Publications

2024

• Multi-Agent Proximal Policy Optimization for Dynamic Multi-Channel URLLC Access
  
  • Robaglia Benoît-Marie
  • Coupechoux Marceau
  • Tsilimantos Dimitrios
  , 2024, pp.1-7. This work addresses the challenge of Dynamic Multi-Channel Access (DMCA) in the context of Ultra Reliable Low Latency Communications (URLLC), a framework subjected to notably stringent constraints, required by numerous Internet of Things (IoT) applications across various sectors. We introduce a theoretically grounded approach, leveraging Deep Multi-Agent Reinforcement Learning (MARL) to tackle this problem. While prior research has not fully addressed the DMCA problem in URLLC networks under time-varying heterogeneous channels and traffic profiles, nor provided robust theoretical guarantees in the multi-agent context, this paper adapts the recent theoretical framework of Trust Region Policy Optimization (TRPO) in MARL to meet the specific challenges and requirements of the URLLC-DMCA problem. Specifically, we introduce Multi Channel Access - Proximal Policy Optimization (MCA-PPO), a MARL algorithm that benefits from theoretical guarantees and effectively handles the partial observability and the combinatorial nature of the DMCA challenge. We validate the superiority of our proposed method across a variety of heterogeneous scenarios, in terms of traffic models and system parameters, and show that we outperform the traditional multiple access benchmark and learning algorithms. (10.1109/PIMRC59610.2024.10817242)
  DOI : 10.1109/PIMRC59610.2024.10817242
• RIR-in-a-Box: Estimating Room Acoustics from 3D Mesh Data through Shoebox Approximation
  
  • Kelley Liam
  • Carlo Diego Di
  • Nugraha Aditya Arie
  • Fontaine Mathieu
  • Bando Yoshiaki
  • Yoshii Kazuyoshi
  , 2024. This paper describes a method for estimating the room impulse response (RIR) for a microphone and a sound source located at arbitrary positions from the 3D mesh data of the room. Simulat- ing realistic RIRs with pure physics-driven methods often fails the balance between physical consistency and computational ef- ficiency, hindering application to real-time speech processing. Alternatively, one can use MESH2IR, a fast black-box estima- tor that consists of an encoder extracting latent code from mesh data with a graph convolutional network (GCN) and a decoder generating the RIR from the latent code. Combining these two approaches, we propose a fast yet physically coherent estimator with interpretable latent code based on differentiable digital sig- nal processing (DDSP). Specifically, the encoder estimates a vir- tual shoebox room scene that acoustically approximates the real scene, accelerating physical simulation with the differentiable image-source model in the decoder. Our experiments showed that our method outperformed MESH2IR for real mesh data ob- tained with the depth scanner of Microsoft HoloLens 2, and can provide correct spatial consistency for binaural RIRs.
• Predefined Prototypes for Intra-Class Separation and Disentanglement
  
  • Mariotte Théo
  • Almudévar Antonio
  • Ortega Alfonso
  • Tahon Marie
  • Vicente Luis
  • Miguel Antonio
  • Lleida Eduardo
  , 2024, pp.3809-3813. Prototypical Learning is based on the idea that there is a point (which we call prototype) around which the embeddings of a class are clustered. It has shown promising results in scenarios with little labeled data or to design explainable models. Typically, prototypes are either defined as the average of the embeddings of a class or are designed to be trainable. In this work, we propose to predefine prototypes following human-specified criteria, which simplify the training pipeline and brings different advantages. Specifically, in this work we explore two of these advantages: increasing the inter-class separability of embeddings and disentangling embeddings with respect to different variance factors, which can translate into the possibility of having explainable predictions. Finally, we propose different experiments that help to understand our proposal and demonstrate empirically the mentioned advantages. (10.21437/Interspeech.2024-825)
  DOI : 10.21437/Interspeech.2024-825
• Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization
  
