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Weiyu Huang

Researcher at University of Pennsylvania

Publications -  33
Citations -  995

Weiyu Huang is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Metric (mathematics) & Graph (abstract data type). The author has an hindex of 15, co-authored 33 publications receiving 776 citations. Previous affiliations of Weiyu Huang include Massachusetts Institute of Technology & King Juan Carlos University.

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A Graph Signal Processing Perspective on Functional Brain Imaging

TL;DR: How brain activity can be meaningfully filtered based on concepts of spectral modes derived from brain structure is reviewed and GSP offers a novel framework for the analysis of brain imaging data.
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Graph Frequency Analysis of Brain Signals

TL;DR: It is observed that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning, and a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed is noticed.
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Functional Alignment with Anatomical Networks is Associated with Cognitive Flexibility.

TL;DR: In this article, the authors applied emerging tools from graph signal processing to examine whether BOLD signals measured at each point in time correspond to complex underlying anatomical networks in 28 individuals performing a perceptual task that probed cognitive flexibility.
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Functional Alignment with Anatomical Networks is Associated with Cognitive Flexibility

TL;DR: It is found that the alignment between functional signals and the architecture of the underlying white matter network was associated with greater cognitive flexibility across subjects, uncovering an integrated structure–function relation of human behaviour.
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Rating Prediction via Graph Signal Processing

TL;DR: New designs for recommendation systems inspired by recent advances in graph signal processing are developed, and it is demonstrated that linear latent factor models can be viewed as bandlimited interpolation algorithms that operate in a frequency domain given by the spectrum of a joint user and item network.