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Monday, 30 October
Time Speaker Title Location
15:00 - 16:00 Dr. Dominik Kwietniak
Jagiellonian University
Abstract
< BR > < p对齐= "证明" >我的同构问题n dynamics dates back to a question of von Neumann from 1932: Is it possible to classify (in some reasonable sense) the ergodic measure-preserving diffeomorphisms of a compact manifold up to isomorphism? We want to study a similar problem: Let C be the Cantor set and let Min(C) stand for the space of all minimal homeomorphisms of the Cantor set. Recall that a homeomorphism f is in Min(C) if every orbit of f is dense in C. We say that f and g in Min(C) are topologically conjugate if there is a Cantor set homeomorphism h such that f o h = h o g. We prove an anti-classification result showing that even for very liberal interpretations of what a "reasonable'' classification scheme might be, a classification of minimal Cantor set homeomorphism up to topological conjugacy is impossible. We see it as a consequence of the following: we prove that the topological conjugacy relation of Cantor minimal systems TopConj treated as a subset of Min(C)xMin(C) is complete analytic. In particular, TopConj is a non-Borel subset of Min(C)xMin(C). Roughly speaking, it is impossible to tell if two minimal Cantor set homeomorphisms are topologically conjugate using only a countable amount of information and computation.

Our result is proved by applying a Foreman-Rudolph-Weiss-type construction used for an anti-classification theorem for ergodic automorphisms of the Lebesgue space. We find a continuous map F from the space of all trees over non-negative integers with arbitrarily long branches into the class of minimal homeomorphisms of the Cantor set. Furthermore, F is a reduction, which means that a tree T is ill-founded if and only if F(T) is topologically conjugate to its inverse. Since the set of ill-founded trees is a well-known example of a complete analytic set, it is impossible to classify which minimal Cantor set homeomorphisms are topologically conjugate to their inverses.

This is joint work with Konrad Deka, Felipe García-Ramos, Kosma Kasprzak, Philipp Kunde (all from the Jagiellonian University in Kraków).

Ergodic theory and dynamical systems seminar
An anti-classification theorem for the topological conjugacy of Cantor minimal systems
Y27H 25
15:00 - 16:30 Dr. Alessandro Giacchetto
ETH Zürich
Abstract
可以递归地计算通过阶乘ir definition, but the computation gets difficult quite quickly when the number of interest gets larger and larger. A workaround is given by Stirling’s approximation: a closed, asymptotic formula for factorials. A similar (but much more complicated) situation occurs when trying to compute Witten’s intersection numbers. Virasoro constraints recursively compute these numbers, but the computation gets difficult when the genus gets larger and larger. An approximation has been recently proved by Aggarwal by studying the structure of the associated Virasoro constraints. I will present an alternative proof of Aggarwal’s result based on quantum curves and resurgence. The advantage of this strategy is that it easily generalises to several problems (like r-spin intersection numbers, Norbury’s intersection numbers, etc) and gives higher-order corrections.
Algebraic Geometry and Moduli Seminar
Resurgent large genus asymptotics of intersection numbers
HG G 43
17:15 - 18:00 Jonas Peters

HG F 30
17:15 - 18:15 Prof. Dr. Jonas Peters
ETH Zurich, Switzerland
Abstract
Inaugural Lectures
What happens if? Can we learn from data how ecosystems react to changes?
HG F 30
Tuesday, 31 October
Time Speaker Title Location
14:15 - 15:15 Prof. Dr. Ivan Dokmanić
University of Basel
Abstract
Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are elusive. Traditional complexity measures from statistical learning theory fail to account for observed phenomena like the double descent or the impact of relational semantics on generalization error. Motivated by experimental observations of ``transductive'' double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. Our results illuminate the nuances of learning on homophilic versus heterophilic data and predict double descent whose existence in GNNs has been questioned by recent work. We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. Our findings apply beyond stylized models, capturing qualitative trends in real-world GNNs and datasets. As a case in point, we use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets.
DACO Seminar
Statistical Mechanics of Graph Convolution Networks
HG G 19.1
16:15 - 18:30 Hjalti Isleifsson

