@book{27872,
  author       = {{Vennemann, Mario and Eickelmann, Birgit and Labusch, Amelie and Drossel, Kerstin}},
  publisher    = {{Waxmann}},
  title        = {{{ICILS 2018 #Deutschland. Dokumentation der Erhebungsinstrumente der zweiten Computer and Information Literacy Study}}},
  year         = {{2021}},
}

@inbook{27877,
  author       = {{Eickelmann, Birgit and Drossel, Kerstin and Heldt, Melanie}},
  booktitle    = {{Quality in Teacher Education and Professional Development}},
  editor       = {{Chi-Kin Lee, John and Ehmke, Timo}},
  pages        = {{107--124}},
  publisher    = {{Routledge}},
  title        = {{{ICT in teacher education and ICT-related teacher professional development in Germany}}},
  year         = {{2021}},
}

@book{27878,
  author       = {{Heldt, Melanie and Drossel, Kerstin}},
  publisher    = {{Empirische Pädagogik}},
  title        = {{{Typen unterrichtsbezogener Lehrerkooperation und ihr Zusammenhang mit Einstellungen und der Nutzung digitaler Medien}}},
  year         = {{2021}},
}

@article{27881,
  abstract     = {{<jats:title>Zusammenfassung</jats:title><jats:p>Lehrkräftekooperation wird generell eine positive Bedeutung in Bezug auf Schul- und Unterrichtsentwicklung zugeschrieben. Dabei sind empirische Belege für eine positive Wirksamkeit nach wie vor kaum vorhanden, es gibt sogar Befunde zu ‚negativen‘ Konsequenzen von Lehrkräftekooperation. Um diese Widersprüchlichkeit zu klären, wurde in der vorliegenden Arbeit Kooperation nicht als Instrument bzw. als Technik betrachtet, sondern als soziale Praxis verstanden, in der eigenlogisches, kollektiv-implizites Wissen (re)produziert wird (Community of Practice). Parallel dazu wurde ein praxeologisches Kompetenzverständnis (Praxiskompetenz) eingeführt, das wesentlich auf die Praxeologie Pierre Bourdieus zurückgeht und den Zusammenhang zwischen Lehrkräftekooperation als Community of Practice und kollektiv strukturierter, individueller Kompetenz theoretisch verdeutlicht. Die empirischen Befunde, welche mittels der Dokumentarischen Methode generiert wurden, verweisen auf die Bedeutung unterschiedlicher Relationslogiken (Nicht-Passung, Entfaltung, Herausforderung) für das ‚Lernen‘ von oder innerhalb von Praxiskompetenz(en) und damit auch auf die Wichtigkeit einer grundlegend kollektiv gerahmten Perspektive auf Lehrkräftekooperation. Vor diesem Hintergrund ist ein allzu positiver Blick auf Lehrkräftekooperationsprozesse kritisch zu betrachten.</jats:p>}},
  author       = {{Bloh, Thiemo}},
  issn         = {{2190-6890}},
  journal      = {{Zeitschrift für Bildungsforschung}},
  title        = {{{Entwicklung von Praxiskompetenz durch Kooperationsprozesse von Lehrkräften}}},
  doi          = {{10.1007/s35834-021-00328-0}},
  year         = {{2021}},
}

@article{21004,
  abstract     = {{Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}},
  author       = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}},
  issn         = {{0162-8828}},
  journal      = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
  keywords     = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}},
  pages        = {{1--1}},
  title        = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}},
  doi          = {{10.1109/tpami.2021.3051276}},
  year         = {{2021}},
}

@inproceedings{21005,
  abstract     = {{Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering.  LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.}},
  author       = {{Hasnain, Asif and Karl, Holger}},
  booktitle    = {{IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}},
  keywords     = {{Coflow scheduling, Reinforcement learning, Deadlines}},
  location     = {{Vancouver BC Canada}},
  publisher    = {{IEEE Communications Society}},
  title        = {{{Learning Coflow Admissions}}},
  doi          = {{10.1109/INFOCOMWKSHPS51825.2021.9484599}},
  year         = {{2021}},
}

@article{21065,
  abstract     = {{The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions and, consequently, quite different processing pipelines have emerged compared to ASR for close-talk speech. A signal enhancement front-end for dereverberation, source separation and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multi-condition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions.}},
  author       = {{Haeb-Umbach, Reinhold and Heymann, Jahn and Drude, Lukas and Watanabe, Shinji and Delcroix, Marc and Nakatani, Tomohiro}},
  journal      = {{Proceedings of the IEEE}},
  number       = {{2}},
  pages        = {{124--148}},
  title        = {{{Far-Field Automatic Speech Recognition}}},
  doi          = {{10.1109/JPROC.2020.3018668}},
  volume       = {{109}},
  year         = {{2021}},
}

