Publications

2025

• Partitions in quantum theory
  
  • Vanrietvelde Augustin
  • Mestoudjian Octave
  • Arrighi Pablo
  , 2025. Decompositional theories describe the ways in which a global physical system can be split into subsystems, facilitating the study of how different possible partitions of a same system interplay, e.g. in terms of inclusions or signalling. In quantum theory, subsystems are usually framed as sub-C* algebras of the algebra of operators on the global system. However, most decompositional approaches have so far restricted their scope to the case of systems corresponding to factor algebras. We argue that this is a mistake: one should cater for the possibility for non-factor subsystems, arising for instance from symmetry considerations. Building on simple examples, we motivate and present a definition of partitions into an arbitrary number of parts, each of which is a possibly non-factor sub-C* algebra. We discuss its physical interpretation and study its properties, in particular with regards to the structure of algebras' centres. We prove that partitions, defined at the C*-algebraic level, can be represented in terms of a splitting of Hilbert spaces, using the framework of routed quantum circuits. For some partitions, however, such a representation necessarily retains a residual pseudo-nonlocality. We provide an example of this behaviour, given by the partition of a fermionic system into local modes.
• Semi-supervised graph learning for underwater source localization using ship-of-opportunity spectrograms
  
  • Castro-Correa Jhon
  • Badiey Mohsen
  • Giraldo Jhony
  • Malliaros Fragkiskos
  Journal of the Acoustical Society of America, Acoustical Society of America, 2025, 158 (3), pp.1836-1848. Conventional techniques for underwater source localization have traditionally relied on optimization methods, matched-field processing, beamforming, and, more recently, deep learning. However, these methods often fall short to fully exploit the data correlation crucial for accurate source localization. This correlation can be effectively captured using graphs, which consider the spatial relationship among data points through edges. This work introduces a novel graph learning module for source localization using spectrograms from ships-of-opportunity, which represent mid-frequency acoustic broadband signals from ship-radiated noise ranging from 360 to 1100 Hz, collected during the 2017 Seabed Characterization Experiment (SBCEX 2017). The proposed approach follows a two-step process: first, a pre-trained convolutional neural network (CNN) module is used for feature extraction via self-supervised learning, and then a graph neural network model is trained using semi-supervised learning for source localization. The graph is constructed using a k-nearest neighbor algorithm, incorporating features extracted by the CNN from the spectrograms. By employing this two-stage training strategy, our framework addresses the challenge of limited labeled data availability while achieving performance comparable to conventional supervised learning models. The effectiveness of our approach is demonstrated through model evaluation on both synthetic and measured data, showcasing the architecture's ability to generalize well to unseen scenarios. (10.1121/10.0039042)
  DOI : 10.1121/10.0039042
• QINCODEC: Neural Audio Compression with Implicit Neural Codebooks
  
  • Lahrichi Zineb
  • Hadjeres Gaëtan
  • Richard Gael
  • Peeters Geoffroy
  , 2026. Neural audio codecs, neural networks which compress a waveform into discrete tokens, play a crucial role in the recent development of audio generative models. State-of-the-art codecs rely on the end-to-end training of an autoencoder and a quantization bottleneck. However, this approach restricts the choice of the quantization methods as it requires to define how gradients propagate through the quantizer and how to update the quantization parameters online. In this work, we revisit the common practice of joint training and propose to quantize the latent representations of a pre-trained autoencoder offline, followed by an optional finetuning of the decoder to mitigate degradation from quantization. This strategy allows to consider any off-the-shelf quantizer, especially state-of-the-art trainable quantizers with implicit neural codebooks such as QINCO2. We demonstrate that with the latter, our proposed codec termed QINCODEC, is competitive with baseline codecs while being notably simpler to train. Finally, our approach provides a general framework that amortizes the cost of autoencoder pretraining, and enables more flexible codec design.
• Proceedings of the 5th Conference on Language, Data, and Knowledge
  
