[2025 Fall Semester]
지정 날짜 목요일 13:30
운영위원 : 표성인 ( luinn27@ajou.ac.kr, 팔달관 426호 )
감독위원 : 장설( jangseol@ajou.ac.kr @ajou.ac.kr, 팔달관 426호 )
Title : On the full subcomplexes of Bier spheres
Abstract : In 1992, Thomas Bier introduced a combinatorial construction that yields many simplicial (m-2)-dimensional PL-spheres on 2m vertices. The study of full subcomplexes of a simplicial complex is important for understanding the structure of simplicial complexes, or its associated topological spaces. In this talk, we will discuss the homological types of full subcomplexes of Bier spheres.
Title : Real toric varieties from nested fans
Abstract :
Title : Sparse FEONet: Neural Networks with FEM-Based Local Connections
Abstract :
Title : Exact Post-Selection Inference with Application to the LASSO
Abstract :
Title : A Systematic Review and Meta-Analysis of Self-Controlled Case Series Studies on Vaccine-Associated Stroke
Abstract :
Title : Universality of Cokernels for p-adic Random Matrices under Inhomogeneous Conditions
Abstract :
Title : Fairness-Aware Score Adjustment for Optimizing the λ value in Recommendation Systems
Abstract :
Title : TDA in Longitudinal Time Series Clustering
Abstract :
Title : Subsurface Utility Detection through Integrated Analysis of Electrical Resistivity and GPR Using Kriging, Topological Data Analysis, and Deep Learning
Abstract : Ground Penetrating Radar (GPR) and Electrical Resistivity (ER) are widely used geophysical techniques for subsurface exploration, but each method has inherent limitations when applied independently. To address this, we propose an integrated framework that combines ER analysis via Kriging-based spatial interpolation with GPR interpretation enhanced by Topological Data Analysis (TDA) and deep learning models. The ER data provide continuous resistivity distributions through Kriging, while GPR B-scan images are processed with TDA to extract shape-aware topological descriptors that are further utilized in deep learning networks for buried object detection. Experimental results using both simulated and field datasets demonstrate that this integrated approach achieves higher accuracy and robustness than single-modality analyses, offering a novel pathway for reliable subsurface utility detection and contributing to improved safety in civil infrastructure and excavation practices.
Title : Flux Reconstruction in the Raviart–Thomas Space for the HDG Method
Abstract :
Title: Physics-Informed Convolutional Operator Network for Efficient PDE Solvers
Abstract: