standards, information sharing in ITS still faces challenges such as the V2X communication overload when a large number of vehicles suddenly appeared in one area. This flash crowd situation is mainly due to the uncertainty of traffic especially in the urban areas during traffic rush hours and will significantly increase the V2X communication latency. In order to solve such flash crowd issues, we propose a novel system that can accurately predict the traffic flow and density in the urban area that can be used to avoid the V2X communication flash crowd situation. By combining the existing grid-based and graph-based traffic flow prediction methods, we use a Topological Graph Convolutional Network (ToGCN) followed with a Sequence-to-sequence (Seq2Seq) framework to predict future traffic flow and density with temporal correlations. The experimentation on a real-world taxi trajectory traffic data set is performed and the evaluation results prove the effectiveness of our method.

We demonstrate the detection of speech signals and footsteps through a novel polarization-multiplexed coded differential-phase OTDR (optical time domain reflectometry) interrogation of standard telecom fibers in different configurations. We assess the sensitivity to audio signals by measuring the induced phase variation with respect to the sound pressure level of the perturbation, and manage to detect acoustic signals with high reliability. Further experiments are performed using an installed fiber cable showing the ability to track footsteps in an indoor scenario. These experiments pave the way for numerous applications of Distributed Acoustic Sensing (DAS) where a high sensitivity is required.

We study the task, for a given language L, of enumerating the (generally infinite) sequence of its words, without repetitions, while bounding the delay between two consecutive words. To allow for delay bounds that do not depend on the current word length, we assume a model where we produce each word by editing the preceding word with a small edit script, rather than writing out the word from scratch. In particular, this witnesses that the language is orderable, i.e., we can write its words as an infinite sequence such that the Levenshtein edit distance between any two consecutive words is bounded by a value that depends only on the language. For instance, (a+b)^* is orderable (with a variant of the Gray code), but a^* + b^* is not. We characterize which regular languages are enumerable in this sense, and show that this can be decided in PTIME in an input deterministic finite automaton (DFA) for the language. In fact, we show that, given a DFA A, we can compute in PTIME automata A₁, …, A_t such that L(A) is partitioned as L(A₁) ⊔ … ⊔ L(A_t) and every L(A_i) is orderable in this sense. Further, we show that the value of t obtained is optimal, i.e., we cannot partition L(A) into less than t orderable languages. In the case where L(A) is orderable (i.e., t = 1), we show that the ordering can be produced by a bounded-delay algorithm: specifically, the algorithm runs in a suitable pointer machine model, and produces a sequence of bounded-length edit scripts to visit the words of L(A) without repetitions, with bounded delay - exponential in |A| - between each script. In fact, we show that we can achieve this while only allowing the edit operations push and pop at the beginning and end of the word, which implies that the word can in fact be maintained in a double-ended queue. By contrast, when fixing the distance bound d between consecutive words and the number of classes of the partition, it is NP-hard in the input DFA A to decide if L(A) is orderable in this sense, already for finite languages. Last, we study the model where push-pop edits are only allowed at the end of the word, corresponding to a case where the word is maintained on a stack. We show that these operations are strictly weaker and that the slender languages are precisely those that can be partitioned into finitely many languages that are orderable in this sense. For the slender languages, we can again characterize the minimal number of languages in the partition, and achieve bounded-delay enumeration., LIPIcs, Vol. 254, 40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023), pages 8:1-8:18

International audience; Background: Software development results in the production of various types of artifacts: source code, version control system metadata, bug reports, mailing list conversations, test data, etc. Empirical software engineering (ESE) has thrived mining those artifacts to uncover the inner workings of software development and improve its practices. But which artifacts are studied in the field is a moving target, which we study empirically in this paper.Aims: We quantitatively characterize the most frequently mined and co-mined software artifacts in ESE research and the research purposes they support.Method: We conduct a meta-analysis of artifact mining studies published in 11 top conferences in ESE, for a total of 9621 papers. We use natural language processing (NLP) techniques to characterize the types of software artifacts that are most often mined and their evolution over a 16-year period (2004-2020). We analyze the combinations of artifact types that are most often mined together, as well as the relationship between study purposes and mined artifacts.Results: We find that: (1) mining happens in the vast majority of analyzed papers, (2) source code and test data are the most mined artifacts, (3) there is an increasing interest in mining novel artifacts, together with source code, (4) researchers are most interested in the evaluation of software systems and use all possible empirical signals to support that goal.

International audience; We tackle in this work the problem for a player to efficiently bid in Simultaneous Ascending Auctions (SAA). Although the success of SAA partially comes from its relative simplicity, bidding efficiently in such an auction is complicated as it presents a number of complex strategical problems. No generic algorithm or analytical solution has yet been able to compute the optimal bidding strategy in face of such complexities. By modelling the auction as a turn-based deterministic game with complete information, we propose the first algorithm which tackles simultaneously two of its main issues: exposure and own price effect. Our bidding strategy is computed by Monte Carlo Tree Search (MCTS) which relies on a new method for the prediction of closing prices. We show that our algorithm significantly outperforms state-of-the-art existing bidding methods. More precisely, our algorithm achieves a higher expected utility by taking lower risks than existing strategies.

