@inproceedings{22894,
abstract = {{The first order optimality conditions of optimal control problems (OCPs) can
be regarded as boundary value problems for Hamiltonian systems. Variational or
symplectic discretisation methods are classically known for their excellent
long term behaviour. As boundary value problems are posed on intervals of
fixed, moderate length, it is not immediately clear whether methods can profit
from structure preservation in this context. When parameters are present,
solutions can undergo bifurcations, for instance, two solutions can merge and
annihilate one another as parameters are varied. We will show that generic
bifurcations of an OCP are preserved under discretisation when the OCP is
either directly discretised to a discrete OCP (direct method) or translated
into a Hamiltonian boundary value problem using first order necessary
conditions of optimality which is then solved using a symplectic integrator
(indirect method). Moreover, certain bifurcations break when a non-symplectic
scheme is used. The general phenomenon is illustrated on the example of a cut
locus of an ellipsoid.}},
author = {{Offen, Christian and Ober-Blöbaum, Sina}},
issn = {{2405-8963}},
keywords = {{optimal control, catastrophe theory, bifurcations, variational methods, symplectic integrators}},
location = {{Berlin, Germany}},
pages = {{334--339}},
title = {{{Bifurcation preserving discretisations of optimal control problems}}},
doi = {{https://doi.org/10.1016/j.ifacol.2021.11.099}},
volume = {{54(19)}},
year = {{2021}},
}
@inproceedings{21572,
author = {{Ridderbusch, Steffen and Offen, Christian and Ober-Blöbaum, Sina and Goulart, Paul}},
booktitle = {{2021 60th IEEE Conference on Decision and Control (CDC)}},
location = {{Austin, TX, USA}},
pages = {{2896}},
publisher = {{IEEE}},
title = {{{Learning ODE Models with Qualitative Structure Using Gaussian Processes }}},
doi = {{10.1109/CDC45484.2021.9683426}},
year = {{2021}},
}
@inproceedings{21592,
abstract = {{We propose a reachability approach for infinite and finite horizon multi-objective optimization problems for low-thrust spacecraft trajectory design. The main advantage of the proposed method is that the Pareto front can be efficiently constructed from the zero level set of the solution to a Hamilton-Jacobi-Bellman equation. We demonstrate the proposed method by applying it to a low-thrust spacecraft trajectory design problem. By deriving the analytic expression for the Hamiltonian and the optimal control policy, we are able to efficiently compute the backward reachable set and reconstruct the optimal trajectories. Furthermore, we show that any reconstructed trajectory will be guaranteed to be weakly Pareto optimal. The proposed method can be used as a benchmark for future research of applying reachability analysis to low-thrust spacecraft trajectory design.}},
author = {{Vertovec, Nikolaus and Ober-Blöbaum, Sina and Margellos, Kostas}},
location = {{Rotterdam, the Netherlands}},
pages = {{1975--1980}},
title = {{{Multi-objective minimum time optimal control for low-thrust trajectory design}}},
year = {{2021}},
}
@inproceedings{29868,
author = {{Jiménez, F. and Ober-Blöbaum, Sina}},
booktitle = {{Nichtlineare Sci 31}},
title = {{{Fractional Damping Through Restricted Calculus of Variations}}},
volume = {{46}},
year = {{2021}},
}
@inproceedings{21272,
author = {{Ködding, Patrick and Dumitrescu, Roman}},
booktitle = {{Digitalisisierung souverän gestalten}},
editor = {{Hartmann, Ernst A.}},
pages = {{pp. 59--73}},
publisher = {{Springer Vieweg}},
title = {{{Forschungsfelder für Künstliche Intelligenz in der strategischen Produktplanung}}},
year = {{2021}},
}
@inproceedings{48286,
author = {{Habernal, Ivan}},
booktitle = {{Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}},
publisher = {{Association for Computational Linguistics}},
title = {{{When differential privacy meets NLP: The devil is in the detail}}},
doi = {{10.18653/v1/2021.emnlp-main.114}},
year = {{2021}},
}
@inproceedings{29893,
abstract = {{Phase-shift modulated full bridge converters suffer from thermal imbalances of the inverter switches. The lagging leg switches are subject to larger commutation currents compared to those of the leading leg as the transformer current reduces in the freewheeling interval. Furthermore, after this interval, the energy in the series inductance may not be large enough to achieve zero-voltage switching (ZVS) for the leading leg. Both effects result in thermal imbalances. This paper analyzes the alternating-asymmetrical phase-shift modulation to achieve balanced conduction and switching losses for all four switches while showing that this modulation is easily implemented on standard DSPs. The modulation has been implemented to LLC converters where experimental measurement results proved its effectiveness for LLC converters by reducing the temperature deviation from 6.3 K to only 0.2 K such that the peak temperature is reduced from 95 °C to 92 °C. The paper also proves that the modulation can be utilized to improve the efficiency of LLC converters operated at very low gains while simultaneously reducing the junction temperature of all four switches compared to the conventional complementary modulation. Finally, EMI implications are analyzed, which show that the modulation may be beneficial for reducing the common-mode emissions around the operating frequency.}},
author = {{Rehlaender, Philipp and Unruh, Roland and Schafmeister, Frank and Böcker, Joachim}},
booktitle = {{2021 IEEE Applied Power Electronics Conference and Exposition (APEC)}},
isbn = {{978-1-7281-8950-5}},
keywords = {{Phase-Shifted Full Bridge, Full-Bridge Converter, Phase-Shift Control, Phase-Shift Modulation, LLC Converter, Thermal Balancing}},
location = {{Phoenix, AZ, USA}},
publisher = {{IEEE}},
title = {{{Alternating Asymmetrical Phase-Shift Modulation for Full-Bridge Converters with Balanced Switching Losses to Reduce Thermal Imbalances}}},
doi = {{10.1109/apec42165.2021.9487104}},
year = {{2021}},
}
@article{34827,
abstract = {{ZusammenfassungZu den ersten geometrischen Begriffen, die Kinder bereits im Elementar- und Primarbereich lernen, zählen u. a. Viereck, Rechteck und Quadrat. Studien zeigen, dass Lernende bereits früh individuelle Vorstellungen, sog. individuelle Begriffskonzepte, zu diesen Begriffen aufbauen. Zwar wird die Entwicklung von Begriffsverständnis in verschiedenen mathematikdidaktischen Stufenmodellen dargestellt, diese sind jedoch generisch und beschreiben nicht explizit die Entwicklung der ersten individuellen Begriffskonzepte von Lernenden zu Viereck, Rechteck und Quadrat. Aus empirischer Sicht liegen verschiedene Studien vor, die einzelne Aspekte der individuellen Begriffskonzepte von Lernenden unterschiedlicher Altersgruppen zu diesen Begriffen ausleuchten. Um Begriffsbildungsprozesse aus empirischer Sicht detaillierter entlang der jeweils vorherrschenden individuellen Begriffskonzepte zu beschreiben, fehlen insbesondere Studien in der Grundschule, die alle vier Klassenstufen betrachten und dabei differenzierte Erkenntnisse zu verschiedenen theoretischen Indikatoren des Begriffsverständnisses liefern. Daher geht die vorliegende Studie der Frage nach, welches Verständnis der Begriffe Viereck, Rechteck und Quadrat Schülerinnen und Schüler der Jahrgangsstufen 1, 2, 3 und 4 zeigen. Dazu wurde eine Quasi-Längsschnittstudie mit N = 456 Grundschulkindern (ca. 100 pro Jahrgangsstufe) durchgeführt. Die Ergebnisse geben detaillierte Einblicke in die individuellen Begriffskonzepte der Lernenden und zeigen, dass Lernende zunehmend Eigenschaften der Figuren berücksichtigen, jedoch individuelle Begriffskonzepte über lange Zeit auch prototypisch geprägt sind. Implikationen dieser Ergebnisse für Forschung und Praxis werden diskutiert.}},
author = {{Bruns, Julia and Unterhauser, Elisabeth and Gasteiger, Hedwig}},
issn = {{0173-5322}},
journal = {{Journal für Mathematik-Didaktik}},
keywords = {{Education, General Mathematics}},
number = {{2}},
pages = {{581--623}},
publisher = {{Springer Science and Business Media LLC}},
title = {{{Geometrisches Begriffsverständnis in der Grundschule am Beispiel der Begriffe Viereck, Rechteck und Quadrat}}},
doi = {{10.1007/s13138-021-00185-4}},
volume = {{42}},
