Engineering topics
Ma, Yong
Publications and source records attributed to Ma, Yong.
Identifying COVID-19 cases and extracting patient reported symptoms from Reddit using natural language processing
We used social media data from “covid19positive” subreddit, from 03/2020 to 03/2022 to identify COVID-19 cases and extract their reported symptoms automatically using natural language processing (NLP). We trained a Bidirectional Encoder Representations from Transformers classification model with chunking to identify COVID-19 cases; also, we developed a novel QuadArm model, which incorporates Question-answering, dual-corpus expansion, Adaptive rotation clustering, and mapping, to extract symptoms. Our classification model achieved a 91.2% accuracy for the early period (03/2020-05/2020) and was applied to the Delta (07/2021–09/2021) and Omicron (12/2021–03/2022) periods for case identification. We identified 310, 8794, and 12,094 COVID-positive authors in the three periods, respectively. The top five common symptoms extracted in the early period were coughing (57%), fever (55%), loss of sense of smell (41%), headache (40%), and sore throat (40%). During the Delta period, these symptoms remained as the top five symptoms with percent authors reporting symptoms reduced to half or fewer than the early period. During the Omicron period, loss of sense of smell was reported less while sore throat was reported more. Our study demonstrated that NLP can be used to identify COVID-19 cases accurately and extracted symptoms efficiently.
X-ray Pump-Probe Measurements using a Laser-Plasma Accelerator (Final Report)
This project funded one graduate student, Mario Balcazar, to perform experiments on high power laser facilities to study the application of X-rays generated by laser wakefield accelerated electrons for advanced radiography, in particular X-ray phase contrast imaging of hydrodynamic shocks/instabilities. The work made use of the DOE’s LaserNetUS facilities. We performed dynamic phase-contrast X-ray imaging of the shock generated by a 200 ps duration laser pulse (E = 1 J, I = 10 15 Wcm –2 ) with a 30 µm diameter liquid target, with unprecedented spatio-temporal resolution. This includes multi-shock generation within the liquid jet and plasma instability formation. Innovative electron-beam radiography was used to probe the laser-plasma interplay finding evidence of bilateral heating of the water followed by strong electric field generation. These measurements help explain some of the discrepancies between simulation and experiment and pave the way to better plasma diagnostic systems in HED and ICF physics experiments. This work was performed in collaboration with researchers from Michigan, LLNL, LBL and Sandia National Laboratories, Queen’s University Belfast and Imperial College.
Beyond optimization—supervised learning applications in relativistic laser-plasma experiments
We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change caused by a deformable mirror. Machine learning enables feature analysis beyond merely searching for an optimal beam charge, showing that specific aberrations in the laser wavefront are favored in generating higher beam charges. Supervised learning models allow characterizing the measured data quality as well as recognizing irreproducible data and potential outliers. Furthermore, we also include virtual measurement errors in the experimental data to examine the model robustness under these conditions. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of relativistic laser-plasma interaction.
Towards the optimization of direct laser acceleration
Experimental measurements using the OMEGA EP laser facility demonstrated direct laser acceleration (DLA) of electron beams to (505 ± 75) MeV with (140 ± 30) nC of charge from a low-density plasma target using a 400 J, picosecond duration pulse. Similar trends of electron energy with target density are also observed in self-consistent two-dimensional particle-in-cell simulations. The intensity of the laser pulse is sufficiently large that the electrons are rapidly expelled from along the laser pulse propagation axis to form a channel. The dominant acceleration mechanism is confirmed to be DLA and the effect of quasi-static channel fields on energetic electron dynamics is examined. A strong channel magnetic field, self-generated by the accelerated electrons, is found to play a comparable role to the transverse electric channel field in defining the boundary of electron motion.