Thèses sur le sujet « Modèles génératifs de séquences »
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Ponty, Yann. « Modélisation de séquences génomiques structurées, génération aléatoire et applications ». Phd thesis, Université Paris Sud - Paris XI, 2006. http://tel.archives-ouvertes.fr/tel-00144130.
Texte intégralgénération aléatoire, d'évaluer la significativité d'un phénomène observé. Tout d'abord, nous nous intéressons aux propriétés des grammaires pondérées, un formalisme particulièrement adapté à la modélisation de la structure des ARN, dérivant des algorithmes de génération aléatoire efficaces implémentés au sein du prototype GenRGenS. Nous abordons le calcul automatique des pondérations réalisant des valeurs observées pour les paramètres du modèle, ainsi qu'une implémentation basée sur une approche optimisation. Dans un second temps, nous abordons la modélisation de la structure secondaire d'ARN. Après quelques rappels de biologie moléculaire, nous proposons plusieurs modèles basés sur des grammaires pondérées permettant la génération de structures d'ARN réalistes. L'utilisation d'un algorithme d'optimisation permet le calculer des pondérations correspondant à certaines familles d'ARN. Nous proposons enfin un algorithme d'extraction de structure secondaire maximale dans une structure générale, qui permet de profiter des données récentes issues de la cristallographie. Le dernier chapitre de cette thèse s'intéresse à l'analyse d'un algorithme de recherche de similarité heuristique, dont la sensibilité s'avère étroitement liée à la probabilité de présence d'un motif au sein de marches aléatoires particulières, les chemins culminants. Ces marches restent positives, et atteignent une altitude maximale en leur dernier pas. Nous proposons un algorithme récursif de génération aléatoire pour ces chemins. En combinant des techniques issues de la combinatoire énumérative, l'analyse asymptotique et la théorie des langages, nous dérivons des algorithmes de génération aléatoire par rejet linéaires dans de nombreux cas.
Ressencourt, Hervé. « Diagnostic hors-ligne à base de modèles : approche multi-modèle pour la génération automatique de séquences de tests : application au domaine de l'automobile ». Toulouse 3, 2008. http://thesesups.ups-tlse.fr/2151/.
Texte intégralThis thesis deals with the problem of off-board diagnosis in the automotive domain. The work has consisted in proposing and implementing an operational model based approach that determines the best sequences of tests to be performed by the garage mechanic to localise a faulty component on a vehicle. A multi-model approach is proposed for the description of mechatronic systems, which allows us to handle the functional complexity of embedded systems and to match functional symptoms with a set of faults on hardware / software components. The test sequencing problem is approached along a next best test strategy based on a local heuristic. This strategy enables an interactive diagnostic session, allowing more flexibility and leaving with the human operator the initiative to accept or reject the proposed test. A software prototype has been developed and tested on the rear wiper system of real vehicles. This thesis, supported by a CIFRE grant, is the result of collaboration between the company ACTIA and the research center LAAS-CNRS in the framework of the common laboratory Autodiag (LAAS, IRIT, ACTIA) which aims at developing new methods for diagnosis in the automotive domain
Tubiana, Jérôme. « Restricted Boltzmann machines : from compositional representations to protein sequence analysis ». Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.
Texte intégralRestricted Boltzmann machines (RBM) are graphical models that learn jointly a probability distribution and a representation of data. Despite their simple architecture, they can learn very well complex data distributions such the handwritten digits data base MNIST. Moreover, they are empirically known to learn compositional representations of data, i.e. representations that effectively decompose configurations into their constitutive parts. However, not all variants of RBM perform equally well, and little theoretical arguments exist for these empirical observations. In the first part of this thesis, we ask how come such a simple model can learn such complex probability distributions and representations. By analyzing an ensemble of RBM with random weights using the replica method, we have characterised a compositional regime for RBM, and shown under which conditions (statistics of weights, choice of transfer function) it can and cannot arise. Both qualitative and quantitative predictions obtained with our theoretical analysis are in agreement with observations from RBM trained on real data. In a second part, we present an application of RBM to protein sequence analysis and design. Owe to their large size, it is very difficult to run physical simulations of proteins, and to predict their structure and function. It is however possible to infer information about a protein structure from the way its sequence varies across organisms. For instance, Boltzmann Machines can leverage correlations of mutations to predict spatial proximity of the sequence amino-acids. Here, we have shown on several synthetic and real protein families that provided a compositional regime is enforced, RBM can go beyond structure and extract extended motifs of coevolving amino-acids that reflect phylogenic, structural and functional constraints within proteins. Moreover, RBM can be used to design new protein sequences with putative functional properties by recombining these motifs at will. Lastly, we have designed new training algorithms and model parametrizations that significantly improve RBM generative performance, to the point where it can compete with state-of-the-art generative models such as Generative Adversarial Networks or Variational Autoencoders on medium-scale data
Cochard, Thomas. « Contribution à la génération de séquences pour la conduite de systèmes complexes critiques ». Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0355/document.
Texte intégralThe works presented in this manuscript deals with critical complex systems operation. They are part of the CONNEXION project (Investissements d'Avenir, BGLE2), which involves the main actors in the French nuclear industry around the design of control systems for power plants and their operation. In the operation field, the actions developed by the project concern the engineering phase with the aim of integrating the operator's point of view as soon as possible in the validation of control architectures, and the operation phase with the aim of providing assistance in the preparation and execution of operation procedures. In this context, the contribution presented in this manuscript deals with the generation and verification of action sequences that meet a given objective and that can be safely operated on the process. The proposed approach relies on verifying a reachability property on a network of timed automata modelling the behavior of architectures. The originality is in the definition of a formal modelling framework using patterns promoting the reusability of models, as well as in the proposition of abstraction and reachability iterative analysis algorithms exploiting the intrinsic hierarchization of architectures in order to scale-up of the proposed approach. The contribution was evaluated on the CISPI experimental platform of the CRAN, and on an industrial scale case study proposed within the framework of the CONNEXION project
Shimagaki, Kai. « Advanced statistical modeling and variable selection for protein sequences ». Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS548.
