Cette thèse porte sur les problèmes de sécurité sur le réseau électrique français exploité par RTE, le Gestionnaire de Réseau de Transport (GRT). Les progrès en matière d'énergie durable, d'efficacité du marché de l'électricité ou de nouveaux modes de consommation poussent les GRT à exploiter le réseau plus près de ses limites de sécurité. Pour ce faire, il est essentiel de rendre le réseau plus "intelligent". Pour s'attaquer à ce problème, ce travail explore les avantages des réseaux neuronaux artificiels. Nous proposons de nouveaux algorithmes et architectures d'apprentissage profond pour aider les opérateurs humains (dispatcheurs) à prendre des décisions que nous appelons " guided dropout ". Ceci permet de prévoir les flux électriques consécutifs à une modification volontaire ou accidentelle du réseau. Pour se faire, les données continues (productions et consommations) sont introduites de manière standard, via une couche d'entrée au réseau neuronal, tandis que les données discrètes (topologies du réseau électrique) sont encodées directement dans l'architecture réseau neuronal. L’architecture est modifiée dynamiquement en fonction de la topologie du réseau électrique en activant ou désactivant des unités cachées. Le principal avantage de cette technique réside dans sa capacité à prédire les flux même pour des topologies de réseau inédites. Le "guided dropout" atteint une précision élevée (jusqu'à 99% de précision pour les prévisions de débit) tout en allant 300 fois plus vite que des simulateurs de grille physiques basés sur les lois de Kirchoff, même pour des topologies jamais vues, sans connaissance détaillée de la structure de la grille. Nous avons également montré que le "guided dropout" peut être utilisé pour classer par ordre de gravité des évènements pouvant survenir. Dans cette application, nous avons démontré que notre algorithme permet d'obtenir le même risque que les politiques actuellement mises en œuvre tout en n'exigeant que 2 % du budget informatique. Le classement reste pertinent, même pour des cas de réseau jamais vus auparavant, et peut être utilisé pour avoir une estimation globale de la sécurité globale du réseau électrique. / This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid.

Contexte: La microscopie joue un rôle important en biologie depuis plusieurs siècles, mais sa résolution a longtemps été limitée à environ 250 nm, de sorte que nombre de structures biologiques (virus, vésicules, pores nucléaires, synapses) ne pouvaient être résolues. Au cours de la dernière décennie, plusieurs méthodes de super-résolution ont été développées pour dépasser cette limite. Parmi ces techniques, les plus puissantes et les plus utilisées reposent sur la localisation de molécules uniques (microscopie à localisation de molécule unique, ou SMLM), comme PALM et STORM. En localisant précisément les positions de molécules fluorescentes isolées dans des milliers d'images de basse résolution acquises de manière séquentielle, la SMLM peut atteindre des résolutions de 20 à 50 nm voire mieux. Cependant, cette technique est intrinsèquement lente car elle nécessite l’accumulation d’un très grand nombre d’images et de localisations pour obtenir un échantillonnage super-résolutif des structures fluorescentes. Cette lenteur (typiquement ~ 30 minutes par image super-résolutive) rend difficile l'utilisation de la SMLM pour l'imagerie cellulaire à haut débit ou en cellules vivantes. De nombreuses méthodes ont été proposées pour pallier à ce problème, principalement en améliorant les algorithmes de localisation pour localiser des molécules proches, mais la plupart de ces méthodes compromettent la résolution spatiale et entraînent l’apparition d’artefacts. Méthodes et résultats: Nous avons adopté une stratégie de transformation d’image en image basée sur l'apprentissage profond dans le but de restaurer des images SMLM parcimonieuses et par là d’améliorer la vitesse d’acquisition et la qualité des images super-résolutives. Notre méthode, ANNA-PALM, s’appuie sur des développements récents en apprentissage profond, notamment l’architecture U-net et les modèles génératifs antagonistes (GANs). Nous montrons des validations de la méthode sur des images simulées et des images expérimentales de différentes structures cellulaires (microtubules, pores nucléaires et mitochondries). Ces résultats montrent qu’après un apprentissage sur moins de 10 images de haute qualité, ANNA-PALM permet de réduire le temps d’acquisition d’images SMLM, à qualité comparable, d’un facteur 10 à 100. Nous avons également montré que ANNA-PALM est robuste à des altérations de la structure biologique, ainsi qu’à des changements de paramètres de microscopie. Nous démontrons le potentiel applicatif d’ANNA-PALM pour la microscopie à haut débit en imageant ~ 1000 cellules à haute résolution en environ 3 heures. Enfin, nous avons conçu un outil pour estimer et réduire les artefacts de reconstruction en mesurant la cohérence entre l’image reconstruite et l’image en épi-fluorescence. Notre méthode permet une microscopie super-résolutive plus rapide et plus douce, compatible avec l’imagerie haut débit, et ouvre une nouvelle voie vers l'imagerie super-résolutive des cellules vivantes. La performance des méthodes d'apprentissage profond augmente avec la quantité des données d’entraînement. Le partage d’images au sein de la communauté de microscopie offre en principe un moyen peu coûteux d’augmenter ces données. Cependant, il est souvent difficile d'échanger ou de partager des données de SMLM, car les tables de localisation seules ont souvent une taille de plusieurs gigaoctets et il n'existe pas de plate-forme de visualisation dédiée aux données SMLM. Nous avons développé un format de fichier pour compresser sans perte des tables de localisation, ainsi qu’une plateforme web (https://shareloc.xyz) qui permet de visualiser et de partager facilement des données SMLM 2D ou 3D. A l’avenir, cette plate-forme pourrait grandement améliorer les performances des modèles d'apprentissage en profondeur, accélérer le développement des outils, faciliter la réanalyse des données et promouvoir la recherche reproductible et la science ouverte. / Background: Microscopy plays an important role in biology since several centuries, but its resolution has long been limited to ~250nm due to diffraction, leaving many important biological structures (e.g. viruses, vesicles, nuclear pores, synapses) unresolved. Over the last decade, several super-resolution methods have been developed that break this limit. Among the most powerful and popular super-resolution techniques are those based on single molecular localization (single molecule localization microscopy, or SMLM) such as PALM and STORM. By precisely localizing positions of isolated fluorescent molecules in thousands or more sequentially acquired diffraction limited images, SMLM can achieve resolutions of 20-50 nm or better. However, SMLM is inherently slow due to the necessity to accumulate enough localizations to achieve high resolution sampling of the fluorescent structures. The drawback in acquisition speed (typically ~30 minutes per super-resolution image) makes it difficult to use SMLM in high-throughput and live cell imaging. Many methods have been proposed to address this issue, mostly by improving the localization algorithms to localize overlapping spots, but most of them compromise spatial resolution and cause artifacts.Methods and results: In this work, we applied deep learning based image-to-image translation framework for improving imaging speed and quality by restoring information from rapidly acquired low quality SMLM images. By utilizing recent advances in deep learning including the U-net and Generative Adversarial Networks, we developed our method Artificial Neural Network Accelerated PALM (ANNA-PALM) which is capable of learning structural information from training images and using the trained model to accelerate SMLM imaging by tens to hundreds folds. With experimentally acquired images of different cellular structures (microtubules, nuclear pores and mitochondria), we demonstrated that deep learning can efficiently capture the structural information from less than 10 training samples and reconstruct high quality super-resolution images from sparse, noisy SMLM images obtained with much shorter acquisitions than usual for SMLM. We also showed that ANNA-PALM is robust to possible variations between training and testing conditions, due either to changes in the biological structure or to changes in imaging parameters. Furthermore, we take advantage of the acceleration provided by ANNA-PALM to perform high throughput experiments, showing acquisition of ~1000 cells at high resolution in ~3 hours. Additionally, we designed a tool to estimate and reduce possible artifacts is designed by measuring the consistency between the reconstructed image and the experimental wide-field image. Our method enables faster and gentler imaging which can be applied to high-throughput, and provides a novel avenue towards live cell high resolution imaging. Deep learning methods rely on training data and their performance can be improved even further with more training data. One cheap way to obtain more training data is through data sharing within the microscopy community. However, it often difficult to exchange or share localization microscopy data, because localization tables alone are typically several gigabytes in size, and there is no dedicated platform for localization microscopy data which provide features such as rendering, visualization and filtering. To address these issues, we developed a file format that can losslessly compress localization tables into smaller files, alongside with a web platform called ShareLoc (https://shareloc.xyz) that allows to easily visualize and share 2D or 3D SMLM data. We believe that this platform can greatly improve the performance of deep learning models, accelerate tool development, facilitate data re-analysis and further promote reproducible research and open science.