Abstract
We will recall the now classical notion of Gromov hyperbolicity and then discuss results due to Wenger and Kleiner-Lang on how hyperbolic phenomena arise in dimensions greater than or equal to the rank of spaces which satisfy non-positive curvature conditions.
Zurich Graduate Colloquium
What is... higher rank hyperbolicity?
KO2F 150
Wednesday, 1 November
Time Speaker Title Location
13:30 - 15:00 Dr. Johannes Schmitt
ETH Zürich
Abstract
Algebraic Geometry and Moduli Seminar
Log intersection theory: from toric varieties to moduli of curves III
HG G 43
15:45 - 16:45 Peter Teichner
Max Planck Institute Bonn
Abstract
Geometry Seminar
A new extension of 4-dimensional mapping class groups
HG G 43
16:30 - 17:30 Prof. Dr. Daniele Boffi
KAUST
Abstract
In this talk I will discuss the numerical approximation of PDE eigenvalue problems depending on a finite number of deterministic parameters. The parameters can be part of the problem or can be introduced by the discretization. It turns out that eigenvalue problems are influenced by the presence of parameters in a way that doesn't compare to the corresponding source problem. We present several examples and counterexamples, showing the difficulties arising when eigenvalues and eigenfunctions need to be approximated accurately. A crucial aspect of parametric eigenvalue problems is the lack of regularity with respect to the parameter, unless a special sorting is considered, taking into account appropriately possible crossings and clustering. On the other hand, parameters arising from the discretizing scheme can be source of spurious solutions.
Zurich Colloquium in Applied and Computational Mathematics
On the numerical approximation of parameter dependent PDE eigenvalue problems
HG E 1.2
17:15 - 18:45 Prof. Dr. Jean Bertoin
Universität Zürich, Switzerland
Abstract
Seminar on Stochastic Processes
Working group step-reinforced random walk: general presentation
Y27H12
Thursday, 2 November
Time Speaker Title Location
15:00 - 16:00 Hjalti Isleifsson
ETH Zurich, Switzerland
Abstract
I will explain what is is meant by an asymptotic Plateau problem in Hadamard spaces, give a survey of some classical results for such problems and also discuss more recent ones. Only very basic knowledge will be assumed so I will spend some time introducing the concepts and tools needed.
Geometry Graduate Colloquium
Asymptotic Plateau problems in Hadamard spaces
HG G 19.1
16:15 - 17:15 Quentin Berthet
Google DeepMind
Abstract
Machine learning pipelines often rely on optimizers procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths). Although these discrete decisions are easily computed in a forward manner, they break the back-propagation of computational graphs. In order to expand the scope of learning problems that can be solved in an end-to-end fashion, we propose a systematic method to transform optimizers into operations that are differentiable and never locally constant. Our approach relies on stochastically perturbed optimizers, and can be used readily within existing solvers. Their derivatives can be evaluated efficiently, and smoothness tuned via the chosen noise amplitude. We also show how this framework can be connected to a family of losses developed in structured prediction, and give theoretical guarantees for their use in learning tasks. We demonstrate experimentally the performance of our approach on various tasks, including recent applications on protein sequences.
ETH-FDS seminar
Joint talk DACO-FDS: Perturbed Optimizers for Machine Learning
HG G 19.1
16:15 - 17:15 Dr. Quentin Berthet
Google DeepMind
Abstract
Machine learning pipelines often rely on optimizers procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths). Although these discrete decisions are easily computed in a forward manner, they break the back-propagation of computational graphs. In order to expand the scope of learning problems that can be solved in an end-to-end fashion, we propose a systematic method to transform optimizers into operations that are differentiable and never locally constant. Our approach relies on stochastically perturbed optimizers, and can be used readily within existing solvers. Their derivatives can be evaluated efficiently, and smoothness tuned via the chosen noise amplitude. We also show how this framework can be connected to a family of losses developed in structured prediction, and give theoretical guarantees for their use in learning tasks. We demonstrate experimentally the performance of our approach on various tasks, including recent applications on protein sequences.
DACO Seminar
DACO-FDS: Perturbed Optimizers for Machine Learning
HG G 19.1
17:15 - 18:15 Prof. Dr. Alexander McNeil
University of York
Abstract
We present some new approaches to modelling and forecasting macroeconomic and financial time series using stationary d-vine (s-vine) copula processes. We show how non-Gaussian extensions of ARMA processes can be constructed to model data that have non-Gaussian marginal distributions and/or non-Gaussian and non-linear serial dependence structures. We also show how these models can be combined with uniform-measure-preserving transformations known as v-transforms to construct processes for volatile financial return series which can outperform classical econometric models in certain cases. Methods will be illustrated with a variety of applications to data.
Talks in Financial and Insurance Mathematics
Copula-based models for financial and macroeconomic time series
HG G 43
Friday, 3 November
Time Speaker Title Location
14:15 - 15:15 Prof. Dr. Sarah Zerbes
ETH Zurich, Switzerland
Abstract
The arithmetic of the adjoint, or symmetric square, of an elliptic curve over Q (or, more generally, of a modular form) is a particularly interesting case from the viewpoint of Iwasawa theory, not least because of its close connection with modularity-lifting problems and hence with Fermat's last theorem. In this talk I will describe ongoing work with David Loeffler in which we prove the cyclotomic Iwasawa main conjecture in this setting, using Euler systems for Hilbert modular surfaces.
Number Theory Seminar
Iwasawa theory for the symmetric square of an elliptic curve
HG G 43
16:00 - 17:30 Prof. Dr. Radu Laza
SUNY Stony Brook
Abstract
It is a question of major interest to construct meaningful compactifications for moduli spaces of algebraic varieties. A case that proved challenging is that of polarized K3 surfaces. I will survey the problem and the proposed solutions to it. Then, I will discus an alternative, variational approach to it, proposed by O’Grady and I, which is particularly relevant in the case of low degree K3 surfaces. Namely, in the case of low degree K3 surfaces there are at least two natural compactifications — the Baily-Borel compactifciation and the GIT compactification. They are related by a series of simple birational transformations “wall crossings”, that can be described explicitly. The degree 2 case is classical, going back to the 80s, the degree 4 was completed a few years ago by myself and O’Grady. Here, I report on the degree 6 case, which is work in progress with Francois Greer, Zhiyuan Li, and Fei Si.
Algebraic Geometry and Moduli Seminar
Birational geometry of the moduli space of low degree K3 surfaces
HG G 43
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