Monumenta illustrata. Raumwissen und antiquarische Gelehrsamkeit

Bereits zur Zeit der europäischen Renaissance, lange vor der Ausrufung eines spatial turn in den Kulturwissenschaften, wurde das wechselseitige Verhältnis von Raum und Wissen als Analysekategorie eingeführt. Der Band demonstriert das mit Untersuchungen zu den archäologischen Landeskunden des 15. bis 17. Jahrhundert. In der geographisch-historischen Betrachtung erschlossen sich im 15. Jahrhundert Raumkonzepte, die wiederum auf das eigene Selbstverständnis zurückwirkten. Der vorliegende Band geht in Fallstudien zu landeskundlichen Forschungen der frühen Neuzeit der Geschichte des Raumwissens nach. Dabei kommt Flavio Biondos Italia Illustrata (erschienen 1474) ein besonderes Interesse zu, da das Werk in vielen Bereichen Europas ähnliche Untersuchungen angeregt hat, etwa in Spanien, Skandinavien, der Schweiz und im Rheingebiet

Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Abstract—The paper's main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.</p

Robust Constraint Satisfaction for C/GMRES based on NMPC with Parameter Uncertainties

Using optimization for solving control problems has become much more accessible due to computational advancements in recent years. With the introduction of Model Predictive Control (MPC) in the petro-chemical industry for linear plant models, nonlinear applications followed and enabled finding optimal control solutions for complex tasks. These tasks often require the abidance by certain limitations, which are referred to as constraints in a Nonlinear Model Predictive Control (NMPC) environment. This development entailed two major hurdles to be negotiated. The first one is finding efficient algorithms for problems such as NMPC optimizations, where Ohtsukas Continuation/Generalised Minimum Residual (C/GMRES) provides a remedy and is heavily used in this work. Second, improving model accuracy is an ongoing topic, which tries to eliminate model-plant mismatches, but usually fails to do so, because models solely approximate real plants. Hence, robust control approaches were introduced in order to deal with disturbances and inaccuracies. In this work, ideas from robust control are used and developed in order to deal with the problem of robustly satisfying inequality constraints on models with parameter uncertainties. Three approaches are presented, where the main goal is to find a worst case from a given parameter set. One method makes use of the fact that quasi-convex functions find their maximum at an extreme point of the functions argument and therefore enables finding the worst case via extreme point scenarios. Next, a method to find the worst case directly from the continuous parameter set is discussed, which is treated as a separate maximization problem, resulting in a bilevel optimization problem. A way of transforming such problems into singlelevel tasks via Karush- Kuhn-Tucker (KKT) conditions is presented, requiring the constraint function to be pseudo-concave. At last, sensitivity is discussed and used as a separate tool and to facilitate previous approaches.submitted by Markus KrierUniversität Linz, Masterarbeit, 2018(VLID)267997

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br /

Assessing Trustworthy AI in Times of COVID-19.: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic