5月4日
研討會, 演講, 講座
Department of Mathematics - PhD Student Seminar - A Moving Mesh Finite Element Method for Topology Optimization
Many partial differential equations may have solutions with nearly singular behaviors, such as shock waves and boundary layers.
5月4日
研討會, 演講, 講座
Department of Mathematics - PhD Student Seminar - Highest weight crystals for Schur Q-functions
In 1990s, Kashiwara and Lusztig defined crystals as abstraction of crystal bases of quantum group representations.
5月4日
研討會, 演講, 講座
Department of Mathematics - PhD Student Seminar - Integration of single-cell atlases with generative adversarial networks
As single-cell technologies evolved over years, diverse single-cell atlas datasets have been rapidly accumulated. Integrative analyses harmonizing such datasets provide opportunities for gaining deep biological insights.
5月4日
研討會, 演講, 講座
Physics Department - Condensed Matter Seminar: What is “Qiu Ku” and How to Measure Quantum Entanglement with It
5月4日
研討會, 演講, 講座
Department of Mathematics - PhD Student Seminar - Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
The tensor train (TT) format enjoys appealing advantages in handling structural high-order tensors. The recent decade has witnessed the wide applications of TT-format tensors from diverse disciplines, among which tensor completion has drawn considerable attention.
5月4日
研討會, 演講, 講座
Department of Mathematics - PhD Student Seminar - Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks
Pruning is a model compression method that removes redundant parameters and accelerates the inference speed of deep neural networks while maintaining accuracy. Most available pruning methods impose various conditions on parameters or features directly.
5月3日
研討會, 演講, 講座
Department of Mathematics - PhD Student Seminar - Apply threshold dynamics algorithm to minimal compliance problem in topology optimization
Inspired by the simple two-step threshold dynamics algorithm which iteratively does convolution and thresholding to simulate the motion of grain boundaries, we developed an algorithm to approach the minimal compliance problem in topology optimization with
5月2日
研討會, 演講, 講座
Department of Mathematics - PhD Student Seminar - Application of Reinforcement Learning to High-frequency Market Making Strategy
With the increasing usage of the electronic limit order book (LOB) in modern financial markets, high-frequency algorithmic trading has captured over 70 percent of the whole trading volume in various financial markets.