Published Article / A worldwide concern are focusing on the quality of postgraduate training in higher education institutions, the length of time it takes postgraduate students to complete their studies, and the high percentage of postgraduate students who terminate there studies. Furthermore the involvement in research is making increasing quality demands on higher education institutions in terms of sustaining high-level research capability and involvement on an efficient and effective basis. It is clear that the postgraduate environment will have certain expectations as well as obstacles for the students and therefore if the undergraduate students are prepared beforehand for the postgraduate environment, they will be able to bridge the gap between undergraduate and postgraduate studies more successfully.

The actin cytoskeleton plays a vital role in various biological processes such as cell migration, morphogenesis, and intracellular trafficking. The polymerization of actin filaments at membranes provides the force for generating dynamic actin structures such as protrusions and invaginations that drive these processes. In filopodia, which are finger-like protrusions comprised of bundled actin filaments, actin regulatory proteins are believed to assemble a distal 'tip complex' which stimulates actin nucleation at the membrane. However how these regulators collectively behave in a macromolecular complex still remains poorly understood. To understand the macromolecular nature of these complexes, I investigated the dynamic properties and spatial organization of actin regulatory factors, using an in vitro reconstitution assay for filopodia-like structures (FLS) utilizing artificial lipid bilayers and Xenopus laevis egg extracts. FRAP analysis of seven actin regulatory factors (Toca-1, N-WASP, GTPase-binding domain, Ena, VASP, Diaph3, Fascin) revealed that the FLS tip complex has both dynamic and stable properties, with different proteins displaying distinct dynamics. Further analyses on the membrane-binding protein Toca-1 showed that its dynamic turnover is controlled by interactions with actin and exchange of molecules with solution. Single-molecule localization microscopy resolved the nanoscale organization of Toca-1, showing its arrangement into flat plaque-like and narrowly elevated tubular substructures. Plaque-like structures showed similarities to phase-transition patterns, while tubule-like structures closely resembled those previously found to decorate membrane tubules in vitro, which are thought to be involved in endocytic membrane remodeling. Endocytic accessory proteins such as SNX9 and Dynamin2 were also found to localize to FLS tips. This work provides new insights into the dynamics and organization of protein ensembles at actin nucleation sites, and proposes a novel link between endocytosis and filopodia formation, which is relevant to understanding how cells decide when and where to assemble actin at the membrane.

Synapses are the communication junctions of the nervous system and contain protein machinery necessary for cognitive functions such as learning and memory. Postsynaptic density protein-95 (PSD-95) is a key scaffolding molecule at the PSD of synapses, yet its sub-synaptic organisation in the mammalian brain remains poorly understood. This thesis presents the use of genetically labelled PSD-95 with super-resolution imaging to resolve its nano-architecture in the mouse brain. To visualize PSD-95, two knock-in mouse lines were generated where the fluorescent proteins eGFP or mEos2 was fused to the carboxyl terminus of the endogenous PSD- 95 protein (PSD-95-eGFP or PSD-95-mEos2). Methods were developed by which fixed tissue sections of PSD-95-eGFP mice were examined using gated-stimulated emission depletion (g-STED) microscopy and PSD-95-mEos2 sections were examined with photoactivatable localisation microscopy (PALM) and quantitative image analysis was developed for both methods. From these platforms it was demonstrated that PSD-95 has a two tiered organisation: it is assembled into nanoclusters (NCs) approximately 140 nm diameter, which form part of the greater envelope of the PSD within synapses. Synapse subtypes were observed as characterised by the number of NCs per PSD. Using double colour g- STED microscopy. It was then asked whether PSD-95 nano-architecture remained the same across different sub-regions of the brain. A survey of PSD-95 was performed from seven different sub-regions of the hippocampus, quantifying ~110,000 NCs within ~70,000 PSDs from across the two super-resolution platforms. It was found that synapses displayed structural diversity both within and between different brain subregions as a function of the number of NCs per PSD. PSD-95 NCs were structurally conserved across the hippocampus, but showed molecular diversity in the abundance of PSD-95 molecules within. The findings of this thesis are: 1) genetic labelling of endogenous proteins combined with super-resolution microscopy is a powerful tool to study synaptic protein organisation in tissue. 2) Synaptic structural diversity in the brain is underlined by the number of PSD-95 NC units per synapse 3) PSD-95 NCs are structurally conserved but molecularly diverse synaptic units of synapses throughout the brain. These findings suggest that cognitive processing at the synapse is based upon a conserved, fundamental, molecular architecture.