Texte intégralOver the last few decades, protein sequencing techniques have been developed and continuous experiments have been done. Thanks to all of these efforts, nowadays, we have obtained more than two hundred million protein sequence data. In order to deal with such a huge amount of biological data, now, we need theories and technologies to extract information that we can understand and interpret.The key idea to resolve this problem is statistical physics and the state of the art of machine learning (ML). Statistical physics is a field of physics that can successfully describe many complex systems by extracting or reducing variables to be interpretable variables based on simple principles. ML, on the other hand, can represent data (such as reconstruction and classification) without assuming how the data was generated, i.e. physical phenomenon behind of data. In this dissertation, we report studies of protein sequence generative modeling and protein-residue contact predictions using statistical physics-inspired modeling and ML-oriented methods. In the first part, we review the general background of biology and genomics. Then we discuss statistical modelings for protein sequence. In particular, we review Direct Coupling Analysis (DCA), which is the core technology of our research. We also discuss the effects of higher-order statistics contained in protein sequences and introduces deep learning-based generative models as a model that can go beyond pairwise interaction
Lucas, Thomas. « Modèles génératifs profonds : sur-généralisation et abandon de mode ». Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM049.
Texte intégralThis dissertation explores the topic of generative modelling of natural images,which is the task of fitting a data generating distribution.Such models can be used to generate artificial data resembling the true data, or to compress images.Latent variable models, which are at the core of our contributions, seek to capture the main factors of variations of an image into a variable that can be manipulated.In particular we build on two successful latent variable generative models, the generative adversarial network (GAN) and Variational autoencoder (VAE) models.Recently GANs significantly improved the quality of images generated by deep models, obtaining very compelling samples.Unfortunately these models struggle to capture all the modes of the original distribution, ie they do not cover the full variability of the dataset.Conversely, likelihood based models such as VAEs typically cover the full variety of the data well and provide an objective measure of coverage.However these models produce samples of inferior visual quality that are more easily distinguished from real ones.The work presented in this thesis strives for the best of both worlds: to obtain compelling samples while modelling the full support of the distribution.To achieve that, we focus on i) the optimisation problems used and ii) practical model limitations that hinder performance.The first contribution of this manuscript is a deep generative model that encodes global image structure into latent variables, built on the VAE, and autoregressively models low level detail.We propose a training procedure relying on an auxiliary loss function to control what information is captured by the latent variables and what information is left to an autoregressive decoder.Unlike previous approaches to such hybrid models, ours does not need to restrict the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables.The second contribution builds on the standard GAN model, which trains a discriminator network to provide feedback to a generative network.The discriminator usually assesses the quality of individual samples, which makes it hard to evaluate the variability of the data.Instead we propose to feed the discriminator with emph{batches} that mix both true and fake samples, and train it to predict the ratio of true samples in the batch.These batches work as approximations of the distribution of generated images and allows the discriminator to approximate distributional statistics.We introduce an architecture that is well suited to solve this problem efficiently,and show experimentally that our approach reduces mode collapse in GANs on two synthetic datasets, and obtains good results on the CIFAR10 and CelebA datasets.The mutual shortcomings of VAEs and GANs can in principle be addressed by training hybrid models that use both types of objective.In our third contribution, we show that usual parametric assumptions made in VAEs induce a conflict between them, leading to lackluster performance of hybrid models.We propose a solution based on deep invertible transformations, that trains a feature space in which usual assumptions can be made without harm.Our approach provides likelihood computations in image space while being able to take advantage of adversarial training.It obtains GAN-like samples that are competitive with fully adversarial models while improving likelihood scores over existing hybrid models at the time of publication, which is a significant advancement
Hadjeres, Gaëtan. « Modèles génératifs profonds pour la génération interactive de musique symbolique ». Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS027/document.
Texte intégralThis thesis discusses the use of deep generative models for symbolic music generation. We will be focused on devising interactive generative models which are able to create new creative processes through a fruitful dialogue between a human composer and a computer. Recent advances in artificial intelligence led to the development of powerful generative models able to generate musical content without the need of human intervention. I believe that this practice cannot be thriving in the future since the human experience and human appreciation are at the crux of the artistic production. However, the need of both flexible and expressive tools which could enhance content creators' creativity is patent; the development and the potential of such novel A.I.-augmented computer music tools are promising. In this manuscript, I propose novel architectures that are able to put artists back in the loop. The proposed models share the common characteristic that they are devised so that a user can control the generated musical contents in a creative way. In order to create a user-friendly interaction with these interactive deep generative models, user interfaces were developed. I believe that new compositional paradigms will emerge from the possibilities offered by these enhanced controls. This thesis ends on the presentation of genuine musical projects like concerts featuring these new creative tools
Hadjeres, Gaëtan. « Modèles génératifs profonds pour la génération interactive de musique symbolique ». Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS027.
Texte intégralThis thesis discusses the use of deep generative models for symbolic music generation. We will be focused on devising interactive generative models which are able to create new creative processes through a fruitful dialogue between a human composer and a computer. Recent advances in artificial intelligence led to the development of powerful generative models able to generate musical content without the need of human intervention. I believe that this practice cannot be thriving in the future since the human experience and human appreciation are at the crux of the artistic production. However, the need of both flexible and expressive tools which could enhance content creators' creativity is patent; the development and the potential of such novel A.I.-augmented computer music tools are promising. In this manuscript, I propose novel architectures that are able to put artists back in the loop. The proposed models share the common characteristic that they are devised so that a user can control the generated musical contents in a creative way. In order to create a user-friendly interaction with these interactive deep generative models, user interfaces were developed. I believe that new compositional paradigms will emerge from the possibilities offered by these enhanced controls. This thesis ends on the presentation of genuine musical projects like concerts featuring these new creative tools
Chevrier, Christophe. « Test de conformité de protocoles de communication modèle de fautes et génération automatique de séquences de tests ». Bordeaux 1, 1996. http://www.theses.fr/1996BOR10503.
Texte intégralFranceschi, Jean-Yves. « Apprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques ». Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS014.
