In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic congestion points, pinpoint military targets in satellite images, determine possible locations of oil reservoirs, and detect water pollution incidents. Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on "global comparison" and identifies deviations from the remainder of the entire data set. In contrast, spatial outlier detection concentrates on discovering neighborhood instabilities that break the spatial continuity. In recent years, a number of techniques have been proposed for spatial outlier detection. However, they have the following limitations. First, most of them focus primarily on single-attribute outlier detection. Second, they may not accurately locate outliers when multiple outliers exist in a cluster and correlate with each other. Third, the existing algorithms tend to abstract spatial objects as isolated points and do not consider their geometrical and topological properties, which may lead to inexact results. This dissertation reports a study of the problem of abnormal spatial pattern recognition, and proposes a suite of novel algorithms. Contributions include: (1) formal definitions of various spatial outliers, including single-attribute outliers, multi-attribute outliers, and region outliers; (2) a set of algorithms for the accurate detection of single-attribute spatial outliers; (3) a systematic approach to identifying and tracking region outliers in continuous meteorological data sequences; (4) a novel Mahalanobis-distance-based algorithm to detect outliers with multiple attributes; (5) a set of graph-based algorithms to identify point outliers and region outliers; and (6) extensive analysis of experiments on several spatial data sets (e.g., West Nile virus data and NOAA meteorological data) to evaluate the effectiveness and efficiency of the proposed algorithms. / Ph. D.

The Appalachian National Scenic Trail is a unit of the National Park System created by the National Trails Act of 1968. Commonly referred to as the Appalachian Trail, or the AT, this National Park has some of the longest boundaries of any park. The AT is routed more than 2000 miles along the mountains of the eastern United States. The land purchased for the protection of the AT creates a separate boundary on each side of the trail. Monitoring these boundaries for intrusions or encroachments is a difficult and time-consuming task when done totally by field methods. This thesis presents a more efficient and consistent monitoring process using remote sensing data and change detection algorithms. Using Landsat TM images, Normalized Difference Vegetation Index (NDVI), and image difference change detection, this research shows that major boundary encroachments can be detected. Detection of sub-pixel vegetation index decreases identifies specific locations for field inspection. Assuming low cost multispectral Landsat imagery is available, simple NDVI difference calculation allows this technique to be applied to the entire AT one or more times per year. This procedure would improve the response time for encroachment mediation. The producer's accuracy for finding possible encroachments was 100 percent and the consumer's accuracy for possible encroachments indicated was 78.3 percent. Due to limited image availability, this study only examines change between one pair of Landsat images. Further refinement of these techniques should investigate other Landsat images at other times. Use of other remote sensing systems and change detection algorithms could be the focus of further research. / Master of Science

Not many studies are currently devoted to the estimation of soil moisture from space-borne SAR data at the field scale. Superficial soil moisture is indeed generally estimated from SAR images at lower resolutions, rarely reaching the sub-kilometric scale. This is mainly due to the lack of in situ data, such as measured soil moisture and parameters indicative of the soil roughness and vegetation conditions. Moreover, when working at the kilometric scale, some hypothesis assumed while modelling the backscattered SAR signal over a vegetated area are more likely satisfied, whereas when working at higher resolutions such as the field scale, other interactions should be taken into account. Indeed, over a vegetated area the total backscattered SAR signal is usually modelled as the incoherent sum of the vegetation and the soil components, and only in the last years has been added a further contribution provoked from the presence of subsurface scatterers. In the present thesis, the just mentioned contributions are considered and modelled at the field scale for soil moisture estimation purposes. A long term Change Detection method is applied to copolarized Sentinel-1 data, with a focus on taking into account the component of the total backscattering coefficient due to the presence of subsurface scatterers, recently proposed in literature. By exploiting the strong relationships detected over the study area between the copolarized signal and the observed soil moisture, the inversion algorithm for soil moisture retrieval is adapted for considering the cases of dominant subsurface scattering mechanism. Moreover, the proper time scale of detection of subsurface scattering is identified at the field scale, providing helpful information for correcting retrieval algorithms based on SAR data also at lower spatial scales.

