2018.6.29-7.5 学术活动预告
2018/6/28 10:40:03 中国科学院数学与系统科学研究院
Speaker:
文志英 教授, 清华大学数学系
Title:
分形几何：内容、意义和方法
Time & Venue:
2018.6.29 10:00-11:00 N202
Abstract:
介绍分形几何的特点，特别是研究内容、方法与经典几何的重要差异，同时也介绍一些前沿进展。
Speaker:
Prof. Li Xiaochun, Illinois University, USA
Title:
Recent progress on pointwise convergence of Schrodinger equations
Time & Venue:
2018.6.29 15:00-17:00 N820
Abstract:
We report some recent progress on the pointwise convergence of the solutions to Schrodinger equations. The talk will be divided into two parts. In the first part, we will give a general description of the recent progress, the main ideas and ingredients in the proofs. In the second part, we will go to some technical details for a better understanding.
Speaker:
骆顺龙, 中国科学院数学与系统科学研究院
Title:
Symmetry Quantification
Time & Venue:
2018.6.29 15:00-16:00 N620
Abstract:
By exploiting the algebraic and geometric structure of operation-state coupling, we show that an information-theoretic measure of symmetry emerges naturally from the formalism of quantum mechanics. This is achieved by decomposing the operation-state coupling into a symmetric part and an asymmetric part, which satisfy a conservation relation. The symmetric part is represented by the symmetric Jordan product, and the asymmetric part is synthesized by the skew-symmetric Lie product. The latter leads to a significant extension of the celebrated Wigner-Yanase skew information, and has an operational interpretation as quantum coherence of a state with respect to an operation. This not only puts the study of coherence in a broad context involving operations, but also presents a basic framework for quantitatively addressing symmetry-asymmetry complementarity.
Speaker:
Prof. Xiaotong Shen, University of Minnesota
Title:
Personalized prediction and recommender systems
Time & Venue:
2018.6.29 15:00-17:00 N222
Abstract:
Personalized prediction predicts a user's preference for a large number of items through user-specific as well as content-specific information, based on a very small amount of observed preference scores. The problem of this kind involves unknown parameters of high-dimensionality in the presence of a high percentage of missing observations. In this situation, the predictive accuracy depends on how to pool the information from similar users and items. Two major approaches are collaborative filtering and content-based filtering. Whereas the former utilizes the information on users that think alike for a specific item, the latter acts on characteristics of the items that a user prefers, on which two kinds of recommender systems Grooveshark and Pandora are built. In this talk, I will present our recent research on regularized latent-factor modeling and compare with state-of-art recommenders in terms of predictive performance. Special attention will be devoted to the impact of nonignorable missing and social networks on personalized prediction.
Speaker:
Prof. Shigeru Mukai, 京都RIMS研究所
Title:
Playing with plane cubics, rational sextics and Cremona transformations
Time & Venue:
2018.6.29 15:30-16:30 N109
Abstract:
Several theorems, such as Pappus’, Miquel’s and Desargues’ in elementary geometry are theorems of projective geometry also. Moreover, they have infinite symmetries of Cremona transformations of the projective plane. After explaining it for pencil of cubics, I will move to rational sextics and present an example with action of a non-commutative free group consisting of infinte Cremona transformations.
Speaker:
Prof. Lei Liu, Division of Biostatistics, Washington University in St. Louis, Missouri, U.S.A.
Title:
Variable selection for random effects two-part models
Time & Venue:
2018.7.2 10:00-11:00 N613
Abstract:
Random effects two-part models have been applied to longitudinal studies for zero-inflated (or semi-continuous) data, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include monthly medical costs, daily alcohol drinks, relative abundance of microbiome, etc. With the advance of information technology for data collection and storage, the number of variables available to researchers can be rather large in such studies. To avoid curse of dimensionality and facilitate decision making, it is critically important to select covariates that are truly related to the outcome. However, owing to its intricate nature, there is not yet a satisfactory variable selection method available for such sophisticated models. In this paper, we seek a feasible way of conducting variable selection for random effects two-part models on the basis of the recently proposed “minimum information criterion" (MIC) method. We demonstrate that the MIC formulation leads to a reasonable formulation of sparse estimation, which can be conveniently solved with SAS Proc NLMIXED. The performance of our approach is evaluated through simulation, and an application to a longitudinal alcohol dependence study is provided.
Speaker:
杨静, 广西民族大学软件与信息安全学院
Title:
The Second Discriminant of a Univariate Polynomial
Time & Venue:
2018.7.5 15:00-16:00 N205
Abstract:
In this talk, we define the second discriminant $D_2$ of a univariate polynomial $f$ of degree greater than $2$ as the product of the linear forms $2\,r_k-r_i-r_j$ for all triples of roots $r_i, r_k, r_j$ of $f$ with $i
Speaker:
林志聪, 集美大学数学系
Title:
Restricted inversion sequences and enhanced $3$-noncrossing partitions
Time & Venue:
2018.7.5 16:00-17:00 N205
Abstract
Yan and Martinez--Savage conjectured independently that inversion sequences with no weakly decreasing subsequence of length $3$ and enhanced $3$-noncrossing partitions have the same cardinality. In this talk, I will present an algebraic proof, which applies both the generating tree technique and the so-called obstinate kernel method
developed by Bousquet-M\'elou. As one application of this equinumerosity, an intriguing identity involving numbers of classical and enhanced $3$-noncrossing partitions was discovered.I will also pose a related functional equation, enumerating another interesting class of restricted inversion sequences, that I could not solve.
来源：中国科学院数学与系统科学研究院
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