In this talk, we study the emergence of spatially localised coherent structures induced by a compact region of spatial heterogeneity that is motivated by numerical studies into the formation of tornados. While one-dimensional localised patterns induced by spatial heterogeneities have been well studied, proving the existence of fully localised patterns in higher dimensions remains an open problem in pattern formation. We present a general approach to prove the existence of fully localised two-dimensional patterns in partial differential equations containing a compact spatial heterogeneity. This includes patterns with radial and dihedral symmetries, but also extends to patterns beyond these standard rotational symmetry groups. In order to demonstrate the approach, we consider the planar Swift--Hohenberg equation whose linear bifurcation parameter is modified with a radially-symmetric step function. In this case the trivial state is unstable in a compact neighbourhood of the origin and linearly stable outside. The introduction of a spatial heterogeneity results in an infinite family of bifurcation points with finite dimensional kernels, allowing one to prove local and global bifurcation theorems. We prove the existence of local bifurcation branches of fully localised patterns, characterise their stability and bifurcation structure, and then rigorously continue to large amplitude via analytic global bifurcation theory. Notably, the primary (possibly stable) bifurcating branch in the Swift--Hohenberg equation alternates between an axisymmetric spot and a non-axisymmetric `dipole' pattern, depending on the width of the spatial heterogeneity. We also discuss how one can use geometric singular perturbation theory to prove the persistence of the patterns to smooth spatial heterogeneities.

This work is in collaboration with Daniel Hill and Matthew Turner.

"We consider a Kuramoto-Shivashinsky like equation close to the threshold of instability with additive white noise and spatially periodic boundary conditions which simultaneously exhibit Turing bifurcations with a spatial 1:3 resonance of the critical wave numbers. For the description of the bifurcating solutions we derive a system of coupled stochastic Landau equations. It is the goal of this paper to prove error estimates between the associated approximation obtained through this amplitude system and true solutions of the original system. The Kuramoto-Shivashinsky like equation serves as a prototype model for so-called super-pattern forming systems with quadratic nonlinearity and additive white noise."

Multispecies asymmetric exclusion processes (ASEPs) are interacting particle systems characterised by simple, local dynamics, where particles occupy lattice sites and interact only with their adjacent neighbors, following asymmetric exchange rules based on their species labels. I will present recent results on two-point correlation functions in multispecies ASEPs, including models on finite rings and their continuous-space limit as the number of sites tends to infinity. Using combinatorial tools such as Ferrari–Martin multiline queues, projection techniques, and bijective arguments, we derive exact formulas for adjacent particle correlations and resolve a conjecture in the continuous multispecies TASEP (Aas and Linusson, AIHPD 2018). We also extend finite-ring results of Ayyer and Linusson (Trans AMS, 2017) to the partially asymmetric case (PASEP), formulating new correlation functions that depend on the asymmetry parameter. I will briefly outline ongoing work on boundary-driven multispecies B-TASEP and long-time limiting states in periodic ASEPs, suggesting connections between pairwise correlations and stationary-state structure.

Das Beweisen ist für die Mathematik als Disziplin von zentraler Bedeutung und spielt daher auch in der mathematischen Ausbildung eine wichtige Rolle. Lernende sollen die Mathematik als deduktives System begreifen, die Art der Absicherung mathematischer Ergebnisse verstehen, argumentative Herausforderungen erfolgreich bewältigen können und so ein adäquates Verständnis von mathematischen Beweisen aufbauen. Ausgehend von einem theoretischen Rahmenmodell zum mathematischen Beweisverständnis werden Ergebnisse empirischer Studien vorgestellt, die das Beweisverständnis von Lernenden in unterschiedlichen Phasen der mathematischen Ausbildung untersuchen und Möglichkeiten der Förderung aufzeigen. ______________________

Invited by Prof. Stefan Ufer

Factor analysis is a statistical technique that explains correlations among observed random variables with the help of a smaller number of unobserved factors. In traditional full-factor analysis, each observed variable is influenced by every factor. However, many applications exhibit interesting sparsity patterns, that is, each observed variable only depends on a subset of the factors. In this talk, we will discuss parameter identifiability of sparse factor analysis models. In particular, we present a sufficient condition for parameter identifiability that generalizes the well-known Anderson-Rubin condition and is tailored to the sparse setup. This is joint work with Mathias Drton, Miriam Kranzlmüller, and Irem Portakal.

