Reasoning about Explanations for Negative Query Answers in DL-Lite

In order to meet usability requirements, most logic-based applications provide explanation facilities for reasoning services. This holds also for Description Logics, where research has focused on the explanation of both TBox reasoning and, more recently, query answering. Besides explaining the presence of a tuple in a query answer, it is important to explain also why a given tuple is missing. We address the latter problem for instance and conjunctive query answering over DL-Lite ontologies by adopting abductive reasoning; that is, we look for additions to the ABox that force a given tuple to be in the result. As reasoning tasks we consider existence and recognition of an explanation, and relevance and necessity of a given assertion for an explanation. We characterize the computational complexity of these problems for arbitrary, subset minimal, and cardinality minimal explanations

Contributions to Statistical Reproducibility and Small-Sample Bootstrap

This thesis consists of three contributions: an investigation of bootstrap methods for small samples, an overview of reproducibility, and advances on the topic of test reproducibility. These contributions are inspired by statistical practice in preclinical research. Small samples are a common feature in preclinical research. In this thesis, an extensive simulation study is carried out to explore whether bootstrap methods can perform well with such samples. This study compares four bootstrap methods: nonparametric predictive inference bootstrap, Banks bootstrap, Hutson bootstrap, and Efron bootstrap. The thesis concludes that bootstrap methods can provide a useful estimation and prediction inference for small samples. Some initial recommendations for practitioners are provided. There are no standardised definitions for reproducibility. This work further contributes to the existing literature by classifying reproducibility definitions from the literature into five types, and providing an overview of reproducibility with a focus on issues related to preclinical research, and on statistical reproducibility and its quantification. This research explores the variability of statistical methods from the statistical reproducibility perspective. It considers reproducibility as a predictive inference problem. The nonparametric predictive inference (NPI) method, which is focused on the prediction of future observations based on existing data, is applied. In this work, statistical reproducibility is defined as the probability of the event that, if the test was repeated under identical circumstances and with the same sample size, the same test outcome would be reached. This thesis presents contributions to NPI reproducibility for the t-test and the Wilcoxon-Mann Whitney test. As one of the prevailing patterns, a test statistic falling close to the test threshold leads to low reproducibility. In a preclinical test scenario, reproducibility of a final decision involving multiple pairwise comparisons is studied

Effects Of Improvement Size On Contentment, Aspiration Level, Perceived Strength, Perceived Conflict, And Instrumental Action Escalation

This dissertation investigated whether improvement can sometimes increase the likelihood that the recipient will use increasingly more coercive actions to attempt to obtain further improvement from the giver. It also investigated how it would do so. The source of the research question was a paradoxical real-life phenomenon, that of improvement sometimes preceding such extreme actions as revolutions and strikes.;Two studies were performed in which subjects worked on tasks, received pay from a fictitious manager, and communicated with the manager about the pay. In study 1, there was evidence that large and small improvement increased contentment and decreased perceived strength. Their effects on aspiration level were not clear. There was evidence that large improvement decreased perceived conflict and that small improvement increased it, though the latter only among males. Finally, there was evidence that large improvement decreased escalation likelihood and that small improvement increased it.;It was proposed that the mixed results regarding small improvement\u27s effects on perceived conflict may have been due to small improvement having different short and long term effects on perceived conflict. Study 2 tested whether a short series of small improvements decreased perceived conflict and thereby escalation likelihood, and whether a long series increased both. The results of study 2 replicated those of study 1, except that small improvement\u27s effect on contentment was not clear. Also, there was evidence that both large and small improvement increased aspiration level. Small improvement did not have different short and long term effects on perceived conflict, but there was other evidence that as in study 1 a long series of small improvements increased perceived conflict among males and decreased it among females. There were no differences between the short and long term effects of small improvement on contentment, aspiration level or perceived strength. However, there was evidence that the short series of small improvements decreased escalation likelihood and the long series of small improvements increased it. Thus, the present research demonstrated that long term small improvement received in response to active seeking leads to the escalation of instrumental actions, but exactly how it does so was not uncovered

Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling

Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable. A principled choice for asymptotically exact conditional sampling is Metropolis-within-Gibbs (MWG). However, we observe that the tendency of VAEs to learn a structured latent space, a commonly desired property, can cause the MWG sampler to get "stuck" far from the target distribution. This paper mitigates the limitations of MWG: we systematically outline the pitfalls in the context of VAEs, propose two original methods that address these pitfalls, and demonstrate an improved performance of the proposed methods on a set of sampling tasks