The west coast of North America is under the threat of future great megathrust earthquakes and associated tsunamis. This dissertation addresses three urgent but unresolved issues in tsunami hazard assessment and risk mitigation at northernmost Cascadia. (1) Plate subduction is actively taking place along the Explorer segment of the northern Cascadia subduction zone and probably also its Winona fragment, and therefore their seismogenic and tsunamigenic potential should be investigated. (2) It needs to be investigated whether the shallowest portion of the Cascadia megathrust can undergo highly tsunamigenic trench-breaching rupture in great earthquakes like in the 2011 Tohoku-Oki earthquake at the Japan Trench. (3) For tsunami hazard assessment and early warning in southwestern British Columbia, high-resolution megathrust rupture models need to be systematically developed. To address the first issue, I develop finite element models for the Explorer segment to estimate thermally allowed potential seismic rupture zone of the megathrust. The results suggest a potential rupture zone of ~60 km downdip width located offshore. For the Winona fragment, where there are large uncertainties in the tectonic history and the age of the oceanic lithosphere, a preliminary estimate by considering only the thermal effect of sedimentation on a cooling lithosphere suggests a potential rupture zone of a minimum downdip width of 35 km. I address the second issue by reanalyzing seismic survey images off Vancouver Island with a focus on secondary faults around the accretionary wedge deformation front. No strong evidence suggests trench-breaching megathrust rupture being a dominant mode of fault behaviour at northern Cascadia, although the possibility cannot be excluded from tsunami hazard assessment. Buried rupture and coseismic activation of secondary faults may be more important at Cascadia. To address the third issue and also to investigate how the different secondary faults can contribute to tsunami generation, I compile a new Cascadia megathrust geometry and develop 21 tsunami sources using a three-dimensional (3D) dislocation model, including hypothetical models of frontal thrust, back-thrust, and splay faults. The dislocation models indicate that the buried rupture, splay-faulting rupture, and trench-breaching rupture can result in large seafloor uplift and coastal subsidence, and hence will lead to tsunamis that seriously affect the local coastal area. Back-thrust rupture near the deformation front is unimportant for tsunami generation. The model results also show that properly configured land-based Global Navigational Satellite System (GNSS) monitoring can distinguish between ruptures along the Cascadia megathrust and along the strike-slip Nootka fault and between megathrust ruptures of difference strike lengths and therefore can effectively contribute to real-time tsunami early warning. However, the results also reveal that these land-based measurements are not sensitive to the slip behaviour of the shallow portion of the megathrust farther offshore, demonstrating urgent need for near-trench, seafloor observations. / Graduate / 0373 / gaodawei999@126.com

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<p>In this thesis, the improved synthesis and investigations into the properties of some 3,7-diazabicyclo[3.3.1]nonane (bispidine) derivatives are described. These compounds are structurally related to the naturally occurring lupanine alkaloids, they are of interest because of their cardiac antiarrhythmic function as well as their use as bases or ligands in organic chemical reactions. Their chemical properties are related to the presence of a rigid molecular scaffold with two nitrogen atoms that can be utilized for binding interactions with a variety of Lewis acids.</p><p>An improved synthesis has been developed, providing access to bispidines <i>via</i> bispidinones while avoiding the use of highly toxic hydrazine, which is required as reducing agent in alternative methods.</p><p>A series of bispidine derivatives with a variety of substituents were characterized regarding their basicity, which spans thirteen orders of magnitude. Correlations between structure and basicity are discussed and computational methods have been used to propose further derivatives with even higher basicity.</p><p>The structures of several bispidine derivatives and their protonated forms have been characterized in the solid state by X-ray crystallography and in solution using NMR spectroscopy. Structure and solution dynamics in a sterically congested (π-allyl)palladium complex with a bispidine ligand have been investigated, revealing mechanistic insight into the dynamic process. Using a bulky bispidine as a temporary ligand for a (η³-propenyl) palladium complex, the novel adamantanoid [{(η³-propenyl)Pd}₆(μ₃-OH)₄] cluster was prepared.</p>

