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Award ID contains: 1952180

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  1. ; ; ; ; ; (, Disability and Rehabilitation: Assistive Technology)
    Free, publicly-accessible full text available July 4, 2026
  2. ; ; ; ; ; ; (, Journal of Urban Health)
    Free, publicly-accessible full text available February 27, 2026
  3. ; ; ; ; ; (, Disability and Rehabilitation: Assistive Technology)
    Free, publicly-accessible full text available February 15, 2026
  4. ; ; ; ; ; ; (, IEEE Open Journal of the Communications Society)
    Free, publicly-accessible full text available January 1, 2026
  5. ; ; ; (, IEEE)
    Full Text Available 
  6. ; ; ; ; ; ; (, IEEE Transactions on Wireless Communications)
    Full Text Available 
  7. ; ; ; ; (, IEEE Transactions on Multimedia)
    Full Text Available 
  8. ; ; ; ; ; (, IEEE)
    Full Text Available 
  9. ; ; (, IEEE Transactions on Haptics)
    Full Text Available 
  10. (, In MLSys Workshop on Resource-Constrained Learning in Wireless Networks)
    Split-computing has recently emerged as a paradigm for offloading computation of visual analytics models from low-powered mobile devices to edge or cloud servers, by which the mobiles execute part of the model and compress and send the intermediate features, and the servers complete the remaining model computation. Prior feature compression approaches train different compression models and possibly visual analytics models to reach different target bit rates. We propose a scalable compression model that compresses the intermediate features of the YOLO object detection model into a layered bitstream, which can be easily adapted to meet the rate constraint of a dynamic network. Our approach achieves comparable rate-accuracy performance compared to prior non-scalable compression approaches over a large bitrate range. 
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    Accepted Manuscript Full Text Available