Publications

2024

• Entropy Decay for Davies Semigroups of a One Dimensional Quantum Lattice
  
  • Bardet Ivan
  • Capel Ángela
  • Gao Li
  • Lucia Angelo
  • Pérez-García David
  • Rouzé Cambyse
  Communications in Mathematical Physics, Springer Verlag, 2024, 405 (2), pp.42 (1-51). Given a finite-range, translation-invariant commuting system Hamiltonians on a spin chain, we show that the Davies semigroup describing the reduced dynamics resulting from the joint Hamiltonian evolution of a spin chain weakly coupled to a large heat bath thermalizes rapidly at any temperature. More precisely, we prove that the relative entropy between any evolved state and the equilibrium Gibbs state contracts exponentially fast with an exponent that scales logarithmically with the length of the chain. Our theorem extends a seminal result of Holley and Stroock [40] to the quantum setting, up to a logarithmic overhead, as well as provides an exponential improvement over the non-closure of the gap proved by Brandao and Kastoryano [43]. This has wide-ranging applications to the study of many-body in and out-of-equilibrium quantum systems. Our proof relies upon a recently derived strong decay of correlations for Gibbs states of one dimensional, translation-invariant local Hamiltonians, and tools from the theory of operator spaces (10.1007/s00220-023-04869-5)
  DOI : 10.1007/s00220-023-04869-5
• 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
• Poisson approximation of fixed-degree nodes in weighted random connection models
  
  • Hirsch Christian
  • Jahnel Benedikt
  • Jhawar Sanjoy Kumar
  • Juhász Péter
  , 2025, pp.104593. We present a process-level Poisson-approximation result for the degree-k vertices in a highdensity weighted random connection model with preferential-attachment kernel in the unit volume. Our main focus lies on the impact of the left tails of the weight distribution for which we establish general criteria based on their small-weight quantiles. To illustrate that our conditions are broadly applicable, we verify them for weight distributions with polynomial and stretched exponential left tails. The proofs rest on truncation arguments and a recently established quantitative Poisson approximation result for functionals of Poisson point processes. (10.1016/j.spa.2025.104593)
  DOI : 10.1016/j.spa.2025.104593
• 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. (10.70675/f042c834z623bz40f6z9423z8270239612c6)
  DOI : 10.70675/f042c834z623bz40f6z9423z8270239612c6
• 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
• Subverting or preserving the institution: Competing IT firm and foundation discourses about open source
  
  • Muselli Laure
  • O'Neil Mathieu
  • Pailler Fred
  • Zacchiroli Stefano
  New Media and Society, SAGE Publications, 2024. The data economy depends on digital infrastructure produced in self-managed projects and communities. To understand how information technology (IT) firms communicate to a volunteer workforce, we examine IT firm and foundation employee discourses about open source. We posit that organizations employ rhetorical strategies to advocate for or resist changing the meaning of this institution. Our analysis of discourses collected at three open source professional conferences in 2019 is complemented by computational methods, which generate semantic clusters from presentation summaries. In terms of defining digital infrastructure, business models, and the firm-community relationship, we find a clear division between the discourses of large firm and consortia foundation employees, on one hand, and small firm and non-profit foundation employees, on the other. These divisions reflect these entities’ roles in the data economy and levels of concern about predatory “Big Tech” practices, which transform common goods to be shared into proprietary assets to be sold. (10.1177/14614448231222249)
  DOI : 10.1177/14614448231222249
• Comment les biais cognitifs affectent la prise de décision assistée par l'IA explicable
  
  • Belloum Rafik
  • Bertrand Astrid
  • Eagan James R.
  • Maxwell Winston
  , 2024. This paper summarizes a literature review on cognitive biases influencing XAI-assisted decision-making. It goes beyond mere identification of cognitive biases in XAI, providing a heuristic map, guiding the future development of XAI systems that are more attuned to human cognitive processes. This map contributes to the evolution of the XAI field by emphasizing alignment with how individuals comprehend and use explanations provided by AI systems.
• An Information Theoretic Condition for Perfect Reconstruction
  
  • Delsol Idris
  • Rioul Olivier
  • Béguinot Julien
  • Rabiet Victor
  • Souloumiac Antoine
  Entropy, MDPI, 2024, 26 (1), pp.86. A new information theoretic condition is presented for reconstructing a discrete random variable X based on the knowledge of a set of discrete functions of X. The reconstruction condition is derived from Shannon’s 1953 lattice theory with two entropic metrics of Shannon and Rajski. Because such a theoretical material is relatively unknown and appears quite dispersed in different references, we first provide a synthetic description (with complete proofs) of its concepts, such as total, common, and complementary information. The definitions and properties of the two entropic metrics are also fully detailed and shown to be compatible with the lattice structure. A new geometric interpretation of such a lattice structure is then investigated, which leads to a necessary (and sometimes sufficient) condition for reconstructing the discrete random variable X given a set {X1,…,Xn} of elements in the lattice generated by X. Intuitively, the components X1,…,Xn of the original source of information X should not be globally “too far away” from X in the entropic distance in order that X is reconstructable. In other words, these components should not overall have too low of a dependence on X; otherwise, reconstruction is impossible. These geometric considerations constitute a starting point for a possible novel “perfect reconstruction theory”, which needs to be further investigated and improved along these lines. Finally, this condition is illustrated in five specific examples of perfect reconstruction problems: the reconstruction of a symmetric random variable from the knowledge of its sign and absolute value, the reconstruction of a word from a set of linear combinations, the reconstruction of an integer from its prime signature (fundamental theorem of arithmetic) and from its remainders modulo a set of coprime integers (Chinese remainder theorem), and the reconstruction of the sorting permutation of a list from a minimal set of pairwise comparisons. (10.3390/e26010086)
  DOI : 10.3390/e26010086
• Bidding efficiently in Simultaneous Ascending Auctions using Monte Carlo Tree Search
  