  • Conti Jean-Rémy
  • Clémençon Stéphan
  , 2025, 15614, pp.371-385. The urging societal demand for fair AI systems has put pressure on the research community to develop predictive models that are not only globally accurate but also meet new fairness criteria, reflecting the lack of disparate mistreatment with respect to sensitive attributes (e.g. gender, ethnicity, age). In particular, the variability of the errors made by certain Facial Recognition (FR) systems across specific segments of the population compromises the deployment of the latter, and was judged unacceptable by regulatory authorities. Designing fair FR systems is a very challenging problem, mainly due to the complex and functional nature of the performance measure used in this domain (i.e. ROC curves) and because of the huge heterogeneity of the face image datasets usually available for training. In this paper, we propose a novel post-processing approach to improve the fairness of pre-trained FR models by optimizing a regression loss which acts on centroid-based scores. Beyond the computational advantages of the method, we present numerical experiments providing strong empirical evidence of the gain in fairness and of the ability to preserve global accuracy. (10.1007/978-3-031-87657-8_26)
  DOI : 10.1007/978-3-031-87657-8_26
• Explainable by-design Audio Segmentation through Non-Negative Matrix Factorization and Probing
  
  • Lebourdais Martin
  • Mariotte Théo
  • Almudévar Antonio
  • Tahon Marie
  • Ortega Alfonso
  , 2024. Audio segmentation is a key task for many speech technologies, most of which are based on neural networks, usually considered as black boxes, with high-level performances.However, in many domains, among which health or forensics, there is not only a need for good performance but also for explanations about the output decision.Explanations derived directly from latent representations need to satisfy "good" properties, such as informativeness, compactness, or modularity, to be interpretable.In this article, we propose an explainable-by-design audio segmentation model based on non-negative matrix factorization (NMF) which is a good candidate for the design of interpretable representations.This paper shows that our model reaches good segmentation performances, and presents deep analyses of the latent representation extracted from the non-negative matrix.The proposed approach opens new perspectives toward the evaluation of interpretable representations according to "good" properties.
• STOCHASTIC MIRROR DESCENT FOR NONPARAMETRIC ADAPTIVE IMPORTANCE SAMPLING
  
  • Bianchi Pascal
  • Delyon Bernard
  • Portier François
  • Priser Victor
  , 2024. This paper addresses the problem of approximating an unknown probability distribution with density $f$ - which can only be evaluated up to an unknown scaling factor - with the help of a sequential algorithm that produces at each iteration $n\geq 1$ an estimated density $q_n$. The proposed method optimizes the Kullback-Leibler divergence using a mirror descent (MD) algorithm directly on the space of density functions, while a stochastic approximation technique helps to manage between algorithm complexity and variability. One of the key innovations of this work is the theoretical guarantee that is provided for an algorithm with a fixed MD learning rate \(\eta \in (0,1 )\). The main result is that the sequence \(q_n\) converges almost surely to the target density \(f\) uniformly on compact sets. Through numerical experiments, we show that fixing the learning rate \(\eta \in (0,1 )\) significantly improves the algorithm's performance, particularly in the context of multi-modal target distributions where a small value of $\eta$ allows to increase the chance of finding all modes. Additionally, we propose a particle subsampling method to enhance computational efficiency and compare our method against other approaches through numerical experiments.
• Speech dereverberation constrained on room impulse response characteristics
  
  • Bahrman Louis
  • Fontaine Mathieu
  • Le Roux Jonathan
  • Richard Gaël
  , 2024. Single-channel speech dereverberation aims at extracting a dry speech signal from a recording affected by the acoustic reflections in a room. However, most current deep learning-based approaches for speech dereverberation are not interpretable for room acoustics, and can be considered as black-box systems in that regard. In this work, we address this problem by regularizing the training loss using a novel physical coherence loss which encourages the room impulse response (RIR) induced by the dereverberated output of the model to match the acoustic properties of the room in which the signal was recorded. Our investigation demonstrates the preservation of the original dereverberated signal alongside the provision of a more physically coherent RIR.
• Meta-learners for few-shot weakly-supervised medical image segmentation
  
  • Oliveira Hugo
  • Gama Pedro H.T.
  • Bloch Isabelle
  • Cesar Roberto Marcondes
  Pattern Recognition, Elsevier, 2024, 153, pp.110471-1:110471-13. Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of work in other tasks such as segmentation and detection. We propose a new generic Meta-Learning framework for few-shot weakly supervised segmentation in medical imaging domains. The proposed approach includes a meta-training phase that uses a meta-dataset. It is deployed on an out-of-distribution few-shot target task, where a single highly generalizable model, trained via a selective supervised loss function, is used as a predictor. The model can be trained in several distinct ways, such as second-order optimization, metric learning, and late fusion. Some relevant improvements of existing methods that are part of the proposed approach are presented. We conduct a comparative analysis of meta-learners from distinct paradigms adapted to few-shot image segmentation in different sparsely annotated radiological tasks. The imaging modalities include 2D chest, mammographic, and dental X-rays, as well as 2D slices of volumetric tomography and resonance images. Our experiments consider in total 9 meta-learners, 4 backbones, and multiple target organ segmentation tasks. We explore small-data scenarios in radiology with varying weak annotation styles and densities. Our analysis shows that metric-based meta-learning approaches achieve better segmentation results in tasks with smaller domain shifts compared to the meta-training datasets, while some gradient- and fusion-based meta-learners are more generalizable to larger domain shifts. Guidelines learned from the comparative performance assessment of the analyzed methods are summarized to support those readers interested in the field. (10.1016/j.patcog.2024.110471)
  DOI : 10.1016/j.patcog.2024.110471
• Simulation Secure Multi-input Quadratic Functional Encryption
  
  • Alborch Escobar Ferran
  • Canard Sébastien
  • Laguillaumie Fabien
  , 2025, 15516, pp.26-53. Multi-input functional encryption is a primitive that allows for the evaluation of an -ary function over multiple ciphertexts, without learning any information about the underlying plaintexts. This type of computation is useful in many cases where one has to compute over encrypted data, such as privacy-preserving cloud services, federated learning, or more generally delegation of computation from multiple clients. In this work we propose the first secret-key multi-input quadratic functional encryption scheme satisfying simulation security. On contrary, current constructions supporting quadratic functionalities, proposed by Agrawal et al. in CRYPTO '21 and TCC '22, only reach indistinguishibility-based security. Our proposed construction is generic, and for a concrete instantiation, we propose a new function-hiding innerproduct functional encryption scheme proven simulation secure against one challenge ciphertext in the standard model, which is of independent interest. (10.1007/978-3-031-82852-2_2)
  DOI : 10.1007/978-3-031-82852-2_2
• Leveraging Reusable Code and Proofs to Design Complex DRAM Controllers - A Case Study
  
  • Malaquias Felipe Lisboa
  • Asavoae Mihail
  • Brandner Florian
  , 2024, pp.298-305. Critical real-time systems are getting more and more complex and require ever more computing power. Multi-core platforms, GPUs, and custom accelerators promise to deliver this needed performance. However, these platforms are notoriously hard to analyze and lack predictability in terms of timing properties. Computer architectures and platforms that offer both predictability and performance are thus needed. This work investigates the use of the interactive proof assistant Coq in order to model complex DRAM memory controllers (MCs) for multi-core platforms. The design of predictable high- performance MCs is particularly challenging, since memory requests have to be processed efficiently, while facing interference from other cores in the system. The problem is exacerbated by the complexity of DRAM devices and the various timing constraints they impose. Specifically, this work extends a previous Coq framework by focusing on reusability, which allows designers to develop and prove complex MCs. As a use-case, we present TDMShelve, an MC balancing performance and isolation. (10.1109/DSD64264.2024.00047)
  DOI : 10.1109/DSD64264.2024.00047
• Device-independent lower bounds on the conditional von Neumann entropy
  
