Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers - von Neumann, Thilo; Kinoshita, Keisuke; Boeddeker, Christoph; Delcroix, Marc; Haeb-Umbach, Reinhold (Interspeech 2021)
Automatic transcription of meetings requires handling of overlapped speech, which calls for continuous speech separation (CSS) systems. The uPIT criterion was proposed for utterance-level separation with neural networks and introduces the constraint that the total number of speakers must not exceed the number of output channels. When processing meeting-like data in a segment-wise manner, i.e., by separating overlapping segments independently and stitching adjacent segments to continuous output streams, this constraint has to be fulfilled for any segment. In this contribution, we show that this constraint can be significantly relaxed. We propose a novel graph-based PIT criterion, which casts the assignment of utterances to output channels in a graph coloring problem. It only requires that the number of concurrently active speakers must not exceed the number of output channels. As a consequence, the system can process an arbitrary number of speakers and arbitrarily long segments and thus can handle more diverse scenarios. Further, the stitching algorithm for obtaining a consistent output order in neighboring segments is of less importance and can even be eliminated completely, not the least reducing the computational effort. Experiments on meeting-style WSJ data show improvements in recognition performance over using the uPIT criterion.
Keywords: Continuous speech separation; automatic speech recognition; overlapped speech; permutation invariant training

Speeding Up Permutation Invariant Training for Source Separation - von Neumann, Thilo; Boeddeker, Christoph; Kinoshita, Keisuke; Delcroix, Marc; Haeb-Umbach, Reinhold (Speech Communication; 14th ITG Conference)

Self-Trained Audio Tagging and Sound Event Detection in Domestic Environments - Ebbers, Janek; Haeb-Umbach, Reinhold (Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021))
In this paper we present our system for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 Challenge Task 4: Sound Event Detection and Separation in Domestic Environments, where it scored the fourth rank. Our presented solution is an advancement of our system used in the previous edition of the task.We use a forward-backward convolutional recurrent neural network (FBCRNN) for tagging and pseudo labeling followed by tag-conditioned sound event detection (SED) models which are trained using strong pseudo labels provided by the FBCRNN. Our advancement over our earlier model is threefold. First, we introduce a strong label loss in the objective of the FBCRNN to take advantage of the strongly labeled synthetic data during training. Second, we perform multiple iterations of self-training for both the FBCRNN and tag-conditioned SED models. Third, while we used only tag-conditioned CNNs as our SED model in the previous edition we here explore sophisticated tag-conditioned SED model architectures, namely, bidirectional CRNNs and bidirectional convolutional transformer neural networks (CTNNs), and combine them. With metric and class specific tuning of median filter lengths for post-processing, our final SED model, consisting of 6 submodels (2 of each architecture), achieves on the public evaluation set poly-phonic sound event detection scores (PSDS) of 0.455 for scenario 1 and 0.684 for scenario as well as a collar-based F1-score of 0.596 outperforming the baselines and our model from the previous edition by far. Source code is publicly available at https://github.com/fgnt/pb_sed.

Adapting Sound Recognition to A New Environment Via Self-Training - Ebbers, Janek; Keyser, Moritz Curt; Haeb-Umbach, Reinhold (Proceedings of the 29th European Signal Processing Conference (EUSIPCO))
Recently, there has been a rising interest in sound recognition via Acoustic Sensor Networks to support applications such as ambient assisted living or environmental habitat monitoring. With state-of-the-art sound recognition being dominated by deep-learning-based approaches, there is a high demand for labeled training data. Despite the availability of large-scale data sets such as Google's AudioSet, acquiring training data matching a certain application environment is still often a problem. In this paper we are concerned with human activity monitoring in a domestic environment using an ASN consisting of multiple nodes each providing multichannel signals. We propose a self-training based domain adaptation approach, which only requires unlabeled data from the target environment. Here, a sound recognition system trained on AudioSet, the teacher, generates pseudo labels for data from the target environment on which a student network is trained. The student can furthermore glean information about the spatial arrangement of sensors and sound sources to further improve classification performance. It is shown that the student significantly improves recognition performance over the pre-trained teacher without relying on labeled data from the environment the system is deployed in.

OpenCL-based FPGA Accelerator for Semi-Global Approximate String Matching Using Diagonal Bit-Vectors - Castells-Rufas, David; Marco-Sola, Santiago; Aguado-Puig, Quim; Espinosa-Morales, Antonio; Moure, Juan Carlos; Alvarez, Lluc; Moreto, Miquel (2021 31st International Conference on Field-Programmable Logic and Applications (FPL))
An FPGA accelerator for the computation of the semi-global Levenshtein distance between a pattern and a reference text is presented. The accelerator provides an important benefit to reduce the execution time of read-mappers used in short-read genomic sequencing. Previous attempts to solve the same problem in FPGA use the Myers algorithm following a column approach to compute the dynamic programming table. We use an approach based on diagonals that allows for some resource savings while maintaining a very high throughput of 1 alignment per clock cycle. The design is implemented in OpenCL and tested on two FPGA accelerators. The maximum performance obtained is 91.5 MPairs/s for 100 × 120 sequences and 47 MPairs/s for 300 × 360 sequences, the highest ever reported for this problem.
Keywords: pc2-harp-ressources

High-Performance Spectral Element Methods on Field-Programmable Gate Arrays : Implementation, Evaluation, and Future Projection - Karp, Martin; Podobas, Artur; Jansson, Niclas; Kenter, Tobias; Plessl, Christian; Schlatter, Philipp; Markidis, Stefano (2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

Algorithm-hardware co-design of a discontinuous Galerkin shallow-water model for a dataflow architecture on FPGA - Kenter, Tobias; Shambhu, Adesh; Faghih-Naini, Sara; Aizinger, Vadym (Proceedings of the Platform for Advanced Scientific Computing Conference (PASC))

Non-adiabatic transitions in the reaction of fluorine with methane - Zhao, Bin; Manthe, Uwe (The Journal of Chemical Physics)
Keywords: pc2-ressources

Hybrid density functional theory benchmark study on lithium manganese oxides - Eckhoff, Marco; Blöchl, Peter E.; Behler, Jörg (Physical Review B)
Keywords: pc2-ressources

Multistable circular currents of polariton condensates trapped in ring potentials - Barkhausen, Franziska; Schumacher, Stefan; Ma, Xuekai (Optics Letters)
Keywords: pc2-ressources

Externally controlled Lotka-Volterra dynamics in a linearly polarized polariton fluid - Pukrop, Matthias; Schumacher, Stefan (Physical Review E)
Keywords: pc2-ressources

Learning Choice Functions via Pareto-Embeddings - Pfannschmidt, Karlson; Hüllermeier, Eyke (Lecture Notes in Computer Science)

Minimizing crane times in pre-marshalling problems - Parreño-Torres, Consuelo; Alvarez-Valdes, Ramon; Ruiz, Rubén; Tierney, Kevin (Transportation Research Part E: Logistics and Transportation Review)
Keywords: pc2-ressources

Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types - Sellmann, Meinolf; Tierney, Kevin (Lecture Notes in Computer Science)
Keywords: pc2-ressources

A genetic algorithm for finding realistic sea routes considering the weather - Kuhlemann, Stefan; Tierney, Kevin (Journal of Heuristics)
Keywords: pc2-ressources

“On-The-Fly” Calculation of the Vibrational Sum-Frequency Generation Spectrum at the Air-Water Interface - Ojha, Deepak; Kühne, Thomas D. (Molecules)
In the present work, we provide an electronic structure based method for the “on-the-fly” determination of vibrational sum frequency generation (v-SFG) spectra. The predictive power of this scheme is demonstrated at the air-water interface. While the instantaneous fluctuations in dipole moment are obtained using the maximally localized Wannier functions, the fluctuations in polarizability are approximated to be proportional to the second moment of Wannier functions. The spectrum henceforth obtained captures the signatures of hydrogen bond stretching, bending, as well as low-frequency librational modes.

Disordered crystals from first principles II: Transport coefficients - Kühne, Thomas; Heske, Julian Joachim; Prodan, Emil (Annals of Physics)
This is the second part of a project on the foundations of first-principle calculations of the electron transport in crystals at finite temperatures, aiming at a predictive first-principles platform that combines ab-initio molecular dynamics (AIMD) and a finite-temperature Kubo-formula with dissipation for thermally disordered crystalline phases. The latter are encoded in an ergodic dynamical system (Ω,G,dP), where Ω is the configuration space of the atomic degrees of freedom, G is the space group acting on Ω and dP is the ergodic Gibbs measure relative to the G-action. We first demonstrate how to pass from the continuum Kohn–Sham theory to a discrete atomic-orbitals based formalism without breaking the covariance of the physical observables w.r.t. (Ω,G,dP). Then we show how to implement the Kubo-formula, investigate its self-averaging property and derive an optimal finite-volume approximation for it. We also describe a numerical innovation that made possible AIMD simulations with longer orbits and elaborate on the details of our simulations. Lastly, we present numerical results on the transport coefficients of crystal silicon at different temperatures.

Insight from energy decomposition analysis on a hydrogen-bond-mediated mechanism for on-water catalysis - Salem, M. Alaraby; Kühne, Thomas D. (Molecular Physics)

Correlating facet orientation, defect-level density and dipole layer formation at the surface of polycrystalline CuInSe2 thin films - Elizabeth, Amala; Conradi, Hauke; K. Sahoo, Sudhir; Kodalle, Tim; A. Kaufmann, Christian; Kühne, Thomas; Mirhosseini, Hossein; Abou-Ras, Daniel; Mönig, Harry (Acta Materialia)
Individual grains of chalcopyrite solar cell absorbers can facet in different crystallographic directions at their surfaces. To gain a deeper understanding of the junction formation in these devices, we correlate variations in the surface facet orientation with the defect electronic properties. We use a combined analytical approach based on scanning tunneling spectroscopy (STS), scanning electron microscopy, and electron back scatter diffraction (EBSD), where we perform these experiments on identical surface areas as small as 2 × 2 µm2 with a lateral resolution well below 50 nm. The topography of the absorber surfaces indicates two main morphological features: micro-faceted, long basalt-like columns and their short nano-faceted terminations. Our STS results reveal that the long columns exhibit spectral signatures typical for the presence of pronounced oxidation-induced surface dipoles in conjunction with an increased density of electronic defect levels. In contrast, the nano-faceted terminations of the basalt-like columns are largely passivated in terms of electronic defect levels within the band gap region. Corresponding crystallographic data based on EBSD experiments show that the surface of the basalt-like columns can be assigned to intrinsically polar facet orientations, while the passivated terminations are assigned to non-polar planes. Ab-initio calculations suggest that the polar surfaces are more prone to oxidation and resulting O-induced defects, in comparison to non-polar planes. Our results emphasize the correlation between morphology, surface facet orientations and surface electronic properties. Furthermore, this work aids in gaining a fundamental understanding of oxidation induced lateral inhomogeneities in view of the p-n junction formation in chalcopyrite thin-film solar cells.
Keywords: Chalcopyrite absorber; Scanning tunneling spectroscopy; Electron backscatter diffraction; Density functional theory; Surface dipole

Nonlinear dielectric properties of random paraelectric-dielectric composites - Myroshnychenko, Viktor; Smirnov, Stanislav; Jose, Pious Mathews Mulavarickal; Brosseau, Christian; Förstner, Jens (Acta Materialia)
The challenge of designing new tunable nonlinear dielectric materials with tailored properties has attracted an increasing amount of interest recently. Herein, we study the effective nonlinear dielectric response of a stochastic paraelectric-dielectric composite consisting of equilibrium distributions of circular and partially penetrable disks (or parallel, infinitely long, identical, partially penetrable, circular cylinders) of a dielectric phase randomly dispersed in a continuous matrix of a paraelectric phase. The random microstructures were generated using the Metropolis Monte Carlo algorithm. The evaluation of the effective permittivity and tunability were carried out by employing either a Landau thermodynamic model or its Johnson’s approximation to describe the field-dependent permittivity of the paraelectric phase and solving continuum-electrostatics equations using finite element calculations. We reveal that the percolation threshold in this composite governs the critical behavior of the effective permittivity and tunability. For microstructures below the percolation threshold, our simulations demonstrate a strong nonlinear behaviour of the field-dependent effective permittivity and very high tunability that increases as a function of dielectric phase concentration. Above the percolation threshold, the effective permittivity shows the tendency to linearization and the tunability dramatically drops down. The highly reduced permittivity and extraordinarily high tunability are obtained for the composites with dielectric impenetrable disks at high concentrations, in which the triggering of the percolation transition is avoided. The reported results cast light on distinct nonlinear behaviour of 2D and 3D stochastic composites and can guide the design of novel composites with the controlled morphology and tailored permittivity and tunability.

