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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
101

Étude asymptotique de modèles en transition de phase / Asymptotic study of phase transition models

Wehbe, Charbel 05 December 2014 (has links)
Ce rapport de thèse est consacré à l'étude de modèles de champ de phase de type Caginalp. Nous considérons ici, deux parties : la première étant une généralisation du modèle de champ de phase de Caginalp basée sur la loi de Maxwell-Cattaneo et la seconde traite le même modèle dans sa version conservative. L'étude dans les deux parties est faite dans un domaine borné. De plus, dans la première partie on distingue les cas de conditions aux bords de type Dirichlet ainsi que Neumann, tandis que dans la deuxième partie le modèle est étudié uniquement avec les conditions Dirichlet (avec un potentiel régulier puis un potentiel singulier). Tout d'abord, l'existence, l'unicité, et la régularité des solutions sont analysées aux moyens d'arguments classiques. Ensuite, l'existence d'ensembles bornés absorbants est établie. Enfin, dans certains cas, l'existence de l'attracteur global et d'attracteurs exponentiels sont analysés. / This thesis report is devoted to the study of Caginalp type phase-field Models. Here, we consider two parts : the first is a generalization of the Caginalp type phase-field model based on a generalization of the Maxwell-Cattaneo law and the second with the same model in its conservative version. The study in the two parts is made in a bounded domain. In addition, in the first part we distinguish cases of boundary conditions of Dirichlet and Neumann, while in the second part the model is studied only with Dirichlet conditions (with a regular potential and a singular potential). First, the existence, uniqueness, and regularity of solutions are analyzed by means of classical arguments. Then, the existence of bounded absorbing sets is established. Finally, in some cases, the existence of the global attractor and exponential attractors are analyzed.
102

Connectionist modelling in cognitive science: an exposition and appraisal

Janeke, Hendrik Christiaan 28 February 2003 (has links)
This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic approach in cognitive science. Classical researchers have approached the description of cognition by concentrating mainly on an abstract, algorithmic level of description in which the information processing properties of cognitive processes are emphasised. The approach is founded on seminal ideas about computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain are highlighted. Although neural networks are generally accepted to be more neurally plausible than their classical counterparts, some classical researchers have argued that these networks are best viewed as implementation models, and that they are therefore not of much relevance to cognitive researchers because information processing models of cognition can be developed independently of considerations about implementation in physical systems. In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms underlying some neural network models have interesting properties such as similarity-based representation, content-based retrieval, and coarse coding which do not have straightforward equivalents in classical systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition, but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned to study the effect of such damage on the behaviour of the system, and these systems can be used to study the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational endeavour. / Psychology / D. Litt. et Phil. (Psychology)
103

Modelling neuronal mechanisms of the processing of tones and phonemes in the higher auditory system

