1、邓亚博士——12月23日15:00-16:00

邓亚，研究方向为复几何，师从于J.-P. Demailly，现任职于法国国家科学研究中心。 

Title: Recent progress on hyperbolicity of algebraic varieties

Abstract: Hyperbolicity of algebraic varieties is a tantalizing topic of complex algebraic geometry in the last two decades, partly because of its fascinating analogy with the study of rational points of algebraic varieties, a central topic of number theory.  It interacts directly with many areas like Hodge theory, Nevanlinna theory, differential geometry etc. In this talk I will present some recent progress in this subject.

2、韩笑博士——12月23日16:00-17:00

韩笑，研究方向为量子群（胚）和非交换几何，现就职于伦敦玛丽女王大学，职位：玛丽居里学者。

Title: On the gauge groups and bialgebroids

Abstract：For starters, we will recall the fundamental concept of a Hopf-Galois extension, and instantiate it through quantum principal SU(2)-bundles with noncommutative seven-spheres as total spaces and noncommutative four-spheres as base spaces, together with monopole bundle over the quantum sphere.

Then we will recall the construction of the Ehresmann-Schauenburg bialgebroid of a Hopf-Galois extension, which is a noncommutative analogue of the Ehresmann groupoid of a classical principal bundle. Next, we will show that, the gauge group of any Hopf Galois extension is isomorphic to the group of bisections of its Ehresmann-Schauenburg bialgebroid. Then, we will prove that the group of bisections and the group of automorphisms of the bialgebroid form a crossed module. In particular, we will consider and Galois objects (non-trivial noncommutative principal bundles over a point), whose base-space subalgebra is the ground field and the corresponding Ehresmann-Schauenburg bialgebroid becomes a Hopf algebra. Examples will include monopole bundle and Galois objects of Taft algebras.

3、王杨抟风博士——12月23日17:00-17:30 

王杨抟风，研究方向为几何建模、计算机图形学、计算机视觉、布料仿真，现就职于Adobe Research，职位：Research Scientist

报告标题：人像合成与分解

报告摘要：人像的合成在图片/视频编辑、虚拟/增强现实、电子商务中有极为基础的应用。其中，图像的分解亦与之息息相关。在处理此类问题时，已有的方法不能很好处理动态中复杂服装的非刚性变化。在这个报告中，我将汇报近年来我们提出的一系列基于视频的人像合成技术，通过基于风格化的神经网络以及更紧凑有效的动态表达，我们的方法取得了非常好的视觉效果和运动通用性。此外，图像、视频的分解也是编辑的重要一环。我将介绍我们最新提出的基于自监督学习的纹理坐标（UV）构造算法，以及基于模式识别的构造算法。

 报告地点：腾讯会议694 418 928，密码9363。