Electrochemical N2 Reduction to Ammonia Using Single Au/Fe Atoms Supported on Nitrogen-Doped Porous Carbon - Sahoo, Sudhir K.; Heske, Julian Joachim; Antonietti, Markus; Qin, Qing; Oschatz, Martin; Kühne, Thomas (ACS Applied Energy Materials)
The electrochemical nitrogen reduction reaction (NRR) to ammonia (NH3) is a promising alternative route for an NH3 synthesis at ambient conditions to the conventional high temperature and pressure Haber--Bosch process without the need for hydrogen gas. Single metal ions or atoms are attractive candidates for the catalytic activation of non-reactive nitrogen (N2), and for future targeted improvement of NRR catalysts, it is of utmost importance to get detailed insights into structure-performance relationships and mechanisms of N2 activation in such structures. Here, we report density functional theory studies on the NRR catalyzed by single Au and Fe atoms supported in graphitic C2N materials. Our results show that the metal atoms present in the structure of C2N are the reactive sites, which catalyze the aforesaid reaction by strong adsorption and activation of N2. We further demonstrate that a lower onset electrode potential is required for Fe--C2N than for Au--C2N. Thus, Fe--C2N is theoretically predicted to be a potentially better NRR catalyst at ambient conditions than Au--C2N owing to the larger adsorption energy of N2 molecules. Furthermore, we have experimentally shown that single sites of Au and Fe supported on nitrogen-doped porous carbon are indeed active NRR catalysts. However, in contrast to our theoretical results, the Au-based catalyst performed slightly better with a Faradaic efficiency (FE) of 10.1{%} than the Fe-based catalyst with an FE of 8.4{%} at −0.2 V vs. RHE. The DFT calculations suggest that this difference is due to the competitive hydrogen evolution reaction and higher desorption energy of ammonia.

Comparison of Feature Selection Techniques to Improve Approximate Circuit Synthesis - Chandrakar, Khushboo

Preselection Bandits - Bengs, Viktor; Hüllermeier, Eyke (International Conference on Machine Learning)

Multi-Armed Bandits with Censored Consumption of Resources - Bengs, Viktor; Hüllermeier, Eyke (arXiv:2011.00813)
We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates the actual amount of allocated resources of each learning round as well as the optimality of realizable rewards. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We derive the theoretical lower bound on the cumulative regret and propose a learning algorithm having a regret upper bound that matches the lower bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms.

Investigation of the Surface Equivalence Principle on a Metal Surface for a Near-Field to Far-Field Transformation by the NFS3000 - Lange, Sven; Schroder, Dominik; Hedayat, Christian; Hangmann, Christian; Otto, Thomas; Hilleringmann, Ulrich (2020 International Symposium on Electromagnetic Compatibility - EMC EUROPE)
In this publication, the near-field to far-field transformation using the self-built near-field scanner NFS3000 is examined with regard to its geometry. This device allows to measure electric and magnetic fields in small distances to the DUT (Device under Test) with high geometric precision and high sensitivity. Leading to a fast examination of EMC (Electromagnetic Compatibility) problems, because the electromagnetic properties are better understandable and therefore easier to solve than e.g. measurements in a far-field chamber. In addition, it is possible to extrapolate the near-fields into the far-field and to determine the radiation pattern of antennas and emitting objects. For this purpose, this paper deals with the basis of this transformation, the so-called surface equivalence theorem. This principle is then adapted to the measurement of near-field scanners and implemented accordingly. Due to the non-ideal design of the near-field scanner, the effects on a far-field transformation are finally presented and discussed.
Keywords: Near-Field Scanner; Near-Field to Far-Field Transformation; Directivity; Surface Equivalence Theorem; Huygens’ Box

Far-field prediction combining simulations with near-field measurements for EMI assessment of PCBs - Schröder, Dominik; Lange, Sven; Hangmann, Christian; Hedayat, Christian (Tensorial Analysis of Networks (TAN) Modelling for PCB Signal Integrity and EMC Analysis)
Using near-field (NF) scan data to predict the far-field (FF) behaviour of radiating electronic systems represents a novel method to accompany the whole RF design process. This approach involves so-called Huygens' box as an efficient radiation model inside an electromagnetic (EM) simulation tool and then transforms the scanned NF measured data into the FF. For this, the basic idea of the Huygens'box principle and the NF-to-FF transformation are briefly presented. The NF is measured on the Huygens' box around a device under test using anNF scanner, recording the magnitude and phase of the site-related magnetic and electric components. A comparison between a fullwave simulation and the measurement results shows a good similarity in both the NF and the simulated and transformed FF.Thus, this method is applicable to predict the FF behaviour of any electronic system by measuring the NF. With this knowledge, the RF design can be improved due to allowing a significant reduction of EM compatibility failure at the end of the development flow. In addition, the very efficient FF radiation model can be used for detailed investigations in various environments and the impact of such an equivalent radiation source on other electronic systems can be assessed.
Keywords: Huygens' box; NF-to-FF transformation; efficient FF radiation model; FF behaviour; EMI assessment; PCB; near-field measurements; efficient radiation model; far-field behaviour; RF design process; far-field prediction; Huygens'box principle; fullwave simulation; electronic system radiation; equivalent radiation source; electromagnetic simulation tool; near-field scan data; EM compatibility failure reduction

On the Possibility of Helium Adsorption in Nitrogen Doped Graphitic Materials - Kumar Sahoo, Sudhir; Heske, Julian Joachim; Azadi, Sam; Zhang, Zhenzhe; V Tarakina, Nadezda; Oschatz, Martin; Z. Khaliullin, Rustam; Antonietti, Markus; Kühne, Thomas (Scientific Reports)

Energy transfer within the hydrogen bonding network of water following resonant terahertz excitation - Elgabarty, Hossam; Kampfrath, Tobias; Bonthuis, Douwe Jan; Balos, Vasileios; Kaliannan, Naveen Kumar; Loche, Philip; Netz, Roland R.; Wolf, Martin; K{\, Thomas D.; Sajadi, Mohsen (Science Advances)

Accurate Sampling with Noisy Forces from Approximate Computing - Rengaraj, Varadarajan; Lass, Michael; Plessl, Christian; Kühne, Thomas D. (Computation)

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations - Kühne, Thomas D.; Iannuzzi, Marcella; Del Ben, Mauro; Rybkin, Vladimir V.; Seewald, Patrick; Stein, Frederick; Laino, Teodoro; Khaliullin, Rustam Z.; Schütt, Ole; Schiffmann, Florian; al., et (The Journal of Chemical Physics)

Extreme Algorithm Selection with Dyadic Feature Representation - Tornede, Alexander; Wever, Marcel Dominik; Hüllermeier, Eyke (Discovery Science)

Hybrid Ranking and Regression for Algorithm Selection - Hanselle, Jonas Manuel; Tornede, Alexander; Wever, Marcel Dominik; Hüllermeier, Eyke (KI 2020: Advances in Artificial Intelligence)

AutoML for Predictive Maintenance: One Tool to RUL Them All - Tornede, Tanja; Tornede, Alexander; Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke (Proceedings of the ECMLPKDD 2020)

Light backscattering from large clusters of densely packed irregular particles - Grynko, Yevgen; Shkuratov, Yuriy; Förstner, Jens (Journal of Quantitative Spectroscopy and Radiative Transfer)
We numerically simulate multiple light scattering in discrete disordered media represented by large clusters of irregular non-absorbing particles. The packing density of clusters is 0.5. With such conditions diffuse scattering is significantly reduced and light transport follows propagation channels that are determined by the particle size and topology of the medium. This kind of localization produces coherent backscattering intensity surge and enhanced negative polarization branch if compared to lower density samples.
Keywords: tet_topic_scattering

Ansatz zur effizienteren Auslegung von Hybridbauteilen - Hielscher, Christian; Grenz, Julian; Camberg, Alan Adam; Wingenbach, Nils (ATZ - Automobiltechnische Zeitschrift)

Approach to More Efficient Design of Hybrid Components - Hielscher, Christian; Grenz, Julian; Camberg, Alan Adam; Wingenbach, Nils (ATZ worldwide)

CFD-basierte Untersuchung von Zweiphasenströmungen in strukturierten Packungen - Bertling, René; Hack, Mathias; Ausner, Ilja; Kenig, Eugeny

CFD-Simulationen von Film- und Rinnsalströmungen auf mikrostrukturierten Oberflächen - Bertling, René; Hack, Mathias; Ausner, Ilja; Kenig, Eugeny

Numerical investigation of liquid flow morphology in structured packings - Olenberg, Alexander; Kenig, Eugeny (Chemical Engineering Science)

Numerical investigation of conjugate heat transfer in a pillow-plate heat exchanger - Zibart, A.; Kenig, E.Y. (International Journal of Heat and Mass Transfer)

Erstellung eines CFD-Modells für die Entwicklung eines innovativen Trennapparates zur Stickstoffrückgewinnung aus landwirtschaftlichen Abfällen - Bernemann, Sören Antonius
Keywords: CFD; landwirtschaftliche Abfälle; Volume-of-Fluid

Towards Meta-Algorithm Selection - Tornede, Alexander; Wever, Marcel Dominik; Hüllermeier, Eyke (Workshop MetaLearn 2020 @ NeurIPS 2020)

Light diffraction in slab waveguide lenses simulated with the stepwise angular spectrum method - Ebers, Lena; Hammer, Manfred; Förstner, Jens (Optics Express)
A stepwise angular spectrum method (SASM) for curved interfaces is presented to calculate the wave propagation in planar lens-like integrated optical structures based on photonic slab waveguides. The method is derived and illustrated for an effective 2D setup first and then for 3D slab waveguide lenses. We employ slab waveguides of different thicknesses connected by curved surfaces to realize a lens-like structure. To simulate the wave propagation in 3D including reflection and scattering losses, the stepwise angular spectrum method is combined with full vectorial finite element computations for subproblems with lower complexity. Our SASM results show excellent agreement with rigorous numerical simulations of the full structures with a substantially lower computational effort and can be utilized for the simulation-based design and optimization of complex and large scale setups.
Keywords: tet_topic_waveguides

Impact of Chlorine on the Internal Transition Rates and Excited States of the Thermally Delayed Activated Fluorescence Molecule 3CzClIPN - Streiter, Martin; Fischer, Tillmann G.; Wiebeler, Christian; Reichert, Sebastian; Langenickel, Jörn; Zeitler, Kirsten; Deibel, Carsten (The Journal of Physical Chemistry C)
Keywords: pc2-ressources

Jointly Optimal Dereverberation and Beamforming - Boeddeker, Christoph; Nakatani, Tomohiro; Kinoshita, Keisuke; Haeb-Umbach, Reinhold (ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

Towards a speaker diarization system for the CHiME 2020 dinner party transcription - Boeddeker, Christoph; Cord-Landwehr, Tobias; Heitkaemper, Jens; Zorila, Catalin; Hayakawa, Daichi; Li, Mohan; Liu, Min; Doddipatla, Rama; Haeb-Umbach, Reinhold (Proc. CHiME 2020 Workshop on Speech Processing in Everyday Environments)

Predicting fracture at non-isothermal forming conditions: A temperature dependent extension of the LS-DYNA GISSMO fracture indicator framework - Camberg, Alan Adam; Erhart, Tobias; Tröster, Thomas

Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach - El Mesaoudi-Paul, Adil; Weiß, Dimitri; Bengs, Viktor; Hüllermeier, Eyke; Tierney, Kevin (Learning and Intelligent Optimization. LION 2020.)

Online Preselection with Context Information under the Plackett-Luce Model - El Mesaoudi-Paul, Adil; Bengs, Viktor; Hüllermeier, Eyke (arXiv:2002.04275)
We consider an extension of the contextual multi-armed bandit problem, in which, instead of selecting a single alternative (arm), a learner is supposed to make a preselection in the form of a subset of alternatives. More specifically, in each iteration, the learner is presented a set of arms and a context, both described in terms of feature vectors. The task of the learner is to preselect $k$ of these arms, among which a final choice is made in a second step. In our setup, we assume that each arm has a latent (context-dependent) utility, and that feedback on a preselection is produced according to a Plackett-Luce model. We propose the CPPL algorithm, which is inspired by the well-known UCB algorithm, and evaluate this algorithm on synthetic and real data. In particular, we consider an online algorithm selection scenario, which served as a main motivation of our problem setting. Here, an instance (which defines the context) from a certain problem class (such as SAT) can be solved by different algorithms (the arms), but only $k$ of these algorithms can actually be run.

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis - Tornede, Alexander; Wever, Marcel Dominik; Werner, Stefan; Mohr, Felix; Hüllermeier, Eyke (ACML 2020)
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.

Molecular Mechanism of the pH-Dependent Calcium Affinity in Langerin - Joswig, Jan-O.; Anders, Jennifer; Zhang, Hengxi; Rademacher, Christoph; Keller, Bettina G. (bioRxiv)
Abstract The C-type lectin receptor langerin plays a vital role in the mammalian defense against invading pathogens. Its function hinges on the affinity to its co-factor Ca 2+ which in turn is regulated by the pH. We studied the structural consequences of pro-tonating the allosteric pH-sensor histidine H294 by molecular dynamics simulations (total simulation time: about 120 μs) and Markov models. We discovered a mechanism in which the signal that the pH has dropped is transferred to the Ca 2+ -binding site without transferring the initial proton. Instead, protonation of H294 unlocks a conformation in which a protonated lysine side-chain forms a hydrogen bond with a Ca 2+ -coordinating aspartic acid. This destabilizes Ca 2+ in the binding pocket, which we probed by steered molecular dynamics. After Ca 2+ -release, the proton is likely transferred to the aspartic acid and stabilized by a dyad with a nearby glutamic acid, triggering a conformational transition and thus preventing Ca 2+ -rebinding.
Keywords: pc2-ressources

Bulk viscosity of liquid noble gases - Chatwell, René Spencer; Vrabec, Jadran (The Journal of Chemical Physics)
Keywords: pc2-ressources

The role of clay surfaces in the heterogeneous nucleation of calcite: Molecular dynamics simulations of cluster formation and attachment - Fodor, Melinda A.; Ható, Zoltán; Kristóf, Tamás; Pósfai, Mihály (Chemical Geology)
Keywords: pc2-ressources

Structural Elements Regulating the Photochromicity in a Cyanobacteriochrome - Xu, Xiuling; Port, Astrid; Wiebeler, Christian; Zhao, Kai-Hong; Schapiro, Igor; Gärtner, Wolfgang (Proceedings of the National Academy of Sciences)
The three-dimensional (3D) crystal structures of the GAF3 domain of cyanobacteriochrome Slr1393 ( Synechocystis PCC6803) carrying a phycocyanobilin chromophore could be solved in both 15- Z dark-adapted state, Pr, λ max = 649 nm, and 15- E photoproduct, Pg, λ max = 536 nm (resolution, 1.6 and 1.86 Å, respectively). The structural data allowed identifying the large spectral shift of the Pr-to-Pg conversion as resulting from an out-of-plane rotation of the chromophore’s peripheral rings and an outward movement of a short helix formed from a formerly unstructured loop. In addition, a third structure (2.1-Å resolution) starting from the photoproduct crystals allowed identification of elements that regulate the absorption maxima. In this peculiar form, generated during X-ray exposition, protein and chromophore conformation still resemble the photoproduct state, except for the D-ring already in 15- Z configuration and tilted out of plane akin the dark state. Due to its formation from the photoproduct, it might be considered an early conformational change initiating the parental state-recovering photocycle. The high quality and the distinct features of the three forms allowed for applying quantum-chemical calculations in the framework of multiscale modeling to rationalize the absorption maxima changes. A systematic analysis of the PCB chromophore in the presence and absence of the protein environment showed that the direct electrostatic effect is negligible on the spectral tuning. However, the protein forces the outer pyrrole rings of the chromophore to deviate from coplanarity, which is identified as the dominating factor for the color regulation.

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification - Wever, Marcel Dominik; Tornede, Alexander; Mohr, Felix; Hüllermeier, Eyke
In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.

Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets - Wever, Marcel Dominik; van Rooijen, Lorijn; Hamann, Heiko (Evolutionary Computation)
In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.

Determination of Murnaghan constants of plate-shaped polymers under uniaxial tensile load - Johannesmann, Sarah; Becker, Sebastian; Webersen, Manuel; Henning, Bernd (SMSI 2020 - Measurement Science)

Stofftransportmessung am ruhenden und bewegten Einzeltropfen - Heine, Jens; Wecker, Christian; Kenig, Eugeny; Bart, Hans-Joerg

CFD-basierte Untersuchung stofftransportinduzierter Marangoni-konvektion in Flüssig-Flüssig-Systemen - Wecker, Christian; Schulz, Andreas; Heine, Jens; Bart, Hans-Joerg; Kenig, Eugeny

Deep model predictive flow control with limited sensor data and online learning - Bieker, Katharina; Peitz, Sebastian; Brunton, Steven L.; Kutz, J. Nathan; Dellnitz, Michael (Theoretical and Computational Fluid Dynamics)
The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.

Numerische Untersuchungen des Stofftransports in Flüssig-Flüssig-Systemen unter Berücksichtigung der Marangonikonvektion - Wecker, Christian; Hoppe, Anna; Schulz, Andreas; Heine, Jens; Bart, Hans-Jörg; Kenig, Eugeny

Ein PLIC-basierter Ansatz zur Erfassung des Stoffübergangs an bewegten Phasengrenzflächen - Schulz, Andreas; Wecker, Christian; Kenig, Eugeny

Numerical investigation of the hole-drilling method applied to intrinsic manufactured metal-CFRP hybrids - Tinkloh, Steffen Rainer; Wu, Tao; Tröster, Thomas; Niendorf, Thomas (Proceedings of the 4th International Conference Hybrid 2020 Materials and Structures)

In silico investigation of Cu(In,Ga)Se2-based solar cells - Mirhosseini, Hossein; Kormath Madam Raghupathy, Ramya; Sahoo, Sudhir K.; Wiebeler, Hendrik; Chugh, Manjusha; Kühne, Thomas D. (Physical Chemistry Chemical Physics)
State-of-the-art methods in materials science such as artificial intelligence and data-driven techniques advance the investigation of photovoltaic materials.
Keywords: Physical and Theoretical Chemistry; General Physics and Astronomy

Multi-Armed Bandits with Censored Consumption of Resources - Bengs, Viktor; Hüllermeier, Eyke (arXiv:2011.00813)
We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates the actual amount of allocated resources of each learning round as well as the optimality of realizable rewards. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We derive the theoretical lower bound on the cumulative regret and propose a learning algorithm having a regret upper bound that matches the lower bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms.

Oxidation/reduction cycles and their reversible effect on the dipole formation at CuInSe2 surfaces - Elizabeth, Amala; Sahoo, Sudhir K.; Lockhorn, David; Timmer, Alexander; Aghdassi, Nabi; Zacharias, Helmut; Kühne, Thomas; Siebentritt, Susanne; Mirhosseini, Hossein; Mönig, Harry (Phys. Rev. Materials)
The defect-electronic properties of {112} microfaceted surfaces of epitaxially grown CuInSe2 thin films are investigated by scanning tunneling spectroscopy and photoelectron spectroscopy techniques after various surface treatments. The intrinsic CuInSe2 surface is found to be largely passivated in terms of electronic defect levels in the band-gap region. However, surface oxidation leads to an overall high density of defect levels in conjunction with a considerable net surface dipole, which persists even after oxide removal. Yet, a subsequent annealing under vacuum restores the initial condition. Such oxidation/reduction cycles are reversible for many times providing robust control of the surface and interface properties in these materials. Based on ab initio simulations, a mechanism where oxygen dissociatively adsorbs and subsequently diffuses to a subsurface site is proposed as the initial step of the observed dipole formation. Our results emphasize the relevance of oxidation-induced dipole effects at the thin film surface and provide a comprehensive understanding toward passivation strategies of these surfaces.

In silico investigation of Cu(In,Ga)Se2-based solar cells - Mirhosseini, S. Hossein; Kormath Madam Raghupathy, Ramya; Sahoo, Sudhir K.; Wiebeler, Hendrik; Chugh, Manjusha; Kühne, Thomas (Phys. Chem. Chem. Phys.)
Photovoltaics is one of the most promising and fastest-growing renewable energy technologies. Although the price-performance ratio of solar cells has improved significantly over recent years{,} further systematic investigations are needed to achieve higher performance and lower cost for future solar cells. In conjunction with experiments{,} computer simulations are powerful tools to investigate the thermodynamics and kinetics of solar cells. Over the last few years{,} we have developed and employed advanced computational techniques to gain a better understanding of solar cells based on copper indium gallium selenide (Cu(In{,}Ga)Se2). Furthermore{,} we have utilized state-of-the-art data-driven science and machine learning for the development of photovoltaic materials. In this Perspective{,} we review our results along with a survey of the field.

A High-Rate Two-Dimensional Polyarylimide Covalent Organic Framework Anode for Aqueous Zn-Ion Energy Storage Devices - Yu, Minghao; Chandrasekhar, Naisa; Kormath Madam Raghupathy, Ramya; Ly, Khoa Hoang; Zhang, Haozhe; Dmitrieva, Evgenia; Liang, Chaolun; Lu, Xihong; Kühne, Thomas; Mirhosseini, S. Hossein; Weidinger, Inez M.; Feng, Xinliang (Journal of the American Chemical Society)
Rechargeable aqueous Zn-ion energy storage devices are promising candidates for next-generation energy storage technologies. However, the lack of highly reversible Zn2+-storage anode materials with low potential windows remains a primary concern. Here, we report a two-dimensional polyarylimide covalent organic framework (PI-COF) anode with high-kinetics Zn2+-storage capability. The well-organized pore channels of PI-COF allow the high accessibility of the build-in redox-active carbonyl groups and efficient ion diffusion with a low energy barrier. The constructed PI-COF anode exhibits a specific capacity (332 C g–1 or 92 mAh g–1 at 0.7 A g–1), a high rate capability (79.8% at 7 A g–1), and a long cycle life (85% over 4000 cycles). In situ Raman investigation and first-principle calculations clarify the two-step Zn2+-storage mechanism, in which imide carbonyl groups reversibly form negatively charged enolates. Dendrite-free full Zn-ion devices are fabricated by coupling PI-COF anodes with MnO2 cathodes, delivering excellent energy densities (23.9 ∼ 66.5 Wh kg–1) and supercapacitor-level power densities (133 ∼ 4782 W kg–1). This study demonstrates the feasibility of covalent organic framework as Zn2+-storage anodes and shows a promising prospect for constructing reliable aqueous energy storage devices.

Vibrational dynamics in lead halide hybrid perovskites investigated by Raman spectroscopy - Ibaceta-Jaña, Josefa; Muydinov, Ruslan; Rosado, Pamela; Mirhosseini, Hossein; Chugh, Manjusha; Nazarenko, Olga; Dirin, Dmitry N.; Heinrich, Dirk; Wagner, Markus R.; Kühne, Thomas; Szyszka, Bernd; Kovalenko, Maksym V.; et al. (Phys. Chem. Chem. Phys.)
Lead halide perovskite semiconductors providing record efficiencies of solar cells have usually mixed compositions doped in A- and X-sites to enhance the phase stability. The cubic form of formamidinium (FA) lead iodide reveals excellent opto-electronic properties but transforms at room temperature (RT) into a hexagonal structure which does not effectively absorb visible light. This metastable form and the mechanism of its stabilization by Cs+ and Br− incorporation are poorly characterized and insufficiently understood. We report here the vibrational properties of cubic FAPbI3 investigated by DFT calculations on phonon frequencies and intensities, and micro-Raman spectroscopy. The effects of Cs+ and Br− partial substitution are discussed. We support our results with the study of FAPbBr3 which expands the identification of vibrational modes to the previously unpublished low frequency region (<500 cm−1). Our results show that the incorporation of Cs+ and Br− leads to the coupling of the displacement of the A-site components and weakens the bonds between FA+ and the PbX6 octahedra. We suggest that the enhancement of α-FAPbI3 stability can be a product of the release of tensile stresses in the Pb–X bond, which is reflected in a red-shift of the low frequency region of the Raman spectrum (<200 cm−1).