@article{21067,
  abstract     = {{Acoustic waves in plates have proven a viable tool for testing and material characterisation purposes. There are a multitude of options for excitation and detection of theses waves, such as optical and piezoelectric systems. While optical systems, with thermoelastic excitation and interferometric detection, have the benefit of being contactless, they usually require rather complex and expensive experimental setups. Piezoelectric systems are more easily realised but require direct contact with the specimen and usually have a limited bandwidth, especially in case of piezoelectric excitation. In this work, the authors compare the properties of piezoelectric and optical detection methods for broad-band acoustic signals. The shape (e. g. the displacement) of a propagating plate wave is given by its frequency and wave number, allowing to investigate correlations between mode shapes and received signal strengths. This is aided by evaluations in normalised frequency and wavenumber space, facilitating comparisons of different specimens. Further, the authors explore possibilities to utilise the specific properties of the detection methods to determine acoustic material parameters.}},
  author       = {{Claes, Leander and Schmiegel, Hanna and Grünsteidl, Clemens and Johannesmann, Sarah and Webersen, Manuel and Henning, Bernd}},
  issn         = {{2196-7113}},
  journal      = {{tm - Technisches Messen}},
  number       = {{3}},
  pages        = {{147--155}},
  title        = {{{Investigating peculiarities of piezoelectric detection methods for acoustic plate waves in material characterisation applications}}},
  doi          = {{10.1515/teme-2020-0098}},
  volume       = {{88}},
  year         = {{2021}},
}

@article{21082,
  author       = {{Itner, Dominik and Gravenkamp, Hauke and Dreiling, Dmitrij and Feldmann, Nadine and Henning, Bernd}},
  issn         = {{1617-7061}},
  journal      = {{PAMM}},
  title        = {{{Simulation of guided waves in cylinders subject to arbitrary boundary conditions for applications in material characterization}}},
  doi          = {{10.1002/pamm.202000232}},
  year         = {{2021}},
}

@misc{21084,
  author       = {{Werthmann, Julian}},
  title        = {{{Derandomization and Local Graph Problems in the Node-Capacitated Clique}}},
  year         = {{2021}},
}

@article{21092,
  abstract     = {{Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which   are costly but often ineffective because they are canceled due to a timeout.
In this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions.}},
  author       = {{Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}},
  journal      = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
  publisher    = {{IEEE}},
  title        = {{{Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}}},
  year         = {{2021}},
}

@article{21094,
  author       = {{Aldahhak, Hazem and Hogan, Conor and Lindner, Susi and Appelfeller, Stephan and Eisele, Holger and Schmidt, Wolf Gero and Dähne, Mario and Gerstmann, Uwe and Franz, Martin}},
  issn         = {{2469-9950}},
  journal      = {{Physical Review B}},
  title        = {{{Electronic structure of the Si(111)3×3R30°−B surface from theory and photoemission spectroscopy}}},
  doi          = {{10.1103/physrevb.103.035303}},
  year         = {{2021}},
}

@article{21125,
  author       = {{Kismann, Michael and Riedl, Dr. Thomas and Lindner, Prof. Dr. Jörg KN}},
  journal      = {{Materials Science in Semiconductor Processing}},
  title        = {{{Ordered arrays of Si nanopillars with alternating diameters fabricated by nanosphere lithography and metal-assisted chemical etching}}},
  year         = {{2021}},
}

@phdthesis{21183,
  abstract     = {{Die präzise Kenntnis der Eigenschaften verwendeter Materialien hat große Bedeutung für den Entwurf technischer Systeme aller Art, aber auch für die Überwachung solcher Systeme im Betrieb. Für verschiedene physikalische Eigenschaften, Betriebsbedingungen und Materialklassen werden daher geeignete messtechnische Verfahren zur Materialcharakterisierung benötigt. In der vorliegenden Arbeit wird ein Verfahren zur ultraschallbasierten Charakterisierung der mechanischen Eigenschaften von homogenen und faserverstärkten thermoplastischen Polymeren unter Berücksichtigung der Richtungsabhängigkeit vorgestellt. Plattenförmige Probekörper werden dazu mittels Laser-Pulsen hoher Energie breitbandig angeregt und die resultierenden akustischen Lamb-Wellen aufgezeichnet. Auf Basis der dispersiven Eigenschaften der detektierten Wellenleitermoden werden in einem inversen Verfahren die Parameter eines linear-elastischen Materialmodells identifiziert. Darüber hinaus wird ein Verfahren zur vollständigen Charakterisierung der Richtungsabhängigkeit in orthotropen Materialien wie Faserverbundwerkstoffen unter Verwendung eines zweidimensionalen Simulationsmodells vorgestellt. Das Messverfahren wird anhand einer Untersuchungsreihe an künstlich gealterten Polymer- und Faserverbundwerkstoffen verifiziert und die Übertragbarkeit der Ergebnisse auf den quasistatischen Fall betrachtet. Im Vergleich mit den Ergebnissen mechanischer Zugversuche werden die Voraussetzungen und Einschränkungen, insbesondere durch die Annahme eines ideal-elastischen Materialmodells, diskutiert.}},
  author       = {{Webersen, Manuel}},
  publisher    = {{Universitätsbibliothek Paderborn}},
  title        = {{{Zerstörungsfreie Charakterisierung der elastischen Materialeigenschaften thermoplastischer Polymerwerkstoffe mittels Ultraschall}}},
  doi          = {{10.17619/UNIPB/1-1088}},
  year         = {{2021}},
}

