Publications

2023

• Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code
  
  • Sun Yiming
  • German Daniel M.
  • Zacchiroli Stefano
  Empirical Software Engineering, Springer Verlag, 2023. We consider the problem of identifying the provenance of free/open source software (FOSS) and specifically the need of identifying where reused source code has been copied from. We propose a lightweight approach to solve the problem based on software identifiers—such as the names of variables, classes, and functions chosen by programmers. The proposed approach is able to efficiently narrow down to a small set of candidate origin products, to be further analyzed with more expensive techniques to make a final provenance determination. By analyzing the PyPI (Python Packaging Index) open source ecosystem we find that globally defined identifiers are very distinct. Across PyPI's 244 K packages we found 11.2 M different global identifiers (classes and method/function names—with only 0.6% of identifiers shared among the two types of entities); 76% of identifiers were used only in one package, and 93% in at most 3. Randomly selecting 3 non-frequent global identifiers from an input product is enough to narrow down its origins to a maximum of 3 products within 89% of the cases. We validate the proposed approach by mapping Debian source packages implemented in Python to the corresponding PyPI packages; this approach uses at most five trials, where each trial uses three randomly chosen global identifiers from a randomly chosen python file of the subject software package, then ranks results using a popularity index and requires to inspect only the top result. In our experiments, this method is effective at finding the true origin of a project with a recall of 0.9 and precision of 0.77. (10.1007/s10664-023-10317-8)
  DOI : 10.1007/s10664-023-10317-8
• Phase estimation at the point-ahead angle for AO pre-compensated ground to GEO satellite telecoms
  
  • Lognoné Perrine
  • Conan Jean-Marc
  • Rekaya Ghaya
  • Védrenne Nicolas
  Optics Express, Optical Society of America - OSA Publishing, 2023, 31 (3), pp.3441. We present a new method to estimate the off-axis adaptive optics pre-compensation phase of a ground to GEO satellite telecom link suffering from point-ahead anisoplanatism. The proposed phase estimator relies on the downlink phase and log-amplitude measurements that are available at the optical ground station. We introduce the analytical tools, extended from the literature, to build the estimator as well as a general modal formalism to express the reciprocal residual phase covariance matrix resulting from any estimation linear with measurements. We use this residual phase covariance matrix to generate independent coupled flux samples thanks to a pseudo-analytical approach and study the gain offered by the proposed estimator on the coupled flux statistics, in various atmospheric conditions. The estimator is shown to reduce the anisoplanatic residual phase variance by at least 35%, and 46% at best, with a greater impact on the lower modes, especially on the tip and tilt residual phase variances. The phase variance reduction brings a gain up to 15 dB on the cumulative density function at probability 10 −3 . This gain should allow to relax the power constraints on the link budget at the OGS and renews the interest in large aperture diameter (60 cm class telescopes) for GEO Feeder links by reducing the atmospheric turbulence impact on the uplink coupled signal. (10.1364/OE.476328)
  DOI : 10.1364/OE.476328
• Multi-Agent Systems
  
  • Malvone Vadim
  • Murano Aniello
  , 2023, 14282, pp.XX-554. This volume LNCS 14282 constitutes the refereed proceedings of the 20th European Conference EUMAS 2023, held in Naples, Italy, during September 2023. This volume includes 24 full papers and 5 short papers, carefully selected from 47 submissions. Additionally, the volume features 16 short papers, rigorously reviewed from 20 submissions for the PhD day. The conference focused on the theory and practice of autonomous agents and multi-agent systems, covering a wide range of topics. (10.1007/978-3-031-43264-4)
  DOI : 10.1007/978-3-031-43264-4
• (Adversarial) Electromagnetic Disturbance in the Industry
  
