Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Early Determinants of Women in the IT Workforce: A Model of Girls’ Career Choices

Purpose – To develop a testable model for girls’ career choices in technology fields based on past research and hypotheses about the future of the information technology (IT) workforce. Design/Methodology/Approach – Review and assimilation of literature from education, psychology, sociology, computer science, IT, and business in a model that identifies factors that can potentially influence a girl’s choice towards or against IT careers. The factors are categorized into social factors (family, peers, and media), structural factors (computer use, teacher/counselor influence, same sex versus coeducational schools), and individual differences. The impact of culture on these various factors is also explored. Findings – The model indicates that parents, particularly fathers, are the key influencers of girls’ choice of IT careers. Teachers and counselors provide little or no career direction. Hypotheses propose that early access to computers may reduce intimidation with technology and that same-sex education may serve to reduce career bias against IT. Research Limitations/Implications – While the model is multidisciplinary, much of research from which it draws is five to eight years old. Patterns of career choices, availability of technology, increased independence of women and girls, offshore/nearshore outsourcings of IT jobs are just some of the factors that may be insufficiently addressed in this study. Practical Implications – A “Recommendations” section provides some practical steps to increase the involvement of girls in IT-related careers and activities at an early age. The article identifies cultural research as a limitation and ways to address this. Originality/value – The paper is an assimilation of literature from diverse fields and provides a testable model for research on gender and IT

Top 10 technologies and their impact on CPA\u27s

https://egrove.olemiss.edu/aicpa_guides/2474/thumbnail.jp

Advances in the Convergence of Blockchain and Artificial Intelligence

Blockchain (BC) and artificial intelligence (AI) are currently two of the hottest computer science topics and their future seems bright. However, their convergence is not straightforward, and more research is needed in both fields. Thus, this book presents some of the latest advances in the convergence of BC and AI, gives useful guidelines for future researchers on how BC can help AI and how AI can become smarter, thanks to the use of BC. This book specifically analyzes the past of BC through the history of Bitcoin and then looks into the future: from massive internet-of-things (IoT) deployments, to the so-called metaverse, and to the next generation of AI-powered BC-based cyber secured applications

MAGNETIC FIELDS AND OTHER PHYSICAL CONDITIONS IN THE INTERSTELLAR MEDIUM

This document consists of two very different projects but the common thread is in the interest of magnetic fields. It describes the effect of magnetic fields in two Interstellar Medium regions in the Galaxy. Electromagnetic force is one of the four fundamental forces in physics. It is not known where magnetic field has initially risen in the Universe, but what is certain is that it has significant effect in the dynamics of star formation and galaxy formation. The studies aim to better understand the effects of field in an active star forming region and in the halo of the Galaxy. We observed the HI 21 cm spectral line via the Zeeman effect in attempt to detect line-of-sight magnetic field strengths in both of the projects. For the star forming region project in Chapter 2, towards the Eagle Nebula, an upper limit of the field strength was determined. From the observational results, physical conditions of the region were modeled. For the second project in Chapter 3, we attempted to detect magnetic fields via Zeeman effect towards non galactic disk objects. All of the observed positions have radial velocities that cannot be explained by the simple galactic rotation. Hence, they are considered to be non galactic disk sources and often grouped as High Velocity Clouds. With a unique observational technique and analysis, we derived the best fit line-of-sight magnetic fields. A particular interest to us is the Smith Cloud. From the detection of magnetic field, we attempted to estimate the density of the ambient medium in the halo, which will be useful for studying the galaxy formation

Current, March 08, 2004

https://irl.umsl.edu/current2000s/1182/thumbnail.jp

Top 10 technology opportunities : tips and tools

https://egrove.olemiss.edu/aicpa_guides/1610/thumbnail.jp

An Inventory of Student Recollections of Their Past Misconceptions as a Tool for Improved Classroom Astronomy Instruction

My Ph.D. research is about examining the persistence of 215 common misconceptions in astronomy. Each misconception is based on an often commonlyheld incorrect belief by college students taking introductory astronomy. At the University of Maine, the course is taught in alternating semesters by Prof. Neil F. Comins and Prof. David J. Batuski. In this dissertation, I examine the persistence of common astronomy misconceptions by the administration of a retrospective survey. The survey is a new instrument in that it permits the student to indicate either endorsement or rejection of each misconception at various stages in the student’s life. I analyze data from a total of 639 students over six semesters. I compare the survey data to the results of exams taken by the students and additional instruments that assess students’ misconceptions prior to instruction. I show that the consistency of the students’ recollection of their own misconceptions is on par with the consistency of responses between prelims and the final exam. I also find that students who report higher increased childhood interest in astronomy are more likely to have accurate recalls of their own past recollections. I then discuss the use of principal components analysis as a technique for describing the extent to which misconceptions are correlated with each other. The analysis yields logical groupings of subtopics from which to teach. I then present a brief overview of item response theory, the methodology of which calculates relative difficulties of the items. My analysis reveals orders to teach the associated topics in ways that are most effective at dispelling misconceptions during instruction. I also find that the best order to teach the associated concepts is often different for high school and college level courses