10月10日
研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - Use of Large Animal Models to Investigate Brain Diseases
Abstract Genetically modified animal models have been extensively used to investigate the pathogenesis of age-dependent neurodegenerative diseases, such as Alzheimer (AD), Parkinson (PD), Huntington (HD) diseases, and Amyotrophic Lateral Sclerosis (ALS). The common feature of these diseases…
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5月4日
研讨会, 演讲, 讲座
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.
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5月4日
研讨会, 演讲, 讲座
Physics Department - Condensed Matter Seminar: What is “Qiu Ku” and How to Measure Quantum Entanglement with It
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5月4日
研讨会, 演讲, 讲座
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.
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5月4日
研讨会, 演讲, 讲座
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.
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5月4日
研讨会, 演讲, 讲座
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.
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5月3日
研讨会, 演讲, 讲座
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
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5月2日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - SDE-based deep generative model
Deep generative models are a category of machine learning models that utilizes deep neural networks to model data distributions and generate new samples.
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5月2日
研讨会, 演讲, 讲座
Department of Mathematics - PhD Student Seminar - Data Adaptive Early Stopping in Split LBI: towards Controlling the False Discovery Rate
Early stopping is a widely-used regularization technique to avoid overfitting in iterative algorithms.
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