Topic: 
ContentAware Image Restoration for Light and Electron Microscopy 
Date: 
27.01.20 
Time: 
16:15 
Place: 
H6 
Guest: 

Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 

Abstract: 
In recent years, fluorescent light microscopy and cryoelectron microscopy saw tremendous technological advances. Using light microscopes, we routinely image beyond the resolution limit, acquire large volumes at high temporal resolution, and capture many hours of video material showing processes of interest inside cells, in tissues, and in developing organisms. Cryoelectron microscopes, at the same time, are capable of visualizing cellular buildingblocks in their native environment at close to atomic resolution. Despite these possibilities, the analysis of raw images is usually nontrivial, errorprone, and cumbersome. Here we show how machine learning, ie. neural networks, can help to tap the full potential of raw microscopy data by applying contentaware image restoration (CARE) techniques. Several examples in the context of light microscopy (LM) and cryoelectron microscopy (EM) illustrate how downstream analysis pipelines lead to improved (automated) results when applied to contentaware restorations. While our recently published results on LM data [1] do profit from the fact that single highquality, lownoise acquisitions can directly be recorded, in other occasions, this is not possible (e.g. for cryoEM). Hence, we developed CARE variations [2,3,4] that do not require the acquisition of highquality examples but can be trained from noisy images alone. [1] M Weigert, U Schmidt, et al.; Contentaware image restoration: pushing the limits of fluorescence microscopy; bioRxiv, 236463 [2] TO Buchholz, M Jordan, G Pigino, F Jug; CryoCARE: ContentAware Image Restoration for CryoTransmission Electron Microscopy Data; ISBI'19; preprint: arXiv:1810.05420 [3] A Krull, TO Buchholz, F Jug; Noise2VoidLearning Denoising from Single Noisy Images; CVPR'19, preprint: arXiv:1811.10980 [4] A Krull, T Vicar, F Jug; Probabilistic Noise2Void: Unsupervised ContentAware Denoising; arXiv arXiv:1906.00651 [eess.IV] 
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Topic: 
Numerics for resonances of Schottky surfaces 
Date: 
10.01.20 
Time: 
16:15 
Place: 
V3201 
Guest: 

Universität Bremen 

Abstract: 
Resonances of Riemannian manifolds are of great importance in many areas of mathematics and physics. Even though many fascinating results about these spectral entities have already been found, an enormous amount of their properties, also some very elementary ones, is still undiscovered. A few years ago, by means of numerical experiments, Borthwick noticed for some classes of Schottky surfaces (certain hyperbolic surfaces of infinite area) that their sets of resonances exhibit unexcepted and nice patterns, which are not yet fully understood. After a survey of some parts of this field, we will discuss an alternative numerical method, combining tools from dynamics, zeta functions, transfer operators and thermodynamic formalism, functional analysis and approximation theory. This is joint work with Oscar Bandtlow, Torben Schick and Alexander Weiße. 
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Topic: 
QCD phase transition and thermal properties in the functional QCD method 
Date: 
28.01.20 
Time: 
15:00 
Place: 
D6135 
Guest: 

Heidelberg 

Abstract: 
A detailed understanding of the QCD phase structure at finite temperature and density are essential for our understanding of the formation of matter and the evolution of our universe. We access the QCD phase structure within the functional QCD approach. The phase diagram of QCD in the finite temperature and density has now been obtained without the need of phenomenological infrared parameters by combining the two functional approaches, DysonSchwinger equations and the functional renormalisation group. The thermal properties of the states including the first order phase transition regime can also be computed after introducing a subtraction scheme for the effective potential. 
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Topic: 
Multiplescale stochastic processes: decimation, averaging and beyond 
Date: 
30.01.20 
Time: 
14:15 
Place: 
D5153 
Guest: 
Stefano Bo 
MPI for the Physics of Complex Systems 

Abstract: 
Many systems of interest involve processes taking place on widely separated time scales. For an efficient modeling one usually focuses on the slower degrees of freedom and it is of great importance to accurately eliminate the fast variables in a controlled fashion, carefully accounting for their net effect on the slower dynamics. Multiplescale techniques provide a systematic approach to this task. I will present such procedures and discuss their application to some stochastic systems of physical, biological and chemical relevance. I will then consider functionals of the stochastic trajectories such as residence times, counting statistics, fluxes, entropy production, etc.. For such functionals, the elimination of the fast degrees of freedom can present additional difficulties and naive procedures can lead to blatantly inconsistent results. These difficulties can be overcome by systematic multiplescales approaches, which are less covered in the literature but can be seen as natural extensions of the ones employed for the trajectories. 
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Topic: 
Critical behaviour and characteristic polynomials of nonHermitian random matrices 
Date: 
23.05.19 
Time: 
16:15 
Place: 
D5153 
Guest: 

University of Sussex 

Abstract: 
I will discuss some recent developments regarding the normal matrix model. In particular my interest will be in certain critical models where the limiting support of the eigenvalues can radically change its topology by slightly adjusting an external parameter. I will discuss how aspects of the model can be explicitly mapped to the study of expectations of characteristic polynomials of nonHermitian random matrices (e.g. Ginibre or truncated unitary). Many of these averages are related to Painlevé transcendents, and by exploiting this, a precise and nontrivial asymptotic expansion of partition functions can be calculated in the critical models. This is joint work with Alfredo Deaño (University of Kent). 
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Topic: 
Dimensional reduction for elliptic SPDE's: integrable structures and large deviations 
Date: 
18.12.19 
Time: 
16:15 
Place: 
V3201 
Guest: 

University of Warwick 

Abstract: 
I will review the phenomenon of dimensional reduction for elliptic stochastic PDE's in two and three dimensions due to hidden supersymmetry discovered by Parisi and Sourlas. I will use dimensional reduction to establish a link between matrixvalued elliptic SPDE's and determinantal point processes. I will show that the large deviations principle can be established for a class of equations without any reference to supersymmetry. The talk is based on joint work with Roger Tribe and David Elworthy 
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