  • Pacaud Alexandre
  , 2024. Since its introduction in 1994 in the United States, the Simultaneous Ascending Auction (SAA) has become the privileged mechanism for spectrum auctions. As sometimes billions of euros are at stake in an SAA, and a mobile operator’s business plan highly depends on the auction outcome, establishing an efficient bidding strategy is crucial. Despite the importance of this problem, there is a lack of research dedicated to developing an efficient bidding strategy for the SAA. The intrinsic complexity of the SAA makes its analysis very challenging for auction theory and exact game resolution methods. Additionally, the mechanism introduces strategical issues such as the exposure problem, adding an extra layer of complexity to its study.This thesis proposes the use of Monte Carlo Tree Search (MCTS) to compute an efficient bidding strategy for the SAA. The six chapters of the thesis are structured as follows. The first chapter introduces spectrum auction mechanisms, highlighting their pros and cons. The second chapter details the bidding problem in the SAA, along with relevant related research.The third chapter provides a summary of adversarial search methods, with a specific focus on MCTS. Chapters four to six are dedicated to developing an efficient MCTS bidding strategy for the SAA. The fourth chapter considers a turn-based deterministic model of the SAA with perfect and complete information. Numerical experiments are only undertaken on small instances.The fifth chapter considers a n-player simultaneous move model of SAA with incomplete information. Extensive numerical experiments are undertaken on instances of realistic size. The sixth chapter extends the preceding game to incomplete information to introduce uncertainties. For each model, an algorithm that significantly outperforms state-of-the-art bidding strategies is proposed, notably by better tackling the exposure problem. Moreover, a final price prediction method is developed throughout the chapters, upon which each MCTS algorithm relies. (10.70675/19f74eaez240fz46c2zb1e8zb058262d3492)
  DOI : 10.70675/19f74eaez240fz46c2zb1e8zb058262d3492
• TD2: Source and detection in quantum communications
  
  • Fabre N
  , 2024.
• Teleportation of polarized single photon states
  
  • Fabre N.
  , 2024.
• A tight and general finite-size security proof for Quantum Key Distribution
  
  • van Himbeeck Thomas
  • Brown Peter
  , 2024. Quantum Key Distribution is one of the most mature quantum protocols. This technological advancement comes with a need for new security proofs that work with realistic devices and perform well in the finite-size regime, where the users exchange a large but finite set of messages. In recent years, we have seen a new generation of proof techniques utilizing ideas from convex optimization or information theory. In this talk, I will present these new ideas and review some challenges and opportunities for the future work.
• Le théorème d’échantillonnage... de Shannon ?
  
  • Rioul Olivier
  , 2024. Le théorème d'échantillonnage, souvent appelé théorème de Shannon, constitue une des bases du domaine du traitement de l'information. Mais Shannon lui-même ne s'en attribue pas le mérite et effectivement, on le retrouve sous une forme ou une autre dans de nombreux travaux antérieurs. Cet article nous permet de remonter le temps aux sources de ce théorème, aussi bien chez les ingénieurs que les mathématiciens.
• Exploiting temporal information to detect conversational groups in videos and predict the next speaker
  
  • Tosato Lucrezia
  • Fortier Victor
  • Bloch Isabelle
  • Pelachaud Catherine
  Pattern Recognition Letters, Elsevier, 2024, 177, pp.164-168. Studies in human-human interaction have introduced the concept of F-formation to describe the spatial arrangement of participants during social interactions. This paper has two objectives. It aims at detecting F-formations in video sequences and at predicting the next speaker in a group conversation. The proposed approach exploits time information and multimodal signals of humans in video sequences. In particular, we rely on measuring the engagement level of people as a feature of group belonging. Our approach makes use of a recursive neural network, the Long Short Term Memory (LSTM), to predict who will take the speaker's turn in a conversation group. Experiments on the MatchNMingle dataset led to 85% true positives in group detection and 98% accuracy in predicting the next speaker. (10.1016/j.patrec.2023.10.002)
  DOI : 10.1016/j.patrec.2023.10.002
• Ausgewählte Themen des Malliavin-Kalküls
  
  • Decreusefond Laurent
  , 2024. Dieses Buch ist keine Forschungsmonographie zum Malliavin-Kalkül mit neuesten Ergebnissen und besonders anspruchsvollen Beweisen. Es enthält nicht alle Ergebnisse, die für die behandelten grundlegenden Themen bekannt sind. Das Ziel ist vielmehr, eine möglichst große Vielfalt an Beweistechniken zu bieten. Zum Beispiel haben wir uns nicht auf den Beweis der Konzentrationsungleichung für Funktionale der Brownschen Bewegung konzentriert, da er sich eng an das analoge Ergebnis für Poisson-Funktionale anlehnt. Dieses Buch ist aus den Graduiertenkursen entstanden, die ich in den letzten Jahren an den Universitäten Paris-Sorbonne und Paris-Saclay gehalten habe. Es soll so zugänglich wie möglich für Studierende sein, die über Kenntnisse der Itô-Kalkulation und einige Grundlagen der Funktionalanalysis verfügen. Die Übersetzung wurde mit Hilfe von künstlicher Intelligenz durchgeführt. Eine anschließende menschliche Überarbeitung erfolgte vor allem in Bezug auf den Inhalt.