@techreport{49113,
  abstract     = {{In this report we present our system for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 4: Sound Event Detection in Domestic Environments 1 . As in previous editions of the Challenge, we use forward-backward convolutional recurrent neural networks (FBCRNNs) [1, 2] for weakly labeled and semi-supervised sound event detection (SED) and eventually generate strong pseudo labels for weakly labeled and unlabeled data. Then, (tag-conditioned) bidirectional CRNNs (Bi-CRNNs) [1, 2] are trained in a strongly supervised manner as our final SED models. In each of the training stages we use multiple iterations of self-training. Compared to previous editions, we improved our system performance by 1) some tweaks regarding data augmentation, pseudo labeling and inference 2) using weakly labeled AudioSet data [3] for pretraining larger networks and 3) augmenting the DESED data [4] with strongly labeled AudioSet data [5] for finetuning of the networks. Source code is publicly available at https://github.com/fgnt/pb_sed.}},
  author       = {{Ebbers, Janek and Haeb-Umbach, Reinhold}},
  title        = {{{Pre-Training And Self-Training For Sound Event Detection In Domestic Environments}}},
  year         = {{2022}},
}

@inproceedings{33509,
  abstract     = {{In this publication a novel method for far-field prediction from magnetic Huygens box data based on the boundary element method (BEM) is presented. Two examples are considered for the validation of this method. The first example represents an electric dipole so that the obtained calculations can be compared to an analytical solution. As a second example, a printed circuit board is considered and the calculated far-field is compared to a fullwave simulation. In both cases, the calculations for different field integral equations are under comparison, and the results indicate that the presented method performs very well with a combined field integral equation, for the specified problem, when only magnetic Huygens box data is given.}},
  author       = {{Marschalt, Christoph and Schroder, Dominik and Lange, Sven and Hilleringmann, Ulrich and Hedayat, Christian and Kuhn, Harald and Sievers, Denis and Förstner, Jens}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  keywords     = {{Near-Field Scanning, Huygens Box, Boundary Element Method, Method of Moments, tet_topic_hf, tet_enas}},
  location     = {{Grenoble, France}},
  publisher    = {{IEEE}},
  title        = {{{Far-field Calculation from magnetic Huygens Box Data using the Boundary Element Method}}},
  doi          = {{10.1109/ssi56489.2022.9901431}},
  year         = {{2022}},
}

@inproceedings{33848,
  abstract     = {{Impressive progress in neural network-based single-channel speech source
separation has been made in recent years. But those improvements have been
mostly reported on anechoic data, a situation that is hardly met in practice.
Taking the SepFormer as a starting point, which achieves state-of-the-art
performance on anechoic mixtures, we gradually modify it to optimize its
performance on reverberant mixtures. Although this leads to a word error rate
improvement by 7 percentage points compared to the standard SepFormer
implementation, the system ends up with only marginally better performance than
a PIT-BLSTM separation system, that is optimized with rather straightforward
means. This is surprising and at the same time sobering, challenging the
practical usefulness of many improvements reported in recent years for monaural
source separation on nonreverberant data.}},
  author       = {{Cord-Landwehr, Tobias and Boeddeker, Christoph and von Neumann, Thilo and Zorila, Catalin and Doddipatla, Rama and Haeb-Umbach, Reinhold}},
  booktitle    = {{2022 International Workshop on Acoustic Signal Enhancement (IWAENC)}},
  publisher    = {{IEEE}},
  title        = {{{Monaural source separation: From anechoic to reverberant environments}}},
  year         = {{2022}},
}

@inproceedings{33819,
  author       = {{von Neumann, Thilo and Kinoshita, Keisuke and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  publisher    = {{IEEE}},
  title        = {{{SA-SDR: A Novel Loss Function for Separation of Meeting Style Data}}},
  doi          = {{10.1109/icassp43922.2022.9746757}},
  year         = {{2022}},
}

@misc{33816,
  author       = {{Gburrek, Tobias and Boeddeker, Christoph and von Neumann, Thilo and Cord-Landwehr, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  publisher    = {{arXiv}},
  title        = {{{A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network}}},
  doi          = {{10.48550/ARXIV.2205.00944}},
  year         = {{2022}},
}

