German Unity Day
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03. October
Sat W39
Sat, 03. Oct
07. October
Wed W40
Georg
Loho
FU Berlin
14. October
Wed W41
Chloe
Rouyer
Universtität Potsdam
Azadeh
Parvaneh
Bielefeld University
Large Deviation Principle for Friendship Biases in Galton-Watson Trees
Abstract.
The friendship bias of a vertex is defined as the difference between the average degree of its neighbours and its own degree; for isolated vertices, this bias is considered to be zero. According to the sign of the friendship bias, vertices can naturally be classified as "negative", "neutral", or "positive". The friendship paradox says that the average friendship bias is non-negative for all finite undirected graphs, whether simple graphs or multigraphs. However, the combined number of neutral and positive vertices can be significantly smaller than the number of negative vertices. For instance, in a complete graph on a large number of vertices with a single edge removed, only the two endpoints of the removed edge are positive, while all other vertices are negative. The fractions of different vertex types can also vary across graphs and reflect aspects of the geometry of a graph. The typical behaviour of these fractions has been studied for sparse random graphs that are locally tree-like, as well as for finite and infinite Galton-Watson trees. In this talk, we analyse the atypical behaviour of the fractions of vertex types along a random downward path in an infinite Galton-Watson tree by deriving a large deviation principle as the branching depth grows. The rate function is characterised through a variational problem involving relative entropy under a linear constraint. We discuss its properties in the case of binary branching. Based on joint work with Frank den Hollander.
Tom
ter Elst
Department of Mathematics, University of Auckland, New Zealand
Dan
Kral
Leipzig University & MPI MiS
15. October
Thu W41
Organisatorial meeting: Overview and distribution of talks
20. October
Tue W42
Giuseppe
Ancona
Strasburg
21. October
Wed W42
Kolyan
Ray
Imperial College London
27. October
Tue W43
Alexander
Bobenko
TU Berlin
28. October
Wed W43
Olivier
de Gaay Fortman
Imperial College
Nicolas
Verzelen
INRAE
Lars
Schröder
University of Twente
03. November
Tue W44
Nitin Kumar
Chidambaram
UNED Madrid
Refining Witten-Kontsevich
Abstract.
The moduli space of metric Möbius graphs, which are non-orientable ribbon graphs, has one component homeomorphic to the moduli space of Riemann surfaces and another component homeomorphic to the moduli space of Klein surfaces. I'll discuss a lattice point count on this moduli space, weighted by a polynomial in b known as the measure of non-orientability. This "refined" lattice point count satisfies a refined version of Norbury's recursion for the count of lattice points on the moduli space of curves. Consequently, we obtain a recursion for the volumes of these moduli spaces that reduces to the Witten-Kontsevich recursion when b=0.
Charles
Vial
Bielefeld
04. November
Wed W44
Mark
Podolski
Universität Luxemburg
Martin
Kružík
Czech Academy of Sciences Prague, CZ
10. November
Tue W45
Gregorio
Baldi
Jussieau
11. November
Wed W45
Simon
Weißmann
Universität Mannheim
17. November
Tue W46
Ivan
Dneprov
University of Mons
18. November
Wed W46
Botond
Szabo
Bocconi
Ulrik
Thinggaard Hansen
University of Innsbruck
Gianna
Götzmann
U Augsburg
Perturbed minimizing movements of time-dependent functionals on metric spaces
Abstract
24. November
Tue W47
Javier
Fresán
Jussieau
25. November
Wed W47
Christoph
von Tycowicz
Zuse Institut Berlin (ZIB)
Symmetry-preserving Geodesic Regression on Lie Groups for Longitudinal Medical Imaging
Abstract.
Many medical imaging tasks require statistical modeling of continuous transformations, including longitudinal anatomical shape change and articulated skeletal motion. These transformations naturally live on Lie groups, where meaningful statistical analysis should respect group symmetries to remain invariant to arbitrary coordinate choices and reference frames. In this talk, I will present a geodesic regression framework on Lie groups for longitudinal imaging data. Common approaches rely on Riemannian metrics, but many Lie groups do not admit a metric fully compatible with the group structure. This mismatch breaks symmetry and leads to unstable regression estimates. We therefore introduce a non-metric, bi-invariant estimator that is equivariant under both left and right group actions. We evaluate the method on synthetic data and on an open-access clinical dataset of longitudinal knee joint configurations acquired for osteoarthritis research. The proposed approach yields stable trajectories and reproducible statistical conclusions, while state-of-the-art Riemannian methods exhibit sensitivity and instability. These results highlight the practical advantages of symmetry-preserving statistical modeling in longitudinal medical imaging studies.
02. December
Wed W48
Hugo
Cue
Orsay
04. December
Fri W48
08. December
Tue W49
Lukas
Müller
LMU München
09. December
Wed W49
Yuta
Koike
Tokyo University
16. December
Wed W50
Eardi
Lila
University of Washington
Geometric Deep Operator Learning for Inverse Problems with Functional Data
25. December
Fri W51
Fri, 25. Dec
Christmas Day
26. December
Sat W51
Sat, 26. Dec
St. Stephen's Day
01. January
Fri W00
Fri, 01. Jan
New Year's Day
06. January
Wed W01
Claudia
Strauch
Universität Heidelberg
13. January
Wed W02
Matthias
Hieber
TU Darmstadt
10. February
Wed W06
Alexander
Kreiß
Universität Hildesheim
08. March
Mon W10
Mon, 08. Mar
International Women's Day (Regional Holiday)
26. March
Fri W12
Fri, 26. Mar
Good Friday