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Статті в журналах з теми "Computationnelle"
Varenne, Franck. "Les simulations computationnelles dans les sciences sociales." Nouvelles perspectives en sciences sociales 5, no. 2 (July 6, 2010): 17–49. http://dx.doi.org/10.7202/044073ar.
Повний текст джерелаMeunier, Jean-Guy. "Enjeux de la modélisation formelle en sémiotique computationnelle." Cygne noir, no. 7 (June 1, 2022): 42–78. http://dx.doi.org/10.7202/1089329ar.
Повний текст джерелаLe Bouc, Raphaël, and Mathias Pessiglione. "La motivation dans tous ses K." médecine/sciences 34, no. 3 (March 2018): 238–46. http://dx.doi.org/10.1051/medsci/20183403012.
Повний текст джерелаFrançois, Thomas. "La Lisibilité Computationnelle." ITL - International Journal of Applied Linguistics 160 (January 1, 2010): 75–99. http://dx.doi.org/10.1075/itl.160.04fra.
Повний текст джерелаGoldbeter, Albert. "Biologie computationnelle des rythmes circadiens." Bulletin de la Classe des sciences 14, no. 1 (2003): 67–82. http://dx.doi.org/10.3406/barb.2003.28343.
Повний текст джерелаLareau, Francis. "Approche computationnelle de l’analyse conceptuelle." Philosophiques 49, no. 2 (2022): 413. http://dx.doi.org/10.7202/1097460ar.
Повний текст джерелаAydede, Murat. "Computation and Intentional Psychology." Dialogue 39, no. 2 (2000): 365–80. http://dx.doi.org/10.1017/s0012217300005977.
Повний текст джерелаLe Bouc, Raphaël, and Mathias Pessiglione. "Approche neuro-computationnelle de la procrastination." médecine/sciences 39, no. 11 (November 2023): 819–22. http://dx.doi.org/10.1051/medsci/2023151.
Повний текст джерелаDondero, Maria Giulia. "Les forces dans l’image et les gestualités émotionnelles." SHS Web of Conferences 81 (2020): 03005. http://dx.doi.org/10.1051/shsconf/20208103005.
Повний текст джерелаAyache, Nicholas. "De l’imagerie médicale à la médecine computationnelle." La lettre du Collège de France, no. 39 (March 1, 2015): 39. http://dx.doi.org/10.4000/lettre-cdf.1964.
Повний текст джерелаДисертації з теми "Computationnelle"
Debarnot, Valentin. "Microscopie computationnelle." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30156.
Повний текст джерелаThe contributions of this thesis are numerical and theoretical tools for the resolution of blind inverse problems in imaging. We first focus in the case where the observation operator is unknown (e.g. microscopy, astronomy, photography). A very popular approach consists in estimating this operator from an image containing point sources (microbeads or fluorescent proteins in microscopy, stars in astronomy). Such an observation provides a measure of the impulse response of the degradation operator at several points in the field of view. Processing this observation requires robust tools that can rapidly use the data. We propose a toolbox that estimates a degradation operator from an image containing point sources. The estimated operator has the property that at any location in the field of view, its impulse response is expressed as a linear combination of elementary estimated functions. This makes it possible to estimate spatially invariant (convolution) and variant (product-convolution expansion) operators. An important specificity of this toolbox is its high level of automation: only a small number of easily accessible parameters allows to cover a large majority of practical cases. The size of the point source (e.g. bead), the background and the noise are also taken in consideration in the estimation. This tool, coined PSF-estimator, comes in the form of a module for the Fiji software, and is based on a parallelized implementation in C++. The operators generated by an optical system are usually changing for each experiment, which ideally requires a calibration of the system before each acquisition. To overcome this, we propose to represent an optical system not by a single operator (e.g. convolution blur with a fixed kernel for different experiments), but by subspace of operators. This set allows to represent all the possible states of a microscope. We introduce a method for estimating such a subspace from a collection of low rank operators (such as those estimated by the toolbox PSF-Estimator). We show that under reasonable assumptions, this subspace is low-dimensional and consists of low rank elements. In a second step, we apply this process in microscopy on large fields of view and with spatially varying operators. This implementation is possible thanks to the use of additional methods to process real images (e.g. background, noise, discretization of the observation).The construction of an operator subspace is only one step in the resolution of blind inverse problems. It is then necessary to identify the degradation operator in this set from a single observed image. In this thesis, we provide a mathematical framework to this operator identification problem in the case where the original image is constituted of point sources. Theoretical conditions arise from this work, allowing a better understanding of the conditions under which this problem can be solved. We illustrate how this formal study allows the resolution of a blind deblurring problem on a microscopy example.[...]
