Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Magnetic Non-Potentiality of Solar Active Regions and Peak X-Ray Flux of the Associated Flares

Predicting the severity of the solar eruptive phenomena like flares and Coronal Mass Ejections (CMEs) remains a great challenge despite concerted efforts for several decades. The advent of high quality vector magnetograms obtained from Hinode (SOT/SP) has increased the possibility of meeting this challenge. In particular, the Spatially Averaged Signed Shear Angle (SASSA) seems to be an unique parameter to quantify the non-potentiality of the active regions. We demonstrate the usefulness of SASSA for predicting the flare severity. For this purpose we present case studies of the evolution of magnetic non-potentiality using 115 vector magnetograms of four active regions namely ARs NOAA 10930, 10960, 10961 and 10963 during December 08-15, 2006, June 03-10, 2007, June 28-July 5, 2007 and July 10-17, 2007 respectively. The NOAA ARs 10930 and 10960 were very active and produced X and M class flares respectively, along with many smaller X-ray flares. On the other hand, the NOAA ARs 10961 and 10963 were relatively less active and produced only very small (mostly A and B-class) flares. For this study we have used a large number of high resolution vector magnetograms obtained from Hinode (SOT/SP). The analysis shows that the peak X-ray flux of the most intense solar flare emanating from the active regions depends on the magnitude of the SASSA at the time of the flare. This finding of the existence of a lower limit of SASSA for a given class of X-ray flare will be very useful for space weather forecasting. We have also studied another non-potentiality parameter called mean weighted shear angle (MWSA) of the vector magnetograms along with SASSA. We find that the MWSA does not show such distinction as the SASSA for upper limits of GOES X-Ray flux of solar flares, however both the quantities show similar trends during the evolution of all active regions studied.Comment: 25 pages, 5 figures, accepted for publication in the Astrophysical Journa

Education Research Using Data Mining and Machine Learning with Computer Science Undergraduates

In recent decades, we are witness to an explosion of technology use and integration of everyday life. The engine of technology application in every aspect of life is Computer Science (CS). Appropriate CS education to fulfill the demand from the workforce for graduates is a broad and challenging problem facing many universities. Research into this ‘supply–chain’ problem is a central focus of CS education research. As of late, Educational Data Mining (EDM) emerges as an area connecting CS education research with the goal to help students stay in their program, improve performance in their program, and graduate with a degree. We contribute to this work with several research studies and future work focusing on CS undergraduate students relating to their program success and course performance analyzed through the lens of data mining. We perform research into student success predictors beyond diversity and gender. We examine student behaviors in course load and completion. We study workforce readiness with creation of a new teaching strategy, its deployment in the classroom, and the analysis shows us relevant Software Engineering (SE) topics for computing jobs. We look at cognitive learning in the beginning CS course its relations to course performance. We use decision trees in machine learning algorithms to predict student success or failure of CS core courses using performance and semester span of core curriculum. These research areas refine pathways for CS course sequencing to improve retention, reduce time-to–graduation, and increase success in the work field

Bayesian Item Response Modeling in R with brms and Stan

Item Response Theory (IRT) is widely applied in the human sciences to model persons' responses on a set of items measuring one or more latent constructs. While several R packages have been developed that implement IRT models, they tend to be restricted to respective prespecified classes of models. Further, most implementations are frequentist while the availability of Bayesian methods remains comparably limited. We demonstrate how to use the R package brms together with the probabilistic programming language Stan to specify and fit a wide range of Bayesian IRT models using flexible and intuitive multilevel formula syntax. Further, item and person parameters can be related in both a linear or non-linear manner. Various distributions for categorical, ordinal, and continuous responses are supported. Users may even define their own custom response distribution for use in the presented framework. Common IRT model classes that can be specified natively in the presented framework include 1PL and 2PL logistic models optionally also containing guessing parameters, graded response and partial credit ordinal models, as well as drift diffusion models of response times coupled with binary decisions. Posterior distributions of item and person parameters can be conveniently extracted and post-processed. Model fit can be evaluated and compared using Bayes factors and efficient cross-validation procedures.Comment: 54 pages, 16 figures, 3 table