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Creators/Authors contains: "Liu, Shengzhong"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, ACM The Web Conference)
    Free, publicly-accessible full text available April 28, 2026
  2. ; ; ; ; ; ; ; ; ; ; et al (, ACN The Web Conference)
    Free, publicly-accessible full text available April 28, 2026
  3. ; ; ; ; (, IEEE)
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  6. ; ; ; ; ; ; ; ; (, Communications of the ACM)
    The paper discussesalgorithmic priority inversionin mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general,priority inversionoccurs in computing systems when computations that are less important are performed together with or ahead of those that are more important. Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. 
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  10. ; ; ; ; ; ; ; ; ; ; et al (, IEEE)