Texte intégralThe recent rise of deep learning has been motivated by numerous scientific breakthroughs, particularly regarding representation learning and generative modeling. However, most of these achievements have been obtained on image or text data, whose evolution through time remains challenging for existing methods. Given their importance for autonomous systems to adapt in a constantly evolving environment, these challenges have been actively investigated in a growing body of work. In this thesis, we follow this line of work and study several aspects of temporality and dynamical systems in deep unsupervised representation learning and generative modeling. Firstly, we present a general-purpose deep unsupervised representation learning method for time series tackling scalability and adaptivity issues arising in practical applications. We then further study in a second part representation learning for sequences by focusing on structured and stochastic spatiotemporal data: videos and physical phenomena. We show in this context that performant temporal generative prediction models help to uncover meaningful and disentangled representations, and conversely. We highlight to this end the crucial role of differential equations in the modeling and embedding of these natural sequences within sequential generative models. Finally, we more broadly analyze in a third part a popular class of generative models, generative adversarial networks, under the scope of dynamical systems. We study the evolution of the involved neural networks with respect to their training time by describing it with a differential equation, allowing us to gain a novel understanding of this generative model
Schmitt, Louise-Amelie. « Développement de modèles spécifiques aux séquences génomique virales ». Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0649/document.
Texte intégralDNA sequencing of complex samples containing various living species is a choice approach to study the viral landscape of a given environment. Viral genomes are hard to identify due to their extreme variability and the tight relationship they have with their hosts. We hereby provide new leads for the development of a virusesspecific solution to the need for accurate identification that hasn't found a satisfactory solution in the existing universal software so far
Binsztok, Henri. « Apprentissage de modèles Markoviens pour l'analyse de séquences ». Paris 6, 2007. http://www.theses.fr/2007PA066568.
Texte intégralInitially, Machine Learning allowed to learn models from labeled data. But, for numerous tasks, notably for the task of user modeling, if the available quantity of data is potentially without limit, the quantity of labeled data is almost nonexistent. Within the framework of this thesis, we are interested in the unsupervised learning of sequence models. The information of sequence constitutes the first level of structured data, where the data are no more simple vectors of characteristics. We propose approaches that we apply to the automatic learning of Hidden Markov Models ( HMMs) and Hierarchical HMMs (HHMMs). Our purpose is to learn simultaneously the structure and the parameters of these Markovian Models, to minimize the quantity of prior information necessary to learn them
Jacques, Julien. « Contribution à l'apprentissage statistique à base de modèles génératifs pour données complexes ». Habilitation à diriger des recherches, Université des Sciences et Technologie de Lille - Lille I, 2012. http://tel.archives-ouvertes.fr/tel-00761184.
Texte intégralArribas, Gil Ana. « Estimation dans des modèles à variables cachées : alignement des séquences biologiques et modèles d'évolution ». Paris 11, 2007. http://www.theses.fr/2007PA112054.
Texte intégralThis thesis is devoted to parameter estimation in models for biological sequence alignment. These are models constructed considering an evolution process on the sequences. In the case of two sequences evolving under the classical evolution process, the alignment model is called a pair-Hidden Markov Model (pair-HMM). Observations in a pair-HMM are formed by the couple of sequences to be aligned and the hidden alignment is a Markov chain. From a theoretical point of view, we provide a rigorous formalism for these models and study consistency of maximum likelihood and bayesian estimators. From the point of view of applications, we are interested in detection of conserved motifs in the sequences. To do this we present an evolution process that allows heterogeneity along the sequence. The alignment under this process still fits the pair-HMM. We propose efficient estimation algorithms for alignments and evolution parameters. Finally we are interested in multiple alignment (more than two sequences). The classical evolution process for the sequences provides a complex hidden variable model for the alignment in which the phylogenetic relationships between the sequences must be taken into account. We provide a theoretical framework for this model and study, as for the pairwise alignment, the consistency of estimators
Baelde, Maxime. « Modèles génératifs pour la classification et la séparation de sources sonores en temps-réel ». Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I058/document.
Texte intégralThis thesis is part of the A-Volute company, an audio enhancement softwares editor. It offers a radar that translates multi-channel audio information into visual information in real-time. This radar, although relevant, lacks intelligence because it only analyses the audio stream in terms of energy and not in terms of separate sound sources. The purpose of this thesis is to develop algorithms for classifying and separating sound sources in real time. On the one hand, audio source classification aims to assign a label (e.g. voice) to a monophonic (one label) or polyphonic (several labels) sound. The developed method uses a specific feature, the normalized power spectrum, which is useful in both monophonic and polyphonic cases due to its additive properties of the sound sources. This method uses a generative model that allows to derive a decision rule based on a non-parametric estimation. The real-time constraint is achieved by pre-processing the prototypes with a hierarchical clustering. The results are encouraging on different databases (owned and benchmark), both in terms of accuracy and computation time, especially in the polyphonic case. On the other hand, source separation consists in estimating the sources in terms of signal in a mixture. Two approaches to this purpose were considered in this thesis. The first considers the signals to be found as missing data and estimates them through a generative process and probabilistic modelling. The other approach consists, from sound examples present in a database, in computing optimal transformations of several examples whose combination tends towards the observed mixture. The two proposals are complementary, each having advantages and drawbacks (computation time for the first, interpretability of the result for the second). The experimental results seem promising and allow us to consider interesting research perspectives for each of the proposals
Kermorvant, Christopher. « Apprentissage de modèles à états finis stochastiques pour les séquences ». Saint-Etienne, 2003. http://www.theses.fr/2003STET4002.
Texte intégralThis thesis deals with learning stochastic finite state automata for sequence modelling. We aimed at developing both their structural and probabilistic aspects, through the extension of the models and the design of new learning algorithms. On the one hand, we have developed statistical aspects of stochastic finite state automaton learning algorithms in order to deal with practical cases. We have designed a new learning algorithm based on statistical tests for sample comparison. This framework allows to take into account the size of the learning set in the inference process. On the other hand, we have developed syntactic aspects of finite state automaton and their ability to model the underlying structure of sequences. We have defined typed automata, an extension of classical finite state automata, which permits the introduction of a priori knowledge in the models. From a theoretical point of view, we have studied the search space for the typed automata. We have proposed a modified version of classical automata learning algorithms in the framework of typed automata. Finally, we have applied these models and algorithms to a language modelling task. The obtained automata were competitive with state of the art models on a classical corpus
Barrat-Charlaix, Pierre. « Comprendre et améliorer les modèles statistiques de séquences de protéines ». Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS378.