Revealing the origin of the beneficial effect of cesium in highly efficient Cu(In,Ga)Se2 solar cells - Schöppe, Philipp; Schönherr, Sven; Chugh, Manjusha; Mirhosseini, Hossein; Jackson, Philip; Wuerz, Roland; Ritzer, Maurizio; Johannes, Andreas; Martínez-Criado, Gema; Wisniewski, Wolfgang; Schwarz, Torsten; T. Plass, Christian; et al. (Nano Energy)
The record conversion efficiency of thin-film solar cells based on Cu(In,Ga)Se2 (CIGS) absorbers has exceeded 23%. Such a high performance is currently only attainable by the incorporation of heavy alkali metals like Cs into the absorber through an alkali fluoride post-deposition treatment (PDT). As the effect of the incorporated heavy alkali metals is under discussion, we investigated the local composition and microstructure of high efficiency CIGS solar cells via various high-resolution techniques in a combinatory approach. An accumulation of Cs is clearly detected at the p-n junction along with variations in the local CIGS composition, showing the formation of a beneficial secondary phase with a laterally inhomogeneous distribution. Additionally, Cs accumulations were detected at grain boundaries with a random misorientation of the adjacent grains where a reduced Cu concentration and increased In and Se concentrations are detected. No accumulation was found at Σ3 twin boundaries as well as the grain interior. These experimental findings are in excellent agreement with complementary ab-initio calculations, demonstrating that the grain boundaries are passivated by the presence of Cs. Further, it is unlikely that Cs with its large ionic radius is incorporated into the CIGS grains where it would cause detrimental defects.

Demystifying TasNet: A Dissecting Approach - Heitkaemper, Jens; Jakobeit, Darius; Boeddeker, Christoph; Drude, Lukas; Haeb-Umbach, Reinhold (ICASSP 2020 Virtual Barcelona Spain)
In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradually replacing components of an utterance-level permutation invariant training (u-PIT) based separation system in the frequency domain until the TasNet system is reached, thus blending components of frequency domain approaches with those of time domain approaches. Some of the intermediate variants achieve comparable signal-to-distortion ratio (SDR) gains to TasNet, but retain the advantage of frequency domain processing: compatibility with classic signal processing tools such as frequency-domain beamforming and the human interpretability of the masks. Furthermore, we show that the scale invariant signal-to-distortion ratio (si-SDR) criterion used as loss function in TasNet is related to a logarithmic mean square error criterion and that it is this criterion which contributes most reliable to the performance advantage of TasNet. Finally, we critically assess which gains in a noise-free single channel environment generalize to more realistic reverberant conditions.
Keywords: voice activity detection; speech activity detection; neural network; statistical speech processing

CauseNet: Towards a Causality Graph Extracted from the Web - Heindorf, Stefan; Scholten, Yan; Wachsmuth, Henning; Ngonga Ngomo, Axel-Cyrille; Potthast, Martin (Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020))

Electrically controlled rapid adiabatic passage in a single quantum dot - Mukherjee, Amlan; Widhalm, Alex; Siebert, Dustin; Krehs, Sebastian; Sharma, Nandlal; Thiede, Andreas; Reuter, Dirk; Förstner, Jens; Zrenner, Artur (Applied Physics Letters)
Keywords: tet_topic_qd

Band Alignment at Ga x In 1– x P/Al y In 1– y P Alloy Interfaces from Hybrid Density Functional Theory Calculations - Meier, Lukas; Braun, Christian; Hannappel, Thomas; Schmidt, Wolf Gero (physica status solidi (b))
Keywords: Condensed Matter Physics; Electronic; Optical and Magnetic Materials

Vibrational Raman spectroscopy on adsorbate-induced low-dimensional surface structures - Speiser, Eugen; Esser, Norbert; Halbig, Benedikt; Geurts, Jean; Schmidt, Wolf Gero; Sanna, Simone (Surface Science Reports)

Molecular doping in few-molecule polymer-dopant complexes shows reduced Coulomb binding - Dong, Chuan-Ding; Schumacher, Stefan (Journal of Materials Chemistry C)
Coulomb binding energy is reduced when a few-molecule integer charge transfer complex (ICTC) is formed.
Keywords: Materials Chemistry; General Chemistry

Circular polarization reversal of half-vortex cores in polariton condensates - Pukrop, Matthias; Schumacher, Stefan; Ma, Xuekai (Physical Review B)

Spiraling vortices in exciton-polariton condensates - Ma, Xuekai; Kartashov, Yaroslav V.; Gao, Tingge; Torner, Lluis; Schumacher, Stefan (Physical Review B)

Free and defect-bound (bi)polarons in LiNbO3: Atomic structure and spectroscopic signatures from ab initio calculations - Schmidt, Falko; Kozub, Agnieszka L.; Biktagirov, Timur; Eigner, Christof; Silberhorn, Christine; Schindlmayr, Arno; Schmidt, Wolf Gero; Gerstmann, Uwe (Physical Review Research)
Polarons in dielectric crystals play a crucial role for applications in integrated electronics and optoelectronics. In this work, we use density-functional theory and Green's function methods to explore the microscopic structure and spectroscopic signatures of electron polarons in lithium niobate (LiNbO3). Total-energy calculations and the comparison of calculated electron paramagnetic resonance data with available measurements reveal the formation of bound polarons at Nb_Li antisite defects with a quasi-Jahn-Teller distorted, tilted configuration. The defect-formation energies further indicate that (bi)polarons may form not only at Nb_Li antisites but also at structures where the antisite Nb atom moves into a neighboring empty oxygen octahedron. Based on these structure models, and on the calculated charge-transition levels and potential-energy barriers, we propose two mechanisms for the optical and thermal splitting of bipolarons, which provide a natural explanation for the reported two-path recombination of bipolarons. Optical-response calculations based on the Bethe-Salpeter equation, in combination with available experimental data and new measurements of the optical absorption spectrum, further corroborate the geometries proposed here for free and defect-bound (bi)polarons.

A photoredox catalysed Heck reaction via hole transfer from a Ru(ii)-bis(terpyridine) complex to graphene oxide - Rosenthal, Marta; Lindner, Jörg; Gerstmann, Uwe; Meier, Armin; Schmidt, Wolf Gero; Wilhelm, René (RSC Advances)
A hole transfer from an excited Ru unit towards graphene oxide significantly improved the photocatalytic activity of the complexes.
Keywords: General Chemical Engineering; General Chemistry

Toward Efficient Toxic-Gas Detectors: Exploring Molecular Interactions of Sarin and Dimethyl Methylphosphonate with Metal-Centered Phthalocyanine Structures - Aldahhak, Hazem; Powroźnik, Paulina; Pander, Piotr; Jakubik, Wiesław; Dias, Fernando B.; Schmidt, Wolf Gero; Gerstmann, Uwe; Krzywiecki, Maciej (The Journal of Physical Chemistry C)

Vibration-Driven Self-Doping of Dangling-Bond Wires on Si(553)-Au Surfaces - Braun, Christian; Neufeld, Sergej; Gerstmann, Uwe; Sanna, S.; Plaickner, J.; Speiser, E.; Esser, N.; Schmidt, Wolf Gero (Physical Review Letters)

Spin decontamination for magnetic dipolar coupling calculations: Application to high-spin molecules and solid-state spin qubits - Biktagirov, Timur; Schmidt, Wolf Gero; Gerstmann, Uwe (Physical Review Research)

Spin decontamination for magnetic dipolar coupling calculations: Application to high-spin molecules and solid-state spin qubits - Biktagirov, Timur; Schmidt, Wolf Gero; Gerstmann, Uwe (Physical Review Research)

Tetracene Ultrathin Film Growth on Hydrogen-Passivated Silicon - Niederhausen, Jens; MacQueen, Rowan W.; Lips, Klaus; Aldahhak, Hazem; Schmidt, Wolf Gero; Gerstmann, Uwe (Langmuir)

Photochemical Ring Opening of Oxirane Modeled by Constrained Density Functional Theory - Krenz, Marvin; Gerstmann, Uwe; Schmidt, Wolf Gero (ACS Omega)

Carrier-wave population transfer in semiconductors - Zuo, R; Song, X; Meier, Torsten; Yang, W (Journal of Physics: Conference Series)

Carbon vacancy-related centers in 3C-silicon carbide: Negative-U properties and structural transformation - von Bardeleben, H. J.; Rauls, E.; Gerstmann, Uwe (Physical Review B)

Electron paramagnetic resonance study of ferroelectric phase transition and dynamic effects in a Mn2+ doped [NH4][Zn(HCOO)3] hybrid formate framework - Navickas, Marius; Giriūnas, Laisvydas; Kalendra, Vidmantas; Biktagirov, Timur; Gerstmann, Uwe; Schmidt, Wolf Gero; Mączka, Mirosław; Pöppl, Andreas; Banys, Jūras; Šimėnas, Mantas (Physical Chemistry Chemical Physics)
EPR spectroscopy reveals the universality class and dynamic effects of the [NH4][Zn(HCOO)3] hybrid formate framework.

Spin-orbit driven electrical manipulation of the zero-field splitting in high-spin centers in solids - Biktagirov, Timur; Gerstmann, Uwe (Physical Review Research)
Keywords: General Engineering

Photocatalytic properties of graphene‐supported titania clusters from density‐functional theory - Badalov, Sabuhi; Wilhelm, René; Schmidt, Wolf Gero (Journal of Computational Chemistry)
Density-functional theory calculations of (TiO2)n clusters (n = 1–5) in the gas phase and adsorbed on pristine graphene as well as graphene quantum dots are presented. The cluster adsorption is found to be dominated by van der Waals forces. The electronic structure and in particular the excitation energies of the bare clusters and the TiO2/graphene composites are found to vary largely in dependence on the size of the respective constituents. This holds in particular for the energy and the spatial localization of the highest occupied and lowest unoccupied molecular orbitals. In addition to a substantial gap narrowing, a pronounced separation of photoexcited electrons and holes is predicted in some instances. This is expected to prolong the lifetime of photoexcited carriers. Altogether, TiO2/graphene composites are predicted to be promising photocatalysts with improved electronic and photocatalytic properties compared to bulk TiO2.

k.p-based multiband simulations of non-degenerate two-photon absorption in bulk GaAs - Hannes, Wolf-Rüdiger; Meier, Torsten (Ultrafast Phenomena and Nanophotonics XXIV)

Understanding gray track formation in KTP: Ti^3+ centers studied from first principles - Bocchini, Adriana; Eigner, Christof; Silberhorn, Christine; Schmidt, Wolf Gero; Gerstmann, Uwe (Phys. Rev. Materials)

Modeling and Optimization of Rectangular Latent Heat Storage Elements in an Air-Guided Heat Storage System - Grabo, Matti; Staggenborg, Christoph; Philippi, Kai Alexander; Kenig, Eugeny (Frontiers in Energy Research)

A micromechanical-based finite element simulation of process-induced residual stresses in metal-CFRP-hybrid structures - Tinkloh, Steffen Rainer; Wu, Tao; Tröster, Thomas; Niendorf, Thomas (Composite Structures)

CFD Simulation of Film and Rivulet Flows on Microstructured Surfaces - Bertling, René; Hack, Mathias; Ausner, Ilja; Kenig, Eugeny

Realization of all-optical vortex switching in exciton-polariton condensates - Ma, Xuekai; Berger, B; Aßmann, M; Driben, R; Meier, Torsten; Schneider, C; Höfling, S; Schumacher, Stefan (Nature Communications)

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations - Kühne, Thomas; Iannuzzi, Marcella; Ben, Mauro Del; Rybkin, Vladimir V.; Seewald, Patrick; Stein, Frederick; Laino, Teodoro; Khaliullin, Rustam Z.; Schütt, Ole; Schiffmann, Florian; Golze, Dorothea; Wilhelm, Jan; et al. (The Journal of Chemical Physics)
CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-theart ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CP2K - Lass, Michael; Schade, Robert; Kühne, Thomas; Plessl, Christian (Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC))
Electronic structure calculations based on density-functional theory (DFT) represent a significant part of today's HPC workloads and pose high demands on high-performance computing resources. To perform these quantum-mechanical DFT calculations on complex large-scale systems, so-called linear scaling methods instead of conventional cubic scaling methods are required. In this work, we take up the idea of the submatrix method and apply it to the DFT computations in the software package CP2K. For that purpose, we transform the underlying numeric operations on distributed, large, sparse matrices into computations on local, much smaller and nearly dense matrices. This allows us to exploit the full floating-point performance of modern CPUs and to make use of dedicated accelerator hardware, where performance has been limited by memory bandwidth before. We demonstrate both functionality and performance of our implementation and show how it can be accelerated with GPUs and FPGAs.

