Speaker: Brandon Alexander and Dana Botesteanu (AMSC) -

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Speaker: Daniel Kaufman (UMD) -

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Speaker: Nicholas Paskal (UMD) -

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Speaker: Samuel Punshon-Smith (University of Maryland) -

Abstract: http://www.terpconnect.umd.edu/~lvrmr/2017-2018-F/Classes/RIT.shtml

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Speaker: Discussion (UMD) - http://math.umd.edu/~lcw/IwasawaRIT.html

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Speaker: Richard Wentworth (UMCP) -

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Speaker: Eric Slud (U-M STAT Program) - http://www.math.umd.edu/~evs

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Speaker: Brad Lackey (University of Maryland) - http://www.umiacs.umd.edu/~bclackey

Abstract: I will continue with a rapid introduction to the foundations of quantum theory, focusing on bipartite systems and measurement.

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Speaker: John Benedetto (UMD) -

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Speaker: Bruno de Mendonça Braga (York University) - https://sites.google.com/site/demendoncabraga/home

Abstract: In 1981, J. Krivine and B. Maurey introduced the definition of stable Banach spaces, and, in 1983, Y. Raynaud introduced the notion of superstability and studied uniform embeddings of Banach spaces into superstable Banach spaces. In this talk, we will talk about coarse embeddings into superstable spaces. This is a joint work with Andrew Swift.

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Speaker: Jing ZHOU (UMD) -

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Speaker: Klaus Kroencke (Hambrug) -

Abstract: We prove that if an ALE Ricci-flat manifold (M,g) is linearly stable and integrable, it is dynamically stable under Ricci flow, i.e. any Ricci flow starting close to g exists for all time and converges modulo diffeomorphism to an ALE Ricci-flat metric close to g. By adapting Tian's approach in the closed case, we show that integrability holds for ALE Calabi-Yau manifolds which implies that they are dynamically stable. This is joint work with Alix Deruelle.

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Speaker: Scott Andrew Smith (Max Plank Institute, Leipzig) -

Abstract: The present talk is concerned with quasi-linear parabolic equations which are ill-posed in the classical distributional sense. In the semi-linear context, the theory of regularity structures provides a solution theory which applies to a large class of equations with suitably randomized inputs. The quasi-linear setting has seen recent advances, but a general theory remains open. We will present some partial progress in this direction based on joint work with Felix Otto, Jonas Sauer, and Hendrik Weber.

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Speaker: Kasso Okoudjou (UMD) -

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Speaker: Prof. Markus Kirkilionis (Warwick Mathematics Institute, University of Warwick) - http://homepages.warwick.ac.uk/~mascac/

Abstract: In this talk I present a general framework to model cell-cycle structured

populations living in a chemostat. The main examples are E. coli, or yeast, both

model organisms which have been intensively investigated to understand cell-cycle controls. In this simple case the cells' cell cycle influence each other only by the level of nutrients found in the culture medium. Otherwise the cell cycle in each cell behaves autonomously. As the cell-cycle depends on many cell-internal biochemical concentrations, most importantly on the cyclin protein family, the dynamical system describing the internal cell dynamics can be of arbitrary high dimension, making the model extremely complex. In order to investigate the model behaviour we decided not to use numerical time-integration, but numerical continuation and bifurcation techniques. The respective numerical algorithm is again of immense complexity, and uses a cell cohort discretisation. The plan is to refine the model in future, most importantly bringing it to a tissue level in order to describe cancer dynamics.

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Speaker: (CMNS Dean's Office) -

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Speaker: Jaideep Pathak (IREAP, UMD)

Abstract: Networks of nonlinearly interacting neuron-like units have the capacity to approximately reproduce the dynamical behavior of a wide variety of dynamical systems. We demonstrate the use of such neural networks for reconstruction of chaotic attractors from limited time series data using a machine learning technique known as reservoir computing. The orbits of the reconstructed attractor can be used to obtain approximate estimates of the ergodic properties of the original system. As a specic example, we focus on the task of determining the Lyapunov exponents of a system from limited time series data. Using the example of the Kuramoto-Sivashinsky system, we show that this technique oers a robust estimate of a large number of Lyapunov exponents of a high dimensional spatiotemporal chaotic system. We further develop an eective, computationally parallelizable technique for model-free prediction of spatiotemporal chaotic systems of arbitrarily large spatial extent and dimension purely from observations of the system's past evolution.

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Speaker: Peter Nandori (UMD) - http://math.umd.edu/~pnandori/

Abstract: We consider a special flow over a mixing map with some hyperbolicity.

In case the roof function is square integrable, we find a set of conditions, under which the flow is mixing and also satisfies the local limit theorem. In case the roof function is non-integrable, we identify another set of conditions that imply Krickeberg mixing. The most important condition is the local limit theorem for the underlying map. We check that the conditions are satisfied for some examples, such as Axiom A flows, Sinai billiards, geometric Lorenz attractors (finite measure case) and suspensions over Pomeau-Manneville maps (finite and infinite measure cases). The talk is based on joint work with Dmitry Dolgopyat.

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Speaker: Ian Teixeira (UMCP)

Abstract: We will start on Tong's notes on the quantum Hall effect (QHE).

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Speaker: Shujie Kang (UMD) -

Abstract: I introduce commonly used algorithms to train a convolutional neural network.

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Speaker: TBA () -

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Speaker: ensemble () -

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