  • Beckers Arthur
  • Guilley Sylvain
  • Maurine Philippe
  • O'Flynn Colin
  • Picek Stjepan
  IEEE Transactions on Computers, Institute of Electrical and Electronics Engineers, 2023, 72 (2), pp.414-422. Faults occur naturally and are responsible for reliability concerns. Faults are also an interesting tool for attackers to extract sensitive information from secure chips. In particular, non-invasive fault attacks have received a fair amount of attention. One easy way to perturb a chip without altering it is the so-called Electromagnetic Fault Injection (EMFI). Such attack has been studied in great depth, and nowadays, it is part and parcel of the state-of-the-art. Indeed, new capabilities have emerged where EM experimental benches are used to cryptanalyze chips. The progress of this "field" is fast, in terms of reproducibility, accuracy, and number of use-cases. However, there is too little awareness about such advances. In this paper, we aim to expose the true harmfulness of EMFI (including reproducibility) to enable reasonable security quotations. We also analyze protections (at hardware/firmware/system levels) in light of their efficiency. We characterize the specificity of EM fault injection compared to other injection means (laser, glitch, probing). (10.1109/TC.2022.3224373)
  DOI : 10.1109/TC.2022.3224373
• Interband cascade technology for energy-efficient mid-infrared free-space communication
  
  • Didier Pierre
  • Knötig Hedwig
  • Spitz Olivier
  • Cerutti Laurent
  • Lardschneider Anna
  • Awwad Elie
  • Diaz-Thomas Daniel
  • Baranov A.
  • Weih Robert
  • Koeth Johannes
  • Schwarz Benedikt
  • Grillot Frédéric
  Photonics research, Optical Society of America, 2023, 11 (4), pp.582. Space-to-ground high-speed transmission is of utmost importance for the development of a worldwide broadband network. Mid-infrared wavelengths offer numerous advantages for building such a system, spanning from low atmospheric attenuation to eye-safe operation and resistance to inclement weather conditions. We demonstrate a full interband cascade system for high-speed transmission around a wavelength of 4.18 µm. The low-power consumption of both the laser and the detector in combination with a large modulation bandwidth and sufficient output power makes this technology ideal for a free-space optical communication application. Our proof-of-concept experiment employs a radio-frequency optimized Fabry–Perot interband cascade laser and an interband cascade infrared photodetector based on a type-II InAs/GaSb superlattice. The bandwidth of the system is evaluated to be around 1.5 GHz. It allows us to achieve data rates of 12 Gbit/s with an on–off keying scheme and 14 Gbit/s with a 4-level pulse amplitude modulation scheme. The quality of the transmission is enhanced by conventional pre- and post-processing in order to be compatible with standard error-code correction. (10.1364/PRJ.478776)
  DOI : 10.1364/PRJ.478776
• Integration of heterogeneous components for co-simulation
  
  • Jerray Jawher
  • Ameur-Boulifa Rabéa
  • Apvrille Ludovic
  , 2023. Because of their complexity, embedded systems are designed with sub-systems or components taken in charge by different development teams or entities and with different modeling frameworks and simulation tools, depending on the characteristics of each component. Unfortunately, this diversity of tools and semantics makes the integration of these heterogeneous components difficult. Thus, to evaluate their integration before their hardware or software is available, one solution would be to merge them into a common modeling framework. Yet, such a holistic environment supporting many computation and computation semantics seems hard to settle. Another solution we investigate in this paper is to generically link their respective simulation environments in order to keep the strength and semantics of each component environment. The paper presents a method to simulate heterogeneous components of embedded systems in real-time. These components can be described at any abstraction level. Our main contribution is a generic glue that can analyze in real-time the state of different simulation environments and accordingly enforce the correct communication semantics between components. Once presented in a generic way, our glue is illustrated with Apache Kafka as the communication facility between simulation engines. It is then applied to two model and simulation frameworks: TTool and SystemC. Finally, Zigbee serves as a case study to illustrate the strengths of our approach.
• MPEG immersive video
  