  • Alam Mehwish
  • Tchechmedjiev Andon
  • Gracia Jorge
  • Gromann Dagmar
  • di Buono Maria Pia
  • Monti Johanna
  • Ionov Maxim
  , 2025. Questo volume contiene gli atti della quinta conferenza Language Data and Knowledge (LDK), che si è tenuta a Napoli, Italia, dal 9 all’11 settembre 2025. L’evento si è svolto in modalità ibrida, con la maggior parte dei partecipanti presenti in loco. La conferenza biennale, inaugurata nel 2017, riunisce esperti in tecnologie del linguaggio, scienza dei dati e rappresentazione della conoscenza. Supportata da un comitato scientifico internazionale, LDK è cresciuta costantemente, con le edizioni precedenti ospitate in Irlanda, Germania, Spagna e Austria. Gli atti di questa edizione di LDK raccolgono 34 articoli e una prefazione. Ciascun articolo è stato sottoposto a revisione single-blind da almeno tre esperti. La conferenza si concentra sull’acquisizione e sull’uso dei dati linguistici in contesti scientifici e industriali, con particolare attenzione all’elaborazione del linguaggio naturale, all’apprendimento automatico e alle tecnologie semantiche. I temi principali includono grafi della conoscenza, risorse multilingue e approcci neuro-simbolici che combinano modelli linguistici di grandi dimensioni e semantica esplicita. (10.6093/978-88-6719-333-2)
  DOI : 10.6093/978-88-6719-333-2
• Bidding efficiently in Simultaneous Ascending Auctions with incomplete information using Monte Carlo Tree Search and determinization
  
  • Pacaud Alexandre
  • Bechler Aurelien
  • Coupechoux Marceau
  IEEE Transactions on Games, Institute of Electrical and Electronics Engineers, 2025, 17 (3), pp.813-826. In this paper, we tackle the problem of designing an efficient bidding strategy for Simultaneous Ascending Auctions (SAA). SAA is well-known mechanism for allocating spectrum to mobile networks operators and has been used for example to allocate 5G licenses in many countries. Although the rules are relatively simple, there is no known optimal bidding strategy for SAA. In a previous work, we proposed a Simultaneous Move Monte-Carlo Tree Search (SM-MCTS) based algorithm named SMSα that we extend here to an incomplete information framework. We consider and compare three determinization approaches of SMSα, and show how they are able to tackle four key strategic issues of SAA, namely the exposure problem, the own price effect, the budget constraints and the eligibility management. Extensive numerical experiments on instances of realistic size and including an uncertain framework show that our extensions of SMSα outperform state-of-the-art algorithms by achieving higher expected utility while taking less risks. (10.1109/TG.2025.3552025)
  DOI : 10.1109/TG.2025.3552025
• On the Effect of Feature Reduction on Energy Consumption: An Exploratory Study
  
  • Tërnava Xhevahire
  • Lefeuvre Romain
  • Perez Quentin
  • Khelladi Djamel Eddine
  • Acher Mathieu
  • Combemale Benoît
  , 2025, pp.1-11. Energy consumption is a growing concern for sustainable software. Although increasingly studied, it remains largely unexplored in configurable systems growing in complexity with features. Feature reduction can eliminate software bloat, but to our knowledge, its impact on energy use has not been investigated. To fill this gap, we investigated how both on-demand and built-in feature reduction (defined later) affect the energy consumption of configurable systems. We conducted a first exploratory study using 28 programs from three systems with built-in feature reduction, namely ToyBox, BusyBox, and GNU, as well as 6 GNU programs debloated on-demand using the Chisel, Debop, and Cov tools. In our results, built-in feature reduction led to statistically significant energy decreases in 7% of the cases, while on-demand reduction, despite achieving energy decreases in 67% of cases, showed no statistical significance. However, when energy consumption increased, it was often more substantial than the reductions observed (occurring in 25% of built-in cases and 11% of on-demand cases) showing the complex and sometimes counterintuitive interplay between feature reduction and energy. Additionally, the observed strong correlation between energy consumption and execution time motivates a shift from traditional debloating goals, centered on binary size/attack surface, to energy-aware strategies that prioritize performance concerns. Finally, we provide an in-depth analysis and discuss the perspective. (10.1145/3744915.3748463)
  DOI : 10.1145/3744915.3748463
• Quantum Reupload Units: A Scalable and Expressive Approach for Time Series Learning
  