Evaluating FPGA Accelerator Performance with a Parameterized OpenCL Adaptation of Selected Benchmarks of the HPCChallenge Benchmark Suite - Meyer, Marius; Kenter, Tobias; Plessl, Christian (2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC))
FPGAs have found increasing adoption in data center applications since a new generation of high-level tools have become available which noticeably reduce development time for FPGA accelerators and still provide high-quality results. There is, however, no high-level benchmark suite available, which specifically enables a comparison of FPGA architectures, programming tools, and libraries for HPC applications. To fill this gap, we have developed an OpenCL-based open-source implementation of the HPCC benchmark suite for Xilinx and Intel FPGAs. This benchmark can serve to analyze the current capabilities of FPGA devices, cards, and development tool flows, track progress over time, and point out specific difficulties for FPGA acceleration in the HPC domain. Additionally, the benchmark documents proven performance optimization patterns. We will continue optimizing and porting the benchmark for new generations of FPGAs and design tools and encourage active participation to create a valuable tool for the community. To fill this gap, we have developed an OpenCL-based open-source implementation of the HPCC benchmark suite for Xilinx and Intel FPGAs. This benchmark can serve to analyze the current capabilities of FPGA devices, cards, and development tool flows, track progress over time, and point out specific difficulties for FPGA acceleration in the HPC domain. Additionally, the benchmark documents proven performance optimization patterns. We will continue optimizing and porting the benchmark for new generations of FPGAs and design tools and encourage active participation to create a valuable tool for the community.
Keywords: FPGA; OpenCL; High Level Synthesis; HPC benchmarking

Accurate Sampling with Noisy Forces from Approximate Computing - Rengaraj, Varadarajan; Lass, Michael; Plessl, Christian; Kühne, Thomas (Computation)
In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever-growing application. A major limitation, however, is that the high efficiency in terms of performance and low power consumption entails the massive usage of low precision computing units. Here, based on the approximate computing paradigm, we present an algorithmic method to compensate for numerical inaccuracies due to low accuracy arithmetic operations rigorously, yet still obtaining exact expectation values using a properly modified Langevin-type equation.

Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments - Heitkaemper, Jens; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (INTERSPEECH 2020 Virtual Shanghai China)
Speech activity detection (SAD), which often rests on the fact that the noise is "more'' stationary than speech, is particularly challenging in non-stationary environments, because the time variance of the acoustic scene makes it difficult to discriminate speech from noise. We propose two approaches to SAD, where one is based on statistical signal processing, while the other utilizes neural networks. The former employs sophisticated signal processing to track the noise and speech energies and is meant to support the case for a resource efficient, unsupervised signal processing approach. The latter introduces a recurrent network layer that operates on short segments of the input speech to do temporal smoothing in the presence of non-stationary noise. The systems are tested on the Fearless Steps challenge database, which consists of the transmission data from the Apollo-11 space mission. The statistical SAD achieves comparable detection performance to earlier proposed neural network based SADs, while the neural network based approach leads to a decision cost function of 1.07% on the evaluation set of the 2020 Fearless Steps Challenge, which sets a new state of the art.
Keywords: voice activity detection; speech activity detection; neural network; statistical speech processing

End-to-End Training of Time Domain Audio Separation and Recognition - von Neumann, Thilo; Kinoshita, Keisuke; Drude, Lukas; Boeddeker, Christoph; Delcroix, Marc; Nakatani, Tomohiro; Haeb-Umbach, Reinhold (ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multispeaker speech recognition. However, up until now, state-of-theart neural network–based time domain source separation has not yet been combined with E2E speech recognition. We here demonstrate how to combine a separation module based on a Convolutional Time domain Audio Separation Network (Conv-TasNet) with an E2E speech recognizer and how to train such a model jointly by distributing it over multiple GPUs or by approximating truncated back-propagation for the convolutional front-end. To put this work into perspective and illustrate the complexity of the design space, we provide a compact overview of single-channel multi-speaker recognition systems. Our experiments show a word error rate of 11.0% on WSJ0-2mix and indicate that our joint time domain model can yield substantial improvements over cascade DNN-HMM and monolithic E2E frequency domain systems proposed so far.

Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source Counting, Separation and ASR - von Neumann, Thilo; Boeddeker, Christoph; Drude, Lukas; Kinoshita, Keisuke; Delcroix, Marc; Nakatani, Tomohiro; Haeb-Umbach, Reinhold (Proc. Interspeech 2020)
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown. To cope with this, we extend an iterative speech extraction system with mechanisms to count the number of sources and combine it with a single-talker speech recognizer to form the first end-to-end multi-talker automatic speech recognition system for an unknown number of active speakers. Our experiments show very promising performance in counting accuracy, source separation and speech recognition on simulated clean mixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a new state-of-the-art word error rate on the WSJ0-2mix database. Furthermore, our system generalizes well to a larger number of speakers than it ever saw during training, as shown in experiments with the WSJ0-4mix database.

Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection - Ebbers, Janek; Haeb-Umbach, Reinhold (Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020))
In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domestic environments. We introduce two new models: the forward-backward convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers sharing the same CNN for preprocessing. With one RNN processing a recording in forward direction and the other in backward direction, the two networks are trained to jointly predict audio tags, i.e., weak labels, at each time step within a recording, given that at each time step they have jointly processed the whole recording. The proposed training encourages the classifiers to tag events as soon as possible. Therefore, after training, the networks can be applied to shorter audio segments of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned CNN to complement SED. It is trained to predict strong labels while using (predicted) tags, i.e., weak labels, as additional input. For training pseudo strong labels from a FBCRNN ensemble are used. The presented system scored the fourth and third place in the systems and teams rankings, respectively. Subsequent improvements allow our system to even outperform the challenge baseline and winner systems in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source code is publicly available at https://github.com/fgnt/pb_sed.

Electronic Structure of the Hieber Anion [Fe(CO)3(NO)]− Revisited by X-ray Emission and Absorption Spectroscopy - Burkhardt, Lukas; Vukadinovic, Yannik; Nowakowski, Michał; Kalinko, Aleksandr; Rudolph, Julian; Carlsson, Per-Anders; Jacob, Christoph R.; Bauer, Matthias (Inorganic Chemistry)

Molecular Doping of PCPDT–BT Copolymers: Comparison of Molecular Complexes with and without Integer Charge Transfer - Dong, Chuan-Ding; Schumacher, Stefan (The Journal of Physical Chemistry C)
Keywords: pc2-ressources

Controllable high-speed polariton waves in a PT-symmetric lattice - Ma, Xuekai; Kartashov, Yaroslav Y; Gao, Tingge; Schumacher, Stefan (New Journal of Physics)
Keywords: pc2-ressources

Learning Choice Functions: Concepts and Architectures - Pfannschmidt, Karlson; Gupta, Pritha; Hüllermeier, Eyke (arXiv:1901.10860)
We study the problem of learning choice functions, which play an important role in various domains of application, most notably in the field of economics. Formally, a choice function is a mapping from sets to sets: Given a set of choice alternatives as input, a choice function identifies a subset of most preferred elements. Learning choice functions from suitable training data comes with a number of challenges. For example, the sets provided as input and the subsets produced as output can be of any size. Moreover, since the order in which alternatives are presented is irrelevant, a choice function should be symmetric. Perhaps most importantly, choice functions are naturally context-dependent, in the sense that the preference in favor of an alternative may depend on what other options are available. We formalize the problem of learning choice functions and present two general approaches based on two representations of context-dependent utility functions. Both approaches are instantiated by means of appropriate neural network architectures, and their performance is demonstrated on suitable benchmark tasks.

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation - Witschen, Linus Matthias; Wiersema, Tobias; Ghasemzadeh Mohammadi, Hassan; Awais, Muhammad; Platzner, Marco (Microelectronics Reliability)
Existing approaches and tools for the generation of approximate circuits often lack generality and are restricted to certain circuit types, approximation techniques, and quality assurance methods. Moreover, only few tools are publicly available. This hinders the development and evaluation of new techniques for approximating circuits and their comparison to previous approaches. In this paper, we ﬁrst analyze and classify related approaches and then present CIRCA, our ﬂexible framework for search-based approximate circuit generation. CIRCA is developed with a focus on modularity and extensibility. We present the architecture of CIRCA with its clear separation into stages and functional blocks, report on the current prototype, and show initial experiments.
Keywords: Approximate Computing; Framework; Pareto Front; Accuracy

Inductive Locating Method to Locate Miniaturized Wireless Sensors within Inhomogeneous Dielectrics - Lange, Sven; Schröder, Dominik; Hedayat, Christian; Otto, Thomas; Hilleringmann, Ulrich (2019 17th IEEE International New Circuits and Systems Conference (NEWCAS))
For the measurement of process data in bioreactors, very small wireless sensors are currently under development to replace the conventional rod probes. The so-called Sens-o-Spheres measure the temperature and in future the oxygen content and the pH of fluids. In order to evaluate the distribution of the measured values within the process, it is necessary to locate the wireless sensors. Because of the small size of the sphere (diameter 8 mm), inhomogeneous ambient media and the size of the reactor (less than 2 m), an inductive locating by magnetic fields with a frequency of f = 13.56 MHz is necessary. Since the behaviour of the magnetic field is very different from that of the electromagnetic wave, new locating methods are required, which are presented in this paper.
Keywords: oxygen content; inhomogeneous ambient media; magnetic field; inductive locating method; miniaturized wireless sensors; inhomogeneous dielectrics; conventional rod probes; Sens-o-Spheres measure; frequency 13.56 MHz

SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition - Drude, Lukas; Heitkaemper, Jens; Boeddeker, Christoph; Haeb-Umbach, Reinhold (ArXiv e-prints)
We present a multi-channel database of overlapping speech for training, evaluation, and detailed analysis of source separation and extraction algorithms: SMS-WSJ -- Spatialized Multi-Speaker Wall Street Journal. It consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases we consider all WSJ0+1 utterances and take care of strictly separating the speaker sets present in the training, validation and test sets. When spatializing the data we ensure a high degree of randomness w.r.t. room size, array center and rotation, as well as speaker position. Furthermore, this paper offers a critical assessment of recently proposed measures of source separation performance. Alongside the code to generate the database we provide a source separation baseline and a Kaldi recipe with competitive word error rates to provide common ground for evaluation.

Numerische Untersuchung des Eisbildungsprozesses auf der welligen Oberfläche von Kissenplatten - Zibart, Alexander; Kenig, Eugeny

Divide and Compact - Stochastic Space Compaction for Faster-than-At-Speed Test - Sprenger, Alexander; Hellebrand, Sybille (Journal of Circuits, Systems and Computers)

Towards Automated Machine Learning for Multi-Label Classification - Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke; Hetzer, Alexander

Characterization of the linear-acoustic material behavior of fiber-reinforced composites using lamb waves - Johannesmann, Sarah; Webersen, Manuel; Düchting, Julia; Claes, Leander; Henning, Bernd (45th Annual Review of Progress in Quantitative Nondestructive Evaluation)

A holistic approach to the lightweight design of tailored structural components using the example of a hybrid A-pillar - Camberg, Alan Adam; Hielscher, Christian (Aachen Body Engineering Days 2019)

Challenges in fracture modeling under non-isothermal forming conditions using the example of a new forming process for aluminum blanks - Camberg, Alan Adam; Tröster, Thomas (26. Sächsische Fachtagung Umformtechnik)

A micromechanical based finite element simulation of process induced residual stresses in metal-CFRP-hybrid structures - Tinkloh, Steffen Rainer; Wu, Tao; Tröster, Thomas; Niendorf, Thomas

Jump Search: A Fast Technique for the Synthesis of Approximate Circuits - Witschen, Linus Matthias; Ghasemzadeh Mohammadi, Hassan; Artmann, Matthias; Platzner, Marco (Fourth Workshop on Approximate Computing (AxC 2019))
State-of-the-art frameworks for generating approximate circuits usually rely on information gained through circuit synthesis and/or verification to explore the search space and to find an optimal solution. Throughout the process, a large number of circuits may be subject to processing, leading to considerable runtimes. In this work, we propose a search which takes error bounds and pre-computed impact factors into account to reduce the number of invoked synthesis and verification processes. In our experimental results, we achieved speed-ups of up to 76x while area savings remain comparable to the reference search method, simulated annealing.
Keywords: Approximate computing; parameter selection; search space exploration; verification; circuit synthesis

