Speaker:Shiyang Ma (Shanghai Jiaotong University)Venue: Zijingang Campus, Administration Building, Room 1417Time: 20th December, 2024 14:00Abstract: Local genetic correlation analysis is an important tool for identifying genetic loci with shared biology across traits. Recently Border et al. have shown that the results of these analyses are confounded by cross-trait assortative mating (xAM) leading to many false positive findings. Here we describe LAVA-Knock, a local genetic correlation method that builds off an existing genetic correlation method, LAVA, and augments it by generating synthetic data in a way that preserves local and long-range linkage disequilibrium (LD), allowing us to reduce the confounding induced by xAM. We show in simulations based on a realistic xAM model and in GWAS applications for 630 trait pairs that LAVA-Knock can greatly reduce the bias due to xAM relative to LAVA. Furthermore, we show a significant positive correlation between the reduction in local genetic correlations and estimates in the literature of cross-mate phenotype correlations; in particular, pairs of traits that are known to have high cross-mate phenotype correlation values have a significantly higher reduction in the number of local genetic correlations compared with other trait pairs. A few representative examples include education and intelligence, education and alcohol consumption, and attention-deficit hyperactivity disorder and depression. These results suggest that LAVA-Knock can reduce confounding due to both short range LD but also long-range LD induced by xAM.

Speaker: Prof Chengchen GuoVenue: ZJE科研楼，A203-1Time: 19th December, 2024  15:00-18:00

Speaker: Prof LI Congming (上海交通大学)Venue: Hainayuan Building 2, room 210Abstract: We give a brief introduction on the qualitative analysis of nonlinear problems arising from geometry, functional analysis and tluid mechanics, we focus on the research work of our team on various maximum principles, Liouville type theorems, the method of moving planes and classification of solutions, We mainly work on the Hardy-LittlewoodSobolev type systems, curvatures related geometric equations, and incompressible fluid equations.

Speaker: Carl-Philipp Heisenberg (ISTA)Venue: Zijingang Campus, Limoxi Building, Zijingang HallTime: 19th, Nov  16:00

Speaker: Lucia BoldriniVenue: Zijingang Campus, East 5-102Time: Nov 8th, 10:00-12:00

Speaker: Michel SadelainVenue: Zijingang Campus, School of Medicine, Room 205Time: Nov 8th, 10:00-11:00

Speaker: Ottmar EtteVenue: Zijingang Campus, East 1B-315Time: 10:00-12:00, NOV 1st, 2024

Speaker: ZHENG ZeminVenue: Administration building Room 1417, Zijingang CampusTime: Nov 5th, 2024 15:30Abstract: Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the sparse singular value decomposition (SVD) within the coefficient matrix, was introduced for interpretable multi-task learning, enabling the discovery of meaningful latent feature-response association networks across different layers. However, conducting precise inference on the latent factor matrices has remained challenging due to the orthogonality constraints inherited from the sparse SVD constraints. In this paper, we suggest a novel approach called the high-dimensional manifold-based SOFAR inference (SOFARI), drawing on the Neyman near-orthogonality inference while incorporating the Stiefel manifold structure imposed by the SVD constraints. By leveraging the underlying Stiefel manifold structure that is crucial to enabling inference, SOFARI provides easy-to-use bias-corrected estimators for both latent left factor vectors and singular values, for which we show to enjoy the asymptotic mean-zero normal distributions with estimable variances. We illustrate the effectiveness of SOFARI and justify our theoretical results through simulation examples and a real data application in economic forecasting.

Speaker: Prof. Leon Chai, UIUCModerator: Prof. Hao Tianhu, Zhejiang UniversityTime: 19:00-21:00 pm, Oct. 28, 2024 (Monday)Venue: 腾讯会议 690 262 214Bilibili Live：https://live.bilibili.com/24519212I propose a new way of arriving at a Renaissance-Medieval distinction. Medieval theology had favored the independent status of ideas, defined by Aquinas and others as existing in the mind of God. By contrast Pico sees only a divine emanationism, by which God creates or produces only a first hypostasis from which ideas arise subsequently. Thus we can associate the Renaissance with a new primacy of creative activity or process over a previously-stable medieval world order based on ideationism.