  • Cassé Léa
  • Ponnambalam Sabarikirishwaran
  • Pfahringer Bernhard
  • Bifet Albert
  , 2025, pp.1815-1825. <div><p>We propose a single-qubit Quantum Machine Learning (QML) model for time series forecasting, built around the concept of a Quantum Reupload Unit (QRU), a hardwareefficient quantum circuit architecture with shallow depth. The proposed model demonstrates enhanced predictive power compared to variational methods such as quantum circuits (VQC), parameterized quantum circuits (PQC), and quantum residual blocks (QRB). The proposed QRU outperforms classical learning models such as Recurrent Neural Networks (RNNs) and Long-Short Term Memory (LSTM) with the same number of parameters. The novelty of this approach is its ability to model temporal patterns without relying on an extensive memory state, which reduces resource demands while preserving forecast accuracy. The expressivity of the model is evaluated through Fourier spectral decomposition. We analyze the trainability of our model using the absorption witness metric. We benchmarked the proposed model on the Mackey-Glass chaotic time series and the real-world river level dataset from TAIAO. The proposed model consistently exhibits enhanced expressivity over both of the datasets. These results highlight the significance of QRUs as promising candidates for learning models that can be conveniently deployed on noisy intermediate-scale quantum (NISQ) hardware.</p></div> (10.1109/QCE65121.2025.00199)
  DOI : 10.1109/QCE65121.2025.00199
• Blind Polarisation Demultiplexing and Carrier Recovery Using FIR-based Variational AutoEncoder Equaliser for Probabilistic Constellation Shaping in Optical Fibre Communications
  
  • Tomczyk Louis
  • Awwad Élie
  • Ware Cédric
  Journal of Lightwave Technology, Institute of Electrical and Electronics Engineers (IEEE)/Optical Society of America(OSA), 2025, pp.1-15. <div><p>We investigate through simulations the potential of Finite Impulse Response (FIR)-based Variational AutoEncoderinspired (VAE-FIR) equaliser for polarisation demultiplexing, Carrier Phase Recovery (CPR), and Carrier Frequency Offset (CFO) estimation in the context of Probabilistic Constellation Shaped (PCS) transmissions in coherent optical fibre communication systems. Additionally, we compare the performance of this novel estimator with the conventional Constant Modulus Algorithm (CMA) and Pilot-Aided Carrier Phase Recovery (PA-CPR). Our study shows that the VAE-FIR clearly outperforms the conventional approach in terms of polarisation demultiplexing, even with PCS where the CMA fails. We also show the ability of the VAE-FIR to track the phase evolution. Its ability to compensate for the carrier's phase effects is however limited to linewidths of a few dozen kHz and a hundred kHz for CFO, showing that the VAE-FIR may be used to compensate for the small residual phase noise or residual frequency mismatch.</p></div> (10.1109/JLT.2025.3603685)
  DOI : 10.1109/JLT.2025.3603685
• Graph Neural Networks for Moving Objects Detection in Videos
  
  • Prummel Wieke
  • Giraldo Jhony
  • Zakharova Anastasia
  • Bouwmans Thierry
  , 2025, 09, pp.121-143. Deep learning has been widely applied for the detection of moving objects from static cameras. Recently, many methods using graph neural networks for background subtraction have been reported with very promising performance. This chapter provides a survey of different graph neural for moving object detection. First, a comparison of the transductive and inductive architectures of each method is provided, followed by a discussion of the specific application requirements, such as spatio-temporal and real-time constraints. After analyzing the strategies of each method and showing their limitations, a comparative evaluation of the large-scale CDnet2014 dataset is provided. Finally, we conclude with some potential future research directions. (10.1142/9789819807154_0006)
  DOI : 10.1142/9789819807154_0006
• Désentrelacement fréquentiel doux pour les codecs audio neuronaux
  
  • Giniès Benoît
  • Bie Xiaoyu
  • Fercoq Olivier
  • Richard Gaël
  , 2025. Bien que les modèles basés sur les réseaux de neurones aient permis des avancées significatives dans l'extraction de représentations audio, l'interprétabilité des représentations apprises reste un défi majeur. Pour y remédier, des techniques de désentrelacement ont été intégrées dans les codecs audio neuronaux discrets afin d'imposer une structure aux tokens extraits. Cependant, ces approches sont souvent fortement dépendantes de tâches ou d'ensembles de données spécifiques. Dans ce travail, nous proposons un codec audio neuronal désentrelacé qui tire parti de la décomposition spectrale des signaux temporels pour améliorer l'interprétabilité de la représentation. Des évaluations expérimentales démontrent que notre méthode surpasse un modèle de référence en termes de fidélité de reconstruction et de qualité perceptuelle.
• Reconstruction 3D depuis des couples SAR ascendant/descendant
  
  • Barbier--Renard Emile
  • Tupin Florence
  • Denis Loïc
  , 2025, pp.1-4. Afin d'exploiter les disparités géométriques entre des images SAR acquises sous différents points de vue, nous présentons une méthode de reconstruction de surface par rendu inverse inspirée de NeRF. Elle optimise une carte d'élévation et une carte de coefficients de rétrodiffusion à partir d'un minimum de deux images, et s'appuie sur un modèle de rendu différentiable adapté à cette représentation en carte d'élévation ainsi qu'une stratégie multi-échelles assurant une convergence rapide. Nous validons les capacités de reconstruction sur des données synthétiques réalistes générées par le simulateur EMPRISE ® de l'ONERA.
• Vers une axiomatique minimale de l'information quantique : Schrödinger ex-nihilo
  
  • Rioul Olivier
  , 2025. Partant de deux simples postulats quantiques, on peut retrouver l’équation de Schrödinger qui modélise l’évolution d’un système quelconque en dimension finie. Cette équation de la physique fondamentale, qui peut sembler absconse pour la communauté du traitement du signal, n’est en effet qu’une conséquence de propriétés d’orthogonalité dans un espace de Hilbert et d’une hypothèse probabiliste sur la mesure quantique. Sa preuve mathématique utilise des versions simplifiées des théorèmes de Wigner et de Stone.
• Generalizability and sample complexity of quadratic shallow neural networks under low-rank learning
  
  • Wang Xiaolin
  • Rioul Olivier
  • Mokraoui Anissa
  • Duhamel Pierre
  • Benesty Jacob
  , 2025. We investigate the interplay between sample complexity and model complexity in low-rank learning of quadratic shallow neural networks (QSNN), within a novel doubly-correlated teacher-student framework that incorporates parameter correlations to reflect real-world data properties. This framework generalizes existing theories for QSNN by analyzing the impact of sample size on generalization loss for models under low-rank learning or exhibiting inherent bias. We observe a two-regime behavior in the scaling law of generalization ability with respect to sample size and show that parameter correlations in the teacher model significantly enhance the generalization of rank-reduced models. Extensive numerical simulations confirm the results and offer theoretical insights and practical guidance for designing efficient neural network architectures under low-rank learning.
• Reconstruction 3D en tomographie radar : apprentissage profond basé sur un Matching Pursuit déroulé
  
  • Ulondu Mendes Cristiano
  • Denis Loïc
  • Kervazo Christophe
  • Tupin Florence
  , 2025. La tomographie radar en milieu urbain consiste à séparer des réflecteurs situés à des hauteurs différentes mais vus dans un même pixel car situés à une distance similaire du radar. Les méthodes d'apprentissage profond proposées récemment pour résoudre cette tâche sont basées sur le déroulement d'algorithmes de poursuites de base avec contrainte de parcimonie. Ils dépendent d'une discrétisation des hauteurs et ne permettent pas un contrôle simple du nombre de réflecteurs détectés. On présente dans cet article une approche alternative permettant d'estimer la position des cibles sur un intervalle continu. Notre approche s'inspire des itérations des algorithmes gloutons de reconstruction parcimonieuse tels que Matching Pursuit ou RELAX. Nous montrons des résultats de reconstruction satisfaisants sur des données simulées et sur une pile d'images satellitaires.
• La recherche en TdSI aux temps du transhumanisme
  
