@article{34098, author = {{Yusuf, Maha and LaManna, Jacob M. and Paul, Partha P. and Agyeman-Budu, David N. and Cao, Chuntian and Dunlop, Alison R. and Jansen, Andrew N. and Polzin, Bryant J. and Trask, Stephen E. and Tanim, Tanvir R. and Dufek, Eric J. and Thampy, Vivek and Steinrück, Hans-Georg and Toney, Michael F. and Nelson Weker, Johanna}}, issn = {{2666-3864}}, journal = {{Cell Reports Physical Science}}, keywords = {{General Physics and Astronomy, General Energy, General Engineering, General Materials Science, General Chemistry}}, number = {{11}}, pages = {{101145}}, publisher = {{Elsevier BV}}, title = {{{Simultaneous neutron and X-ray tomography for visualization of graphite electrode degradation in fast-charged lithium-ion batteries}}}, doi = {{10.1016/j.xcrp.2022.101145}}, volume = {{3}}, year = {{2022}}, } @inproceedings{34103, abstract = {{It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic problem, which is the most suitable algorithm to solve it? As such, the AS problem has received considerable attention resulting in various approaches - many of which either solve a regression or ranking problem under the hood. Although both of these formulations yield very natural ways to tackle AS, they have considerable weaknesses. On the one hand, correctly predicting the performance of an algorithm on an instance is a sufficient, but not a necessary condition to produce a correct ranking over algorithms and in particular ranking the best algorithm first. On the other hand, classical ranking approaches often do not account for concrete performance values available in the training data, but only leverage rankings composed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon foreSts - a new algorithm selector leveraging special forests, combining the strengths of both approaches while alleviating their weaknesses. HARRIS' decisions are based on a forest model, whose trees are created based on splits optimized on a hybrid ranking and regression loss function. As our preliminary experimental study on ASLib shows, HARRIS improves over standard algorithm selection approaches on some scenarios showing that combining ranking and regression in trees is indeed promising for AS.}}, author = {{Fehring, Lukass and Hanselle, Jonas Manuel and Tornede, Alexander}}, booktitle = {{Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022}}, location = {{Baltimore}}, title = {{{HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection}}}, year = {{2022}}, } @article{33251, author = {{Robra-Bissantz, Susanne and Lattemann, Christoph and Laue, Ralf and Leonhard-Pfleger, Raphaela and Wagner, Luisa and Gerundt, Oliver and Schlimbach, Ricarda and Baumann, Sabine and Vorbohle, Christian and Gottschalk, Sebastian and Kundisch, Dennis and Engels, Gregor and Wünderlich, Nancy and Nissen, Volker and Lohrenz, Lisa and Michalke, Simon}}, journal = {{HMD Praxis der Wirtschaftsinformatik}}, number = {{5}}, pages = {{1227 -- 1257}}, title = {{{Methoden zum Design digitaler Plattformen, Geschäftsmodelle und Service-Ökosysteme}}}, volume = {{59}}, year = {{2022}}, } @inproceedings{33502, author = {{Althaus, Maike and Poniatowski, Martin and Kundisch, Dennis}}, booktitle = {{Proceedings of the 43rd International Conference on Information Systems (ICIS)}}, location = {{Copenhagen, Denmark}}, title = {{{Tackling Crises Together? – An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic}}}, year = {{2022}}, } @inproceedings{33882, author = {{Laux, Florian and Poniatowski, Martin and Kundisch, Dennis}}, location = {{Copenhagen, Denmark}}, title = {{{May I have your attention, please? Analyzing the effects of attention screening mechanisms on crowdworking platforms}}}, year = {{2022}}, } @inproceedings{33885, author = {{Seutter, Janina}}, location = {{Copenhagen, Denmark}}, title = {{{Online Reviews in B2B Markets: A Qualitative Study on the Underlying Motives }}}, year = {{2022}}, } @inproceedings{32602, author = {{Padalkin, Andreas and Scheideler, Christian and Warner, Daniel}}, booktitle = {{28th International Conference on DNA Computing and Molecular Programming (DNA 28)}}, editor = {{Ouldridge, Thomas E. and Wickham, Shelley F. J.