  • Brown Peter
  • Fawzi Hamza
  • Fawzi Omar
  Quantum, Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften, 2024, 8, pp.1445. The rates of several device-independent (DI) protocols, including quantum key-distribution (QKD) and randomness expansion (RE), can be computed via an optimization of the conditional von Neumann entropy over a particular class of quantum states. In this work we introduce a numerical method to compute lower bounds on such rates. We derive a sequence of optimization problems that converge to the conditional von Neumann entropy of systems defined on general separable Hilbert spaces. Using the Navascu\'es-Pironio-Ac\'in hierarchy we can then relax these problems to semidefinite programs, giving a computationally tractable method to compute lower bounds on the rates of DI protocols. Applying our method to compute the rates of DI-RE and DI-QKD protocols we find substantial improvements over all previous numerical techniques, demonstrating significantly higher rates for both DI-RE and DI-QKD. In particular, for DI-QKD we show a new minimal detection efficiency threshold which is within the realm of current capabilities. Moreover, we demonstrate that our method is capable of converging rapidly by recovering instances of known tight analytical bounds. Finally, we note that our method is compatible with the entropy accumulation theorem and can thus be used to compute rates of finite round protocols and subsequently prove their security. (10.22331/q-2024-08-27-1445)
  DOI : 10.22331/q-2024-08-27-1445
• Anomalous Sound Detection For Road Surveillance Based On Graph Signal Processing
  
  • Mnasri Zied
  • Giraldo Zuluaga Jhony Heriberto
  • Bouwmans Thierry
  , 2024. Recently, Anomalous Sound Detection (ASD) has emerged as a promising method for road surveillance. However, since the ratio of anomalous events is generally too small, anomaly detection in general, and ASD in particular, are mainly treated as one-class classification problems. Besides, a common problem in road surveillance is the background noise, which makes it difficult to train effective models based on normal sounds only. Therefore, this work aims to experiment with the use of graph signal processing (GSP) to improve ASD performance. Thus, we propose a Graph-based One-Class SVM technique (GOC-SVM) where the features extracted from audio signals are firstly embedded on graphs, and then filtered through a graph filterbank, before computing their joint Fourier transform magnitude. Subsequently, they are fed into a one-class SVM classifier trained on normal data only. Evaluation results show a threefold advantage of using graph embedding and filtering for ASD: (a) improving the anomaly detection results in comparison to plain features, (b) outperforming the classical OC-SVM baseline, (c) enhancing the performance of the proposed semi-supervised GOC-SVM, so as to reach a comparable level of performance of the fully-supervised binary classification SVM, yielding 0.91 of Area-under-the-curve (AUC), 98% of overall accuracy, 99% and 88% of F1 score for normal and anomalous classes, respectively. Such a performance proves the potential of using GSP to solve the ASD problem in road traffic monitoring. (10.23919/EUSIPCO63174.2024.10715291)
  DOI : 10.23919/EUSIPCO63174.2024.10715291
• Using Random Codebooks for Audio Neural AutoEncoders
  
  • Giniès Benoît
  • Bie Xiaoyu
  • Fercoq Olivier
  • Richard Gaël
  , 2024. Latent representation learning has been an active field of study for decades in numerous applications. Inspired among others by the tokenization from Natural Language Processing and motivated by the research of a simple data representation, recent works have introduced a quantization step into the feature extraction. In this work, we propose a novel strategy to build the neural discrete representation by means of random codebooks. These codebooks are obtained by randomly sampling a large, predefined fixed codebook. We experimentally show the merits and potential of our approach in a task of audio compression and reconstruction. (10.23919/EUSIPCO63174.2024.10715290)
  DOI : 10.23919/EUSIPCO63174.2024.10715290
• Invariance-based layer regularization for sound event detection
  
