Mitigating Label Noise through Data Ambiguation - Lienen, Julian; Hüllermeier, Eyke (arXiv:2305.13764)
Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field. Models of that kind are prone to memorizing incorrect labels, thereby harming generalization performance. Many methods have been proposed to address this problem, including robust loss functions and more complex label correction approaches. Robust loss functions are appealing due to their simplicity, but typically lack flexibility, while label correction usually adds substantial complexity to the training setup. In this paper, we suggest to address the shortcomings of both methodologies by "ambiguating" the target information, adding additional, complementary candidate labels in case the learner is not sufficiently convinced of the observed training label. More precisely, we leverage the framework of so-called superset learning to construct set-valued targets based on a confidence threshold, which deliver imprecise yet more reliable beliefs about the ground-truth, effectively helping the learner to suppress the memorization effect. In an extensive empirical evaluation, our method demonstrates favorable learning behavior on synthetic and real-world noise, confirming the effectiveness in detecting and correcting erroneous training labels.

Reconfigurable Random Forest Implementation on FPGA - Raeisi Nafchi, Masood

Data Analytics for Predictive Maintenance of Time Series Data - Yadalam Murali Kumar, Nihal

Design approach to extend and decarbonise existing district heating systems - case study for German cities - Divkovic, Denis; Knorr, Lukas; Meschede, Henning (International Journal of Sustainable Energy Planning and Management)
This paper aims to present an approach for the planning of carbon low heat supply in a future district heating system based on open data for German cities with existing district heating networks. One focus is on the integration of industrial waste heat and the uncertainty of future waste heat sources as well as restrictions on the use of biomass. For that purpose, knowledge about the energy demand is necessary. In a first step it is shown how the demand around a heating network is estimated with spatial data and a load profile is generated. Local available heat sources are examined according to their suitability and their kind of integration in the heating network. As heat production from different units are optimised, the development of a simulation model will be presented. The simulation is based on the optimisation of the operational costs of the used technologies for heating supply. Different scenarios covering various technologies and economic assumptions are applied. The results show the levelized costs of heating as well as the ecological performance. A sensitivity analysis shows the importance of uncertainties for the economic assumptions. The results showing levelized costs of heating as well as the ecological performance underlining the advantage of excess heat integration.
Keywords: Energy Engineering and Power Technology; Renewable Energy; Sustainability and the Environment; Geography; Planning and Development

„Löwenstark in der Schwangerschaft!“ Ressourcenaktivierung und Stressreduktion mit dem Zürcher Ressourcen Modell (ZRM®) – eine unkontrollierte Interventionsstudie - Schneider, Tamara; Weber, Julia; Bauer, Nicola H. (Prävention und Gesundheitsförderung)
Jede Schwangerschaft bringt physische, psychische und soziale Veränderungen mit sich. Diese können zu Stress und ambivalenten Gefühlen bei Schwangeren führen, die einen starken Einfluss auf das Wohlbefinden und die Gesundheit von Mutter und Kind haben. Zur Förderung eines komplikationslosen Verlaufs der Schwangerschaft wird in der Forschungsliteratur die Stärkung der gesundheitlichen Ressourcen gefordert. Das Zürcher Ressourcen Modell (ZRM) ist ein ressourcenaktivierendes Selbstmanagementtraining, welches nach aktuellen Studienergebnissen die Gefühlsregulierung unterstützen und Stress reduzieren kann. Ziel dieser Studie war die erstmalige Wirksamkeitsprüfung des ZRM in der Schwangerschaft. Angenommen wurde, dass durch die Aktivierung der persönlichen Ressourcen der Schwangeren das Stresserleben reduziert und das subjektive Wohlbefinden gesteigert werden kann. Die Konstrukte „Stress“ und „Wohlbefinden“ wurden anhand von fünf psychometrischen Parametern operationalisiert. Durchgeführt wurde eine quasiexperimentelle unkontrollierte 6‑stündige Online-Interventionsstudie mit dem ZRM im Januar 2021 an N = 32 Schwangeren, basierend auf einem Prä-Post-Follow-up-Design. Zur Überprüfung der gebildeten Hypothesen wurden einfaktorielle Varianzanalysen (rmANOVAs) durchgeführt. Die 1-faktoriellen rmANOVAs ergaben, dass die ZRM-Intervention das Stresserleben allgemein und schwangerschaftsspezifisch sowie das Angsterleben signifikant senken konnte. Handlungsorientierung nach Misserfolg und emotionales Wohlbefinden konnten signifikant gesteigert werden. Hinsichtlich der subjektiven Vitalität ließ sich lediglich deskriptiv eine Steigerung der Werte verzeichnen. Die ZRM-Intervention erwies sich als wirksame Methode zur Optimierung der Stressbewältigung und Steigerung des Wohlbefindens während der Schwangerschaft. Es kann zielführend sein, das ZRM-Training als Ergänzung zur üblichen Geburtsvorbereitung einzusetzen. Zukünftige Forschungsarbeiten sollten v. a. die Übertragbarkeit der Ergebnisse unter Einbindung einer Kontrollgruppe sicherstellen.
Keywords: Public Health; Environmental and Occupational Health