In this thesis, the improved synthesis and investigations into the properties of some 3,7-diazabicyclo[3.3.1]nonane (bispidine) derivatives are described. These compounds are structurally related to the naturally occurring lupanine alkaloids, they are of interest because of their cardiac antiarrhythmic function as well as their use as bases or ligands in organic chemical reactions. Their chemical properties are related to the presence of a rigid molecular scaffold with two nitrogen atoms that can be utilized for binding interactions with a variety of Lewis acids. An improved synthesis has been developed, providing access to bispidines via bispidinones while avoiding the use of highly toxic hydrazine, which is required as reducing agent in alternative methods. A series of bispidine derivatives with a variety of substituents were characterized regarding their basicity, which spans thirteen orders of magnitude. Correlations between structure and basicity are discussed and computational methods have been used to propose further derivatives with even higher basicity. The structures of several bispidine derivatives and their protonated forms have been characterized in the solid state by X-ray crystallography and in solution using NMR spectroscopy. Structure and solution dynamics in a sterically congested (π-allyl)palladium complex with a bispidine ligand have been investigated, revealing mechanistic insight into the dynamic process. Using a bulky bispidine as a temporary ligand for a (η3-propenyl) palladium complex, the novel adamantanoid [{(η3-propenyl)Pd}6(μ3-OH)4] cluster was prepared.

No / Purpose: To develop a perimetric test strategy, Structure Estimation of Minimum Uncertainty (SEMU), that uses structural information to drive stimulus choices. Methods: Structure Estimation of Minimum Uncertainty uses retinal nerve fiber layer (RNFL) thickness data as measured by optical coherence tomography to predict perimetric sensitivity. This prediction is used to set suprathreshold levels that then alter a prior probability distribution of the final test output. Using computer simulation, we studied SEMU’s performance under three different patient error response conditions: No Error, Typical False Positive errors, and Extremely Unreliable patients. In experiment 1, SEMU was compared with an existing suprathreshold cum thresholding combination test procedure, Estimation of Minimum Uncertainty (EMU), on single visual field locations. We used these results to finalize SEMU parameters. In experiment 2, SEMU was compared with full threshold (FT) on 163 glaucomatous visual fields. Results: On individual locations, SEMU has similar accuracy to EMU, but is, on average, one presentation faster than EMU. For the typical false-positive error condition, SEMU has significantly lower error compared with FT (SEMU average 0.33 dB lower; p < 0.001) and the 90% measured sensitivity range for SEMU is also smaller than that for FT. For unreliable patients, however, FT has lower mean and SD of error. Structure Estimation of Minimum Uncertainty makes significantly fewer presentations than FT (1.08 presentation on average fewer in a typical false-positive condition; p < 0.001). Assuming that a location in the field is marked abnormal if it falls below the 5th percentile of normal, SEMU has a false-positive rate of less than 10% for all error conditions compared with FT’s rate of 20% or more. Conclusions: On average, simulations show that using RNFL information to guide stimulus placement in a perimetric test procedure maintains accuracy, improves precision, and decreases test duration for patients with less than 15% false-positive rates.