}}, isbn = {{978-3-95977-253-2}}, issn = {{1868-8969}}, pages = {{8:1–8:22}}, publisher = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}}, title = {{{The Structural Power of Reconfigurable Circuits in the Amoebot Model}}}, doi = {{10.4230/LIPIcs.DNA.28.8}}, volume = {{238}}, year = {{2022}}, } @inproceedings{32603, author = {{Kostitsyna, Irina and Scheideler, Christian and Warner, Daniel}}, booktitle = {{28th International Conference on DNA Computing and Molecular Programming (DNA 28)}}, editor = {{Ouldridge, Thomas E. and Wickham, Shelley F. J.}}, isbn = {{978-3-95977-253-2}}, issn = {{1868-8969}}, pages = {{9:1–9:22}}, publisher = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}}, title = {{{Fault-Tolerant Shape Formation in the Amoebot Model}}}, doi = {{10.4230/LIPIcs.DNA.28.9}}, volume = {{238}}, year = {{2022}}, } @inproceedings{30916, author = {{Seutter, Janina}}, booktitle = {{Proceedings of the 30th European Conference on Information Systems (ECIS)}}, location = {{Timișoara, Romania}}, title = {{{Online Reviews in B2B Markets: A Qualitative Study of Underlying Motivations}}}, year = {{2022}}, } @inproceedings{31062, author = {{Poniatowski, Martin}}, booktitle = {{Proceedings of the 28th Americas Conference on Information Systems (AMCIS)}}, location = {{Minneapolis, USA}}, title = {{{How the Display of the Transaction Count Affects the Purchase Intention}}}, year = {{2022}}, } @inproceedings{30939, author = {{Vorbohle, Christian and Kundisch, Dennis}}, booktitle = {{Proceedings of the 30th European Conference on Information Systems (ECIS)}}, location = {{Timișoara, Romania}}, title = {{{Overcoming Silos: A Review of Business Model Modeling Languages for Business Ecosystems}}}, year = {{2022}}, } @inproceedings{30734, author = {{Althaus, Maike and Poniatowski, Martin and Kundisch, Dennis}}, location = {{Madrid, Spain}}, title = {{{Tackling Crises Together? - An Econometric Analysis of Charitable Crowdfunding During the COVID-19 Pandemic}}}, year = {{2022}}, } @inproceedings{30212, author = {{Vorbohle, Christian and Kundisch, Dennis}}, title = {{{Key Properties of Sustainable Business Ecosystem Relationships}}}, year = {{2022}}, } @inproceedings{32811, abstract = {{The decentralized nature of multi-agent systems requires continuous data exchange to achieve global objectives. In such scenarios, Age of Information (AoI) has become an important metric of the freshness of exchanged data due to the error-proneness and delays of communication systems. Communication systems usually possess dependencies: the process describing the success or failure of communication is highly correlated when these attempts are ``close'' in some domain (e.g. in time, frequency, space or code as in wireless communication) and is, in general, non-stationary. To study AoI in such scenarios, we consider an abstract event-based AoI process $\Delta(n)$, expressing time since the last update: If, at time $n$, a monitoring node receives a status update from a source node (event $A(n-1)$ occurs), then $\Delta(n)$ is reset to one; otherwise, $\Delta(n)$ grows linearly in time. This AoI process can thus be viewed as a special random walk with resets. The event process $A(n)$ may be nonstationary and we merely assume that its temporal dependencies decay sufficiently, described by $\alpha$-mixing. We calculate moment bounds for the resulting AoI process as a function of the mixing rate of $A(n)$. Furthermore, we prove that the AoI process $\Delta(n)$ is itself $\alpha$-mixing from which we conclude a strong law of large numbers for $\Delta(n)$. These results are new, since AoI processes have not been studied so far in this general strongly mixing setting. This opens up future work on renewal processes with non-independent interarrival times.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{Proceedings of the 58th Allerton Conference on Communication, Control, and Computing}}, title = {{{Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law}}}, year = {{2022}}, } @inproceedings{30793, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{Proceedings of the 14th International Conference on Agents and Artificial Intelligence}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication}}}, doi = {{10.5220/0010845400003116}}, year = {{2022}}, } @inproceedings{34082, abstract = {{Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless ("She accepted her future'') and men as proactive and powerful ("He chose his future''). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs' probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better.}}, author = {{Stahl, Maja and Spliethöver, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science}}, location = {{Abu Dhabi, United Arab Emirates}}, title = {{{To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation}}}, year = {{2022}}, } @unpublished{30790, abstract = {{Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence of unreliable communication networks, the Age-of-Information (AoI), which measures the freshness of data received, may be large and hence hinder algorithmic convergence. In this paper, we study the convergence of general distributed gradient-based optimization algorithms in the presence of communication that neither happens periodically nor at stochastically independent points in time. We show that convergence is guaranteed provided the random variables associated with the AoI processes are stochastically dominated by a random variable with finite first moment. This improves on previous requirements of boundedness of more than the first moment. We then introduce stochastically strongly connected (SSC) networks, a new stochastic form of strong connectedness for time-varying networks. We show: If for any $p \ge0$ the processes that describe the success of communication between agents in a SSC network are $\alpha$-mixing with $n^{p-1}\alpha(n)$ summable, then the associated AoI processes are stochastically dominated by a random variable with finite $p$-th moment. In combination with our first contribution, this implies that distributed stochastic gradient descend converges in the presence of AoI, if $\alpha(n)$ is summable.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{arXiv:2201.11343}}, title = {{{Distributed gradient-based optimization in the presence of dependent aperiodic communication}}}, year = {{2022}}, } @article{34109, author = {{Hagengruber, Ruth Edith}}, journal = {{La Lettre clandestine n° 30: Émilie Du Châtelet et la littérature clandestine. Moreau, P.F. & Seguin, M.S. (Eds.). Paris}}, pages = {{99 – 115}}, publisher = {{Classiques Garnier}}, title = {{{Du Châtelet et la tradition critique de la Bible. De la ‘philofolie’ aux Examens}}}, year = {{2022}}, } @inproceedings{32855, author = {{Clausing, Lennart and Platzner, Marco}}, booktitle = {{2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}}, location = {{ Lyon, France}}, pages = {{120--127}}, publisher = {{IEEE}}, title = {{{ReconOS64: A Hardware Operating System for Modern Platform FPGAs with 64-Bit Support}}}, doi = {{10.1109/ipdpsw55747.2022.00029}}, year = {{2022}}, } @unpublished{30791, abstract = {{We present sufficient conditions that ensure convergence of the multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. It is an example of one of the most popular paradigms of Deep Reinforcement Learning (DeepRL) for tackling continuous action spaces: the actor-critic paradigm. In the setting considered herein, each agent observes a part of the global state space in order to take local actions, for which it receives local rewards. For every agent, DDPG trains a local actor (policy) and a local critic (Q-function). The analysis shows that multi-agent DDPG using neural networks to approximate the local policies and critics converge to limits with the following properties: The critic limits minimize the average squared Bellman loss; the actor limits parameterize a policy that maximizes the local critic's approximation of $Q_i^*$, where $i$ is the agent index. The averaging is with respect to a probability distribution over the global state-action space. It captures the asymptotics of all local training processes. Finally, we extend the analysis to a fully decentralized setting where agents communicate over a wireless network prone to delays and losses; a typical scenario in, e.g., robotic applications.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{arXiv:2201.00570}}, title = {{{Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms}}}, year = {{2022}}, }