Touret, Alain. "Vers une géométrie computationnelle." Nice, 2000. http://www.theses.fr/2000NICE5491.
Повний текст джерелаLe, Calvez Rozenn. "Approche computationnelle de l'acquisition précoce des phonèmes." Paris 6, 2007. http://www.theses.fr/2007PA066345.
Повний текст джерелаPanel, Nicolas. "Étude computationnelle du domaine PDZ de Tiam1." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX062/document.
Повний текст джерелаSmall protein domains often direct protein-protein interactions and regulate eukaryotic signalling pathways. PDZ domains are among the most widespread and best-studied. They specifically recognize the 4-10 C-terminal amino acids of target proteins. Tiam1 is a Rac GTP exchange factor that helps control cellmigration and proliferation and whose PDZ domain binds the proteins syndecan-1 (Sdc1), Caspr4, and Neurexin. Short peptides and peptidomimetics can potentially inhibit or modulate its action and act as bioreagents or therapeutics. We used computational protein design (CPD) and molecular dynamics (MD) free energy simulations to understand and engineer its peptide specificity. CPD uses a structural model and an energy function to explore the space of sequences and structures and identify stable and functional protein or peptide variants. We used our in-house Proteus CPD package to completely redesign the Tiam1 PDZ domain. The designed sequences were similar to natural PDZ domains, with similarity and fold recognition scores comarable to the widely-used Rosetta CPD package. Selected sequences, containing around 60 mutated positions out of 90, were tested by microsecond MD simulations and biophysical experiments. Four of five sequences tested experimentally (by our collaborators) displayed reversible unfolding around 50°C. Proteus also accurately scored the binding specificity of several protein and peptide variants. As a more refined model for specificity, we parameterized a semi-empirical free energy model of the Poisson-Boltzmann Linear Interaction Energy or PB/LIE form, which scores conformations extracted from explicit solvent MD simulations of PDZ:peptide complexes. With three adjustable parameters, the model accurately reproduced the experimental binding affinities of 41 variants, with a mean unsigned error of just 0.4 kcal/mol, andgave predictions for 10 new variants. The PB/LIE model was tested further by comparing to non-empirical, alchemical, MD free energy simulations, which have no adjustable parameters and were found to give chemical accuracy for 12 Tiam1:peptide complexes. The tools and insights obtained should help discover new tight binding peptides or peptidomimetics and have broad implications for engineering PDZ:peptide interactions
Pauwels, Edouard. "Applications de l'apprentissage statistique à la biologie computationnelle." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2013. http://pastel.archives-ouvertes.fr/pastel-00958432.
Повний текст джерелаJacob, Laurent. "A priori structurés pour l'apprentissage supervisé en biologie computationnelle." Phd thesis, École Nationale Supérieure des Mines de Paris, 2009. http://pastel.archives-ouvertes.fr/pastel-00005743.
Повний текст джерелаZheng, Léon. "Frugalité en données et efficacité computationnelle dans l'apprentissage profond." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0009.
Повний текст джерелаThis thesis focuses on two challenges of frugality and efficiency in modern deep learning: data frugality and computational resource efficiency. First, we study self-supervised learning, a promising approach in computer vision that does not require data annotations for learning representations. In particular, we propose a unification of several self-supervised objective functions under a framework based on rotation-invariant kernels, which opens up prospects to reduce the computational cost of these objective functions. Second, given that matrix multiplication is the predominant operation in deep neural networks, we focus on the construction of fast algorithms that allow matrix-vector multiplication with nearly linear complexity. More specifically, we examine the problem of sparse matrix factorization under the constraint of butterfly sparsity, a structure common to several fast transforms like the discrete Fourier transform. The thesis establishes new theoretical guarantees for butterfly factorization algorithms, and explores the potential of butterfly sparsity to reduce the computational costs of neural networks during their training or inference phase. In particular, we explore the efficiency of GPU implementations for butterfly sparse matrix multiplication, with the goal of truly accelerating sparse neural networks
Sander, David. "Approche computationnelle des mécanismes émotionnels : test de l'hypothèse de polarité." Lyon 2, 2002. http://demeter.univ-lyon2.fr:8080/sdx/theses/lyon2/2002/sander_d.
