Development of the economic situation in recent years raises number of issues, including defining what are the factors of this development and whether it is possible to affect them. This thesis deals with the demographic factors of economic growth; those are factors associated with general population and factors which may have an impact on the country's economy. The main aim of this work is to precisely identify the demographic factor and analyze their development in the Czech Republic since the early 1990s to the present days. Furthermore, the economic development is analyzed along with the indicators of economic activity in sorting by various demographic factors. Special attention is given to the status of working foreigners within the labor market. To emphasize the specifics of the development of the various sectors of national economy the construction industry was selected as a case example. The analysis of the employment in the construction industry was conducted in regard to demographic and economic indicators. To illustrate the results of the analysis column, line and pie charts were used in addition to the figures in the tables.

IN ENGLISH Drug dependency in inflammatory bowel disease (IBD), Crohn's disease (CD) and ulcerative colitis (UC), is a specific disease phenotype which determines disease prognosis and hence may be used as a prognostic marker for treatment management. Drug dependency in IBD has been well described in corticosteroid treatment and recently also in infliximab (IFX) therapy. The aims of this thesis were: 1) to assess the occurrence of IFX dependency in paediatric and adult patients with CD; further to search for clinical and genetic predictors of IFX outcome and to evaluate the impact of IFX dependency on surgical rate; 2) to assess in CD patients the outcome of the first course of 5-ASA monotherapy with emphasis on 5-ASA dependency and to define clinical predictors of 5-ASA treatment outcome. We found that 66% of children and 29% of adults with CD became IFX dependent. The high frequency in paediatrics is in agreement with previously published studies, while the finding in adult patients indicates a lower rate of IFX dependency in the only study to date. Perianal disease and no bowel surgery prior to IFX start were predicative of IFX dependency in paediatric patients. In adult cohort, 2 genetic variants LTA c.207 A>G and CASP9 c.93 C>T were associated with IFX outcome, whereas no relevant clinical...

<p dir="ltr">Semantic pairs, which consist of related entities or concepts, serve as the foundation for comprehending the meaning of language in both written and spoken forms. These pairs enable to grasp the nuances of relationships between words, phrases, or ideas, forming the basis for more advanced language tasks like entity recognition, sentiment analysis, machine translation, and question answering. They allow to infer causality, identify hierarchies, and connect ideas within a text, ultimately enhancing the depth and accuracy of automated language processing.</p><p dir="ltr">Nevertheless, the task of extracting semantic pairs from sentences poses a significant challenge, necessitating the relevance of syntactic dependency patterns (SDPs). Thankfully, semantic relationships exhibit adherence to distinct SDPs when connecting pairs of entities. Recognizing this fact underscores the critical importance of extracting these SDPs, particularly for specific semantic relationships like hyponym-hypernym, meronym-holonym, and cause-effect associations. The automated extraction of such SDPs carries substantial advantages for various downstream applications, including entity extraction, ontology development, and question answering. Unfortunately, this pivotal facet of pattern extraction has remained relatively overlooked by researchers in the domains of natural language processing (NLP) and information retrieval.</p><p dir="ltr">To address this gap, I introduce an attention-based supervised deep learning model, ASPER. ASPER is designed to extract SDPs that denote semantic relationships between entities within a given sentential context. I rigorously evaluate the performance of ASPER across three distinct semantic relations: hyponym-hypernym, cause-effect, and meronym-holonym, utilizing six datasets. My experimental findings demonstrate ASPER's ability to automatically identify an array of SDPs that mirror the presence of these semantic relationships within sentences, outperforming existing pattern extraction methods by a substantial margin.</p><p dir="ltr">Second, I want to use the SDPs to extract semantic pairs from sentences. I choose to extract cause-effect entities from medical literature. This task is instrumental in compiling various causality relationships, such as those between diseases and symptoms, medications and side effects, and genes and diseases. Existing solutions excel in sentences where cause and effect phrases are straightforward, such as named entities, single-word nouns, or short noun phrases. However, in the complex landscape of medical literature, cause and effect expressions often extend over several words, stumping existing methods, resulting in incomplete extractions that provide low-quality, non-informative, and at times, conflicting information. To overcome this challenge, I introduce an innovative unsupervised method for extracting cause and effect phrases, PatternCausality tailored explicitly for medical literature. PatternCausality employs a set of cause-effect dependency patterns as templates to identify the key terms within cause and effect phrases. It then utilizes a novel phrase extraction technique to produce comprehensive and meaningful cause and effect expressions from sentences. Experiments conducted on a dataset constructed from PubMed articles reveal that PatternCausality significantly outperforms existing methods, achieving a remarkable order of magnitude improvement in the F-score metric over the best-performing alternatives. I also develop various PatternCausality variants that utilize diverse phrase extraction methods, all of which surpass existing approaches. PatternCausality and its variants exhibit notable performance improvements in extracting cause and effect entities in a domain-neutral benchmark dataset, wherein cause and effect entities are confined to single-word nouns or noun phrases of one to two words.</p><p dir="ltr">Nevertheless, PatternCausality operates within an unsupervised framework and relies heavily on SDPs, motivating me to explore the development of a supervised approach. Although SDPs play a pivotal role in semantic relation extraction, pattern-based methodologies remain unsupervised, and the multitude of potential patterns within a language can be overwhelming. Furthermore, patterns do not consistently capture the broader context of a sentence, leading to the extraction of false-positive semantic pairs. As an illustration, consider the hyponym-hypernym pattern <i>the w of u</i> which can correctly extract semantic pairs for a sentence like <i>the village of Aasu</i> but fails to do so for the phrase <i>the moment of impact</i>. The root cause of this limitation lies in the pattern's inability to capture the nuanced meaning of words and phrases in a sentence and their contextual significance. These observations have spurred my exploration of a third model, DepBERT which constitutes a dependency-aware supervised transformer model. DepBERT's primary contribution lies in introducing the underlying dependency structure of sentences to a language model with the aim of enhancing token classification performance. To achieve this, I must first reframe the task of semantic pair extraction as a token classification problem. The DepBERT model can harness both the tree-like structure of dependency patterns and the masked language architecture of transformers, marking a significant milestone, as most large language models (LLMs) predominantly focus on semantics and word co-occurrence while neglecting the crucial role of dependency architecture.</p><p dir="ltr">In summary, my overarching contributions in this thesis are threefold. First, I validate the significance of the dependency architecture within various components of sentences and publish SDPs that incorporate these dependency relationships. Subsequently, I employ these SDPs in a practical medical domain to extract vital cause-effect pairs from sentences. Finally, my third contribution distinguishes this thesis by integrating dependency relations into a deep learning model, enhancing the understanding of language and the extraction of valuable semantic associations.</p>

