2022-09-06

The discussion on the Out of Africa hypothesis, OOAH and PPA (Progressive Phylogenetic Analysis), that identified the African human populations as paraphyletic 

Seminar on Bioinformatics

报告人 关梦岑 清华大学数学系 博士生

时 间 15:00-15:30 Tue

Tencent 801 3489 7723

摘要

The Out of Africa hypothesis, OOAH, was challenged recently in an extended mtDNA analysis, PPA (Progressive Phylogenetic Analysis), that identified the African human populations as paraphyletic, a finding that contradicted the common OOAH understanding that Hss had originated in Africa and invaded Eurasia from there. The results were consistent with the molecular Out of Eurasia hypothesis, OOEH, and Eurasian palaeontology, a subject that has been largely disregarded in the discussion of OOAH. In the present study the mtDNA tree, a phylogeny based on maternal inheritance, was compared to the nuclear DNA tree of the paternally transmitted Y-chromosome haplotypes, Y-DNAs. The comparison showed full phylogenetic coherence between these two separate sets of data. The results were consistent with potentially four translocations of modern humans from Eurasia into Africa, the earliest taking place ≈ 250,000 years before present, YBP. The results were in accordance with the postulates behind OOEH at the same time as they lent no support to the OOAH.

组织者：丘成栋

2022-09-06

Prediction of binding free energy changes of viral surface proteins and host receptor proteins 

Seminar on Bioinformatics

报告人 任若寒 清华大学致理书院 本科生

时 间 15:30-16:00 Tue

Tencent 801 3489 7723

摘要

Protein-protein interactions play an important role in many aspects such as virus infection and immune response. Although there are experimental methods for determining the structure of protein complexes, the experimental methods are time-consuming and labor-intensive, and are difficult to apply in large-scale screening. This project hopes to use the method of extended natural vector to extract the information of the protein-protein binding region, combine some molecular dynamics or topology information, and then use the deep learning method to analyze the protein-protein binding free energy changes after single or multiple point mutation. Then we hope to apply the method to the SARS-CoV-2, HIV and Ebola viruses. This helps greatly improve the efficiency of the optimal design of antiviral drugs. In this report, we mainly introduce the overall ideas and data types.

组织者：丘成栋

2022-09-07

Infinitesimal Torelli for Weighted Complete Intersections and Certain Fano Threefolds

Seminar on Algebraic, complex geometry and singularities

报告人 胡创强 BIMSA

时 间 15:30-16:30 Wed

Tencent 639 1488 3821

摘要

We generalize the classical approach of describing the infinitesimal Torelli map in terms of multiplication in a Jacobi ring to the case of quasi-smooth complete intersections in weighted projective space. As an application, we prove the infinitesimal Torelli theorem for hyperelliptic Fano threefolds of Picard rank 1, index 1, degree 4 and study the action of the automorphism group on cohomology. The results of this talk are used to prove Lang-Vojta’s conjecture for the moduli of such Fano threefolds.

组织者：丘成栋

2022-09-08

From Neural PDEs to Neural Operators: Blending data and physics for fast predictions

BIMSA-Tsinghua Seminar on Machine Learning and Differential Equations

报告人 George Em Karniadakis Brown University

时 间 08:50-12:15 Thu

地 点 1129B

Zoom 537 192 5549（PW: BIMSA）

摘要

We will review physics-informed neural network and summarize available extensions for applications in computational mechanics and beyond. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. The universal approximation theorem of operators is suggestive of the potential of NNs in learning from scattered data any continuous operator or complex system. We first generalize the theorem to deep neural networks, and subsequently we apply it to design a new composite NN with small generalization error, the deep operator network (DeepONet), consisting of a NN for encoding the discrete input function space (branch net) and another NN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, e.g., integrals, Laplace transforms and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. More generally, DeepOnet can learn multiscale operators spanning across many scales and trained by diverse sources of data simultaneously.

人物介绍

George Karniadakis is from Crete. He is a member of the National Academy of Engineering and a Vannvar Bush Faculty Fellow. He received his S.M. and Ph.D. from Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018-), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt award in 2017, the SIAM Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics. His h-index is 123 and he has been cited about 70,000 times.

