What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

PubMed Central

2017-01-01

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274, 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105, 2745–2750; Thiessen & Yee 2010 Child Development 81, 1287–1303; Saffran 2002 Journal of Memory and Language 47, 172–196; Misyak & Christiansen 2012 Language Learning 62, 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39, 246–263; Thiessen et al. 2013 Psychological Bulletin 139, 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37, 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'. PMID:27872374

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

PubMed

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Workshop in computational molecular biology, April 15, 1991--April 14, 1994

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tavare, S.

Funds from this award were used to the Workshop in Computational Molecular Biology, `91 Symposium entitled Interface: Computing Science and Statistics, Seattle, Washington, April 21, 1991; the Workshop in Statistical Issues in Molecular Biology held at Stanford, California, August 8, 1993; and the Session on Population Genetics a part of the 56th Annual Meeting, Institute of Mathematical Statistics, San Francisco, California, August 9, 1993.

"Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs"

ERIC Educational Resources Information Center

Konstantopoulos, Spyros

2009-01-01

Power computations for one-level experimental designs that assume simple random samples are greatly facilitated by power tables such as those presented in Cohen's book about statistical power analysis. However, in education and the social sciences experimental designs have naturally nested structures and multilevel models are needed to compute the…

A Computer-Assisted Instruction in Teaching Abstract Statistics to Public Affairs Undergraduates

ERIC Educational Resources Information Center

Ozturk, Ali Osman

2012-01-01

This article attempts to demonstrate the applicability of a computer-assisted instruction supported with simulated data in teaching abstract statistical concepts to political science and public affairs students in an introductory research methods course. The software is called the Elaboration Model Computer Exercise (EMCE) in that it takes a great…

Mathematics and Statistics Research Department progress report, period ending June 30, 1982

DOE Office of Scientific and Technical Information (OSTI.GOV)

Denson, M.V.; Funderlic, R.E.; Gosslee, D.G.

1982-08-01

This report is the twenty-fifth in the series of progress reports of the Mathematics and Statistics Research Department of the Computer Sciences Division, Union Carbide Corporation Nuclear Division (UCC-ND). Part A records research progress in analysis of large data sets, biometrics research, computational statistics, materials science applications, moving boundary problems, numerical linear algebra, and risk analysis. Collaboration and consulting with others throughout the UCC-ND complex are recorded in Part B. Included are sections on biology, chemistry, energy, engineering, environmental sciences, health and safety, materials science, safeguards, surveys, and the waste storage program. Part C summarizes the various educational activities inmore » which the staff was engaged. Part D lists the presentations of research results, and Part E records the staff's other professional activities during the report period.« less

Effect of Robotics on Elementary Preservice Teachers' Self-Efficacy, Science Learning, and Computational Thinking

NASA Astrophysics Data System (ADS)

Jaipal-Jamani, Kamini; Angeli, Charoula

2017-04-01

The current impetus for increasing STEM in K-12 education calls for an examination of how preservice teachers are being prepared to teach STEM. This paper reports on a study that examined elementary preservice teachers' ( n = 21) self-efficacy, understanding of science concepts, and computational thinking as they engaged with robotics in a science methods course. Data collection methods included pretests and posttests on science content, prequestionnaires and postquestionnaires for interest and self-efficacy, and four programming assignments. Statistical results showed that preservice teachers' interest and self-efficacy with robotics increased. There was a statistically significant difference between preknowledge and postknowledge scores, and preservice teachers did show gains in learning how to write algorithms and debug programs over repeated programming tasks. The findings suggest that the robotics activity was an effective instructional strategy to enhance interest in robotics, increase self-efficacy to teach with robotics, develop understandings of science concepts, and promote the development of computational thinking skills. Study findings contribute quantitative evidence to the STEM literature on how robotics develops preservice teachers' self-efficacy, science knowledge, and computational thinking skills in higher education science classroom contexts.

Introductory life science mathematics and quantitative neuroscience courses.

PubMed

Duffus, Dwight; Olifer, Andrei

2010-01-01

We describe two sets of courses designed to enhance the mathematical, statistical, and computational training of life science undergraduates at Emory College. The first course is an introductory sequence in differential and integral calculus, modeling with differential equations, probability, and inferential statistics. The second is an upper-division course in computational neuroscience. We provide a description of each course, detailed syllabi, examples of content, and a brief discussion of the main issues encountered in developing and offering the courses.

The IT Gender Gap: Experience, Motivation and Differences in Undergraduate Studies of Computer Science

ERIC Educational Resources Information Center

Mirjana, Ivanovic; Zoran, Putnik; Anja, Sisarica; Zoran, Budimac

2011-01-01

This paper reports on progress and conclusions of two-year research of gender issues in studying computer science at Department of Mathematics and Informatics, Faculty of Science, University of Novi Sad. Using statistics on data gathered by a survey, the work presented here focused on identifying, understanding, and correlating both female and…

Modified Bayesian Kriging for Noisy Response Problems for Reliability Analysis

DTIC Science & Technology

2015-01-01

52242, USA nicholas-gaul@uiowa.edu Mary Kathryn Cowles Department of Statistics & Actuarial Science College of Liberal Arts and Sciences , The...Forrester, A. I. J., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences , 45(1–3), 50-79. doi...Wiley. [27] Sacks, J., Welch, W. J., Toby J. Mitchell, & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science , 4

Network-based statistical comparison of citation topology of bibliographic databases

PubMed Central

Šubelj, Lovro; Fiala, Dalibor; Bajec, Marko

2014-01-01

Modern bibliographic databases provide the basis for scientific research and its evaluation. While their content and structure differ substantially, there exist only informal notions on their reliability. Here we compare the topological consistency of citation networks extracted from six popular bibliographic databases including Web of Science, CiteSeer and arXiv.org. The networks are assessed through a rich set of local and global graph statistics. We first reveal statistically significant inconsistencies between some of the databases with respect to individual statistics. For example, the introduced field bow-tie decomposition of DBLP Computer Science Bibliography substantially differs from the rest due to the coverage of the database, while the citation information within arXiv.org is the most exhaustive. Finally, we compare the databases over multiple graph statistics using the critical difference diagram. The citation topology of DBLP Computer Science Bibliography is the least consistent with the rest, while, not surprisingly, Web of Science is significantly more reliable from the perspective of consistency. This work can serve either as a reference for scholars in bibliometrics and scientometrics or a scientific evaluation guideline for governments and research agencies. PMID:25263231

