Wed, 02 Dec 2015

11:30 - 12:30
S2.37

Representation Dimension and Quasihereditary algebras

Teresa Conde
(Oxford)
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.

Tue, 01 Dec 2015
15:00
L1

Data Assimilation for Weather Forecasting: Reducing the Curse of Dimensionality

Professor Philippe Toint
(University of Namur)
Abstract
Weather prediction and, more generally, data assimilation in earth sciences, set a significant computing challenge 
because the size of the problem involved is very large.  The talk discusses algorithmic aspects related to the numerical 
solution of such problems and, in particular, focusses on how the lower dimensionality of the (dual) observation space 
may be used to advantage for computing a primal solution.  This is achieved both by adapting the preconditioned 
conjugate gradient and trust-region algorithms to dual space and by reducing the dimensionality of the latter as much 
as possible using observation hierarchies.
 
 
Mon, 30 Nov 2015

17:00 - 18:00
L1

Slightly Rubbish Modular Ax-Lindemann

Haden Spence
(Oxford University)
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.

Fri, 04 Dec 2015

10:00 - 11:00
L4

Analysis of images in multidimensional single molecule microscopy

Michael Hirsch
(STFC Rutherford Appleton Laboratory)
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.

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