  • Maitre Henri
  , 2025, pp.1-4. <div><p>À travers des start-ups de la Tech et certains GAFAM, l'agenda transhumaniste a fait irruption dans les laboratoires de TdSI et d'IA, avec en étendard ses thèmes favoris : l'homme augmenté, la communication cerveaumachine, l'assistance mentale… Pour certains, cette confiance hardie dans le rôle de la Science pour sauver l'humanité est le meilleur défenseur de la Raison dans un monde qui doute de ses scientifiques. On montre ici le double visage des projets transhumanistes : d'une part un engagement très noble dans la résolution de quelques grands projets de la société, d'autre part une idéologie mortifère pour l'humanité qui l'engagera violemment dans une quête technosolutionniste au service d'une minorité.</p><p>Abstract -With the start-ups of Tech and GAFAM, the transhumanist agenda has burst into the laboratories of Signal &amp; Image Processing and AI, brandishing its favorite themes: augmented man, brain-machine communication, mental assistance ... For many, this bold confidence in the power of Science is the best guarantee of maintaining Reason in a world that doubts its scientists. Here we show the double face of transhumanistic projects: on the one hand a very noble commitment in the resolution of some great projects of society, on the other hand a mortifying ideology for mankind that will engage it violently in a techno-solutionist quest at the service of a minority.</p></div>
• Détection non supervisée de changements radiométriques en imagerie radar à synthèse d'ouverture
  
  • Bultingaire Thomas
  • Kervazo Christophe
  • Denis Loïc
  • Tupin Florence
  , 2025, pp.1-4. L'imagerie radar à synthèse d'ouverture est un mode d'imagerie clé pour la détection de changements en télédétection. Cette tâche est difficile à cause du phénomène de chatoiement, un phénomène qui nécessite de réaliser une étape de débruitage pour y être davantage robuste. Cependant, il est nécessaire de prendre en compte les incertitudes de débruitage pour contrôler la probabilité de fausse alarme des changements détectés car les instabilités de débruitage doivent être distinguées des changements. Nous proposons donc un réseau, entraîné de manière auto-supervisée, pour prédire les incertitudes de débruitage menant à une détection de changements radiométriques dont la performance est évaluée sur des images du satellite TerraSAR-X.
• Une distance de style stochastique pour la synthèse de textures multispectrales
  
  • Ollivier Sélim
  • Gousseau Yann
  • Lefebvre Sidonie
  , 2025, pp.985-988/2025-1702. In this paper, we propose a novel multispectral style distance that relies on a RGB network. It consists in the stochastic evaluation of a classical style distance, over images formed by random triplets of spectral bandes. It constitutes a simple and efficient extension of state-of-the-art methods for multispectral texture synthesis, while avoiding the additional training of a multispectral network.
• Modèle physique variationnel pour l’estimation de réponses impulsionnelles de salles
  
  • Lalay Louis
  • Fontaine Mathieu
  • Badeau Roland
  , 2025. Estimer la réponse impulsionnelle d’une salle est essentiel pour des tâches comme la déréverbération, qui améliore la reconnaissance automatique de la parole. La plupart des méthodes existantes reposent soit sur du traitement du signal statistique, soit sur des réseaux de neurones profonds s’inspirant du traitement du signal. Cependant, la combinaison des modélisations statistique et physique reste largement inexploré en estimation de réponse impulsionnelle de salle. Cet article propose une approche novatrice intégrant les deux aspects à travers un modèle physique. La réponse de salle est décomposée en paramètres interprétables : un bruit blanc gaussien modulé par une décroissance exponentielle dépendante de la fréquence (modélisant l’absorption des murs) et un filtre autorégressif (modélisant par exemple la réponse du microphone). L’optimisation d’une fonction d’énergie libre variationnelle permet une estimation pratique des paramètres. Nous montrons que, connaissant les signaux secs et réverbérants, la méthode proposée surpasse la déconvolution classique dans des environnements bruités, comme le confirment les mesures objectives.
• SigN: SIMBox Activity Detection Through Latency Anomalies at the Cellular Edge
  