Introductory Life Science Mathematics and Quantitative Neuroscience Courses

ERIC Educational Resources Information Center

Duffus, Dwight; Olifer, Andrei

2010-01-01

We describe two sets of courses designed to enhance the mathematical, statistical, and computational training of life science undergraduates at Emory College. The first course is an introductory sequence in differential and integral calculus, modeling with differential equations, probability, and inferential statistics. The second is an…

Introductory Life Science Mathematics and Quantitative Neuroscience Courses

PubMed Central

Olifer, Andrei

2010-01-01

We describe two sets of courses designed to enhance the mathematical, statistical, and computational training of life science undergraduates at Emory College. The first course is an introductory sequence in differential and integral calculus, modeling with differential equations, probability, and inferential statistics. The second is an upper-division course in computational neuroscience. We provide a description of each course, detailed syllabi, examples of content, and a brief discussion of the main issues encountered in developing and offering the courses. PMID:20810971

Attitudes and Achievement in Introductory Psychological Statistics Classes: Traditional versus Computer-Supported Instruction.

ERIC Educational Resources Information Center

Gratz, Zandra S.; And Others

A study was conducted at a large, state-supported college in the Northeast to establish a mechanism by which a popular software package, Statistical Package for the Social Sciences (SPSS), could be used in psychology program statistics courses in such a way that no prior computer expertise would be needed on the part of the faculty or the…

For the Love of Statistics: Appreciating and Learning to Apply Experimental Analysis and Statistics through Computer Programming Activities

ERIC Educational Resources Information Center

Mascaró, Maite; Sacristán, Ana Isabel; Rufino, Marta M.

2016-01-01

For the past 4 years, we have been involved in a project that aims to enhance the teaching and learning of experimental analysis and statistics, of environmental and biological sciences students, through computational programming activities (using R code). In this project, through an iterative design, we have developed sequences of R-code-based…

Computer Science in High School Graduation Requirements. ECS Education Trends

ERIC Educational Resources Information Center

Zinth, Jennifer Dounay

2015-01-01

Computer science and coding skills are widely recognized as a valuable asset in the current and projected job market. The Bureau of Labor Statistics projects 37.5 percent growth from 2012 to 2022 in the "computer systems design and related services" industry--from 1,620,300 jobs in 2012 to an estimated 2,229,000 jobs in 2022. Yet some…

Why Don't All Professors Use Computers?

ERIC Educational Resources Information Center

Drew, David Eli

1989-01-01

Discusses the adoption of computer technology at universities and examines reasons why some professors don't use computers. Topics discussed include computer applications, including artificial intelligence, social science research, statistical analysis, and cooperative research; appropriateness of the technology for the task; the Computer Aptitude…

Overview of the SAMSI year-long program on Statistical, Mathematical and Computational Methods for Astronomy

NASA Astrophysics Data System (ADS)

Jogesh Babu, G.

2017-01-01

A year-long research (Aug 2016- May 2017) program on `Statistical, Mathematical and Computational Methods for Astronomy (ASTRO)’ is well under way at Statistical and Applied Mathematical Sciences Institute (SAMSI), a National Science Foundation research institute in Research Triangle Park, NC. This program has brought together astronomers, computer scientists, applied mathematicians and statisticians. The main aims of this program are: to foster cross-disciplinary activities; to accelerate the adoption of modern statistical and mathematical tools into modern astronomy; and to develop new tools needed for important astronomical research problems. The program provides multiple avenues for cross-disciplinary interactions, including several workshops, long-term visitors, and regular teleconferences, so participants can continue collaborations, even if they can only spend limited time in residence at SAMSI. The main program is organized around five working groups:i) Uncertainty Quantification and Astrophysical Emulationii) Synoptic Time Domain Surveysiii) Multivariate and Irregularly Sampled Time Seriesiv) Astrophysical Populationsv) Statistics, computation, and modeling in cosmology.A brief description of each of the work under way by these groups will be given. Overlaps among various working groups will also be highlighted. How the wider astronomy community can both participate and benefit from the activities, will be briefly mentioned.

Computational Ecology and Open Science: Tools to Help Manage Lakes for Cyanobacteria in Lakes

EPA Science Inventory

Computational ecology is an interdisciplinary field that takes advantage of modern computation abilities to expand our ecological understanding. As computational ecologists, we use large data sets, which often cover large spatial extents, and advanced statistical/mathematical co...

Traveling front solutions to directed diffusion-limited aggregation, digital search trees, and the Lempel-Ziv data compression algorithm.

PubMed

Majumdar, Satya N

2003-08-01

We use the traveling front approach to derive exact asymptotic results for the statistics of the number of particles in a class of directed diffusion-limited aggregation models on a Cayley tree. We point out that some aspects of these models are closely connected to two different problems in computer science, namely, the digital search tree problem in data structures and the Lempel-Ziv algorithm for data compression. The statistics of the number of particles studied here is related to the statistics of height in digital search trees which, in turn, is related to the statistics of the length of the longest word formed by the Lempel-Ziv algorithm. Implications of our results to these computer science problems are pointed out.

Traveling front solutions to directed diffusion-limited aggregation, digital search trees, and the Lempel-Ziv data compression algorithm

NASA Astrophysics Data System (ADS)

Majumdar, Satya N.

2003-08-01

We use the traveling front approach to derive exact asymptotic results for the statistics of the number of particles in a class of directed diffusion-limited aggregation models on a Cayley tree. We point out that some aspects of these models are closely connected to two different problems in computer science, namely, the digital search tree problem in data structures and the Lempel-Ziv algorithm for data compression. The statistics of the number of particles studied here is related to the statistics of height in digital search trees which, in turn, is related to the statistics of the length of the longest word formed by the Lempel-Ziv algorithm. Implications of our results to these computer science problems are pointed out.

Archaeology Through Computational Linguistics: Inscription Statistics Predict Excavation Sites of Indus Valley Artifacts.

