Speaker: Dr. LIN Yongxiao (Shandong University)Venue: Online Tencent Meeting ID: 321-4296-7396Abstract: Let $(\lambda_f(n))_{n\geq 1}$ be the Fourier coefficients of a Hecke--Maass $f$ on $GL_2$. The classical Rankin--Selberg problem asks for a better error term in the asymptotic formula for $\sum_{n\leq X} \lambda_f(n)^2$, as $X\to \infty$. We study an arithmetic analogue of this problem where the coefficients are restricted to arithmetic progressions $n\equiv a (mod q)$ with varying moduli $q$ (as $q\to \infty$).   The  functional  equation  for  the  related  L-functions  (subject  to  the  Ramanujan--Petersson conjecture) implies a level of distribution 2/5. We explain how one can obtain a better exponent $2/5+\eta$ (some $\eta>0$). Key to our proof is convolution identity for the coefficients of the L-function $L(f\times f,s)$. This is a joint work with E. Kowalski and Ph. Michel.

Speaker:Dr.XU ZhenliAvenue: Online     Tencent Meeting ID: 527-899-518Abstract:The development of efficient methods for long-range systems plays important role in all-atom simulations of biomolecules and drug design. We present a random-batch Ewald (RBE) and a random-batch sum-of-Gaussians (SOG) methods for molecular dynamics simulations of particle systems with long-range Coulomb interactions. These algorithms take advantage of the random minibatch strategy for the force calculation between particles, leading to an order N algorithm. It is based on the Ewald or the SOG splitting of the Coulomb kernel and the random importance sampling is employed in the Fourier part such that the force variance can be reduced. It avoids the use of the FFT and greatly improves the scalability of the molecular simulations. We also discuss the treatment of the short-range interactions by using random batch idea, and present the comparison between different approaches. Numerical results, including protein solution and phase-separated electrolytes, are presented to show the attractive performance of the algorithm, including the superscalability in parallel computing.

Speaker: PAN JiayinAvenue: Online    Tencent Meeting ID: 324-158-036Abstract:I had a short conversation with Richard Montgomery. It led to a  surprising connection between Ricci curvature and sub-Riemannian geometry:  Pan-Wei's example of Ricci limit space is isometric to half of the Grushin plane, a classical example in sub-Riemannian geometry. Inspired by this  connection, we construct the Grushin hemisphere as a Ricci limit space with  curvature >=1.

Speaker:Dr.CAO Juan(Jane)Aveneu: Online    Tencent Meeting: 870-353-157Abstract:Recently, triangle configuration-based bivariate simplex splines (referred to as TCB-spline) have been introduced to the geometric computing community. TCB-splines retain many attractive theoretic properties of classical B-splines, such as the partition of unity, local support, polynomial reproduction, and automatic inbuilt high-order smoothness. Moreover, TCB-splines have appealing properties superior to tensor-product splines. For example, compared to tensor-product splines, TCB-splines support local refinement and are more flexible to accommodate general parametric domains. The attractive theoretical properties of TCB-splines make them an ideal basis for geometric shape modeling and CAD/CAE integration. In this talk, we will briefly introduce the concept of TCB-splines and then show their applications in complex geometric shape modeling, image vectorization, finite element analysis, isogeometric analysis, shell analysis, etc.

Speaker: LIN Huazhen，Southwestern University of Finance and EconomicsVenue: Online,Tencent Conference, ID: 441 837 276Abstract: Functional data analysis has emerged as a powerful tool in response to the ever increasing  resources and efforts devoted to collecting information about response curves or anything varying over a continuum. However, limited progress has been made to link the covariance structure of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing the variances of the random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions which govern the patterns of variation across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure, which combines regularization and B-spline smoothing, for model selection and estimation, and establish the convergence rate and asymptotic normality for the proposed estimators. The utility of the method is demonstrated via simulations as well as an analysis of the Avon Longitudinal Study of Parents and Children on parental effects on the growth curves of their offspring, which yields biologically interesting results.

Speaker: PEI Guangyu,Department of Economics,Chinese University of Hong KongVenue: Online, Tencent Conference(ID:803 511 877)Abstract: Using firm-level earnings forecasts and managerial guidance data, we construct guidance surprises for analysts, i.e., differences between managerial guidance and analysts’ initial forecasts. We document new evidence on expectation formation: (i) analysts overreact to guidance surprises; (ii) the overreaction is stronger for negative guidance surprises but weaker for larger surprises; and (iii) forecast revisions are neither symmetric in guidance surprises nor monotonic. We organize these facts with a model where analysts are uncertain about the quality of managerial guidance. Structural estimation reveals that a reasonable degree of ambiguity aversion is necessary to account for the documented heterogeneous overreaction.

Speaker: Carol SONGVenue: OnlineAbstract: This lecture highlights using images to illustrate and organize research ideas, and using graphics to demonstrate key points in academic research, and will introduce related softwares (Corel and Autodesk).

Speaker: Alan Plattus(Yale University)Abstract: Organized by the Zhejiang University , this seminar invites Alan Plattus, professor of Yale School of Architecture, to introduce the Advanced Studio at Yale as a teaching and research methodology, as well as share the past achievements and experiences of Yale School of Architecture's China studio. This seminar will introduce the outstanding examples of the Yale School of Architecture in international design education, as well as its future exploration of urban design in China.

Speaker:Yang Yunan （ETH Zurich）Avenue: Online    Tencent Meeting ID: 153-737-210 153 737 210Abstract: Many models in machine learning and PDE-based inverse problems exhibit intrinsic spectral properties, which have been used to explain the generalization capacity and the ill-posedness of such problems. In this talk, we discuss weighted training for computational learning and inversion with noisy data. The highlight of the proposed framework is that we allow weighting in both the parameter space and the data space. The weighting scheme encodes both a priori knowledge of the object to be learned and a strategy to weight the contribution of training data in the loss function. We demonstrate that appropriate weighting from prior knowledge can improve the generalization capability of the learned model in both machine learning and PDE-based inverse problems.