Date/Time : 16 Jul 2019 16:00
Venue : S17 #04-06 SR1
Speaker : Andre Nies, University of Auckland

Date/Time:16 Jul 2019 16:00 Venue: S17 #04-06 SR1 Speaker: Andre Nies, University of Auckland Random sequences of quantum bits Martin-Loef formalised in 1966 the intuitive notion of randomness of infinite sequences of bits via algorithmic tests. In this talk, we investigate what happens if we replace classical bits by quantum bits. We first provide a framework to formalise infinite sequences of quantum bits as states of a suitable C* algebra. Thereafter we introduce an analog of Martin-Loef’s notion. We show that for classical bit sequences the two notions coincide. We also discuss quantum Kolmogorov complexity for finite sequences of quantum bits and its relationship to quantum Martin-Loef randomness. Finally, we consider an effective version of the Shannan-McMillan-Breiman theorem in the quantum setting. This is joint work with Volkher Scholz. The paper is available at http://arxiv.org/abs/1709.08422. Add to calendar: 

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Date/Time : 12 Jun 2019 16:00
Venue : S17 #04-04 SR3
Speaker : Simon Weissmann, University of Mannheim

Date/Time:12 Jun 2019 15:00 Venue: S17 #04-04 SR3 Speaker: Simon Weissmann, University of Mannheim On the incorporation of box-constraints for ensemble Kalman inversion The Bayesian approach to inverse problems is widely used in practice to infer un- known parameters from noisy observations. In this framework, the ensemble Kalman inversion has been successfully applied for the quantification of uncertainties in various areas of applica- tions. In recent years, a complete analysis of the method has been developed for linear inverse problems adopting an optimization viewpoint. However, many applications require the incorpo- ration of additional constraints on the parameters, e.g. arising due to physical constraints. We propose a new variant of the ensemble Kalman inversion to include box constraints on the un- known parameters motivated by the theory of projected preconditioned gradient flows. Based on the continuous time limit of the constrained ensemble Kalman inversion, we discuss a complete convergence analysis for linear forward problems. We adopt techniques from filtering which are crucial in order to improve the performance and establish a correct descent, such as variance inflation. These benefits are highlighted through a number of numerical examples on various inverse problems based on partial differential equations. Add to calendar: 

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Date/Time : 12 Jun 2019 10:00
Venue : S17 #04-05 SR2
Speaker : Tomasz Tkocz , Carnegie Mellon University

Date/Time:12 Jun 2019 11:00 Venue: S17 #04-05 SR2 Speaker: Tomasz Tkocz, Carnegie Mellon University Volume of intersections of convex bodies with their symmetric images and efficient coverings Let K be a convex body with volume one and barycentre at the origin. How small is the volume of the intersection of K and -K? I shall discuss such lower bounds and present applications to the Hadwidger covering/illumination conjecture. Based on joint work with H. Huang, B. Slomka and B. Vritsiou. Add to calendar: 

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Date/Time : 12 Jun 2019 14:00
Venue : S17 #04-06 SR1
Speaker : Weinan E, Princeton University

Modern machine learning has had remarkable success in all kinds of AI applications, and is also poised to change fundamentally the way we do physical modeling. In this talk, I will give an overview on some of the theoretical and practical issues that I consider most important in this exciting area. The first part of this talk will be focused on the fol-lowing question: How can we make use of modern machine learning tools to help build reliable and practical physi-cal models? Here we will address two issues (mostly using the example of molecular dynamics): (1) building machine learning models that satisfy physical constraints; (2) using microscopic models to generate the optimal data set.

