Aktuelle Publikationen

Juni 2023

Assessing the Value of Multimodal Interfaces: A Study on Human–Machine Interaction in Weld Inspection Workstations

Paul Chojecki, Peter Eisert, Sebastian Bosse, Detlef Runde, David Przewozny, Niklas Gard, Niklas Hoerner, Dominykas Strazdas, Ayoub Al-Hamadi

Multimodal user interfaces promise natural and intuitive human–machine interactions. However, is the extra effort for the development of a complex multisensor system justified, or can users also be satisfied with only one input modality? This...


Juni 2023

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

Alexander Binder, Klaus-Robert Müller, Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Leander Weber

While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing is often overestimated and regarded...


Juni 2023

Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations

Maximilian Dreyer, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin, Reduan Achtibat

Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or...


Juni 2023

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models

Frederick Pahde, Wojciech Samek, Sebastian Lapuschkin, Maximilian Dreyer

State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To...


Juni 2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus

Anna Hedström, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne, Philine Bommer, Kristoffer K. Wickstrøm

Explainable AI (XAI) is a rapidly evolving field that aims to improve transparency and trustworthiness of AI systems to humans. One of the unsolved challenges in XAI is estimating the performance of these explanation methods for neural networks,...


Juni 2023

Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement

Leander Weber, Wojciech Samek, Alexander Binder, Sebastian Lapuschkin

Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically to...


Juni 2023

Sydnone Methides: Intermediates between Mesoionic Compounds and Mesoionic N-Heterocyclic Olefins

Sebastian Mummel, Eike Hübner, Felix Lederle, Jan C. Namyslo, Martin Nieger, Andreas Schmidt

Sydnone methides represent an almost unknown class of mesoionic compounds which possess exocyclic carbon substituents instead of oxygen (sydnones) or nitrogen (sydnone imines) in the 5-position of a 1,2,3-oxadiazolium ring. Unsubstituted...


Juni 2023

Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models

Daniel Krakowczyk, Sebastian Lapuschkin, David Robert Reich, Paul Prasse, Lena Ann Jäger, Tobias Scheffer

Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. In this work, we employ established...


Mai 2023

Semantic modeling of cell damage prediction: A machine learning approach at human-level performance in dermatology

Patrick Wagner, Jackie Ma, Maximilian Springenberg, Marius Kröger, Rose K. C. Moritz, Johannes Schleusener, Martina C. Meinke

In this work we investigate cell damage in whole slice images of the epidermis. A common way for pathologists to annotate a score, characterising the degree of damage for these samples, is the ratio between healthy and unhealthy nuclei. The...


Mai 2023

Data Models for Dataset Drift Controls in Machine Learning With Optical Images

Luis Oala, Wojciech Samek, Gabriel Nobis, Christian Matek, Bruno Sanguinetti, Marco Aversa, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Jerome Extermann, Enrico Pomarico, Roderick Murray-Smith, Christoph Clausen

In this study, we pair traditional machine learning with physical optics to obtain explicit and differentiable data models. We demonstrate how such data models can be constructed for image data and used to control downstream machine learning...


Mai 2023

Demonstration of a 15-Mode Network Node Supported by a Field-Deployed 15-Mode Fiber

Ruben S. Luis, A. Mecozzi, Colja Schubert, F. Achten, Robert Emmerich, Nicolas Braig-Christophersen, Georg Rademacher, Hideaki Furukawa, Giammarco Di Sciullo, Andrea Marotta, Ralf Stolte, Fabio Graziosi, Cristian Antonelli, Pierre Sillard, Giuseppe Ferri, Benjamin J. Puttnam, Roland Ryf, Lauren Dallachiesa, Satoshi Shinada

Researchers from NICT, University of L’Aquila, Finisar, Prysmian and Nokia Bell Lab demonstrate a 2-line side 15-mode spatial division multiplexing network node based on fifteen 2×2 wavelength cross-connects to direct up to six 5 Tb/s, 15-mode,...


Mai 2023

Experimental and Numerical Evaluation of CAZAC-type Training Sequences for MxM SDM-MIMO Channel Estimation

Nicolas Braig-Christophersen, Colja Schubert, Carsten Schmidt-Langhorst, Robert Elschner, Johannes Fischer, Robert Emmerich, Andreas Maaßen, Juan L. Morrone

In this work, we experimentally and numerically compare cyclic shifted constant-amplitude zero-autocorrelation (CAZAC) training sequences (TS) with different number of repetitions, sequence lengths and scalings for channel estimation in an...


