Aktuelle Publikationen

November 2022

Towards the Interpretability of Deep Learning Models for Multi-Modal Neuroimaging: Finding Structural Changes of the Ageing Brain

Simon M. Hofmann, Klaus-Robert Müller, Wojciech Samek, Arno Villringer, Sebastian Lapuschkin, Frauke Beyer, Markus Loeffler, A. Veronica Witte, Ole Goltermann

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with...

Oktober 2022

Optical Generation and Transmission of mmWave Signals in 5G ERA: Experimental Evaluation Paradigm

Efstathios Andrianopoulos, Christos Kouloumentas, Norbert Keil, David de Felipe Mesquida, Simon Nellen, Panos Groumas, Lefteris Gounaridis, Christos Tsokos, Tianwen Qian, Herkules Avramopoulos, Adam Raptakis, Nikolaos K. Lyras

We demonstrate the generation, of a mmWave signal via the injection of an optical frequency comb (OFC) into an integrated tunable dual distributed Bragg reflector (DBR) laser as well as the fiber transmission and the processing of this signal by...

September 2022

Experimental Investigation of Information Bit Scrambling for Physical-Layer Security in Coherent Fiber-Optic Systems

Carsten Schmidt-Langhorst, Colja Schubert, Robert Elschner, Robert F. H. Fischer, Robert Emmerich, Johannes Pfeiffer, Fabian Chowanek, In-Ho Baek

We experimentally demonstrate tap-proof coherent optical 640-Gb/s transmission based on encryption-less physical layer security. Information bit scrambling combined with soft-decision error-correction coding yields favorably small security gaps,...

August 2022

Perfusion Assessment via Local Remote Photoplethysmography (rPPG)

Benjamin Kossack, Peter Eisert, Anna Hilsmann, Eric Wisotzky, Sebastian Schraven, Brigitta Globke

We present an approach to assess the perfusion of visible human tissue from RGB video files. We show that locally resolved rPPG-signals can be used for intraoperative perfusion analysis and visualization during skin and organ transplantation as...

August 2022

Customizing the Appearance of Sparks with Binary Metal Alloys

Philipp Memmel, Wolfgang Schade, Jannis Koch, Mingji Li, Eike Hübner, Felix Lederle, Martin Söftje

Alloys consisting of >65 at. % of a brightly emitting and low-boiling-point metal and a carrier metal allow achieving long-flying deeply colored sparks. Besides the color, branching of sparks is crucial for the visual appearance. Rare-earth...

August 2022

Explaint to not Forget: Defending Against Catastrophic Forgetting with XAI

Sami Ede, Wojciech Samek, Sebastian Lapuschkin, Leander Weber, Serop Baghdadlian, An Nguyen, Dario Zanca

The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, the traditional optimization algorithms often require large amounts...

August 2022

Towards the Interpretability of Deep Learning Models for Human Neuroimaging

Simon M. Hofmann, Klaus-Robert Müller, Wojciech Samek, Arno Villringer, Sebastian Lapuschkin, Frauke Beyer, Markus Loeffler, A. Veronica Witte

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with...

Juli 2022

Characterization of Dispersion-Tailored Silicon Strip Waveguide for Wideband Wavelength Conversion

Hidenobu Muranaka, Colja Schubert, Carsten Schmidt-Langhorst, Tomoyuki Kato, Isaac Sackey, Takeshi Hoshida, Gregor Ronniger, Shun Okada, Tokuharu Kimura, Yu Tanaka, Tsuyoshi Yamamoto

In view of application to wideband wavelength conversion, an SOI waveguide was fabricated and characterized. Conversion of C- band WDM test signals into S- and L- bands in a single waveguide is demonstrated.

Juli 2022

DSP-Based Link Tomography for Amplifier Gain Estimation and Anomaly Detection in C+L-Band Systems

Matheus Ribeiro Sena, Ronald Freund, Robert Emmerich, Johannes K. Fischer, Mohammad Behnam Shariati, Caio Marciano Santos

In this work, we propose a spatially-resolved and wavelength-dependent DSP-based monitoring scheme to accurately estimate the spectral gain profile of C+L-band in-line Erbium-doped fiber amplifiers deployed in a 280-km single mode fiber link.

Juli 2022

Bayesian Optimization for Nonlinear System Identification and Pre-distortion in Cognitive Transmitters

Matheus Ribeiro Sena, Ronald Freund, Johannes Fischer, Robert Emmerich, Mustafa Sezer Erkilinc, Mohammad Behnam Shariati, Thomas Dippon

We present a digital signal processing (DSP) scheme that performs hyperparameter tuning (HT) via Bayesian optimization (BO) to autonomously optimize memory tap distribution of Volterra series and adapt parameters used in the synthetization of a...

Juli 2022

Toward Explainable AI for Regression Models

Simon Letzgus, Klaus-Robert Müller, Wojciech Samek, Grégoire Montavon, Patrick Wagner, Jonas Lederer

While such Explainable AI (XAI) techniques have reached significant popularity for classifiers, so far little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI...

Juni 2022

Multiparametric MRI for characterization of the basal ganglia and the midbrain

Till M. Schneider, Jackie Ma, Patrick Wagner, Nicolas Behl, Armin Michael Nagel, Mark E. Ladd, Sabine Heiland, Martin Bendszus, Sina Straub

In this joint work with the University of Heidelberg, German Cancer Research Center, University Hospital of Erlangen we showed that multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with...

Juni 2022

Communication-Efficient Federated Distillation via Soft-Label Quantization and Delta Coding

Felix Sattler, Wojciech Samek, Arturo Marban, Roman Rischke

Communication constraints prevent the wide-spread adoption of Federated Learning systems. In this work, we investigate Federated Distillation (FD) from the perspective of communication efficiency by analyzing the effects of active...

Juni 2022

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, an augmentation of federated...

Juni 2022

Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

Andreas Rieckmann, Wojciech Samek, Sebastian Lapuschkin, Leila Arras, Piotr Dworzynski, Onyebuchi A. Arah, Naja H. Rod, Claus T. Ekstrom

Nearly all diseases are caused by different combinations of exposures. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of...

Juni 2022

A Benchmark Dataset for the Ground Truth Evaluation of Neural Network Explanations

Leila Arras, Wojciech Samek, Ahmed Osman

Recently, the field of explainable AI (XAI) has developed methods that provide such explanations for already trained neural networks. So far XAI methods along with their heatmaps were mainly validated qualitatively via human-based assessment, or...

Mai 2022

Improve the Deep Learning Models in Forestry Based on Explanations and Expertise

Ximeng Cheng, Ali Doosthosseini, Julian Kunkel

This research improves deep learning models based on explanations and expertise. The way is to set the annotation matrix for each training sample. Three image classification tasks in forestry verify the method.

Mai 2022

Coherent Wireless Link at 300 GHz with 160 Gbit/s Enabled by a Photonic Transmitter

Simon Nellen, Sebastian Lauck, Emilien Peytavit, Pascal Szriftgiser, Martin Schell, Guillaume Ducourn, Björn Globisch

We demonstrate a wireless link at 300 GHz using a fiber-coupled PIN photodiode as the transmitter. We achieved a maximum line rate of 160 Gbit/s with 32QAM modulation. The highest spectral efficiency was achieved with 64QAM at 8 GBaud, i.e. 48...

Mai 2022

Overview of the Neural Network Compression and Representation (NNR) Standard

Heiner Kirchhoffer, Karsten Müller, Werner Bailer, Fabien Racape, Wojciech Samek, Shan Liu, Miska M. Hannuksela, Paul Haase, Hamed Rezazadegan-Tavakoli, Francesco Cricri, Emre Aksu, Wei Jiang, Wei Wang, Swayambhoo Jain, Shahab Hamidi-Rad

Neural Network Coding and Representation (NNR) is the first international standard for efficient compression of neural networks. The NNR standard contains quantization and an arithmetic coding scheme as core encoding and decoding technologies, as...

Mai 2022

Low-Loss Bragg-ReflectionWaveguides for On-Chip Time-Bin Entanglement

Hannah Thiel, Moritz Kleinert, Hauke Conradi, Lennart Jehle, Robert Chapman, Stefan Frick, Gregor Weihs, Marita Wagner, Bianca Nardi, Alexander Schlager, Holger Suchomel, Martin Kamp, Sven Hofling, Christian Schneider

We fabricate low-loss AlGaAs Bragg-reflection waveguides for the creation of C-band photon pairs via parametric down-conversion. These photon pairs are used in a hybrid on-chip time-bin entanglement scheme.

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