no code implementations • 4 Jul 2023 • Sara Shoouri, Mingyu Yang, Gordy Carichner, Yuyang Li, Ehab A. Hamed, Angela Deng, Delbert A. Green II, Inhee Lee, David Blaauw, Hun-Seok Kim
A new GPS-less, daily localization method is proposed with deep learning sensor fusion that uses daylight intensity and temperature sensor data for Monarch butterfly tracking.
1 code implementation • 9 Jul 2022 • Yichen Gu, David Blaauw, Joshua Welch
By using a simple family of ODEs informed by the biochemistry of gene expression to constrain the likelihood of a deep generative model, we can simultaneously infer the latent time and latent state of each cell and predict its future gene expression state.
no code implementations • 9 Mar 2022 • Andrea Bejarano-Carbo, Hyochan An, Kyojin Choo, Shiyu Liu, Qirui Zhang, Dennis Sylvester, David Blaauw, Hun-Seok Kim
Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert.
1 code implementation • 14 Aug 2021 • Tim Dunn, Harisankar Sadasivan, Jack Wadden, Kush Goliya, Kuan-Yu Chen, Reetuparna Das, David Blaauw, Satish Narayanasamy
The MinION is a recent-to-market handheld nanopore sequencer.
no code implementations • 14 Dec 2019 • Mingyu Yang, Roger Hsiao, Gordy Carichner, Katherine Ernst, Jaechan Lim, Delbert A. Green II, Inhee Lee, David Blaauw, Hun-Seok Kim
Details of Monarch butterfly migration from the U. S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration.
no code implementations • 9 May 2018 • Charles Eckert, Xiaowei Wang, Jingcheng Wang, Arun Subramaniyan, Ravi Iyer, Dennis Sylvester, David Blaauw, Reetuparna Das
This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks.