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1 [CSIAM]

[CSIAM]

Session Date & Time

CSIAM (1/3) : 2C (Aug.22, 13:20-15:00) @~~A208~~ D604
CSIAM (2/3) : 2D (Aug.22, 15:30-17:10) @~~A208~~ D604
CSIAM (3/3) : 2E (Aug.22, 17:40-19:20) @~~A208~~ D604

[EM001] Current State and outlook of applied math in China

Session Date & Time : 2C (Aug.22, 13:20-15:00) @~~A208~~ D604
Type : Talk in Embedded Meeting
Abstract : Firstly, I will introduce CSIAM: who we are and what we do. Then I will introduce the current state of applied math in China. I will introduce national policies and opportunities for researcher in China. I will share some of my thinking about the outlook of applied math research as well as practical applications.
Format : Talk at Waseda University
Author(s) :
- Pingwen Zhang (Wuhan University)

[EM002] Fluid dynamical system, numerical analysis and its applications in AMSS, CAS

Session Date & Time : 2C (Aug.22, 13:20-15:00) @A208
Type : Talk in Embedded Meeting
Abstract : In this talk, I will briefly introduce recent progress on the fluid dynamical system, numerical analysis and its applications in Academy of Mathematics and Systems Science, Chinese Academy of Sciences.
Format : Talk at Waseda University
Author(s) :
- Feimin Huang (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

[EM003] Efficient and Trustworthy AI and its Applications to 5G networks

Session Date & Time : 2C (Aug.22, 13:20-15:00) @A208
Type : Talk in Embedded Meeting
Abstract : As the main workhorse of artificial intelligence, deep neural networks (DNN) have led to spectacular successes in voice/face recognition applications among other things. However, training a good neural network that can generalize well and is robust to data perturbation is quite challenging. This talk will discuss efficient and trustworthy DNN training methods and their applications in 5G networks. In particular, three fundamental research topics will be discussed: theoretical analysis of the most popular DNN training algorithm called ADAM; efficient distributed DNN training algorithms; and understanding why DNNs are fragile and how to obtain robustness. Applications of DNN in modeling and optimization of the performance of 5G networks will be presented. Potential application of other AI techniques such as reinforcement learning to future communication networks will also be discussed.
Format : Talk at Waseda University
Author(s) :
- Zhi-Quan Luo (Shenzhen Research Institute of Big Data/The Chinese University of Hong Kong, Shenzhen)

[EM004] Efficient estimation and computation of parameters and nonparametric functions for semi/non-parametric models

Session Date & Time : 2C (Aug.22, 13:20-15:00) @A208
Type : Talk in Embedded Meeting
Abstract : The efficiency of estimation for the parameters in semiparametric models has been widely studied in the literature. However, efficient estimation of nonparametric functions are still not clear. We study efficient estimators for both parameters and nonparametric functions with distribution known and unknown, including non-parametric models for sparse functional data, and generalized semi-parametric models, which cover commonly used semiparametric models such as partially linear models, partially linear single index models, and two-sample semiparametric models. We propose a (quasi)-likelihood principle combined with the local linear technique for estimating the parameters and nonparametric functions. The proposed estimators of the parameters and a linear functional of the nonparametric functions are consistent and asymptotically normal and are further shown to be semiparametrically efficient. Efficient computational algorithms to achieve the maximization are proposed. Extensive simulation experiments show the superiority of the proposed methods. Real data examples are analyzed and presented as an illustration.
Format : Talk at Waseda University
Author(s) :
- Huazhen Lin (Southwestern University of Finance and Economics)

[EM005] Deep adaptive sampling for numerical PDEs

Session Date & Time : 2D (Aug.22, 15:30-17:10) @A208
Type : Talk in Embedded Meeting
Abstract : In this talk, we shall propose a deep adaptive sampling method for solving PDEs where deep neural networks are utilized to approximate the solutions. In particular, we propose the failure informed PINNs (FI-PINNs), which can adaptively refine the training set with the goal of reducing the failure probability. Compared to the neural network approximation obtained with uniformly distributed collocation points, the developed algorithms can significantly improve the accuracy, especially for low regularity and high-dimensional problems.
Format : Talk at Waseda University
Author(s) :
- Tao Tang (BNU-HKBU United International College)

[EM006] Energy transfer and Generalized Fermi's Golden Rule in Hamiltonian nonlinear Klein-Gordon equations

Session Date & Time : 2D (Aug.22, 15:30-17:10) @A208
Type : Talk in Embedded Meeting
Abstract : More than 20 years ago, Soffer-Weinstein proved that spatially localized and time-periodic solutions of the linear Klein-Gordon problem are destroyed by generic nonlinear Hamiltonian perturbations via slow radiation of energy to infinity, via energy transfer from the discrete to continuum modes, under the condition that the discrete modes are close to the continuous spectral modes. Since then, a long-standing open question is to study the corresponding small eigenvalues problem, which will be reported in this talk.
Format : Talk at Waseda University
Author(s) :
- Zhen Lei (Fudan University)
- Jie Liu (Fudan University)
- Zhaojie Yang (Fudan University)