O objetivo deste trabalho é a definição de um conjunto de roteiros para o ensino de arquitetura de computadores com enfoque em arquiteturas superescalares. O procedimento é baseado em simulação e verificação da influência dos parâmetros arquiteturais dos processadores, em termos funcionais e de desempenho. É dada ênfase a conceitos como memória cache, predição de desvio, execução fora de ordem, unidades funcionais e etc. Através do estudo e avaliação dos parâmetros que constituem estes conceitos, procurava-se através dos roteiros identificar as configurações com melhor desempenho. Para a implementação destes roteiros é dotado o conjunto de ferramentas de simulação SimpleScalar. Este conjunto, além de estar disponibilizado em código aberto na página oficial das ferramentas, traz como vantagem a possibilidade de alteração do código para fins de pesquisa. Este trabalho e os roteiros que o compõem têm como objetivos auxiliar professores e estimular os alunos através de simulações, como forma didática de testar conceitos vistos em sala de aula. Os roteiros são apresentados com os respectivos resultados de simulação e incrementados com comentários e sugestões de um conjunto de perguntas e respostas para que o trabalho possa ter continuidade necessária, partindo da sala de aula para a simulação, busca de respostas e culminando com um relatório final a ser avaliado.

In recent years, microlenses (ML), which are micro-scale spheres, have been used to overcome physical diffraction limit of optical microscopy (~200 nm). Although the use of such ML has provided highly resolved images of objects beyond the Abbe optical diffraction limit, the process needs to be refined before it can be applied widespread in materials, biological and clinical research. In this research work, we have implemented experiments on super-resolution imaging utilizing MLs of different refractive indices (n) and diameters to provide the scientific and engineering communities with practical guidelines for obtaining high resolution images with ease. With the support from experimental imaging data as well as FDTD simulations, we have shown that optimal super-resolution imaging with microspheres was accomplished under specific parameter range. We have identified ML with n=1.51 as a preferable choice over those MLs with n=1.4, 1.93, and 2.2, because of high reliability and high magnification for ML with n=1.51. With n=1.51 in mind, we have identified a diameter range from 15 μm to 50 μm provides high resolution and magnification for practical purposes. We show that other ML diameters provided high resolution as well; we believe that ML diameters between 15 μm and 50 μm are practically preferred. We were able to achieve <150 nm resolution and further refinement of this tool can potentially yield higher quality imaging results. Ideally, MLs will eventually be directly incorporated as a modular device in an optical microscope providing the researchers an effective, noninvasive, and economical alternative to complex super resolution microscopy techniques. To improve scanning efficiency, we also proposed microtubule (MT) based imaging. With the demonstration of theoretical optics, we conclude, at present time, that there are some practical concerns for MT-based imaging technique that may limit its application as super-resolution imaging technique. For example, MT-based imaging appears to possess a lower contrast than ML-based technique. Thus, although the concept of MT-based imaging is theoretically possible, we think that more work is needed to utilization of this tool for practical applications.

O objetivo deste trabalho é a definição de um conjunto de roteiros para o ensino de arquitetura de computadores com enfoque em arquiteturas superescalares. O procedimento é baseado em simulação e verificação da influência dos parâmetros arquiteturais dos processadores, em termos funcionais e de desempenho. É dada ênfase a conceitos como memória cache, predição de desvio, execução fora de ordem, unidades funcionais e etc. Através do estudo e avaliação dos parâmetros que constituem estes conceitos, procurava-se através dos roteiros identificar as configurações com melhor desempenho. Para a implementação destes roteiros é dotado o conjunto de ferramentas de simulação SimpleScalar. Este conjunto, além de estar disponibilizado em código aberto na página oficial das ferramentas, traz como vantagem a possibilidade de alteração do código para fins de pesquisa. Este trabalho e os roteiros que o compõem têm como objetivos auxiliar professores e estimular os alunos através de simulações, como forma didática de testar conceitos vistos em sala de aula. Os roteiros são apresentados com os respectivos resultados de simulação e incrementados com comentários e sugestões de um conjunto de perguntas e respostas para que o trabalho possa ter continuidade necessária, partindo da sala de aula para a simulação, busca de respostas e culminando com um relatório final a ser avaliado.