The Optimal Depth of Variational Quantum Algorithms Is QCMA-Hard to Approximate - Bittel, Lennart; Gharibian, Sevag; Kliesch, Martin (Proceedings of the 38th Computational Complexity Conference (CCC))
Variational Quantum Algorithms (VQAs), such as the Quantum Approximate Optimization Algorithm (QAOA) of [Farhi, Goldstone, Gutmann, 2014], have seen intense study towards near-term applications on quantum hardware. A crucial parameter for VQAs is the depth of the variational ansatz used - the smaller the depth, the more amenable the ansatz is to near-term quantum hardware in that it gives the circuit a chance to be fully executed before the system decoheres. This potential for depth reduction has made VQAs a staple of Noisy Intermediate-Scale Quantum (NISQ)-era research. In this work, we show that approximating the optimal depth for a given VQA ansatz is intractable. Formally, we show that for any constant $ε>0$, it is QCMA-hard to approximate the optimal depth of a VQA ansatz within multiplicative factor $N^{1-ε}$, for $N$ denoting the encoding size of the VQA instance. (Here, Quantum Classical Merlin-Arthur (QCMA) is a quantum generalization of NP.) We then show that this hardness persists even in the "simpler" setting of QAOAs. To our knowledge, this yields the first natural QCMA-hard-to-approximate problems. To achieve these results, we bypass the need for a PCP theorem for QCMA by appealing to the disperser-based NP-hardness of approximation construction of [Umans, FOCS 1999].

Zur Darstellung der Afrikanischen Europäerin in Geschichte und Gegenwart - Sharon Dodua Otoos Roman 'Adas Raum' - Müller, Inez (Interkulturelle Konstellationen in Literaturwissenschaft und Literaturdidaktik. Festschrift für Michael Hofmann)

Empirical Analysis in Markets for OTF Services - Elrich, Alina; Kaimann, Daniel; Fahr, René; Kundisch, Dennis; Mir Djawadi, Behnud; Müller, Michelle; Poniatowski, Martin; Schäfers, Sabrina; Frick, Bernd (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Engineering Sub‐Nanometer Hafnia‐Based Ferroelectric to Break The Scaling Relation for High‐Efficiency Piezocatalytic Water Splitting - Su, Ran; Zhang, Jiahui; Wong, Vienna; Zhang, Dawei; Yang, Yong; Luo, Zheng‐Dong; Wang, Xiaojing; Wen, Hui; Liu, Yang; Seidel, Jan; Yang, Xiaolong; Pan, Ying; et al. (Advanced Materials)
Keywords: Mechanical Engineering; Mechanics of Materials; General Materials Science

Die junge Käthe Kollwitz in ihrer künstlerischen Findungsphase - Schreckenberg, Carolin

Zusatzkosten der Besteuerung – Eine Analyse des steuerlichen Verwaltungsaufwands und der subjektiv wahrgenommenen Steuerbelastung - Fochmann, Martin; Heinemann-Heile, Vanessa; Huber, Hans-Peter; Maiterth, Ralf; Sureth-Sloane, Caren (Steuer und Wirtschaft)

Anticipatory Assignment of Passengers to Meeting Points for Taxi-Ridesharing - Dieter, Peter; Stumpe, Miriam; Ulmer, Marlin Wolf; Schryen, Guido (Transportation Research Part D)

Cation-dependent assembly of hexagonal DNA origami lattices on SiO2 surfaces - Pothineni, Bhanu Kiran; Grundmeier, Guido; Keller, Adrian (Nanoscale)
DNA origami nanostructures have emerged as functional materials for applications in various areas of science and technology. In particular, the transfer of the DNA origami shape into inorganic materials using...
Keywords: General Materials Science

On Cloud Assisted, Restricted, and Reosurce Constrained Scheduling - Pukrop, Simon

Utilizing Redundancy in Distributed Heterogeneous Storage - Schneider, Fabian

Gelingensbedingungen gestaltungsorientierter Schulentwicklung - Schwabl, Franziska; Daniel-Söltenfuß, Desiree (Organisation zwischen Theorie und Praxis)

Potenziale der Kulturerbe-Konvention von 2003. Beispiele aus der Beratungspraxis der Landesstelle Immaterielles Kulturerbe NRW - Harnack, Maria (Kulturerbe als kulturelle Praxis – Kulturerbe in der Beratungspraxis)

Peers: Powerful or Negligible? A Systematic Review on Peer Factors and Internal Whistleblowing - Mir Djawadi, Behnud; Plaß, Sabrina; Schäfers, Sabrina (Organizational Wrongdoing as the “Foundational” Grand Challenge: Consequences and Impact)

Graph Neural Network-based Anomaly Detection in Smart Grid Energy Consumption - Ranade, Amruta

Highly accurate deep compressed facial recognition - Ali, Osama