Publications

2024

• Développement d'un modèle de «Machine Learning» d'aide à la prescription de posologies individualisées destinées aux patients traités par amoxicilline en perfusion continue
  
  • Guillot Robin
  • El-Helali N.
  • Mory Celine
  • Gutton Johann
  • Hocquet Grégory
  • Le Monnier Alban
  • Buronfosse Anne
  • Billuart Olivier
  • Le Folgoc Loïc
  • Maynadier Xavier
  Journal of Epidemiology and Population Health, Elsevier Masson SAS, 2024, 72 (Supplément 1), pp.202294. Introduction Dans les situations infectieuses impliquant une hospitalisation, l'obtention d'une concentration sanguine efficace d'antibiotique constitue un enjeu qui passe par le choix d'une posologie adaptée. Si un pharmacologue peut être sollicité afin de tenir compte des caractéristiques individuelles, sa démarche d'adaptation posologique repose encore sur des principes très généraux. L'objectif de notre étude était de construire un modèle de Machine Learning de prédiction de la concentration d'amoxicilline à partir de la dose administrée et des caractéristiques du patient impliqué. Méthodes Etude rétrospective sur données qui porte sur les patients hospitalisés au sein du Groupe hospitalier Paris Saint-Joseph entre 2018 et 2023, traités par amoxicilline en perfusion continue et pour lesquels un dosage de la concentration sérique de cet antibiotique a été réalisé. Les caractéristiques démographiques (âge, sexe, IMC) et biologiques (fonctions rénale, hépatique, cardiaque) ont été extraites du dossier médical informatisé. Plusieurs modèles de prédiction de la concentration sanguine ont été construits. Le plus simple s'est fondé sur une régression linéaire utilisant la seule dose administrée. Trois modèles plus avancés - régression linéaire multivariée, forêt aléatoire, XGBoost – se sont appuyés sur un sous-ensemble de caractéristiques patient déterminé par la méthode de sélection de variables LASSO. La prédiction a été considérée comme performante lorsqu'elle correspondait à la concentration observée avec une marge d'erreur de 20 %. Résultats L'entrainement des modèles a porté sur 237 dosages sériques, l’évaluation de leur performance sur 57 dosages; 19 % des prédictions de la concentration sanguine à partir de la seule dose administrée étaient adaptées. Les modèles prenant aussi en compte les caractéristiques liées au poids et à la fonction rénale du patient amélioraient les performances. La régression linéaire multivariée, la forêt aléatoire et le XGBoost atteignaient respectivement 47 % [35 %-60 %] (IC95%), 47 % [35 %-61 %] et 51 % [37 %-63 %] de bonnes prédictions. Conclusion Les résultats suggèrent qu'un ajustement des posologies en fonction de caractéristiques individuelles est bénéfique. Une évaluation de la performance de ces modèles en prospectif est en cours afin de déterminer leur plus-value par rapport aux pratiques actuelles des pharmacologues. (10.1016/j.jeph.2024.202294)
  DOI : 10.1016/j.jeph.2024.202294
• Estimation of a causal directed acyclic graph process using non-gaussianity
  
  • Einizade Aref
  • Giraldo Jhony H.
  • Malliaros Fragkiskos D.
  • Hajipour Sardouie Sepideh
  Digital Signal Processing, Elsevier, 2024, 146, pp.104400. In machine learning and data mining, causal relationship discovery is a critical task. While the state-of-the-art Vector Auto-Regressive Linear Non-Gaussian Acyclic Model (VAR-LiNGAM) method excels in uncovering both instantaneous and time-lagged connections, it entails analyzing multiple VAR matrices, leading to heightened parameter complexity. To address this challenge, we introduce the Causal Graph Process-LiNGAM (CGP-LiNGAM), a novel approach that significantly reduces parameter load by focusing on a single causal graph, a Directed Acyclic Graph (DAG). Leveraging Graph Signal Processing (GSP) techniques, our method interprets causal relations with graph shift invariance and uniqueness. Our experimental results demonstrate the superiority and robustness of CGP-LiNGAM, particularly in high-noise environments. Moreover, we showcase its real-world applicability in studying brain connectivity during sleep, underlining its compatibility with previous sleep-related neuroscientific research. (10.1016/j.dsp.2024.104400)
  DOI : 10.1016/j.dsp.2024.104400
• Shaping Strategies to Embrace Nonlinear Effects in Optical Fiber Communications
  