Dissertação (mestrado)—Universidade de Brasília, Instituto de Letras, Departamento de Teoria Literária e Literaturas, Programa de Pós-Graduação em Literatura, 2014. / Submitted by Albânia Cézar de Melo (albania@bce.unb.br) on 2014-06-02T16:29:02Z No. of bitstreams: 1 2014_MairaBassoMotta.pdf: 859320 bytes, checksum: 51b422447be76a4e6cfc3f3be74b9ba7 (MD5) / Approved for entry into archive by Guimaraes Jacqueline(jacqueline.guimaraes@bce.unb.br) on 2014-06-03T12:51:04Z (GMT) No. of bitstreams: 1 2014_MairaBassoMotta.pdf: 859320 bytes, checksum: 51b422447be76a4e6cfc3f3be74b9ba7 (MD5) / Made available in DSpace on 2014-06-03T12:51:04Z (GMT). No. of bitstreams: 1 2014_MairaBassoMotta.pdf: 859320 bytes, checksum: 51b422447be76a4e6cfc3f3be74b9ba7 (MD5) / A representação do indígena tem uma tradição consolidada na literatura brasileira. A transfiguração é o processo literário utilizado para essa representação. Esse conceito de transfiguração é entendido aqui, primeiramente, como um processo interno ao sistema literário nacional, no sentido de que os elementos anteriores que figuravam nas obras literárias cujo tema se aproximava do indígena permanecem, de forma renovada, nas obras posteriores. Nesse sentido, estudamos Maíra (1976), de Darcy Ribeiro, em que ocorre a transfiguração do indígena, porém, inserida em um contexto literário que Antonio Candido chama de super-regionalismo, ou seja, por ser uma literatura que já tem consciência do subdesenvolvimento do país e que já não pode amenizar o fato de que o indígena teve sua etnia desfigurada devido à colonização, ocorre no romance uma explosão conceitual dos elementos anteriores. Consideramos que o processo de transfiguração literária do indígena está associado à formação da literatura e do país e envolve as questões da formação do povo brasileiro e do desaparecimento das nações indígenas em um mundo civilizado pela ação colonizadora dos europeus e influenciado pela religião católica. ______________________________________________________________________________ ABSTRACT / The representations of indigenous people have one consolidated tradition in Brazilian literature. The transfiguration is the literary process used to make that representation. This concept of transfiguration is understood here, firstly, as one internal process of national literary system, in the sense that the old elements which figured in Brazilian literature whose theme approach indigenous issues rest, somehow renewed, in newly works. In that way, we analyzed Maíra (1976), from Darcy Ribeiro, in which occur the indigenous transfiguration, but, inserted in a literary context which Antonio Candido called super-regionalism, which means, that the literature already have conscience of country’s undevelopment and that cannot ignore the fact that the indigenous had his ethnic disfigured because of colonization, in the romance occur one conceptual explosion of before elements. We state that the process of literary transfiguration of indigenous is linked to the formation of Brazil as country and its literature and involves issues about the formation of Brazilian people and the disappearing of indigenous nations in such civilized world lead by the colonizing action of Europeans and influenced by the catholic religion.

O objetivo deste trabalho é a definição de um conjunto de roteiros para o ensino de arquitetura de computadores com enfoque em arquiteturas superescalares. O procedimento é baseado em simulação e verificação da influência dos parâmetros arquiteturais dos processadores, em termos funcionais e de desempenho. É dada ênfase a conceitos como memória cache, predição de desvio, execução fora de ordem, unidades funcionais e etc. Através do estudo e avaliação dos parâmetros que constituem estes conceitos, procurava-se através dos roteiros identificar as configurações com melhor desempenho. Para a implementação destes roteiros é dotado o conjunto de ferramentas de simulação SimpleScalar. Este conjunto, além de estar disponibilizado em código aberto na página oficial das ferramentas, traz como vantagem a possibilidade de alteração do código para fins de pesquisa. Este trabalho e os roteiros que o compõem têm como objetivos auxiliar professores e estimular os alunos através de simulações, como forma didática de testar conceitos vistos em sala de aula. Os roteiros são apresentados com os respectivos resultados de simulação e incrementados com comentários e sugestões de um conjunto de perguntas e respostas para que o trabalho possa ter continuidade necessária, partindo da sala de aula para a simulação, busca de respostas e culminando com um relatório final a ser avaliado.