Portail HAL du Collège de France
Not a member yet
22122 research outputs found
Sort by
Le contextualisme conceptuel et la polysémie mentale
No full textInternational audienceIl y a deux François Recanati : l'un est philosophe du langage, l'autre philosophe de l'esprit. Tous deux sont contextualistes. Mais il y a une différence entre leurs formes respectives de contextualisme. En philosophie du langage, le contextualisme radical de Recanati repose sur une double généralisation de la dépendance contextuelle -celle de l'indexicalité à tous les termes référentiels, et celle de la polysémie à toutes les expressions de classe ouverte. En philosophie de l'esprit, l'indexicalité est aussi quasi-généralisée, à travers l'identification des concepts singuliers à des fichiers mentaux, qui sont eux-mêmes conçus comme des indexicaux. Mais la polysémie ne fait pas l'objet d'une généralisation parallèle au niveau des concepts. Le présent article vise à encourager les deux Recanati à harmoniser leurs positions. Il invite à postuler des concepts/fichiers polysémiques, et vise à montrer qu'une telle postulation, contrairement à ce qu'on pourrait croire, ne remet en cause ni le contextualisme radical en philosophie du langage, ni les principes fondamentaux du modèle des fichiers mentaux
Puissants laïcs et religieux : continuité aristocratique en Provence (V<sup>e</sup>-VIII<sup>e</sup> siècle)
No full textNational audienc
Advances in supercritical fluid chromatography for lipid analysis and purification – A review of the past decade
No full textInternational audienceThis review explores the advances in Supercritical Fluid Chromatography (SFC) for the analysis and purification of lipids achieved over the past decade. Lipidomics has become an essential tool in numerous fields such as biology and food science. Compared to liquid and gas chromatography, SFC, which utilizes supercritical COQ as a mobile phase, offers faster analysis, reduced solvent use, and improved separation of complex lipidic substances. This review synthesizes findings from over 80 studies, evaluating various stationary phases, retention mechanisms, and optimized conditions for both lipid class and intra-class separations. It provides detailed comparisons of columns for different lipid types, such as fatty acids, glycerolipids, and phospholipids, and explores preparative-scale applications for bioactive lipid purification, including omega-3 fatty acids and sterols. By consolidating this knowledge, this review will serve as a practical guide for chemists to select optimal columns and conditions without extensive testing, thereby streamlining method development and reducing costs
The Grotte du Bison Neandertals (Arcy-sur-Cure, France)
No full textInternational audienceThe Grotte du Bison, in Arcy-sur-Cure (Yonne, France), yielded a large assemblage of 49 Neandertal remains from late Mousterian layers, offering critical insights for the study of Middle to Upper Paleolithic populations of Western Europe. Previous studies described the external morphology of 13 isolated teeth and a partial maxilla. Building on this previous work, the current study provides further descriptions and analyses of the remains, including one postcranial fragment, six cranial fragments, two maxillary fragments, and 40 isolated teeth. The dental remains are examined for a more detailed assessment of the metric and nonmetric variability of their external and internal morphologies. We focus our description on preservation, health status, and age at death, and we assess the minimum number of individuals. The dental variability is also compared to that of Middle and Upper Pleistocene hominins. Our results indicate that the collection represents at least nine to 17 individuals, comprising mostly children and adolescents. Five to seven pairings are identified based on shared dental traits, developmental criteria, such as perikymata and pitted hypoplasia, wear patterns, and taphonomic alterations. This collection exhibits characteristic Neandertal features, including occasionally markedly expressed traits (e.g., I1 and P3 ridging and tubercular expressions), as well as a homogenous expression of accessory structures (particularly for the molars). The highest morphological variability is observed on maxillary premolar roots, which display different stages of root fusion, mesially placed hypercementosis, and pulp cavity extension. This collection also reflects the morphological and behavioral diversity observed in the other Arcy-sur-Cure caves
Can non-human primates extract the linear trend from a noisy scatterplot ?