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Date/Time : 10 Jun 2019
Venue :
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Date/Time : 21 May 2019 10:00
Venue : S17 #04-06 SR1
Speaker : Wei Cai, Southern Methodist University

Date/Time:21 May 2019 10:00 Venue: S17 #04-06 SR1 Speaker: Wei Cai, Southern Methodist University Algorithms for Wave Scattering of Random Media: Fast multipole method in layered media and a phase shift deep neural network for wideband learning In this talk, we will present two algorithms and numerical results for solving electromagnetic wave scattering of random meta-materials. Firstly, a fast multipole method for 3-D Helmholtz equation for layered media will be presented based on new multipole expansion (ME) and multipole to local translation (M2L) operators for layered media Green’s functions. Secondly, a parallel phase shift deep neural network (PhaseDNN) is proposed for wideband data learning. In order to achieve uniform convergence for low to high frequency content of data, phase shifts are used to convert high frequency learning to low frequency learning. Due to the fast learning of many DNNs in the low frequency range, PhaseDNN is able to learn wideband data uniformly in all frequencies. Reference: [1] B.Wang, W.Z. Zhang, W. Cai, Fast Multipole Method For 3-D Helmholtz Equation In Layered Media, arXiv:1902.05132 [2] W. Cai, X.G. Li, L.Z. Liu, PhaseDNN – A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning , arXiv:1905.01389 Add to calendar: 

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Date/Time : 15 May 2019 11:00
Venue : S17 #05-11 SR5
Speaker : Zhong Guolei , National University of Singapore

Date/Time:15 May 2019 11:00 Venue: S17 #05-11 SR5 Speaker: Zhong Guolei, National University of Singapore Totally invariant divisors of amplified endomorphisms of normal projective varieties This work is encouraged by Professor De-Qi Zhang. We consider an arbitrary int-amplified surjective endomorphism $f$ of a normal projective variety $X$ over $\mathbb{C}$ and its $f^{-1}$-stable prime divisors. We extend the early result for the case of polarized endomorphisms to the case of int-amplified endomorphisms. Assume further that $X$ has at worst Kawamata log terminal singularities. We prove that the total number of $f^{-1}$-stable prime divisors has an optimal upper bound $\dim X+\rho(X)$, where $\rho(X)$ is the Picard number. Also, we give a sufficient condition for $X$ to be rationally connected and simply connected. Finally, by running the minimal model program (MMP), we prove that, under some extra conditions, the end product of the MMP can only be an elliptic curve or a single point. Add to calendar: 

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Date/Time : 08 May 2019 17:00
Venue : S17 #04-06 SR1
Speaker : Borisa Kuzeljevic, University of Novi Sad

Date/Time:08 May 2019 17:00 Venue: S17 #04-06 SR1 Speaker: Borisa Kuzeljevic, University of Novi Sad Matrices of countable elementary submodels We present an application of the forcing notion of finite matrices whose rows consist of isomorphic countable elementary submodels of a given structure of the form $H_{\theta}$. We will explain how forcing with this poset adds a Kurepa tree. If a minor modification of the poset is considered, then the tree added is actually an almost Souslin Kurepa tree. This is a joint work with Stevo Todorcevic. Add to calendar: 

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Date/Time : 02 May 2019 16:00
Venue : S17 #06-11
Speaker : Zhang Shuangjian, École Nationale de la Statistique et de l'Administration Économique, Paris, France

Date/Time:02 May 2019 16:00 Venue: S17 #06-11 Speaker: Zhang Shuangjian, École Nationale de la Statistique et de l’Administration Économique, Paris, France On concavity of the principal’s profit maximization facing agents who respond nonliearly to prices A monopolist wishes to maximize her profits by finding an optimal price policy. After she announces a menu of products and prices, each agent will choose to buy that product which maximizes his own utility, if positive. The principal’s profits are the sum of the net earnings produced by each product sold. These are determined by the costs of production and the distribution of products sold, which in turn are based on the distribution of anonymous agents and the choices they make in response to the principal’s price menu. In this talk, we describe a necessary and sufficient condition for the convexity or concavity of the principal’s problem, assuming each agent’s disutility is a strictly increasing but not necessarily affine (i.e. quasilinear) function of the price paid. Concavity when present, makes the problem more amenable to computational and theoretical analysis; it is key to obtaining uniqueness and stability results for the principal’s strategy in particular. Even in the quasilinear case, our analysis goes beyond previous work by addressing convexity as well as concavity, by establishing conditions which are not only sufficient but necessary, and by requiring fewer hypotheses on the agents’ preferences. Moreover, the analytic and geometric interpretations of a specific condition relevant to the concavity of the problem have been explored. This talk represents joint work with Robert McCann. Add to calendar: 

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