Earth observation satellites (EOS) such as Landsat provide image datasets that can be immensely useful in numerous application domains. One way of analyzing satellite images for land use and land cover change (LULCC) is time series analysis (TSA). Several algorithms for time series analysis have been proposed by various groups in remote sensing; more algorithms (that can be adapted) are available in the general time series literature. However, in spite of an abundance of algorithms, the choice of algorithm to be used for analyzing an image stack is presently an open question. A concurrent issue is the prohibitive size of Landsat datasets, currently of the order of petabytes and growing. This makes them computationally unwieldy --- both in storage and processing. An EOS image stack typically consists of multiple images of a fixed area on the Earth's surface (same latitudes and longitudes) taken at different time points. Experiments on multicore servers indicate that carrying out meaningful time series analysis on one such interannual, multitemporal stack with existing state of the art codes can take several days. This work proposes using multiple algorithms to analyze a given image stack in a polyalgorithmic framework. A polyalgorithm combines several basic algorithms, each meant to solve the same problem, producing a strategy that unites the strengths and circumvents the weaknesses of constituent algorithms. The foundation of the proposed TSA based polyalgorithm is laid using three algorithms (LandTrendR, EWMACD, and BFAST). These algorithms are precisely described mathematically, and chosen to be fundamentally distinct from each other in design and in the phenomena they capture. Analysis of results representing success, failure, and parameter sensitivity for each algorithm is presented. Scalability issues, important for real simulations, are also discussed, along with scalable implementations, and speedup results. For a given pixel, Hausdorff distance is used to compare the distance between the change times (breakpoints) obtained from two different algorithms. Timesync validation data, a dataset that is based on human interpretation of Landsat time series in concert with historical aerial photography, is used for validation. The polyalgorithm yields more accurate results than EWMACD and LandTrendR alone, but counterintuitively not better than BFAST alone. This nascent work will be directly useful in land use and land cover change studies, of interest to terrestrial science research, especially regarding anthropogenic impacts on the environment, and in much broader applications such as health monitoring and urban transportation. / M. S. / Numerous manmade satellites circling around the Earth regularly take pictures (images) of the Earth’s surface from up above. These images naturally provide information regarding the land cover of any given piece of land at the moment of capture (for e.g., whether the land area in the picture is covered with forests or with agriculture or housing). Therefore, for a fixed land area, if a person looks at a chronologically arranged series of images, any significant changes in land use can be identified. Identifying such changes is of critical importance, especially in this era where deforestation, urbanization, and global warming are major concerns. The goal of this thesis is to investigate the design of methodologies (algorithms) that can efficiently and accurately use satellite images for answering questions regarding land cover trend and change. Experience shows that the state-of-the-art methodologies produce great results for the region they were originally designed on but their performance on other regions is unpredictable. In this work, therefore, a ‘polyalgorithm’ is proposed. A ‘polyalgorithm’ utilizes multiple simple methodologies and strategically combines them so that the outcome is better than the individual components. In this introductory work, three component methodologies are utilized; each component methodology is capable of capturing phenomenon different from the other two. Mathematical formulation of each component methodology is presented. Initial strategy for combining the three component algorithms is proposed. The outcomes of each component methodology as well the polyalgorithm are tested on human interpreted data. The strengths and limitations of each methodology are also discussed. Efficiency of the codes used for implementing the polyalgorithm is also discussed; this is important because the satellite data that needs to be processed is known to be huge (petabytes sized already and growing). This nascent work will be directly useful especially in understanding the impact of human activities on the environment. It will also be useful in other applications such as health monitoring and urban transportation.

Dans cette thèse, nous présentons notre travail sur la gestion des pannes dans les systèmes distribués, avec comme motivation principale le suivi de fautes et de changements brusques dans de grands systèmes informatiques comme la grille et le cloud.Au lieu de construire une connaissance complète a priori du logiciel et des infrastructures matérielles comme dans les méthodes traditionnelles de détection ou de diagnostic, nous proposons d'utiliser des techniques spécifiques pour effectuer une surveillance de bout en bout dans des systèmes de grande envergure, en laissant les détails inaccessibles des composants impliqués dans une boîte noire.Pour la surveillance de pannes d'un système distribué, nous modélisons tout d'abord cette application basée sur des sondes comme une tâche de prédiction statique de collaboration (CP), et démontrons expérimentalement l'efficacité des méthodes de CP en utilisant une méthode de la max margin matrice factorisation. Nous introduisons en outre l’apprentissage actif dans le cadre de CP et exposons son avantage essentiel dans le traitement de données très déséquilibrées, ce qui est particulièrement utile pour identifier la class de classe de défaut de la minorité.Nous étendons ensuite la surveillance statique de défection au cas séquentiel en proposant la méthode de factorisation séquentielle de matrice (SMF). La SMF prend une séquence de matrices partiellement observées en entrée, et produit des prédictions comportant des informations à la fois sur les fenêtres temporelles actuelle et passé. L’apprentissage actif est également utilisé pour la SMF, de sorte que les données très déséquilibrées peuvent être traitées correctement. En plus des méthodes séquentielles, une action de lissage pris sur la séquence d'estimation s'est avérée être une astuce pratique utile pour améliorer la performance de la prédiction séquentielle.Du fait que l'hypothèse de stationnarité utilisée dans le surveillance statique et séquentielle devient irréaliste en présence de changements brusques, nous proposons un framework en ligne semi-Supervisé de détection de changement (SSOCD) qui permette de détecter des changements intentionnels dans les données de séries temporelles. De cette manière, le modèle statique du système peut être recalculé une fois un changement brusque est détecté. Dans SSOCD, un procédé hors ligne non supervisé est proposé pour analyser un échantillon des séries de données. Les points de changement ainsi détectés sont utilisés pour entraîner un modèle en ligne supervisé, qui fournit une décision en ligne concernant la détection de changement à parti de la séquence de données en entrée. Les méthodes de détection de changements de l’état de l’art sont utilisées pour démontrer l'utilité de ce framework.Tous les travaux présentés sont vérifiés sur des ensembles de données du monde réel. Plus précisément, les expériences de surveillance de panne sont effectuées sur un ensemble de données recueillies auprès de l’infrastructure de grille Biomed faisant partie de l’European Grid Initiative et le framework de détection de changement brusque est vérifié sur un ensemble de données concernant le changement de performance d'un site en ligne ayant un fort trafic. / In this dissertation, we present our work on fault management in distributed systems, with motivating application roots in monitoring fault and abrupt change of large computing systems like the grid and the cloud. Instead of building a complete a priori knowledge of the software and hardware infrastructures as in conventional detection or diagnosis methods, we propose to use appropriate techniques to perform end-To-End monitoring for such large scale systems, leaving the inaccessible details of involved components in a black box.For the fault monitoring of a distributed system, we first model this probe-Based application as a static collaborative prediction (CP) task, and experimentally demonstrate the effectiveness of CP methods by using the max margin matrix factorization method. We further introduce active learning to the CP framework and exhibit its critical advantage in dealing with highly imbalanced data, which is specially useful for identifying the minority fault class.Further we extend the static fault monitoring to the sequential case by proposing the sequential matrix factorization (SMF) method. SMF takes a sequence of partially observed matrices as input, and produces predictions with information both from the current and history time windows. Active learning is also employed to SMF, such that the highly imbalanced data can be coped with properly. In addition to the sequential methods, a smoothing action taken on the estimation sequence has shown to be a practically useful trick for enhancing sequential prediction performance.Since the stationary assumption employed in the static and sequential fault monitoring becomes unrealistic in the presence of abrupt changes, we propose a semi-Supervised online change detection (SSOCD) framework to detect intended changes in time series data. In this way, the static model of the system can be recomputed once an abrupt change is detected. In SSOCD, an unsupervised offline method is proposed to analyze a sample data series. The change points thus detected are used to train a supervised online model, which gives online decision about whether there is a change presented in the arriving data sequence. State-Of-The-Art change detection methods are employed to demonstrate the usefulness of the framework.All presented work is verified on real-World datasets. Specifically, the fault monitoring experiments are conducted on a dataset collected from the Biomed grid infrastructure within the European Grid Initiative, and the abrupt change detection framework is verified on a dataset concerning the performance change of an online site with large amount of traffic.

