Skyline Computation for Low-Latency Image-Activated Cell Identification

Abstract

High-throughput label-free single cell screening technology has been studied for noninvasive analysis of various kinds of cells. We tackle the cell identification task in the cell sorting system as a continuous skyline computation. Skyline Computation is a method for extracting interesting entries from a large population with multiple attributes. Jointed rooted-tree (JR-tree) is continuous skyline computation algorithm that manages entries using a rooted-tree structure. JR-tree delays extend the tree to deeper levels to accelerate tree construction and traversal. In this study, we proposed the JR-tree-based parallel skyline computation accelerator. We implemented it on a field-programmable gate array (FPGA). We evaluated our proposed software and hardware algorithms against an existing software algorithm using synthetic and real-world datasets.

Cite

Text

Koizumi et al. "Skyline Computation for Low-Latency Image-Activated Cell Identification." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12134

Markdown

[Koizumi et al. "Skyline Computation for Low-Latency Image-Activated Cell Identification." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/koizumi2018aaai-skyline/) doi:10.1609/AAAI.V32I1.12134

BibTeX

@inproceedings{koizumi2018aaai-skyline,
  title     = {{Skyline Computation for Low-Latency Image-Activated Cell Identification}},
  author    = {Koizumi, Kenichi and Hiraki, Kei and Inaba, Mary},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8101-8102},
  doi       = {10.1609/AAAI.V32I1.12134},
  url       = {https://mlanthology.org/aaai/2018/koizumi2018aaai-skyline/}
}