Publications

2025

• Routing Quantum Control of Causal Order
  
  • Grothus Maarten
  • Abbott Alastair A.
  • Vanrietvelde Augustin
  • Branciard Cyril
  , 2025. In recent years, various frameworks have been proposed for the study of quantum processes with indefinite causal order. In particular, quantum circuits with quantum control of causal order (QC-QCs) form a broad class of physical supermaps obtained from a bottom-up construction and are believed to represent all quantum processes physically realisable in a fixed spacetime. Complementarily, the formalism of routed quantum circuits introduces quantum operations constrained by "routes" to represent processes in terms of a more fine-grained routed circuit decomposition. This decomposition, formalised using a so-called routed graph, represents the information flow within the respective process. However, the existence of routed circuit decompositions has only been established for a small set of processes so far, including both certain specific QC-QCs and more exotic processes as examples. In this work, we remedy this fact by connecting these two frameworks. We prove that for any given $N$, one can use a single routed graph to systematically obtain a routed circuit decomposition for any QC-QC with $N$ parties. We detail this construction explicitly and contrast it with other routed circuit decompositions of QC-QCs, which we obtain from alternative routed graphs. We conclude by pointing out how this connection can be useful to tackle various open problems in the field of indefinite causal order, particularly establishing circuit representations of subclasses of QC-QCs.
• Exact distinguishability between real-valued and complex-valued Haar random quantum states
  
  • Nemoz Tristan
  • Alléaume Romain
  • Brown Peter
  Quantum, Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften, 2025, 10, pp.2120. Haar random states are fundamental objects in quantum information theory and quantum computing. We study the density matrix resulting from sampling $t$ copies of a $d$-dimensional quantum state according to the Haar measure on the orthogonal group. In particular, we analytically compute its spectral decomposition. This allows us to compute exactly the trace distance between $t$-copies of a real Haar random state and $t$-copies of a complex Haar random state. Using this we show a lower-bound on the approximation parameter of real-valued state $t$-designs and improve the lower-bound on the number of copies required for imaginarity testing. (10.22331/q-2026-05-29-2120)
  DOI : 10.22331/q-2026-05-29-2120
• Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study
  
  • Mammadov Ali
  • Le Folgoc Loic
  • Adam Julien
  • Buronfosse Anne
  • Hayem Gilles
  • Hocquet Guillaume
  • Gori Pietro
  Journal of Medical Imaging, SPIE Digital Library, 2025. Multiple Instance Learning (MIL) has emerged as the best solution for Whole Slide Image (WSI) classification. It consists of dividing each slide into patches, which are treated as a bag of instances labeled with a global label. MIL includes two main approaches: instance-based and embedding-based. In the former, each patch is classified independently, and then the patch scores are aggregated to predict the bag label. In the latter, bag classification is performed after aggregating patch embeddings. Even if instance-based methods are naturally more interpretable, embedding-based MILs have usually been preferred in the past due to their robustness to poor feature extractors. However, recently, the quality of feature embeddings has drastically increased using self-supervised learning (SSL). Nevertheless, many authors continue to endorse the superiority of embedding-based MIL. To investigate this further, we conduct 710 experiments across 4 datasets, comparing 10 MIL strategies, 6 self-supervised methods with 4 backbones, 4 foundation models, and various pathology-adapted techniques. Furthermore, we introduce 4 instance-based MIL methods never used before in the pathology domain. Through these extensive experiments, we show that with a good SSL feature extractor, simple instance-based MILs, with very few parameters, obtain similar or better performance than complex, state-of-the-art (SOTA) embedding-based MIL methods, setting new SOTA results on the BRACS and Camelyon16 datasets. Since simple instance-based MIL methods are naturally more interpretable and explainable to clinicians, our results suggest that more effort should be put into well-adapted SSL methods for WSI rather than into complex embedding-based MIL methods.
• White matter hyperintensities and their role in major depressive episodes: a cross-sectional study in adults under 65
  
  • Baudouin Édouard
  • Corruble Emmanuelle
  • Gori Pietro
  • Bloch Isabelle
  • Becquemont Laurent
  • Duron Emmanuelle
  • Colle Romain
  Brazilian Journal of Psychiatry, Brazilian Psychiatric Association, 2025, 47, pp.e20243921. Objective: White matter hyperintensities (WMH) are associated with major depressive episodes (MDE) in individuals aged 65 and older. WMH are prevalent in adults under 65, yet the association between their volume and MDE in this population remains uncertain. This study aimed to assess the association of WMH volume with MDE and its severity in patients aged < 65. Methods: Cross-sectional study (ancillary to clinical trial NCT02051413) of subjects under the age of 65. Overall, 69 patients with MDE and 32 healthy controls (HCs) were included. Severity was assessed with the Hamilton Rating Scale (HRS) and WMH were quantified by two experts. Post-hoc mediation analyses were conducted if associations were found between independent variables and WMH. Results: Mean age was 34.5 (12.4) years. There was no difference in WMH between patients and HCs. Higher WMH volumes were observed in extremely severe MDE (2,170.2 [3,767.9] mm3 vs. 416.6 [594.9] mm3 [r = 0.21; p < 0.05]), which completely mediated the effect of age on severity. Conclusions: In a sample of adults under 65, this study failed to identify higher WMH volume in patients with MDE compared to HCs. However, WMH may act as a mediator of the association between age and MDE severity. This finding suggests that WMH could contribute to more severe depression in late life. (10.47626/1516-4446-2024-3921)
  DOI : 10.47626/1516-4446-2024-3921
• When Statistical Physics Meets Image Processing
  