BACKGROUND.One group of theories aiming at providing a framework explaining the etiology, maintenance and phenomenology of anxiety disorders is classified as cognitive models of anxiety. These approaches assume that distortions in specific levels of information processing are relevant for the onset and maintenance of the disorder. A detailed knowledge about the nature of these distortions would have important implications for the therapy of anxiety, as the implementation of confrontative or cognitive elements precisely fitting the distortions might enhance efficacy. Still, these models and related empirical evidence provide conflicting assumptions about the nature of disorder-linked processing distortions. Many cognitive models of anxiety (e.g., Fox, Russo, & Dutton, 2002; Mathews & Mackintosh, 1998; Williams, Watts, MacLeod, & Mathews, 1997) postulate that anxiety-linked biases of attention imply hypervigilance to threat and distractibility from other stimuli in the presence of feared materials. This is convincingly confirmed by various experimentalclinical studies assessing attention for threat in anxious participants compared to non-anxious controls (for a review, seeMathews &MacLeod, 2005). In contrast, assumptions concerning anxiety-linked biased memory for threat are less convincing; based on the shared tendency for avoidance of deeper elaboration in anxiety disorders, some models predict memory biases only for implicit memory tasks (Williams et al., 1997) or even disclaim the relevance of memory in anxiety at all (e.g., Mogg, Bradley, Miles, & Dixon, 2004). Other theories restrict the possibility of measuring disorder-specific memory biases to tasks that require merely perceptual encoding of the materials instead of verbal-conceptual memory (e.g., Fox et al., 2002; Mathews &Mackintosh, 1998). On the one hand, none of these models has integrated all the inconsistencies in empirical data on the topic. On the other hand, the numerous empirical studies on memory in anxiety that have been conducted with varying materials, anxiety disorders, encoding and retrieval conditions do not allow final conclusions about the prerequisites for finding memory biases (for a review, see MacLeod & Mathews, 2004). A more detailed investigation of the complete spectrum of memory for threat utilizing carefully controlled variations of depth of encoding and materials is needed. In view of these inconsistencies, it is all the more surprising that one important part of this spectrum has so far remained completely uninvestigated: visual working memory (VWM). No study has ever differentially addressed VWM for threat in anxious vs. nonanxious participants and none of the cognitive models of anxiety provides any predictions concerning this stage of information processing. Research on cognitive biases in anxiety has thus far only addressed the two extremes of the processing continuum: attention and longer-term memory. In between, a gap remains, the bridging of which might bring us closer to defining the prerequisites of memory biases in anxiety. As empirical research has provided substantial and coherent knowledge concerning attention in anxiety, and as attention and VWM are so closely linked (see, for instance, Cowan, 1995), the thorough investigation of VWM may provide important clues for models of anxiety. Is anxiety related to VWM biases favoring the processing of threatening information, or does the avoidance presumed by cognitive models of anxiety already begin at this stage? RESEARCH AIMS. To investigate the relevance of biased VWM in anxiety, the present research focused in eight experiments on the following main research questions: (1) Is threat preferably stored in VWM in anxious individuals? (2) Does threat preference occur at the cost of the storage of other items, or is extra storage capacity provided? (3) Would the appearance of threat interrupt ongoing encoding of non-threatening items? (4) Does prioritized encoding of threat in anxiety occur strategically or automatically? (5) Are disorder-specific VWM biases also materials-specific? (6) Are VWM biases in anxiety modifiable through cognitive-behavioral therapy? METHODS. In Experiments 1-4, a spatial-sequential cueing paradigm was used. A subset of real-object display items was successively cued on each trial by a sudden change of the picture background for 150 ms each. After the cueing, one of the display pictures was hidden and probed for a memory test. On most trials, a cued item was tested, and memory accuracy was determined depending on the item’s position within the cue string and depending on its valence. In some cases, memory for an uncued item was tested. Experiment 1 and 2 were directed at discovering whether spider fearfuls and non-anxious controls would differ with respect to the accuracy in memorizing cued spiders and uncued spiders and, thus, reveal disorder-specific biases of VWM. In addition, the question whether the presence of a spider image is related to costs for the memorization of other images was tested. Experiment 3 addressed whether any disorder-specific VWM biases found earlier were specific to the feared spiders. Therefore, the critical stimuli here were a snake and a spider. Participants were spider fearfuls and non-anxious controls, both without snake anxiety. In Experiment 4, it was tested whether disorder-specific biases found in Experiment 1 and 2 were modifiable through cognitive-behavioral treatment. The critical stimulus was a spider image. Spider fearfuls were tested three times. Half of them received a cognitive-behavioral intervention after the first test, the other half only after the second test. In two additional experiments, VWM was assessed with a change-detection paradigm. The main aim was to clarify whether disorder-specific effects found in the previous experiments were associated with automatic or with strategic selective encoding of threatening materials, and whether any group differences in spider change detection were materials-specific to spiders, but not to snakes. In Experiment 5, several images were presented simultaneously in a study display for either 100 or 500 