  • Kouam Anne Josiane
  • Carneiro Viana Aline
  • Martins Philippe
  • Adjih Cédric
  • Tchana Alain
  , 2025. Despite their widespread adoption, cellular networks face growing vulnerabilities due to their inherent complexity and the integration of advanced technologies. One of the major threats in this landscape is Voice over IP (VoIP) to GSM gateways, known as SIMBox devices. These devices use multiple SIM cards to route VoIP traffic through cellular networks, enabling international bypass fraud with losses of up to $3.11 billion annually. Beyond financial impact, SIMBox activity degrades network performance, threatens national security, and facilitates eavesdropping on communications. Existing detection methods for SIMBox activity are hindered by evolving fraud techniques and implementation complexities, limiting their practical adoption in operator networks. This paper addresses the limitations of current detection methods by introducing SigN , a novel approach to identifying SIMBox activity at the cellular edge. The proposed method focuses on detecting remote SIM card association, a technique used by SIMBox appliances to mimic human mobility patterns. The method detects latency anomalies between SIMBox and standard devices by analyzing cellular signaling during network attachment. Extensive indoor and outdoor experiments demonstrate that SIMBox devices generate significantly higher attachment latencies, particularly during the authentication phase, where latency is up to 23 times greater than that of standard devices. We attribute part of this overhead to immutable factors such as LTE authentication standards and Internet-based communication protocols. Therefore, our approach offers a robust, scalable, and practical solution to mitigate SIMBox activity risks at the network edge. (10.1145/3708821.3733902)
  DOI : 10.1145/3708821.3733902
• La borne bayesienne de Schützenberger-van Trees : Un principe d'incertitude sur l'a posteriori
  
  • Rioul Olivier
  • Renaux Alexandre
  , 2025. Cette communication propose une perspective historique sur la borne de Cramér-Rao bayésienne (BCRB), généralement attribuée à van Trees qui l’a découverte en 1968. Selon la loi de Stigler sur l’éponymie, aucune découverte scientifique ne porte le nom de son premier découvreur. C’est non seulement le cas de la borne Cramér-Rao elle-même – due notamment aux mathématiciens français Fréchet et Darmois – mais aussi de l’inégalité de van Trees. En effet, le médecin, généticien, épidémiologiste et mathématicien français Marcel-Paul (Marco) Schützenberger, dans un petit article d’une quinzaine de lignes seulement, écrit en 1956 – plus d’une décennie avant van Trees – avait non seulement démontré la BCRB, mais comme le montre une lecture approfondie de sa preuve, l’avait fait avec une démarche très originale en la reliant au principe d’incertitude de Weyl-Heisenberg sur l’a posteriori. Nous passons en revue et comparons les contributions de Schützenberger et de van Trees ainsi que celles de Gart en 1959. L’équivalence générale entre BCRB et principe d’incertitude sur l’a posteriori ouvre de nouvelles perspectives.
• Déréverbération non-supervisée de la parole par modèle hybride
  
  • Bahrman Louis
  • Fontaine Mathieu
  • Richard Gaël
  , 2025, pp.1-4. Cet article introduit une nouvelle stratégie d'apprentissage pour améliorer des systèmes de déréverbération de la parole de manière non-supervisée en n'utilisant que des signaux réverbérants. La plupart des algorithmes existants nécessitent des paires de signaux (sec, réverbérant), qui sont difficiles à obtenir. Notre approche utilise en revanche des informations acoustiques limitées, comme le temps de réverbération (RT60), pour entraîner un système de déréverbération. Les résultats expérimentaux démontrent que notre méthode permet d'obtenir des performances plus cohérentes que l'état de l'art sur différentes mesures objectives.
• Super-résolution non supervisée d'images hyperspectrales de télédétection utilisant un entraînement entièrement synthétique
  
  • Xu Xinxin
  • Gousseau Yann
  • Kervazo Christophe
  • Ladjal Saïd
  , 2025. Hyperspectral single-image super-resolution (SISR) aims to improve the spatial resolution of images while preserving their spectral richness. Most current methods rely on supervised learning that requires high-resolution reference data, which are often unavailable in practice. To overcome this limitation, we propose an unsupervised learning approach based on the generation of synthetic data. The hyperspectral image is first decomposed into materials and abundances using a hyperspectral unmixing algorithm. A neural network is then trained to super-resolve the abundance maps from synthetic data generated through a dead leaves model, which imitates the statistical properties of real abundances. The high-resolution hyperspectral image is finally reconstructed by recombining the super-resolved abundance maps with the materials. Experimental results validate the effectiveness of this approach and highlight the usefulness of synthetic data for training.
• Unified Variational and Physics-aware Model for Room Impulse Response Estimation
  