Evaporation sampled by stationary molecular dynamics simulation - Heinen, Matthias; Vrabec, Jadran (The Journal of Chemical Physics)
Keywords: pc2-ressources

Thermodynamic factor of quaternary mixtures from Kirkwood–Buff integration - Fingerhut, Robin; Herres, Gerhard; Vrabec, Jadran (Molecular Physics)
Keywords: pc2-ressources

Controlling the Electronic Contact at the Terpyridine/Metal Interface - Mennicken, Max; Peter, Sophia Katharina; Kaulen, Corinna; Simon, Ulrich; Karthäuser, Silvia (The Journal of Physical Chemistry C)
Keywords: pc2-ressources

QM/MM Benchmarking of Cyanobacteriochrome Slr1393g3 Absorption Spectra - Wiebeler, Christian; Schapiro, Igor (Molecules)
Cyanobacteriochromes are compact and spectrally diverse photoreceptor proteins that are promising candidates for biotechnological applications. Computational studies can contribute to an understanding at a molecular level of their wide spectral tuning and diversity. In this contribution, we benchmark methods to model a 110 nm shift in the UV/Vis absorption spectrum from a red- to a green-absorbing form of the cyanobacteriochrome Slr1393g3. Based on an assessment of semiempirical methods to describe the chromophore geometries of both forms in vacuo, we find that DFTB2+D leads to structures that are the closest to the reference method. The benchmark of the excited state calculations is based on snapshots from quantum mechanics/molecular mechanics molecular dynamics simulations. In our case, the methods RI-ADC(2) and sTD-DFT based on CAM-B3LYP ground state calculations perform the best, whereas no functional can be recommended to simulate the absorption spectra of both forms with time-dependent density functional theory. Furthermore, the difference in absorption for the lowest energy absorption maxima of both forms can already be modelled with optimized structures, but sampling is required to improve the shape of the absorption bands of both forms, in particular for the second band. This benchmark study can guide further computational studies, as it assesses essential components of a protocol to model the spectral tuning of both cyanobacteriochromes and the related phytochromes.
Keywords: pc2-ressources

Jump Search: A Fast Technique for the Synthesis of Approximate Circuits - Witschen, Linus Matthias; Ghasemzadeh Mohammadi, Hassan; Artmann, Matthias; Platzner, Marco (Proceedings of the 2019 on Great Lakes Symposium on VLSI - GLSVLSI '19)
State-of-the-art frameworks for generating approximate circuits automatically explore the search space in an iterative process - often greedily. Synthesis and verification processes are invoked in each iteration to evaluate the found solutions and to guide the search algorithm. As a result, a large number of approximate circuits is subjected to analysis - leading to long runtimes - but only a few approximate circuits might form an acceptable solution. In this paper, we present our Jump Search (JS) method which seeks to reduce the runtime of an approximation process by reducing the number of expensive synthesis and verification steps. To reduce the runtime, JS computes impact factors for each approximation candidate in the circuit to create a selection of approximate circuits without invoking synthesis or verification processes. We denote the selection as path from which JS determines the final solution. In our experimental results, JS achieved speed-ups of up to 57x while area savings remain comparable to the reference search method, Simulated Annealing.
Keywords: Approximate computing; design automation; parameter selection; circuit synthesis

Unsupervised training of neural mask-based beamforming - Drude, Lukas; Heymann, Jahn; Haeb-Umbach, Reinhold (INTERSPEECH 2019, Graz, Austria)
We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood criterion derived from a spatial mixture model of the observations. It is trained from scratch without requiring any parallel data consisting of degraded input and clean training targets. Thus, training can be carried out on real recordings of noisy speech rather than simulated ones. In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely. We demonstrate the effectiveness of our approach by speech recognition experiments on two different datasets: one mainly deteriorated by noise (CHiME 4) and one by reverberation (REVERB). The results show that the performance of the proposed system is on par with a supervised system using oracle target masks for training and with a system trained using a model-based teacher.

Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation - Drude, Lukas; Hasenklever, Daniel; Haeb-Umbach, Reinhold (ICASSP 2019, Brighton, UK)
We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26% over the unsupervised teacher.

Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR - Heymann, Jahn; Drude, Lukas; Haeb-Umbach, Reinhold; Kinoshita, Keisuke; Nakatani, Tomohiro (ICASSP 2019, Brighton, UK)
Signal dereverberation using the Weighted Prediction Error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. First proposed as an iterative algorithm, follow-up works have reformulated it as a recursive least squares algorithm and therefore enabled its use in online applications. For this algorithm, the estimation of the power spectral density (PSD) of the anechoic signal plays an important role and strongly influences its performance. Recently, we showed that using a neural network PSD estimator leads to improved performance for online automatic speech recognition. This, however, comes at a price. To train the network, we require parallel data, i.e., utterances simultaneously available in clean and reverberated form. Here we propose to overcome this limitation by training the network jointly with the acoustic model of the speech recognizer. To be specific, the gradients computed from the cross-entropy loss between the target senone sequence and the acoustic model network output is backpropagated through the complex-valued dereverberation filter estimation to the neural network for PSD estimation. Evaluation on two databases demonstrates improved performance for on-line processing scenarios while imposing fewer requirements on the available training data and thus widening the range of applications.

Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation - Drude, Lukas; Haeb-Umbach, Reinhold (IEEE Journal of Selected Topics in Signal Processing)
We formulate a generic framework for blind source separation (BSS), which allows integrating data-driven spectro-temporal methods, such as deep clustering and deep attractor networks, with physically motivated probabilistic spatial methods, such as complex angular central Gaussian mixture models. The integrated model exploits the complementary strengths of the two approaches to BSS: the strong modeling power of neural networks, which, however, is based on supervised learning, and the ease of unsupervised learning of the spatial mixture models whose few parameters can be estimated on as little as a single segment of a real mixture of speech. Experiments are carried out on both artificially mixed speech and true recordings of speech mixtures. The experiments verify that the integrated models consistently outperform the individual components. We further extend the models to cope with noisy, reverberant speech and introduce a cross-domain teacher–student training where the mixture model serves as the teacher to provide training targets for the student neural network.

Time-dependent vibrational sum-frequency generation spectroscopy of the air-water interface - Ojha, Deepak; Kaliannan, Naveen Kumar; Kühne, Thomas D. (Communications Chemistry)

Unconventional phase III of high-pressure solid hydrogen - Azadi, Sam; Kühne, Thomas D. (Physical Review B)

Vibrational Properties of RbInSe2: Raman Scattering Spectroscopy and First-Principle Calculations - Guc, Maxim; Kodalle, Tim; Kormath Madam Raghupathy, Ramya; Mirhosseini, Hossein; Kühne, Thomas D.; Becerril-Romero, Ignacio; Pérez-Rodríguez, Alejandro; Kaufmann, Christian A.; Izquierdo-Roca, Victor (The Journal of Physical Chemistry C)

Improving CTC Using Stimulated Learning for Sequence Modeling - Heymann, Jahn; Khe Chai Sim, Bo Li (ICASSP 2019, Brighton, UK)
Connectionist temporal classification (CTC) is a sequence-level loss that has been successfully applied to train recurrent neural network (RNN) models for automatic speech recognition. However, one major weakness of CTC is the conditional independence assumption that makes it difficult for the model to learn label dependencies. In this paper, we propose stimulated CTC, which uses stimulated learning to help CTC models learn label dependencies implicitly by using an auxiliary RNN to generate the appropriate stimuli. This stimuli comes in the form of an additional stimulation loss term which encourages the model to learn said label dependencies. The auxiliary network is only used during training and the inference model has the same structure as a standard CTC model. The proposed stimulated CTC model achieves about 35% relative character error rate improvements on a synthetic gesture keyboard recognition task and over 30% relative word error rate improvements on the Librispeech automatic speech recognition tasks over a baseline model trained with CTC only.

An Investigation Into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party Transcription - Zorila, Catalin; Boeddeker, Christoph; Doddipatla, Rama; Haeb-Umbach, Reinhold (ASRU 2019, Sentosa, Singapore)
Despite the strong modeling power of neural network acoustic models, speech enhancement has been shown to deliver additional word error rate improvements if multi-channel data is available. However, there has been a longstanding debate whether enhancement should also be carried out on the ASR training data. In an extensive experimental evaluation on the acoustically very challenging CHiME-5 dinner party data we show that: (i) cleaning up the training data can lead to substantial error rate reductions, and (ii) enhancement in training is advisable as long as enhancement in test is at least as strong as in training. This approach stands in contrast and delivers larger gains than the common strategy reported in the literature to augment the training database with additional artificially degraded speech. Together with an acoustic model topology consisting of initial CNN layers followed by factorized TDNN layers we achieve with 41.6% and 43.2% WER on the DEV and EVAL test sets, respectively, a new single-system state-of-the-art result on the CHiME-5 data. This is a 8% relative improvement compared to the best word error rate published so far for a speech recognizer without system combination.

Predicting plasticity and fracture of severe pre-strained EN AW-5182 by Yld2000 yield locus and Hosford-Coulomb fracture model in sheet forming applications - Camberg, Alan Adam; Tröster, Thomas; Bohner, F.; Tölle, J. (IOP Conference Series: Materials Science and Engineering)

Ranking on Very Large Knowledge Graphs - Desouki, Abdelmoneim Amer; Röder, Michael; Ngonga Ngomo, Axel-Cyrille (Proceedings of the 30th ACM Conference on Hypertext and Social Media - HT '19)
Ranking plays a central role in a large number of applications driven by RDF knowledge graphs. Over the last years, many popular RDF knowledge graphs have grown so large that rankings for the facts they contain cannot be computed directly using the currently common 64-bit platforms. In this paper, we tackle two problems: Computing ranks on such large knowledge bases efficiently and incrementally. First, we present D-HARE, a distributed approach for computing ranks on very large knowledge graphs. D-HARE assumes the random surfer model and relies on data partitioning to compute matrix multiplications and transpositions on disk for matrices of arbitrary size. Moreover, the data partitioning underlying D-HARE allows the execution of most of its steps in parallel. As very large knowledge graphs are often updated periodically, we tackle the incremental computation of ranks on large knowledge bases as a second problem. We address this problem by presenting I-HARE, an approximation technique for calculating the overall ranking scores of a knowledge without the need to recalculate the ranking from scratch at each new revision. We evaluate our approaches by calculating ranks on the 3 × 10^9 and 2.4 × 10^9 triples from Wikidata resp. LinkedGeoData. Our evaluation demonstrates that D-HARE is the first holistic approach for computing ranks on very large RDF knowledge graphs. In addition, our incremental approach achieves a root mean squared error of less than 10E−7 in the best case. Both D-HARE and I-HARE are open-source and are available at: https://github.com/dice-group/incrementalHARE.
Keywords: Knowledge Graphs; Ranking; RDF

A Study on Online Source Extraction in the Presence of Changing Speaker Positions - Heitkaemper, Jens; Feher, Thomas; Freitag, Michael; Haeb-Umbach, Reinhold (International Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia)
Multi-talker speech and moving speakers still pose a significant challenge to automatic speech recognition systems. Assuming an enrollment utterance of the target speakeris available, the so-called SpeakerBeam concept has been recently proposed to extract the target speaker from a speech mixture. If multi-channel input is available, spatial properties of the speaker can be exploited to support the source extraction. In this contribution we investigate different approaches to exploit such spatial information. In particular, we are interested in the question, how useful this information is if the target speaker changes his/her position. To this end, we present a SpeakerBeam-based source extraction network that is adapted to work on moving speakers by recursively updating the beamformer coefficients. Experimental results are presented on two data sets, one with articially created room impulse responses, and one with real room impulse responses and noise recorded in a conference room. Interestingly, spatial features turn out to be advantageous even if the speaker position changes.

Multi-Channel Block-Online Source Extraction based on Utterance Adaptation - Martin-Donas, Juan M.; Heitkaemper, Jens; Haeb-Umbach, Reinhold; Gomez, Angel M.; Peinado, Antonio M. (INTERSPEECH 2019, Graz, Austria)
This paper deals with multi-channel speech recognition in scenarios with multiple speakers. Recently, the spectral characteristics of a target speaker, extracted from an adaptation utterance, have been used to guide a neural network mask estimator to focus on that speaker. In this work we present two variants of speakeraware neural networks, which exploit both spectral and spatial information to allow better discrimination between target and interfering speakers. Thus, we introduce either a spatial preprocessing prior to the mask estimation or a spatial plus spectral speaker characterization block whose output is directly fed into the neural mask estimator. The target speaker’s spectral and spatial signature is extracted from an adaptation utterance recorded at the beginning of a session. We further adapt the architecture for low-latency processing by means of block-online beamforming that recursively updates the signal statistics. Experimental results show that the additional spatial information clearly improves source extraction, in particular in the same-gender case, and that our proposal achieves state-of-the-art performance in terms of distortion reduction and recognition accuracy.

Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR - Kanda, Naoyuki; Boeddeker, Christoph; Heitkaemper, Jens; Fujita, Yusuke; Horiguchi, Shota; Haeb-Umbach, Reinhold (INTERSPEECH 2019, Graz, Austria)
In this paper, we present Hitachi and Paderborn University’s joint effort for automatic speech recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for dinner party recordings obtained by multiple microphone arrays are (1) heavy speech overlaps, (2) severe noise and reverberation, (3) very natural onversational content, and possibly (4) insufficient training data. As an example of a dinner party scenario, we have chosen the data presented during the CHiME-5 speech recognition challenge, where the baseline ASR had a 73.3% word error rate (WER), and even the best performing system at the CHiME-5 challenge had a 46.1% WER. We extensively investigated a combination of the guided source separation-based speech enhancement technique and an already proposed strong ASR backend and found that a tight combination of these techniques provided substantial accuracy improvements. Our final system achieved WERs of 39.94% and 41.64% for the development and evaluation data, respectively, both of which are the best published results for the dataset. We also investigated with additional training data on the official small data in the CHiME-5 corpus to assess the intrinsic difficulty of this ASR task.

Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking - Tornede, Alexander; Wever, Marcel Dominik; Hüllermeier, Eyke (Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019)

Fracture prediction of additively manufactured AlSi10Mg materials - Akbulut Irmak, Emine Fulya; Tröster, Thomas (Procedia Structural Integrity)

Modeling the Energy Absorption Characteristics of Wood Crash Elements - Akbulut Irmak, Emine Fulya; Hanses, Julius; Schweizer, Swetlana; Tröster, Thomas

Evolutionary algorithm for the design of passive electric matching networks for ultrasonic transducers - Hetkämper, Tim; Claes, Leander; Henning, Bernd (2019 International Congress on Ultrasonics)

Experimental and Theoretical High Energy Resolution Hard X-ray Absorption and Emission Spectroscopy on Biomimetic Cu2S2 Complexes - Müller, Patrick; Neuba, Adam; Flörke, Ulrich; Henkel, Gerald; Kühne, Thomas D.; Bauer, Matthias (The Journal of Physical Chemistry A)

Probing Iron-Ligand interactions by combining modern high-resolution hard X-ray spectroscopy and density functional theory : a powerful methodology in cases where conventional methods fail / vorgelegt von Lukas Burkhardt ; [Prof. Dr. Matthias Bauer (Erstgutachter), Prof. Dr. Thomas Kühne (Zweitgutachter), Jun. Prof. Dr. Stephan Hohloch (Drittprüfer)] - Burkhardt, Lukas

Experimental and theoretical (high energy resolution) X-ray absorption and emission spectroscopy / vorgelegt von Patrick Müller ; [Promotionskommission: Prof. Dr.-Ing. Hans-Joachim Warnecke, Vorsitz; Prof. Dr. Matthias Bauer, Erstgutachter; Prof. Dr. Thomas D. Kühne, Zweitgutachter; Prof. Dr. Wolf Gero Schmidt] - Müller, Patrick

Undiscovered potential: Ge catalysts for lactide polymerization - Herres-Pawlis, Sonja; Rittinghaus, Ruth D.; Tremmel, Jakub; Ruzicka, Ales; Conrads, Christian; Albrecht, Pascal; Hoffmann, Alexander; Ksiazkiewicz, Agnieszka; Pich, Andrij; Jambor, Roman (Chemistry – A European Journal)

Stepwise Growth of Ruthenium Terpyridine Complexes on Au Surfaces - Peter, Sophia Katharina; Kaulen, Corinna; Hoffmann, Alexander; Ogieglo, Wojciech; Karthäuser, Silvia; Homberger, Melanie; Herres-Pawlis, Sonja; Simon, Ulrich (The Journal of Physical Chemistry C)

Random network models with variable disorder of geometry - Klümper, A.; Nuding, W.; Sedrakyan, A. (Physical Review B)

Acoustic absorption measurement for the determination of the volume viscosity of pure fluids / Messverfahren für die akustischen Absorption zur Bestimmung der Volumenviskosität reiner Fluide - Claes, Leander; Hülskämper, Lars Moritz; Baumhögger, Elmar; Feldmann, Nadine; Chatwell, René Spencer; Vrabec, Jadran; Henning, Bernd (tm - Technisches Messen)

Stepwise Growth of Ruthenium Terpyridine Complexes on Au Surfaces - Peter, Sophia Katharina; Kaulen, Corinna; Hoffmann, Alexander; Ogieglo, Wojciech; Karthäuser, Silvia; Homberger, Melanie; Herres-Pawlis, Sonja; Simon, Ulrich (The Journal of Physical Chemistry C)

New Kids in Lactide Polymerization: Highly Active and Robust Iron Guanidine Complexes as Superior Catalysts - Rittinghaus, Ruth D.; Schäfer, Pascal M.; Albrecht, Pascal; Conrads, Christian; Hoffmann, Alexander; Ksiazkiewicz, Agnieszka N.; Bienemann, Olga; Pich, Andrij; Herres-Pawlis, Sonja (ChemSusChem)
Abstract Polylactide is a biodegradable versatile material based on annually renewable resources and thus CO2-neutral in its lifecycle. Until now, tin(II)octanoate [Sn(Oct2)] was used as catalyst for the industrial ring-opening polymerization of lactide in spite of its cytotoxicity. On the way towards a sustainable catalyst, three iron(II) hybrid guanidine complexes were investigated concerning their molecular structure and applied to the ring-opening polymerization of lactide. The complexes could polymerize unpurified technical-grade rac-lactide as well as recrystallized l-lactide to long-chain polylactide in bulk with monomer/initiator ratios of more than 5000:1 in a controlled manner following the coordination–insertion mechanism. For the first time, a biocompatible complex has surpassed Sn(Oct)2 in its polymerization activity under industrially relevant conditions.
Keywords: bioplastics; guanidines; iron; lactide; ring-opening polymerization

Properties of Co-Evaporated RbInSe2 Thin Films - Kodalle, Tim; Kormath Madam Raghupathy, Ramya; Bertram, Tobias; Maticiuc, Natalia; Yetkin, Hasan A; Gunder, René; Schlatmann, Rutger; Kühne, Thomas D; Kaufmann, Christian A; Mirhosseini, Hossein (physica status solidi (RRL)--Rapid Research Letters)

Opposing Electronic and Nuclear Quantum Effects on Hydrogen Bonds in H2O and D2O - Clark, Timothy; Heske, Julian Joachim; Kühne, Thomas (ChemPhysChem)
Abstract The effect of extending the O−H bond length(s) in water on the hydrogen-bonding strength has been investigated using static ab initio molecular orbital calculations. The “polar flattening” effect that causes a slight σ-hole to form on hydrogen atoms is strengthened when the bond is stretched, so that the σ-hole becomes more positive and hydrogen bonding stronger. In opposition to this electronic effect, path-integral ab initio molecular-dynamics simulations show that the nuclear quantum effect weakens the hydrogen bond in the water dimer. Thus, static electronic effects strengthen the hydrogen bond in H2O relative to D2O, whereas nuclear quantum effects weaken it. These quantum fluctuations are stronger for the water dimer than in bulk water.
Keywords: ab initio calculations; bond theory; hydrogen bonds; isotope effects; solvent effects

Impact of intermolecular vibrational coupling effects on the sum-frequency generation spectra of the water/air interface - Kaliannan, Naveen Kumar; Henao Aristizabal, Andres; Wiebeler, Hendrik; Zysk, Frederik; Ohto, Tatsuhiko; Nagata, Yuki; D. Kühne, Thomas (Molecular Physics)

Experimental and Theoretical High Energy Resolution Hard X-ray Absorption and Emission Spectroscopy on Biomimetic Cu2S2 Complexes - Müller, Patrick; Neuba, Adam; Flörke, Ulrich; Henkel, Gerald; Kühne, Thomas D.; Bauer, Matthias (The Journal of Physical Chemistry A)

Controlling the strength of interaction between carbon dioxide and nitrogen-rich carbon materials by molecular design - Walczak, Ralf; Savateev, Aleksandr; Heske, Julian Joachim; Tarakina, Nadezda V.; Sahoo, Sudhir; Epping, Jan D.; Kühne, Thomas; Kurpil, Bogdan; Antonietti, Markus; Oschatz, Martin (Sustainable Energy Fuels)
Thermal treatment of hexaazatriphenylene-hexacarbonitrile (HAT-CN) in the temperature range from 500 °C to 700 °C leads to precise control over the degree of condensation{,} and thus atomic construction and porosity of the resulting C2N-type materials. Depending on the condensation temperature of HAT-CN{,} nitrogen contents of more than 30 at% can be reached. In general{,} these carbons show adsorption properties which are comparable to those known for zeolites but their pore size can be adjusted over a wider range. At condensation temperatures of 525 °C and below{,} the uptake of nitrogen gas remains negligible due to size exclusion{,} but the internal pores are large and polarizing enough that CO2 can still adsorb on part of the internal surface. This leads to surprisingly high CO2 adsorption capacities and isosteric heat of adsorption of up to 52 kJ mol−1. Theoretical calculations show that this high binding enthalpy arises from collective stabilization effects from the nitrogen atoms in the C2N layers surrounding the carbon atom in the CO2 molecule and from the electron acceptor properties of the carbon atoms from C2N which are in close proximity to the oxygen atoms in CO2. A true CO2 molecular sieving effect is achieved for the first time in such a metal-free organic material with zeolite-like properties{,} showing an IAST CO2/N2 selectivity of up to 121 at 298 K and a N2/CO2 ratio of 90/10 without notable changes in the CO2 adsorption properities over 80 cycles.

Enhancement of the asymmetry in the hydrogen bond network of liquid water by an ultrafast electric field pulse - Elgabarty, Hossam; Kaliannan, Naveen Kumar; Kühne, Thomas D. (Scientific Reports)

A Branch and Bound Approach for Large Pre-Marshalling Problems - Tanaka, Shunji; Tierney, Kevin; Parreño-Torres, Consuelo; Alvarez-Valdes, Ramon; Ruiz, Rubén (European Journal of Operational Research)

Hyper-Reactive Tabu Search for MaxSAT - Ansótegui, Carlos; Heymann, Britta; Pon, Josep; Sellmann, Meinolf; Tierney, Kevin (Learning and Intelligent Optimization)

Diffusion of Methane in Supercritical Carbon Dioxide Across the Widom Line - Guevara-Carrion, Gabriela; Ancherbak, Sergiy; Mialdun, Aliaksandr; Vrabec, Jadran; Shevtsova, Valentina (Scientific Reports)

Diffusion Limited Evaporation of a Binary Liquid Film - Chatwell, René Spencer; Heinen, Matthias; Vrabec, Jadran (International Journal of Heat and Mass Transfer)

Kirkwood-Buff Integration: A Promising Route to Entropic Properties? - Fingerhut, Robin; Vrabec, Jadran (Fluid Phase Equilibria)

Investigation of Near-Surface Defects of Nanodiamonds by High-Frequency EPR and DFT Calculation - Cho, F. H.; Peng, Z.; Biktagirov, T.; Gerstmann, Uwe; Takahashi, S. (The Journal of Chemical Physics)

Proton Irradiation Induced Defects in β-Ga2O3: A Combined EPR and Theory Study - von Bardeleben, Hans Jürgen; Zhou, Shengqiang; Gerstmann, Uwe; Skachkov, Dmitry; Lambrecht, Walter R. L.; Ho, Quoc Duy; Deák, Peter (APL Materials)

Methode zur Erfassung von Stofftransport an fluiden Phasengrenzflächen - Schulz, Andreas; Wecker, Christian; Kenig, Eugeny (Chemie Ingenieur Technik)

Ein Finite-Volumen Ansatz für den Stoffübergang an bewegten Phasengrenzflächen - Schulz, Andreas; Wecker, Christian; Kenig, Eugeny