PubMed

Recchia, Gabriel L; Louwerse, Max M

2016-11-01

Computational techniques comparing co-occurrences of city names in texts allow the relative longitudes and latitudes of cities to be estimated algorithmically. However, these techniques have not been applied to estimate the provenance of artifacts with unknown origins. Here, we estimate the geographic origin of artifacts from the Indus Valley Civilization, applying methods commonly used in cognitive science to the Indus script. We show that these methods can accurately predict the relative locations of archeological sites on the basis of artifacts of known provenance, and we further apply these techniques to determine the most probable excavation sites of four sealings of unknown provenance. These findings suggest that inscription statistics reflect historical interactions among locations in the Indus Valley region, and they illustrate how computational methods can help localize inscribed archeological artifacts of unknown origin. The success of this method offers opportunities for the cognitive sciences in general and for computational anthropology specifically. Copyright © 2015 Cognitive Science Society, Inc.

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance

NASA Astrophysics Data System (ADS)

McCray, Wilmon Wil L., Jr.

The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI RTM) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization model and dashboard that demonstrates the use of statistical methods, statistical process control, sensitivity analysis, quantitative and optimization techniques to establish a baseline and predict future customer satisfaction index scores (outcomes). The American Customer Satisfaction Index (ACSI) model and industry benchmarks were used as a framework for the simulation model.

Women in Community College: Factors Related to Intentions to Pursue Computer Science

ERIC Educational Resources Information Center

Denner, Jill; Werner, Linda; O'Connor, Lisa

2015-01-01

Community colleges (CC) are obvious places to recruit more women into computer science. Enrollment at CCs has grown in response to a struggling economy, and students are more likely to be from underrepresented groups than students enrolled in 4-year universities (National Center for Education Statistics, 2008). However, we know little about why so…

Computational methods to extract meaning from text and advance theories of human cognition.

PubMed

McNamara, Danielle S

2011-01-01

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. Copyright © 2010 Cognitive Science Society, Inc.

DOE Advanced Scientific Advisory Committee (ASCAC): Workforce Subcommittee Letter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chapman, Barbara; Calandra, Henri; Crivelli, Silvia

2014-07-23

Simulation and computing are essential to much of the research conducted at the DOE national laboratories. Experts in the ASCR ¬relevant Computing Sciences, which encompass a range of disciplines including Computer Science, Applied Mathematics, Statistics and domain Computational Sciences, are an essential element of the workforce in nearly all of the DOE national laboratories. This report seeks to identify the gaps and challenges facing DOE with respect to this workforce. This letter is ASCAC’s response to the charge of February 19, 2014 to identify disciplines in which significantly greater emphasis in workforce training at the graduate or postdoctoral levels ismore » necessary to address workforce gaps in current and future Office of Science mission needs.« less

Quantifying uncertainty in climate change science through empirical information theory.

PubMed

Majda, Andrew J; Gershgorin, Boris

2010-08-24

Quantifying the uncertainty for the present climate and the predictions of climate change in the suite of imperfect Atmosphere Ocean Science (AOS) computer models is a central issue in climate change science. Here, a systematic approach to these issues with firm mathematical underpinning is developed through empirical information theory. An information metric to quantify AOS model errors in the climate is proposed here which incorporates both coarse-grained mean model errors as well as covariance ratios in a transformation invariant fashion. The subtle behavior of model errors with this information metric is quantified in an instructive statistically exactly solvable test model with direct relevance to climate change science including the prototype behavior of tracer gases such as CO(2). Formulas for identifying the most sensitive climate change directions using statistics of the present climate or an AOS model approximation are developed here; these formulas just involve finding the eigenvector associated with the largest eigenvalue of a quadratic form computed through suitable unperturbed climate statistics. These climate change concepts are illustrated on a statistically exactly solvable one-dimensional stochastic model with relevance for low frequency variability of the atmosphere. Viable algorithms for implementation of these concepts are discussed throughout the paper.

The Behavioral and Social Sciences Survey: Mathematical Sciences and Social Sciences.

ERIC Educational Resources Information Center

Kruskal, William, Ed.

This book, one of a series prepared in connection with the Behavioral and Social Sciences Survey (BASS) conducted between 1967 and 1969, deals with problems of statistics, mathematics, and computation as they related to the social sciences. Chapter 1 shows how these subjects help in their own ways for studying learning behavior with irregular…

GPU Acceleration of the Locally Selfconsistent Multiple Scattering Code for First Principles Calculation of the Ground State and Statistical Physics of Materials

NASA Astrophysics Data System (ADS)

Eisenbach, Markus

The Locally Self-consistent Multiple Scattering (LSMS) code solves the first principles Density Functional theory Kohn-Sham equation for a wide range of materials with a special focus on metals, alloys and metallic nano-structures. It has traditionally exhibited near perfect scalability on massively parallel high performance computer architectures. We present our efforts to exploit GPUs to accelerate the LSMS code to enable first principles calculations of O(100,000) atoms and statistical physics sampling of finite temperature properties. Using the Cray XK7 system Titan at the Oak Ridge Leadership Computing Facility we achieve a sustained performance of 14.5PFlop/s and a speedup of 8.6 compared to the CPU only code. This work has been sponsored by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Material Sciences and Engineering Division and by the Office of Advanced Scientific Computing. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

ENVIRONMENTAL STATISTICS INITIATIVE

EPA Science Inventory

EPA's Center of Excellence (COE) for Environmental Computational Science is intended to integrate cutting-edge science and emerging information technology (IT) solutions for input to the decision-making process. Complementing the research goals of EPA's COE, the NERL has initiat...