International audience; Claude Shannon, who laid the foundations of the celebrated information theory in 1948, preferred the term “communication theory”. In a 1953 paper, he proposed a new approach to information and its lattice structure. Surprisingly, this remained relatively unknown. We briefly recall the features of this theory and the characteristics that could explain its poor impact; finally we consider possible interesting developments.; Claude Shannon, qui a posé les bases de la célèbre théorie de l’information en 1948, préférait parler de théorie de la communication. Dans un article de 1953, il a proposé une nouvelle approche de l’information et de sa structure en treillis. Cela n’a étonnamment eu qu’un très faible écho. Nous rappelons brièvement les traits principaux de cette théorie et les caractéristiques qui pourraient en expliquer l’oubli, et nous envisageons enfin les développements intéressants qu’elle pourrait susciter.

International audience; Ces dernières années, plusieurs efforts ont été consacrés à la réduction des différentes sources de latence dans la transmission vidéo, notamment l'acquisition, le codage et la transmission réseau. L'objectif est d'améliorer la qualité d'expérience dans les applications nécessitant une interaction en temps réel. Néanmoins, ces efforts sont fondamentalement contraints par des limites technologiques et physiques. Dans cet article, nous étudions une approche radicalement différente qui peut réduire arbitrairement la latence globale au moyen de l'extrapolation vidéo. Nous proposons deux schémas de compensation de latence dans lesquels l'extrapolation vidéo est effectuée soit au niveau de l'encodeur, soit au niveau du décodeur. Puisqu'une perte de fidélité est le prix à payer pour compenser arbitrairement la latence, nous étudions le compromis latence-fidélité en utilisant trois schémas de prédiction vidéo récents. Nos résultats préliminaires montrent qu'en acceptant une perte de qualité, nous pouvons compenser une latence typique de 100 ms avec une perte de 8 dB en PSNR avec le meilleur extrapolateur. Cette approche est prometteuse mais suggère également que des travaux supplémentaires doivent être réalisés dans le domaine de la prédiction vidéo afin de poursuivre la transmission vidéo sans latence.

International audience

International audience; Non-orthogonal multiple access (NOMA) is a technology proposed for next generation cellular networks because of its high spectral efficiency and enhanced user connectivity. However, in the literature the optimal joint power and sub-carrier allocation for NOMA has been proposed for single cell only. Consequently, a global optimal algorithm for the joint power and sub-carrier allocation for NOMA system in multi-cell scenario is still an open problem. In this work, we propose a polyblock optimization based algorithm for obtaining a global optimal solution. It has reduced complexity due to a necessary and sufficient condition for feasible successive interference cancellation (SIC). Besides, we can adjust its optimization approximation parameter to serve as benchmark solution or to provide suitable solution for multi-cell multi-carrier NOMA systems. Numerical studies have shown its effectiveness.

Understanding generalization in modern machine learning settings has been one of the major challenges in statistical learning theory. In this context, recent years have witnessed the development of various generalization bounds suggesting different complexity notions such as the mutual information between the data sample and the algorithm output, compressibility of the hypothesis space, and the fractal dimension of the hypothesis space. While these bounds have illuminated the problem at hand from different angles, their suggested complexity notions might appear seemingly unrelated, thereby restricting their high-level impact. In this study, we prove novel generalization bounds through the lens of rate-distortion theory, and explicitly relate the concepts of mutual information, compressibility, and fractal dimensions in a single mathematical framework. Our approach consists of (i) defining a generalized notion of compressibility by using source coding concepts, and (ii) showing that the `compression error rate' can be linked to the generalization error both in expectation and with high probability. We show that in the `lossless compression' setting, we recover and improve existing mutual information-based bounds, whereas a `lossy compression' scheme allows us to link generalization to the rate-distortion dimension -- a particular notion of fractal dimension. Our results bring a more unified perspective on generalization and open up several future research directions., Accepted for presentation at the Conference on Learning Theory (COLT) 2022

Side channel attacks (SCAs) on neural networks (NNs) are particularly efficient for retrieving secret information from NNs. We differentiate multiple types of threat scenarios regarding what kind of information is available before the attack and its purpose: recovering hyperparameters (the architecture) of the targeted NN, its weights (parameters), or its inputs. In this survey article, we consider the most relevant attacks to extract the architecture of CNNs. We also categorize SCAs, depending on access with respect to the victim: physical, local, or remote. Attacks targeting the architecture via local SCAs are most common. As of today, physical access seems necessary to retrieve the weights of an NN. We notably describe cache attacks, which are local SCAs aiming to extract the NN's underlying architecture. Few countermeasures have emerged; these are presented at the end of the survey.

International audience

International audience; Cyber physical systems are built upon digital and analog circuits, making it necessary to handle different models of computation during their design and verification (e.g., by simulation). When designing these systems, an important aspect to consider is the causality between the different domains. For this, we introduce a new model-driven framework able to identify causality problems and to suggest a valid schedule between the analog and digital domains. Once a valid schedule has been computed, our framework can generate cycle and bit accurate virtual prototypes (in SystemC/SystemC AMS) from high-level SysML models.

Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. Multi-temporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field., The manuscript is the accepted version of IEEE STARS. Code is made available at https://gitlab.telecom-paris.fr/RING/SAR2SAR

International audience

International audience; The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing presence-based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding sequential miner with novel strategies to prune the search space efficiently. Experiments on both real and synthetic data show the utility of our approach.