Texte intégralIn the last decades, progress in experimental techniques have given rise to a vast increase in the number of known DNA and protein sequences. This has prompted the development of various statistical methods in order to make sense of this massive amount of data. Among those are pairwise co-evolutionary methods, using ideas coming from statistical physics to construct a global model for protein sequence variability. These methods have proven to be very effective at extracting relevant information from sequences, such as structural contacts or effects of mutations. While co-evolutionary models are for the moment used as predictive tools, their success calls for a better understanding of they functioning. In this thesis, we propose developments on existing methods while also asking the question of how and why they work. We first focus on the ability of the so-called Direct Coupling Analysis (DCA) to reproduce statistical patterns found in sequences in a protein family. We then discuss the possibility to include other types of information such as mutational effects in this method, and then potential corrections for the phylogenetic biases present in available data. Finally, considerations about limitations of current co-evolutionary models are presented, along with suggestions on how to overcome them
Albet, Joël. « Simulation rigoureuse de colonnes de distillation discontinue à séquences opératoires multiples ». Toulouse, INPT, 1992. http://www.theses.fr/1992INPT008G.
Texte intégralPelletier, Sylvain. « Modèle multi-couches pour l'analyse de séquences vidéo ». Paris 9, 2007. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2007PA090029.
Texte intégralWe propose to study in this thesis the layer model and its applications to video analysis. According to this model, a video sequence is obtained by the projection of a three dimensional scene composed of several opaque objects located at different depths from the camera. The background is the last layer, and the moving objects are projected upon it, in order opposite of their depth. In the first chapter, we set forth a real-time video segmentation method based upon the layer model. Given a known background and a fixed camera, we compare the current frame and the background, detecting the occluded zones. Likewise we use a contrario detection to detect moving objects as meaningful clusters of changes. In Chapter 2, we look for reconstruction of layers from the video. We propose a deformation model of the objects' projection on the image, valid under some hypothesis on the objects' movement. Chapter 3 proposes a variational method to extract moving object layers from the sequence, even if these are hidden during several images
Côme, Etienne. « Apprentissage de modèles génératifs pour le diagnostic de systèmes complexes avec labellisation douce et contraintes spatiales ». Compiègne, 2009. http://www.theses.fr/2009COMP1796.
Texte intégralThe main topic of this thesis concerns the formalisation and the resolution of statistical learning problem involving imperfect information on one or several discrete variables of interest. The solution advocates is build on top of the Dempster-Shaffer theory of evidence and a generative approach. We show first, how « soft » labels defined as a Dempster-Shafer basic belief assignments can be employed to define a criterion generalizing the likelihood function which can be used to compute estimates of mixture model parameters. A variant of the EM algorithm dedicated to the optimization of this criterion is furthermore proposed. A similar approach is also studied in the context of independent factor analysis, a parsimonious generative model dealing with several discrete variables. A solution to leverage prior knowledge on the generative process underlying this model is also supplied. Finally, results from a real diagnosis application demonstrates the interest of these proposis. This diagnosis application concerns an essential component of the French railway infrastructure : the track circuit
Bourguignon, Pierre Yves Vincent. « Parcimonie dans les modèles Markoviens et application à l'analyse des séquences biologiques ». Thesis, Evry-Val d'Essonne, 2008. http://www.theses.fr/2008EVRY0042.
Texte intégralMarkov chains, as a universal model accounting for finite memory, discrete valued processes, are omnipresent in applied statistics. Their applications range from text compression to the analysis of biological sequences. Their practical use with finite samples, however, systematically require to draw a compromise between the memory length of the model used, which conditions the complexity of the interactions the model may capture, and the amount of information carried by the data, whose limitation negatively impacts the quality of estimation. Context trees, as an extension of the model class of Markov chains, provide the modeller with a finer granularity in this model selection process, by allowing the memory length to vary across contexts. Several popular modelling methods are based on this class of models, in fields such as text indexation of text compression (Context Tree Maximization and Context Tree Weighting). We propose an extension of the models class of context trees, the Parcimonious context trees, which further allow the fusion of sibling nodes in the context tree. They provide the modeller with a yet finer granularity to perform the model selection task, at the cost of an increased computational cost for performing it. Thanks to a bayesian approach of this problem borrowed from compression techniques, we succeeded at desiging an algorithm that exactly optimizes the bayesian criterion, while it benefits from a dynamic programming scheme ensuring the minimisation of the computational complexity of the model selection task. This algorithm is able to perform in reasonable space and time on alphabets up to size 10, and has been applied on diverse datasets to establish the good performances achieved by this approach
Jaziri, Rakia. « Modèles de mélanges topologiques pour la classification de données structurées en séquences ». Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_jaziri.pdf.
Texte intégralRecent years have seen the development of data mining techniques in various application areas, with the purpose of analyzing sequential, large and complex data. In this work, the problem of clustering, visualization and structuring data is tackled by a three-stage proposal. The first proposal present a generative approach to learn a new probabilistic Self-Organizing Map (PrSOMS) for non independent and non identically distributed data sets. Our model defines a low dimensional manifold allowing friendly visualizations. To yield the topology preserving maps, our model exhibits the SOM like learning behavior with the advantages of probabilistic models. This new paradigm uses HMM (Hidden Markov Models) formalism and introduces relationships between the states. This allows us to take advantage of all the known classical views associated to topographic map. The second proposal concerns a hierarchical extension of the approach PrSOMS. This approach deals the complex aspect of the data in the classification process. We find that the resulting model ”H-PrSOMS” provides a good interpretability of classes built. The third proposal concerns an alternative approach statistical topological MGTM-TT, which is based on the same paradigm than HMM. It is a generative topographic modeling observation density mixtures, which is similar to a hierarchical extension of time GTM model. These proposals have then been applied to test data and real data from the INA (National Audiovisual Institute). This work is to provide a first step, a finer classification of audiovisual broadcast segments. In a second step, we sought to define a typology of the chaining of segments (multiple scattering of the same program, one of two inter-program) to provide statistically the characteristics of broadcast segments. The overall framework provides a tool for the classification and structuring of audiovisual programs
Groussin, Mathieu. « Résurrection du passé à l’aide de modèles hétérogènes d’évolution des séquences protéiques ». Thesis, Lyon 1, 2013. http://www.theses.fr/2013LYO10201/document.