  • Garus Patrick
  • Milovanović Marta
  • Jung Joël
  • Cagnazzo Marco
  , 2023, pp.327-356. MPEG immersive video (MIV) is a novel standard, enabling the compression of volumetric video content. In this chapter, we describe MIV, its tools, and its profiles. Given that MIV is a video-based solution, the texture and geometry information is coded using available 2D video codecs, which are independent of MIV. We present the performance of MIV with several state-of-the-art 2D codecs: VVC, AV1, and AVS3, highlighting that the eventual success of MIV does not depend on the market share of any particular 2D codec. However, using suitable tools for the coding of MIV texture or depth map atlases is an important requirement for efficient compression of immersive video. In this context, we present results related to screen content coding tools of VVC and show their potential for the compression of MIV atlases. (10.1016/B978-0-32-391755-1.00018-3)
  DOI : 10.1016/B978-0-32-391755-1.00018-3
• Limitations of local update recovery in stabilizer-GKP codes: a quantum optimal transport approach
  
  • Koenig Robert
  • Rouzé Cambyse
  , 2023. Local update recovery seeks to maintain quantum information by applying local correction maps alternating with and compensating for the action of noise. Motivated by recent constructions based on quantum LDPC codes in the finite-dimensional setting, we establish an analytic upper bound on the fault-tolerance threshold for concatenated GKP-stabilizer codes with local update recovery. Our bound applies to noise channels that are tensor products of one-mode beamsplitters with arbitrary environment states, capturing, in particular, photon loss occurring independently in each mode. It shows that for loss rates above a threshold given explicitly as a function of the locality of the recovery maps, encoded information is lost at an exponential rate. This extends an early result by Razborov from discrete to continuous variable (CV) quantum systems. To prove our result, we study a metric on bosonic states akin to the Wasserstein distance between two CV density functions, which we call the bosonic Wasserstein distance. It can be thought of as a CV extension of a quantum Wasserstein distance of order 1 recently introduced by De Palma et al. in the context of qudit systems, in the sense that it captures the notion of locality in a CV setting. We establish several basic properties, including a relation to the trace distance and diameter bounds for states with finite average photon number. We then study its contraction properties under quantum channels, including tensorization, locality and strict contraction under beamsplitter-type noise channels. Due to the simplicity of its formulation, and the established wide applicability of its finite-dimensional counterpart, we believe that the bosonic Wasserstein distance will become a versatile tool in the study of CV quantum systems. (10.48550/arXiv.2309.16241)
  DOI : 10.48550/arXiv.2309.16241
• Efficient learning of the structure and parameters of local Pauli noise channels
  
  • Rouzé Cambyse
  • Stilck Franca Daniel
  , 2023. The unavoidable presence of noise is a crucial roadblock for the development of large-scale quantum computers and the ability to characterize quantum noise reliably and efficiently with high precision is essential to scale quantum technologies further. Although estimating an arbitrary quantum channel requires exponential resources, it is expected that physically relevant noise has some underlying local structure, for instance that errors across different qubits have a conditional independence structure. Previous works showed how it is possible to estimate Pauli noise channels with an efficient number of samples in a way that is robust to state preparation and measurement errors, albeit departing from a known conditional independence structure. We present a novel approach for learning Pauli noise channels over n qubits that addresses this shortcoming. Unlike previous works that focused on learning coefficients with a known conditional independence structure, our method learns both the coefficients and the underlying structure. We achieve our results by leveraging a groundbreaking result by Bresler for efficiently learning Gibbs measures and obtain an optimal sample complexity of O(log(n)) to learn the unknown structure of the noise acting on n qubits. This information can then be leveraged to obtain a description of the channel that is close in diamond distance from O(poly(n)) samples. Furthermore, our method is efficient both in the number of samples and postprocessing without giving up on other desirable features such as SPAM-robustness, and only requires the implementation of single qubit Cliffords. In light of this, our novel approach enables the large-scale characterization of Pauli noise in quantum devices under minimal experimental requirements and assumptions. (10.48550/arXiv.2307.02959)
  DOI : 10.48550/arXiv.2307.02959
• Proceedings of The Semantic Web: ESWC 2023 Satellite Events
  
