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Code-multiplexed Coulter sensors can easily be integrated into microfluidic devices and provide information on spatiotemporal manipulations of suspended particles for quantitative sample assessment. In this paper, we introduced a deep learning-based decoding algorithm to process the output waveform from a network of code- multiplexed Coulter sensors on a microfluidic device. Our deep learning-based algorithm both simplifies the design of coded Coulter sensors and increases the signal processing speed. As a proof of principle, we designed and fabricated a microfluidic platform with 10 code-multiplexed Coulter sensors, and used a suspension of human ovarian cancer cells as a test sample to characterize the system. Our deep learning-based algorithm resulted in an 87% decoding accuracy at a sample processing speed of 800 particles/s.
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Coded-InvNet for Resilient Prediction Serving Systems
Inspired by a new coded computation algorithm for invertible functions, we propose Coded-InvNet a new approach to design resilient prediction serving systems that can gracefully handle stragglers or node failures. Coded-InvNet leverages recent findings in the deep learning literature such as invertible neural networks, Manifold Mixup, and domain translation algorithms, identifying interesting research directions that span across machine learning and systems. Our experimental results show that Coded-InvNet can outperform existing approaches, especially when the compute resource overhead is as low as 10%. For instance, without knowing which of the ten workers is going to fail, our algorithm can design a backup task so that it can correctly recover the missing prediction result with an accuracy of 85.9%, significantly outperforming the previous SOTA by 32.5%.
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- Award ID(s):
- 2003129
- PAR ID:
- 10311444
- Editor(s):
- Meila, Marina; Zhang, Tong
- Date Published:
- Journal Name:
- Proceedings of Machine Learning Research
- Volume:
- 139
- ISSN:
- 2640-3498
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
