TY - JOUR A1 - Wienrich, Carolin A1 - Latoschik, Marc Erich T1 - eXtended Artificial Intelligence: New Prospects of Human-AI Interaction Research JF - Frontiers in Virtual Reality N2 - Artificial Intelligence (AI) covers a broad spectrum of computational problems and use cases. Many of those implicate profound and sometimes intricate questions of how humans interact or should interact with AIs. Moreover, many users or future users do have abstract ideas of what AI is, significantly depending on the specific embodiment of AI applications. Human-centered-design approaches would suggest evaluating the impact of different embodiments on human perception of and interaction with AI. An approach that is difficult to realize due to the sheer complexity of application fields and embodiments in reality. However, here XR opens new possibilities to research human-AI interactions. The article’s contribution is twofold: First, it provides a theoretical treatment and model of human-AI interaction based on an XR-AI continuum as a framework for and a perspective of different approaches of XR-AI combinations. It motivates XR-AI combinations as a method to learn about the effects of prospective human-AI interfaces and shows why the combination of XR and AI fruitfully contributes to a valid and systematic investigation of human-AI interactions and interfaces. Second, the article provides two exemplary experiments investigating the aforementioned approach for two distinct AI-systems. The first experiment reveals an interesting gender effect in human-robot interaction, while the second experiment reveals an Eliza effect of a recommender system. Here the article introduces two paradigmatic implementations of the proposed XR testbed for human-AI interactions and interfaces and shows how a valid and systematic investigation can be conducted. In sum, the article opens new perspectives on how XR benefits human-centered AI design and development. KW - human-artificial intelligence interface KW - human-artificial intelligence interaction KW - XR-artificial intelligence continuum KW - XR-artificial intelligence combination KW - research methods KW - human-centered, human-robot KW - recommender system Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-260296 VL - 2 ER - TY - THES A1 - Niebler, Thomas T1 - Extracting and Learning Semantics from Social Web Data T1 - Extraktion und Lernen von Semantik aus Social Web-Daten N2 - Making machines understand natural language is a dream of mankind that existed since a very long time. Early attempts at programming machines to converse with humans in a supposedly intelligent way with humans relied on phrase lists and simple keyword matching. However, such approaches cannot provide semantically adequate answers, as they do not consider the specific meaning of the conversation. Thus, if we want to enable machines to actually understand language, we need to be able to access semantically relevant background knowledge. For this, it is possible to query so-called ontologies, which are large networks containing knowledge about real-world entities and their semantic relations. However, creating such ontologies is a tedious task, as often extensive expert knowledge is required. Thus, we need to find ways to automatically construct and update ontologies that fit human intuition of semantics and semantic relations. More specifically, we need to determine semantic entities and find relations between them. While this is usually done on large corpora of unstructured text, previous work has shown that we can at least facilitate the first issue of extracting entities by considering special data such as tagging data or human navigational paths. Here, we do not need to detect the actual semantic entities, as they are already provided because of the way those data are collected. Thus we can mainly focus on the problem of assessing the degree of semantic relatedness between tags or web pages. However, there exist several issues which need to be overcome, if we want to approximate human intuition of semantic relatedness. For this, it is necessary to represent words and concepts in a way that allows easy and highly precise semantic