SearcharxivSearch

arXiv subjects

Mei Lin

Publications and source records attributed to Mei Lin.

2 recordsLinked to original sources

The 256-antenna Coherent All-Sky Monitor

Radio astronomy is uniquely coupled to exponential trends in computation because the optics (cross-correlation, beamforming, and imaging) and spectrometry (i.e. channelization) can now be done digitally. Inexpensive analog-to-digital converters (ADCs) can sample signals from large numbers of antennas and graphics processing units (GPUs) allow us to coherently process wide-field radio data in real time, motivating large-$N$ aperture arrays at moderate cost. We describe the 256-antenna Coherent All-Sky Monitor (CASM-256), a dense aperture array operating at 375-500\,MHz, currently being deployed at the Owens Valley Radio Observatory (OVRO) in Big Pine, California. The large field-of-view (FoV$\sim10^4$\,deg$^2$) and point-source sensitivity of CASM-256 will allow it to detect local Universe fast radio bursts (FRBs). The nearby sample is ideal for unveiling the physical origin of FRBs, measuring the baryonic content of nearby galaxy halos, and discovering prompt multi-wavelength and multi-messenger counterparts to FRBs. CASM will search for fast transients in the Milky Way such as FRB analogs, pulsar giant pulses, and the new source class known as long-period radio transients. We describe the instrument and present on-sky data from the first two dozen antennas, including an operational real-time GPU based FRB search pipeline. We emphasize the scalability of the concept and describe paths to a future CASM array with tens of thousands of antennas that could detect one million FRBs.

astro-ph.IM

Application of Deep Learning on Predicting Prognosis of Acute Myeloid Leukemia with Cytogenetics, Age, and Mutations

We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD). In our DL network, autoencoders are stacked to form a hierarchical DL model from which raw data are compressed and organized and high-level features are extracted. The network is written in R language and is designed to predict prognosis of AML for a given case (DTD of more than or less than 730 days). The DL network achieves an excellent accuracy of 83% in predicting prognosis. As a proof-of-concept study, our preliminary results demonstrate a practical application of DL in future practice of prognostic prediction using next-gen sequencing (NGS) data.

cs.LG