  • Lalay Louis
  • Fontaine Mathieu
  • Badeau Roland
  , 2025. Room impulse response estimation is essential for tasks like speech dereverberation, which improves automatic speech recognition. Most existing methods rely on either statistical signal processing or deep neural networks designed to replicate signal processing principles. However, combining statistical and physical modeling for RIR estimation remains largely unexplored. This paper\footnote{This paper was submitted to Interspeech 2025} proposes a novel approach integrating both aspects through a theoretically grounded model. The RIR is decomposed into interpretable parameters: white Gaussian noise filtered by a frequency-dependent exponential decay (e.g. modeling wall absorption) and an autoregressive filter (e.g. modeling microphone response). A variational free-energy cost function enables practical parameter estimation. As a proof of concept, we show that given dry and reverberant speech signals, the proposed method outperforms classical deconvolution in noisy environments, as validated by objective metrics.
• Emotional speech markers of psychiatric disturbance in Huntington’s disease
  
  • Chenain Lucie
  • Fabre Audrey
  • Titeux Hadrien
  • Morgado Graça
  • Youssov Katia
  • Clavel Chloé
  • Bachoud-Lévi Anne-Catherine
  Frontiers in Psychiatry, Frontiers, 2025, 16, pp.1633492-1:1633492-20. Introduction: Psychiatric disorders and difficulties in emotional expression represent a major problem in the management of Huntington's Disease (HD). To improve patient follow-up, we propose to investigate the link between emotional expression and psychiatric symptoms, measured by the Problem Behaviors Assessment (PBA) scale. To this aim we developed the first emotional/psychiatric speech corpus, emoHD. Methods: We included 102 HD gene carriers and 35 healthy controls (HC). Psychiatric symptoms were assessed using PBA sub-scales for Depression, Irritability/aggressivity, Apathy, and Obsessive/compulsive symptoms. Speech was annotated using three emotional descriptors: primary emotions, affective phenomena, and activation levels. Affective phenomena labels were selected based on PBA statements by external participants unaware of the study's aims. We analyzed (1) emotional descriptors' relationships, (2) emotional expression differences between HD and HC, and (3) the associations between emotions and psychiatric symptoms. Results: HD patients showed reduced emotional expressiveness than HC with more neutral activation levels (=0). Only the primary emotion "angry" was less expressed in HD compared to HC. In contrast they expressed more affective phenomena states like apathetic, confused, "depressed", "disoriented", "frustrated", and "pessimistic" than HC, whereas they expressed less "other" and "irritable" than HC. Expressed emotions were congruent with psychiatric symptoms (e.g., "anxious" and "nervous" are positively associated with Depression PBA sub-scale; "frustrated" with Irritability/aggressivity PBA sub-scale). (10.3389/fpsyt.2025.1633492)
  DOI : 10.3389/fpsyt.2025.1633492
• Efficient Negative Weight Realization for Analog Resistive Neural Networks
  
  • Kiraz Zulal
  • Pham Dang-Kièn Germain
  • Desgreys Patricia
  , 2025. Most analog nonlinear resistive neural networks for machine learning training use doubling input and output neuron nodes to implement negative weights. However, this approach increases network size, modifies the gradient computation, and complicates circuit design. We propose an alternative circuit topology that retains a one-to-one correspondence between neurons in the original model and their analog counterparts. Our design employs a emph{single} input source for all first-layer weights, a emph{single} resistor per weight, and a bidirectional amplifier for the rest of the layers' weight to handle negative connections without duplicating neurons. We validate our design on a binary XOR classification task over SI{100}{} training epochs and SI{100}{} randomized initializations. Our textbf{single-resistor} approach achieved an average final error of SI{-6.6}{dB} and required approximately SI{568}{} minutes of total CPU time. In comparison, the textbf{doubled-node} design reached SI{-4.6}{dB} error and consumed around SI{1104}{} minutes of CPU time. This equates to nearly 49% less computation for the single-resistor circuit while preserving the standard gradient update procedure—demonstrating that negative weights can be realized more efficiently without doubling input/output neurons.