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Viele Veranstaltungen werden aktuell auch online durchgeführt. Informationen zum Zugang finden Sie jeweils unter „mehr” bei dem betreffenden Eintrag.


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Dienstag, 20.04.2021, 15:00 Uhr (Online Event)
Seminar Modern Methods in Applied Stochastics and Nonparametric Statistics
Dr. Caroline Geiersbach, WIAS Berlin:
Stochastic approximation with applications to PDE-constrained optimization under uncertainty (online talk)
mehr ... Veranstaltungsort
Online Event

Weitere Informationen
Dieser Vortrag findet bei Zoom statt: https://zoom.us/j/492088715

Veranstalter
WIAS Berlin
Dienstag, 20.04.2021, 15:15 Uhr (Online Event)
Oberseminar Nonlinear Dynamics
Alan Rendall, Johannes Gutenberg-Universität Mainz:
Bogdanov--Takens bifurcations and the regulation of enzymatic activity by autophosphorylation
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Online Event

Abstrakt
An important mechanism of information storage in molecular biology is the binding of phosphate groups to proteins. In this talk we consider the case of autophosphorylation, where the protein is an enzyme and the substrate to which it catalyses the binding of a phosphate group is that enzyme itself. It turns out that this often leads to more complicated dynamics than those seen in the case where enzyme and substrate are distinct. We focus on the example of the enzyme Lck (lymphocyte-associated tyrosine kinase) which is of central importance in the function of immune cells. We study a model for the activation of Lck due to Kaimachnikov and Kholodenko and give a rigorous proof that it admits periodic solutions. We do so by showing that it exhibits a generic Bogdanov-Takens bifurcation. This is an example where this approach gives a simpler proof of the existence of periodic solutions than ones

Weitere Informationen
Diesen Vortrag können Sie mit Zoom verfolgen unter: https://zoom.us/j/84203723799.

Veranstalter
Freie Universität Berlin
WIAS Berlin
Mittwoch, 21.04.2021, 09:00 Uhr (Online Event)
Forschungsseminar Mathematische Statistik
Prof. Hans-Georg Müller, University of California, Davis, USA:
Functional models for time-varying random objects (online talk)
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Online Event

Abstrakt
In recent years, samples of random objects and time-varying object data such as time-varying distributions or networks that are not in a vector space have become increasingly prevalent. Such data can be viewed as elements of a general metric space that lacks local or global linear structure. Common approaches that have been used with great success for the analysis of functional data, such as functional principal component analysis, are therefore not applicable. The concept of metric covariance makes it possible to define a metric auto-covariance function for a sample of random curves that take values in a general metric space and it can be shown to be non-negative definite when the squared semi-metric of the underlying space is of negative type. Then the eigenfunctions of the linear operator with the auto-covariance function as kernel can be used as building blocks for an object functional principal component analysis, which includes real-valued Frechet scores and metric-space valued object functional principal components. Sample based estimates of these quantities are shown to be asymptotically consistent and are illustrated with various data. (Joint work with Paromita Dubey, Stanford University.)

Weitere Informationen
Der Vortrag findet bei Zoom statt: https://zoom.us/j/159082384

Veranstalter
Humboldt-Universität zu Berlin
WIAS Berlin
Mittwoch, 21.04.2021, 15:15 Uhr (Online Event)
Berliner Oberseminar „Nichtlineare partielle Differentialgleichungen” (Langenbach-Seminar)
Dr. Nikolas Nüsken, Universität Potsdam:
The Stein geometry in machine learning: gradient flows, large deviations and convergence properties (online talk)
mehr ... Veranstaltungsort
Online Event

Abstrakt
Sampling or approximating high-dimensional probability distributions is a key challenge in computational statistics and machine learning. This talk will present connections to gradient flow PDEs and interacting particle systems, focusing on the recently introduced Stein variational gradient descent methodology. The construction induces a novel geometrical structure on the set of probability distributions related to a positive definite kernel function.We discuss the corresponding geodesic equations as well as large deviation functionals and leverage those to shed some light on the convergence properties of the algorithm. This is joint work with A. Duncan (Imperial College London), L. Szpruch (University of Edinburgh) and M. Renger (Weierstrass Institute Berlin).

Veranstalter
Humboldt-Universität zu Berlin
WIAS Berlin