characterization. This also largely depends on the quality of data from which these representations are constructed. In this thesis, we extract semantic information from both tagging data created by users of social tagging systems and human navigation data in different semantic-driven social web systems. Our main goal is to construct high quality and robust vector representations of words which can the be used to measure the relatedness of semantic concepts. First, we show that navigation in the social media systems Wikipedia and BibSonomy is driven by a semantic component. After this, we discuss and extend methods to model the semantic information in tagging data as low-dimensional vectors. Furthermore, we show that tagging pragmatics influences different facets of tagging semantics. We then investigate the usefulness of human navigational paths in several different settings on Wikipedia and BibSonomy for measuring semantic relatedness. Finally, we propose a metric-learning based algorithm in adapt pre-trained word embeddings to datasets containing human judgment of semantic relatedness. This work contributes to the field of studying semantic relatedness between words by proposing methods to extract semantic relatedness from web navigation, learn highquality and low-dimensional word representations from tagging data, and to learn semantic relatedness from any kind of vector representation by exploiting human feedback. Applications first and foremest lie in ontology learning for the Semantic Web, but also semantic search or query expansion. N2 - Einer der großen Träume der Menschheit ist es, Maschinen dazu zu bringen, natürliche Sprache zu verstehen. Frühe Versuche, Computer dahingehend zu programmieren, dass sie mit Menschen vermeintlich intelligente Konversationen führen können, basierten hauptsächlich auf Phrasensammlungen und einfachen Stichwortabgleichen. Solche Ansätze sind allerdings nicht in der Lage, inhaltlich adäquate Antworten zu liefern, da der tatsächliche Inhalt der Konversation nicht erfasst werden kann. Folgerichtig ist es notwendig, dass Maschinen auf semantisch relevantes Hintergrundwissen zugreifen können, um diesen Inhalt zu verstehen. Solches Wissen ist beispielsweise in Ontologien vorhanden. Ontologien sind große Datenbanken von vernetztem Wissen über Objekte und Gegenstände der echten Welt sowie über deren semantische Beziehungen. Das Erstellen solcher Ontologien ist eine sehr kostspielige und aufwändige Aufgabe, da oft tiefgreifendes Expertenwissen benötigt wird. Wir müssen also Wege finden, um Ontologien automatisch zu erstellen und aktuell zu halten, und zwar in einer Art und Weise, dass dies auch menschlichem Empfinden von Semantik und semantischer Ähnlichkeit entspricht. Genauer gesagt ist es notwendig, semantische Entitäten und deren Beziehungen zu bestimmen. Während solches Wissen üblicherweise aus Textkorpora extrahiert wird, ist es möglich, zumindest das erste Problem - semantische Entitäten zu bestimmen - durch Benutzung spezieller Datensätze zu umgehen, wie zum Beispiel Tagging- oder Navigationsdaten. In diesen Arten von Datensätzen ist es nicht notwendig, Entitäten zu extrahieren, da sie bereits aufgrund inhärenter Eigenschaften bei der Datenakquise vorhanden sind. Wir können uns also hauptsächlich auf die Bestimmung von semantischen Relationen und deren Intensität fokussieren. Trotzdem müssen hier noch einige Hindernisse überwunden werden. Beispielsweise ist es notwendig, Repräsentationen für semantische Entitäten zu finden, so dass es möglich ist, sie einfach und semantisch hochpräzise zu charakterisieren. Dies hängt allerdings auch erheblich von der Qualität der Daten ab, aus denen diese Repräsentationen konstruiert werden. In der vorliegenden Arbeit extrahieren wir semantische Informationen sowohl aus Taggingdaten, von Benutzern sozialer Taggingsysteme erzeugt, als auch aus Navigationsdaten von Benutzern semantikgetriebener Social Media-Systeme. Das Hauptziel dieser Arbeit ist es, hochqualitative und robuste Vektordarstellungen von Worten zu konstruieren, die dann dazu benutzt werden können, die semantische Ähnlichkeit von Konzepten zu bestimmen. Als erstes zeigen wir, dass Navigation in Social Media Systemen unter anderem durch eine semantische Komponente getrieben wird. Danach diskutieren und erweitern wir Methoden, um die semantische Information in Taggingdaten als niedrigdimensionale sogenannte “Embeddings” darzustellen. Darüberhinaus demonstrieren wir, dass die Taggingpragmatik verschiedene Facetten der Taggingsemantik beeinflusst. Anschließend untersuchen wir, inwieweit wir menschliche Navigationspfade zur Bestimmung semantischer Ähnlichkeit benutzen können. Hierzu betrachten wir mehrere Datensätze, die Navigationsdaten in verschiedenen Rahmenbedingungen beinhalten. Als letztes stellen wir einen neuartigen Algorithmus vor, um bereits trainierte Word Embeddings im Nachhinein an menschliche Intuition von Semantik anzupassen. Diese Arbeit steuert wertvolle Beiträge zum Gebiet der Bestimmung von semantischer Ähnlichkeit bei: Es werden Methoden vorgestellt werden, um hochqualitative semantische Information aus Web-Navigation und Taggingdaten zu extrahieren, diese mittels niedrigdimensionaler Vektordarstellungen zu modellieren und selbige schließlich besser an menschliches Empfinden von semantischer Ähnlichkeit anzupassen, indem aus genau diesem Empfinden gelernt wird. Anwendungen liegen in erster Linie darin, Ontologien für das Semantic Web zu lernen, allerdings auch in allen Bereichen, die Vektordarstellungen von semantischen Entitäten benutzen. KW - Semantik KW - Maschinelles Lernen KW - Soziale Software KW - Semantics KW - User Behavior KW - Social Web KW - Machine Learning Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-178666 ER - TY - THES A1 - Budig, Benedikt T1 - Extracting Spatial Information from Historical Maps: Algorithms and Interaction T1 - Extraktion räumlicher Informationen aus historischen Landkarten: Algorithmen und Interaktion N2 - Historical maps are fascinating documents and a valuable source of information for scientists of various disciplines. Many of these maps are available as scanned bitmap images, but in order to make them searchable in useful ways, a structured representation of the contained information is desirable. This book deals with the extraction of spatial information from historical maps. This cannot be expected to be solved fully automatically (since it involves difficult semantics), but is also too tedious to be done manually at scale. The methodology used in this book combines the strengths of both computers and humans: it describes efficient algorithms to largely automate information extraction tasks and pairs these algorithms with smart user interactions to handle what is not understood by the algorithm. The effectiveness of this approach is shown for various kinds of spatial documents from the 16th to the early 20th century. N2 - Historische Landkarten sind faszinierende Dokumente und eine wertvolle Informationsquelle für Wissenschaftler verschiedener Fächer. Viele dieser Karten liegen als gescannte Bitmap-Bilder vor, aber um sie auf nützliche Art durchsuchbar zu machen ist eine strukturierte Repräsentation der enthaltenen Informationen wünschenswert. Dieses Buch beschäftigt sich mit der Extraktion räumlicher Informationen aus historischen Landkarten. Man kann nicht erwarten, dass dies vollautomatisch geschieht (da komplizierte Semantik involviert ist), aber es ist auch zu aufwändig, um im großen Stil manuell durchgeführt zu werden. Die Methodik, die in diesem Buch verwendet wird, kombiniert die Stärken von Computern und Menschen: Es werden effiziente Algorithmen beschrieben, die Extraktionsaufgaben weitgehend automatisieren, und dazu passende Nutzerinteraktionen entworfen, mit denen Fälle gelöst werden, die die Algorithmen nicht vestehen. Die Effekitivität dieses Ansatzes wird anhand verschiedener räumlicher Dokumente aus dem 16. bis frühen 20. Jahrhundert gezeigt. KW - Karte KW - Effizienter Algorithmus KW - Interaktion KW - Information Extraction KW - Smart User Interaction KW - Historical Maps KW - Itineraries KW - Deep Georeferencing KW - Benutzerinteraktion KW - Historische Landkarten KW - Itinerare KW - Georeferenzierung KW - Historische Karte KW - Raumdaten Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-160955 SN - 978-3-95826-092-4 SN - 