Numerische Untersuchungen zum Stofftransport und Fluidmechanik bei der Tropfenbildung - Wecker, Christian; Schulz, Andreas; Heine, Jens; Bart, Hans-Joerg; Kenig, Eugeny

Visualization of Marangoni Phenomena during Droplet Formation - Heine, Jens; Wecker, Christian; Kenig, Eugeny; Bart, Hans-Joerg

In-situ Messung des Stofftransports bei der Tropfenbildung - Heine, Jens; Wecker, Christian; Kenig, Eugeny; Bart, Hans-Joerg

Stofftransport und Fluidmechanik bei der Tropfenbildung unter Berücksichtigung von Marangonikonvektion mittels CFD - Wecker, Christian; Schulz, Andreas; Heine, Jens; Bart, Hans-Joerg; Kenig, Eugeny

Stofftransport bei der Tropfenbildung - Heine, Jens; Wecker, Christian; Kenig, Eugeny; Bart, Hans-Joerg

Reduktion von parasitären Strömungen in Mehrphasensimulationen durch Verwendung der Height-Function Methode - Zibart, Alexander; Kenig, Eugeny

TAILORED STACKED HYBRIDS – AN OPTIMIZATION-BASED APPROACH IN MATERIAL DESIGN FOR FURTHER IMPROVEMENT IN LIGHTWEIGHT CAR BODY STRUCTURES - Camberg, Alan Adam; Stratmann, Ina; Tröster, Thomas (Technologies for economical and functional lightweight design)

Numerical Simulation of Forced Convection in a Microchannel with Realistic Roughness of 3D Printed Surface - Manavi, Seyed Alborz; Kenig, Eugeny (Computer Aided Chemical Engineering)

Deep reinforcement learning for scheduling in large-scale networked control systems - Redder, Adrian; Ramaswamy, Arunselvan; Quevedo, Daniel (Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems)
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.
Keywords: Networked control systems; deep reinforcement learning; large-scale systems; resource scheduling; stochastic control

Automating Multi-Label Classification Extending ML-Plan - Wever, Marcel Dominik; Mohr, Felix; Tornede, Alexander; Hüllermeier, Eyke
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.

Quantification of frequency-dependent absorption phenomena - Claes, Leander; Johannesmann, Sarah; Baumhögger, Elmar; Henning, Bernd (2019 International Congress on Ultrasonics)

Diffusion of Alkali Metals in Polycrystalline CuInSe2 and Their Role in the Passivation of Grain Boundaries - Chugh, Manjusha; Kühne, Thomas D.; Mirhosseini, Hossein (ACS Applied Materials & Interfaces)
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.

Curved Polar Dibenzocoronene Esters and Imides versus Their Planar Centrosymmetric Homologs: Photophysical and Optoelectronic Analysis - Vollbrecht, Joachim; Wiebeler, Christian; Bock, Harald; Schumacher, Stefan; Kitzerow, Heinz-Siegfried (The Journal of Physical Chemistry C)

Externally Controlled Lotka-Volterra Dynamics in a Linearly Polarized Polariton Fluid - Pukrop, Matthias; Schumacher, Stefan (arXiv:1903.12534)
Spontaneous formation of transverse patterns is ubiquitous in nonlinear dynamical systems of all kinds. An aspect of particular interest is the active control of such patterns. In nonlinear optical systems this can be used for all-optical switching with transistor-like performance, for example realized with polaritons in a planar quantum-well semiconductor microcavity. Here we focus on a specific configuration which takes advantage of the intricate polarization dependencies in the interacting optically driven polariton system. Besides detailed numerical simulations of the coupled light-field exciton dynamics, in the present paper we focus on the derivation of a simplified population competition model giving detailed insight into the underlying mechanisms from a nonlinear dynamical systems perspective. We show that such a model takes the form of a generalized Lotka-Volterra system for two competing populations explicitly including a source term that enables external control. We present a comprehensive analysis both of the existence and stability of stationary states in the parameter space spanned by spatial anisotropy and external control strength. We also construct phase boundaries in non-trivial regions and characterize emerging bifurcations. The population competition model reproduces all key features of the switching observed in full numerical simulations of the rather complex semiconductor system and at the same time is simple enough for a fully analytical understanding of the system dynamics.

Molecular Doping of PCPDT-BT Copolymers: Comparison of Molecular Complexes with and Without Integer Charge Transfer - Dong, Chuan-Ding; Schumacher, Stefan
Molecular doping in conjugated polymers is a crucial process for their application in organic photovoltaics and optoelectronics. In the present work we theoretically investigate p-type molecu- lar doping in a series of (poly[2,6-(4,4-bis(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b”]dithiophene)-alt- 4,7-(2,1,3-benzothiadiazole)] (PCPDT-BT) conjugated oligomers with different lengths and three widely-used dopants with different electron affinities, namely F4TCNQ, F6TCNNQ, and CN6-CP. We study in detail the molecular geometry of possible oligomer-dopant complexes and its influence on the doping mechanisms and electronic system properties. We find that the mechanisms of dop- ing and charge transfer observed sensitively depend on the specific geometry of the oligomer-dopant complexes. For a given complex different geometries may exist, some of which show transfer of an entire electron from the oligomer chain onto the dopant molecule resulting in an integer-charge transfer complex, leaving the system in a ground state with broken spin symmetry. In other ge- ometries merely hybridization of oligomer and dopant frontier orbitals occurs with partial charge transfer but spin-symmetric ground state. Considering the resulting electronic density of states both cases may well contribute to an increased electrical conductivity of corresponding film samples while the underlying physical mechanisms are entirely different.

Controllable high-speed polariton waves in a PT-symmetric lattice - Ma, Xuekai; Kartashov, Yaroslav Y; Gao, Tingge; Schumacher, Stefan (New Journal of Physics)

Theoretical description of a multimode SU(1,1) interferometer - Ferreri, A.; Sharapova, P.; Luo, Kai Hong; Herrmann, H.; Silberhorn, C. (Quantum Information and Measurement (QIM) V: Quantum Technologies)
Keywords: pc2-ressources

Generating two-mode squeezing and Schrödinger cat states with multimode measurement-induced nonlinearity - Riabinin, Matvei; Sharapova, Polina; Bartley, Tim; Meier, Torsten
Keywords: pc2-ressources

Quasiparticle and excitonic effects in the optical response of KNbO3 - Schmidt, Falko; Riefer, Arthur; Schmidt, Wolf Gero; Schindlmayr, Arno; Imlau, Mirco; Dobener, Florian; Mengel, Nils; Chatterjee, Sangam; Sanna, Simone (Physical Review Materials)
The cubic, tetragonal, and orthorhombic phase of potassium niobate (KNbO3) are studied based on density-functional theory. Starting from the relaxed atomic geometries, we analyze the influence of self-energy corrections on the electronic band structure within the GW approximation. We find that quasiparticle shifts widen the direct (indirect) band gap by 1.21 (1.44), 1.58 (1.55), and 1.67 (1.64) eV for the cubic, tetragonal, and orthorhombic phase, respectively. By solving the Bethe-Salpeter equation, we obtain the linear dielectric function with excitonic and local-field effects, which turn out to be essential for good agreement with experimental data. From our results, we extract an exciton binding energy of 0.6, 0.5, and 0.5 eV for the cubic, tetragonal, and orthorhombic phase, respectively. Furthermore, we investigate the nonlinear second-harmonic generation (SHG) both theoretically and experimentally. The frequency-dependent second-order polarization tensor of orthorhombic KNbO3 is measured for incoming photon energies between 1.2 and 1.6 eV. In addition, calculations within the independent-(quasi)particle approximation are performed for the tetragonal and orthorhombic phase. The novel experimental data are in excellent agreement with the quasiparticle calculations and resolve persistent discrepancies between earlier experimental measurements and ab initio results reported in the literature.

Excited-state band mapping and momentum-resolved ultrafast population dynamics in In/Si(111) nanowires investigated with XUV-based time- and angle-resolved photoemission spectroscopy - Nicholson, C. W.; Puppin, M.; Lücke, A.; Gerstmann, Uwe; Krenz, Marvin; Schmidt, Wolf Gero; Rettig, L.; Ernstorfer, R.; Wolf, M. (Physical Review B)

Water Splitting Reaction at Polar Lithium Niobate Surfaces - Dues, Christof; Schmidt, Wolf Gero; Sanna, Simone (ACS Omega)

Nonlinear integrated quantum electro-optic circuits - Luo, Kai-Hong; Brauner, Sebastian; Eigner, Christof; Sharapova, Polina; Ricken, Raimund; Meier, Torsten; Herrmann, Harald; Silberhorn, Christine (Science Advances)
An integrated chip with quantum state generation, active polarization manipulation, and precise time control is demonstrated.
Keywords: Multidisciplinary

Intensity-dependent degenerate and non-degenerate nonlinear optical absorption of direct-gap semiconductors - Hannes, Wolf-Rüdiger; Krauß-Kodytek, Laura; Ruppert, Claudia; Betz, Markus; Meier, Torsten (Ultrafast Phenomena and Nanophotonics XXIII)

Higher-order contributions and nonperturbative effects in the nondegenerate nonlinear optical absorption of semiconductors using a two-band model - Hannes, Wolf-Rüdiger; Meier, Torsten (Physical Review B)

Potassium titanyl phosphate (KTP) quasiparticle energies and optical response - Neufeld, Sergej; Bocchini, Adriana; Gerstmann, Uwe; Schindlmayr, Arno; Schmidt, Wolf Gero (Journal of Physics: Materials)
The KTiOPO4 (KTP) band structure and dielectric function are calculated on various levels of theory starting from density-functional calculations. Within the independent-particle approximation an electronic transport gap of 2.97 eV is obtained that widens to about 5.23 eV when quasiparticle effects are included using the GW approximation. The optical response is shown to be strongly anisotropic due to (i) the slight asymmetry of the TiO6 octahedra in the (001) plane and (ii) their anisotropic distribution along the [001] and [100] directions. In addition, excitonic effects are very important: The solution of the Bethe–Salpeter equation indicates exciton binding energies of the order of 1.5 eV. Calculations that include both quasiparticle and excitonic effects are in good agreement with the measured reflectivity.

Attosecond temporal confinement of interband excitation by intraband motion - Song, Xiaohong; Zuo, Ruixin; Yang, Shidong; Li, Pengcheng; Meier, Torsten; Yang, Weifeng (Optics Express)

Ballistic photocurrents in semiconductor quantum wells caused by the excitation of asymmetric excitons - Duc, Huynh Thanh; Ngo, Cong; Meier, Torsten (Physical Review B)

Oxygen and potassium vacancies in KTP calculated from first principles - Bocchini, Adriana; Neufeld, Sergej; Gerstmann, Uwe; Schmidt, Wolf Gero (Journal of Physics: Condensed Matter)

A General Algorithm to Calculate the Inverse Principal p-th Root of Symmetric Positive Definite Matrices - Richters, Dorothee; Lass, Michael; Walther, Andrea; Plessl, Christian; Kühne, Thomas (Communications in Computational Physics)
We address the general mathematical problem of computing the inverse p-th root of a given matrix in an efficient way. A new method to construct iteration functions that allow calculating arbitrary p-th roots and their inverses of symmetric positive definite matrices is presented. We show that the order of convergence is at least quadratic and that adaptively adjusting a parameter q always leads to an even faster convergence. In this way, a better performance than with previously known iteration schemes is achieved. The efficiency of the iterative functions is demonstrated for various matrices with different densities, condition numbers and spectral radii.

Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision - Ebbers, Janek; Haeb-Umbach, Reinhold (DCASE2019 Workshop, New York, USA)
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.

Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks - Ebbers, Janek; Drude, Lukas; Haeb-Umbach, Reinhold; Brendel, Andreas; Kellermann, Walter (CAMSAP 2019, Guadeloupe, West Indies)
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.

Hard X-ray spectroscopy: an exhaustive toolbox for mechanistic studies (?) - Schoch, Anke; Burkhardt, Lukas; Schoch, Roland; Stührenberg, Kai; Bauer, Matthias (Faraday Discussions)
The hard X-ray spectroscopy methods XAS, valence-to-core XES and higher solution XANES offer unique insights into organometallic reaction mechanisms.

Modellierung und Optimierung von makroverkapselten Latentwärmespeicherelementen - Grabo, Matti; Staggenborg, Christoph; Kenig, Eugeny