  • Liu Jingtian
  , 2024. The main impediment in long-distance communications is nonlinear interference (NLI), stemming from nonlinear effects in optical fibers. While Digital signal processing algorithms offer partial mitigation, the inherent nonlinear nature of the fiber, coupled with predominant dispersion effects, continues to challenge the increase of transmission throughputs. Addressing nonlinearity at the information source through signal modulation technology is at the heart of our research. Traditional modulation schemes, as spectral efficiency climbs, such as QAM, become increasingly susceptible to NLI while their Mean Squared Euclidean Distance (MSED) diminishes. While multi-dimensional (MD) modulation yields improved linear and partial nonlinear gains, it has not yet demonstrated tangible benefits. On the other hand, the emergence of probabilistic constellation shaping (PCS), preferred for its enhanced linear gain and compatibility with conventional modulation hardware and software, introduces additional NLI. Consequently, the design of nonlinear-tolerant PCS is emerging as a pivotal research direction. Our thesis begins with a novel MD modulation for uniformly distributed signals. Then, we propose a novel approach combining MD with PCS to examine performance variations. Delving into PCS, we investigate the enumerative sphere shaping distribution matcher (DM), initially from an MD stance, and design a DM optimized for nonlinear tolerance over shorter distances. Subsequently, we introduce a new NLI measurement technique, accounting for dispersion effects. Integrating this with the sequence selection framework of PCS, we achieve successful long-distance transmission with notable nonlinear gains.
• Improving the automatic diagnosis of hepatocellular carcinoma with contrastive learning
  
  • Sarfati Emma
  , 2024. Several deep learning methods have been proposed to automatically classify liver lesions in MRI or CT, with good performance [1,2,3]. They all share a classical training procedure. Contrastive learning (CL) is a novel deep learning paradigm where pairs of cases, instead of cases taken in isolation, are leveraged to train the model. CL methods can be either trained without labels, in this case it learns global mathematical representations of the input images [4], or it can use labels during training in order to help to discriminate input images based on a specific characteristic [5]. In this study, we evaluate the potential of CL to improve the automatic classification of hepatocellular carcinoma (HCC) in CT-scans. We formulate it through a binary classification problem (HCC versus no HCC).
• Exploring nonlinear dynamics and amplitude squeezing of quantum dot lasers
  
  • Ding Shihao
  , 2024. Photonic integrated circuits (PICs) utilizing silicon photonics technology show significant potential in high-speed communication systems, optical computing, and quantum technology. Quantum dot (QD) lasers, particularly those epitaxially grown on silicon, exhibit notable characteristics such as strong defect tolerance, low threshold current, and good temperature stability. As a result, they are gradually emerging as promising on-chip laser sources for PICs. This thesis aims to explore the nonlinear dynamics and quantum state properties of QD lasers, laying the foundation for various potential applications. The first section of the thesis delves into the role of the linewidth enhancement factor (αH-factor) in QD lasers. I employed an optical phase modulation technique to extract the above-threshold αH-factor of QD lasers. The small αH-factor of QD lasers strongly improved optical feedback tolerance. This facilitates the development of high-speed optical transmission on chips without an optical isolator and increases integration density. The second section explores the dynamic characteristics of QD lasers under optoelectronic feedback (OEF). Unlike optical feedback, I demonstrated that QD lasers exhibit enhanced sensitivity to OEF, leading to various complex dynamics. This extreme sensitivity is crucial for the advancement of on-chip silicon-based optical computing and Ising machine applications, a phenomenon not observed in quantum well (QW) lasers. In the third section, I analyzed the dependency of frequency noise on external carrier noise. The research revealed that quiet pumping is highly advantageous for minimizing the αH-factor, reducing frequency noise, and consequently narrowing the optical linewidth of QD lasers. The last section highlights the generation of amplitude-squeezed light directly from a constant-current-driven semiconductor QD laser. With the quiet pump, I achieved a substantial gigahertz squeezing bandwidth at room temperature with a squeezing ratio of 3.5 dB. The extreme reflection insensitivity of the squeezed QD generator under optical feedback, in contrast to a reference laser using standard QW technology, is also demonstrated with a squeezing ratio further improved to 5.7 dB. Three distinct measurements, including the sub-shot-noise radiofrequency spectrum, sub-Poissonian photon statistics, and second-order correlation function at zero delay, validate my findings. This research establishes a foundational framework for compact and highly efficient photonic quantum integrated circuits, showcasing the immense application potential of QD lasers in both classical and quantum photonics fields.
• Photonic quantum information processing using the frequency continuous-variable of single photons
  