978-3-95826-093-1 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, ISBN 978-3-95826-092-4, 32,90 Euro. PB - Würzburg University Press CY - Würzburg ET - 1. Auflage ER - TY - JOUR A1 - Krenzer, Adrian A1 - Makowski, Kevin A1 - Hekalo, Amar A1 - Fitting, Daniel A1 - Troya, Joel A1 - Zoller, Wolfram G. A1 - Hann, Alexander A1 - Puppe, Frank T1 - Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists JF - BioMedical Engineering OnLine N2 - Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object’s annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source. KW - object detection KW - machine learning KW - deep learning KW - annotation KW - endoscopy KW - gastroenterology KW - automation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300231 VL - 21 IS - 1 ER - TY - RPRT A1 - Dworzak, Manuel A1 - Großmann, Marcel A1 - Le, Duy Thanh T1 - Federated Learning for Service Placement in Fog and Edge Computing T2 - KuVS Fachgespräch - Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS’23) N2 - Service orchestration requires enormous attention and is a struggle nowadays. Of course, virtualization provides a base level of abstraction for services to be deployable on a lot of infrastructures. With container virtualization, the trend to migrate applications to a micro-services level in order to be executable in Fog and Edge Computing environments increases manageability and maintenance efforts rapidly. Similarly, network virtualization adds effort to calibrate IP flows for Software-Defined Networks and eventually route it by means of Network Function Virtualization. Nevertheless, there are concepts like MAPE-K to support micro-service distribution in next-generation cloud and network environments. We want to explore, how a service distribution can be improved by adopting machine learning concepts for infrastructure or service changes. Therefore, we show how federated machine learning is integrated into a cloud-to-fog-continuum without burdening single nodes. KW - fog computing KW - SDN KW - orchestration KW - federated learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322193 ER - TY - JOUR A1 - Kaltdorf, Kristin Verena A1 - Schulze, Katja A1 - Helmprobst, Frederik A1 - Kollmannsberger, Philip A1 - Dandekar, Thomas A1 - Stigloher, Christian T1 - Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms JF - PLoS Computational Biology N2 - Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial. KW - Biology KW - Vesicles KW - Caenorhabditis elegans KW - Zebrafish KW - Septins KW - Synaptic vesicles KW - Neuromuscular junctions KW - Computer software KW - Synapses Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172112 VL - 13 IS - 1 ER - TY - JOUR A1 - Toepfer, Martin A1 - Corovic, Hamo A1 - Fette, Georg A1 - Klügl, Peter A1 - Störk, Stefan A1 - Puppe, Frank T1 - Fine-grained information extraction from German transthoracic echocardiography reports JF - BMC Medical Informatics and Decision Making N2 - Background Information extraction techniques that get structured representations out of unstructured data make a large amount of clinically relevant information about patients accessible for semantic applications. These methods typically rely on standardized terminologies that guide this process. Many languages and clinical domains, however, lack appropriate resources and tools, as well as evaluations of their applications, especially if detailed conceptualizations of the domain are required. For instance, German transthoracic echocardiography reports have not been targeted sufficiently before, despite of their importance for clinical trials. This work therefore aimed at development and evaluation of an information extraction component with a fine-grained terminology that enables to recognize almost all relevant information stated in German transthoracic echocardiography reports at the University Hospital of Würzburg. Methods A domain expert validated and iteratively refined an automatically inferred base terminology. The terminology was used by an ontology-driven information extraction system that outputs attribute value pairs. The final component has been mapped to the central elements of a standardized terminology, and