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This thesis is concerned with the solution of the blind source separation problem (BSS). The BSS problem occurs frequently in various scientific and technical applications. In essence, it consists in separating meaningful underlying components out of a mixture of a multitude of superimposed signals. In the recent research literature there are two related approaches to the BSS problem: The first is known as Independent Component Analysis (ICA), where the goal is to transform the data such that the components become as independent as possible. The second is based on the notion of diagonality of certain characteristic matrices derived from the data. Here the goal is to transform the matrices such that they become as diagonal as possible. In this thesis we study the latter method of approximate joint diagonalization (AJD) to achieve a solution of the BSS problem. After an introduction to the general setting, the thesis provides an overview on particular choices for the set of target matrices that can be used for BSS by joint diagonalization. As the main contribution of the thesis, new algorithms for approximate joint diagonalization of several matrices with non-orthogonal transformations are developed. These newly developed algorithms will be tested on synthetic benchmark datasets and compared to other previous diagonalization algorithms. Applications of the BSS methods to biomedical signal processing are discussed and exemplified with real-life data sets of multi-channel biomagnetic recordings.
1 Introduction 1.1 Project formulation 1.2 Our contribution 2 Pedagogical Aspect 4 2.1 Modern teaching 2.2 Our Contribution 2.2.1 Autonomous and exploratory learning 2.2.2 Human machine interaction 2.2.3 Short multimedia clips 3 Ontology Aspect 3.1 Ontology driven expert systems 3.2 Our contribution 3.2.1 Ontology language 3.2.2 Concept Taxonomy 3.2.3 Knowledge base annotation 3.2.4 Description Logics 4 Natural language approach 4.1 Natural language processing in computer science 4.2 Our contribution 4.2.1 Explored strategies 4.2.2 Word equivalence 4.2.3 Semantic interpretation 4.2.4 Various problems 5 Information Retrieval Aspect 5.1 Modern information retrieval 5.2 Our contribution 5.2.1 Semantic query generation 5.2.2 Semantic relatedness 6 Implementation 6.1 Prototypes 6.2 Semantic layer architecture 6.3 Development 7 Experiments 7.1 Description of the experiments 7.2 General characteristics of the three sessions, instructions and procedure 7.3 First Session 7.4 Second Session 7.5 Third Session 7.6 Discussion and conclusion 8 Conclusion and future work 8.1 Conclusion 8.2 Open questions A Description Logics B Probabilistic context-free grammars
It is predicted that Service-oriented Architectures (SOA) will have a high impact on future electronic business and markets. Services will provide an self-contained and standardised interface towards business and are considered as the future platform for business-to-business and business-toconsumer trades. Founded by the complexity of real world business scenarios a huge need for an easy, flexible and automated creation and enactment of service compositions is observed. This survey explores the relationship of service composition with workflow management—a technology/ concept already in use in many business environments. The similarities between the both and the key differences between them are elaborated. Furthermore methods for composition of services ranging from manual, semi- to full-automated composition are sketched. This survey concludes that current tools for service composition are in an immature state and that there is still much research to do before service composition can be used easily and conveniently in real world scenarios. However, since automated service composition is a key enabler for the full potential of Service-oriented Architectures, further research on this field is imperative. This survey closes with a formal sample scenario presented in appendix A to give the reader an impression on how full-automated service composition works.
Quantified Boolean formulas (QBFs) play an important role in theoretical computer science. QBF extends propositional logic in such a way that many advanced forms of reasoning can be easily formulated and evaluated. In this dissertation we present our ZQSAT, which is an algorithm for evaluating quantified Boolean formulas. ZQSAT is based on ZBDD: Zero-Suppressed Binary Decision Diagram , which is a variant of BDD, and an adopted version of the DPLL algorithm. It has been implemented in C using the CUDD: Colorado University Decision Diagram package. The capability of ZBDDs in storing sets of subsets efficiently enabled us to store the clauses of a QBF very compactly and let us to embed the notion of memoization to the DPLL algorithm. These points led us to implement the search algorithm in such a way that we could store and reuse the results of all previously solved subformulas with a little overheads. ZQSAT can solve some sets of standard QBF benchmark problems (known to be hard for DPLL based algorithms) faster than the best existing solvers. In addition to prenex-CNF, ZQSAT accepts prenex-NNF formulas. We show and prove how this capability can be exponentially beneficial.
Modern biological analysis techniques supply scientists with various forms of data. One category of such data are the so called "expression data". These data indicate the quantities of biochemical compounds present in tissue samples. Recently, expression data can be generated at a high speed. This leads in turn to amounts of data no longer analysable by classical statistical techniques. Systems biology is the new field that focuses on the modelling of this information. At present, various methods are used for this purpose. One superordinate class of these methods is machine learning. Methods of this kind had, until recently, predominantly been used for classification and prediction tasks. This neglected a powerful secondary benefit: the ability to induce interpretable models. Obtaining such models from data has become a key issue within Systems biology. Numerous approaches have been proposed and intensively discussed. This thesis focuses on the examination and exploitation of one basic technique: decision trees. The concept of comparing sets of decision trees is developed. This method offers the possibility of identifying significant thresholds in continuous or discrete valued attributes through their corresponding set of decision trees. Finding significant thresholds in attributes is a means of identifying states in living organisms. Knowing about states is an invaluable clue to the understanding of dynamic processes in organisms. Applied to metabolite concentration data, the proposed method was able to identify states which were not found with conventional techniques for threshold extraction. A second approach exploits the structure of sets of decision trees for the discovery of combinatorial dependencies between attributes. Previous work on this issue has focused either on expensive computational methods or the interpretation of single decision trees a very limited exploitation of the data. This has led to incomplete or unstable results. That is why a new method is developed that uses sets of decision trees to overcome these limitations. Both the introduced methods are available as software tools. They can be applied consecutively or separately. That way they make up a package of analytical tools that usefully supplement existing methods. By means of these tools, the newly introduced methods were able to confirm existing knowledge and to suggest interesting and new relationships between metabolites.
For interactive construction of CSG models understanding the layout of a model is essential for its efficient manipulation. To understand position and orientation of aggregated components of a CSG model, we need to realize its visible and occluded parts as a whole. Hence, transparency and enhanced outlines are key techniques to assist comprehension. We present a novel real-time rendering technique for visualizing design and spatial assembly of CSG models. As enabling technology we combine an image-space CSG rendering algorithm with blueprint rendering. Blueprint rendering applies depth peeling for extracting layers of ordered depth from polygonal models and then composes them in sorted order facilitating a clear insight of the models. We develop a solution for implementing depth peeling for CSG models considering their depth complexity. Capturing surface colors of each layer and later combining the results allows for generating order-independent transparency as one major rendering technique for CSG models. We further define visually important edges for CSG models and integrate an image-space edgeenhancement technique for detecting them in each layer. In this way, we extract visually important edges that are directly and not directly visible to outline a model’s layout. Combining edges with transparency rendering, finally, generates edge-enhanced depictions of image-based CSG models and allows us to realize their complex, spatial assembly.