Enhancing interest in statistics among computer science students using computer tool entrepreneur role play

NASA Astrophysics Data System (ADS)

Judi, Hairulliza Mohamad; Sahari @ Ashari, Noraidah; Eksan, Zanaton Hj

2017-04-01

Previous research in Malaysia indicates that there is a problem regarding attitude towards statistics among students. They didn't show positive attitude in affective, cognitive, capability, value, interest and effort aspects although did well in difficulty. This issue should be given substantial attention because students' attitude towards statistics may give impacts on the teaching and learning process of the subject. Teaching statistics using role play is an appropriate attempt to improve attitudes to statistics, to enhance the learning of statistical techniques and statistical thinking, and to increase generic skills. The objectives of the paper are to give an overview on role play in statistics learning and to access the effect of these activities on students' attitude and learning in action research framework. The computer tool entrepreneur role play is conducted in a two-hour tutorial class session of first year students in Faculty of Information Sciences and Technology (FTSM), Universiti Kebangsaan Malaysia, enrolled in Probability and Statistics course. The results show that most students feel that they have enjoyable and great time in the role play. Furthermore, benefits and disadvantages from role play activities were highlighted to complete the review. Role play is expected to serve as an important activities that take into account students' experience, emotions and responses to provide useful information on how to modify student's thinking or behavior to improve learning.

FY 1999 Laboratory Directed Research and Development annual report

DOE Office of Scientific and Technical Information (OSTI.GOV)

PJ Hughes

2000-06-13

A short synopsis of each project is given covering the following main areas of research and development: Atmospheric sciences; Biotechnology; Chemical and instrumentation analysis; Computer and information science; Design and manufacture engineering; Ecological science; Electronics and sensors; Experimental technology; Health protection and dosimetry; Hydrologic and geologic science; Marine sciences; Materials science; Nuclear science and engineering; Process science and engineering; Sociotechnical systems analysis; Statistics and applied mathematics; and Thermal and energy systems.

Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research.

PubMed

Tang, Qi-Yi; Zhang, Chuan-Xi

2013-04-01

A comprehensive but simple-to-use software package called DPS (Data Processing System) has been developed to execute a range of standard numerical analyses and operations used in experimental design, statistics and data mining. This program runs on standard Windows computers. Many of the functions are specific to entomological and other biological research and are not found in standard statistical software. This paper presents applications of DPS to experimental design, statistical analysis and data mining in entomology. © 2012 The Authors Insect Science © 2012 Institute of Zoology, Chinese Academy of Sciences.

Science and data science.

PubMed

Blei, David M; Smyth, Padhraic

2017-08-07

Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combination of all three components is the essence of what data science is about.

Testing meta tagger

DTIC Science & Technology

2017-12-21

rank , and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on...Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.[1] Arthur Samuel...an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning " in 1959 while at IBM[2]. Evolved

The changing landscape of astrostatistics and astroinformatics

NASA Astrophysics Data System (ADS)

Feigelson, Eric D.

2017-06-01

The history and current status of the cross-disciplinary fields of astrostatistics and astroinformatics are reviewed. Astronomers need a wide range of statistical methods for both data reduction and science analysis. With the proliferation of high-throughput telescopes, efficient large scale computational methods are also becoming essential. However, astronomers receive only weak training in these fields during their formal education. Interest in the fields is rapidly growing with conferences organized by scholarly societies, textbooks and tutorial workshops, and research studies pushing the frontiers of methodology. R, the premier language of statistical computing, can provide an important software environment for the incorporation of advanced statistical and computational methodology into the astronomical community.

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

78 FR 67103 - Request for Nominations of Members To Serve on the Census Scientific Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-08

... analysis, survey methodology, geospatial analysis, econometrics, cognitive psychology, and computer science... following disciplines: demography, economics, geography, psychology, statistics, survey methodology, social... expertise in such areas as demography, economics, geography, psychology, statistics, survey methodology...

[Intranarcotic infusion therapy -- a computer interpretation using the program package SPSS (Statistical Package for the Social Sciences)].

PubMed

Link, J; Pachaly, J

1975-08-01

In a retrospective 18-month study the infusion therapy applied in a great anesthesia institute is examined. The data of the course of anesthesia recorded on magnetic tape by routine are analysed for this purpose bya computer with the statistical program SPSS. It could be proved that the behaviour of the several anesthetists is very different. Various correlations are discussed.

Changing the Paradigm: Preparing Students for the Computing Profession in the 21st Century

NASA Technical Reports Server (NTRS)

Robbins, Kay A.

2003-01-01

The dramatic technological developments of the past decade have led to a tremendous growth in the demand for computer science professionals well-versed in advanced technology and techniques. NASA, traditionally a haven for cutting-edge innovators, is now competing with every industrial and government sector for computer science talent. The computer science program at University of Texas at San Antonio (UTSA) faces challenges beyond those intrinsically presented by rapid technological change, because a significant number of UTSA students come from low-income families with no Internet or computer access at home. An examination of enrollment statistics for the computer science program at UTSA showed that very few students who entered as freshmen successfully graduated. The upper division courses appeared to be populated by graduate students removing deficiencies and by transfer students. The faculty was also concerned that the students who did graduate from the program did not have the strong technical and programming skills that the CS program had been noted for in the community during the 1980's.

GPU-computing in econophysics and statistical physics

NASA Astrophysics Data System (ADS)

Preis, T.

2011-03-01

A recent trend in computer science and related fields is general purpose computing on graphics processing units (GPUs), which can yield impressive performance. With multiple cores connected by high memory bandwidth, today's GPUs offer resources for non-graphics parallel processing. This article provides a brief introduction into the field of GPU computing and includes examples. In particular computationally expensive analyses employed in financial market context are coded on a graphics card architecture which leads to a significant reduction of computing time. In order to demonstrate the wide range of possible applications, a standard model in statistical physics - the Ising model - is ported to a graphics card architecture as well, resulting in large speedup values.

Statistical Methodologies to Integrate Experimental and Computational Research

NASA Technical Reports Server (NTRS)

Parker, P. A.; Johnson, R. T.; Montgomery, D. C.

2008-01-01

Development of advanced algorithms for simulating engine flow paths requires the integration of fundamental experiments with the validation of enhanced mathematical models. In this paper, we provide an overview of statistical methods to strategically and efficiently conduct experiments and computational model refinement. Moreover, the integration of experimental and computational research efforts is emphasized. With a statistical engineering perspective, scientific and engineering expertise is combined with statistical sciences to gain deeper insights into experimental phenomenon and code development performance; supporting the overall research objectives. The particular statistical methods discussed are design of experiments, response surface methodology, and uncertainty analysis and planning. Their application is illustrated with a coaxial free jet experiment and a turbulence model refinement investigation. Our goal is to provide an overview, focusing on concepts rather than practice, to demonstrate the benefits of using statistical methods in research and development, thereby encouraging their broader and more systematic application.