Larsson, Johan P. 15 November 2012 (has links)
S'ha investigat molt tant els mecanismes neuronals bàsics de l'audició com l'organització psicològica de la percepció de la parla. Tanmateix, en ambdós temes n'hi ha una relativa escassetat en quant a modelització. Aquí describim dos treballs de modelització. Un d'ells proposa un nou mecanisme de millora de selectivitat de freqüències que explica resultats de experiments neurofisiològics investigant manifestacions de forward masking y sobretot auditory streaming en l'escorça auditiva principal (A1). El mecanisme funciona en una xarxa feed-forward amb depressió sináptica entre el tàlem y l'escorça, però mostrem que és robust a l'introducció d'una organització realista del circuit de A1, que per la seva banda explica cantitat de dades neurofisiològics. L'altre treball descriu un mecanisme candidat d'explicar la trobada en estudis psicofísics de diferències en la percepció de paraules entre bilinguës primerencs y simultànis. Simulant tasques de decisió lèxica y discriminació de fonemes, fortifiquem l'hipòtesi de que persones sovint exposades a variacions dialectals de paraules poden guardar aquestes en el seu lèxic, sense alterar representacions fonemàtiques . / Though much experimental research exists on both basic neural mechanisms of hearing and the psychological organization of language perception, there is a relative paucity of modelling work on these subjects. Here we describe two modelling efforts. One proposes a novel mechanism of frequency selectivity improvement that accounts for results of neurophysiological experiments investigating manifestations of forward masking and above all auditory streaming in the primary auditory cortex (A1). The mechanism works in a feed-forward network with depressing thalamocortical synapses, but is further showed to be robust to a realistic organization of the neural circuitry in A1, which accounts for a wealth of neurophysiological data. The other effort describes a candidate mechanism for explaining differences in word/non-word perception between early and simultaneous bilinguals found in psychophysical studies. By simulating lexical decision and phoneme discrimination tasks in an attractor neural network model, we strengthen the hypothesis that people often exposed to dialectal word variations can store these in their lexicons, without altering their phoneme representations. / Se ha investigado mucho tanto los mecanismos neuronales básicos de la audición como la organización psicológica de la percepción del habla. Sin embargo, en ambos temas hay una relativa escasez en cuanto a modelización. Aquí describimos dos trabajos de modelización. Uno propone un nuevo mecanismo de mejora de selectividad de frecuencias que explica resultados de experimentos neurofisiológicos investigando manifestaciones de forward masking y sobre todo auditory streaming en la corteza auditiva principal (A1). El mecanismo funciona en una red feed-forward con depresión sináptica entre el tálamo y la corteza, pero mostramos que es robusto a la introducción de una organización realista del circuito de A1, que a su vez explica cantidad de datos neurofisiológicos. El otro trabajo describe un mecanismo candidato de explicar el hallazgo en estudios psicofísicos de diferencias en la percepción de palabras entre bilinguës tempranos y simultáneos. Simulando tareas de decisión léxica y discriminación de fonemas, fortalecemos la hipótesis de que personas expuestas a menudo a variaciones dialectales de palabras pueden guardar éstas en su léxico, sin alterar representaciones fonémicas.
104

Connectionist modelling in cognitive science: an exposition and appraisal

Janeke, Hendrik Christiaan 28 February 2003 (has links)
This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic approach in cognitive science. Classical researchers have approached the description of cognition by concentrating mainly on an abstract, algorithmic level of description in which the information processing properties of cognitive processes are emphasised. The approach is founded on seminal ideas about computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain are highlighted. Although neural networks are generally accepted to be more neurally plausible than their classical counterparts, some classical researchers have argued that these networks are best viewed as implementation models, and that they are therefore not of much relevance to cognitive researchers because information processing models of cognition can be developed independently of considerations about implementation in physical systems. In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms underlying some neural network models have interesting properties such as similarity-based representation, content-based retrieval, and coarse coding which do not have straightforward equivalents in classical systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition, but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned to study the effect of such damage on the behaviour of the system, and these systems can be used to study the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational endeavour. / Psychology / D. Litt. et Phil. (Psychology)
105

Uma condição de injetividade e a estabilidade assintótica global no plano / A injectividade condition and the global asymptotic estability on the plane

SOUZA, Wender José de 29 March 2010 (has links)
Made available in DSpace on 2014-07-29T16:02:15Z (GMT). No. of bitstreams: 1 Dissertacao Wender J de Souza.pdf: 1008440 bytes, checksum: b2d3405f265353a21b9eaaad1c91d71f (MD5) Previous issue date: 2010-03-29 / In this work we are interested in the solution of the following problem: Let Y = ( f ,g) be a vector field of class C1 in R2. Suppose that (x, y) = (0,0) is a singular point of Y and assume that for any q &#8712; R2, the eigenvalues of DY have negative real part, this is, det(DY) > 0 and tr(DY) < 0. Then, the solution (x, y) = (0,0) of Y is globally asymptotically stable. To this end, we show that this problema is equivalent to the following: Let Y : R2 &#8594;R2 be a C1 vector field. If det(DY) > 0 and tr(DY) < 0, then Y is globally injective. This equivalence was proved by C. Olech [1]. So we show the injectivity of the vector field Y under the conditions det(DY) > 0 and tr(DY)<0. In fact, we present a more stronger result, which was obtained by C. Gutierrez and can be found in [4]. This result is given by: Any planar vector field X of class C2 satisfying the r-eigenvalue condition for some r &#8712; [0,¥) is injective. / Neste trabalho, estamos interessados em estudar a solução do seguinte problema: Seja Y = ( f ,g) um campo de vetores, de classe C1, em R2. Suponha que (x, y) = (0,0) é um ponto singular de Y e suponha que, para todo q &#8712; R2, os autovalores de DY tem parte real negativa, isto é, det(DY) > 0 e tr(DY) < 0. Então, a solução (x, y) = (0,0) de Y é globalmente assintoticamente estável. Para este fim, mostramos que este problema é equivalente ao seguinte: Seja Y : R2 &#8594;R2 uma campo de vetores de classe C1. Se det(DY) > 0 e tr(DY) < 0, então Y é globalmente injetora. Esta equivalência foi demonstrada por C. Olech em [1]. Desta forma, a estratégia é estudar a injetividade do campo Y sob as condições det(DY)> 0 e tr(DY) < 0. Na verdade, apresentamos um resultado um pouco mais forte, o qual foi obtido por C. Gutierrez e pode ser encontrado em [4]. Este resultado é dado por: Qualquer campo de vetores X : R2 &#8594;R2 de classe C2 satisfazendo a condição de r-autovalor, para algum r &#8712; [0,¥), é injetora.
106