  • Perera David
  • Essid Slim
  • Gaël Richard
  , 2024. Experimental and theoretical evidences suggest that invariance constraints can improve the performance and generalization capabilities of a classification model. While invariance-based regularization has become part of the standard tool-belt of machine learning practitioners, this regularization is usually applied near the decision layers or at the end of the feature extracting layers of a deep classification network. However, the optimal placement of invariance constraints inside a deep classifier is yet an open question. In particular, it would be beneficial to link it to the structural properties of the network (\textit{e.g.} its architecture), or its dynamical properties (\textit{e.g.} the effectively used volume of its latent spaces). The purpose of this article is to initiate an investigation on these aspects. We use the experimental framework of the DCASE 2023 Task 4A challenge, which considers the training of a sound event classifier in a semi-supervised manner. We show that the optimal placement of invariance constraints improves the performance of the standard baseline for this task. (10.23919/EUSIPCO63174.2024.10715380)
  DOI : 10.23919/EUSIPCO63174.2024.10715380
• Towards a Perfect Reconstruction Theory
  
  • Rioul Olivier
  • Souloumiac Antoine
  , 2024, pp.2547-2551. This paper presents some preliminary considerations on the general problems of missing/complementary information and perfect reconstruction, with the hope to attract the attention of the signal processing researchers that the rigorous derivation of a general theory of reconstruction should be desirable and possible. (10.23919/EUSIPCO63174.2024.10715178)
  DOI : 10.23919/EUSIPCO63174.2024.10715178
• Generating synthetic data to train a deep unrolled network for Hyperspectral Unmixing
  
  • Hadjeres Rassim
  • Kervazo Christophe
  • Tupin Florence
  , 2024. Hyperspectral unmixing is an essential tool for analyzing hyperspectral data, especially in remote sensing. Many approaches have been developed for this problem, ranging from model-based to deep learning-based, and (hybrid) unrolled methods. However, the development of supervisedly trained deep learning-based unmixing methods is hindered by the lack of available labeled training datasets. In this paper, to enable the supervised training of neural networks for hyperspectral unmixing, we propose a methodology to construct a synthetic training database directly from the hyperspectral image to unmix. We use this data generation approach to train an unrolled unmixing method LPALM. The trained LPALM is assessed on two real hyperspectral datasets and shows the best performances compared to other classical, unrolled, and autoencoder-based unmixing methods. The code of this work will be available at https://github.com/rhadjeres/Synthetic
• InSAR2InSAR: A Self-Supervised Method for InSAR Parameters Estimation
  
  • Geara Carla
  • Gelas Colette
  • De Vitry Louis
  • Colin Elise
  • Tupin Florence
  , 2024. <div><p>Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing tool that provides comprehensive information about the Earth's surface. However, InSAR parameters are highly corrupted by speckle, which limits their utility. Deep learning methods have recently achieved promising results in improving the reliability of InSAR parameters. Most of the proposed methods are fully supervised. These methods are usually trained on synthetic data, which are not able to fully take into account all the properties of real images. In this paper, we address this issue by extending the self-supervised denoising approach Noise2Noise, previously proposed by Lehtinen et al. in 2018, for the joint estimation of InSAR parameters. Additionally, the proposed method uses a loss function that is adapted to the InSAR noise model, making it well-suited for the problem we are addressing.</p></div>
• Shrinkage MMSE estimators of covariances beyond the zero-mean and stationary variance assumptions
  
  • Flasseur Olivier
  • Thiébaut Éric
  • Denis Loïc
  • Langlois Maud
  , 2024. We tackle covariance estimation in low-sample scenarios, employing a structured covariance matrix with shrinkage methods. These involve convexly combining a low-bias/highvariance empirical estimate with a biased regularization estimator, striking a bias-variance trade-off. Literature provides optimal settings of the regularization amount through risk minimization between the true covariance and its shrunk counterpart. Such estimators were derived for zero-mean statistics with i.i.d. diagonal regularization matrices accounting for the average sample variance solely. We extend these results to regularization matrices accounting for the sample variances both for centered and noncentered samples. In the latter case, the empirical estimate of the true mean is incorporated into our shrinkage estimators. Introducing confidence weights into the statistics also enhance estimator robustness against outliers. We compare our estimators to other shrinkage methods both on numerical simulations and on real data to solve a detection problem in astronomy.
• Diffusion-based image inpainting with internal learning
  