Are Academic Programs Adequate for the Software Profession?

ERIC Educational Resources Information Center

Koster, Alexis

2010-01-01

According to the Bureau of Labor Statistics, close to 1.8 million people, or 77% of all computer professionals, were working in the design, development, deployment, maintenance, and management of software in 2006. The ACM [Association for Computing Machinery] model curriculum for the BS in computer science proposes that about 42% of the core body…

Computational and Statistical Models: A Comparison for Policy Modeling of Childhood Obesity

NASA Astrophysics Data System (ADS)

Mabry, Patricia L.; Hammond, Ross; Ip, Edward Hak-Sing; Huang, Terry T.-K.

As systems science methodologies have begun to emerge as a set of innovative approaches to address complex problems in behavioral, social science, and public health research, some apparent conflicts with traditional statistical methodologies for public health have arisen. Computational modeling is an approach set in context that integrates diverse sources of data to test the plausibility of working hypotheses and to elicit novel ones. Statistical models are reductionist approaches geared towards proving the null hypothesis. While these two approaches may seem contrary to each other, we propose that they are in fact complementary and can be used jointly to advance solutions to complex problems. Outputs from statistical models can be fed into computational models, and outputs from computational models can lead to further empirical data collection and statistical models. Together, this presents an iterative process that refines the models and contributes to a greater understanding of the problem and its potential solutions. The purpose of this panel is to foster communication and understanding between statistical and computational modelers. Our goal is to shed light on the differences between the approaches and convey what kinds of research inquiries each one is best for addressing and how they can serve complementary (and synergistic) roles in the research process, to mutual benefit. For each approach the panel will cover the relevant "assumptions" and how the differences in what is assumed can foster misunderstandings. The interpretations of the results from each approach will be compared and contrasted and the limitations for each approach will be delineated. We will use illustrative examples from CompMod, the Comparative Modeling Network for Childhood Obesity Policy. The panel will also incorporate interactive discussions with the audience on the issues raised here.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Analysis of reference transactions using packaged computer programs.

PubMed

Calabretta, N; Ross, R

1984-01-01

Motivated by a continuing education class attended by the authors on the measurement of reference desk activities, the reference department at Scott Memorial Library initiated a project to gather data on reference desk transactions and to analyze the data by using packaged computer programs. The programs utilized for the project were SPSS (Statistical Package for the Social Sciences) and SAS (Statistical Analysis System). The planning, implementation and development of the project are described.

Teacher's Guide to Secondary Mathematics.

ERIC Educational Resources Information Center

Duval County Schools, Jacksonville, FL.

This is a teacher's guide to secondary school mathematics. Developed for use in the Duval County Public Schools, Jacksonville, Florida. Areas of mathematics covered are algebra, analysis, calculus, computer literacy, computer science, geometry, analytic geometry, general mathematics, consumer mathematics, pre-algebra, probability and statistics,…

What a Chemist Needs to Know--Other than Chemistry.

ERIC Educational Resources Information Center

Chemical and Engineering News, 1980

1980-01-01

Recommends a range of courses of study which may be important for one pursuing a career in chemistry. Discusses courses in computer science, statistics, public speaking, technical writing, mathematics, physics, economics, market research, psychology, chemical engineering, toxicology, history, foreign language, and science history. (CS)

Identification of natural images and computer-generated graphics based on statistical and textural features.

PubMed

Peng, Fei; Li, Jiao-ting; Long, Min

2015-03-01

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

20 Discoveries that Shaped Our Lives: Century of the Sciences.

ERIC Educational Resources Information Center

Judson, Horace Freeland

1984-01-01

Describes (in separate articles) 20 developments in science, technology, and medicine that were made during the twentieth century and had significant impact on society. They include discoveries related to intelligence tests, plastics, aviation, antibiotics, genetics, evolution, birth control, computers, transistors, DNA, lasers, statistics,…

Workforce Retention Study in Support of the U.S. Army Aberdeen Test Center Human Capital Management Strategy

DTIC Science & Technology

2016-09-01

Sciences Group 6% 1550s Computer Scientists Group 5% Other 1500s ORSAa, Mathematics, & Statistics Group 3% 1600s Equipment & Facilities Group 4...Employee removal based on misconduct, delinquency , suitability, unsatisfactory performance, or failure to qualify for conversion to a career appointment...average of 10.4% in many areas, but over double the average for the 1550s (Computer Scientists) and other 1500s (ORSA, Mathematics, and Statistics ). Also

Education through the prism of computation

NASA Astrophysics Data System (ADS)

Kaurov, Vitaliy

2014-03-01

With the rapid development of technology, computation claims its irrevocable place among research components of modern science. Thus to foster a successful future scientist, engineer or educator we need to add computation to the foundations of scientific education. We will discuss what type of paradigm shifts it brings to these foundations on the example of Wolfram Science Summer School. It is one of the most advanced computational outreach programs run by Wolfram Foundation, welcoming participants of almost all ages and backgrounds. Centered on complexity science and physics, it also covers numerous adjacent and interdisciplinary fields such as finance, biology, medicine and even music. We will talk about educational and research experiences in this program during the 12 years of its existence. We will review statistics and outputs the program has produced. Among these are interactive electronic publications at the Wolfram Demonstrations Project and contributions to the computational knowledge engine Wolfram|Alpa.

Data-Based Detection of Potential Terrorist Attacks: Statistical and Graphical Methods

DTIC Science & Technology

2010-06-01

Naren; Vasquez-Robinet, Cecilia; Watkinson, Jonathan: "A General Probabilistic Model of the PCR Process," Applied Mathematics and Computation 182(1...September 2006. Seminar, Measuring the effect of Length biased sampling, Mathematical Sciences Section, National Security Agency, 19 September 2006...Committee on National Statistics, 9 February 2007. Invited seminar, Statistical Tests for Bullet Lead Comparisons, Department of Mathematics , Butler

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

All biology is computational biology.

PubMed

Markowetz, Florian

2017-03-01

Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts rigorous and testable, and it provides a reference map that holds together individual insights. The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science.

