Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches

Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches

Noise Regularization for Conditional Density Estimation

Modelling statistical relationships beyond the conditional mean is crucial in many settings. Conditional density estimation (CDE) aims to learn the full conditional probability density from data. Though highly expressive, neural network based CDE models can suffer from severe over-fitting when trained with the maximum likelihood objective. Due to the inherent structure of such models, classical regularization approaches in the parameter space are rendered ineffective. To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training. We demonstrate that the proposed approach corresponds to a smoothness regularization and prove its asymptotic consistency. In our experiments, noise regularization significantly and consistently outperforms other regularization methods across seven data sets and three CDE models. The effectiveness of noise regularization makes neural network based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce

Environmental Factors in the Relapse and Recurrence of Inflammatory Bowel Disease:A Review of the Literature

The causes of relapse in patients with Crohn's disease (CD) and ulcerative colitis (UC) are largely unknown. This paper reviews the epidemiological and clinical data on how medications (non-steroidal anti-inflammatory drugs, estrogens and antibiotics), lifestyle factors (smoking, psychological stress, diet and air pollution) may precipitate clinical relapses and recurrence. Potential biological mechanisms include: increasing thrombotic tendency, imbalances in prostaglandin synthesis, alterations in the composition of gut microbiota, and mucosal damage causing increased permeability

Restriction of trophic factors and nutrients induces PARKIN expression

Parkinson’s disease (PD) is the most frequent neurodegenerative movement disorder and manifests at old age. While many details of its pathogenesis remain to be elucidated, in particular the protein and mitochondrial quality control during stress responses have been implicated in monogenic PD variants. Especially the mitochondrial kinase PINK1 and the ubiquitin ligase PARKIN are known to cooperate in autophagy after mitochondrial damage. As autophagy is also induced by loss of trophic signaling and PINK1 gene expression is modulated after deprivation of cytokines, we analyzed to what extent trophic signals and starvation stress regulate PINK1 and PARKIN expression. Time course experiments with serum deprivation and nutrient starvation of human SH-SY5Y neuroblastoma cells and primary mouse neurons demonstrated phasic induction of PINK1 transcript up to twofold and PARKIN transcript levels up to sixfold. The corresponding threefold starvation induction of PARKIN protein was limited by its translocation to lysosomes. Analysis of primary mouse cells from PINK1-knockout mice indicated that PARKIN induction and lysosomal translocation occurred independent of PINK1. Suppression of the PI3K-Akt-mTOR signaling by pharmacological agents modulated PARKIN expression accordingly. In conclusion, this expression survey demonstrates that PARKIN and PINK1 are coregulated during starvation and suggest a role of both PD genes in response to trophic signals and starvation stress

Noise Regularization for Conditional Density Estimation

Modelling statistical relationships beyond the conditional mean is crucial in many settings. Conditional density estimation (CDE) aims to learn the full conditional probability density from data. Though highly expressive, neural network based CDE models can suffer from severe over-fitting when trained with the maximum likelihood objective. Due to the inherent structure of such models, classical regularization approaches in the parameter space are rendered ineffective. To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training. We demonstrate that the proposed approach corresponds to a smoothness regularization and prove its asymptotic consistency. In our experiments, noise regularization significantly and consistently outperforms other regularization methods across seven data sets and three CDE models. The effectiveness of noise regularization makes neural network based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce