@book{Laack2019, author = {Laack, Walter van}, title = {Weltbilder gestern und heute - Was bleibt und wor{\"u}ber lacht man morgen?}, series = {Vortr{\"a}ge \& Einsichten, Lectures \& Insights}, journal = {Vortr{\"a}ge \& Einsichten, Lectures \& Insights}, number = {Band 2}, publisher = {van Laack Buchverlag}, address = {Aachen}, isbn = {ISBN 978-3936624-44-1}, pages = {56 Seiten}, year = {2019}, language = {de} } @incollection{EngelmannBaumann2023, author = {Engelmann, Ulrich M. and Baumann, Martin}, title = {Moderationsexpertise f{\"u}r QMBs - die Methoden}, series = {Qualit{\"a}tsmanagement in Dienstleistungsunternehmen}, booktitle = {Qualit{\"a}tsmanagement in Dienstleistungsunternehmen}, editor = {Thomann, Hermann and Tr{\"a}ger, Thomas}, publisher = {T{\"U}V-Verlag}, address = {K{\"o}ln}, isbn = {978-3-8249-0473-0}, pages = {Kapitel 08631}, year = {2023}, abstract = {Damit Sie als Moderator effektiv und professionell moderieren k{\"o}nnen, sollten Sie die entsprechenden Methoden kennen. Mit den richtigen Methoden k{\"o}nnen Sie Diskussionen leiten, Konflikte l{\"o}sen, die Teilnehmer motivieren und daf{\"u}r sorgen, dass die Ziele der Veranstaltung erreicht werden. Außerdem helfen sie Ihnen, eine positive Atmosph{\"a}re zu schaffen und das Interesse der Teilnehmer zu halten. In diesem zweiten Beitrag der mehrteiligen Serie lernen Sie die grunds{\"a}tzlichen Methoden kennen, um erfolgreiche Teamsitzungen, Arbeitsgruppentreffen, Kick-offs und Meetings durchzuf{\"u}hren.}, language = {de} } @article{BaringhausGaigallThiele2018, author = {Baringhaus, Ludwig and Gaigall, Daniel and Thiele, Jan Philipp}, title = {Statistical inference for L²-distances to uniformity}, series = {Computational Statistics}, volume = {2018}, journal = {Computational Statistics}, number = {33}, publisher = {Springer}, address = {Berlin}, issn = {1613-9658}, doi = {10.1007/s00180-018-0820-0}, pages = {1863 -- 1896}, year = {2018}, abstract = {The paper deals with the asymptotic behaviour of estimators, statistical tests and confidence intervals for L²-distances to uniformity based on the empirical distribution function, the integrated empirical distribution function and the integrated empirical survival function. Approximations of power functions, confidence intervals for the L²-distances and statistical neighbourhood-of-uniformity validation tests are obtained as main applications. The finite sample behaviour of the procedures is illustrated by a simulation study.}, language = {en} } @article{SchoeningBronderWuetal.2017, author = {Sch{\"o}ning, Michael Josef and Bronder, Thomas and Wu, Chunsheng and Scheja, Sabrina and Jessing, Max and Metzger-Boddien, Christoph and Keusgen, Michael and Poghossian, Arshak}, title = {Label-Free DNA Detection with Capacitive Field-Effect Devices—Challenges and Opportunities}, series = {Proceedings}, volume = {1}, journal = {Proceedings}, number = {8}, publisher = {MDPI}, address = {Basel}, issn = {2504-3900}, doi = {10.3390/proceedings1080719}, pages = {Artikel 719}, year = {2017}, abstract = {Field-effect EIS (electrolyte-insulator-semiconductor) sensors modified with a positively charged weak polyelectrolyte layer have been applied for the electrical detection of DNA (deoxyribonucleic acid) immobilization and hybridization by the intrinsic molecular charge. The EIS sensors are able to detect the existence of target DNA amplicons in PCR (polymerase chain reaction) samples and thus, can be used as tool for a quick verification of DNA amplification and the successful PCR process. Due to their miniaturized setup, compatibility with advanced micro- and nanotechnologies, and ability to detect biomolecules by their intrinsic molecular charge, those sensors can serve as possible platform for the development of label-free DNA chips. Possible application fields as well as challenges and limitations will be discussed.}, language = {en} } @article{Gaigall2020, author = {Gaigall, Daniel}, title = {Testing marginal homogeneity of a continuous bivariate distribution with possibly incomplete paired data}, series = {Metrika}, volume = {2020}, journal = {Metrika}, number = {83}, publisher = {Springer}, issn = {1435-926X}, doi = {10.1007/s00184-019-00742-5}, pages = {437 -- 465}, year = {2020}, abstract = {We discuss the testing problem of homogeneity of the marginal distributions of a continuous bivariate distribution based on a paired sample with possibly missing components (missing completely at random). Applying the well-known two-sample Cr{\´a}mer-von-Mises distance to the remaining data, we determine the limiting null distribution of our test statistic in this situation. It is seen that a new resampling approach is appropriate for the approximation of the unknown null distribution. We prove that the resulting test asymptotically reaches the significance level and is consistent. Properties of the test under local alternatives are pointed out as well. Simulations investigate the quality of the approximation and the power of the new approach in the finite sample case. As an illustration we apply the test to real data sets.