Computers and Cognitive Development at Work

ERIC Educational Resources Information Center

Roth, Wolff-Michael; Lee, Yew-Jin

2006-01-01

Data-logging exercises in science classrooms assume that with the proper scaffolding and provision of contexts by instructors, pupils are able to meaningfully comprehend the experimental variables under investigation. From a case study of knowing and learning in a fish hatchery using real-time computer statistical software, we show that…

The 1984 NASA/ASEE summer faculty fellowship program

NASA Technical Reports Server (NTRS)

Mcinnis, B. C.; Duke, M. B.; Crow, B.

1984-01-01

An overview is given of the program management and activities. Participants and research advisors are listed. Abstracts give describe and present results of research assignments performed by 31 fellows either at the Johnson Space Center, at the White Sands test Facility, or at the California Space Institute in La Jolla. Disciplines studied include engineering; biology/life sciences; Earth sciences; chemistry; mathematics/statistics/computer sciences; and physics/astronomy.

A National Study of the Relationship between Home Access to a Computer and Academic Performance Scores of Grade 12 U.S. Science Students: An Analysis of the 2009 NAEP Data

NASA Astrophysics Data System (ADS)

Coffman, Mitchell Ward

The purpose of this dissertation was to examine the relationship between student access to a computer at home and academic achievement. The 2009 National Assessment of Educational Progress (NAEP) dataset was probed using the National Data Explorer (NDE) to investigate correlations in the subsets of SES, Parental Education, Race, and Gender as it relates to access of a home computer and improved performance scores for U.S. public school grade 12 science students. A causal-comparative approach was employed seeking clarity on the relationship between home access and performance scores. The influence of home access cannot overcome the challenges students of lower SES face. The achievement gap, or a second digital divide, for underprivileged classes of students, including minorities does not appear to contract via student access to a home computer. Nonetheless, in tests for significance, statistically significant improvement in science performance scores was reported for those having access to a computer at home compared to those not having access. Additionally, regression models reported evidence of correlations between and among subsets of controls for the demographic factors gender, race, and socioeconomic status. Variability in these correlations was high; suggesting influence from unobserved factors may have more impact upon the dependent variable. Having access to a computer at home increases performance scores for grade 12 general science students of all races, genders and socioeconomic levels. However, the performance gap is roughly equivalent to the existing performance gap of the national average for science scores, suggesting little influence from access to a computer on academic achievement. The variability of scores reported in the regression analysis models reflects a moderate to low effect, suggesting an absence of causation. These statistical results are accurate and confirm the literature review, whereby having access to a computer at home and the predictor variables were found to have a significant impact on performance scores, although the data presented suggest computer access at home is less influential upon performance scores than poverty and its correlates.

PREFACE: ELC International Meeting on Inference, Computation, and Spin Glasses (ICSG2013)

NASA Astrophysics Data System (ADS)

Kabashima, Yoshiyuki; Hukushima, Koji; Inoue, Jun-ichi; Tanaka, Toshiyuki; Watanabe, Osamu

2013-12-01

The close relationship between probability-based inference and statistical mechanics of disordered systems has been noted for some time. This relationship has provided researchers with a theoretical foundation in various fields of information processing for analytical performance evaluation and construction of efficient algorithms based on message-passing or Monte Carlo sampling schemes. The ELC International Meeting on 'Inference, Computation, and Spin Glasses (ICSG2013)', was held in Sapporo 28-30 July 2013. The meeting was organized as a satellite meeting of STATPHYS25 in order to offer a forum where concerned researchers can assemble and exchange information on the latest results and newly established methodologies, and discuss future directions of the interdisciplinary studies between statistical mechanics and information sciences. Financial support from Grant-in-Aid for Scientific Research on Innovative Areas, MEXT, Japan 'Exploring the Limits of Computation (ELC)' is gratefully acknowledged. We are pleased to publish 23 papers contributed by invited speakers of ICSG2013 in this volume of Journal of Physics: Conference Series. We hope that this volume will promote further development of this highly vigorous interdisciplinary field between statistical mechanics and information/computer science. Editors and ICSG2013 Organizing Committee: Koji Hukushima Jun-ichi Inoue (Local Chair of ICSG2013) Yoshiyuki Kabashima (Editor-in-Chief) Toshiyuki Tanaka Osamu Watanabe (General Chair of ICSG2013)

An Assessment of Research-Doctorate Programs in the United States: Mathematical & Physical Sciences.

ERIC Educational Resources Information Center

Jones, Lyle V., Ed.; And Others

The quality of doctoral-level chemistry (N=145), computer science (N=58), geoscience (N=91), mathematics (N=115), physics (N=123), and statistics/biostatistics (N=64) programs at United States universities was assessed, using 16 measures. These measures focused on variables related to: program size; characteristics of graduates; reputational…

Advanced Methodologies for NASA Science Missions

NASA Astrophysics Data System (ADS)

Hurlburt, N. E.; Feigelson, E.; Mentzel, C.

2017-12-01

Most of NASA's commitment to computational space science involves the organization and processing of Big Data from space-based satellites, and the calculations of advanced physical models based on these datasets. But considerable thought is also needed on what computations are needed. The science questions addressed by space data are so diverse and complex that traditional analysis procedures are often inadequate. The knowledge and skills of the statistician, applied mathematician, and algorithmic computer scientist must be incorporated into programs that currently emphasize engineering and physical science. NASA's culture and administrative mechanisms take full cognizance that major advances in space science are driven by improvements in instrumentation. But it is less well recognized that new instruments and science questions give rise to new challenges in the treatment of satellite data after it is telemetered to the ground. These issues might be divided into two stages: data reduction through software pipelines developed within NASA mission centers; and science analysis that is performed by hundreds of space scientists dispersed through NASA, U.S. universities, and abroad. Both stages benefit from the latest statistical and computational methods; in some cases, the science result is completely inaccessible using traditional procedures. This paper will review the current state of NASA and present example applications using modern methodologies.

