11:00
'On the model theory of representations of rings of integers'
Abstract
following the joint paper with L.Shaheen http://people.maths.ox.ac.uk/zilber/wLb.pdf
Representation Dimension and Quasihereditary algebras
Abstract
The representation dimension of an algebra was introduced in the early 70's by M. Auslander, with the goal of measuring how far an algebra is from having finite number of finitely generated indecomposable modules (up to isomorphism). This invariant is not well understood. For instance, it was not until 2002 that O. Iyama proved that every algebra has finite representation dimension. This was done by constructing special quasihereditary algebras. In this talk I will give an introduction to this topic and I shall briefly explain Iyama's construction.
15:00
Data Assimilation for Weather Forecasting: Reducing the Curse of Dimensionality
Abstract
Slightly Rubbish Modular Ax-Lindemann
Abstract
In quite an elementary, hands-on talk, I will discuss some Ax-Lindemann type results in the setting of modular functions. There are some very powerful results in this area due to Pila, but in nonclassical variants we have only quite weak results, for a rather silly reason to be discussed in the talk.
Analysis of images in multidimensional single molecule microscopy
Abstract
Multidimensional single molecule microscopy (MSMM) generates image time series of biomolecules in a cellular environment that have been tagged with fluorescent labels. Initial analysis steps of such images consist of image registration of multiple channels, feature detection and single particle tracking. Further analysis may involve the estimation of diffusion rates, the measurement of separations between molecules that are not optically resolved and more. The analysis is done under the condition of poor signal to noise ratios, high density of features and other adverse conditions. Pushing the boundary of what is measurable, we are facing among others the following challenges. Firstly the correct assessment of the uncertainties and the significance of the results, secondly the fast and reliable identification of those features and tracks that fulfil the assumptions of the models used. Simpler models require more rigid preconditions and therefore limiting the usable data, complexer models are theoretically and especially computationally challenging.