Draft and proof of an algorithm computing incremental changes within a labeled layouted concept lattice upon insertion or removal of an attribute column in the underlying formal context. Furthermore some implementational details and mathematical background knowledge are presented. / Entwurf und Beweis eines Algorithmus zur Berechnung inkrementeller Änderungen in einem beschrifteten dargestellten Begriffsverband beim Einfügen oder Entfernen einer Merkmalsspalte im zugrundeliegenden formalen Kontext. Weiterhin sind einige Details zur Implementation sowie zum mathematischen Hintergrundwissen dargestellt.

Draft and proof of an algorithm computing incremental changes within a labeled layouted concept lattice upon insertion or removal of an attribute column in the underlying formal context. Furthermore some implementational details and mathematical background knowledge are presented.:1 Introduction 1.1 Acknowledgements 1.2 Supporting University: TU Dresden, Institute for Algebra 1.3 Supporting Corporation: SAP AG, Research Center Dresden 1.4 Research Project: CUBIST 1.5 Task Description und Structure of the Diploma Thesis I Mathematical Details 2 Fundamentals of Formal Concept Analysis 2.1 Concepts and Concept Lattice 2.2 Visualizations of Concept Lattices 2.2.1 Transitive Closure and Transitive Reduction 2.2.2 Neighborhood Relation 2.2.3 Line Diagram 2.2.4 Concept Diagram 2.2.5 Vertical Hybridization 2.2.6 Omitting the top and bottom concept node 2.2.7 Actions on Concept Diagrams 2.2.8 Metrics on Concept Diagrams 2.2.9 Heatmaps for Concept Diagrams 2.2.10 Biplots of Concept Diagrams 2.2.11 Seeds Selection 2.3 Apposition of Contexts 3 Incremental Updates for Concept Diagrams 3.1 Insertion & Removal of a single Attribute Column 3.1.1 Updating the Concepts 3.1.2 Structural Remarks 3.1.3 Updating the Order 3.1.4 Updating the Neighborhood 3.1.5 Updating the Concept Labels 3.1.6 Updating the Reducibility 3.1.7 Updating the Arrows 3.1.8 Updating the Seed Vectors 3.1.9 Complete IFOX Algorithm 3.1.10 An Example: Stepwise Construction of FCD(3) 3.2 Setting & Deleting a single cross 4 Iterative Exploration of Concept Lattices 4.1 Iceberg Lattices 4.2 Alpha Iceberg Lattices 4.3 Partly selections 4.3.1 Example with EMAGE data 4.4 Overview on Pruning & Interaction Techniques II Implementation Details 5 Requirement Analysis 5.1 Introduction 5.2 User-Level Requirements for Graphs 5.2.1 Select 5.2.2 Explore 5.2.3 Reconfigure 5.2.4 Encode 5.2.5 Abstract/Elaborate 5.2.6 Filter 5.2.7 Connect 5.2.8 Animate 5.3 Low-Level Requirements for Graphs 5.3.1 Panel 5.3.2 Node and Edge 5.3.3 Interface 5.3.4 Algorithm 5.4 Mapping of Low-Level Requirements to User-Level Requirements 5.5 Specific Visualization Requirements for Lattices 5.5.1 Lattice Zoom/Recursive Lattices/Partly Nested Lattices 5.5.2 Planarity 5.5.3 Labels 5.5.4 Selection of Ideals, Filters and Intervalls 5.5.5 Restricted Moving of Elements 5.5.6 Layout Algorithms 5.5.7 