milliseconds. After a short interruption, a test display was presented including either the same items as the first one or one changed item. Participants’ accuracy in determining whether displays were the same or different was measured depending on the valence of the changed item, set size, and presentation time of the display. There were trials with and without spiders. If a change was made, it could involve either a non-spider or a spider item. Of specific interest was the condition in which a spider image was presented initially, but not in the test phase, as noticing this specific change would require storage of that image in VWM. Would group differences be particularly pronounced in the shorter encoding condition suggesting automatic encoding of threat, or would they occur in the longer encoding condition, suggesting strategic encoding of spiders? In Experiment 6, change detection accuracy for spiders vs. snakes was tested. The participants in both experiments were spider fearfuls vs. controls, but those of Experiment 6 were additionally required to lack snake anxiety. Moreover, a temporal VWM paradigm - an attentional blink task - was applied to assess whether a biased encoding of spider images in spider fearfuls would occur at the expense of non-threatening items undergoing concurrent processing, and whether this effect was specific to spiders, but not to snakes. Series of real-object pictures were presented at rates of 80 ms at the display center. The observer’s task was to identify and report the two target pictures indicated by a brighter background. In Experiment 7, the first target always depicted a neutral item. The valence of the second target was varied - either negative depicting a spider, positive, or neutral. Participants varied with respect to their spider anxiety. In Experiment 8, spider fearfuls and non-anxious controls, both without snake anxiety, were tested. The experiment was nearly the same as the previous one, but two negative target types were tested: disorder-relevant spiders and negative but not feared snakes. Of specific interest was whether the appearance of a threatening target would reduce the report probability of the earlier attended target, indicating the interruption of its VWM encoding in favor of the threat item. RESULTS. (1) Both anxious and non-anxious controls, showed VWM advantages for negative materials such as spider or snake images. (2) In addition, there were disorderspecific VWM biases: some effects were larger in spider fearfuls than in non-anxious controls and some effects occurred exclusively in spider fearfuls. (3) Group differences and, thus, disorder-specificity were particularly pronounced under competitive circumstances, that is, under the condition of numerous stimuli competing for processing resources: when only little orientation time was allowed, when only little time was provided for selecting and encoding items from a crowd, and when VWMfor the critical item required reflexive instead of voluntary attention. (4) Pronounced memory for task-relevant, voluntarily attended spiders was related to difficulties in disengaging attention from these items in the fearful group, reflected in reduced memory accuracy for the item following it. (5) Disorder-specific VWM biases seem to be based on attentional biases to threatening materials resulting in a very quick, automatic memory consolidation. However, this preferential encoding was not at the cost of neutral materials currently undergoing encoding processes. (6) All disorder-specific VWM biases occured only with fear-related materials, not with other negative materials. (7) Automatic and highly disorder-specific fear-related VWM biases – but not strategic VWM biases occuring in both groups - were modifiable through cognitive-behavioral intervention. CONCLUSIONS. This work provides additional information about informationprocessing distortions related to specific anxiety. With the experimental investigation of biased VWM, this work has been performed to fill a gap within research on cognitive biases in anxiety. Moreover, this dissertation contributes to cognitive theories of anxiety by proposing several recommendations for refinements of current theoretical approaches. Most important, it was suggested to extend existing models by a more detailed consideration of attention and memory. In view of numerous previous empirical studies on the topic and the conclusions of this dissertation, a differentiation of the attentional engagement and disengagement component appears inevitable. Even more important, in view of the data presented here predictions concerning VWM for threatening materials need to be taken into account. In addition, suggestions are provided for the differential consideration of biases occuring from prepotent threat value of negative stimuli vs. individual threat value. A proposal for a cognitive model of anxiety extended by all these aspects is provided to serve as an invitation of further research in the investigation of the nature of memory biases in anxiety disorders. REFERENCES: Cowan, N. (1995). Attention and Memory. An integrated framework.New York: Oxford University Press. Fox, E., Russo, R., & Dutton, K. (2002). Attentional bias for threat: Evidence for delayed disengagement from emotional faces. Cognition and Emotion, 16, 355-379. MacLeod, C., & Mathews, A. (2004). Selective memory effects in anxiety disorders: An overview of research findings and their implications. In D. Reisberg & P. Hertel (eds.), Memory and Emotion. Oxford: Oxford University Press. Mathews, A., & Mackintosh, B. (1998). A cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22 (6), 539-560. Mathews, A., & MacLeod, C. (2005). Cognitive vulnerability to emotional disorders. Annual Review of Clinical Psychology, 1, 167-195.Mathews, Mogg, May, & Eysenck (1989). Mogg, K., Bradley, B.P., Miles, F., & Dixon, R. (2004). Time course of attentional bias for threat scenes: Testing the vigilance avoidance hypothesis. Cognition and Emotion, 18(5), 689-700. Williams, J.M.G., Watts, F.N., MacLeod, C., & Mathews, A. (1997). Cognitive psychology and emotional disorders. Chichester: John Wiley.

In this dissertation, we present our work on fault management in distributed systems, with motivating application roots in monitoring fault and abrupt change of large computing systems like the grid and the cloud. Instead of building a complete a priori knowledge of the software and hardware infrastructures as in conventional detection or diagnosis methods, we propose to use appropriate techniques to perform end-to-end monitoring for such large scale systems, leaving the inaccessible details of involved components in a black box.For the fault monitoring of a distributed system, we first model this probe-based application as a static collaborative prediction (CP) task, and experimentally demonstrate the effectiveness of CP methods by using the max margin matrix factorization method. We further introduce active learning to the CP framework and exhibit its critical advantage in dealing with highly imbalanced data, which is specially useful for identifying the minority fault class.Further we extend the static fault monitoring to the sequential case by proposing the sequential matrix factorization (SMF) method. SMF takes a sequence of partially observed matrices as input, and produces predictions with information both from the current and history time windows. Active learning is also employed to SMF, such that the highly imbalanced data can be coped with properly. In addition to the sequential methods, a smoothing action taken on the estimation sequence has shown to be a practically useful trick for enhancing sequential prediction performance.Since the stationary assumption employed in the static and sequential fault monitoring becomes unrealistic in the presence of abrupt changes, we propose a semi-supervised online change detection (SSOCD) framework to detect intended changes in time series data. In this way, the static model of the system can be recomputed once an abrupt change is detected. In SSOCD, an unsupervised offline method is proposed to analyze a sample data series. The change points thus detected are used to train a supervised online model, which gives online decision about whether there is a change presented in the arriving data sequence. State-of-the-art change detection methods are employed to demonstrate the usefulness of the framework.All presented work is verified on real-world datasets. Specifically, the fault monitoring experiments are conducted on a dataset collected from the Biomed grid infrastructure within the European Grid Initiative, and the abrupt change detection framework is verified on a dataset concerning the performance change of an online site with large amount of traffic.

BACKGROUND.One group of theories aiming at providing a framework explaining the etiology, maintenance and phenomenology of anxiety disorders is classified as cognitive models of anxiety. These approaches assume that distortions in specific levels of information processing are relevant for the onset and maintenance of the disorder. A detailed knowledge about the nature of these distortions would have important implications for the therapy of anxiety, as the implementation of confrontative or cognitive elements precisely fitting the distortions might enhance efficacy. Still, these models and related empirical evidence provide conflicting assumptions about the nature of disorder-linked processing distortions. Many cognitive models of anxiety (e.g., Fox, Russo, & Dutton, 2002; Mathews & Mackintosh, 1998; Williams, Watts, MacLeod, & Mathews, 1997) postulate that anxiety-linked biases of attention imply hypervigilance to threat and distractibility from other stimuli in the presence of feared materials. This is convincingly confirmed by various experimentalclinical studies assessing attention for threat in anxious participants compared to non-anxious controls (for a review, seeMathews &MacLeod, 2005). In contrast, assumptions concerning anxiety-linked biased memory for threat are less convincing; based on the shared tendency for avoidance of deeper elaboration in anxiety disorders, some models predict memory biases only for implicit memory tasks (Williams et al., 1997) or even disclaim the relevance of memory in anxiety at all (e.g., Mogg, Bradley, Miles, & Dixon, 2004). Other theories restrict the possibility of measuring disorder-specific memory biases to tasks that require merely perceptual encoding of the materials instead of verbal-conceptual memory (e.g., Fox et al., 2002; Mathews &Mackintosh, 1998). On the one hand, none of these models has integrated all the inconsistencies in empirical data on the topic. On the other hand, the numerous empirical studies on memory in anxiety that have been conducted with varying materials, anxiety disorders, encoding and retrieval conditions do not allow final conclusions about the prerequisites for finding memory biases (for a review, see MacLeod & Mathews, 2004). A more detailed investigation of the complete spectrum of memory for threat utilizing carefully controlled variations of depth of encoding and materials is needed. In view of these inconsistencies, it is all the more surprising that one important part of this spectrum has so far remained completely uninvestigated: visual working memory (VWM). No study has ever differentially addressed VWM for threat in anxious vs. nonanxious participants and none of the cognitive models of anxiety provides any predictions concerning this stage of information processing. Research on cognitive biases in anxiety has thus far only addressed the two extremes of the processing continuum: attention and longer-term memory. In between, a gap remains, the bridging of which might bring us closer to defining the prerequisites of memory biases in anxiety. As empirical research has provided substantial and coherent knowledge concerning attention in anxiety, and as attention and VWM are so closely linked (see, for instance, Cowan, 1995), the thorough investigation of VWM may provide important clues for models of anxiety. Is anxiety related to VWM biases favoring the processing of threatening information, or does the avoidance presumed by cognitive models of anxiety already begin at this stage? RESEARCH AIMS. To investigate the relevance of biased VWM in anxiety, the present research focused in eight experiments on the following main research questions: (1) Is threat preferably stored in VWM in anxious individuals? (2) Does threat preference occur at the cost of the storage of other items, or is extra storage capacity provided? (3) Would the appearance of threat interrupt ongoing encoding of non-threatening items? (4) Does prioritized encoding of threat in anxiety occur strategically or automatically? (5) Are disorder-specific VWM biases also materials-specific? (6) Are VWM biases in anxiety modifiable through cognitive-behavioral therapy? METHODS. In Experiments 1-4, a spatial-sequential cueing paradigm was used. A subset of real-object display items was successively cued on each trial by a sudden change of the picture background for 150 ms each. After the cueing, one of the display pictures was hidden and probed for a memory test. On most trials, a cued item was tested, and memory accuracy was determined depending on the item’s position within the cue string and depending on its valence. In some cases, memory for an uncued item was tested. Experiment 1 and 2 were directed at discovering whether spider fearfuls and non-anxious controls would differ with respect to the accuracy in memorizing cued spiders