Criteria for Modification of Complex Infrastructure Networks

Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network.Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network

ON RACAH WIGNERCALCULUS FOR CLASSICAL LIE GROUPS VIA SCHUR –WEYL DUALITY

Lo scopo della tesi è di fornire un approccio sistematico e unitario per la trattazione della struttura di vari coefficienti di accoppiamento tra rappresentazioni irriducibili di gruppi di Lie della serie classica. La strategia più promettente a tale scopo è stata quella basata sulla ben nota connessione tra gruppi simmetrici e gruppi unitari, conosciuta in letteratura come Dualità di Schur-Weyl. Estendendo opportunamente tale concetto di dualità, è possibile provare che il problema della determinazione dei coefficienti di accoppiamento per i gruppi di Lie della serie classica è equivalente al problema della subduzione per le relative algebre centralizzanti. Scegliendo un approccio puramente algebrico al problema della subduzione per gruppi simmetrici e algebre di Brauer, analizziamo il Metodo delle Equazioni Lineari fornendo una descrizione combinatoria del sistema di equazioni da esso prodotte e descriviamo un nuovo algoritmo per la sua soluzione. Pertanto, risolvendo il problema della subduzione per le algebre centralizzanti, abbiamo un approccio unitario al calcolo di Racah-Wigner per i gruppi di Lie della serie classica

Forging the City Image during the French Colonial Period (1883-1962) in the Case of Jijel (Algeria)

The urban configuration of Algerian cities reflects the influence of French colonization (1883-1962). This is characterized by a collection of contrasts and overlays of different forms of architecture and urbanism. In all urban agglomerations in Algeria, the colonial section remains the most prominent and structured. This legacy of colonial architecture and urban planning has ignited a national debate in political and academic circles regarding its classification as heritage. This current study contributes to the debate by adopting a neutral and scientific approach in order to smooth things out and shed light on the role and creation of urban form and its image, specifically through the example of Jijel. The notion of urban image is explored through colonial architectural achievements, urban planning, and artistic endeavours that were emblematic of the city during the colonial period and continue to be so today. This article showcases various works created during the colonial period in Jijel, those that still convey an identity that defines the city. The concern for this identity is substantiated by a research project that seeks to identify the city\u27s image through significant architectural works across different epochs and determine those that accurately convey the city\u27s identity within the country

Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material

This book focuses on the use of GIScience in conjunction with historical visual sources to resolve past scenarios. The themes, knowledge gained and methodologies conducted might be of interest to a variety of scholars from the social science and humanities disciplines

Doctor of Philosophy

dissertationWith modern computational resources rapidly advancing towards exascale, large-scale simulations useful for understanding natural and man-made phenomena are becoming in- creasingly accessible. As a result, the size and complexity of data representing such phenom- ena are also increasing, making the role of data analysis to propel science even more integral. This dissertation presents research on addressing some of the contemporary challenges in the analysis of vector fields--an important type of scientific data useful for representing a multitude of physical phenomena, such as wind flow and ocean currents. In particular, new theories and computational frameworks to enable consistent feature extraction from vector fields are presented. One of the most fundamental challenges in the analysis of vector fields is that their features are defined with respect to reference frames. Unfortunately, there is no single ""correct"" reference frame for analysis, and an unsuitable frame may cause features of interest to remain undetected, thus creating serious physical consequences. This work develops new reference frames that enable extraction of localized features that other techniques and frames fail to detect. As a result, these reference frames objectify the notion of ""correctness"" of features for certain goals by revealing the phenomena of importance from the underlying data. An important consequence of using these local frames is that the analysis of unsteady (time-varying) vector fields can be reduced to the analysis of sequences of steady (time- independent) vector fields, which can be performed using simpler and scalable techniques that allow better data management by accessing the data on a per-time-step basis. Nevertheless, the state-of-the-art analysis of steady vector fields is not robust, as most techniques are numerical in nature. The residing numerical errors can violate consistency with the underlying theory by breaching important fundamental laws, which may lead to serious physical consequences. This dissertation considers consistency as the most fundamental characteristic of computational analysis that must always be preserved, and presents a new discrete theory that uses combinatorial representations and algorithms to provide consistency guarantees during vector field analysis along with the uncertainty visualization of unavoidable discretization errors. Together, the two main contributions of this dissertation address two important concerns regarding feature extraction from scientific data: correctness and precision. The work presented here also opens new avenues for further research by exploring more-general reference frames and more-sophisticated domain discretizations

Representing

The earliest recorded use of the term representation is in France in the mid-13th century, when it referred to the presentation of letters, documents or evidence for view. Since then, representation has taken on various meanings, which concern the symbolic denotation of something; ‘standing in for’ others with the authority to act on their behalf; a discursive or written account; and the visual portrayal of a person or thing (Oxford English Dictionary, 2009). Today, many people around the world are immersed in representations, such as adverts, newspapers, debates and art. It is important to differentiate between the meaning of a representation, and the medium or form that it takes

A new class of neural architectures to model episodic memory : computational studies of distal reward learning

A computational cognitive neuroscience model is proposed, which models episodic memory based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an environment in which the embodiment exists are simulated. This simulated environment realizes the Morris Water Maze task, a well established biological experimental test of distal reward learning. The embodied neural architecture is treated as a virtual rat and the environment it acts in as a virtual water tank. Performance levels of the neural architectures are evaluated through analysis of embodied behavior in the distal reward learning task. Comparison is made to biological rat experimental data, as well as comparison to other published models. In addition, differences in performance are compared between the normal and categorically informed versions of the architecture

Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data casual discovery.Comment: 52 pages, 6 figure

Comparative Uncertainty Visualization for High-Level Analysis of Scalar- and Vector-Valued Ensembles

With this thesis, I contribute to the research field of uncertainty visualization, considering parameter dependencies in multi valued fields and the uncertainty of automated data analysis. Like uncertainty visualization in general, both of these fields are becoming more and more important due to increasing computational power, growing importance and availability of complex models and collected data, and progress in artificial intelligence. I contribute in the following application areas: Uncertain Topology of Scalar Field Ensembles. The generalization of topology-based visualizations to multi valued data involves many challenges. An example is the comparative visualization of multiple contour trees, complicated by the random nature of prevalent contour tree layout algorithms. I present a novel approach for the comparative visualization of contour trees - the Fuzzy Contour Tree. Uncertain Topological Features in Time-Dependent Scalar Fields. Tracking features in time-dependent scalar fields is an active field of research, where most approaches rely on the comparison of consecutive time steps. I created a more holistic visualization for time-varying scalar field topology by adapting Fuzzy Contour Trees to the time-dependent setting. Uncertain Trajectories in Vector Field Ensembles. Visitation maps are an intuitive and well-known visualization of uncertain trajectories in vector field ensembles. For large ensembles, visitation maps are not applicable, or only with extensive time requirements. I developed Visitation Graphs, a new representation and data reduction method for vector field ensembles that can be calculated in situ and is an optimal basis for the efficient generation of visitation maps. This is accomplished by bringing forward calculation times to the pre-processing. Visually Supported Anomaly Detection in Cyber Security. Numerous cyber attacks and the increasing complexity of networks and their protection necessitate the application of automated data analysis in cyber security. Due to uncertainty in automated anomaly detection, the results need to be communicated to analysts to ensure appropriate reactions. I introduce a visualization system combining device readings and anomaly detection results: the Security in Process System. To further support analysts I developed an application agnostic framework that supports the integration of knowledge assistance and applied it to the Security in Process System. I present this Knowledge Rocks Framework, its application and the results of evaluations for both, the original and the knowledge assisted Security in Process System. For all presented systems, I provide implementation details, illustrations and applications