Additional Feature: Three Dimensions and Rotation 5.5.8 Additional Feature: Nesting 6 FCAFOX Framework for Formal Concept Analysis in JAVA 6.1 Architecture A Appendix A.1 Synonym Lexicon A.2 Galois Connections & Galois Lattices A.3 Fault Tolerance Extensions to Formal Concept Analysis / Entwurf und Beweis eines Algorithmus zur Berechnung inkrementeller Änderungen in einem beschrifteten dargestellten Begriffsverband beim Einfügen oder Entfernen einer Merkmalsspalte im zugrundeliegenden formalen Kontext. Weiterhin sind einige Details zur Implementation sowie zum mathematischen Hintergrundwissen dargestellt.:1 Introduction 1.1 Acknowledgements 1.2 Supporting University: TU Dresden, Institute for Algebra 1.3 Supporting Corporation: SAP AG, Research Center Dresden 1.4 Research Project: CUBIST 1.5 Task Description und Structure of the Diploma Thesis I Mathematical Details 2 Fundamentals of Formal Concept Analysis 2.1 Concepts and Concept Lattice 2.2 Visualizations of Concept Lattices 2.2.1 Transitive Closure and Transitive Reduction 2.2.2 Neighborhood Relation 2.2.3 Line Diagram 2.2.4 Concept Diagram 2.2.5 Vertical Hybridization 2.2.6 Omitting the top and bottom concept node 2.2.7 Actions on Concept Diagrams 2.2.8 Metrics on Concept Diagrams 2.2.9 Heatmaps for Concept Diagrams 2.2.10 Biplots of Concept Diagrams 2.2.11 Seeds Selection 2.3 Apposition of Contexts 3 Incremental Updates for Concept Diagrams 3.1 Insertion & Removal of a single Attribute Column 3.1.1 Updating the Concepts 3.1.2 Structural Remarks 3.1.3 Updating the Order 3.1.4 Updating the Neighborhood 3.1.5 Updating the Concept Labels 3.1.6 Updating the Reducibility 3.1.7 Updating the Arrows 3.1.8 Updating the Seed Vectors 3.1.9 Complete IFOX Algorithm 3.1.10 An Example: Stepwise Construction of FCD(3) 3.2 Setting & Deleting a single cross 4 Iterative Exploration of Concept Lattices 4.1 Iceberg Lattices 4.2 Alpha Iceberg Lattices 4.3 Partly selections 4.3.1 Example with EMAGE data 4.4 Overview on Pruning & Interaction Techniques II Implementation Details 5 Requirement Analysis 5.1 Introduction 5.2 User-Level Requirements for Graphs 5.2.1 Select 5.2.2 Explore 5.2.3 Reconfigure 5.2.4 Encode 5.2.5 Abstract/Elaborate 5.2.6 Filter 5.2.7 Connect 5.2.8 Animate 5.3 Low-Level Requirements for Graphs 5.3.1 Panel 5.3.2 Node and Edge 5.3.3 Interface 5.3.4 Algorithm 5.4 Mapping of Low-Level Requirements to User-Level Requirements 5.5 Specific Visualization Requirements for Lattices 5.5.1 Lattice Zoom/Recursive Lattices/Partly Nested Lattices 5.5.2 Planarity 5.5.3 Labels 5.5.4 Selection of Ideals, Filters and Intervalls 5.5.5 Restricted Moving of Elements 5.5.6 Layout Algorithms 5.5.7 Additional Feature: Three Dimensions and Rotation 5.5.8 Additional Feature: Nesting 6 FCAFOX Framework for Formal Concept Analysis in JAVA 6.1 Architecture A Appendix A.1 Synonym Lexicon A.2 Galois Connections & Galois Lattices A.3 Fault Tolerance Extensions to Formal Concept Analysis