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Cette thèse porte sur le développement, l'évaluation et l'optimisation de nouvelles stratégies de maintenance pour des systèmes multi-composants. Cette démarche est justifiée par le fait que contrairement aux systèmes monolithiques qui ont été largement traités dans la littérature sur la maintenance, les systèmes multi-composants avec dépendances économique et stochastique sont encore peu étudiés à cause essentiellement de la difficulté de modélisation de ces types de dépendance. Dans ce cadre d'étude de la maintenance des systèmes multi-composants, cette thèse contient trois volets indépendants. Le premier volet porte sur un type particulier d'équipements dont l'état ne peut être connu que suite à une inspection. De tels équipements constitués de deux composants en série sont considérés. Une nouvelle stratégie quasi-optimale de maintenance conditionnelle basée sur des inspections séquentielles des deux composants est proposée. Dans le deuxième volet on considère des systèmes multi-composants complexes dans le sens où leurs composants peuvent être sujets à des défaillances aléatoires locales et/ou propagées avec ou sans effet d'isolation. Une stratégie de maintenance sélective est proposée pour ce type de systèmes. Dans le troisième et dernier volet la notion de systèmes multi-composants est étendue aux systèmes de production multi-lignes. Dans cette perspective, cette partie porte sur le développement d'une politique intégrée de production-maintenance pour un système de production à deux lignes fonctionnant en parallèle en présence d'une dépendance de type stochastique entre les deux lignes / This thesis focuses on the development, the evaluation and the optimization of new maintenance policies for multi-component systems. This approach is justified by the fact that contrarily to single component systems which have been extensively treated in the literature on maintenance strategies, multi-component systems with economic and stochastic dependence have been much less studied due to the difficulty in modeling such kind of dependence. In this context of studying multi-component systems maintenance, this thesis is made of three independent parts. The first part focuses on a particular type of equipment whose state can only be known following inspection. Such equipment made of two components in series is considered. A new nearly optimal condition based maintenance policy based on sequential inspections of both components is proposed. In the second part, we consider complex multi-component systems in which the components are subject to random local and/or propagated failures, with or without isolation effect. A selective maintenance strategy is applied to such systems. In the third and last part, the concept of multi-component systems is extended to manufacturing systems with multiple machines. In this context, this part focuses on the development of an integrated production-maintenance policy for a production system consisting of two machines in parallel in presence of a form of stochastic dependency between them

by Cheung Yiu Kwong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 59-68). / Abstract --- p.ii / Acknowledgments --- p.iv / Table of contents --- p.v / List of tables --- p.vi / List of appendix --- p.vii / Chapter Chapter 1 - --- Introduction --- p.1 / Chapter Chapter 2 - --- Method --- p.18 / Chapter Chapter 3 - --- Results --- p.24 / Chapter Chapter 4 - --- Discussion --- p.44 / References --- p.59 / Appendix --- p.69

The main objective of this thesis is to analyze the problem of population ageing in terms of the cessation of professional activity (and especially premature labour market withdrawals) and non-medical care needs of persons who are dependent or have lost their autonomy, in order to provide the various public and private administrations active in these fields with some food for thought.

