@article{13274,
  author       = {{Fingerhut, Robin and Vrabec, Jadran}},
  issn         = {{0378-3812}},
  journal      = {{Fluid Phase Equilibria}},
  pages        = {{270--281}},
  title        = {{{Kirkwood-Buff Integration: A Promising Route to Entropic Properties?}}},
  doi          = {{10.1016/j.fluid.2018.12.015}},
  year         = {{2019}},
}

@article{13294,
  author       = {{Cho, F. H. and Peng, Z. and Biktagirov, T. and Gerstmann, Uwe and Takahashi, S.}},
  journal      = {{The Journal of Chemical Physics}},
  number       = {{13}},
  pages        = {{134702}},
  title        = {{{Investigation of Near-Surface Defects of Nanodiamonds by High-Frequency EPR and DFT Calculation}}},
  doi          = {{10.1063/1.5085351}},
  volume       = {{150}},
  year         = {{2019}},
}

@article{13295,
  author       = {{von Bardeleben, Hans Jürgen and Zhou, Shengqiang and Gerstmann, Uwe and Skachkov, Dmitry and Lambrecht, Walter R. L. and Ho, Quoc Duy and Deák, Peter}},
  journal      = {{APL Materials}},
  title        = {{{Proton Irradiation Induced Defects in β-Ga2O3: A Combined EPR and Theory Study}}},
  doi          = {{10.1063/1.5053158}},
  volume       = {{7}},
  year         = {{2019}},
}

@article{13390,
  author       = {{Schulz, Andreas and Wecker, Christian and Kenig, Eugeny}},
  journal      = {{Chemie Ingenieur Technik}},
  title        = {{{Methode zur Erfassung von Stofftransport an fluiden Phasengrenzflächen}}},
  doi          = {{10.1002/cite.201900030}},
  year         = {{2019}},
}

@inproceedings{13393,
  author       = {{Schulz, Andreas and Wecker, Christian and Kenig, Eugeny}},
  location     = {{Frankfurt}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Computational Fluid Dynamics}},
  title        = {{{Ein Finite-Volumen Ansatz für den Stoffübergang an bewegten Phasengrenzflächen}}},
  year         = {{2019}},
}

@inproceedings{13394,
  author       = {{Wecker, Christian and Schulz, Andreas and Heine, Jens and Bart, Hans-Joerg and Kenig, Eugeny}},
  location     = {{Essen}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Wärme- und Stofftransport}},
  title        = {{{Numerische Untersuchungen zum Stofftransport und Fluidmechanik bei der Tropfenbildung}}},
  year         = {{2019}},
}

@inproceedings{13395,
  author       = {{Heine, Jens and Wecker, Christian and Kenig, Eugeny and Bart, Hans-Joerg}},
  location     = {{Rio de Janeiro, Brasilien}},
  publisher    = {{10th International Conference on Multiphase Flow}},
  title        = {{{Visualization of Marangoni Phenomena during Droplet Formation}}},
  year         = {{2019}},
}

@inproceedings{13396,
  author       = {{Heine, Jens and Wecker, Christian and Kenig, Eugeny and Bart, Hans-Joerg}},
  location     = {{Essen}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Wärme- und Stofftransport}},
  title        = {{{In-situ Messung des Stofftransports bei der Tropfenbildung}}},
  year         = {{2019}},
}

@inproceedings{13397,
  author       = {{Wecker, Christian and Schulz, Andreas and Heine, Jens and Bart, Hans-Joerg and Kenig, Eugeny}},
  location     = {{Muttenz, Schweiz}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Extraktion}},
  title        = {{{Stofftransport und Fluidmechanik bei der Tropfenbildung unter Berücksichtigung von Marangonikonvektion mittels CFD}}},
  year         = {{2019}},
}

@inproceedings{13398,
  author       = {{Heine, Jens and Wecker, Christian and Kenig, Eugeny and Bart, Hans-Joerg}},
  location     = {{Muttenz, Schweiz}},
  publisher    = {{Jahrestreffen der ProcessNet-Fachgruppe Extraktion}},
  title        = {{{Stofftransport bei der Tropfenbildung}}},
  year         = {{2019}},
}

@inproceedings{13399,
  author       = {{Zibart, Alexander and Kenig, Eugeny}},
  location     = {{Frankfurt}},
  publisher    = {{ProcessNet-Fachgruppe Computational Fluid Dynamics}},
  title        = {{{Reduktion von parasitären Strömungen in Mehrphasensimulationen durch Verwendung der Height-Function Methode}}},
  year         = {{2019}},
}

@inproceedings{13442,
  author       = {{Manavi, Seyed Alborz and Kenig, Eugeny}},
  booktitle    = {{Computer Aided Chemical Engineering}},
  isbn         = {{9780128186343}},
  issn         = {{1570-7946}},
  publisher    = {{29th European Symposium on Computer Aided Process Engineering}},
  title        = {{{Numerical Simulation of Forced Convection in a Microchannel with Realistic Roughness of 3D Printed Surface}}},
  doi          = {{10.1016/b978-0-12-818634-3.50138-7}},
  year         = {{2019}},
}

@inproceedings{13443,
  abstract     = {{This work considers the problem of control and resource allocation in networked
systems. To this end, we present DIRA a Deep reinforcement learning based Iterative Resource
Allocation algorithm, which is scalable and control-aware. Our algorithm is tailored towards
large-scale problems where control and scheduling need to act jointly to optimize performance.
DIRA can be used to schedule general time-domain optimization based controllers. In the present
work, we focus on control designs based on suitably adapted linear quadratic regulators. We
apply our algorithm to networked systems with correlated fading communication channels. Our
simulations show that DIRA scales well to large scheduling problems.}},
  author       = {{Redder, Adrian and Ramaswamy, Arunselvan and Quevedo, Daniel}},
  booktitle    = {{Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems}},
  keywords     = {{Networked control systems, deep reinforcement learning, large-scale systems, resource scheduling, stochastic control}},
  location     = {{Chicago, USA}},
  title        = {{{Deep reinforcement learning for scheduling in large-scale networked control systems}}},
  year         = {{2019}},
}

