Zheyuan Zhou on Muck Rack

Zheyuan Zhou

As seen in: MDPI, Amazon Science
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Articles

CAE: Character-level autoencoder for non-semantic relational data grouping

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By Veera Nunna, Shinae Kang, Zheyuan Zhou, Virginia Wang
| Amazon Science
Enterprise relational databases increasingly contain vast amounts of non-semantic data—IP addresses, product identifiers, encoded keys, and timestamps—that challenge traditional semantic analysis. This paper introduces a novel Character-Level Autoencoder (CAE) approach that automatically identifies and groups semantically identical columns in nonsemantic relational datasets by detecting column similarities based on data patterns and structures.
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CAE: Character-Level Autoencoder for Non-Semantic Relational Data Grouping

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By Shinae Kang, Zheyuan Zhou, Virginia Wang, Sucharitha Boinapally
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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A Twisted-Integrated Multifunctional Fiber Sensor for Real-Time Metabolic Monitoring and Management of Sepsis

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By Sijia Yu, Ying Jiang, Yiyang Liu, Zheyuan Zhou
| dx.doi.org
Introduction Click to copy section linkSection link copied! Sepsis is a systemic inflammatory response syndrome caused by infection, characterized by an abnormal immune response that can lead to systemic inflammation, organ dysfunction, and even organ failure, with a fatality rate that remains alarmingly high. (1,2) According to data from the World Health Organization, sepsis is responsible for approximately 11 million deaths annually.
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