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How could a contemporary archive in an art institution be organized, what can or should its parameters be? In recent years archiving has become an important subject for museums and galleries as they consider and organize the documentation and accessibility of their past activities and exhibitions. The article deals with the Fundació Antoní Tàpies in Barcelona and its special programmes to engage visitors in working with archive material in the museum space. The institutional framework developed at the Fundació includes the activation of different visitor groups, the establishing of online tools and the creation of new exhibitions based on archive material. These aspects aim at an open access to the archive for the public.

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Teil I: Herausgeberbericht mit Benutzung einer Briefedition von Frank Hieronymus. Teil II: Edition als PDF

In etwa 2-10% aller Krebsleiden findet man eine Metastase vor bei unbekanntem Primärtumor, der mit der üblichen Diagnostik nicht bestimmt werden kann. Meist handelt es sich hierbei um ein metastasierendes Adenokarzinom. Als Hilfsmittel bei der immunhistochemischen Differenzierung solcher Metastasen ist ein statistisches Verfahren entwickelt worden. Dabei können Wahrscheinlichkeitsangaben für die mögliche Organlokalisation des Primärtumors auf der Grundlage von immunhistologischen Färbeergebnissen mit 7 verschiedenen Tumormarkern (CEA, CK7, CK20, ER, GCDFP-15, Surfactant A, Vimentin) geliefert werden. Das histologische Untersuchungsmaterial umfaßte 313 Adenokarzinommetastasen mit bekanntem Primärtumor in Mamma, Ovar, Lunge, Niere, Kolon, Magen und Pankreas. Unter der Annahme einer ausreichenden Diagnosesicherheit bei einer Zuordnungswahrscheinlichkeit von >=90% konnten mit Hilfe des Verfahrens 46% der Metastasen ihrem Primärtumor zugeordnet werden. Die Methode erreicht dabei eine Spezifität von 95%. Mamma- und Lungenadenokarzinommetastasen wurden vor allem aufgrund des positiven Färbeergebnisses für ihren spezifischen Marker GCDFP-15 bzw. Surfactant A differenziert. Da die Diagnose hierbei unabhängig von den übrigen Ergebnissen gestellt werden konnte, wird die Anwendung des Verfahrens besonders bedeutsam für Metastasen, die ihren organspezifischen Marker nicht exprimieren, bzw. für Kolon-, Nieren- und Ovarialkarzinommetastasen, die zum Teil charakteristische Markerspektren aufweisen und damit recht gut zu differenzieren waren. Eine Differenzierung von Magen- und Pankreaskarzinommetastasen war dagegen aufgrund der sehr ähnlichen Markerprofile nicht möglich. Das vorgestellte Programm läßt sich nicht nur als Diagnosehilfe für den Immunhistologen nutzen, sondern läßt auch eine bessere Beurteilung der diagnostischen Wertigkeit verwendeter Markerkombinationen zu, so dass evtl. eine effektivere Antikörper-Auswahl getroffen werden kann. / Cancer of unknown primary origin is a common clinical syndrome and accounts for 2-10% of all cancer diagnoses. Most of these cases are related to a metastatic adenocarcinoma. We have developed and tested a statistical method which can serve as a diagnostic tool for the immunhistochemical differentiation of such metastases. Based on the different expression patterns of 7 tumor markers (CEA, CK 7, CK20, GCDFP-15, Vimentin, Surfactant A), the probability for a certain primary cancer site is calculated. To test the method, 313 metastases of adenocarcinoma with primary sites in the breast, the ovary, the kidney, the colon, the stomach, the pancreas and the lung were examined. Taking the diagnosis to be reliable if the method identifies a certain cancer site with a probability of 90 %, we find that we could differentiate 46 % of the metastases with a specificity of 95 %. Metastases of breast and lung carcinoma were identified mainly based on the expression of their specific markers Surfactant A and GCDFP-15. The procedure becomes especially useful, if a specific marker does not exist, or if the staining result is negative. Metastases of carcinoma from the kidney, the colon and the ovary could be differentiated, because they often exhibit specific expression patterns. In contrast, metastases from stomach and pancreas carcinoma have very similar immunhistochemical properties. Here, a differentiation was not possible. The suggested method can help immunhistochemists not only in the diagnosis, but also to estimate the diagnostic value of certain combinations of tumor markers.

Zugl.: Köln, Univ., Diss., 2007

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La millora de la productivitat i la qualitat són indubtablement dues de les principals exigències del sector productiu modern i factors clau per la competitivitat i la supervivència. Dins aquest sector,la fabricació per arrancada de material juga encara avui en dia un paper protagonista tot i l'aparició de noves tècniques de conformat per addició.Indústries com l'aeronàutica, l'automobilística,la del motlle o l'energètica, depenen en bona part de les prestacions de les màquines-eina. Aquesta Tesi aborda dos aspectes rellevants quan es tracta de millorar de la productivitat i la qualitat del sector productiu: el problema del fimbrament, més conegut per la denominació anglosaxona chatter,i la monitorització de la rugositat superficial en el mecanitzat a alta velocitat. / Productivity and quality improvement are undoubtedly two of the main demands of themodern manufacturing sector and key factors for competitiveness and survival. Within this sector, material removal processes play, still nowadays, a principal role despite the emergence of additive manufacturing techniques. Industries such as aerospace, automotive, molds and dies or energy largely depend on machine tools performance for improved productivity and quality. This Thesis is focused on two important aspects when it comes to improving productivity and quality of the manufacturing sector: chatter problem, and surface roughness monitoring in high speed milling.