and uncued spiders and, thus, reveal disorder-specific biases of VWM. In addition, the question whether the presence of a spider image is related to costs for the memorization of other images was tested. Experiment 3 addressed whether any disorder-specific VWM biases found earlier were specific to the feared spiders. Therefore, the critical stimuli here were a snake and a spider. Participants were spider fearfuls and non-anxious controls, both without snake anxiety. In Experiment 4, it was tested whether disorder-specific biases found in Experiment 1 and 2 were modifiable through cognitive-behavioral treatment. The critical stimulus was a spider image. Spider fearfuls were tested three times. Half of them received a cognitive-behavioral intervention after the first test, the other half only after the second test. In two additional experiments, VWM was assessed with a change-detection paradigm. The main aim was to clarify whether disorder-specific effects found in the previous experiments were associated with automatic or with strategic selective encoding of threatening materials, and whether any group differences in spider change detection were materials-specific to spiders, but not to snakes. In Experiment 5, several images were presented simultaneously in a study display for either 100 or 500 milliseconds. After a short interruption, a test display was presented including either the same items as the first one or one changed item. Participants’ accuracy in determining whether displays were the same or different was measured depending on the valence of the changed item, set size, and presentation time of the display. There were trials with and without spiders. If a change was made, it could involve either a non-spider or a spider item. Of specific interest was the condition in which a spider image was presented initially, but not in the test phase, as noticing this specific change would require storage of that image in VWM. Would group differences be particularly pronounced in the shorter encoding condition suggesting automatic encoding of threat, or would they occur in the longer encoding condition, suggesting strategic encoding of spiders? In Experiment 6, change detection accuracy for spiders vs. snakes was tested. The participants in both experiments were spider fearfuls vs. controls, but those of Experiment 6 were additionally required to lack snake anxiety. Moreover, a temporal VWM paradigm - an attentional blink task - was applied to assess whether a biased encoding of spider images in spider fearfuls would occur at the expense of non-threatening items undergoing concurrent processing, and whether this effect was specific to spiders, but not to snakes. Series of real-object pictures were presented at rates of 80 ms at the display center. The observer’s task was to identify and report the two target pictures indicated by a brighter background. In Experiment 7, the first target always depicted a neutral item. The valence of the second target was varied - either negative depicting a spider, positive, or neutral. Participants varied with respect to their spider anxiety. In Experiment 8, spider fearfuls and non-anxious controls, both without snake anxiety, were tested. The experiment was nearly the same as the previous one, but two negative target types were tested: disorder-relevant spiders and negative but not feared snakes. Of specific interest was whether the appearance of a threatening target would reduce the report probability of the earlier attended target, indicating the interruption of its VWM encoding in favor of the threat item. RESULTS. (1) Both anxious and non-anxious controls, showed VWM advantages for negative materials such as spider or snake images. (2) In addition, there were disorderspecific VWM biases: some effects were larger in spider fearfuls than in non-anxious controls and some effects occurred exclusively in spider fearfuls. (3) Group differences and, thus, disorder-specificity were particularly pronounced under competitive circumstances, that is, under the condition of numerous stimuli competing for processing resources: when only little orientation time was allowed, when only little time was provided for selecting and encoding items from a crowd, and when VWMfor the critical item required reflexive instead of voluntary attention. (4) Pronounced memory for task-relevant, voluntarily attended spiders was related to difficulties in disengaging attention from these items in the fearful group, reflected in reduced memory accuracy for the item following it. (5) Disorder-specific VWM biases seem to be based on attentional biases to threatening materials resulting in a very quick, automatic memory consolidation. However, this preferential encoding was not at the cost of neutral materials currently undergoing encoding processes. (6) All disorder-specific VWM biases occured only with fear-related materials, not with other negative materials. (7) Automatic and highly disorder-specific fear-related VWM biases – but not strategic VWM biases occuring in both groups - were modifiable through cognitive-behavioral intervention. CONCLUSIONS. This work provides additional information about informationprocessing distortions related to specific anxiety. With the experimental investigation of biased VWM, this work has been performed to fill a gap within research on cognitive biases in anxiety. Moreover, this dissertation contributes to cognitive theories of anxiety by proposing several recommendations for refinements of current theoretical approaches. Most important, it was suggested to extend existing models by a more detailed consideration of attention and memory. In view of numerous previous empirical studies on the topic and the conclusions of this dissertation, a differentiation of the attentional engagement and disengagement component appears inevitable. Even more important, in view of the data presented here predictions concerning VWM for threatening materials need to be taken into account. In addition, suggestions are provided for the differential consideration of biases occuring from prepotent threat value of negative stimuli vs. individual threat value. A proposal for a cognitive model of anxiety extended by all these aspects is provided to serve as an invitation of further research in the investigation of the nature of memory biases in anxiety disorders. REFERENCES: Cowan, N. (1995). Attention and Memory. An integrated framework.New York: Oxford University Press. Fox, E., Russo, R., & Dutton, K. (2002). Attentional bias for threat: Evidence for delayed disengagement from emotional faces. Cognition and Emotion, 16, 355-379. MacLeod, C., & Mathews, A. (2004). Selective memory effects in anxiety disorders: An overview of research findings and their implications. In D. Reisberg & P. Hertel (eds.), Memory and Emotion. Oxford: Oxford University Press. Mathews, A., & Mackintosh, B. (1998). A cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22 (6), 539-560. Mathews, A., & MacLeod, C. (2005). Cognitive vulnerability to emotional disorders. Annual Review of Clinical Psychology, 1, 167-195.Mathews, Mogg, May, & Eysenck (1989). Mogg, K., Bradley, B.P., Miles, F., & Dixon, R. (2004). Time course of attentional bias for threat scenes: Testing the vigilance avoidance hypothesis. Cognition and Emotion, 18(5), 689-700. Williams, J.M.G., Watts, F.N., MacLeod, C., & Mathews, A. (1997). Cognitive psychology and emotional disorders. Chichester: John Wiley.