it has been evaluated according to documents with different layouts. Results The final system achieved state-of-the-art precision (micro average.996) and recall (micro average.961) on 100 test documents that represent more than 90 % of all reports. In particular, principal aspects as defined in a standardized external terminology were recognized with f 1=.989 (micro average) and f 1=.963 (macro average). As a result of keyword matching and restraint concept extraction, the system obtained high precision also on unstructured or exceptionally short documents, and documents with uncommon layout. Conclusions The developed terminology and the proposed information extraction system allow to extract fine-grained information from German semi-structured transthoracic echocardiography reports with very high precision and high recall on the majority of documents at the University Hospital of Würzburg. Extracted results populate a clinical data warehouse which supports clinical research. Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-125509 VL - 15 IS - 91 ER - TY - THES A1 - Glaßer, Christian T1 - Forbidden-Patterns and Word Extensions for Concatenation Hierarchies T1 - Verbotsmuster und Worterweiterungen für Konkatenationshierarchien N2 - Starfree regular languages can be build up from alphabet letters by using only Boolean operations and concatenation. The complexity of these languages can be measured with the so-called dot-depth. This measure leads to concatenation hierarchies like the dot-depth hierarchy (DDH) and the closely related Straubing-Thérien hierarchy (STH). The question whether the single levels of these hierarchies are decidable is still open and is known as the dot-depth problem. In this thesis we prove/reprove the decidability of some lower levels of both hierarchies. More precisely, we characterize these levels in terms of patterns in finite automata (subgraphs in the transition graph) that are not allowed. Therefore, such characterizations are called forbidden-pattern characterizations. The main results of the thesis are as follows: forbidden-pattern characterization for level 3/2 of the DDH (this implies the decidability of this level) decidability of the Boolean hierarchy over level 1/2 of the DDH definition of decidable hierarchies having close relations to the DDH and STH Moreover, we prove/reprove the decidability of the levels 1/2 and 3/2 of both hierarchies in terms of forbidden-pattern characterizations. We show the decidability of the Boolean hierarchies over level 1/2 of the DDH and over level 1/2 of the STH. A technique which uses word extensions plays the central role in the proofs of these results. With this technique it is possible to treat the levels 1/2 and 3/2 of both hierarchies in a uniform way. Furthermore, it can be used to prove the decidability of the mentioned Boolean hierarchies. Among other things we provide a combinatorial tool that allows to partition words of arbitrary length into factors of bounded length such that every second factor u leads to a loop with label u in a given finite automaton. N2 - Sternfreie reguläre Sprachen können aus Buchstaben unter Verwendung Boolescher Operationen und Konkatenation aufgebaut werden. Die Komplexität solcher Sprachen lässt sich durch die sogenannte "Dot-Depth" messen. Dieses Maß führt zu Konkatenationshierarchien wie der Dot-Depth-Hierachie (DDH) und der Straubing-Thérien-Hierarchie (STH). Die Frage nach der Entscheidbarkeit der einzelnen Stufen dieser Hierarchien ist als (immer noch offenes) Dot-Depth-Problem bekannt. In dieser Arbeit beweisen wir die Entscheidbarkeit einiger unterer Stufen beider Hierarchien. Genauer gesagt charakterisieren wir diese Stufen durch das Verbot von bestimmten Mustern in endlichen Automaten. Solche Charakterisierungen werden Verbotsmustercharakterisierungen genannt. Die Hauptresultate der Arbeit lassen sich wie folgt zusammenfassen: Verbotsmustercharakterisierung der Stufe 3/2 der DDH (dies hat die Entscheidbarkeit dieser Stufe zur Folge) Entscheidbarkeit der Booleschen Hierarchie über der Stufe 1/2 der DDH Definition von entscheidbaren Hierarchien mit engen Verbindungen zur DDH und STH Darüber hinaus beweisen