Munk, Axel

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03.03.2020

Statistical Molecule Counting in Super-Resolution Fluorescence Microscopy: Towards Quantitative Nanoscopy

Authors Staudt T, Aspelmeier T, Laitenberger O, Geisler C, Egner A, Munk A Journal Statistical Science Citation Statist. Sci., Volume 35, Number 1 (2020), 92-111. Abstract Super-resolution microscopy is rapidly gaining importance as an analytical tool in the life sciences. A compelling feature is the ability to label biological units
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22.07.2019

Molecular contribution function in RESOLFT nanoscopy

Authors Frahm L, Keller-Findeisen J, Alt P, Schnorrenberg S, Del Alamo Ruiz M, Aspelmeier T, Munk A, Jakobs S, Hell SW Journal Optics Express Citation Opt Express. 2019 Jul 22;27(15):21956-21987. Abstract BACKGROUND: The ultimate objective of a microscope of the highest resolution is to map the molecules of interest in
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04.02.2019

High-resolution experimental and computational electrophysiology reveals weak beta-lactam binding events in the porin PorB

Authors Bartsch A, Llabres S, Pein F, Kattner C, Schon M, Diehn M, Tanabe M, Munk A, Zachariae U, Steinem C Journal Scientific Reports Citation Sci Rep. 2019 Feb 4;9(1):1264. Abstract The permeation of most antibiotics through the outer membrane of Gram-negative bacteria occurs through porin channels. To design drugs
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05.01.2019

Multiscale change-point segmentation: beyond step functions

Authors Li H, Guo Q, Munk A Journal Electronic Journal of Statistics Citation Electron. J. Statist. Volume 13, Number 2 (2019), 3254-3296. Abstract Modern multiscale type segmentation methods are known to detect multiple change-points with high statistical accuracy, while allowing for fast computation. Underpinning (minimax) estimation theory has been developed
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01.01.2019

Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications

Authors Tameling C, Sommerfeld M, Munk A Journal Annals of Applied Probability Citation Ann. Appl. Probab. 29 (2019), no. 5, 2744–2781. Abstract We derive distributional limits for empirical transport distances between probability measures supported on countable sets. Our approach is based on sensitivity analysis of optimal values of infinite dimensional
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