Statistical Physics in the Era of Big Data

ERIC Educational Resources Information Center

Wang, Dashun

2013-01-01

With the wealth of data provided by a wide range of high-throughout measurement tools and technologies, statistical physics of complex systems is entering a new phase, impacting in a meaningful fashion a wide range of fields, from cell biology to computer science to economics. In this dissertation, by applying tools and techniques developed in…

New Trends in E-Science: Machine Learning and Knowledge Discovery in Databases

NASA Astrophysics Data System (ADS)

Brescia, Massimo

2012-11-01

Data mining, or Knowledge Discovery in Databases (KDD), while being the main methodology to extract the scientific information contained in Massive Data Sets (MDS), needs to tackle crucial problems since it has to orchestrate complex challenges posed by transparent access to different computing environments, scalability of algorithms, reusability of resources. To achieve a leap forward for the progress of e-science in the data avalanche era, the community needs to implement an infrastructure capable of performing data access, processing and mining in a distributed but integrated context. The increasing complexity of modern technologies carried out a huge production of data, whose related warehouse management and the need to optimize analysis and mining procedures lead to a change in concept on modern science. Classical data exploration, based on local user own data storage and limited computing infrastructures, is no more efficient in the case of MDS, worldwide spread over inhomogeneous data centres and requiring teraflop processing power. In this context modern experimental and observational science requires a good understanding of computer science, network infrastructures, Data Mining, etc. i.e. of all those techniques which fall into the domain of the so called e-science (recently assessed also by the Fourth Paradigm of Science). Such understanding is almost completely absent in the older generations of scientists and this reflects in the inadequacy of most academic and research programs. A paradigm shift is needed: statistical pattern recognition, object oriented programming, distributed computing, parallel programming need to become an essential part of scientific background. A possible practical solution is to provide the research community with easy-to understand, easy-to-use tools, based on the Web 2.0 technologies and Machine Learning methodology. Tools where almost all the complexity is hidden to the final user, but which are still flexible and able to produce efficient and reliable scientific results. All these considerations will be described in the detail in the chapter. Moreover, examples of modern applications offering to a wide variety of e-science communities a large spectrum of computational facilities to exploit the wealth of available massive data sets and powerful machine learning and statistical algorithms will be also introduced.

Monte Carlo Analysis of the Commissioning Phase Maneuvers of the Soil Moisture Active Passive (SMAP) Mission

NASA Technical Reports Server (NTRS)

Williams, Jessica L.; Bhat, Ramachandra S.; You, Tung-Han

2012-01-01

The Soil Moisture Active Passive (SMAP) mission will perform soil moisture content and freeze/thaw state observations from a low-Earth orbit. The observatory is scheduled to launch in October 2014 and will perform observations from a near-polar, frozen, and sun-synchronous Science Orbit for a 3-year data collection mission. At launch, the observatory is delivered to an Injection Orbit that is biased below the Science Orbit; the spacecraft will maneuver to the Science Orbit during the mission Commissioning Phase. The delta V needed to maneuver from the Injection Orbit to the Science Orbit is computed statistically via a Monte Carlo simulation; the 99th percentile delta V (delta V99) is carried as a line item in the mission delta V budget. This paper details the simulation and analysis performed to compute this figure and the delta V99 computed per current mission parameters.

Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis

ERIC Educational Resources Information Center

Young, Cristobal; Holsteen, Katherine

2017-01-01

Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

Student Response to Hypermedia in the Lecture Theatre: A Case Study.

ERIC Educational Resources Information Center

Conway, Damian

The Computer Science Department at Monash University (Victoria, Australia) recently began presenting lectures using projection of a hypertext system, HyperLecture, running on a notebook computer as the primary medium. This paper presents a statistical analysis of student reactions to this approach, focusing on the effects, as perceived by the…

An Integrated Approach to Teaching Students the Use of Computers in Science.

ERIC Educational Resources Information Center

Hood, B. James

1991-01-01

Reported is an approach to teaching the use of Macintosh computers to sixth, seventh, and eighth grade students within the context of a simplified model of scientific research including proposal, data collection and analyses, and presentation of findings. Word processing, graphing, statistical, painting, and poster software were sequentially…

Statistical physics of hard combinatorial optimization: Vertex cover problem

NASA Astrophysics Data System (ADS)

Zhao, Jin-Hua; Zhou, Hai-Jun

2014-07-01

Typical-case computation complexity is a research topic at the boundary of computer science, applied mathematics, and statistical physics. In the last twenty years, the replica-symmetry-breaking mean field theory of spin glasses and the associated message-passing algorithms have greatly deepened our understanding of typical-case computation complexity. In this paper, we use the vertex cover problem, a basic nondeterministic-polynomial (NP)-complete combinatorial optimization problem of wide application, as an example to introduce the statistical physical methods and algorithms. We do not go into the technical details but emphasize mainly the intuitive physical meanings of the message-passing equations. A nonfamiliar reader shall be able to understand to a large extent the physics behind the mean field approaches and to adjust the mean field methods in solving other optimization problems.

Computational methods in the exploration of the classical and statistical mechanics of celestial scale strings: Rotating Space Elevators

NASA Astrophysics Data System (ADS)

Knudsen, Steven; Golubovic, Leonardo

2015-04-01

With the advent of ultra-strong materials, the Space Elevator has changed from science fiction to real science. We discuss computational and theoretical methods we developed to explore classical and statistical mechanics of rotating Space Elevators (RSE). An RSE is a loopy string reaching deep into outer space. The floppy RSE loop executes a motion which is nearly a superposition of two rotations: geosynchronous rotation around the Earth, and yet another faster rotational motion of the string which goes on around a line perpendicular to the Earth at its equator. Strikingly, objects sliding along the RSE loop spontaneously oscillate between two turning points, one of which is close to the Earth (starting point) whereas the other one is deeply in the outer space. The RSE concept thus solves a major problem in space elevator science which is how to supply energy to the climbers moving along space elevator strings. The exploration of the dynamics of a floppy string interacting with objects sliding along it has required development of novel finite element algorithms described in this presentation. We thank Prof. Duncan Lorimer of WVU for kindly providing us access to his computational facility.

Mathematics and statistics research department. Progress report, period ending June 30, 1981

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lever, W.E.; Kane, V.E.; Scott, D.S.