Texte intégralThe molecular reconstruction and resurrection of ancestral proteins is the major issue tackled in this thesis manuscript. While fossil molecular data are almost nonexistent, phylogenetic methods allow to estimate what were the most likely ancestral protein sequences along a phylogenetic tree describing the relationships between extant sequences. With these ancestral sequences, several biological hypotheses can be tested, from the evolution of protein function to the inference of ancient environments in which the ancestors were adatapted. These probabilistic estimations of ancestral sequences depend on substitution models giving the different probabilities of substitution between all pairs of amino acids. Classicaly, substitution models assume in a simplistic way that the evolutionary process remains homogeneous (constant) among sites of the multiple sequence alignment or between lineages. During the last decade, several methodological improvements were realised, with the description of substitution models allowing to account for the heterogeneity of the process among sites and in time. During my thesis, I developed new heterogeneous substitution models in Maximum Likelihood that were proved to better fit the data than any other homogeneous or heterogeneous models. I also demonstrated their better performance regarding the accuracy of ancestral sequence reconstruction. With the use of these models to reconstruct or resurrect ancestral proteins, my coworkers and I showed the adapation to temperature is a major determinant of evolutionary rates in Archaea. Furthermore, we also deciphed the nature of the phylogenetic signal informing substitution models to infer a non-parsimonious scenario for the adaptation to temperature during early Life on Earth, with a non-hyperthermophilic last universal common ancestor living at lower temperatures than its two descendants. Finally, we showed that the use of heterogeneous models allow to improve the functionality of resurrected proteins, opening the way to a better understanding of evolutionary mechanisms acting on biological sequences
Bouchard, Guillaume. « Les modèles génératifs en classification supervisée et applications à la catégorisation d'images et à la fiabilité industrielle ». Phd thesis, Université Joseph Fourier (Grenoble), 2005. http://tel.archives-ouvertes.fr/tel-00541059.
Texte intégralAzeraf, Elie. « Classification avec des modèles probabilistes génératifs et des réseaux de neurones. Applications au traitement des langues naturelles ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. https://theses.hal.science/tel-03880848.
Texte intégralMany probabilistic models have been neglected for classification tasks with supervised learning for several years, as the Naive Bayes or the Hidden Markov Chain. These models, called generative, are criticized because the induced classifier must learn the observations' law. This problem is too complex when the number of observations' features is too large. It is especially the case with Natural Language Processing tasks, as the recent embedding algorithms convert words in large numerical vectors to achieve better scores.This thesis shows that every generative model can define its induced classifier without using the observations' law. This proposition questions the usual categorization of the probabilistic models and classifiers and allows many new applications. Therefore, Hidden Markov Chain can be efficiently applied to Chunking and Naive Bayes to sentiment analysis.We go further, as this proposition allows to define the classifier induced from a generative model with neural network functions. We "neuralize" the models mentioned above and many of their extensions. Models so obtained allow to achieve relevant scores for many Natural Language Processing tasks while being interpretable, able to require little training data, and easy to serve
Dib, Linda. « Détection des mutations simultanées dans les séquences protéiques non-divergentes ». Paris 6, 2012. http://www.theses.fr/2012PA066016.
Texte intégralThomas, Dave. « Simulation des séquences de sciage du bois par des modèles logiques et numériques ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0011/MQ33764.pdf.
Texte intégralBergogne, Laurent. « Quelques algorithmes parallèles sur des "séquences de" pour différents modèles de calcul parallèle ». Amiens, 1999. http://www.theses.fr/1999AMIE0130.
Texte intégralBaghdadi, Siwar. « Extraction multimodale de métadonnées de séquences vidéo dans un cadre bayésien ». Rennes 1, 2010. https://tel.archives-ouvertes.fr/tel-00512706.
Texte intégralThe description of multimedia contents field is a relatively recent one which takes a large importance in both industrial and research world, considering the massive increase of content production. A growing need for systems able to provide a semantic description is more than ever within the order of the day. In this domain, Bayesian networks are largely used to model the video data in order to extract semantic metadata. However, the bayesian networks based systems require a beforehand fixed structure. This operation is done, generally, wether using « a priori » knowledge, which results in a not very generalizable system, or by using the assumption of independence of the data flows, which results in a not very optimal system. Moved by the need for providing generic systems capable of adapting themselves to the great diversity of applicaitons, we use training of structure to automatically build the Bayesian network. By automatically learning the structure from a database, we no longer need external knowledge or not very realistic assumptions to build the structure of the used Bayesian network. Various structure training techniques were used. We conclude with the need to adapt training of structure in the static and dynamic Bayesian network in classification. By associating training of structure and selection of attributes, we obtain a framework allowing to automatically building content description systems without being dependent on external knowledge
Zidouni, Azeddine. « Modèles graphiques discriminants pour l'étiquetage de séquences : application à la reconnaissance d'entités nommées radiophiniques ». Thesis, Aix-Marseille 2, 2010. http://www.theses.fr/2010AIX22125/document.
Texte intégralRecent researches in Information Extraction are designed to extract fixed types of information from data. Sequence annotation systems are developed to associate structured annotations to input data presented in sequential form. The named entity recognition (NER) task consists of identifying and classifying every word in a document into some predefined categories such as person name, locations, organizations, and dates. The complexity of the NER is largely related to the definition of the task and to the complexity of the relationships between words and the semantic associated. Our first contribution is devoted to solving the NER problem using discriminative graphical models. The proposed approach investigates the use of various contexts of the words to improve recognition. NER systems are fixed in accordance with a specific annotation protocol. Thus, new applications are developed for new protocols. The challenge is how we can adapt an annotation system which is performed for a specific application to other target application? We will propose in this work an adaptation approach of sequence labelling task based on annotation enrichment using conditional random fields (CRF). Experimental results show that the proposed approach outperform rules-based approach in NER task. Finally, we propose a multimodal approach of NER by integrating low level features as contextual information in radio broadcast news data. The objective of this study is to measure the correlation between the speaker voicing quality and the importance of his speech
Antoine-Lorquin, Aymeric. « De l'intérêt des modèles grammaticaux pour la reconnaissance de motifs dans les séquences génomiques ». Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S086/document.
