A note on SPKI's authorisation syntax

Tuple reduction is the basic mechanism used in SPKI to make authorisation decisions. A basic problem with the SPKI authorisation syntax is that straightforward implementations of tuple reduction are quadratic in both time and space. In the paper we introduce a restricted version of the SPKI authorisation syntax, which appears to conform well with practice, and for which authorisation decisions can be made in nearly linear time

Proof-Producing Symbolic Execution for Binary Code Verification

We propose a proof-producing symbolic execution for verification of machine-level programs. The analysis is based on a set of core inference rules that are designed to give control over the tradeoff between preservation of precision and the introduction of overapproximation to make the application to real world code useful and tractable. We integrate our symbolic execution in a binary analysis platform that features a low-level intermediate language enabling the application of analyses to many different processor architectures. The overall framework is implemented in the theorem prover HOL4 to be able to obtain highly trustworthy verification results. We demonstrate our approach to establish sound execution time bounds for a control loop program implemented for an ARM Cortex-M0 processor

Lung Segmentation from Chest X-rays using Variational Data Imputation

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.Comment: Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML). Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE

Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high- and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans

NS-Raubgut und Restitution in Bibliotheken - Ausbildungsinhalte für Informationsfachleute

60 Jahre nach Ende des Zweiten Weltkrieges und der nationalsozialistischen Herrschaft befindet sich noch immer NS-Raubgut im Bestand deutscher Bibliotheken. In der vorliegenden Bachelorarbeit wird eine Übersicht zur Thematik „NS-Raubgut und Restitution in Bibliotheken“ erarbeitet. Dies geschieht mit dem Ziel, im weiteren Verlauf der Arbeit aufzuzeigen, wie diese Inhalte in die Ausbildung von Informationsfachleuten integriert werden können. Nach einer Einführung in den Themenkomplex erfolgt zunächst eine Statusermittlung des derzeitigen Umgangs mit der Thematik innerhalb der Ausbildung von Informationsfachleuten an deutschen Hochschulen. Darauf aufbauend werden Vorlesungsinhalte erarbeitet, die sich für die Vermittlung in der Ausbildung eignen. Die Betrachtung möglicher Vermittlungsformen ist ebenfalls enthalten

Walking and Sensing Mobile Lives

In this position paper, we discuss how mindful walking with people allow us to explore sensory aspects of mobile lives that are typically absent from research. We present an app that aids researchers collect impressions from a walk