Graphical continuous Lyapunov models offer a novel framework for the statistical modeling of correlated multivariate data. These models define the covariance matrix through a continuous Lyapunov equation, parameterized by the drift matrix of the underlying dynamic process. In this talk, I will discuss key results on the defining equations of these models and explore the challenge of structural identifiability. Specifically, I will present conditions under which models derived from different directed acyclic graphs (DAGs) are equivalent and provide a transformational characterization of such equivalences. This is based on ongoing work with Carlos Amendola, Tobias Boege, and Ben Hollering.

We study opportunistic traders that try to detect and exploit the order flow of dealers hedging their net exposure to the FX fix. We also discuss how dealers can take this into account to balance not only risk and trading costs but also information leakage in an appropriate manner. It turns out that information leakage significantly expands the set of scenarios where both dealers and the clients whose orders they execute benefit from hedging part of the exposure before the fixing window itself. (Joint work in progress with Roel Oomen (Deutsche Bank) and Mateo Rodriguez Polo (ETH Zurich))

The exact treatment of Markovian models on complex networks requires knowledge of probability distributions expo- nentially large in the number of nodes n. Mean-field approximations provide an effective reduction in complexity of the models, requiring only a number of phase space variables polynomial in system size. However, this comes at the cost of losing accuracy close to critical points in the systems dynamics and an inability to capture correlations in the system. In this talk, we introduce a tunable approximation scheme for Markovian spreading models on networks based on matrix product states (MPSs). By controlling the bond dimensions of the MPS, we can investigate the effective dimensional- ity needed to accurately represent the exact 2n dimensional steady-state distribution. We introduce the entanglement entropy as a measure of the compressibility of the system and find that it peaks just after the phase transition on the disordered side, in line with the intuition that more complex states are at the ’edge of chaos’. The MPS provides a systematic way to tune the accuracy of the approximation by reducing the dimensionality of the systems state vector, leading to an improvement over second-order mean-field approximations for sufficiently large bond dimensions.

In this talk, I will discuss two of our recent results on three-dimensional (3D) packing problems: the 3D Knapsack problem and the 3D Bin Packing problem. In both settings, we are given a collection of axis-aligned cuboids. In the Knapsack problem, each cuboid is associated with a profit, and the objective is to pack a subset of cuboids non-overlappingly into a unit cube to maximize total profit. In contrast, the Bin Packing problem seeks to pack all the cuboids using the minimum number of unit cubes (bins). Both problems are NP-hard and unlike their two-dimensional counterparts that have been extensively studied, the 3D variants have received much less attention. The previously best-known approximation ratios for 3D Knapsack and 3D Bin Packing are 7 + ε and (T_∞)^2 + ε ≈ 2.86, respectively for any constant ε > 0, where T_∞ ≈ 1.691 is the well-known Harmonic constant in Bin Packing. We provide improved approximation ratios of 139/29 + ε ≈ 4.794, and 3T_∞/2 + ε ≈ 2.54, for 3D Knapsack and 3D Bin Packing, respectively. Our key technical contribution is container packing -- a structured packing in 3D wherein all items are assigned into a constant number of containers, and each container is packed using a specific strategy based on its type. I shall also discuss few extensions of our techniques to related 3D packing problems.

This talk will provide a basic introduction to the three dimensional Dirac equation that describes an electron interacting with a magnetic field. Over the years a lot of work has gone into constructing zero energy solutions, also known as zero modes, for said equation. In this talk I will explain the importance of zero modes e.g. I will show how they relate to the stability of the hydrogen atom. After presenting explicit examples, I will state necessary conditions for the magnetic field so that zero modes exist. Here, of particular interest is a sharp inequality that is optimized by a magnetic field whose field lines are interlinking circles. This inequality relates to sharp inequalities for spinors of which some more examples will be given.

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Invited by Prof. Christian Hainzl

Quantum mechanics, now about a century old, is a very successful physical theory of matter on a small scale. From its first description until today, it has surprised scientists and laypersons alike by the strange behaviour it attributes to particles, atoms, and molecules. This behaviour can be characterized by the keywords Uncertainty, Superposition, and Entanglement. It took about sixty years before it was realized that these three characteristics do not just express a certain vagueness and strangeness of matter on a small scale but can actually be USEd to our advantage. In 1994 Peter Shor made this idea concrete by devising an algorithm that would enable large arrays of quantum systems to perform specific calculations (factoring large integers), which are impossible to do in practice on any classical device. With this algorithm, present-day cryptographic schemes can be broken, provided such "quantum computers” can be made to work. Starting from a discussion of the "two-slit experiment”, we sketch the working of Shor's algorithm and discuss the possibilities of future quantum computers.

About the speaker: Hans Maassen is a dutch mathematical physicist and emeritus professor specializing in quantum probability and quantum information theory. Standing out among his discoveries is the entropic uncertainty relation, named after himself and Jos Uffink, a fundamental  inequality in quantum mechanics.

This talk is open to the general public and all interested persons, and is presented by the SFB TRR352 "Mathematics of Many-Body Quantum systems and their collective phenomena" in cooperation with the TUM-IAS Workshop "Beyond IID in Information Theory".

Please register for the workshop. More information can be found at s. https://www.fm.math.lmu.de/en/news/events-overview/event/frontiers-in-mathematical-finance-between-theory-and-applications.html

Please register for the workshop. More information can be found at s. https://www.fm.math.lmu.de/en/news/events-overview/event/frontiers-in-mathematical-finance-between-theory-and-applications.html

Interconnected real-world systems oftentimes contain non-pairwise interactions between agents. These groupwise interactions are referred to as higher order interactions and can be encoded by means of hypergraphs or hypernetworks. Countless works in recent years have pointed out how this structural feature crucially shapes the collective behavior. This talk will, in particular, focus on dynamics of systems with higher order interactions. We observe that the restriction to undirected higher order interactions obstructs the emergence of certain heteroclinic structures in phase space. The directed counterparts, on the other hand, do not. Motivated by this, we define a general class of directed hypernetworks and corresponding maps that respect a given interaction structure, so-called admissible maps. For this class, all robust patterns of (cluster) synchrony that a given hypernetwork supports can be classified. Interestingly, these are only determined by higher degree polynomial admissible maps. In particular, unlike in classical networks, cluster synchronization is a higher order, that is, nonlinear effect. This feature induces a novel type of “reluctant” synchrony breaking bifurcation when a high order tangency of the solution branch to a non-robust synchrony space causes formerly synchronous nodes to separate unusually slowly.

After motivating the Stefan problem from the random growth model perspective, I will discuss its discontinuities in time. These turn out to be characterized by the cascade equation, a second-order hyperbolic PDE. Questions of existence and regularity for the latter can be answered by expressing its solution as the value function of a player in an equilibrium of a suitable mean field game. Based on joint work with Yucheng Guo and Sergey Nadtochiy.

While the theory of causality is widely viewed as an extension of probability theory, a view which we share, there was no universally accepted, axiomatic framework for causality analogous to Kolmogorov's measure-theoretic axiomatization for the theory of probabilities. Instead, many competing frameworks exist, such as the structural causal models or the potential outcomes framework, that mostly have the flavor of statistical models. To fill this gap, we propose the notion of causal spaces, consisting of a probability space along with a collection of transition probability kernels, called causal kernels, which satisfy two simple axioms and which encode causal information that probability spaces cannot encode. The proposed framework is not only rigorously grounded in measure theory, but it also sheds light on long-standing limitations of existing frameworks, including, for example, cycles, latent variables, and stochastic processes. Our hope is that causal spaces will play the same role for the theory of causality that probability spaces play for the theory of probabilities.

Clinical data often include a mix of continuous measurements and covariates that have been discretized, typically to protect privacy, meet reporting obligations, or simplify clinical interpretation. This combination, along with the nonlinear and tail-asymmetric dependence frequently observed in clinical data, affects the behavior of regression and variable-selection methods. Copula models, which separate marginal behavior from the dependence structure, provide a principled approach to studying these effects. In this talk, we analyze how discretizing a continuous covariate into equiprobable categories impacts conditional quantiles and likelihoods in bivariate copula models. For the Clayton and Frank families, we derive closed-form anchor points: for a given category, we identify the continuous covariate value at which the conditional quantile under the continuous model matches that of the discretized one. These anchors provide an exact measure of discretization bias, which is small near the center but can be substantial in the tails. Simulations across five copula families show that likelihood-based variable selection may over- or under-weight discretized covariates, depending on the dependence structure. Through simulations, we conclude by comparing polyserial and Pearson correlations, as well as Kendall’s tau (-b), in the same settings. Our results have practical implications for copula-based modeling of mixed-type data.

Inference of the conditional dependence structure is challenging when many covariates are present. In numerous applications, only a low-dimensional projection of the covariates influences the conditional distribution. The smallest subspace that captures this effect is called the central subspace in the literature. We show that inference of the central subspace of a vector random variable Y conditioned on a vector of covariates can be separated into inference of the marginal central subspaces of the components of Y conditioned on X and on the copula central subspace, which we define in this paper. Further discussion addresses sufficient dimension reduction subspaces for conditional association measures. An adaptive nonparametric method is introduced for estimating the central dependence subspaces, achieving parametric convergence rates under mild conditions. Simulation studies illustrate the practical performance of the proposed approach.

TBA