Texte intégralThis thesis studies the interest to look for patterns in genomic sequences using grammars. Since the 80s, work has shown that, in theory, high level grammars offer enough expressivity to allow the description of complex biological patterns. In particular David Searls has proposed a new grammar dedicated to biology: string variable grammar (SVG). This formalism has resulted in Logol, a grammatical language and an analysis tool developed by Dyliss team where this thesis is taking place. Logol is a language designed to be flexible enough to express a wide range of biological patterns. The fact that the grammars remain unknown to model biological patterns raises questions. Is the grammatical formalism really relevant to the recognition of biological patterns? This thesis attempts to answer this question through an exploratory approach. We study the relevance of using the grammatical patterns, by using Logol on six different applications of genomic pattern matching. Through the practical resolution of biological problems, we have highlighted some features of grammatical patterns. First, the use of grammatical models presents a cost in terms of performance. Second the expressiveness of grammatical models covers a broad spectrum of biological patterns, unlike the others alternatives, and some patterns modeled by grammars have no other alternative solutions. It also turns out that for some complex patterns, such as those combining sequence and structure, the grammatical approach is the most suitable. Finally, a thesis conclusion is that there was no real competition between different approaches, but rather everything to gain from successful cooperation
Crivelli, Tomás. « Modèles de Markov à états mixtes pour l'analyse du mouvement dans des séquences d'images ». Rennes 1, 2010. http://www.theses.fr/2010REN1S009.
Texte intégralThis thesis deals with mixed-state random fields and their application to image motion analysis. The approach allows us to consider both discrete and continuous values within a single statistical model, exploiting the interaction between the two types of states. In this context, we identify two possible scenarios. First, we are concerned with the modeling of mixed-state observations. Typically they are obtained from image motion measurements depicting a discrete value at zero (null-motion) and continuous motion values. Such motion maps extracted from dynamic texture video sequences are suitable to be modeled as mixed-state Markov fields. We thus design parametric models of motion textures based on the theory of mixed-state Markov random fields and mixed-state Markov chains. We apply them for motion texture characterization, recognition, segmentation and tracking. The second scenario involves inferring mixed-state random variables for simultaneous decision-estimation problems. In this case, the discrete state is a symbolic value indicating an abstract label. Such problems need to be solved jointly and the mixed-state framework can be exploited in order to model the natural coupling that exists between them. In this context, we address the problem of motion detection (decision problem) and background reconstruction (estimation problem). An accurate estimation of the background is only possible if we locate the moving objects; meanwhile, a correct motion detection is achieved if we have a good available background representation. Solving the motion detection and the background reconstruction jointly reduces to obtain a single optimal estimate of a mixed-state process
Belaroussi, Rachid. « Localisation du visage dans des images et séquences vidéo couleur ». Paris 6, 2006. http://www.theses.fr/2006PA066338.
Texte intégralMatias, Catherine. « Statistique asymptotique dans des modèles à variables latentes ». Habilitation à diriger des recherches, Université d'Evry-Val d'Essonne, 2008. http://tel.archives-ouvertes.fr/tel-00349639.
Texte intégralMa présentation s'organise en trois grandes thématiques : les travaux portant sur des séquences, notamment sur la modélisation de leur distribution et des processus d'évolution sous-jacents ; les travaux de statistique semi ou non paramétrique portant sur des signaux observés avec du bruit ; et enfin les travaux (en partie en cours) portant sur les graphes aléatoires.
Noé, Laurent. « Recherche de similarités dans les séquences d'ADN : modèles et algorithmes pour la conception de graines efficaces ». Phd thesis, Université Henri Poincaré - Nancy I, 2005. http://tel.archives-ouvertes.fr/tel-00011482.
Texte intégralElles se basent sur un principe de filtrage du texte qui permet de localiser les régions potentiellement similaires.
Dans cette thèse, nous proposons de nouvelles définitions de filtres pour la recherche de similarités sur les séquences génomiques et des algorithmes associés pour mesurer leurs caractéristiques.
Plus précisément, nous avons étudié le modèle des graines espacées, et proposé un algorithme d'évaluation de l'efficacité des graines sur des similarités d'une classe particulière (similarités dites homogènes). Nous avons également développé un algorithme général pour la mesure de l'efficacité des graines, ainsi qu'un nouveau modèle de graine appelé graine sous-ensemble, extension du modèle des graines espacées. Enfin nous donnons, dans le cadre du filtrage sans perte, une extension à l'aide de graines multiples, que nous analysons et appliquons au problème de la conception d'oligonucléotides.
Nous avons réalisé et donnons accès à des outils pour la conception des filtres, ainsi que pour la recherche de similarités.
Domelevo, Entfellner Jean-Baka. « Combinaison de modèles phylogénétiques et longitudinaux pour l'analyse des séquences biologiques : reconstruction de HMM profils ancestraux ». Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2011. http://tel.archives-ouvertes.fr/tel-00842847.
Texte intégralThéry, Clément. « Model-based covariable decorrelation in linear regression (CorReg) : application to missing data and to steel industry ». Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10060/document.
Texte intégralThis thesis was motivated by correlation issues in real datasets, in particular industrial datasets. The main idea stands in explicit modeling of the correlations between covariates by a structure of sub-regressions, that simply is a system of linear regressions between the covariates. It points out redundant covariates that can be deleted in a pre-selection step to improve matrix conditioning without significant loss of information and with strong explicative potential because this pre-selection is explained by the structure of sub-regressions, itself easy to interpret. An algorithm to find the sub-regressions structure inherent to the dataset is provided, based on a full generative model and using Monte-Carlo Markov Chain (MCMC) method. This pre-treatment does not depend on a response variable and thus can be used in a more general way with any correlated datasets. In a second part, a plug-in estimator is defined to get back the redundant covariates sequentially. Then all the covariates are used but the sequential approach acts as a protection against correlations. Finally, the generative model defined here allows, as a perspective, to manage missing values both during the MCMC and then for imputation. Then we are able to use classical methods that are not compatible with missing datasets. Once again, linear regression is used to illustrate the benefits of this method but it remains a pre-treatment that can be used in other contexts, like clustering and so on. The R package CorReg implements the methods created during this thesis
Palmeira, Leonor. « Analyse et modélisation des dépendances entre sites voisins dans l'évolution des séquences d'ADN ». Phd thesis, Université Claude Bernard - Lyon I, 2007. http://tel.archives-ouvertes.fr/tel-00178453.
Texte intégralBikienga, Moustapha. « Mise en oeuvre applicative de séquences d'ordonnancement hors-ligne ». Thesis, Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2014. http://www.theses.fr/2014ESMA0011/document.
Texte intégralWe address the implementation of periodic task sets for off-line scheduling. Off-line scheduling approach consistsin computing a worst-case schedule before runtime. Implementing a schedule requires to specify what must happenwhen the actual execution times of tasks are lower than the planned execution times. The first contributionconsist of the formalization of implementation policies. These policies consider the date by which a task maystart execution, which may or not occur before the planned start time. The inflexible policy does not allowa task to run before its planned start time, the flexible policy does. Since many implementations can complywith these two policies, we also propose a cost model which enables to perform some comparisons betweenthese implementations. The second contribution is the proposition and the presentation of a set of algorithmswhich implement the pre-computed schedules. We first deal with independent task sets in a non preemptivecontext. These algorithms are then adapted to be used in the context of preemptive scheduling, with sharedcritical ressources and precedence constraints. Using the model driven engeneering, we next provide a Posixcode generation tool. We also present a schedule observation tool. Finally, our work has been tested through apratical case study
Nicolas, Pierre. « Mise au point et utilisation de modèles de chaînes de Markov cachées pour l'étude des séquences d'ADN ». Evry-Val d'Essonne, 2003. http://www.theses.fr/2003EVRY0017.
Texte intégralConsidering the use of self-training approaches, we developed in this thesis three domains in which we applied HMM for the bacterial genome interpretation. First, a segmentation method of DNA sequences into regions of homogeneous composition enables us to identify horizontal gene transfers on the Bacillus subtilis chromosome and also others heterogeneities levels linked to biological properties of genes. Next we developed a gene prediction software and we especially focused on small genes research. Around 30 genes smaller than 50 amino acids have been added to about 20 small genes previously biologically identified on B. Subtilis. Then we proposed a MCMC algorithm for Bayesian model selection in the context of RNA polymerase binding sites modeling
Ahouandjinou, Arnaud. « Reconnaissance de scénario par les Modèles de Markov Cachés Crédibilistes : Application à l'interprétation automatique de séquences vidéos médicales ». Thesis, Littoral, 2014. http://www.theses.fr/2014DUNK0380/document.
Texte intégralThis thesis focuses on the study and the implementation of an intelligent visual monitoring system in hospitals. In the context of an application for patient monitoring in mediacal intensive care unit, we introduce an original concept of the Medical Black Box and we propose a new system for visual monitoring of Automatic Detection of risk Situations and Alert (DASA) based on a CCTV system with network smart camera. The aim is to interpret the visual information flow and to detect at real-time risk situations to prevent the mediacl team and then archive the events in a video that is based Medical Black Box data. The interpretation system is based on scenario recognition algorithms that exploit the Hidden Markov Models (HMM). An extension of the classic model of HMM is proposed to handle the internal reporting structure of the scenarios and to control the duration of each state of the Markov model. The main contribution of this work relies on the integration of an evidential reasoning, in order to manage the recognition decision taking into account the imperfections of available information. The proposed scenarios recognition method have been tested and assessed on database of medical video sequences and compared to standard probabilistic Hidden Markov Models
Di, Franco Arnaud. « Impact des violations des modèles d'annotation et d'évolution de séquences en phylogénomique : application à l'étude des eucaryotes photosynthétiques ». Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30088.
Texte intégralOxygen has shaped life on Earth. Enriching its gaseous form in water then in the atmosphere has allowed the development of multicellular and terrestrial life leading to current biodiversity. This could have taken place thanks to the implementation of the process of the oxidative photosynthesis in living organisms. The latter first appeared in bacteria before being subtilized by nucleated life forms. This action was realized by enslaving the bacterium and keeping it inside the nucleated organism by a phenomenon called endosymbiosis. Different endosymbioses have occurred in history, attributing the ability of photosynthesis to a wide panel of living beings. The aim of this thesis is to study the transmission of photosynthesis in eukaryotic organisms. These present a great diversity of chloroplast, the organelle performing photosynthesis and witnessing the integration of a foreign organism. The inference of phylogeny, i.e. the estimation of the relationship existing between organisms, reveals discrepancies between the story told by the genome of chloroplasts and nuclei. Obtaining these phylogenies and studying their discordances are at the heart of the understanding of the history of the acquisition of photosynthesis. However, the inference of phylogeny is a complex process influenced by the type and the quality of data as well as by the used technology. The considerations of this thesis manuscript focus on the impact of these elements on the resolution of the tree eukaryotes, with the objectif of getting a better understanding of the history of the hosts photosynthetic and their endosymbiont. First, we developed softwares that improve the quality of physical inferences by the removal of segments of sequences determined to be non-relative to the organisms evolution. We demonstrate the effectiveness of the method and its comparative impact to other commonly used sequence filtering methods. Secondly, we create a phylogenomic data set to infer the phylogeny of eukaryotes. This is done semi-automatically and aims to increase the maximum of phylogenetic signal while avoiding the integration of sequences with no trace of homology between organisms. We get a tree of eukaryotes including the greatest diversity of organisms to date and discuss the impact of taxon sampling on the support given to tree topology. Recently, we have studied the impact of the choice of the sequence evolution model on the congruence of the phylogenies obtained between the genomes of the different compartments present in photosynthetic stramenopiles. Our results are in favor of the presence of a weak phylogenetic signal to solve the nodes at the base of this group, the latter can easily be overcome by the non-phylogenetic signal produced by model violations. In the end, this thesis highlights the importance of the development of bioinformatics phylogeny-related method to confidently answer questions in evolution related to old events
Vergne, Nicolas. « Chaînes de Markov régulées et approximation de Poisson pour l'analyse de séquences biologiques ». Phd thesis, Université d'Evry-Val d'Essonne, 2008. http://tel.archives-ouvertes.fr/tel-00322434.
Texte intégralΠt/n = (1-t/n) Π0 + t/n Π1.
