68 citations
- University of LübeckDE6 papers
- CEA GrenobleFR1 paper
- Center for Theoretical PhysicsPL1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR1 paper
- Direction de la Recherche TechnologiqueFR1 paper
- German Research Centre for Artificial IntelligenceDE1 paper
- Hamburg University of TechnologyDE1 paper
- Hochschule für Angewandte Wissenschaften KielDE1 paper
- IkerbasqueES1 paper
- Institut Jean LamourFR1 paper
- Institut Nanosciences et CryogénieFR1 paper
8 papers
Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing
Stefan Fischer, Nihat Ay, Olaf Landsiedel +4
Physical implementations of neural computation now extend far beyond silicon hardware, encompassing substrates such as memristive devices, photonic circuits, mechanical metamateria…
Don't Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysis
Bennet Kahrs, Julia Andresen, Fenja Falta +3
Routine clinical imaging of the retina using optical coherence tomography (OCT) is performed with large slice spacing, resulting in highly anisotropic images and a sparsely scanned…
Accelerated Ostwald ripening by chemical activity
Benjamin Sorkin, Ned S. Wingreen
Phase separation of biomolecular condensates promotes membrane-free compartmentalization in cells. The dynamics of these biocondensates is routinely regulated by energy-consuming p…
Zebrafix: Mitigating Memory-Centric Side-Channel Leakage via Interleaving
Anna Pätschke, Jan Wichelmann, Thomas Eisenbarth
Constant-time code has become the de-facto standard for secure cryptographic implementations. However, some memory-based leakage classes such as ciphertext side-channels and silent…
Laser-induced plasma formation and cavitation in water: from nanoeffects to extreme states of matter
Norbert Linz, Sebastian Freidank, Xiao-Xuan Liang +1
We present an in-depth analysis of the energy dependence of optical breakdown in water by tightly focused laser pulses, from plasma formation to shock waves and cavitation. Laser p…
Deep Convolutional Neural Networks as Generic Feature Extractors
Lars Hertel, Erhardt Barth, Thomas Käster +1
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve conv…