De nos jours les cartes d’utilisation/occupation du sol (USOS) à une échelle régionale sont habituellement générées à partir d’images satellitales de résolution modérée (entre 10 m et 30 m). Le National Land Cover Database aux États-Unis et le programme CORINE (Coordination of information on the environment) Land Cover en Europe, tous deux fondés sur les images LANDSAT, en sont des exemples représentatifs. Cependant ces cartes deviennent rapidement obsolètes, spécialement en environnement dynamique comme les megacités et les territoires métropolitains. Pour nombre d’applications, une mise à jour de ces cartes sur une base annuelle est requise. Depuis 2007, le USGS donne accès gratuitement à des images LANDSAT ortho-rectifiées. Des images archivées (depuis 1984) et des images acquises récemment sont disponibles. Sans aucun doute, une telle disponibilité d’images stimulera la recherche sur des méthodes et techniques rapides et efficaces pour un monitoring continue des changements des USOS à partir d’images à résolution moyenne. Cette recherche visait à évaluer le potentiel de telles images satellitales de résolution moyenne pour obtenir de l’information sur les changements des USOS à une échelle régionale dans le cas de la Communauté Métropolitaine de Montréal (CMM), une métropole nord-américaine typique. Les études précédentes ont démontré que les résultats de détection automatique des changements dépendent de plusieurs facteurs tels : 1) les caractéristiques des images (résolution spatiale, bandes spectrales, etc.); 2) la méthode même utilisée pour la détection automatique des changements; et 3) la complexité du milieu étudié. Dans le cas du milieu étudié, à l’exception du centre-ville et des artères commerciales, les utilisations du sol (industriel, commercial, résidentiel, etc.) sont bien délimitées. Ainsi cette étude s’est concentrée aux autres facteurs pouvant affecter les résultats, nommément, les caractéristiques des images et les méthodes de détection des changements. Nous avons utilisé des images TM/ETM+ de LANDSAT à 30 m de résolution spatiale et avec six bandes spectrales ainsi que des images VNIR-ASTER à 15 m de résolution spatiale et avec trois bandes spectrales afin d’évaluer l’impact des caractéristiques des images sur les résultats de détection des changements. En ce qui a trait à la méthode de détection des changements, nous avons décidé de comparer deux types de techniques automatiques : (1) techniques fournissant des informations principalement sur la localisation des changements et (2)techniques fournissant des informations à la fois sur la localisation des changements et sur les types de changement (classes « de-à »). Les principales conclusions de cette recherche sont les suivantes : Les techniques de détection de changement telles les différences d’image ou l’analyse des vecteurs de changements appliqués aux images multi-temporelles LANDSAT fournissent une image exacte des lieux où un changement est survenu d’une façon rapide et efficace. Elles peuvent donc être intégrées dans un système de monitoring continu à des fins d’évaluation rapide du volume des changements. Les cartes des changements peuvent aussi servir de guide pour l’acquisition d’images de haute résolution spatiale si l’identification détaillée du type de changement est nécessaire. Les techniques de détection de changement telles l’analyse en composantes principales et la comparaison post-classification appliquées aux images multi-temporelles LANDSAT fournissent une image relativement exacte de classes “de-à” mais à un niveau thématique très général (par exemple, bâti à espace vert et vice-versa, boisés à sol nu et vice-versa, etc.). Les images ASTER-VNIR avec une meilleure résolution spatiale mais avec moins de bandes spectrales que LANDSAT n’offrent pas un niveau thématique plus détaillé (par exemple, boisés à espace commercial ou industriel). Les résultats indiquent que la recherche future sur la détection des changements en milieu urbain devrait se concentrer aux changements du couvert végétal puisque les images à résolution moyenne sont très sensibles aux changements de ce type de couvert. Les cartes indiquant la localisation et le type des changements du couvert végétal sont en soi très utiles pour des applications comme le monitoring environnemental ou l’hydrologie urbaine. Elles peuvent aussi servir comme des indicateurs des changements de l’utilisation du sol. De techniques telles l’analyse des vecteurs de changement ou les indices de végétation son employées à cette fin. / Nowadays land use/land cover maps at regional scale are commonly generated with satellite data of medium spatial resolution (between 10 m and 30m). The National Land Cover Database (NLCD) in the United States and the Coordination of Information on the Environment (CORINE) Land Cover program in Europe, both based on LANDSAT images, are two typical examples. However, these maps become rapidly obsolete, especially in highly dynamic areas such as mega cities and metropolitan areas. In many applications, such as to monitor the water quality affected by the Land use/Land cover (LULC) change, the spread of invasive species, policy making for city managers, annual updating of LULC maps is required. Since 2007, the USGS offers access to ortho-rectified LANDSAT imagery free of charge. Both archived (since 1984) and recently acquired images are available. Without doubt, such data availability will stimulate the research on fast and cost effective methods and techniques for “continuous” regional land cover/use map updating using medium resolution satellite imagery. The objective of this research was to evaluate the potential of such medium resolution satellite imagery for providing information on changes useful for the continuous updating of LULC maps at a regional scale in the case of the Montreal Metropolitan Community (MMC) area, a typical North American metropolis. Previous studies have demonstrated that many factors could affect the results of automatic change detection such as: (1) the characteristics of the images (spatial resolution, spectral bands, etc.); (2) the method itself used to automatically detect changes; and (3) the complexity of the landscape. In the study site except for the Central Business District (CBD) and some commercial streets, land uses (industrial, commercial, residential, etc.) are well delimited. Thus this study was focused on the other factors affecting change detection results, namely, the characteristics of the images and the method of change detection. We used 6 spectral bands of LANDSAT TM/ETM+ with 30 m spatial resolution and 3 spectral bands of ASTER-VNIR with 15 m spatial resolution to evaluate the impact of image characteristics on change detection. Concerning the change detection method, we decided to compare two types of automatic techniques: (1) techniques providing information principally on the location of changed areas,and (2) techniques providing information on both the location of changed areas and the type of changes ("from-to" classes). The main conclusions of this research are as follows: Change detection techniques such as image differencing or change vector analysis applied to LANDSAT multi-temporal imagery provide an accurate picture of changed areas in a fast and efficient manner. They can thus be integrated in a continuous monitoring system for a rapid evaluation of the volume of changes. The produced maps could be helpful to guide the acquisition of high spatial resolution imagery if a detailed identification of the type of changes is required. Change detection techniques such as principal component analysis and post-classification comparison applied to LANDSAT multi-temporal imagery could provide a relatively accurate picture of “from-to” classes but at a very general thematic level (for example, built-up to green space and vice-versa, forest lands to bare soil and vice-versa, etc.). ASTER images with better spatial resolution but with less spectral bands than LANDSAT images do not provide more detailed thematic information (for example forest land to commercial or industrial areas). The results indicate that future research should be focused on the detection of changes in the vegetation cover as medium resolution imagery is highly sensitive to this type of surface cover. Maps indicating the location and the type of changes in vegetation cover are in itself very useful for various applications, such as environmental monitoring or urban hydrology, and can be used as indicators on land use changes. Techniques such as change vector analysis or vegetation indices could be used to this end.

L'objectif de ma thèse est d'étudier mathématiquement un indicateur de rupture de volatilité très utilisé par les praticiens en salle de marché. L'indicateur bandes de Bollinger appartient à la famille des méthodes dites d'analyse technique et donc repose exclusivement sur l'historique récente du cours considéré et un principe déduit des observations passées des marchés, indépendamment de tout modèle mathématique. Mon travail consiste à étudier les performances de cet indicateur dans un univers qui serait gouverné par des équations différentielles stochastiques (Black -Scholes) dont le coefficient de diffusion change sa valeur à un temps aléatoire inconnu et inobservable, pour un praticien désirant maximiser une fonction objectif (par exemple, une certaine utilité espérée de la valeur du portefeuille à une certaine maturité). Dans le cadre du modèle, l'indicateur de Bollinger peut s'interpréter comme un estimateur de l'instant de la prochaine rupture. On montre dans le cas des petites volatilités, que le comportement de la densité de l'indicateur dépend de la volatilité, ce qui permet pour un ratio de volatilité assez grand, de détecter via l'estimation de la distribution de l'indicateur dans quel régime de volatilité on se situe. Aussi, dans le cas des grandes volatilités, on montre par une approche via la transformée de Laplace, que le comportement asymptotique des queues de distribution de l'indicateur dépend de la volatilité. Ce qui permet de détecter le changement des grandes volatilités. Ensuite, on s'intéresse à une étude comparative entre l'indicateur de Bollinger et l'estimateur classique de la variation quadratique pour la détection de changement de la volatilité. Enfin, on étudie la gestion optimale de portefeuille qui est décrite par un problème stochastique non standard en ce sens que les contrôles admissibles sont contraints à être des fonctionnelles des prix observés. On résout ce problème de contrôle en s'inspirant de travaux de Pham and Jiao pour décomposer le problème initial d'allocation de portefeuille en un problème de gestion après la rupture et un problème avant la rupture, et chacun de ces problèmes est résolu par la méthode de la programmation dynamique . Ainsi, un théorème de verification est prouvé pour ce problème de contrôle stochastique. / The aim of my thesis is to study mathematically an indicator widely used by the practitioners in the trading market, and designed to detect changes in the volatility term . The Bollinger Bands indicator belongs to the family of methods known as technical analysis which consist in looking t the past price movement in order to predict its future price movements independently of any mathematical model. We study the performance of this indicator in a universe that is governed by a stochastic differential equations (Black-Scholes) such that the volatility changes at an unknown and unobservable random time, for a practitioner seeking to maximize an objective function (for instance, the expected utility of the wealth at a certain maturity). Within the framework of the model, Bollinger indicator can be interpreted as an estimator of the time at which the volatility changes its value. We show that in the case of small volatilities, the density behavior of the indicator depends on the value of the volatility, which allows that for large ratio of volatility, to detect via the distribution estimation in which regime of volatility we are. Also , for the case of large volatilities, we show by an approach via the Laplace transform that the asymptotic tails behavior of the indictor depends on the volatility value. This allows to detect a change for large volatilities. Next, we compare two indicators designed to detect a volatility change: the Bollinger bands and the quadratic variation indicators. Finally, we study the optimal portfolio allocation which is described by a non-standard stochastic problem in view of that the admissible controls need to be adapted to the filtration generated by the prices. We resolve this control problem by an approach used by Pham and Jiao to separate the initial allocation problem into an allocation problem after the rupture and an problem before the rupture, and each one of these problems is resolved by the dynamic programming method. Also, a verification theorem is proved for this stochastic control problem.