  • Fabre Nicolas
  • Chabaud Ulysse
  , 2024.
• A Formal Verification Approach to Handle Attack Graphs
  
  • Catta Davide
  • Leneutre Jean
  • Mijatovic Antonina
  • Ulin Johanna
  • Malvone Vadim
  , 2024, 3, pp.125-132. We propose a formalization of attack graphs through a multi-agent approach. Specifically, we focus on dynamic scenarios that capture the interaction between an attacker and defenders during a cyberattack. We introduce a formal definition of an attack graph using interpreted systems, demonstrating how this formalization enables us to express interesting security properties. Finally, we present a tool AG2IS, which we have developed as an implementation of our formal definitions, to perform the formal verification of attack graphs. (10.5220/0012310000003636)
  DOI : 10.5220/0012310000003636
• Cycle-Accurate Virtual Prototyping with Multiplicity
  
  • Genius Daniela
  • Apvrille Ludovic
  , 2024, 1, pp.187-194. Model-based design for large applications, especially the mapping of applications' tasks to execution nodes, remains a challenge. In this paper, we explore applications comprising multiple identical software tasks intended for deployment across diverse execution nodes. While these tasks are expected to have a unified representation in their SysML-like block diagrams, each must be specifically mapped to individual processor cores to achieve granular performance optimization. Additionally, inter-task communications should be allocated across multiple channels. We further demonstrate a method for automatically generating parallel POSIX C code suitable for a multiprocessor-on-chip. Our approach has proven especially effective for high-performance streaming applications, notably when such applications have a master-worker task structure. (10.5220/0012386100003645)
  DOI : 10.5220/0012386100003645
• System Architects Are not Alone Anymore: Automatic System Modeling with AI
  
  • Apvrille Ludovic
  • Sultan Bastien
  , 2024, 1, pp.27-38. System development cycles typically follow a V-cycle, where modelers first analyze a system specification before proposing its design. When utilizing SysML, this process predominantly involves transforming natural language (the system specification) into various structural and behavioral views employing SysML diagrams. With their proficiency in interpreting natural text and generating results in predetermined formats, Large Language Models (LLMs) could assist such development cycles. This paper introduces a framework where LLMs can be leveraged to automatically construct both structural and behavioral SysML diagrams from system specifications. Through multiple examples, the paper underscores the potential utility of LLMs in this context, highlighting the necessity for feeding these models with a well-defined knowledge base and an automated feedback loop for better outcomes. (10.5220/0012320100003645)
  DOI : 10.5220/0012320100003645
• Find the lady: permutation and re-synchronization of deep neural networks
  
  • de Sousa Trias Carl
  • Mitrea Mihai Petru
  • Fiandrotti Attilio
  • Cagnazzo Marco
  • Chaudhuri Sumanta
  • Tartaglione Enzo
  , 2024, 38 (19), pp.21001-21009. Deep neural networks are characterized by multiple symmetrical, equi-loss solutions that are redundant. Thus, the order of neurons in a layer and feature maps can be given arbitrary permutations, without affecting (or minimally affecting) their output. If we shuffle these neurons, or if we apply to them some perturbations (like fine-tuning) can we put them back in the original order i.e. re-synchronize? Is there a possible corruption threat? Answering these questions is important for applications like neural network white-box watermarking for ownership tracking and integrity verification. We advance a method to re-synchronize the order of permuted neurons. Our method is also effective if neurons are further altered by parameter pruning, quantization, and fine-tuning, showing robustness to integrity attacks. Additionally, we provide theoretical and practical evidence for the usual means to corrupt the integrity of the model, resulting in a solution to counter it. We test our approach on popular computer vision datasets and models, and we illustrate the threat and our countermeasure on a popular white-box watermarking method. (10.1609/aaai.v38i19.30091)
  DOI : 10.1609/aaai.v38i19.30091
• A Statistical analysis of algorithms dedicated for rare events
  
