476 citations
- New York UniversityUS42 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Centre National de la Recherche ScientifiqueFR12 papers
- Fraunhofer Institute for Integrated CircuitsDE12 papers
- University of KonstanzDE12 papers
- University of StuttgartDE12 papers
- Technische Universität BerlinDE10 papers
- Universität HamburgDE10 papers
- Humboldt-Universität zu BerlinDE9 papers
- Occidental CollegeUS9 papers
- Chemnitz University of TechnologyDE8 papers
- Freie Universität BerlinDE8 papers
20 papers · 1 filter
Transitional boundary layers in low-Prandtl-number convection
Jörg Schumacher, Vinodh Bandaru, Ambrish Pandey +1
The boundary layer structure of the velocity and temperature fields in turbulent Rayleigh-Benard flows in closed cylindrical cells of unit aspect ratio is revisited from a transiti…
On the SNR Variability in Noisy Compressed Sensing
Anastasia Lavrenko, Florian Roemer, Giovanni Del Galdo +1
Compressed sensing (CS) is a sampling paradigm that allows to simultaneously measure and compress signals that are sparse or compressible in some domain. The choice of a sensing ma…
Blow-up lemmas for sparse graphs
Peter Allen, Julia Böttcher, Hiep Hàn +2
The blow-up lemma states that a system of super-regular pairs contains all bounded degree spanning graphs as subgraphs that embed into a corresponding system of complete pairs. Thi…
The 3D Limaçon: Properties and Applications
Jakob Kreismann, Martina Hentschel, Stefan Sinzinger
We perform electromagnetic wave simulations of fully three-dimensional optical Limaçon-microcavities, one basis for their future applications in microlasers and photonic devices. T…
Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths
Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger +2
We present an average case analysis of a variant of dual-pivot quicksort. We show that the used algorithmic partitioning strategy is optimal, i.e., it minimizes the expected number…
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods
Antoine Gautier, Quynh Nguyen, Matthias Hein
The optimization problem behind neural networks is highly non-convex. Training with stochastic gradient descent and variants requires careful parameter tuning and provides no guara…