组织者：熊繁升, 杨武岳, 雍稳安, 朱毅

2022-09-09

用带电原子进行量子计算：一些新的进展和探索

YMSC-BIMSA Quantum Information Seminar in 2022 Spring

报告人 张颉颃 中科大

时 间 09:30-12:15 Fri

地 点 JCY-1

Zoom 559 700 6085（PW: BIMSA）

摘要

离子，即带电的原子，是最早被应用于量子计算的平台之一。近些年来离子阱量子计算在国际上得到了广泛的关注和显著的进展：学术界之外，在美国和欧洲的工业界也引起了广泛兴趣，可以操纵任意连通的30多个比特，进行高保真度量子逻辑门与量子态探测。在这个讲座中，我将介绍离子量子比特的工作原理：用完美的（带电）原子来编码量子信息，并用激光进行高保真度操控。在此基础上我们实现了53个量子比特的量子模拟，研究非平衡态演化过程，接近经典计算的极限。我们将这类方法扩展，把量子比特编码在高维的非欧空间里，并在未来期待实现可验证的量子采样与量子纠错。

人物介绍

张颉颃于2009年毕业于中国科学技术大学，赴美国马里兰大学攻读博士学位。期间从事冷原子实验研究，曾在加拿大国家实验室（TRIUMF）从事精密光谱测量。2015-2018年博士后期间师从Christopher Monroe做离子阱量子模拟，第一次实验观测到离散时间晶体等非平衡态量子现象，文章发表于自然杂志。2019年起任纽约大学助理教授，获得美国能源部青年科学家奖。自2021年12月回国，现任中国科学技术大学教授。

组织者：刘正伟

2022-09-09

Semiclassical analysis of elastic surface waves and inverse problems

Seminar on microlocal analysis and applications

报告人 邱凌云 BIMSA, YMSC

时 间 10:00-11:00 Fri

Zoom 618 038 6257（PW: SCMS）

摘要

Non-line-of-sight imaging aims at recovering obscured objects from multiple-scattered light. It has recently received widespread attention due to its potential applications, such as autonomous driving, rescue operations, and remote sensing. However, in cases with high measurement noise, obtaining high-quality reconstructions remains a challenging task. In this work, we establish a unified regularization framework, which can be tailored for different scenarios, including indoor and outdoor scenes with substantial background noise under both confocal and non-confocal settings. The proposed regularization framework incorporates sparseness and non-local self-similarity of the hidden objects as well as smoothness of the measured signals. We show that the estimated signals, albedo, and surface normal of the hidden objects can be reconstructed robustly even with high measurement noise under the proposed framework. Reconstruction results on synthetic and experimental data show that our approach recovers the hidden objects faithfully and outperforms state-of-the-art reconstruction algorithms in terms of both quantitative criteria and visual quality.

组织者：金龙, 陈曦

2022-09-09

Convergence Analysis of the Gaussian mixture particle filters

Seminar on Control theory and Nonlinear Filtering

报告人 孙泽钜 清华大学数学科学系 博士生

时 间 15:00-15:30 Fri

Tencent 735 7908 4302

摘要

In this talk, I will discuss in detail the convergence result of the Gaussian mixture particle filters, in which each particle in the estimation system is regarded as a Gaussian distributed random variable, rather than a simple particle as in the classical particle filters. The convergence rate of the particle system will be given. The relationship between the Gaussian mixture particle filters and radial basis functions will also be discussed.

组织者：丘成栋

2022-09-09

The stability and the convergence results in Yau-Yau methods

Seminar on Control theory and Nonlinear Filtering

报告人 康家熠 清华大学数学科学系 博士生

时 间 15:00-16:00 Fri

Tencent 735 7908 4302

摘要

In this report, I will introduce the semi-group formulation of the SPDE. And I shall give a stability analysis for filtering algorithm based on spectral methods from two aspects. 1. Asymptotic analysis of the relationship between the number of spectra and errors in spectral methods. 2. Analysis of the stability of spectral algorithm by time discretization.

组织者：丘成栋