wir die Entscheidbarkeit der Stufen 1/2 und 3/2 beider Hierarchien (wieder mittels Verbotsmustercharakterisierungen) und die der Booleschen Hierarchien über den Stufen 1/2 der DDH bzw. STH. Dabei stützen sich die Beweise größtenteils auf eine Technik, die von Eigenschaften bestimmter Worterweiterungen Gebrauch macht. Diese Technik erlaubt eine einheitliche Vorgehensweise bei der Untersuchung der Stufen 1/2 und 3/2 beider Hierarchien. Außerdem wird sie in den Beweisen der Entscheidbarkeit der genannten Booleschen Hierarchien verwendet. Unter anderem wird ein kombinatorisches Hilfsmittel zur Verfügung gestellt, das es erlaubt, Wörter beliebiger Länge in Faktoren beschränkter Länge zu zerlegen, so dass jeder zweite Faktor u zu einer u-Schleife in einem gegebenen endlichen Automaten führt. KW - Automatentheorie KW - Formale Sprache KW - Entscheidbarkeit KW - Reguläre Sprache KW - Berechenbarkeit KW - Theoretische Informatik KW - reguläre Sprachen KW - endliche Automaten KW - Dot-Depth Problem KW - Entscheidbarkeit KW - Verbotsmuster KW - Worterweiterungen KW - Theoretical Computer Science KW - regular languages KW - finite automata KW - dot-depth problem KW - decidability KW - forbidden patterns KW - word extensions Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-1179153 ER - TY - THES A1 - Reith, Steffen T1 - Generalized Satisfiability Problems T1 - Verallgemeinerte Erfüllbarkeitsprobleme N2 - In the last 40 years, complexity theory has grown to a rich and powerful field in theoretical computer science. The main task of complexity theory is the classification of problems with respect to their consumption of resources (e.g., running time or required memory). To study the computational complexity (i.e., consumption of resources) of problems, similar problems are grouped into so called complexity classes. During the systematic study of numerous problems of practical relevance, no efficient algorithm for a great number of studied problems was found. Moreover, it was unclear whether such algorithms exist. A major breakthrough in this situation was the introduction of the complexity classes P and NP and the identification of hardest problems in NP. These hardest problems of NP are nowadays known as NP-complete problems. One prominent example of an NP-complete problem is the satisfiability problem of propositional formulas (SAT). Here we get a propositional formula as an input and it must be decided whether an assignment for the propositional variables exists, such that this assignment satisfies the given formula. The intensive study of NP led to numerous related classes, e.g., the classes of the polynomial-time hierarchy PH, P, #P, PP, NL, L and #L. During the study of these classes, problems related to propositional formulas were often identified to be complete problems for these classes. Hence some questions arise: Why is SAT so hard to solve? Are there modifications of SAT which are complete for other well-known complexity classes? In the context of these questions a result by E. Post is extremely useful. He identified and characterized all classes of Boolean functions being closed under superposition. It is possible to study problems which are connected to generalized propositional logic by using this result, which was done in this thesis. Hence, many different problems connected to propositional logic were studied and classified with respect to their computational complexity, clearing the borderline between easy and hard problems. N2 - In den letzten 40 Jahren hat sich die Komplexitätstheorie zu einem reichen und mächtigen Gebiet innerhalb der theoretischen Informatik entwickelt. Dabei ist die hauptsächliche Aufgabenstellung der Komplexitätstheorie die Klassifikation von Problemen bezüglich des Bedarfs von Rechenzeit oder Speicherplatz zu ihrer Lösung. Um die Komplexität von Problemen (d.h. den Bedarf von Resourcen) einzuordnen, werden Probleme mit ähnlichem Ressourcenbedarf in gleiche sogenannte Komplexitätsklassen einsortiert. Bei der systematischen Untersuchung einer Vielzahl von praktisch relevanten Problemen wurden jedoch keine effizienten Algorithmen für viele der untersuchten Probleme gefunden und es ist unklar, ob