  • Descombes Xavier
  • Sigelle Marc
  • Tupin Florence
  Markov Processes And Related Fields, Polymat Publishing Company, 2025, 31 (3-4), pp.177-204. In this paper we review the contribution of Evgueny (Eugène for the French community) Pechersky to image processing. Eugène discovered image processing as he was visiting Telecom Paris in 1994. During his stay at Telecom Paris and later on, during some visits and collaboration at INRIA Sophia Antipolis, his work was focused on designing multi-body interaction models to overcome the limits of the well known Ising and Potts model for image processing applications. Notably, the derived models were applied to different applications both in remote sensing and biomedical images. In this paper we summarize Eugène's contribution to the so-called Bayesian approach in image processing. (10.61102/1024-2953-mprf.2025.31.3-4.001)
  DOI : 10.61102/1024-2953-mprf.2025.31.3-4.001
• Rate Meta-Distribution in Millimeter Wave URLLC Device-to-Device Networks With Beam Misalignment
  
  • Quan Yibo
  • Coupechoux Marceau
  • Kélif Jean-Marc
  IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2025, 74 (1), pp.657-673. <div><p>Using the stochastic geometry framework, we study a millimeter wave (mmWave) Device-to-Device (D2D) network dedicated to Ultra-Reliable Low Latency Communications (URLLC), where users employ multiple antennas to perform beamforming. We leverage the notion of meta-distribution in order to capture the reliability requirement of URLLC. The packet transmission process is divided into two phases: a beam training phase, during which exhaustive beam sweeping is adopted, and a data transmission phase. The paper investigates the misalignment error distribution resulting from an imperfect training phase, due to the finite codebooks resolution and the fast variation of the channel. For the data transmission phase, closed-form expressions for all the moments of the conditional rate coverage probability are derived, and the meta-distribution is approximated using the beta approximation. The study evaluates the overall network performance through the effective rate metadistribution, which accounts for the training overhead and beam misalignment errors. The results show the detrimental impact of misalignment errors when URLLC requirements are stringent and highlight the trade-off between the training overhead and the gain brought by multiple antennas. Insights are provided for optimally and jointly choosing the codebook size and tbe number of antennas.</p></div> (10.1109/TVT.2024.3451487)
  DOI : 10.1109/TVT.2024.3451487
• ding-01 :ARG0 Un corpus AMR pour le français parlé spontané
  
  • Kang Jeongwoo
  • Boritchev Maria
  • Coavoux Maximin
  , 2025, pp.791-801. Nous présentons notre travail en cours sur l'annotation d'un corpus sémantique du français. Nous annotons le corpus DinG, constitué de transcriptions de dialogues spontanés en français enregistrées pendant des parties du jeu de plateau Catan , en Abstract Meaning Representation (AMR), un formalisme de représentation sémantique. Comme AMR a une couverture insuffisante de la dynamique de la parole spontanée, nous étendons le formalisme pour mieux représenter la parole spontanée et les structures de phrases spécifiques au français. En outre, nous diffusons un guide d'annotation détaillant ces extensions. Enfin, nous publions notre corpus sous licence libre (CC-SA-BY). Notre travail contribue au développement de ressources sémantiques pour le dialogue en français.
• A bipartite ranking approach to the two-sample problem
  
  • Clémençon Stéphan
  • Limnios Myrto
  • Vayatis Nicolas
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2025, 19 (1), pp.2733–2779. The two-sample problem consists in testing whether two independent samples are drawn from the same (unknown) distribution. Its study in high-dimension is the subject of much attention, especially because the information acquisition processes at work in the Big Data era often involve various poorly controlled sources, leading to datasets possibly exhibiting strong sampling bias. While the efficiency of classic methods relying on computing a discrepancy measure between the empirical distributions of each sample, is negatively impacted by increasing dimensionality, we develop a two-step approach based on statistical learning and an extension of rank tests. By dividing the initial samples in two, a bipartite ranking algorithm first learns a real-valued scoring function inducing a preorder on the multivariate space. Then, a rank statistic based on the scores of the remaining observations, tests for differences in distribution. Because the ranking algorithm learns how to map the data onto the real line as the likelihood ratio between the original multivariate distributions, the approach resists to large dimensions (ignoring ranking model bias issues) and preserves the advantages of univariate rank tests. We prove nonasymptotic error bounds based on recent results for two-sample linear rank-processes, and experimentally show how the promoted approach surpasses state-of-the-art methods. (10.1214/25-EJS2392)
  DOI : 10.1214/25-EJS2392
• Just Project! Multi-Channel Despeckling, the Easy Way
  
  • Denis Loïc
  • Dalsasso Emanuele
  • Tupin Florence
  IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2025, 63, pp.1-11. Reducing speckle fluctuations in multi-channel SAR images is essential in many applications of SAR imaging such as polarimetric classification or interferometric height estimation. While single-channel despeckling has widely benefited from the application of deep learning techniques, extensions to multi-channel SAR images are much more challenging. This paper introduces MuChaPro, a generic framework that exploits existing single-channel despeckling methods. The key idea is to generate numerous single-channel projections, restore these projections, and recombine them into the final multi-channel estimate. This simple approach is shown to be effective in polarimetric and/or interferometric modalities. A special appeal of MuChaPro is the possibility to apply a self-supervised training strategy to learn sensor-specific networks for single-channel despeckling. (10.1109/TGRS.2025.3531957)
  DOI : 10.1109/TGRS.2025.3531957