  • Pesquita Catia
  • Skaf-Molli Hala
  • Efthymiou Vasilis
  • Kirrane Sabrina
  • Ngonga Axel
  • Collarana Diego
  • Cerqueira Renato
  • Alam Mehwish
  • Trojahn Cassia
  • Hertling Sven
  , 2023, 13998. (10.1007/978-3-031-43458-7)
  DOI : 10.1007/978-3-031-43458-7
• Some Complexity Considerations on the Uniqueness of Graph Colouring
  
  • Hudry Olivier
  • Lobstein Antoine
  WSEAS Transactions on Mathematics, World Scientific and Engineering Academy and Society (WSEAS), 2023, 22, pp.art. #54, 483-493. For some well-known NP-complete problems, linked to the satisfiability of Boolean formulas and the colourability of a graph, we study the variation which consists in asking about the uniqueness of a solution.In particular, we show that five decision problems, Unique Satisfiability (U-SAT), Unique k-Satisfiability (U-k-SAT), k ≥ 3, Unique One-in-Three Satisfiability (U-1-3-SAT), Unique k-Colouring (U-k-COL), k ≥ 3, and Unique Colouring (U-COL), have equivalent complexities, up to polynomials —when dealing with colourings, we forbid permutations ofcolours. As a consequence, all are NP-hard and belong to the class DP. We also consider the problems U-2-SAT, U-2-COL and Unique Optimal Colouring (U-OCOL). (10.37394/23206.2023.22.54)
  DOI : 10.37394/23206.2023.22.54
• Improving Causal Learning Scalability and Performance using Aggregates and Interventions
  
  • Fadiga Kanvaly
  • Houzé Etienne
  • Diaconescu Ada
  • Dessalles Jean-Louis
  ACM Transactions on Autonomous and Adaptive Systems, Association for Computing Machinery (ACM), 2023. Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system self-adaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing “ do-operations ”. The obtained CBN could then be employed for causal inference. The main challenges of this approach included: “non-doable variables” and limited scalability. To address these issues, we propose three extensions: i) early pruning weakly correlated relations to reduce the number of required do-operations; ii) introducing aggregate variables that summarize relations between weakly-coupled sub-systems; iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. We illustrate and evaluate the efficiency of these contributions via examples from the smart home and power grid domain. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way towards applications in large CPS. (10.1145/3607872)
  DOI : 10.1145/3607872
• Dynamic Autoencoders Against Adversarial Attacks
  
  • Chabanne Hervé
  • Despiegel Vincent
  • Gentric Stéphane
  • Guiga Linda
  Procedia Computer Science, Elsevier, 2023, 220, pp.782-787. Neural Networks are the target of numerous adversarial attacks. In those, the adversary perturbs a model's input with a noise that is small, but large enough to fool the model. In this article, we propose to dynamically add autoencoders from a pretrained set to a base model as a countermeasure to such attacks. This doing, we modify the underlying labels regions of the model to be protected, letting the adversary unable to craft relevant adversarial perturbations. Our experiments confirm the efficiency of our protection when the pretrained set has enough elements. (10.1016/j.procs.2023.03.104)
  DOI : 10.1016/j.procs.2023.03.104
• Affine invariant integrated rank-weighted statistical depth: properties and finite sample analysis
  
  • Clémençon Stephan
  • Mozharovskyi Pavlo
  • Staerman Guillaume
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2023, 17 (2), pp.3854 - 3892. Because it determines a center-outward ordering of observations in Rd with d≥2, the concept of statistical depth permits to define quantiles and ranks for multivariate data and use them for various statistical tasks (e.g. inference, hypothesis testing). Whereas many depth functions have been proposed ad-hoc in the literature since the seminal contribution of [50], not all of them possess the properties desirable to emulate the notion of quantile function for univariate probability distributions. In this paper, we propose an extension of the integrated rank-weighted statistical depth (IRW depth in abbreviated form) originally introduced in [40], modified in order to satisfy the property of affine invariance, fulfilling thus all the four key axioms listed in the nomenclature elaborated by [59]. The variant we propose, referred to as the affine invariant IRW depth (AI-IRW in short), involves the precision matrix of the (supposedly square integrable) d-dimensional random vector X under study, in order to take into account the directions along which X is most variable to assign a depth value to any point x∈Rd. The accuracy of the sampling version of the AI-IRW depth is investigated from a non-asymptotic perspective. Namely, a concentration result for the statistical counterpart of the AI-IRW depth is proved. Beyond the theoretical analysis carried out, applications to anomaly detection are considered and numerical results are displayed, providing strong empirical evidence of the relevance of the depth function we propose here. (10.1214/23-EJS2189)
  DOI : 10.1214/23-EJS2189
• Towards a Development Process for Multi-CPU Distributed Synchronous Software Applications
  