@inproceedings{33954,
  author       = {{Boeddeker, Christoph and Cord-Landwehr, Tobias and von Neumann, Thilo and Haeb-Umbach, Reinhold}},
  booktitle    = {{Interspeech 2022}},
  publisher    = {{ISCA}},
  title        = {{{An Initialization Scheme for Meeting Separation with Spatial Mixture Models}}},
  doi          = {{10.21437/interspeech.2022-10929}},
  year         = {{2022}},
}

@article{44088,
  abstract     = {{Hole polarons and defect-bound exciton polarons in lithium niobate are investigated by means of density-functional theory, where the localization of the holes is achieved by applying the +U approach to the oxygen 2p orbitals. We find three principal configurations of hole polarons: (i) self-trapped holes localized at displaced regular oxygen atoms and (ii) two other configurations bound to a lithium vacancy either at a threefold coordinated oxygen atom above or at a two-fold coordinated oxygen atom below the defect. The latter is the most stable and is in excellent quantitative agreement with measured g factors from electron paramagnetic resonance. Due to the absence of mid-gap states, none of these hole polarons can explain the broad optical absorption centered between 2.5 and 2.8 eV that is observed in transient absorption spectroscopy, but such states appear if a free electron polaron is trapped at the same lithium vacancy as the bound hole polaron, resulting in an exciton polaron. The dielectric function calculated by solving the Bethe–Salpeter equation indeed yields an optical peak at 2.6 eV in agreement with the two-photon experiments. The coexistence of hole and exciton polarons, which are simultaneously created in optical excitations, thus satisfactorily explains the reported experimental data.}},
  author       = {{Schmidt, Falko and Kozub, Agnieszka L. and Gerstmann, Uwe and Schmidt, Wolf Gero and Schindlmayr, Arno}},
  issn         = {{2073-4352}},
  journal      = {{Crystals}},
  number       = {{11}},
  publisher    = {{MDPI AG}},
  title        = {{{A density-functional theory study of hole and defect-bound exciton polarons in lithium niobate}}},
  doi          = {{10.3390/cryst12111586}},
  volume       = {{12}},
  year         = {{2022}},
}

@article{31937,
  author       = {{Li, Yao and Ma, Xuekai and Hatzopoulos, Zaharias and Savvidis, Pavlos G. and Schumacher, Stefan and Gao, Tingge}},
  issn         = {{2330-4022}},
  journal      = {{ACS Photonics}},
  number       = {{6}},
  pages        = {{2079--2086}},
  publisher    = {{American Chemical Society (ACS)}},
  title        = {{{Switching Off a Microcavity Polariton Condensate near the Exceptional Point}}},
  doi          = {{10.1021/acsphotonics.2c00288}},
  volume       = {{9}},
  year         = {{2022}},
}

@article{37713,
  author       = {{Murzakhanov, Fadis F. and Mamin, Georgy Vladimirovich and Orlinskii, Sergei Borisovich and Gerstmann, Uwe and Schmidt, Wolf Gero and Biktagirov, Timur and Aharonovich, Igor and Gottscholl, Andreas and Sperlich, Andreas and Dyakonov, Vladimir and Soltamov, Victor A.}},
  issn         = {{1530-6984}},
  journal      = {{Nano Letters}},
  keywords     = {{Mechanical Engineering, Condensed Matter Physics, General Materials Science, General Chemistry, Bioengineering}},
  number       = {{7}},
  pages        = {{2718--2724}},
  publisher    = {{American Chemical Society (ACS)}},
  title        = {{{Electron–Nuclear Coherent Coupling and Nuclear Spin Readout through Optically Polarized V<sub>B</sub><sup>–</sup> Spin States in hBN}}},
  doi          = {{10.1021/acs.nanolett.1c04610}},
  volume       = {{22}},
  year         = {{2022}},
}