@inproceedings{10232,
  abstract     = {{Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with.}},
  author       = {{Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}},
  location     = {{Long Beach, CA, USA}},
  title        = {{{Automating Multi-Label Classification Extending ML-Plan}}},
  year         = {{2019}},
}

@inproceedings{13647,
  author       = {{Claes, Leander and Johannesmann, Sarah and Baumhögger, Elmar and Henning, Bernd}},
  booktitle    = {{2019 International Congress on Ultrasonics}},
  location     = {{Bruges}},
  title        = {{{Quantification of frequency-dependent absorption phenomena}}},
  doi          = {{10.1121/2.0001043}},
  year         = {{2019}},
}

@article{13230,
  abstract     = {{The behavior of alkali atom point defects in polycrystalline CuInSe2 is studied. In this work, three grain boundary models, one coherent twin boundary and two twin boundaries with dislocation cores, are considered. Total energy calculations show that all alkali metals tend to segregate at the grain boundaries. In addition, the segregation of alkali atoms is more pronounced at the grain boundaries with the dislocation cores. The diffusion of alkali metals along and near grain boundaries is studied as well. The results show that the diffusion of alkali atoms in the grain boundary models is faster than within the bulk. In addition, the ion exchange between Na and Rb atoms at the grain boundaries leads to the Rb enrichment at the grain boundaries and the increase of the Na concentration in the bulk. While the effects of Na and Rb point defects on the electronic structure of the grain boundary with the anion-core dislocation are similar, Rb atoms passivate the grain boundary with the cation-core dislocation more effectively than Na. This can explain the further improvement of the solar cell performance after the RbF-postdeposition treatment.}},
  author       = {{ Chugh, Manjusha and Kühne,  Thomas D. and Mirhosseini, Hossein}},
  journal      = {{ACS Applied Materials & Interfaces}},
  number       = {{16}},
  pages        = {{14821−14829}},
  publisher    = {{American Chemical Society}},
  title        = {{{Diffusion of Alkali Metals in Polycrystalline CuInSe2 and Their Role in the Passivation of Grain Boundaries}}},
  doi          = {{10.1021/acsami.9b02158}},
  volume       = {{11}},
  year         = {{2019}},
}

@inproceedings{15794,
  abstract     = {{In this paper we present our audio tagging system for the DCASE 2019 Challenge Task 2. We propose a model consisting of a convolutional front end using log-mel-energies as input features, a recurrent neural network sequence encoder and a fully connected classifier network outputting an activity probability for each of the 80 considered event classes. Due to the recurrent neural network, which encodes a whole sequence into a single vector, our model is able to process sequences of varying lengths. The model is trained with only little manually labeled training data and a larger amount of automatically labeled web data, which hence suffers from label noise. To efficiently train the model with the provided data we use various data augmentation to prevent overfitting and improve generalization. Our best submitted system achieves a label-weighted label-ranking average precision (lwlrap) of 75.5% on the private test set which is an absolute improvement of 21.7% over the baseline. This system scored the second place in the teams ranking of the DCASE 2019 Challenge Task 2 and the fifth place in the Kaggle competition “Freesound Audio Tagging 2019” with more than 400 participants. After the challenge ended we further improved performance to 76.5% lwlrap setting a new state-of-the-art on this dataset.}},
  author       = {{Ebbers, Janek and Haeb-Umbach, Reinhold}},
  booktitle    = {{DCASE2019 Workshop, New York, USA}},
  title        = {{{Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision}}},
  year         = {{2019}},
}

@inproceedings{15796,
  abstract     = {{In this paper we consider human daily activity recognition using an acoustic sensor network (ASN) which consists of nodes distributed in a home environment. Assuming that the ASN is permanently recording, the vast majority of recordings is silence. Therefore, we propose to employ a computationally efficient two-stage sound recognition system, consisting of an initial sound activity detection (SAD) and a subsequent sound event classification (SEC), which is only activated once sound activity has been detected. We show how a low-latency activity detector with high temporal resolution can be trained from weak labels with low temporal resolution. We further demonstrate the advantage of using spatial features for the subsequent event classification task.}},
  author       = {{Ebbers, Janek and Drude, Lukas and Haeb-Umbach, Reinhold and Brendel, Andreas and Kellermann, Walter}},
  booktitle    = {{CAMSAP 2019, Guadeloupe, West Indies}},
  title        = {{{Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks}}},
  year         = {{2019}},
}

@article{16315,
  abstract     = {{<p>The hard X-ray spectroscopy methods XAS, valence-to-core XES and higher solution XANES offer unique insights into organometallic reaction mechanisms.</p>}},
  author       = {{Schoch, Anke and Burkhardt, Lukas and Schoch, Roland and Stührenberg, Kai and Bauer, Matthias}},
  issn         = {{1359-6640}},
  journal      = {{Faraday Discussions}},
  pages        = {{113--132}},
  title        = {{{Hard X-ray spectroscopy: an exhaustive toolbox for mechanistic studies (?)}}},
  doi          = {{10.1039/c9fd00070d}},
  year         = {{2019}},
}

@inproceedings{15249,
  author       = {{Grabo, Matti and Staggenborg, Christoph and Kenig, Eugeny}},
  location     = {{Paderborn}},
  title        = {{{Modellierung und Optimierung von makroverkapselten Latentwärmespeicherelementen}}},
  year         = {{2019}},
}