  • Lubat Eric
  • Jenn Eric
  • Blouin Dominique
  • Kaufmann Marc
  Psychology in Spain, Colegio Oficial de Psicólogos, 2023, pp.549-558. (10.1109/MODELS-C59198.2023.00092)
  DOI : 10.1109/MODELS-C59198.2023.00092
• Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling
  
  • Djebra Yanis
  • Marin Thibault
  • Han Paul K
  • Bloch Isabelle
  • Fakhri Georges El
  • Ma Chao
  IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2023, 42 (1), pp.158-169. The spatial resolution and temporal framerate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled k-space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI. The performance of the proposed method was evaluated and compared to stateof-the-art methods using numerical simulation studies as well as 2D and 3D in vivo cardiac imaging experiments. The proposed method achieved the best performance in image reconstruction among all the compared methods. The proposed method could prove useful for accelerating many MRI applications, including dynamic MRI, multi-parametric MRI, and MR spectroscopic imaging. (10.1109/TMI.2022.3207774)
  DOI : 10.1109/TMI.2022.3207774
• Online Matching in Geometric Random Graphs
  
  • Sentenac Flore
  • Noiry Nathan
  • Lerasle Matthieu
  • Ménard Laurent
  • Perchet Vianney
  , 2023. We investigate online maximum cardinality matching, a central problem in ad allocation. In this problem, users are revealed sequentially, and each new user can be paired with any previously unmatched campaign that it is compatible with. Despite the limited theoretical guarantees, the greedy algorithm, which matches incoming users with any available campaign, exhibits outstanding performance in practice. Some theoretical support for this practical success has been established in specific classes of graphs, where the connections between different vertices lack strong correlations-an assumption not always valid in real-world situations. To bridge this gap, we focus on the following model: both users and campaigns are represented as points uniformly distributed in the interval [0, 1], and a user is eligible to be paired with a campaign if they are "similar enough," meaning the distance between their respective points is less than c/N , where c > 0 is a model parameter. As a benchmark, we determine the size of the optimal offline matching in these bipartite random geometric graphs. We achieve this by introducing an algorithm that constructs the optimal matching and analyzing it. We then turn to the online setting and investigate the number of matches made by the online algorithm CLOSEST, which pairs incoming points with their nearest available neighbors in a greedy manner. We demonstrate that the algorithm's performance can be compared to its fluid limit, which is completely characterized as the solution to a specific partial differential equation (PDE). From this PDE solution, we can compute the competitive ratio of CLOSEST, and our computations reveal that it remains significantly better than its worst-case guarantee. This model turns out to be closely related to the online minimum cost matching problem, and we can extend the results obtained here to refine certain findings in that area of research. Specifically, we determine the exact asymptotic cost of CLOSEST in the ϵ-excess regime, providing a more accurate estimate than the previously known loose upper bound.
• Zero-shot spatial layout conditioning for text-to-image diffusion models
  
  • Couairon Guillaume
  • Careil Marlène
  • Cord Matthieu
  • Lathuilière Stéphane
  • Verbeek Jakob
  , 2023. Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to drive the image generation process. Expressing spatial constraints, e.g. to position specific objects in particular locations, is cumbersome using text; and current text-based image generation models are not able to accurately follow such instructions. In this paper we consider image generation from text associated with segments on the image canvas, which combines an intuitive natural language interface with precise spatial control over the generated content. We propose ZestGuide, a zero-shot segmentation guidance approach that can be plugged into pre-trained text-to-image diffusion models, and does not require any additional training. It leverages implicit segmentation maps that can be extracted from cross-attention layers, and uses them to align the generation with input masks. Our experimental results combine high image quality with accurate alignment of generated content with input segmentations, and improve over prior work both quantitatively and qualitatively, including methods that require training on images with corresponding segmentations. Compared to Paint with Words, the previous state-of-the art in image generation with zero-shot segmentation conditioning, we improve by 5 to 10 mIoU points on the COCO dataset with similar FID scores.
• Test your samples jointly: Pseudo-reference for image quality evaluation
  