  • Aghbalou Anass
  , 2024. This thesis focuses on establishing statistical guarantees for the efficiency of machine learning algorithms in data-scarce environments, particularly within the contexts of extreme value analysis, transfer learning, and imbalanced classification. We develop probability upper bounds that serve as theoretical assurances for the effectiveness of algorithms tailored to these specific scenarios. Our approach begins with a critique of current statistical methods in data-limited set- tings. We identify limitations in existing frameworks and introduce new probability bounds that are specifically designed to provide guarantees for algorithm performance under data scarcity. These bounds are not just theoretically rigorous but are also di- rectly applicable to practical machine learning challenges. We validate our theoretical findings with empirical studies in each of the three focused areas. The results confirm that our derived bounds are effective in certifying the effi- ciency of algorithms in handling extreme values, transferring knowledge in sparse data domains, and classifying imbalanced datasets. Conclusively, the thesis advances the field of statistical learning by providing precise theoretical guarantees for the performance of algorithms in data-scarce situations. This work is particularly relevant for applications where making accurate inferences with limited data is critical.
• Finding Subgraphs with Maximum Total Density and Limited Overlap in Weighted Hypergraphs
  
  • Balalau Oana
  • Bonchi Francesco
  • Chan T-H. Hubert
  • Gullo Francesco
  • Sozio Mauro
  • Xie Hao
  ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2024, 18 (4), pp.1-21. Finding dense subgraphs in large (hyper)graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs, which might correspond to communities in social networks or interesting events. While a large amount of work is devoted to finding a single densest subgraph, perhaps surprisingly, the problem of finding several dense subgraphs in weighted hypergraphs with limited overlap has not been studied in a principled way, to the best of our knowledge. In this work, we define and study a natural generalization of the densest subgraph problem in weighted hypergraphs, where the main goal is to find at most k subgraphs with maximum total aggregate density, while satisfying an upper bound on the pairwise weighted Jaccard coefficient, i.e., the ratio of weights of intersection divided by weights of union on two nodes sets of the subgraphs. After showing that such a problem is NP-Hard, we devise an efficient algorithm that comes with provable guarantees in some cases of interest, as well as, an efficient practical heuristic. Our extensive evaluation on large real-world hypergraphs confirms the efficiency and effectiveness of our algorithms. (10.1145/3639410)
  DOI : 10.1145/3639410
• Fairness-Privacy Issue in Adaptation of ZEXE Protocol for Exchange Between Untrusted Parties
  
  • Languille Victor
  • Menga David
  • Memmi Gerard
  , 2024. We present the main features of ZEXE, a privacy-preserving system extending the Zerocash protocol. Then, through a realistic use case, we show how a slight improvement can make this system more effective. We point out some of its potential vulnerability in term of anonymity followed by methods to overcome them.
• Assessment of Radio Frequency Electromagnetic Field Exposure Induced by Base Stations in Several Micro-Environments in France
  
  • Chikha Wassim Ben
  • Zhang Yarui
  • Liu Jiang
  • Wang Shanshan
  • Sandeep Srikumar
  • Guxens Mònica
  • Veludo Adriana Fernandes
  • Röösli Martin
  • Joseph Wout
  • Wiart Joe
  IEEE Access, IEEE, 2024, 12, pp.21610-21620. Recently, the monitoring of the radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks has received a great deal of attention. In this work, a set of 70 microenvironments (MEs) located in urban and rural areas are selected in France under, on the one hand, the French Beyond5G project, and on the other hand, the 5G expOsure, causaL effects and rIsk perception through citizen engagement (GOLIAT) EU project. The purpose of this study is to assess the RF-EMF DL exposure in residential areas, downtowns, business areas, train stations, and public transport rides. For that, we employ the personal ExpoM-RF4 dosimeter placed inside a backpack to perform the measurements in different MEs. To take into consideration the effect of the presence of the human body near the dosimeter, we propose a correction approach that is mainly based on comparing the measurements given by ExpoM-RF4 to the ones provided by a reference system using the Tektronix real-time spectrum analyzer (RTSA) far from the body. Then, we use metrics, such as the quadratic mean, standard deviation, and median of the electric (E) field to carry out a comparative study between different MEs with different RF bands. It was found that the RF-EMF exposure levels for all MEs are well below the maximum allowable exposure limit prescribed by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). In addition, we perform clustering analyses using the K-Means technique to group the MEs with comparable exposure levels. The results show that the exposure level is low, but generally higher in MEs located in Paris than in the other considered areas (i.e., Massy and three villages, namely Igny, Bures-sur-Yvette and Gif-Sur-Yvette). For example, we observe that outdoor MEs can be grouped into three clusters, where the average total E fields (ATEFs) are 0.77 V/m, 0.35 V/m, and 0.08 V/m for the MEs belonging to the first, second and third clusters, respectively. Note that the first cluster here mainly contains the MEs located in Paris. This can be explained by the important number of antennas deployed in that area to serve the huge amount of users. We also observe few locations with exceptions confirming the presence of heterogeneous environments in the vicinity of some areas. For instance, three MEs in Paris among fifteen have an exposure level similar to Massy MEs in outdoor. (10.1109/ACCESS.2024.3363914)
  DOI : 10.1109/ACCESS.2024.3363914
• Interpretive flexibility in data science and artificial intelligence
  
  • Viard Tiphaine
  • Gornet Mélanie
  • Delarue Simon
  , 2024.
• Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications
  
  • Manokhin Mikhail
  • Chollet Paul
  • Desgreys Patricia
  Sensors, MDPI, 2024, 24 (3), pp.999. Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications. (10.3390/s24030999)
  DOI : 10.3390/s24030999
• Importance sampling for online variational learning
  
  • Chagneux Mathis
  • Gloaguen Pierre
  • Le Corff Sylvain
  • Olsson Jimmy
  , 2024. This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given the observations, using a variational approach together with Monte Carlo importance sampling. We propose an efficient algorithm for computing the gradient of the evidence lower bound (ELBO) in the context of streaming data, where observations arrive sequentially. Our contributions include a computationally efficient online ELBO estimator, demonstrated performance in offline and true online settings, and adaptability for computing general expectations under joint smoothing distributions.
• Adaptive Scalable Online Learning for Handling Heterogeneous Streaming Data in Large-Scale Banking Infrastructure
  
  • Barry Mariam
  , 2024. Artificial Intelligence (AI) is a powerful tool to extract valuable insights for decision-making. However, learning from heterogeneous and unstructured streaming data presents a multitude of challenges that this research aims to tackle. The creation of big data is projected to experience exponential growth, with expectations to surpass 2,000 zettabytes by the year 2035. Such Big Data highlights the importance of efficient, incremental, and adaptive models. Online Learning, known as Streaming Machine Learning (SML), is a dynamic technique for building and updating learning models as new data arrive, without the need for periodic complete model replacement. It is the most efficient technique for big data stream learning. The change detection task is a proactive way to detect and prevent critical events such as cyber-attacks, fraud detection, or IT incidents in an online fashion. The research conducted during this thesis aims to develop adaptive and scalable online machine-learning solutions to learn from heterogeneous streaming data that can be operationalized with large-scale infrastructures, particularly in the banking sector. This Ph.D. thesis delves into algorithmic and infrastructure challenges related to continuous training and serving online machine learning over high-velocity streaming data from diverse sources, specifically focusing on large-scale IT infrastructures (AIOps). Thesis contributions include techniques like StreamFlow for summarizing information from big data streams, Stream2Graph for dynamically building and updating knowledge graphs for batch and online learning tasks, and StreamChange, an efficient and explainable online change detection model. Evaluation results on real-world open data and industrial data demonstrate performance improvements in learned models. StreamChange surpasses state-ofthe-art techniques in detecting gradual and abrupt changes. Additionally, the thesis introduces a conceptual framework, StreamMLOps, for scaling and serving online machine learning in real-time without pausing the inference pipeline. This framework showcases the effectiveness of the proposed MLOps pipeline on a feature-evolving dataset with millions of dimensions for malicious event detection tasks. Finally, we share lessons learned regarding Streaming Machine Learning systems, AI at scale, and online model management in large-scale banking, with a focus on streaming data and real-time applications.
• An Information-Theoretic Approach to Joint Sensing and Communication
  
