Developments in abstract and assumption-based argumentation and their application in logic programming

Logic Programming (LP) and Argumentation are two paradigms for knowledge representation and reasoning under incomplete information. Even though the two paradigms share common features, they constitute mostly separate areas of research. In this thesis, we present novel developments in Argumentation, in particular in Assumption-Based Argumentation (ABA) and Abstract Argumentation (AA), and show how they can 1) extend the understanding of the relationship between the two paradigms and 2) provide solutions to problematic reasoning outcomes in LP. More precisely, we introduce assumption labellings as a novel way to express the semantics of ABA and prove a more straightforward relationship with LP semantics than found in previous work. Building upon these correspondence results, we apply methods for argument construction and conflict detection from ABA, and for conflict resolution from AA, to construct justifications of unexpected or unexplained LP solutions under the answer set semantics. We furthermore characterise reasons for the non-existence of stable semantics in AA and apply these findings to characterise different scenarios in which the computation of meaningful solutions in LP under the answer set semantics fails.Open Acces

Quantification of airfoil geometry-induced aerodynamic uncertainties - comparison of approaches

Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods since it is computationally expensive, especially for the uncertainties caused by random geometry variations which involve a large number of variables. This paper compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and gradient-enhanced version of Kriging, radial basis functions and point collocation polynomial chaos, in their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry which is parameterized by 9 independent Gaussian variables. The results show that gradient-enhanced surrogate methods achieve better accuracy than direct integration methods with the same computational cost

Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!

A large number of embeddings trained on medical data have emerged, but it remains unclear how well they represent medical terminology, in particular whether the close relationship of semantically similar medical terms is encoded in these embeddings. To date, only small datasets for testing medical term similarity are available, not allowing to draw conclusions about the generalisability of embeddings to the enormous amount of medical terms used by doctors. We present multiple automatically created large-scale medical term similarity datasets and confirm their high quality in an annotation study with doctors. We evaluate state-of-the-art word and contextual embeddings on our new datasets, comparing multiple vector similarity metrics and word vector aggregation techniques. Our results show that current embeddings are limited in their ability to adequately encode medical terms. The novel datasets thus form a challenging new benchmark for the development of medical embeddings able to accurately represent the whole medical terminology.Comment: Accepted at AAAI 202

Justifying Answer Sets using Argumentation

An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the Assumption-Based Argumentation (ABA) framework constructed from the same logic program. Attack Trees justify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereas ABA-Based Answer Set Justifications express the same justification structure in logic programming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.Comment: This article has been accepted for publication in Theory and Practice of Logic Programmin

Charakterisierung von Enzymen, welche an der Tetrapyrrolbiosynthese beteiligt sind

During haem biosynthesis uroporphyrinogen III decarboxylase (HemE) catalyses the successive decarboxylation of uroporphyrinogen III acetate subunits to form coproporphyrinogen III. Dysfunction of this enzyme causes the disease porphyria cutanea tarda in humans. Based on amino acid sequence comparison of human HemE with HemE from other organisms,the crystal structure of human HemE and recently density-functional studies of the mechanism, two highly conserved arginine residues were proposed to be important for catalysis. Therefore, the arginine 37 and 41 residues of human HemE were exchanged by site-directed mutagenesis to investigate their functional role. Wild type and mutant HemE were recombinantly produced in E. coli, purified and biochemically characterised. The enzymatically influence of these residues were determined by analysing the HemE activity assay mixture by HPLC analysis. It could be shown that the exchange of the positively charged amino acids Arg37 and Arg41 abolish the enzyme activity of HemE. The necessity of the interaction of these two arginine residues for substrate binding, coordination in the active site cleft and decarboxylation was demonstrated. A second part of this thesis focused on interactions and existence of protein complexes of different enzymes involved into early steps of tetrapyrrole biosynthesis in Thermosynechococcus elongatus. For the protection of highly cell-toxic tetrapyrrole intermediates in the biosynthesis, it is proposed that the involved enzymes form complexes to channel the intermediates. Here T. elongatus hemB, hemE and hemN were cloned into expression vectors. These proteins and an already existing cloned hemD gene were recombinantly produced in E. coli. HemB was successfully chromatographically purified and enzymatic activity could be revealed. However, the overproduced protein HemD, HemE and HemN were found exclusively in the insoluble cellular extract. Initial approaches to solubilise the protein were performed.Während der Biosynthese katalysiert die Uroporphyrinogen III Decarboxylase die sukzessive Decarboxylierung der Uroporphyrinogen III Acetatseitenketten, um Coproporphyrinogen III zu bilden. Fehlfunktionen des Enzymes verursacht im Menschen die Krankheit Porphyria cutanea tarda. Aufgrund von Aminosäurensequenzvergleich des humanen Enzymes mit denen anderer Organismen, der Kristallstruktur des humanen Enzymes und jüngsten Dichte-Funktionsstudien der Katalyse wurden zwei hoch konservierte Argininreste vorgeschlagen, eine entscheidende Rolle in der Katalyse zu spielen. Demzufolge wurden Arg37 und Arg41 des humanen Enzymes für Funktionsuntersuchung durch gerichtete Mutagenese ausgetauscht. Der Wildtyp und die hergestellten Mutanten wurden rekombinant in E. coli produziert, gereinigt und biochemisch charakterisiert. Der enzymatische Einfluss dieser Aminosäurereste wurde durch HPLC Analyse der im Aktivitätstest gebildeten Produkte untersucht. Es wurde gezeigt, dass der Austausch von Arg37 und Arg41 die Aktivität entscheidend beeinflusst. Es konnte die Notwendigkeit der Arginine für die Substratbindung, -koordination im Aktivenzentrum und die Katalyse nachgewiesen werden. Im zweiten Teil der Arbeit wurde die Interaktion und Existenz von Proteinkomplexen verschiedener Enzyme der ersten Schritte in der Biosynthese aus T. elongatus untersucht. Zum Schutz vor hoch-zellgiftigen Biosyntheseintermediaten wird postuliert, dass die beteiligten Enzyme Komplexe bilden, um die Intermediate untereinander weiterzuleiten. Dafür wurden die T. elongatus Gene hemB, hemD, hemE and hemN in Expressionsvektoren kloniert und die entsprechenden Proteine in E. coli recombinant produziert. HemB konnte erfolgreich chromatographisch gereinigt sowie seine enzymatische Aktivität nachgewiesen werden. Hingegen wurden die überproduzierten Proteine HemD, HemE und HemN ausschließlich in der unlöslichen Zellfraktion aufgefunden. Erste Ansätze zur Erhöhung der Löslichkeit dieser Proteine wurden durchgeführt

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges

A Framework for Monitoring and Retraining Language Models in Real-World Applications

In the Machine Learning (ML) model development lifecycle, training candidate models using an offline holdout dataset and identifying the best model for the given task is only the first step. After the deployment of the selected model, continuous model monitoring and model retraining is required in many real-world applications. There are multiple reasons for retraining, including data or concept drift, which may be reflected on the model performance as monitored by an appropriate metric. Another motivation for retraining is the acquisition of increasing amounts of data over time, which may be used to retrain and improve the model performance even in the absence of drifts. We examine the impact of various retraining decision points on crucial factors, such as model performance and resource utilization, in the context of Multilabel Classification models. We explain our key decision points and propose a reference framework for designing an effective model retraining strategy