Cette modélisation correspond à une évolution douce entre deux états. Par exemple cela peut traduire la transition entre deux régimes d'un chaîne de Markov cachée, qui pourrait parfois sembler trop brutale. Ces modèles peuvent donc être vus comme une alternative mais aussi comme un outil complémentaire aux modèles de Markov cachés. Tout au long de ce travail, nous avons considéré des dérives polynomiales de tout degré ainsi que des dérives par splines polynomiales : le but de ces modèles étant de les rendre plus flexibles que ceux des polynômes. Nous avons estimé nos modèles de multiples manières puis évalué la qualité de ces estimateurs avant de les utiliser en vue d'applications telle la recherche de mots exceptionnels. Nous avons mis en oeuvre le software DRIMM (bientôt disponible à http://stat.genopole.cnrs.fr/sg/software/drimm/, dédié à l'estimation de nos modèles. Ce programme regroupe toutes les possibilités offertes par nos modèles, tels le calcul des matrices en chaque position, le calcul des lois stationnaires, des distributions de probabilité en chaque position... L'utilisation de ce programme pour la recherche des mots exceptionnels est proposée dans des programmes auxiliaires (disponibles sur demande).
Plusieurs perspectives à ce travail sont envisageables. Nous avons jusqu'alors décidé de faire varier la matrice seulement en fonction de la position, mais nous pourrions prendre en compte des covariables tels le degré d'hydrophobicité, le pourcentage en gc, un indicateur de la structure des protéines (hélice α, feuillets β...). Nous pourrions aussi envisager de mêler HMM et variation continue, où sur chaque région, au lieu d'ajuster un modèle de Markov, nous ajusterions un modèle de chaînes de Markov régulées.
Grechka, Asya. « Image editing with deep neural networks ». Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS683.pdf.
Texte intégralImage editing has a rich history which dates back two centuries. That said, "classic" image editing requires strong artistic skills as well as considerable time, often in the scale of hours, to modify an image. In recent years, considerable progress has been made in generative modeling which has allowed realistic and high-quality image synthesis. However, real image editing is still a challenge which requires a balance between novel generation all while faithfully preserving parts of the original image. In this thesis, we will explore different approaches to edit images, leveraging three families of generative networks: GANs, VAEs and diffusion models. First, we study how to use a GAN to edit a real image. While methods exist to modify generated images, they do not generalize easily to real images. We analyze the reasons for this and propose a solution to better project a real image into the GAN's latent space so as to make it editable. Then, we use variational autoencoders with vector quantification to directly obtain a compact image representation (which we could not obtain with GANs) and optimize the latent vector so as to match a desired text input. We aim to constrain this problem, which on the face could be vulnerable to adversarial attacks. We propose a method to chose the hyperparameters while optimizing simultaneously the image quality and the fidelity to the original image. We present a robust evaluation protocol and show the interest of our method. Finally, we abord the problem of image editing from the view of inpainting. Our goal is to synthesize a part of an image while preserving the rest unmodified. For this, we leverage pre-trained diffusion models and build off on their classic inpainting method while replacing, at each denoising step, the part which we do not wish to modify with the noisy real image. However, this method leads to a disharmonization between the real and generated parts. We propose an approach based on calculating a gradient of a loss which evaluates the harmonization of the two parts. We guide the denoising process with this gradient
Thivin, Solenne. « Détection automatique de cibles dans des fonds complexes. Pour des images ou séquences d'images ». Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS235/document.
Texte intégralDuring this PHD, we developped an detection algorithm. Our principal objective was to detect small targets in a complex background like clouds for example.For this, we used the spatial covariate structure of the real images.First, we developped a collection of models for this covariate structure. Then, we selected a special model in the previous collection. Once the model selected, we applied the likelihood ratio test to detect the potential targets.We finally studied the performances of our algorithm by testing it on simulated and real images
Alméras, Lionel. « Caractérisation de nouvelles cibles de la réponse immune dans la sclérose en plaques : des séquences rétrovirales endogènes aux auto-antigènes cérébraux ». Lille 2, 2003. http://www.theses.fr/2003LIL2MT02.
Texte intégralPardo, Jérémie. « Méthodes d'inférence de cibles thérapeutiques et de séquences de traitement ». Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG011.
Texte intégralNetwork controllability is a major challenge in network medicine. It consists in finding a way to rewire molecular networks to reprogram the cell fate. The reprogramming action is typically represented as the action of a control. In this thesis, we extended the single control action method by investigating the sequential control of Boolean networks. We present a theoretical framework for the formal study of control sequences.We consider freeze controls, under which the variables can only be frozen to 0, 1 or unfrozen. We define a model of controlled dynamics where the modification of the control only occurs at a stable state in the synchronous update mode. We refer to the inference problem of finding a control sequence modifying the dynamics to evolve towards a desired state or property as CoFaSe. Under this problem, a set of variables are uncontrollable. We prove that this problem is PSPACE-hard. We know from the complexity of CoFaSe that finding a minimal sequence of control by exhaustively exploring all possible control sequences is not practically tractable. By studying the dynamical properties of the CoFaSe problem, we found that the dynamical properties that imply the necessity of a sequence of control emerge from the update functions of uncontrollable variables. We found that the length of a minimal control sequence cannot be larger than twice the number of profiles of uncontrollable variables. From this result, we built two algorithms inferring minimal control sequences under synchronous dynamics. Finally, the study of the interdependencies between sequential control and the topology of the interaction graph of the Boolean network allowed us to investigate the causal relationships that exist between structure and control. Furthermore, accounting for the topological properties of the network gives additional tools for tightening the upper bounds on sequence length. This work sheds light on the key importance of non-negative cycles in the interaction graph for the emergence of minimal sequences of control of size greater than or equal to two
Poli, Emmanuelle. « Stratigraphie séquentielle haute-résolution, modèles de dépôt et géométrie 2D-3D des séquences triasiques de la marge téthysienne ardéchoise ». Dijon, 1997. http://www.theses.fr/1997DIJOS081.
Texte intégralCourbis, Anne-Lise. « Contribution à l'étude et au développement d'un générateur de séquences de test comportemental ». Montpellier 2, 1991. http://www.theses.fr/1991MON20274.
Texte intégralMercier, Sabine. « Statistiques des scores pour l'analyse et la comparaison de séquences biologiques ». Rouen, 1999. http://www.theses.fr/1999ROUES089.
Texte intégral