}, language = {en} } @article{RichterBraunsteinWinnardetal.2017, author = {Richter, Charlotte and Braunstein, Bjoern and Winnard, Andrew and Nasser, Mona and Weber, T.}, title = {Human Biomechanical and Cardiopulmonary Responses to Partial Gravity - A Systematic Review}, series = {Frontiers in physiology}, journal = {Frontiers in physiology}, number = {8, article 583}, doi = {10.3389/fphys.2017.00583}, pages = {22 Seiten}, year = {2017}, language = {en} } @inproceedings{HunkerJungGossmannetal.2019, author = {Hunker, Jan and Jung, Alexander and Goßmann, Matthias and Linder, Peter and Staat, Manfred}, title = {Development of a tool to analyze the conduction speed in microelectrode array measurements of cardiac tissue}, series = {3rd YRA MedTech Symposium 2019 : May 24 / 2019 / FH Aachen}, booktitle = {3rd YRA MedTech Symposium 2019 : May 24 / 2019 / FH Aachen}, editor = {Staat, Manfred and Erni, Daniel}, publisher = {Universit{\"a}t Duisburg-Essen}, address = {Duisburg}, organization = {MedTech Symposium}, isbn = {978-3-940402-22-6}, doi = {10.17185/duepublico/48750}, pages = {7 -- 8}, year = {2019}, abstract = {The discovery of human induced pluripotent stem cells reprogrammed from somatic cells [1] and their ability to differentiate into cardiomyocytes (hiPSC-CMs) has provided a robust platform for drug screening [2]. Drug screenings are essential in the development of new components, particularly for evaluating the potential of drugs to induce life-threatening pro-arrhythmias. Between 1988 and 2009, 14 drugs have been removed from the market for this reason [3]. The microelectrode array (MEA) technique is a robust tool for drug screening as it detects the field potentials (FPs) for the entire cell culture. Furthermore, the propagation of the field potential can be examined on an electrode basis. To analyze MEA measurements in detail, we have developed an open-source tool.}, language = {en} } @inproceedings{JungStaatMueller2016, author = {Jung, Alexander and Staat, Manfred and M{\"u}ller, Wolfram}, title = {Effect of wind on flight style optimisation in ski jumping}, series = {15th International Symposium on Computer Simulation in Biomechanics ; July 9th-11th 2015, Edinburgh, UK}, booktitle = {15th International Symposium on Computer Simulation in Biomechanics ; July 9th-11th 2015, Edinburgh, UK}, publisher = {The University of Edinburgh ; Loughborough University}, address = {Edinburgh}, pages = {53 -- 54}, year = {2016}, language = {en} } @article{Gaigall2019, author = {Gaigall, Daniel}, title = {On a new approach to the multi-sample goodness-of-fit problem}, series = {Communications in Statistics - Simulation and Computation}, volume = {53}, journal = {Communications in Statistics - Simulation and Computation}, number = {10}, publisher = {Taylor \& Francis}, address = {London}, issn = {1532-4141}, doi = {10.1080/03610918.2019.1618472}, pages = {2971 -- 2989}, year = {2019}, abstract = {Suppose we have k samples X₁,₁,…,X₁,ₙ₁,…,Xₖ,₁,…,Xₖ,ₙₖ with different sample sizes ₙ₁,…,ₙₖ and unknown underlying distribution functions F₁,…,Fₖ as observations plus k families of distribution functions {G₁(⋅,ϑ);ϑ∈Θ},…,{Gₖ(⋅,ϑ);ϑ∈Θ}, each indexed by elements ϑ from the same parameter set Θ, we consider the new goodness-of-fit problem whether or not (F₁,…,Fₖ) belongs to the parametric family {(G₁(⋅,ϑ),…,Gₖ(⋅,ϑ));ϑ∈Θ}. New test statistics are presented and a parametric bootstrap procedure for the approximation of the unknown null distributions is discussed. Under regularity assumptions, it is proved that the approximation works asymptotically, and the limiting distributions of the test statistics in the null hypothesis case are determined. Simulation studies investigate the quality of the new approach for small and moderate sample sizes. Applications to real-data sets illustrate how the idea can be used for verifying model assumptions.}, language = {en} } @unpublished{GriegerMehrkanoonBialonski2024, author = {Grieger, Niklas and Mehrkanoon, Siamak and Bialonski, Stephan}, title = {Preprint: Data-efficient sleep staging with synthetic time series pretraining}, series = {arXiv}, journal = {arXiv}, pages = {10 Seiten}, year = {2024}, abstract = {Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.}, language = {en} }