Publications

Huy Viet Le, Sven Mayer, und Niels Henze. InfiniTouch: Finger-Aware Interaction on Fully Touch Sensitive Smartphones. Proceedings of the 31th Annual ACM Symposium on User Interface Software and Technology, ACM, New York, NY, USA, 2018. [PUMA: vis(us) learning machine touchscreen finger-aware interaction vis-sks vis visus:mayersn visus:henzens visus:lehy tiger-generation:639052371226514286] URL

Huy Viet Le, Thomas Kosch, Patrick Bader, Sven Mayer, und Niels Henze. PalmTouch: Using the Palm As an Additional Input Modality on Commodity Smartphones. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 360:1-360:13, ACM, New York, NY, USA, 2018. [PUMA: vis-sks vis vis(us) capacitive machine smartphone learning image palm visus:lehy visus:baderpk visus:koschts visus:mayersn visus:henzens tiger-generation:639052371226514286] URL

Huy Viet Le, Sven Mayer, Thomas Kosch, und Niels Henze. Demonstrating PalmTouch: The Palm as An Additional Input Modality on Commodity Smartphones. Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, ACM, New York, NY, USA, 2018. [PUMA: vis(us) palm smartphone image learning machine capacitive vis-sks vis visus:mayersn visus:henzens visus:koschts visus:lehy tiger-generation:639052371226514286] URL

Michaela Gruber, Michael Beltle, und Stefan Tenbohlen. Evaluating EMI Emission Challenges in Drive Inverter Testing using Electrical Machine Emulators. 367-372, September 2025. [PUMA: Challenges Drive EMI Electrical Emission Emulators Evaluating Inverter Machine Testing in using]

Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, und Andrea Villmann. Appropriate data density models in probabilistic machine learning approaches for data analysis. International Conference on Artificial Intelligence and Soft Computing, 443--454, 2019. [PUMA: data density learning machine probabilistic]

Adrian Schenek, Marcel Görz, Mathias Liewald, und Kim Rouven Riedmüller. Data-Driven Derivation of Sheet Metal Properties Gained from Punching Forces Using an Artificial Neural Network. Key Engineering Materials, (926):2174--2182, 2022. [PUMA: AS KR MG ML Machine characterization;Punching force;Tensile learning;Material properties]

Marcel Görz, Adrian Schenek, Mathias Liewald, und Kim Rouven Riedmüller. Evaluation of Feature Engineering Methods for the Prediction of Sheet Metal Properties from Punching Force Curves by an Artificial Neural Network. In Mingming Zhang, Zhiwei Peng, Bowen Li, Sergio Neves Monteiro, Rajiv Soman, Jiann-Yang Hwang, Yunus Eren Kalay, Juan P. Escobedo-Diaz, John S. Carpenter, Andrew D. Brown, und Shadia Ikhmayies (Hrsg.), Characterization of Minerals, Metals, and Materials 2023, 75--85, Springer Nature Switzerland, Cham, 2023. [PUMA: AS KR MG ML Machine characterization;Punching force;Tensile learning;Material properties]

Axel Körner, Edith Anahi Gonzalez San Martin, Larissa Born, Matthias Ridder, Stephan Moser, Robert Weitlaner, Götz T. Gresser, und Jan Knippers. FlectoLine: eine adaptive Fassade für nachhaltige Architektur FlectoLine: A responsive Façade for sustainable architecture. Stahlbau, Oktober 2025. [PUMA: Aktuatorik Bionik Fassade Kunststoff Lernen Mechanismen Photovoltaik Zwilling actuation adaptive biomimetic compliant digital digitaler faserverstärkter façade fibre learning machine maschinelles mechanisms nachgiebige photo plastic pneumatic pneumatische reinforced responsive twin voltaic] URL

Mario U. Gaimann, und Miriam Klopotek. Optimal information injection and transfer mechanisms for active matter reservoir computing. arXiv, 2025. [PUMA: (cond-mat.soft), (cs.LG), (nlin.AO), (physics.comp-ph), Adaptation Computational Computer Condensed FOS: Learning Machine Matter Physical Physics Self-Organizing Soft Systems and information manybodyml myown publist sciences sciences,] URL

Mario U. Gaimann, und Miriam Klopotek. Robustly optimal dynamics for active matter reservoir computing. arXiv, 2025. [PUMA: (cond-mat.soft), (cs.LG), (nlin.AO), (physics.comp-ph), Adaptation Computational Computer Condensed FOS: Learning Machine Matter Physical Physics Self-Organizing Soft Systems and information manybodyml myown publist sciences sciences,] URL

Konstantin Nikolaou, Sven Krippendorf, Samuel Tovey, und Christian Holm. Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens. 2025. [PUMA: icp, learning, machine myown network, neural] URL

Konstantin Nikolaou, Sven Krippendorf, Samuel Tovey, und Christian Holm. Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens. 2025. [PUMA: myown icp, learning, network, neural machine] URL

Lukas Steinle, Valentin Leipe, Armin Lechler, und Alexander Verl. Automatic preload adaptation for rack-and-pinion drives to maximize performance and energy efficiency. Production Engineering, 19.11.2024. [PUMA: control drive feed isw machine position tool] URL

Saied Ramedani, Ebru Kelesoglu, Norman Stutzig, Hendrik Von Tengg‐Kobligk, Keivan Daneshvar Ghorbani, und Tobias Siebert. Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females. In Tobias Siebert (Hrsg.), Physiological Reports, (13)3Wiley, Januar 2025. [PUMA: body composition deep imaging learning machine magnetic medicine musculoskeletal resonance sports system] URL

Huy Viet Le, Sven Mayer, und Niels Henze. InfiniTouch: Finger-Aware Interaction on Fully Touch Sensitive Smartphones. Proceedings of the 31th Annual ACM Symposium on User Interface Software and Technology, ACM, New York, NY, USA, 2018. [PUMA: vis(us) interaction learning machine touchscreen finger-aware vis-sks vis visus:henzens visus:mayersn visus:lehy tiger-generation:638684700229105045] URL

Huy Viet Le, Thomas Kosch, Patrick Bader, Sven Mayer, und Niels Henze. PalmTouch: Using the Palm As an Additional Input Modality on Commodity Smartphones. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 360:1-360:13, ACM, New York, NY, USA, 2018. [PUMA: vis-sks vis vis(us) smartphone image machine learning palm capacitive visus:lehy visus:baderpk visus:koschts visus:mayersn visus:henzens tiger-generation:638684700229105045] URL

Huy Viet Le, Sven Mayer, Thomas Kosch, und Niels Henze. Demonstrating PalmTouch: The Palm as An Additional Input Modality on Commodity Smartphones. Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, ACM, New York, NY, USA, 2018. [PUMA: vis(us) palm smartphone image learning machine capacitive vis-sks vis visus:henzens visus:mayersn visus:koschts visus:lehy tiger-generation:638684700229105045] URL

Lukas Steinle, Valentin Leipe, Armin Lechler, und Alexander Veri. Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives. 2024 European Control Conference (ECC), 2441-2447, Juni 2024. [PUMA: compensation drive error feed isw learning machine tool]

Andreas Bulling, Daniel Roggen, und Gerhard Tröster. What's in the Eyes for Context-Awareness?. IEEE Pervasive Computing, (10)2:48-57, 2011. [PUMA: Computing Machine Pervasive Wearable computing, hcics learning, processing, signal vis]

Duy Minh Ho Nguyen, Hoang Nguyen, Nghiem T Diep, Tan N Pham, Tri Cao, Binh T Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, und Mathias Niepert. LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023. [PUMA: graph; learning machine] URL