Повний текст джерелаSander, David Koenig Olivier. "Approche computationnelle des mécanismes émotionnels test de l'hypothèse de polarité /." Lyon : Université Lumière Lyon 2, 2002. http://demeter.univ-lyon2.fr:8080/sdx/theses/lyon2/2002/sander_d.
Повний текст джерелаBérenger, François. "Nouveaux logiciels pour la biologie structurale computationnelle et la chémoinformatique." Thesis, Paris, CNAM, 2016. http://www.theses.fr/2016CNAM1047/document.
Повний текст джерелаThis thesis introduces five software useful in three different areas : parallel and distributed computing, computational structural biology and chemoinformatics. The software from the parallel and distributed area is PAR. PAR allows to execute independent experiments in a parallel and distributed way. The software for computational structural biology are Durandal, EleKit and Fragger. Durandal exploits the propagation of geometric constraints to accelerate the exact clustering algorithm for protein models. EleKit allows to measure the electrostatic similarity between a chemical molecule and the protein it is designed to replace at a protein-protein interface. Fragger is a fragment picker able to select protein fragments in the whole protein data-bank. Finally, the chemoinformatics software is ACPC. ACPC encodes in a rotation-translation invariant way a chemical molecule in any or a combination of three chemical spaces (electrostatic, steric or hydrophobic). ACPC is a ligand-based virtual screening tool supporting consensus queries, query molecule annotation and multi-core computers
Книги з теми "Computationnelle"
Venev, Yvan Dimitrov. Dictionnaire russe-français-anglais, linguistique mathématique et computationnelle. Paris: Institut international de philosophie et terminologie "Peter Deunov", 1987.
Знайти повний текст джерелаFodor, Jerry A. L' esprit, ça ne marche pas comme ça: Portée et limites de la psychologie computationnelle. Paris: Odile Jacob, 2003.
Знайти повний текст джерелаRacicot, François-Éric. Finance computationnelle et gestion des risques: Ingénierie financière avec applications Excel (Visual Basic) et Matlab. Québec: Presses de l'Université du Québec, 2006.
Знайти повний текст джерелаSalanskis, J. M. Le monde du computationnel. [Paris]: Les Belles lettres, 2011.
Знайти повний текст джерелаWorkshop on Controlled Natural Language (2009 Marettimo Island, Italy). Controlled natural language: Workshop on Controlled Natural Language, CNL 2009, Marettimo Island, Italy, June 8-10, 2009 : revised papers. Berlin: Springer-Verlag, 2010.
Знайти повний текст джерелаSuzanne, Tyc-Dumont, ed. Le neurone computationnel: Histoire d'un siècle de recherches. Paris: CNRS, 2005.
Знайти повний текст джерелаE, Goodman Jacob, and O'Rourke Joseph, eds. Handbook of discrete and computational geometry. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2004.
Знайти повний текст джерелаMcClelland, James L. Explorations in parallel distributed processing: A handbook of models, programs, and exercises. Cambridge, Mass: MIT Press, 1989.
Знайти повний текст джерелаVenev, Yvan. Elsevier Dictionary of Mathematical & Computational Linguistics inEnglish/ French & Russian Dictionaire Linguistique Mathematique et Computationnelle. French & European Pubns, 1990.
Знайти повний текст джерелаLe monde du computationnel. [Paris]: Les Belles lettres, 2011.
Знайти повний текст джерелаЧастини книг з теми "Computationnelle"
Manes Gallo, M. Caterina. "Créativité langagière et raison computationnelle." In XXVe CILPR Congrès International de Linguistique et de Philologie Romanes, edited by Maria Iliescu, Heidi Siller-Runggaldier, and Paul Danler, 5–485. Berlin, New York: De Gruyter, 2010. http://dx.doi.org/10.1515/9783110231922.5-485.