[EM007] The weak Galerkin finite element method for elliptic eigenvalue problems

Session Date & Time : 2D (Aug.22, 15:30-17:10) @A208
Type : Talk in Embedded Meeting
Abstract : This talk is devoted to studying eigenvalue problem by the weak Galerkin finite element method with an emphasis on obtaining lower bounds. We demonstrate that the WG methods can achieve arbitrary high order convergence. This is in contrast with classical nonconforming finite element methods which can only provide the lower bound approximation by linear elements. We also presented the guaranteed lower bound for k=1 order polynomials and some acceleration techniques are applied to WG method.
Format : Talk at Waseda University
Author(s) :
- Ran Zhang (Peking University)
- Hehu Xie (LSEC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences)
- Zhimin Zhang (Beijing Computational Science Research Center)
- Qilong Zhai (Jilin University)
- Carsten Carstensen (Department of Mathematics, Humboldt-Universit)

[EM008] PMLs for scattering problems in complex media

Session Date & Time : 2D (Aug.22, 15:30-17:10) @A208
Type : Talk in Embedded Meeting
Abstract : Perfectly matched layer (PML) provides a very efficient method for solving exterior scattering problems by designing a layer of artificial material which damps outgoing waves exponentially. The research on PML methods is still very rare for inhomogeneous background media. In this talk, I will focus on the stability and exponential convergence of PML methods for acoustic and electromagnetic scattering problems in two-layered media and half spaces with step-like boundaries.
Format : Talk at Waseda University
Author(s) :
- Weiying Zheng (Institute of Computational Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

[EM009] Semantic Information Theory: Where Shannon Meets Gardner

Session Date & Time : 2E (Aug.22, 17:40-19:20) @A208
Type : Talk in Embedded Meeting
Abstract : With the development of information technologies, we need to explore level-2 information theory, which is named semantic level in Weaver and Shannon’s pioneer work. In this talk, we will first survey some results in reliable communication problem and Shannon capacity. Then, the semantic communication problem will be discussed, where the generalized Gardner capacity will be proposed as the core concept. In the last part, we will introduce our work on graphon entropy for computational semantics.
Format : Talk at Waseda University
Author(s) :
- Bo Bai (Huawei Technology, Co., Ltd.)
- Tianqi Hou (Huawei Technology, Co., Ltd.)
- Xueyan Niu (Huawei Technology, Co., Ltd.)
- Lei Deng (Huawei Technology, Co., Ltd.)

[EM010] Data- and Model-Driven Approaches for Computational Imaging

Session Date & Time : 2E (Aug.22, 17:40-19:20) @A208
Type : Talk in Embedded Meeting
Abstract : Computational imaging, crucial for observing and understanding the natural world, traditionally involved three distinct components: image sensing, reconstruction, and analysis. This talk explores the shift towards their integration, powered by advancements in machine learning, particularly deep learning. The focus is on integrating traditional image reconstruction algorithms with deep learning, enabling data-driven and task-driven imaging algorithms for a holistic approach. The significance and future of computational imaging in life sciences and medicine research is also discussed.
Format : Talk at Waseda University
Author(s) :
- Bin Dong (Peking University)

[EM011] Decentralized Optimization Over the Stiefel Manifold

Session Date & Time : 2E (Aug.22, 17:40-19:20) @A208
Type : Talk in Embedded Meeting
Abstract : We study the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network. The objective is an average of d local functions, which are privately held by d agents in the network. The agents can only communicate with their neighbors in a collaborative effort to solve this problem. Our algorithm DESTINY only invokes a single round of communications per iteration and has guaranteed convergence and promising numerical performance.
Format : Talk at Waseda University
Author(s) :
- Xin Liu (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)
- Lei Wang (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

[EM012] Diversified sample selection via predictive inference

Session Date & Time : 2E (Aug.22, 17:40-19:20) @A208
Type : Talk in Embedded Meeting
Abstract : We consider how to obtain informative individuals that are characterized by their unobserved responses with a given budget. We propose an optimal subsampling procedure that can maximize the diversity of the selected subsample and control the false selection rate simultaneously, allowing us to explore reliable information as much as possible. Further, we extend the algorithm to the online setting, where one encounters a possibly infinite sequence of individuals collected by time with covariate information available.
Format : Talk at Waseda University
Author(s) :
- Xiaoyang Wu (Nankai University)
- Yuyang Huo (Nankai University)
- Haojie Ren (Shanghai Jiao tong University)
- Changliang Zou (Nankai University)