No full textInternational audienceRecent studies showed that humans, regardless of age, education, and culture, can extract the linear trend of a noisy scatterplot. Although this capacity looks sophisticated, it may simply reflect the extraction of the principal trend of the graph, as if the cloud of dots was processed as an oriented object. To test this idea, we trained Guinea baboons to associate arbitrary shapes with the increasing or decreasing trends of noiseless and noisy scatterplots, while varying the number of points, the noise level, and the regression slope. Many baboons successfully learned this conditional match-to-sample task, and their accuracy varied as a sigmoid function of the t-value of the regression, the same statistical index upon which humans also base their answers. The perceptual component of human graphics abilities seems thus to be based on the recycling of a phylogenetically older competence of the primate visual system for extracting the principal axes of visual displays
Deep-learning based Detection and Segmentation in Archaeology
No full textInternational audience1 Introduction Modern archaeology benefits from a convergence between traditional excavation methods and technological advancements, particularly those stemming from computer vision. Among these technologies, image segmentation plays a central role. It involves dividing an image into multiple meaningful regions to isolate specific elements, such as artifacts, architectural structures, or traces of ancient carvings. This task is crucial for extracting, analyzing, and interpreting visual information from archaeological surveys. However, segmentation in the archaeological context poses specific challenges. Images often come from complex scenes where objects of interest may be partially buried, degraded, or blended into their surroundings. Varied textures, shadows, and overlapping elements make it difficult to accurately identify shapes and contours. Despite these challenges, modern approaches, particularly those based on deep learning, have significantly improved segmentation capabilities. In this work, we propose applying two state-of-the-art segmentation methods to two archaeological problems. We will focus, on the one hand, on the detection and segmentation of petroglyphs, and on the other hand, on the segmentation of mosaics into tesserae. 2 Methods and Materials Petroglyphs Detection with YOLOv9 Petroglyphs serve as immutable spatio-temporal markers that hold crucial information about the history of local settlements. The study of these archaeological sites (Danielyan 2020) often requires cataloging all the petroglyphs present. This process traditionally involves photographing the rocks of interest and subsequently analyzing the images, manually detecting and outlining each petroglyph — a labor-intensive task. Using YOLOv9, we propose automating this demanding step. YOLO (You Only Look Once) is a family of real-time object detection models renowned for their speed and efficiency. Designed to simultaneously localize and classify objects in an image in a single step, these networks have evolved over successive versions to deliver increasingly impressive performance. With YOLOv9 (Wang, Yeh, and Mark Liao 2025), the latest iteration in the series, significant improvements have been introduced. This version leverages advancements in network architecture, optimization, and data processing to enhance accuracy while maintaining exceptional speed. YOLOv9 incorporates optimized modules such as advanced attention mechanisms, adaptive anchoring strategies, and better balancing for detecting objects of various sizes. We trained YOLOv9 with annotated images provided by archaeologists. Due to the limited availability of data, data augmentation was essential. Additionally, the images originated from only a few sites, influencing various factors such as rock color. Training was automatically halted after 379 epochs, demonstrating YOLOv9’s capability to adapt to this specialized archaeological dataset. Segmenting Mosaics with Segment Anything Another aspect of our work focuses on the segmentation of tesserae that compose mosaics. These tesserae exhibit varying shapes and sizes, often irregular, and are separated by mortar 155 arranged in a non-uniform manner. Furthermore, the tesserae typically have muted colors and low contrast, making it difficult to distinguish them from the mortar. Automatic segmentation of tesserae thus represents a significant challenge. The goal of this study is to develop a segmentation method specifically tailored to mosaics, concentrating on extracting tesserae as distinct entities. Such an approach would allow archaeologists to analyze the tesserae directly, facilitating their digitization. The detected tesserae would form the basis for deeper analysis, aiding in the interpretation and utilization of the extracted information. While machine learning approaches like Segment Anything (Kirillov et al. 2023) outperform traditional methods, they are not without limitations. When applied to the full image of a mosaic, Segment Anything tends to detect broader shapes, such as characters or decorative elements on the mosaic, rather than focusing on individual tesserae. To counter this, we apply Segment Anything to smaller, localized sections of the image devoid of identifiable forms. This adjustment allows for the generation of more precise masks. This approach, however, requires post-processing. Issues such as duplicate masks and overlaps can arise. To address these, statistical analysis of tesserae sizes and a selection algorithm for the masks are employed to eliminate undesirable duplicates and guarantee segmentation accuracy. 3 Results and Discussion For petroglyphs, our algorithm delivers satisfactory results, particularly when petroglyphs from the same site are included in the training data. However, due to the vast variability in features (e.g., petroglyph shapes, rock types), some elements may go undetected. To address this, an executable application has been developed, allowing archaeologists to manually refine the results produced by the network. This tool will be released as open-source software. Figure 1 illustrates an example of petroglyph segmentation before and after archaeologist intervention. Similarly, the tesserae segmentation will also be integrated into an open-source application. This tool will support the use of various input image types, such as those enhanced through gradient emphasis or captured under different lighting conditions for the same scene. This application has already been employed to conduct a statistical study on the tesserae of Saint-Romain-en-Gal (France), analyzing their size, color, and roughness. This study enabled the tesserae to be grouped based on these characteristics, marking the beginning of an investigation into the materials used in their construction
Electrocatalytic reduction of carbon dioxide using Cu-based ecocatalysts
No full textInternational audienceDifferent biomass wastes were transformed into Cu-based ecocatalysts®, with different CuO concentrations, distribution and morphology, for CO2 electroreduction, for the first time. Samples with high Cu load proved as good as commercial CuO nanoparticles, with faradaic Efficiency for carbonaceous products of about 80% at a high applied current density
Introducing a Standardized and Adaptable Method for Rock Art Recording
No full textInternational audienc
Joint estimation of position and momentum with arbitrarily high precision using non-Gaussian states
No full textInternational audienceWe address the joint estimation of changes in the position and linear momentum of a quantum particle or, equivalently, changes in the complex field of a bosonic mode. Although these changes are generated by non-commuting operators, we show that leveraging non-Gaussianity enables their simultaneous estimation with arbitrarily high precision and arbitrarily low quantum incompatibility. Specifically, we demonstrate that any pure non-Gaussian state provides an advantage over all Gaussian states, whether pure or mixed. Moreover, properly tuned non-Gaussian mixtures of Gaussian states can also serve as a resource
Revue d'Etudes Tibétaines, no. 76, Avril 2025.
No full textEdition du no. 76 de la Revue d'Etudes Tibétaine