MS-B-2a - De fine religiosae perfectionis

Die mittelalterlichen Handschriften der Signaturengruppe B in der ULB DüsseldorfKurzaufnahme einer Handschrift61 MS-B-2

Hierarchical task instance mining in interaction histories

Knowledge work at computer workplaces involves execution of multiple concurrent tasks with frequent task interruptions. The complexity of the resulting work processes makes task externalization a desired goal towards facilitating analysis and support of knowledge work, e.g. by extracting and disseminating best practices. In this paper, we present a task mining method that identifies tasks based on interaction histories. The method generates instances of a semantic hierarchical task model which captures an abstraction of the work processes. A specific characteristic of the method is that it mines tasks based on a combination of semantic and temporal features, extracted from enriched interaction histories. The use of semantic similarity results in a high robustness of the system with respect to task interruption and concurrent task execution. An evaluation of our task mining method based on a study with users executing frequently interrupted tasks is presented. One element of the evaluation is the assessment of different algorithms for semantic similarity computing, namely Term Matching (TM), Vector Space Model (VSM) and Latent Dirichlet Allocation (LDA). For an approach using VSM a precision of 0.83, a recall of 0.76 and a F1-measure of 0.79 is reached

A versatile framework for the analysis of high-throughput screening data

Mitosis is an essential process within the cell life cycle, and research in this field has many applications in medicine. In order to study the proteins involved in mitosis and their function, small molecules (compounds) are required which inhibit the protein under investigation and hence allow to switch off the respective protein. For this purpose, high-throughput screening is performed, where thousands of compounds from commercially available libraries are probed using optical readouts. The vast amounts of data generated in high-throughput screening require dedicated data analysis and visual analytics approaches for evaluation. In this work, a versatile framework is presented which provides data preprocessing and visualization approaches for the analysis of high-throughput screening data

Generation of luminescent defects in hBN by various irradiation methods

Hexagonal Boron Nitride (hBN) is a van der Waals (vdW) crystal with a very wide bandgap and often used as insulating layer for other vdW materials, like graphene. Luminescent centres in hBN have recently been gathering much attention because of their brightness and their excellent quantum properties at room temperature, which would make them competitive with state-of-the-art quantum emitters, like the NV center in diamond. Among the most studied luminescent centres in hBN, there is the charged Boron vacancy (V–B), which features a very broad photoluminescence (PL) spectrum centered around 850 nm, along with magnetic properties which have important applications in quantum sensing schemes. In the present work, we use a Helium Ion Microscope (HIM) for irradiating hBN flakes, either on bare Si/SiO2 substrate or stacked on thick Graphite flakes, to generate luminescent centres like the V–B. We perform in-depth PL characterization of these centres at different laser excitation wavelengths, power and polarization, and at cryogenic temperature, for different hBN thicknesses and for varying HIM irradiation fluences. Our results show that this technique can systematically produce high-quality luminescent emitters, in good agreement with literature, and allow us to have a good benchmark for further studies on V–B emitters in hBN