  • Cherel Nicolas
  • Almansa Andrés
  • Gousseau Yann
  • Newson Alasdair
  , 2024, pp.446-450. Diffusion models are now the undisputed state-of-the-art for image generation and image restoration. However, they require large amounts of computational power for training and inference. In this paper, we propose lightweight diffusion models for image inpainting that can be trained on a single image, or a few images. We show that our approach competes with large state-of-the-art models in specific cases. We also show that training a model on a single image is particularly relevant for image acquisition modality that differ from the RGB images of standard learning databases. We show results in three different contexts: texture images, line drawing images, and materials BRDF, for which we achieve state-of-the-art results in terms of realism, with a computational load that is greatly reduced compared to concurrent methods. (10.23919/EUSIPCO63174.2024.10714982)
  DOI : 10.23919/EUSIPCO63174.2024.10714982
• Unsupervised radiometric change detection from synthetic aperture radar images
  
  • Bultingaire Thomas
  • Meraoumia Inès
  • Kervazo Christophe
  • Denis Loïc
  • Tupin Florence
  , 2024. Change detection is an important data processing task in remote sensing, with applications such as deforestation monitoring or natural disaster assessment. Synthetic Aperture Radar (SAR) imaging offers key advantages for change detection, in particular due to its robustness to weather condition and cloud coverage. Because of the speckle phenomenon, the intensity of SAR images suffer from strong fluctuations, making the detection of radiometric changes challenging. Our method builds on a recently introduced self-supervised despeckling technique. It estimates despeckling uncertainty to better identify meaningful differences between two despeckled images. Conformal prediction permits to approach the change detection problem from the angle of anomaly detection. Thus, we develop a fully unsupervised change detection approach with a controlled probability of false alarm. Experimental results on TerraSAR-X satellite images with metric resolution show the capability of our method to detect changes without any supervision.
• Deep unrolling of the multiplicative updates algorithm for blind source separation, with application to hyperspectral unmixing
  
  • Kervazo Christophe
  • Chetoui Abdelkhalak
  • Cohen Jérémy E.
  , 2024. Blind Source Separation (BSS) has gained a large interest in many fields, including hyperspectral unmixing which is broadly used in remote sensing and astrophysics. BSS being an ill-posed problem, many strategies have been proposed to solve it, ranging from model-based to deep-learning ones. While modelbased algorithms are in general interpretable, in contrast with neural networks, these algorithms often require a large number of iterations and obtain worse unmixing results that their deeplearning counterparts. To try to obtain the best of both worlds, in this work we unroll the multiplicative updates algorithm, leading to two new algorithms. The first one, NALMU, learns some parameters which are fixed once the training is over. The second one, ALMU, enables the parameters of the unrolled algorithm to be predicted by small neural networks, making the whole algorithm adaptative to the specific datasets considered in the test phase. We conduct experiments on two astrophysic datasets, and show that our approach enables to largely outperform the other unmixing unrolled algorithms, while largely reducing the number of iterations compared to the original multiplicative updates algorithm.
• Multifrequency Highly Oscillating Aperiodic Amplitude Estimation for Nonlinear Chirp Signal
  
  • Emelchenkov Anton
  • Fontaine Mathieu
  • Grenier Yves
  • Mahé Hervé
  • Roueff François
  , 2024. This paper addresses the challenge of estimating multiple highly oscillating amplitudes within the nonlinear chirp signal model. The problem is analogous to the mode detection task with fixed instantaneous frequencies, where the oscillating amplitudes signify mechanical vibrations concealing crucial information for predictive maintenance. Existing methods often focus on single-frequency estimation, employ simple amplitude functions, or impose strong noise assumptions. Furthermore, these methods frequently rely on arbitrarily chosen hyperparameters, leading to sub-optimal generalization for a diverse range of amplitudes. To address these limitations, our approach introduces two estimators, based on Capon filters and negative log-likelihood approaches respectively, that leverage locally stationary assumptions and incorporate hyperparameters estimation. The results demonstrate that, even under challenging conditions, these estimators yield competitive outcomes across various noisy scenarios, mitigating the drawbacks associated with existing methods. (10.23919/EUSIPCO63174.2024.10715060)
  DOI : 10.23919/EUSIPCO63174.2024.10715060
• Silicone-Based Haptic Interfaces: Enhancing Multimodal Interactions through Pneumatic Tactile Feedback
  
  • Liu Yang
  • Shangguan Zhegong
  • Tapus Adriana
  • Safin Stéphane
  • Détienne Françoise
  • Lecolinet Eric
  , 2024, pp.140-143. This paper explores the potential of pneumatic haptic interfaces in enhancing human-computer interaction. We present two projects: one augmenting movie experiences with emotion-synchronized haptic feedback, and another integrating pneumatic interfaces into steering wheels for improved driver takeover in autonomous vehicles. The movie experience project demonstrated enhanced emotional engagement, while the automotive application showed improved safety and user trust. These applications highlight the versatility of pneumatic haptic technology across entertainment and safety contexts. We discuss the advantages of the Baromorph technique and outline future research directions, including comparative studies with vibrotactile feedback and machine learning approaches. This work contributes to the development of more insightful and emotionally intelligent interactive systems. (10.1109/IHMSC62065.2024.00039)
  DOI : 10.1109/IHMSC62065.2024.00039
• Platooning in Connected Vehicles: A Review of Current Solutions, Standardization Activities, Cybersecurity, and Research Opportunities
  
  • Braiteh Farah-Emma
  • Bassi Francesca
  • Khatoun Rida
  IEEE Transactions on Intelligent Vehicles, Institute of Electrical and Electronics Engineers, 2024, pp.1-23. Over the past few years, we have witnessed notable advancements in connected vehicle technologies, illustrating significant progress in their interactions with drivers, as well as with various other networks and devices. One remarkable advancement is the Cooperative Adaptive Cruise Control (CACC) and the specialized service known as the platoon system, which entails vehicles traveling closely together in a convoy-like manner. Numerous pilot projects, academic studies, and standardization initiatives have been dedicated to exploring platooning functions. In this paper, we provide an overview of platooning and CACC protocols, examining them from both academic and industry perspectives. We analyze and compare some of the recently published research works and projects related to Platooning. Furthermore, we discuss and analyze the cybersecurity requirements and potential threats associated with platooning. Finally, we discuss some key research challenges and open research issues in the platooning context that require further investigation in the future by the academic and the industrial communities. (10.1109/TIV.2024.3447916)
  DOI : 10.1109/TIV.2024.3447916
• Bounds on Petz-Rényi Divergences and their Applications for Device-Independent Cryptography
  
  • Hahn Thomas
  • Tan Ernest
  • Brown Peter
  , 2024. Variational techniques have been recently developed to find tighter bounds on the von Neumann entropy in a completely device-independent (DI) setting. This, in turn, has led to significantly improved key rates of DI protocols, in both the asymptotic limit as well as in the finite-size regime. In this paper, we discuss two approaches towards applying these variational methods for Petz-Rényi divergences instead. We then show how this can be used to further improve the finite-size key rate of DI protocols, utilizing a fully-Rényi entropy accumulation theorem developed in a partner work. Petz-Rényi divergences can also be applied to study DI advantage distillation, in which two-way communication is used to improve the noise tolerance of quantum key distribution (QKD) protocols. We implement these techniques to derive increased noise tolerances for DIQKD protocols, which surpass all previous known bounds. (10.48550/arXiv.2408.12313)
  DOI : 10.48550/arXiv.2408.12313