In this thesis we propose a novel approach for tracking multiple objects using structural information. The objects are tracked by combining particle filter and frame description with Attributed Relational Graphs (ARGs). We start by learning a structural probabilistic model graph from annotated images. The graphs are then used to evaluate the current tracking state and to correct it, if necessary. By doing so, the proposed method is able to deal with challenging situations such as abrupt motion and tracking loss due to occlusion. The main contribution of this thesis is the exploration of the learned probabilistic structural model. By using it, the structural information of the scene itself is used to guide the object detection process in case of tracking loss. This approach differs from previous works, that use structural information only to evaluate the scene, but do not consider it to generate new tracking hypotheses. The proposed approach is very flexible and it can be applied to any situation in which it is possible to find structural relation patterns between the objects. Object tracking may be used in many practical applications, such as surveillance, activity analysis or autonomous navigation. In this thesis, we explore it to track multiple objects in sports videos, where the rules of the game create some structural patterns between the objects. Besides detecting the objects, the tracking results are also used as an input for recognizing the action each player is performing. This step is performed by classifying a segment of the tracking sequence using Hidden Markov Models (HMMs). The proposed tracking method is tested on several videos of table tennis matches and on the ACASVA dataset, showing that the method is able to continue tracking the objects even after occlusion or when there is a camera cut. / Nesta tese, uma nova abordagem para o rastreamento de múltiplos objetos com o uso de informação estrutural é proposta. Os objetos são rastreados usando uma combinação de filtro de partículas com descrição das imagens por meio de Grafos Relacionais com Atributos (ARGs). O processo é iniciado a partir do aprendizado de um modelo de grafo estrutural probabilístico utilizando imagens anotadas. Os grafos são usados para avaliar o estado atual do rastreamento e corrigi-lo, se necessário. Desta forma, o método proposto é capaz de lidar com situações desafiadoras como movimento abrupto e perda de rastreamento devido à oclusão. A principal contribuição desta tese é a exploração do modelo estrutural aprendido. Por meio dele, a própria informação estrutural da cena é usada para guiar o processo de detecção em caso de perda do objeto. Tal abordagem difere de trabalhos anteriores, que utilizam informação estrutural apenas para avaliar o estado da cena, mas não a consideram para gerar novas hipóteses de rastreamento. A abordagem proposta é bastante flexível e pode ser aplicada em qualquer situação em que seja possível encontrar padrões de relações estruturais entre os objetos. O rastreamento de objetos pode ser utilizado para diversas aplicações práticas, tais como vigilância, análise de atividades ou navegação autônoma. Nesta tese, ele é explorado para rastrear diversos objetos em vídeos de esporte, na qual as regras do jogo criam alguns padrões estruturais entre os objetos. Além de detectar os objetos, os resultados de rastreamento também são usados como entrada para reconhecer a ação que cada jogador está realizando. Esta etapa é executada classificando um segmento da sequência de rastreamento por meio de Modelos Ocultos de Markov (HMMs). A abordagem de rastreamento proposta é testada em diversos vídeos de jogos de tênis de mesa e na base de dados ACASVA, demonstrando a capacidade do método de lidar com situações de oclusão ou cortes de câmera.

In this thesis we propose a novel approach for tracking multiple objects using structural information. The objects are tracked by combining particle filter and frame description with Attributed Relational Graphs (ARGs). We start by learning a structural probabilistic model graph from annotated images. The graphs are then used to evaluate the current tracking state and to correct it, if necessary. By doing so, the proposed method is able to deal with challenging situations such as abrupt motion and tracking loss due to occlusion. The main contribution of this thesis is the exploration of the learned probabilistic structural model. By using it, the structural information of the scene itself is used to guide the object detection process in case of tracking loss. This approach differs from previous works, that use structural information only to evaluate the scene, but do not consider it to generate new tracking hypotheses. The proposed approach is very flexible and it can be applied to any situation in which it is possible to find structural relation patterns between the objects. Object tracking may be used in many practical applications, such as surveillance, activity analysis or autonomous navigation. In this thesis, we explore it to track multiple objects in sports videos, where the rules of the game create some structural patterns between the objects. Besides detecting the objects, the tracking results are also used as an input for recognizing the action each player is performing. This step is performed by classifying a segment of the tracking sequence using Hidden Markov Models (HMMs). The proposed tracking method is tested on several videos of table tennis matches and on the ACASVA dataset, showing that the method is able to continue tracking the objects even after occlusion or when there is a camera cut. / Nesta tese, uma nova abordagem para o rastreamento de múltiplos objetos com o uso de informação estrutural é proposta. Os objetos são rastreados usando uma combinação de filtro de partículas com descrição das imagens por meio de Grafos Relacionais com Atributos (ARGs). O processo é iniciado a partir do aprendizado de um modelo de grafo estrutural probabilístico utilizando imagens anotadas. Os grafos são usados para avaliar o estado atual do rastreamento e corrigi-lo, se necessário. Desta forma, o método proposto é capaz de lidar com situações desafiadoras como movimento abrupto e perda de rastreamento devido à oclusão. A principal contribuição desta tese é a exploração do modelo estrutural aprendido. Por meio dele, a própria informação estrutural da cena é usada para guiar o processo de detecção em caso de perda do objeto. Tal abordagem difere de trabalhos anteriores, que utilizam informação estrutural apenas para avaliar o estado da cena, mas não a consideram para gerar novas hipóteses de rastreamento. A abordagem proposta é bastante flexível e pode ser aplicada em qualquer situação em que seja possível encontrar padrões de relações estruturais entre os objetos. O rastreamento de objetos pode ser utilizado para diversas aplicações práticas, tais como vigilância, análise de atividades ou navegação autônoma. Nesta tese, ele é explorado para rastrear diversos objetos em vídeos de esporte, na qual as regras do jogo criam alguns padrões estruturais entre os objetos. Além de detectar os objetos, os resultados de rastreamento também são usados como entrada para reconhecer a ação que cada jogador está realizando. Esta etapa é executada classificando um segmento da sequência de rastreamento por meio de Modelos Ocultos de Markov (HMMs). A abordagem de rastreamento proposta é testada em diversos vídeos de jogos de tênis de mesa e na base de dados ACASVA, demonstrando a capacidade do método de lidar com situações de oclusão ou cortes de câmera.

