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De nouveaux alliés du bien-être des animaux confrontés aux défis des transitions agroécologiques et climatiques
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CCASL: Counterexamples to comparative analysis of scientific literature—Application to polymers
No full textInternational audienceThe exponential growth of scientific publications has made the exploration and comparative analysis of scientific literature increasingly complex. For instance, identifying pairs of publications that diverge on widely accepted concepts within a domain is extremely difficult, if not impossible, at a large scale. Our work aims to automatically detect such discrepancies using recent artificial intelligence techniques. Given a particular scientific domain, we propose to capture domain knowledge through the definition of arbitrary functions expressed as relaxed functional dependencies (RFDs), and then focus on the large-scale analysis of tables in the publications related to these RFDs. In this context, we propose a four-step method called Counterexamples to Comparative Analysis of Scientific Literature (CCASL), which consists of the following steps: (1) Modeling the domain knowledge with functions expressed as RFDs, (2) Acquiring a corpus of related publications, (3) Analyzing all tables in the PDF documents and producing a consolidated table, (4) Detecting counterexamples of the RFDs in the consolidated table and conducting a comparative analysis of the pairs of papers containing the detected counterexamples. We have applied CCASL to a subfield of polymer research by identifying an arbitrary function relating the storage modulus, the polymer structure, and the glass transition temperature. Based on this function, we implemented the four steps of CCASL for largescale bibliographic confrontation in polymer science, which enabled us to detect several counterexamples. After detailed analysis, these counterexamples were found to originate from two main sources: typographical errors and methodological inconsistencies. The latter led to an update of the initial arbitrary function, specifying that it is valid only for fully reacted mixtures
Quantum Mechanics on Lie Groups: I. Noncommutative Fourier Transforms
No full textInternational audienceStarting from square-integrable wave functions on a Lie group, we build an invertible Fourier transform mapping them on wave functions on the dual of the Lie algebra. This is a group-theoretic version of the map from position space to momentum space, with generally noncommuting momenta owing to the group structure. As a result, the multiplication of momentum-dependent functions involves star products, which makes the construction of noncommutative Fourier series much more involved than that of their commutative cousin. We show that our formalism provides an isometry of Hilbert spaces, and use it to derive a noncommutative Poisson summation formula for any compact Lie group. This is a key preliminary for the computation of Wigner functions and path integrals for quantum systems on group manifolds
Construction automatique d'un graphe de connaissances géo-historiques à partir de textes encyclopédiques anciens
No full textNational audienceLes encyclopédies anciennes, comme celle de Diderot et d'Alembert (1751-1772), offrent une ressource précieuse pour étudier l'évolution des savoirs géographiques, mais leur ampleur complique toute analyse manuelle. Cet article présente une méthode automatique de construction d'un graphe de connaissances géo-historiques à partir de ces textes. Nous proposons des ontologies spatiale et de provenance adaptées au corpus et introduisons un gold standard de 2 750 articles géographiques. Le pipeline combine apprentissage supervisé et grands modèles de langage pour la classification d'articles, le typage d'entités et l'extraction de relations spatiales. Les performances atteignent F1 = 92% pour les relations et F1 > 97% pour la classification, aboutissant à un graphe RDF de 35 000 entités et 46 000 relations. Ce travail ouvre la voie à l'analyse computationnelle des savoirs géographiques anciens
Approche multi-axes basée deep learning pour la segmentation 3D du diaphragme
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Doppler Frequency Estimation in Tensor-Based Modulation via Post-CPD Maximum Likelihood
No full textInternational audienceTensor-Based Modulation (TBM) is a novel wave-form design enabling efficient unsourced massive random access(UMAC) for future wireless networks. By leveraging tensoralgebra and Canonical Polyadic Decomposition (CPD), TBMallows to perform blind user separation at the receiver. However,the impact of Doppler frequency shifts on TBM has not yetbeen addressed. This paper shows that Doppler effects remainencoded mode-wise within the TBM factors, and hence do notdisrupt the blind user separation approach. Furthermore, theDoppler shifts associated to each user can be accurately esti-mated through a post-CPD per-user Maximum-Likelihood (ML)estimator. Simulation results show that the proposed approachonly requires a limited number pilot symbols while achievingaccurate Doppler recovery at low SNRs, thereby restoring thedecoding performance to a level nearly identical to the ideal zero-Doppler case. This makes the proposed approach well suited forshort-packet URLLC and IoT scenarios
Optimal sub-Gaussian variance proxy for truncated Gaussian and exponential random variables
No full textInternational audienceThis paper establishes the optimal sub-Gaussian variance proxy for truncated Gaussian and truncated exponential random variables. The proofs are based initially on reducing each distribution to their standardized versions. Geometrically, for the normal distribution, our argument consists of fitting a parabola to another parabola-looking function, which emerges from its moment generating function. For the exponential case, we show that the optimal variance proxy is the unique solution to a pair of equations and then provide this solution explicitly. Moreover, we demonstrate that truncated Gaussian variables exhibit strict sub-Gaussian behavior if and only if they are symmetric, meaning their truncation is symmetric with respect to the mean. Conversely, truncated exponential variables are shown to never exhibit strict sub-Gaussianity
Open-vocabulary models for object detection and segmentation in visual art: survey and comparative study
No full textInternational audienceObjects present in paintings help art history specialists interpret and decode artworks. The analysis of large, digitized artistic collections became feasible thanks to modern object detection approaches. Nevertheless, the use of object detection models typically requires fine-tuning for specific tasks. Therefore, art history specialists are remain constrained by the categories of objects in existing labeled artistic datasets when using artificial intelligence methods. This limitation can be overcome by using recent models that combine two modalities: vision and text. Vision-language models have made open-vocabulary detection (OVD) possible, allowing detection without restrictions on the applied categories, in contrast to fixed-vocabulary detection. Recent literature lacks a comprehensive review focusing on OVD in artistic images. In this paper we analyze state-of-the-art models for OVD, analyze their transferability to cultural heritage categories and systematically evaluate them on artistic datasets commonly used in literature. The DEArt and IconArt datasets, which are annotated with cultural heritage-specific categories contain paintings from the 11th to the 20th century. While the Watercolor2K dataset, annotated with common object categories consists of watercolor paintings. Based on our analysis, the OWLv2 model achieved the best performance in both object detection and grounding task scenarios on these datasets. Additionally, we discuss existing challenges of open-vocabulary segmentation in artistic images and future tasks