Повний текст джерелаCorradini, Maria Sofia. "Formalisation des variantes à des fins computationnelles: vérification de l’hypothèse expérimentale sur un texte occitan." In Études de langue et de littérature médiévales offertes à Peter T. Ricketts à l’occasion de son 70ème anniversaire, 355–68. Turnhout: Brepols Publishers, 2010. http://dx.doi.org/10.1484/m.stmh-eb.3.2545.
Повний текст джерелаDUCROS, Nicolas. "Une introduction à l’imagerie computationnelle monodétecteur." In Imageries optiques non conventionnelles pour la biologie, 247–74. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9132.ch8.
Повний текст джерелаCorteel, Mathieu. "L’émergence de l’épistémè computationnelle en médecine." In L’épistémologie historique, 227–42. Éditions de la Sorbonne, 2019. http://dx.doi.org/10.4000/books.psorbonne.39341.
Повний текст джерелаCollins, Anne, and Mehdi Khamassi. "Chapitre 9. Initiation à la modélisation computationnelle." In Neurosciences cognitives, 255–81. De Boeck Supérieur, 2021. http://dx.doi.org/10.3917/dbu.khama.2021.01.0255.
Повний текст джерела"Front Matter." In Finance computationnelle et gestion des risques, I—VI. Presses de l'Université du Québec, 2006. http://dx.doi.org/10.2307/j.ctv18ph6c6.1.
Повний текст джерела"LA SIMULATION DE MONTE CARLO." In Finance computationnelle et gestion des risques, 219–46. Presses de l'Université du Québec, 2006. http://dx.doi.org/10.2307/j.ctv18ph6c6.10.
Повний текст джерела"LES MÉTHODES DES DIFFÉRENCES FINIES." In Finance computationnelle et gestion des risques, 247–88. Presses de l'Université du Québec, 2006. http://dx.doi.org/10.2307/j.ctv18ph6c6.11.
Повний текст джерела"LA PROGRAMMATION DYNAMIQUE ET L’ÉQUATION DE BELLMAN." In Finance computationnelle et gestion des risques, 289–300. Presses de l'Université du Québec, 2006. http://dx.doi.org/10.2307/j.ctv18ph6c6.12.
Повний текст джерела"LES CONTRATS À TERME." In Finance computationnelle et gestion des risques, 303–58. Presses de l'Université du Québec, 2006. http://dx.doi.org/10.2307/j.ctv18ph6c6.13.
Повний текст джерелаТези доповідей конференцій з теми "Computationnelle"
Lefort, Claire, Mathieu Chalvidal, Alexis Parenté, Véronique BLANQUET, Henri Massias, Laetitia MAGNOL, and Emilie Chouzenoux. "Imagerie 3D par microscopie multiphotonique appliquée aux sciences du vivant : la chaine instrumentale et computationnelle FAMOUS." In Les journées de l'interdisciplinarité 2022. Limoges: Université de Limoges, 2022. http://dx.doi.org/10.25965/lji.221.
Повний текст джерелаFERRANDON, Erwan, Mathis COURANT, Camélia POPESCU, Yann LAUNAY, Sophie ALAIN, and Claire LEFORT. "Un pipeline instrumental et computationnel pour visualiser des particules virales de SARS-CoV-2 en suspension." In Les journées de l'interdisciplinarité 2022. Limoges: Université de Limoges, 2022. http://dx.doi.org/10.25965/lji.684.
Повний текст джерелаSaint-Aimé, Sébastien, Brigitte Le Pévédic, and Dominique Duhaut. "Évaluation du modèle computationnel d'émotions iGrace." In the 21st International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1629826.1629857.
Повний текст джерелаBiggio, Federico. "Espaces et temps de la transition numérique. Une perspective éco-sémiotique." In Actes du congrès de l’Association Française de Sémiotique. Limoges: Université de Limoges, 2024. http://dx.doi.org/10.25965/as.8600.
Повний текст джерелаЗвіти організацій з теми "Computationnelle"
Gruson-Daniel, Célya, and Maya Anderson-González. Étude exploratoire sur la « recherche sur la recherche » : acteurs et approches. Ministère de l'enseignement supérieur et de la recherche, November 2021. http://dx.doi.org/10.52949/24.
Повний текст джерела