Les réseaux sociaux sont de très grands systèmes permettant de représenter les interactions sociales entre les individus. L'analyse des réseaux sociaux est une collection de méthodes spécialement conçues pour examiner les aspects relationnels des structures sociales. L'un des défis les plus importants dans l'analyse de réseaux sociaux est le problème de prédiction de liens. La prédiction de liens étudie l'existence potentielle de nouvelles associations parmi des entités sociales non connectées. La plupart des approches de prédiction de liens se concentrent sur une seule source d'information, c'est-à-dire sur les aspects topologiques du réseau (par exemple le voisinage des nœuds) en supposant que les données sociales sont entièrement fiables. Pourtant, ces données sont généralement bruitées, manquantes et sujettes à des erreurs d'observation causant des distorsions et des résultats probablement erronés. Ainsi, cette thèse propose de gérer le problème de prédiction de liens sous incertitude. D'abord, deux nouveaux modèles de graphes de réseaux sociaux uniplexes et multiplexes sont introduits pour traiter l'incertitude dans les données sociales. L'incertitude traitée apparaît au niveau des liens et est représentée et gérée à travers le cadre de la théorie des fonctions de croyance. Ensuite, nous présentons huit méthodes de prédiction de liens utilisant les fonctions de croyance fondées sur différentes sources d'information dans les réseaux sociaux uniplexes et multiplexes. Nos contributions s'appuient sur les informations disponibles sur le réseau social. Nous combinons des informations structurelles aux informations des cercles sociaux et aux attributs des nœuds, ainsi que l'apprentissage supervisé pour prédire les nouveaux liens. Des tests sont effectués pour valider la faisabilité et l'intérêt de nos approches à celles de la littérature. Les résultats obtenus sur les données du monde réel démontrent que nos propositions sont pertinentes et valables dans le contexte de prédiction de liens. / Social networks are large structures that depict social linkage between millions of actors. Social network analysis came out as a tool to study and monitor the patterning of such structures. One of the most important challenges in social network analysis is the link prediction problem. Link prediction investigates the potential existence of new associations among unlinked social entities. Most link prediction approaches focus on a single source of information, i.e. network topology (e.g. node neighborhood) assuming social data to be fully trustworthy. Yet, such data are usually noisy, missing and prone to observation errors causing distortions and likely inaccurate results. Thus, this thesis proposes to handle the link prediction problem under uncertainty. First, two new graph-based models for uniplex and multiplex social networks are introduced to address uncertainty in social data. The handled uncertainty appears at the links level and is represented and managed through the belief function theory framework. Next, we present eight link prediction methods using belief functions based on different sources of information in uniplex and multiplex social networks. Our proposals build upon the available information in data about the social network. We combine structural information to social circles information and node attributes along with supervised learning to predict new links. Tests are performed to validate the feasibility and the interest of our link prediction approaches compared to the ones from literature. Obtained results on social data from real-world demonstrate that our proposals are relevant and valid in the link prediction context.