  • Ahmadipour Mehrasa
  • Kobayashi Mari
  • Wigger Michèle
  • Caire Giuseppe
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2024, 70 (2), pp.1124 - 1146. A communication setup is considered where a single transmitter wishes to convey messages to one or two receivers and simultaneously estimate the states of the receivers through the backscattered signals of the emitted waveform. The scenario at hand is motivated by joint radar and communication, which aims to co-design radar sensing and communication over shared spectrum and hardware. In this paper, we model the communication channel as a simple memoryless channel with independent and identically distributed (i.i.d.) time-varying state sequences and we model the backscattered signals by (strictly causal) generalized feedback. For single-receiver systems of this form, we fully characterize the capacity-distortion tradeoff, defined as the largest rate at which a message can reliably be conveyed to the receiver while simultaneously allowing the transmitter to sense the state sequence with a given allowed distortion. Our results show a tradeoff between the achievable rates and distortions, and that this tradeoff only stems from a common choice of the input distribution (the waveform) but not from other properties of the utilized codes. To better illustrate the capacity-distortion tradeoff, we propose a numerical method to compute the optimal inputs (waveforms) that achieve the desired tradeoff. For two-receiver systems with two states, we characterize the capacity-distortion tradeoff region of physically degraded broadcast channels (BC) as a rather straightforward extension of the single receiver case. Here, a tradeoff not only arises between sensing and communication performances but also between the various rates and the distortions of the different states. Similarly to the single-receiver case, the optimal co-design scheme exploits the generalized feedback signals only for sensing but not for improving communication performance. This is different for general two-receiver BCs, where optimal co-design schemes exploit generalized feedback also to improve capacity. However, as we show, also for BCs the optimal sensing performance only depends on the chosen input distribution (waveform) but not on the code construction used to accomplish the communication task. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when this capacity-distortion region is equal to the product of the capacity region and the set of achievable distortions, in which case no tradeoff between sensing and communication occurs. A number of illustrative examples demonstrate that the optimal co-design schemes outperform conventional schemes that split the resources between sensing and communication, both for single-receiver and BC systems. (10.1109/TIT.2022.3176139)
  DOI : 10.1109/TIT.2022.3176139
• Impact of fog on optical chaos transmission
  
  • Breton Alberto
  • Zaminga Sara
  • Sorrente Beatrice
  • Gigan Sylvain
  • Grillot Frédéric
  , 2024, Proceedings Volume 12877, Free-Space Laser Communications XXXVI, pp.128771N. Due to their increased resilience to turbulence and scattering by aerosols or droplets, mid- and long-infrared free-space optical links are gaining recognition as an advantageous technology in challenging environmental conditions compared to those operating at shorter wavelengths, enabling groundbreaking applications like chaos-based secure optical communication. This research numerically investigates the evolution of a chaotic carrier during propagation in the presence of fog, presenting quantitative assessments of attenuation and temporal pulse elongation. The results showcase a better preservation of the properties of chaos when utilizing longer wavelengths, demonstrating enhanced performance and ensuring physical-layer security in adverse environmental conditions. (10.1117/12.3001351)
  DOI : 10.1117/12.3001351
• Modeling the Interplay Between Cohesion Dimensions: a Challenge for Group Affective Emergent States
  
  • Maman Lucien
  • Willenbrock Nale Lehmann-
  • Chetouani Mohamed
  • Likforman-Sulem Laurence
  • Varni Giovanna
  IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers, 2024, pp.1-14. Emergent states are temporal group phenomena that arise from collective affective, behavioral, and cognitive processes shared among the group's members during their interactions. Cohesion is one such state, mainly conceptualized by scholars as affective in nature, and frequently distinguished into the two dimensions social and task cohesion. Whereas social cohesion is related to the need of belonging to a group, task cohesion is related to the group's goals and tasks. In this paper, we emphasize the importance of behavioral interaction dynamics to predict cohesion's dynamics. Drawing from Social Science insights, we investigate the interplay between social and task cohesion to predict their dynamics across group tasks from nonverbal behavioral features. Three computational architectures exploiting transfer learning are presented. Transfer learning capitalizes on information learnt by a model for a specific dimension to predict the dynamics of the other dimension. Results show that integrating the influence of social cohesion to predict dynamics of task cohesion outperforms state-of-the-art. To predict dynamics of social cohesion, a model integrating the reciprocal impact of social and task cohesion significantly improves performance with respect to the state-of-the-art and a model only integrating the impact of task cohesion on dynamics of social cohesion. (10.1109/TAFFC.2024.3349910)
  DOI : 10.1109/TAFFC.2024.3349910
• All-Textile Compact Ultra-Wideband Microstrip Antenna with Full Ground Plane for WBAN Applications
  
