Michael Hanselmann

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MOTIVATION Alignment of multiple liquid chromatography/mass spectrometry (LC/MS) experiments is a necessity today, which arises from the need for biological and technical repeats. Due to limits in sampling frequency and poor reproducibility of retention times, current LC systems suffer from missing observations and non-linear distortions of the retention(More)
Imaging mass spectrometry (IMS) is a promising technology which allows for detailed analysis of spatial distributions of (bio)molecules in organic samples. In many current applications, IMS relies heavily on (semi)automated exploratory data analysis procedures to decompose the data into characteristic component spectra and corresponding abundance maps,(More)
This is a preliminary experience report about designing and implementing a tableaux-based DL reasoner suitable to run on a mobile device. For now the reasoning system only offers pure TBox subsumption. Currently there is no pre-processing for efficient taxonomy computation nor ABox support. The reasoner supports the DIG standard for client communication.(More)
Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email(More)
In this paper, we introduce a new and straightforward criterion for successive insertion and deletion of training points in sparse Gaussian process regression. Our novel approach is based on an approximation of the selection technique proposed by Smola and Bartlett [1]. It is shown that the resulting selection strategies are as fast as the purely randomized(More)
Digital staining for the automated annotation of mass spectrometry imaging (MSI) data has previously been achieved using state-of-the-art classifiers such as random forests or support vector machines (SVMs). However, the training of such classifiers requires an expert to label exemplary data in advance. This process is time-consuming and hence costly,(More)
Watershed segmentation of spectral images is typically achieved by first transforming the high-dimensional input data into a scalar boundary indicator map which is used to derive the watersheds. We propose to combine a Random Forest classifier with the watershed transform and introduce three novel methods to obtain scalar boundary indicator maps from class(More)
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