  • Tworski Marcelin
  • Lathuilière Stéphane
  , 2023. In this paper, we address the well-known image quality assessment problem but in contrast from existing approaches that predict image quality independently for every images, we propose to jointly model different images depicting the same content to improve the precision of quality estimation. This proposal is motivated by the idea that multiple distorted images can provide information to disambiguate image features related to content and quality. To this aim, we combine the feature representations from the different images to estimate a pseudo-reference that we use to enhance score prediction. Our experiments show that at test-time, our method successfully combines the features from multiple images depicting the same new content, improving estimation quality.
• Few-shot Semantic Image Synthesis with Class Affinity Transfer
  
  • Careil Marlène
  • Verbeek Jakob
  • Lathuilière Stéphane
  , 2023. Semantic image synthesis aims to generate photo realistic images given a semantic segmentation map. Despite much recent progress, training them still requires large datasets of images annotated with per-pixel label maps that are extremely tedious to obtain. To alleviate the high annotation cost, we propose a transfer method that leverages a model trained on a large source dataset to improve the learning ability on small target datasets via estimated pairwise relations between source and target classes. The class affinity matrix is introduced as a first layer to the source model to make it compatible with the target label maps, and the source model is then further finetuned for the target domain. To estimate the class affinities we consider different approaches to leverage prior knowledge: semantic segmentation on the source domain, textual label embeddings, and self-supervised vision features. We apply our approach to GAN-based and diffusion-based architectures for semantic synthesis. Our experiments show that the different ways to estimate class affinity can be effectively combined, and that our approach significantly improves over existing state-of-the-art transfer approaches for generative image models.
• Visualization Empowerment: How to Teach and Learn Data Visualization
  
  • Bach Benjamin
  • Carpendale Sheelagh
  • Hinrichs Uta
  • Huron Samuel
  , 2023, pp.10.4230/DagRep.12.6.83. Data visualization is becoming an important asset for a data-literate, informed, and critical society. Despite the variety of existing resources to teach theories and practical skills in this domain, little is known about 1) how learning processes in the context of visualization unfold and 2) best practices for engaging and teaching data visualization to diverse audiences and in different contexts. This Dagstuhl Seminar invited practitioners, researchers, and teachers from the areas of visualization, design, education and cognitive psychology to explore these questions from multiple perspectives. Through a range of practical activities, talks, and discussions, we have begun characterizing and classifying teaching methodologies. We have redacted a pedagogical manifesto, and started formalizing the concept of improvisation with visualization in the context of teaching and learning. We have also interrogated creativity as an important aspect of visualization teaching and learning and explored links between data physicalization and visualization teaching activities. Across these different themes, we have begun to map out the challenges of visualization teaching and learning and the opportunities for research and practice in this area. (10.4230/DagRep.12.6.83)
  DOI : 10.4230/DagRep.12.6.83
• Parallelizable Synthesis of Arbitrary Single-Qubit Gates with Linear Optics and Time-Frequency Encoding
  
  • Henry Antoine
  • Raghunathan Ravi
  • Ricard Guillaume
  • Lefaucher Baptiste
  • Miatto Filippo
  • Belabas Nadia
  • Zaquine Isabelle
  • Alléaume Romain
  Physical Review A, American Physical Society, 2023, 107, pp.062610. We propose novel methods for the exact synthesis of single-qubit unitaries with high success probability and gate fidelity, considering both time-bin and frequency-bin encodings. The proposed schemes are experimentally implementable with a spectral linear-optical quantum computation (S-LOQC) platform, composed of electro-optic phase modulators and phase-only programmable filters (pulse shapers). We assess the performances in terms of fidelity and probability of the two simplest 3-components configurations for arbitrary gate generation in both encodings and give an exact analytical solution for the synthesis of an arbitrary single-qubit unitary in the time-bin encoding, using a single-tone Radio Frequency (RF) driving of the EOMs. We further investigate the parallelization of arbitrary single-qubit gates over multiple qubits with a compact experimental setup, both for spectral and temporal encodings. We systematically evaluate and discuss the impact of the RF bandwidththat conditions the number of tones driving the modulators-and of the choice of encoding for different targeted gates. We moreover quantify the number of high fidelity Hadamard gates that can be synthesized in parallel, with minimal and increasing resources in terms of driving RF tones in a realistic system. Our analysis positions spectral S-LOQC as a promising platform to conduct massively parallel single qubit operations, with potential applications to quantum metrology and quantum tomography. (10.1103/PhysRevA.107.062610)
  DOI : 10.1103/PhysRevA.107.062610
• LEARNING RAW IMAGE DENOISING USING A PARAMETRIC COLOR IMAGE MODEL
  
  • Achddou Raphaël
  • Gousseau Yann
  • Ladjal Saïd
  , 2023. Deep learning methods for image restoration have produced impressive results over recent years. Nevertheless, they generalize poorly and need large learning image datasets to be collected for each new acquisition modality. In order to avoid the building of such datasets, it has been recently proposed to develop synthetic image datasets for training image restoration methods, using scale invariant dead leaves models. While the geometry of such models can be successfully encoded with only a few parameters, the color content cannot be straightforwardly encoded. In this paper, we leverage the concept of color lines prior to build a light parametric color model relying on a chromaticity/luminance factorization. Further, we show that the corresponding synthetic dataset can be used to train neural networks for the denoising of RAW images from different camera-phones, without using any image from these devices. This shows the potential of our approach to increase the generalization capacity of learning-based denoising approaches in real case scenarios.
• Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT
  
  • Legros Quentin
  • Fourer Dominique
  Sensors, MDPI, 2023, 23 (1), pp.85. This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions. (10.3390/s23010085)
  DOI : 10.3390/s23010085
• Provably Efficient Learning of Phases of Matter via Dissipative Evolutions
  
  • Onorati Emilio
  • Rouzé Cambyse
  • Watson James
  • Stilck França Daniel
  , 2023. The combination of quantum many-body and machine learning techniques has recently proved to be a fertile ground for new developments in quantum computing. Several works have shown that it is possible to classically efficiently predict the expectation values of local observables on all states within a phase of matter using a machine learning algorithm after learning from data obtained from other states in the same phase. However, existing results are restricted to phases of matter such as ground states of gapped Hamiltonians and Gibbs states that exhibit exponential decay of correlations. In this work, we drop this requirement and show how it is possible to learn local expectation values for all states in a phase, where we adopt the Lindbladian phase definition by Coser \& Pérez-García [Coser \& Pérez-García, Quantum 3, 174 (2019)], which defines states to be in the same phase if we can drive one to other rapidly with a local Lindbladian. This definition encompasses the better-known Hamiltonian definition of phase of matter for gapped ground state phases, and further applies to any family of states connected by short unitary circuits, as well as non-equilibrium phases of matter, and those stable under external dissipative interactions. Under this definition, we show that $N = O(\log(n/δ)2^{polylog(1/ε)})$ samples suffice to learn local expectation values within a phase for a system with $n$ qubits, to error $ε$ with failure probability $δ$. This sample complexity is comparable to previous results on learning gapped and thermal phases, and it encompasses previous results of this nature in a unified way. Furthermore, we also show that we can learn families of states which go beyond the Lindbladian definition of phase, and we derive bounds on the sample complexity which are dependent on the mixing time between states under a Lindbladian evolution. (10.48550/arXiv.2311.07506)
  DOI : 10.48550/arXiv.2311.07506