@inbook{30288,
  abstract     = {{Lithium niobate (LiNbO3), a material frequently used in optical applications, hosts different kinds of polarons that significantly affect many of its physical properties. In this study, a variety of electron polarons, namely free, bound, and bipolarons, are analyzed using first-principles calculations. We perform a full structural optimization based on density-functional theory for selected intrinsic defects with special attention to the role of symmetry-breaking distortions that lower the total energy. The cations hosting the various polarons relax to a different degree, with a larger relaxation corresponding to a larger gap between the defect level and the conduction-band edge. The projected density of states reveals that the polaron states are formerly empty Nb 4d states lowered into the band gap. Optical absorption spectra are derived within the independent-particle approximation, corrected by the GW approximation that yields a wider band gap and by including excitonic effects within the Bethe-Salpeter equation. Comparing the calculated spectra with the density of states, we find that the defect peak observed in the optical absorption stems from transitions between the defect level and a continuum of empty Nb 4d states. Signatures of polarons are further analyzed in the reflectivity and other experimentally measurable optical coefficients.}},
  author       = {{Schmidt, Falko and Kozub, Agnieszka L. and Gerstmann, Uwe and Schmidt, Wolf Gero and Schindlmayr, Arno}},
  booktitle    = {{New Trends in Lithium Niobate: From Bulk to Nanocrystals}},
  editor       = {{Corradi, Gábor and Kovács, László}},
  isbn         = {{978-3-0365-3340-7}},
  pages        = {{231--248}},
  publisher    = {{MDPI}},
  title        = {{{Electron polarons in lithium niobate: Charge localization, lattice deformation, and optical response}}},
  doi          = {{10.3390/books978-3-0365-3339-1}},
  year         = {{2022}},
}

@article{40371,
  abstract     = {{<jats:p>Multimode integrated interferometers have great potential for both spectral engineering and metrological applications. However, the material dispersion of integrated platforms constitutes an obstacle that limits the performance and precision of such interferometers. At the same time, two-colour nonlinear interferometers present an important tool for metrological applications, when measurements in a certain frequency range are difficult. In this manuscript, we theoretically developed and investigated an integrated multimode two-colour SU(1,1) interferometer operating in a supersensitive mode. By ensuring the proper design of the integrated platform, we suppressed the dispersion, thereby significantly increasing the visibility of the interference pattern. The use of a continuous wave pump laser provided the symmetry between the spectral shapes of the signal and idler photons concerning half the pump frequency, despite different photon colours. We demonstrate that such an interferometer overcomes the classical phase sensitivity limit for wide parametric gain ranges, when up to 3×104 photons are generated.</jats:p>}},
  author       = {{Ferreri, Alessandro and Sharapova, Polina R.}},
  issn         = {{2073-8994}},
  journal      = {{Symmetry}},
  keywords     = {{Physics and Astronomy (miscellaneous), General Mathematics, Chemistry (miscellaneous), Computer Science (miscellaneous)}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  title        = {{{Two-Colour Spectrally Multimode Integrated SU(1,1) Interferometer}}},
  doi          = {{10.3390/sym14030552}},
  volume       = {{14}},
  year         = {{2022}},
}

@inproceedings{27365,
  author       = {{Meyer, Marius}},
  booktitle    = {{Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies}},
  title        = {{{Towards Performance Characterization of FPGAs in Context of HPC using OpenCL Benchmarks}}},
  doi          = {{10.1145/3468044.3468058}},
  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}},
}

@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{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{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}},
}

@inproceedings{21442,
  author       = {{Tinkloh, Steffen Rainer and Wu, Tao and Tröster, Thomas and Niendorf, Thomas}},
  keywords     = {{Micromechanics, Fast Fourier Transform (FFT), Reduced Order Modelling, Homogenization}},
  title        = {{{Development of a submodel technique for FFT-based solvers in micromechanical analysis}}},
  year         = {{2021}},
}

@inproceedings{21570,
  author       = {{Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  title        = {{{Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}}},
  year         = {{2021}},
}

@inproceedings{21573,
  author       = {{Heine, Jens and Wecker, Christian and Kenig, Eugeny and Bart, Hans-Jörg}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Extraktion}},
  title        = {{{Stofftransportmessung und -visualisierung am ruhenden und bewegten Einzeltropfen}}},
  year         = {{2021}},
}

@inproceedings{21574,
  author       = {{Wecker, Christian and Schulz, Andreas and Heine, Jens and Bart, Hans-Jörg and Kenig, Eugeny}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Extraktion}},
  title        = {{{Numerische Untersuchung der Marangonikonvektion in Flüssig-Flüssig-Systemen: Von der Tropfenbildung bis zur Tropfeninteraktion}}},
  year         = {{2021}},
}