April 2023

Optimization and Performance Evaluation of Single-mode SOI Waveguides for Ultra-broadband C-to-S Wavelength Conversion

Gregor Ronniger, Colja Schubert, Carsten Schmidt-Langhorst, Ronald Freund, Robert Elschner, Tomoyuki Kato, Isaac Sackey, Takeshi Hoshida, Hidenobu Muranaka, Shun Okada, Yu Tanaka, Tsuyoshi Yamamoto, Md Mahasin Khan

We implemented an all-optical C-to-S band wavelength converter based on low-complexity SOI strip waveguides. Its bandwidth was optimized and crosstalk issues were modeled numerically. These devices were experimentally evaluated to have a...


April 2023

Telepresence for surgical assistance and training using eXtended reality during and after pandemic periods

Eric Wisotzky, Peter Eisert, Anna Hilsmann, Jean-Claude Rosenthal, Florian Uecker, Philipp Arens, Armin Schneider, Senna Meij, John van den Dobblesteen

Existing challenges in surgical education (See one, do one, teach one) as well as the COVID-19 pandemic make it necessary to develop new ways for surgical training. This work describes the implementation of a scalable remote solution called...


April 2023

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

Rodrigo Hernangómez, Philipp Geuer, Alexandros Palaios, Daniel Schäufele, Cara Watermann, Khawla Taleb-Bouhemadi, Mohammad Parvini, Anton Krause, Sanket Partani, Christian Vielhaus, Martin Kasparick, Daniel F. Külzer, Friedrich Burmeister, Frank H. P. Fitzek, Hans D. Schotten, Gerhard Fettweis, Sławomir Stańczak

The Berlin V2X dataset offers high-resolution GPS-located wireless measurements across diverse urban environments in the city of Berlin for both cellular and sidelink radio access technologies, including information on the physical layer,...


April 2023

Uniform Analysis of Multipath Components From Various Scenarios With Time-Domain Channel Sounding at 300GHz

Alper Schultze, Wilhelm Keusgen, Michael Peter, Ramez Askar, Thomas Kürner, Johannes M.Eckhardt, Tobias Doeker

For the first time, channel measurements at 300 GHz from various environments and different measurement systems are jointly analyzed and published.


April 2023

1x4 Vertical Power Splitter/Combiner: A Basic Building Block for Complex 3D Waveguide Routing Networks

Madeleine Weigel, Norbert Keil, Martin Schell, Moritz Kleinert, Crispin Zawadzki, D. De Felipe, Martin Kresse, Tianwen Qian, Jakob Reck, Klara Mihov, Jan H. Bach, Philipp Winklhofer

A novel polymer-based 1x4 vertical multimode interference (MMI) coupler for 3D photonics is presented. It connects four vertically stacked waveguide layers with a spacing of 21.6 µm. The functionality is demonstrated on a fabricated device.


April 2023

FedAUXfdp: Differentially Private One-Shot Federated Distillation

Haley Hoech, Karsten Müller, Wojciech Samek, Roman Rischke

Federated learning suffers in the case of non-iid local datasets, i.e., when the distributions of the clients’ data are heterogeneous. One promising approach to this challenge is the recently proposed method FedAUX. This work presents a fully...


April 2023

Coherent LiDAR with 2D quasi-static MEMS mirror scanning

Sarah Cwalina, Volker Jungnickel, Ronald Freund, Christoph Kottke, Norman Laske

Coherent LiDAR concepts for automotive applications are of increasing interest due to their superior performance, especially at longer range. Here, MEMS mirrors can be used as a compact scanning device. We demonstrate the operation of such a...


März 2023

Partial MIMO-based Mode Division Multiplexing Transmission over the First Field-Deployed 15-Mode Fiber in Metro Scenario

Alberto Gatto, A. Mecozzi, Colja Schubert, F. Achten, Robert Emmerich, Georg Rademacher, Ruben S. Luis, Giammarco Di Sciullo, Benjamin Puttnam, Andrea Marotta, Fabio Graziosi, Cristian Antonelli, Paola Parolari, Giuseppe Ferri, Pierre Sillard, Paolo Martelli, Pierpaolo Boffi

Researchers from Politecnico di Milano, NICT, Prysmian and University of L’Aquila assess mode division multiplexing transmission based on partial MIMO equalization over 6.1 km of the first deployed 15-mode fiber. They demonstrate more than...



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