year = {{2021}},
}
@article{45382,
author = {{Prediger, Susanne and Dröse, Jennifer}},
journal = {{Lernen und Lernstörungen, 10(2)}},
title = {{{Fehlerbearbeitung bei mathematischen Textaufgaben – Sprachliche und strategische Fehlerursachen und ihre Bearbeitung}}},
doi = {{doi.org/10.1024/2235-0977/a000330}},
year = {{2021}},
}
@inproceedings{26770,
abstract = {{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. }},
author = {{von Neumann, Thilo and Kinoshita, Keisuke and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}},
booktitle = {{Interspeech 2021}},
keywords = {{Continuous speech separation, automatic speech recognition, overlapped speech, permutation invariant training}},
title = {{{Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers}}},
doi = {{10.21437/interspeech.2021-1177}},
year = {{2021}},
}
@inproceedings{29173,
author = {{von Neumann, Thilo and Boeddeker, Christoph and Kinoshita, Keisuke and Delcroix, Marc and Haeb-Umbach, Reinhold}},
booktitle = {{Speech Communication; 14th ITG Conference}},
location = {{Kiel}},
title = {{{Speeding Up Permutation Invariant Training for Source Separation}}},
year = {{2021}},
}
@inproceedings{29308,
abstract = {{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.}},
author = {{Ebbers, Janek and Haeb-Umbach, Reinhold}},
booktitle = {{Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021)}},
isbn = {{978-84-09-36072-7}},
pages = {{226–230}},
title = {{{Self-Trained Audio Tagging and Sound Event Detection in Domestic Environments}}},
year = {{2021}},
}
@inproceedings{29306,
abstract = {{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.}},
author = {{Ebbers, Janek and Keyser, Moritz Curt and Haeb-Umbach, Reinhold}},
booktitle = {{Proceedings of the 29th European Signal Processing Conference (EUSIPCO)}},
pages = {{1135–1139}},
title = {{{Adapting Sound Recognition to A New Environment Via Self-Training}}},
year = {{2021}},
}
@article{24456,
abstract = {{One objective of current research in explainable intelligent systems is to implement social aspects in order to increase the relevance of explanations. In this paper, we argue that a novel conceptual framework is needed to overcome shortcomings of existing AI systems with little attention to processes of interaction and learning. Drawing from research in interaction and development, we first outline the novel conceptual framework that pushes the design of AI systems toward true interactivity with an emphasis on the role of the partner and social relevance. We propose that AI systems will be able to provide a meaningful and relevant explanation only if the process of explaining is extended to active contribution of both partners that brings about dynamics that is modulated by different levels of analysis. Accordingly, our conceptual framework comprises monitoring and scaffolding as key concepts and claims that the process of explaining is not only modulated by the interaction between explainee and explainer but is embedded into a larger social context in which conventionalized and routinized behaviors are established. We discuss our conceptual framework in relation to the established objectives of transparency and autonomy that are raised for the design of explainable AI systems currently.}},
author = {{Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and Horwath, Ilona and Hüllermeier, Eyke and Kern, Friederike and Kopp, Stefan and Thommes, Kirsten and Ngonga Ngomo, Axel-Cyrille and Schulte, Carsten and Wachsmuth, Henning and Wagner, Petra and Wrede, Britta}},
issn = {{2379-8920}},
journal = {{IEEE Transactions on Cognitive and Developmental Systems}},
keywords = {{Explainability, process ofexplaining andunderstanding, explainable artificial systems}},
number = {{3}},
pages = {{717--728}},
title = {{{Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}}},
doi = {{10.1109/tcds.2020.3044366}},
volume = {{13}},
year = {{2021}},
}
@inproceedings{48853,
abstract = {{In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.}},
author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}},
booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
isbn = {{978-1-4503-8350-9}},
keywords = {{evolutionary algorithms, evolutionary diversity optimization, knapsack problem, tailored operators}},
pages = {{556–564}},
publisher = {{Association for Computing Machinery}},
title = {{{Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}}},
doi = {{10.1145/3449639.3459364}},
year = {{2021}},
}
@inproceedings{48855,
abstract = {{Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical knapsack problem. We present an algorithm to count exactly the number of optima to a zero-one knapsack problem instance. In addition, we show how to efficiently sample uniformly at random from the set of all global optima. In our experimental study, we investigate how the number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.}},
author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}},
booktitle = {{Learning and Intelligent Optimization}},
isbn = {{978-3-030-92120-0}},
keywords = {{Dynamic programming, Exact counting, Sampling, Zero-one knapsack problem}},
pages = {{40–54}},
publisher = {{Springer-Verlag}},
title = {{{Exact Counting and~Sampling of Optima for the Knapsack Problem}}},
doi = {{10.1007/978-3-030-92121-7_4}},
year = {{2021}},
}
@inproceedings{48860,
abstract = {{In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality.}},
author = {{Bossek, Jakob and Neumann, Frank}},
booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
isbn = {{978-1-4503-8350-9}},
keywords = {{evolutionary algorithms, evolutionary diversity optimization, minimum spanning tree, runtime analysis}},
pages = {{198–206}},
publisher = {{Association for Computing Machinery}},
title = {{{Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}}},
doi = {{10.1145/3449639.3459363}},
year = {{2021}},
}
@inbook{48862,
abstract = {{Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT {$\cup$} S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT. We show that the answer to our question depends on the number and placement of search points in S. For all black-box algorithms and all fitness functions we show that, if additional optima are placed randomly, even an exponential number of optima has a negligible effect on the expected optimisation time. Considering Hamming balls around all global optima gives an easier target for some algorithms and functions and can shift the phase transition with respect to offspring population sizes in the (1,{$\lambda$}) EA on One-Max. Finally, on functions where search trajectories typically join in a single search point, turning one search point into an optimum drastically reduces the expected optimisation time.}},
author = {{Bossek, Jakob and Sudholt, Dirk}},
booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}},
isbn = {{978-1-4503-8352-3}},
keywords = {{evolutionary algorithms, pseudo-boolean functions, runtime analysis, theory}},
pages = {{1–11}},
publisher = {{Association for Computing Machinery}},
title = {{{Do Additional Optima Speed up Evolutionary Algorithms?}}},
year = {{2021}},
}
@inbook{48881,
abstract = {{Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.}},
author = {{Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}},
booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}},
isbn = {{978-1-4503-8352-3}},
keywords = {{automated algorithm selection, graph theory, instance features, normalization, traveling salesperson problem (TSP)}},
pages = {{1–15}},
publisher = {{Association for Computing Machinery}},
title = {{{On the Potential of Normalized TSP Features for Automated Algorithm Selection}}},
year = {{2021}},
}
@inproceedings{48876,
abstract = {{In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity.}},
author = {{Bossek, Jakob and Wagner, Markus}},
booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
isbn = {{978-1-4503-8351-6}},
keywords = {{evolutionary algorithms, evolving instances, fitness function, instance hardness, traveling thief problem (TTP)}},
pages = {{1423–1432}},
publisher = {{Association for Computing Machinery}},
title = {{{Generating Instances with Performance Differences for More than Just Two Algorithms}}},
doi = {{10.1145/3449726.3463165}},
year = {{2021}},
}