2024年11月15日 – 11月17日
会议, 座谈会, 论坛
Molecular Frontiers Symposium in Hong Kong at HKUST with Nobel Laureates
INTRODUCTION The Molecular Frontiers Symposium, organized by the globally renowned Molecular Frontiers Foundation - founded by the Royal Swedish Academy of Sciences - is recognized as one of the most influential scientific organizations worldwide. For the first time in the organization’s…
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3月21日
研讨会, 演讲, 讲座
Physics Department - Using Anisotropic Strain to Probe Multipolar Order Parameters
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3月20日
研讨会, 演讲, 讲座
Physics Department - Quantum Simulation of Artificial Non-Abelian Gauge Fields Using an Ultracold Gas
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3月20日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Pure Mathematics - The derived birational geometry of nested quiver varieties
Recently, Jeroen Hekking developed the derived blow-up theory of closed embedding of derived schemes, which generalize the theory of Rydh and Khan of the regular embeddings.
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3月17日
研讨会, 演讲, 讲座
Department of Chemistry - PhD Student Seminar - Reduction of CO2 with Hydrosilanes via Homogeneous Catalysis
Student: Mr. Xin XU Department: Department of Chemistry, HKUST Supervisor: Professor Zhenyang LIN
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3月17日
研讨会, 演讲, 讲座
Physics Department - Transport Evidences of Excitonic Condensate in CrOCl-coupled Bernal Bilayer Graphene
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3月17日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on PDE - Rectifiability and uniqueness of blow-ups for points with positive Alt-Caffarelli-Friedman limit
The Alt-Caffarelli-Friedman (ACF) monotonicity formula is an important tool in the study of free boundary problems.
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3月17日
研讨会, 演讲, 讲座
Department of Chemistry - PhD Student Seminar - Phosphinidene: Synthesis, Structure and Reactivity
Student: Miss Junyi WANG Department: Department of Chemistry, HKUST Supervisor: Professor Zhenyang LIN & Professor Qing YE
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3月16日
研讨会, 演讲, 讲座
Department of Mathematics - Seminar on Statistics - Dynamic Matrix/Tensor Factor Models for High Dimensional Time Series
In many applications fields such as economics, finance and engineering, matrix or tensor observations are now commonly observed over time. Often the dimensions of these time series data high.
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