Data integration aims to combine data of different sources and to provide users with a unified view on these data. This task is as challenging as valuable. In this thesis we propose algorithms for dependency discovery to provide necessary information for data integration. We focus on inclusion dependencies (INDs) in general and a special form named conditional inclusion dependencies (CINDs): (i) INDs enable the discovery of structure in a given schema. (ii) INDs and CINDs support the discovery of cross-references or links between schemas. An IND “A in B” simply states that all values of attribute A are included in the set of values of attribute B. We propose an algorithm that discovers all inclusion dependencies in a relational data source. The challenge of this task is the complexity of testing all attribute pairs and further of comparing all of each attribute pair's values. The complexity of existing approaches depends on the number of attribute pairs, while ours depends only on the number of attributes. Thus, our algorithm enables to profile entirely unknown data sources with large schemas by discovering all INDs. Further, we provide an approach to extract foreign keys from the identified INDs. We extend our IND discovery algorithm to also find three special types of INDs: (i) Composite INDs, such as “AB in CD”, (ii) approximate INDs that allow a certain amount of values of A to be not included in B, and (iii) prefix and suffix INDs that represent special cross-references between schemas. Conditional inclusion dependencies are inclusion dependencies with a limited scope defined by conditions over several attributes. Only the matching part of the instance must adhere the dependency. We generalize the definition of CINDs distinguishing covering and completeness conditions and define quality measures for conditions. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. The challenge for this task is twofold: (i) Which (and how many) attributes should be used for the conditions? (ii) Which attribute values should be chosen for the conditions? Previous approaches rely on pre-selected condition attributes or can only discover conditions applying to quality thresholds of 100%. Our approaches were motivated by two application domains: data integration in the life sciences and link discovery for linked open data. We show the efficiency and the benefits of our approaches for use cases in these domains. / Datenintegration hat das Ziel, Daten aus unterschiedlichen Quellen zu kombinieren und Nutzern eine einheitliche Sicht auf diese Daten zur Verfügung zu stellen. Diese Aufgabe ist gleichermaßen anspruchsvoll wie wertvoll. In dieser Dissertation werden Algorithmen zum Erkennen von Datenabhängigkeiten vorgestellt, die notwendige Informationen zur Datenintegration liefern. Der Schwerpunkt dieser Arbeit liegt auf Inklusionsabhängigkeiten (inclusion dependency, IND) im Allgemeinen und auf der speziellen Form der Bedingten Inklusionsabhängigkeiten (conditional inclusion dependency, CIND): (i) INDs ermöglichen das Finden von Strukturen in einem gegebenen Schema. (ii) INDs und CINDs unterstützen das Finden von Referenzen zwischen Datenquellen. Eine IND „A in B“ besagt, dass alle Werte des Attributs A in der Menge der Werte des Attributs B enthalten sind. Diese Arbeit liefert einen Algorithmus, der alle INDs in einer relationalen Datenquelle erkennt. Die Herausforderung dieser Aufgabe liegt in der Komplexität alle Attributpaare zu testen und dabei alle Werte dieser Attributpaare zu vergleichen. Die Komplexität bestehender Ansätze ist abhängig von der Anzahl der Attributpaare während der hier vorgestellte Ansatz lediglich von der Anzahl der Attribute abhängt. Damit ermöglicht der vorgestellte Algorithmus unbekannte Datenquellen mit großen Schemata zu untersuchen. Darüber hinaus wird der Algorithmus erweitert, um drei spezielle Formen von INDs zu finden, und ein Ansatz vorgestellt, der Fremdschlüssel aus den erkannten INDs filtert. Bedingte Inklusionsabhängigkeiten (CINDs) sind Inklusionsabhängigkeiten deren Geltungsbereich durch Bedingungen über bestimmten Attributen beschränkt ist. Nur der zutreffende Teil der Instanz muss der Inklusionsabhängigkeit genügen. Die Definition für CINDs wird in der vorliegenden Arbeit generalisiert durch die Unterscheidung von überdeckenden und vollständigen Bedingungen. Ferner werden Qualitätsmaße für Bedingungen definiert. Es werden effiziente Algorithmen vorgestellt, die überdeckende und vollständige Bedingungen mit gegebenen Qualitätsmaßen auffinden. Dabei erfolgt die Auswahl der verwendeten Attribute und Attributkombinationen sowie der Attributwerte automatisch. Bestehende Ansätze beruhen auf einer Vorauswahl von Attributen für die Bedingungen oder erkennen nur Bedingungen mit Schwellwerten von 100% für die Qualitätsmaße. Die Ansätze der vorliegenden Arbeit wurden durch zwei Anwendungsbereiche motiviert: Datenintegration in den Life Sciences und das Erkennen von Links in Linked Open Data. Die Effizienz und der Nutzen der vorgestellten Ansätze werden anhand von Anwendungsfällen in diesen Bereichen aufgezeigt.