@article{21195,
  author       = {{Goelz, Christian and Mora, Karin and Stroehlein, Julia Kristin and Haase, Franziska Katharina and Dellnitz, Michael and Reinsberger, Claus and Vieluf, Solveig}},
  journal      = {{Cognitive Neurodynamics}},
  title        = {{{Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults}}},
  doi          = {{10.1007/s11571-020-09656-9}},
  year         = {{2021}},
}

@misc{21197,
  author       = {{Mengshi, Ma}},
  title        = {{{Self-stabilizing Arrow Protocol on Spanning Trees with a Low Diameter}}},
  year         = {{2021}},
}

@inproceedings{21204,
  author       = {{Kucklick, Jan-Peter and Müller, Oliver}},
  booktitle    = {{ The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial Services}},
  title        = {{{A Comparison of Multi-View Learning Strategies for Satellite Image-based Real Estate Appraisal}}},
  year         = {{2021}},
}

@article{21207,
  abstract     = {{Simple thermal treatment of guanine at temperatures ranging from 600 to 700 °C leads to C1N1 condensates with unprecedented CO2/N2 selectivity when compared to other carbonaceous solid sorbents. Increasing the surface area of the CN condensates in the presence of ZnCl2 salt melts enhances the amount of CO2 adsorbed while preserving the high selectivity values and C1N1 structure. Results indicate that these new materials show a sorption mechanism a step closer to that of natural CO2 caption proteins and based on metal free structural cryptopores.}},
  author       = {{Kossmann, Janina and Piankova, Diana and V. Tarakina, Nadezda and Heske, Julian Joachim and Kühne, Thomas and Schmidt, Johannes and Antonietti, Markus and López-Salas, Nieves}},
  issn         = {{0008-6223}},
  journal      = {{Carbon}},
  keywords     = {{CN, Cryptopores, Carbon dioxide capture}},
  pages        = {{497--505}},
  title        = {{{Guanine condensates as covalent materials and the concept of cryptopores}}},
  doi          = {{https://doi.org/10.1016/j.carbon.2020.10.047}},
  volume       = {{172}},
  year         = {{2021}},
}

@article{21208,
  abstract     = {{<jats:p>Heterogeneous platforms with FPGAs have started to be employed in the High-Performance Computing (HPC) field to improve performance and overall efficiency. These platforms allow the use of specialized hardware to accelerate software applications, but require the software to be adapted in what can be a prolonged and complex process. The main goal of this work is to describe and evaluate mechanisms that can transparently transfer the control flow between CPU and FPGA within the scope of HPC. Combining such a mechanism with transparent software profiling and accelerator configuration could lead to an automatic way of accelerating regular applications. In this work, a mechanism based on the ptrace system call is proposed, and its performance on the Intel Xeon+FPGA platform is evaluated. The feasibility of the proposed approach is demonstrated by a working prototype that performs the transparent control flow transfer of any function call to a matching hardware accelerator. This approach is more general than shared library interposition at the cost of a small time overhead in each accelerator use (about 1.3ms in the prototype implementation).</jats:p>}},
  author       = {{Granhão, Daniel and Canas Ferreira, João Canas}},
  issn         = {{2079-9292}},
  journal      = {{Electronics}},
  keywords     = {{pc2-harp-ressources}},
  title        = {{{Transparent Control Flow Transfer between CPU and Accelerators for HPC}}},
  doi          = {{10.3390/electronics10040406}},
  year         = {{2021}},
}

@article{21242,
  author       = {{Lüttenberg, Hedda and Beverungen, Daniel and Poniatowski, Martin and Kundisch, Dennis and Wünderlich, Nancy}},
  journal      = {{Wirtschaftsinformatik & Management}},
  number       = {{2}},
  pages        = {{120--131}},
  title        = {{{Drei Strategien zur Etablierung digitaler Plattformen in der Industrie}}},
  volume       = {{13}},
  year         = {{2021}},
}