Network mechanisms of working memory : from persistent dynamics to chaos / Mécanismes de réseau de mémoire de travail : de dynamique persistante à chaos

Harish, Omri 10 December 2013 (has links)
Une des capacités cérébrales les plus fondamentales, qui est essentiel pour tous les fonctions cognitifs de haut niveau, est de garder des informations pertinentes de tâche pendant les périodes courtes de temps; on connaît cette capacité comme la mémoire de travail (WM). Dans des décennies récentes, accumule là l'évidence d'activité pertinente de tâche dans le cortex préfrontal (PFC) de primates pendant les périodes de "delay" de tâches de "delay-response", impliquant ainsi que PFC peut maintenir des informations sensorielles et ainsi la fonction comme un module de WM. Pour la récupération d'informationssensorielles de l'activité de réseau après que le stimulus sensoriel n'est plus présent il est impératif que l'état du réseau au moment de la récupération soit corrélé avec son état au moment de la compensation de stimulus. Un extrême, en vue dans les modèles informatiques de WM, est la coexistence d'attracteurs multiples. Dans cette approche la dynamique de réseau a une multitude d'états stables possibles, qui correspondent aux états différents de mémoire et un stimulus peut forcer le réseau à changer à un tel état stable. Autrement, même en absence d'attracteurs multiples, si la dynamique du réseau estchaotique alors les informations sur des événements passés peuvent être extraites de l'état du réseau, à condition que la durée typique de l'autocorrélation (AC) de dynamique neuronale soit assez grande. Dans la première partie de cette thèse, j'étudie un modèle à base d'attracteur de mémoire d'un emplacement spatial, pour examiner le rôle des non-linéarités de courbes de f-I neuronales dans des mécanismes de WM. Je fournis une théorie analytique et des résultats de simulations montrant que ces nonlinéarités, plutôt que les constants de temps synaptic ou neuronal, peuvent être la base de mécanismes de réseau WM. Dans la deuxième partie j'explore des facteurs contrôlant la durée d'ACs neuronales dans ungrand réseau "balanced" affichant la dynamique chaotique. Je développe une théorie de moyen champ (MF) décrivant l'ACs en termes de plusieurs paramètres d'ordre. Alors, je montre qu'en dehors de la proximité au point de transition-à-chaos, qui peut augmenter la largeur de la courbe d'AC, l'existence de motifs de connectivité peut causer des corrélations de longue durée dans l'état du réseau. / One of the most fundamental brain capabilities, that is vital for any high level cognitive function, is to store task-relevant information for short periods of time; this capability is known as working memory (WM). In recent decades there is accumulating evidence of taskrelevant activity in the prefrontal cortex (PFC) of primates during delay periods of delayedresponse tasks, thus implying that PFC is able to maintain sensory information and so function as a WM module. For retrieval of sensory information from network activity after the sensory stimulus is no longer present it is imperative that the state of the network at the time of retrieval be correlated with its state at the time of stimulus offset. One extreme, prominent in computational models of WM, is the co-existence of multiple attractors. In this approach the network dynamics has a multitude of possible steady states, which correspond to different memory states, and a stimulus can force the network to shift to one such steady state. Alternatively, even in the absence of multiple attractors, if the dynamics of the network is chaotic then information about past events can be extracted from the state of the network, provided that the typical time scale of the autocorrelation (AC) of neuronal dynamics is large enough. In the first part of this thesis I study an attractor-based model of memory of a spatial location to investigate the role of non-linearities of neuronal f-I curves in WM mechanisms. I provide an analytic theory and simulation results showing that these nonlinearities, rather than synaptic or neuronal time constants, can be the basis of WM network mechanisms. In the second part I explore factors controlling the time scale of neuronal ACs in a large balanced network displaying chaotic dynamics. I develop a mean-field (MF) theory describing the ACs in terms of several order parameters. Then, I show that apart from the proximity to the transition-to-chaos point, which can increase the width of the AC curve, the existence of connectivity motifs can cause long-time correlations in the state of the network.
107

Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model

Gönner, Lorenz, Vitay, Julien, Hamker, Fred 23 November 2017 (has links) (PDF)
Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.
108

Modélisation qualitative des réseaux biologiques pour l'innovation thérapeutique / Qualitative modeling of biological networks for therapeutic innovation

Poret, Arnaud 01 July 2015 (has links)
Cette thèse est consacrée à la modélisation qualitative des réseaux biologiques pour l'innovation thérapeutique. Elle étudie comment utiliser les réseaux Booléens, et comment les améliorer, afin d'identifier des cibles thérapeutiques au moyen d'approches in silico. Elle se compose de deux travaux : i) un algorithme exploitant les attracteurs des réseaux Booléens pour l'identification in silico de cibles dans des modèles Booléens de réseaux biologiques pathologiquement perturbés, et ii) une amélioration des réseaux Booléens dans leur capacité à modéliser la dynamique des réseaux biologiques grâce à l'utilisation des opérateurs de la logique floue et grâce au réglage des arrêtes. L'identification de cibles constitue l'une des étapes de la découverte de nouveaux médicaments et a pour but d'identifier des biomolécules dont la fonction devrait être thérapeutiquement modifiée afin de lutter contre la pathologie considérée. Le premier travail de cette thèse propose un algorithme pour l'identification in silico de cibles par l'exploitation des attracteurs des réseaux Booléens. Il suppose que les attracteurs des systèmes dynamiques, tel que les réseaux Booléens, correspondent aux phénotypes produits par le système biologique modélisé. Sous cette hypothèse, et étant donné un réseau Booléen modélisant une physiopathologie, l'algorithme identifie des combinaisons de cibles capables de supprimer les attracteurs associés aux phénotypes pathologiques. L'algorithme est testé sur un modèle Booléen du cycle cellulaire arborant une inactivation constitutive de la protéine du rétinoblastome, tel que constaté dans de nombreux cancers, tandis que ses applications sont illustrées sur un modèle Booléen de l'anémie de Fanconi. Les résultats montrent que l'algorithme est à même de retourner des combinaisons de cibles capables de supprimer les attracteurs associés aux phénotypes pathologiques, et donc qu'il réussit l'identification in silico de cibles proposée. En revanche, comme tout résultat in silico, il y a un pont à franchir entre théorie et pratique, requérant ainsi une utilisation conjointe d'approches expérimentales. Toutefois, il est escompté que l'algorithme présente un intérêt pour l'identification de cibles, notamment par l'exploitation du faible coût des approches computationnelles, ainsi que de leur pouvoir prédictif, afin d'optimiser l'efficience d'expérimentations coûteuses. La modélisation quantitative en biologie systémique peut s'avérer difficile en raison de la rareté des détails quantitatifs concernant les phénomènes biologiques, particulièrement à l'échelle subcellulaire, l'échelle où les médicaments interagissent avec leurs cibles. Une alternative permettant de contourner cette difficulté est la modélisation qualitative étant donné que celle-ci ne requiert que peu ou pas d'informations quantitatives. Parmi les méthodes de modélisation qualitative, les réseaux Booléens en sont l'une des plus populaires. Cependant, les modèles Booléens autorisent leurs variables à n'être évaluées qu'à vrai ou faux, ce qui peut apparaître trop simpliste lorsque des processus biologiques sont modélisés. En conséquence, le second travail de cette thèse propose une méthode de modélisation dérivée des réseaux Booléens où les opérateurs de la logique floue sont utilisés et où les arrêtes peuvent être réglées. Les opérateurs de la logique floue permettent aux variables d'être continues, et ainsi d'être plus finement évaluées qu'avec des méthodes de modélisation discrètes tel que les réseaux Booléens, tout en demeurant qualitatives. De plus, dans le but de considérer le fait que certaines interactions peuvent être plus lentes et/ou plus faibles que d'autres, l'état des arrêtes est calculé afin de moduler en vitesse et en force le signal qu'elles véhiculent. La méthode proposée est illustrée par son implémentation sur un petit échantillon de la signalisation du récepteur au facteur de croissance épidermique... [etc] / This thesis is devoted to the qualitative modeling of biological networks for therapeutic innovation. It investigates how to use the Boolean network formalism, and how to enhance it, for identifying therapeutic targets through in silico approaches. It is composed of two works: i) an algorithm using Boolean network attractors for in silico target identification in Boolean models of pathologically disturbed biological networks, and ii) an enhancement of the Boolean network formalism in modeling the dynamics of biological networks through the incorporation of fuzzy operators and edge tuning. Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. The first work of this thesis proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments. Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale, the scale where drugs interact with there targets. An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic when modeling biological processes. Consequently, the second work of this thesis proposes a modeling approach derived from Boolean networks where fuzzy operators are used and where edges are tuned. Fuzzy operators allow variables to be continuous and then to be more finely valued than with discrete modeling approaches, such as Boolean networks, while remaining qualitative. Moreover, to consider that some interactions are slower and/or weaker relative to other ones, edge states are computed in order to modulate in speed and strength the signal they convey. The proposed formalism is illustrated through its implementation on a tiny sample of the epidermal growth factor receptor signaling pathway. The obtained simulations show that continuous results are produced, thus allowing finer analysis, and that modulating the signal conveyed by the edges allows their tuning according to knowledge about the modeled interactions, thus incorporating more knowledge. The proposed modeling approach is expected to bring enhancements in the ability of qualitative models to simulate the dynamics of biological networks while not requiring quantitative information. The main prospect of this thesis is to use the proposed enhancement of Boolean networks to build a version of the algorithm based on continuous dynamical systems...[etc]
109