  • Du Jinxin
  • Wang Ruimeng
  • Li Haiyan
  • Yang Xue-Xia
  • Roblin Christophe
  International Journal of Antennas and Propagation, Hindawi Publishing Corporation, 2024, 2024, pp.1-10. A novel low-profile all-textile microstrip antenna for ultra-wideband (UWB) applications in wireless body area networks (WBANs) is presented. The antenna incorporates flexible materials such as felt and conductive fabrics which provide optimal wearing comfort and durability. The use of a single dielectric substrate layer facilitates the integration process. The antenna also features a full background plane that minimizes the back radiation towards the human body. Multiple branches are designed in a compact area to generate adjacent resonances, and their combination achieves broadband characteristics across the 4.83–9.57 GHz frequency band. The antenna has a miniaturized size of 60 mm × 60 mm, which is 1.6 λ g × 1.6 λ g (where λ g represents the guided wavelength at the center frequency), and it has a high realized gain of up to 10 dBi. The fully grounded structure also ensures good isolation between the antenna and the human body, thereby alleviating concerns regarding safety and radiation degradation in WBAN context. Simulation results indicate that the antenna maintains high performance levels during various bending tests. Given its favourable properties like ultra-wide bandwidth, compact size, low profile, high flexibility, and low specific absorption rate (SAR), the proposed design could find broad application prospects in high-speed WBAN systems. (10.1155/2024/4236695)
  DOI : 10.1155/2024/4236695
• Point Process Discrimination According to Repulsion
  
  • Adrat Hamza
  • Decreusefond Laurent
  Computo, Société Française de Statistique, 2024. In numerous applications, cloud of points do seem to exhibit repulsion in the intuitive sense that there is no local cluster as in a Poisson process. Motivated by data coming from cellular networks, we devise a classification algorithm based on the form of the Voronoi cells. We show that, in the particular set of data we are given, we can retrieve some repulsiveness between antennas, which was expected for engineering reasons. (10.57750/3r07-aw28)
  DOI : 10.57750/3r07-aw28
• Hilbert space-valued fractionally integrated autoregressive moving average processes with long memory operators
  
  • Durand Amaury
  • Roueff François
  Journal of Statistical Planning and Inference, Elsevier, 2024, 231. Fractionally integrated autoregressive moving average (FIARMA) processes have been widely and successfully used to model and predict univariate time series exhibiting long range dependence. Vector and functional extensions of these processes have also been considered more recently. Here we study these processes by relying on a spectral domain approach in the case where the processes are valued in a separable Hilbert space H0. In this framework, the usual univariate long memory parameter d is replaced by a long memory operator D acting on H0, leading to a class of H0- valued FIARMA(D, p, q) processes, where p and q are the degrees of the AR and MA polynomials. When D is a normal operator, we provide a necessary and sufficient condition for the D-fractional integration of an H0-valued ARMA(p,q) process to be well defined. Then, we derive the best predictor for a class of causal FIARMA processes and study how this best predictor can be consistently estimated from a finite sample of the process. To this end, we provide a general result on quadratic functionals of the periodogram, which incidentally yields a result of independent interest. Namely, for any ergodic stationary process valued in H0 with a finite second moment, the empirical autocovariance operator converges, in trace-norm, to the true autocovariance operator almost surely at each lag. (10.1016/j.jspi.2024.106146)
  DOI : 10.1016/j.jspi.2024.106146
• Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
  
  • Parekh Jayneel
  • Parekh Sanjeel
  • Mozharovskyi Pavlo
  • Richard Gael
  • d'Alché-Buc Florence
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2024, 32, pp.1392--1405. This article tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an inherently interpretable model with high performance. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In particular, an interpreter is trained to generate a regularized intermediate embedding from hidden layers of a target network, learnt as time-activations of a pre-learnt NMF dictionary. Our methodology allows us to generate intuitive audio-based interpretations that explicitly enhance parts of the input signal most relevant for a network's decision. We demonstrate our method's applicability on a variety of classification tasks, including multi-label data for real-world audio and music. (10.1109/TASLP.2024.3358049)
  DOI : 10.1109/TASLP.2024.3358049