Alien Registration- Parent, Joseph B. (Van Buren, Aroostook County)

https://digitalmaine.com/alien_docs/32354/thumbnail.jp

Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress)

We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning tools to gain utility from those data. We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by machine learning approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. With a more capable representation of the organizing parameters and the target electron energy flux observations, PrecipNet achieves a $>$50\% reduction in errors from a current state-of-the-art model (OVATION Prime), better captures the dynamic changes of the auroral flux, and provides evidence that it can capably reconstruct mesoscale phenomena. We create and apply a new framework for space weather model evaluation that culminates previous guidance from across the solar-terrestrial research community. The research approach and results are representative of the `new frontier' of space weather research at the intersection of traditional and data science-driven discovery and provides a foundation for future efforts

Xjava: Exploiting parallelism with objectoriented stream programming

Abstract. This paper presents the XJava compiler for parallel programs. It exploits parallelism based on an object-oriented stream programming paradigm. XJava extends Java with new parallel constructs that do not expose programmers to low-level details of parallel programming on shared memory machines. Tasks define composable parallel activities, and new operators allow an easier expression of parallel patterns, such as pipelines, divide and conquer, or master/worker. We also present an automatic run-time mechanism that extends our previous work to automatically map tasks and parallel statements to threads. We conducted several case studies with an open source desktop search application and a suite of benchmark programs. The results show that XJava reduces the opportunities to introduce synchronization errors. Compared to threaded Java, the amount of code could be reduced by up to 39%. The run-time mechanism helped reduce effort for performance tuning and achieved speedups up to 31.5 on an eight core machine.

A Smartphone App for GNSS Ionospheric Data Collection: Initial Field Test Results

A key remaining problem in ionospheric monitoring via GNSS is that of the global sparseness of GNSS ground receiver distribution. Data transport over the "last mile" from each deployed receiver to data processing environments is still a costly challenge. The Mahali project (mahali.mit.edu) tackles this problem by using off-the-shelf mobile smartphones as relays. In this paper, we present the Mahali GNSS Logger App that connects Android smartphones to GNSS receivers over USB and uploads RINEX files to Dropbox. These files can then be processed to obtain Total Electron Content (TEC) plots of the ionosphere. We report here on the initial testing of this app and the scientific measurements obtained during January and February 2015 field tests in Brazil