Spatial and social dependence shape human activity-travel pattern decisions and their antecedent choices. Although the transportation literature has long recognized the importance of considering spatial and social dependencies in modeling individuals’ choice behavior, there has been less research on techniques to accommodate these dependencies in discrete choice models, mainly because of the modeling complexities introduced by such interdependencies. The main goal of this dissertation, therefore, is to propose new modeling approaches for accommodating flexible spatial and social dependency structures in discrete choice models within the broader context of activity-based travel demand modeling. The primary objectives of this dissertation research are three-fold. The first objective is to develop a discrete choice modeling methodology that explicitly incorporates spatial dependency (or correlation) across location choice alternatives (whether the choice alternatives are contiguous or non-contiguous). This is achieved by incorporating flexible spatial correlations and patterns using a closed-form Generalized Extreme Value (GEV) structure. The second objective is to propose new approaches to accommodate spatial dependency (or correlation) across observational units for different aspatial discrete choice models, including binary choice and ordered-response choice models. This is achieved by adopting different copula-based methodologies, which offer flexible dependency structures to test for different forms of dependencies. Further, simple and practical approaches are proposed, obviating the need for any kind of simulation machinery and methods for estimation. Finally, the third objective is to formulate an enhanced methodology to capture the social dependency (or correlation) across observational units. In particular, a clustered copula-based approach is formulated to recognize the potential dependence due to cluster effects (such as family-related effects) in an ordered-response context. The proposed approaches are empirically applied in the context of both spatial and aspatial choice situations, including residential location and activity participation choices. In particular, the results show that ignoring spatial and social dependencies, when present, can lead to inconsistent and inefficient parameter estimates that, in turn, can result in misinformed policy actions and recommendations. The approaches proposed in this research are simple, flexible and easy-to-implement, applicable to data sets of any size, do not require any simulation machinery, and do not impose any restrictive assumptions on the dependency structure. / text

The practice of composing web services has received an increasing interest with the emerging application development architecture called Service Oriented Architecture (SOA). A web service composition can be done either manually or (semi-) automatically. Doing composition (semi-) automatically minimizes runtime problems that arise due to dynamic nature of runtime environments. However, the implementation of (semi-) automatic composition demands for the automation of a process model or a composition plan generation process. In addition, creating a composite service or applications from component services, that are developed and meant to work independently, causes unavoidable dependencies among the services involved. Consequently, in a composite service development, understanding, analyzing and tracking of such dependencies becomes important. This thesis views the process model generation sub-task of a service composition as a service dependency identifification and analysis problem. In this thesis, we propose a dependency based automatic process model generation methods. For this purpose, the following issues are explored. First, a top layer architecture with a composition engine is developed. The architecture gives a complete picture of dependency based automatic service composition. Second, the process model generation sub-task is formulated as a service dependency identification and analysis problem. Third, a two-stepped method for automatic process model generation, given a set of candidate web service descriptions, is proposed. The first step of the proposed approach deals with the identifification of potential direct and indirect dependencies between abstract services. The direct dependency extraction is done by assuming a semantic I/O matching of service parameters. The extraction of indirect dependency from direct dependency is done using a recursive algorithm derived from the transitive closure property. Alternatively the Warshall algorithm is used. The second step of the proposed approach deals with analysis of dependency information and generation of process model (PM) automatically. To execute this step, we propose two approaches: matrix based and graph based approaches. The matrix based approach utilizes both direct and indirect dependencies. This approach represents dependencies using matrix and takes advantages of a sorting algorithm. The matrix representation facilitates a simplistic mathematical dependency analysis for generating important indicators during automatic process model creation. The process model is generated using a sorting algorithm that uses the analysis result obtained from the dependency matrix as sorting criterion. The graph based approach uses only direct dependency among candidate services. As its name indicates, in this approach the extracted I/O dependencies are represented using a directed graph. A modifified topological sorting algorithm is used for generating a process model that shows the execution order of candidate services. Both of the proposed approaches (matrix and graph based approaches) recognize the existence of cyclic dependencies and provide ways of dealing with them. The resulting process model or composition plan from both approaches has a sequential, concurrent and loop control flows. Finally, the performance of the proposed approaches is studied theoretically as well as experimentally. For the experimental validation and evaluation purpose, the approaches are implemented in a prototype that facilitates the validation and evaluation of the approaches at a larger scale. An extensive experimental performance evaluation is done fifirst on each proposed approach. The two approaches are then compared and their pros and cons under difffferent scenarios are assessed.