Dynamický model nelineárního oscilátoru s piezoelektrickou vrstvou / Dynamic model of nonlinear oscillator with piezoelectric layer

Sosna, Petr January 2021 (has links)
Tato diplomová práce je zaměřena na analýzu chování magnetopiezoelastického kmitajícího nosníku. V teoretické části jsou odvozeny diskretizované parametry, které popisují reálnou soustavu jako model s jedním stupněm volnosti. Tento model je následně použit pro kvalitativní i kvantitativní analýzu chování tohoto harvesteru. Frekvenční odezva harmonicky buzeného systému je zkoumána v dvouparametrické nebo tříparametrické analýze v závislosti na amplitudě buzení, elektrické zátěži a vzdálenosti mezi magnety. Posledně zmíněný parametr je v práci tím hlavním, proto je vliv vzdálenosti magnetů zkoumán také s pomocí bifurkačních diagramů. Tyto diagramy byly navíc použity k vytvoření oscilační "mapy", která pro každé zatěžovací podmínky ukazuje, jakou vzdálenost magnetů je třeba nastavit, aby bylo generováno nejvíce energie. Práce je doplněna o ukázky několika jevů, které mohou značně ovlivnit chování systému, pokud se nejdená o čistě harmonické buzení.
110

Pokročilé algoritmy analýzy datových sekvencí v Matlabu / Advanced algorithms for the analysis of data sequences in Matlab

Götthans, Tomáš January 2010 (has links)
Cílem této práce je se seznámení s možnostmi programu Matlab z hlediska detailní analýzy deterministických dynamických systémů. Jedná se především o analýzu časové posloupnosti a o nalezení Lyapunových exponentů. Dalším cílem je navrhnout algoritmus umožňující specifikovat chování systému na základě znalosti příslušných diferenciálních rovnic. To znamená, nalezení chaotických systémů.

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