solche Algorithmen überhaupt existieren. Ein Durchbruch bei der Untersuchung dieser Problematik war die Einführung der Komplexitätsklassen P und NP und die Identifizierung von schwersten Problemen in NP. Diese schwierigsten Probleme von NP sind heute als sogenannte NP-vollständige Probleme bekannt. Ein prominentes Beispiel für ein NP-vollständiges Problem ist das Erfüllbarkeitsproblem für aussagenlogische Formeln (SAT). Hier ist eine aussagenlogische Formel als Eingabe gegeben und es muss bestimmt werden, ob eine Belegung der Wahrheitswertevariablen existiert, so dass diese Belegung die gegebene Formel erfüllt. Das intensive Studium der Klasse NP führte zu einer Vielzahl von anderen Komplexitätsklassen, wie z.B. die der Polynomialzeithierarchie PH, P, #P, PP, NL, L oder #L. Beim Studium dieser Klassen wurden sehr oft Probleme im Zusammenhang mit aussagenlogischen Formeln als schwierigste (vollständige) Probleme für diese Klassen identifiziert. Deshalb stellt sich folgende Frage: Welche Eigenschaften des Erfüllbarkeitsproblems SAT bewirken, dass es eines der schwersten Probleme der Klasse NP ist? Gibt es Einschränkungen oder Verallgemeinerungen des Erfüllbarkeitsproblems, die vollständig für andere bekannte Komplexitätsklassen sind? Im Zusammenhang mit solchen Fragestellungen ist ein Ergebnis von E. Post von extremem Nutzen. Er identifizierte und charakterisierte alle Klassen von Booleschen Funktionen, die unter Superposition abgeschlossen sind. Mit Hilfe dieses Resultats ist es möglich, Probleme im Zusammenhang mit verallgemeinerten Aussagenlogiken zu studieren, was in der vorliegenden Arbeit durchgeführt wurde. Dabei wurde eine Vielzahl von verschiedenen Problemen, die in Zusammenhang mit der Aussagenlogik stehen, studiert und bezüglich ihrer Komplexität klassifiziert. Dadurch wird die Grenzlinie zwischen einfach lösbaren Problemen und schweren Problemen sichtbar. KW - Erfüllbarkeitsproblem KW - Komplexitätstheorie KW - Boolesche Funktionen KW - Isomorphie KW - abgeschlossene Klassen KW - Zählprobleme KW - Computational complexity KW - Boolean functions KW - Boolean isomorphism KW - Boolean equivalence KW - Dichotomy KW - counting problems Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-74 ER - TY - JOUR A1 - Appel, Mirjam A1 - Scholz, Claus-Jürgen A1 - Müller, Tobias A1 - Dittrich, Marcus A1 - König, Christian A1 - Bockstaller, Marie A1 - Oguz, Tuba A1 - Khalili, Afshin A1 - Antwi-Adjei, Emmanuel A1 - Schauer, Tamas A1 - Margulies, Carla A1 - Tanimoto, Hiromu A1 - Yarali, Ayse T1 - Genome-Wide Association Analyses Point to Candidate Genes for Electric Shock Avoidance in Drosophila melanogaster JF - PLoS ONE N2 - Electric shock is a common stimulus for nociception-research and the most widely used reinforcement in aversive associative learning experiments. Yet, nothing is known about the mechanisms it recruits at the periphery. To help fill this gap, we undertook a genome-wide association analysis using 38 inbred Drosophila melanogaster strains, which avoided shock to varying extents. We identified 514 genes whose expression levels and/or sequences covaried with shock avoidance scores. We independently scrutinized 14 of these genes using mutants, validating the effect of 7 of them on shock avoidance. This emphasizes the value of our candidate gene list as a guide for follow-up research. In addition, by integrating our association results with external protein-protein interaction data we obtained a shock avoidance- associated network of 38 genes. Both this network and the original candidate list contained a substantial number of genes that affect mechanosensory bristles, which are hairlike organs distributed across the fly's body. These results may point to a potential role for mechanosensory bristles in shock sensation. Thus, we not only provide a first list of candidate genes for shock avoidance, but also point to an interesting new hypothesis on nociceptive mechanisms. KW - functional analysis KW - disruption project KW - natural variation KW - complex traits KW - networks KW - behavior KW - flies KW - temperature KW - genetics KW - painful Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-152006 VL - 10 IS - 5 ER -