1981-09-01

This report is the twenty-fourth in the series of progress reports of the Mathematics and Statistics Research Department of the Computer Sciences Division, Union Carbide Corporation - Nuclear Division (UCC-ND). Part A records research progress in biometrics research, materials science applications, model evaluation, moving boundary problems, multivariate analysis, numerical linear algebra, risk analysis, and complementary areas. Collaboration and consulting with others throughout the UCC-ND complex are recorded in Part B. Included are sections on biology and health sciences, chemistry, energy, engineering, environmental sciences, health and safety research, materials sciences, safeguards, surveys, and uranium resource evaluation. Part C summarizes the variousmore » educational activities in which the staff was engaged. Part D lists the presentations of research results, and Part E records the staff's other professional activities during the report period.« less

Caleb Phillips | NREL

Science.gov Websites

, Statistical Analysis and Data Mining: The ASA Data Science Journal (2017) Using GIS-Based Methods and Lidar techniques to the problem of large area coverage mapping for wireless networks. He has also done work in -4297 Dr. Caleb Phillips is a data scientist with the Computational Science Center at NREL. Caleb comes

20 CFR 901.12 - Eligibility for enrollment of individuals applying for enrollment before January 1, 1976.

Code of Federal Regulations, 2010 CFR

2010-04-01

... course of study in which the major area of concentration was actuarial science, or (2) Received a... mathematics, statistics, or computer science, and shall have successfully completed at least 6 semester hours or 9 quarter hours of courses in life contingencies at an accredited college or university. (e...

20 CFR 901.12 - Eligibility for enrollment of individuals applying for enrollment before January 1, 1976.

Code of Federal Regulations, 2011 CFR

2011-04-01

... course of study in which the major area of concentration was actuarial science, or (2) Received a... mathematics, statistics, or computer science, and shall have successfully completed at least 6 semester hours or 9 quarter hours of courses in life contingencies at an accredited college or university. (e...

Analysis of USAREUR Family Housing.

DTIC Science & Technology

1985-04-01

Standard Installation/Division Personnel System SJA ................ Staff Judge Advocate SPSS ............... Statistical Package for the...for Projecting Family Housing Requirements. a. Attempts to define USAREUR’s programmable family housing deficit Sbased on the FHS have caused anguish ...responses using the Statistical Package for the Social Sciences ( SPSS ) computer program. E-2 ANNEX E RESPONSE TO ESC HOUSING QUESTIONNAIRE Section Page I

Statistics, Computation, and Modeling in Cosmology

NASA Astrophysics Data System (ADS)

Jewell, Jeff; Guiness, Joe; SAMSI 2016 Working Group in Cosmology

2017-01-01

Current and future ground and space based missions are designed to not only detect, but map out with increasing precision, details of the universe in its infancy to the present-day. As a result we are faced with the challenge of analyzing and interpreting observations from a wide variety of instruments to form a coherent view of the universe. Finding solutions to a broad range of challenging inference problems in cosmology is one of the goals of the “Statistics, Computation, and Modeling in Cosmology” workings groups, formed as part of the year long program on ‘Statistical, Mathematical, and Computational Methods for Astronomy’, hosted by the Statistical and Applied Mathematical Sciences Institute (SAMSI), a National Science Foundation funded institute. Two application areas have emerged for focused development in the cosmology working group involving advanced algorithmic implementations of exact Bayesian inference for the Cosmic Microwave Background, and statistical modeling of galaxy formation. The former includes study and development of advanced Markov Chain Monte Carlo algorithms designed to confront challenging inference problems including inference for spatial Gaussian random fields in the presence of sources of galactic emission (an example of a source separation problem). Extending these methods to future redshift survey data probing the nonlinear regime of large scale structure formation is also included in the working group activities. In addition, the working group is also focused on the study of ‘Galacticus’, a galaxy formation model applied to dark matter-only cosmological N-body simulations operating on time-dependent halo merger trees. The working group is interested in calibrating the Galacticus model to match statistics of galaxy survey observations; specifically stellar mass functions, luminosity functions, and color-color diagrams. The group will use subsampling approaches and fractional factorial designs to statistically and computationally efficiently explore the Galacticus parameter space. The group will also use the Galacticus simulations to study the relationship between the topological and physical structure of the halo merger trees and the properties of the resulting galaxies.

Bringing Computers into College and University Teaching. Papers Presented at a Symposium Held under the Auspices of the Higher Education Research and Development Society of Australasia (Canberra, Australia, November 19, 1980).

ERIC Educational Resources Information Center

Miller, Allen H., Ed.; Ogilvie, John F., Ed.

The use of computers in higher education teaching programs is discussed in 16 papers and reports. Applications of computers in teaching particular subjects including prehistory and anthropology, mathematics, Hindi, plant science, chemistry, language, medicine, drawing, statistics, and engineering are discussed in 10 of the contributions. The other…

76 FR 36095 - Notice of Submission for OMB Review

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-21

..., mathematics, and science literacy. It was first implemented by the National Center for Education Statistics..., mathematics will be the major subject domain. The field test will also include computer-based assessments in...

Introduction to the Special Issue: Advancing the State-of-the-Science in Reading Research through Modeling.

PubMed

Zevin, Jason D; Miller, Brett

Reading research is increasingly a multi-disciplinary endeavor involving more complex, team-based science approaches. These approaches offer the potential of capturing the complexity of reading development, the emergence of individual differences in reading performance over time, how these differences relate to the development of reading difficulties and disability, and more fully understanding the nature of skilled reading in adults. This special issue focuses on the potential opportunities and insights that early and richly integrated advanced statistical and computational modeling approaches can provide to our foundational (and translational) understanding of reading. The issue explores how computational and statistical modeling, using both observed and simulated data, can serve as a contact point among research domains and topics, complement other data sources and critically provide analytic advantages over current approaches.

From multisensory integration in peripersonal space to bodily self-consciousness: from statistical regularities to statistical inference.

PubMed

Noel, Jean-Paul; Blanke, Olaf; Serino, Andrea

2018-06-06

Integrating information across sensory systems is a critical step toward building a cohesive representation of the environment and one's body, and as illustrated by numerous illusions, scaffolds subjective experience of the world and self. In the last years, classic principles of multisensory integration elucidated in the subcortex have been translated into the language of statistical inference understood by the neocortical mantle. Most importantly, a mechanistic systems-level description of multisensory computations via probabilistic population coding and divisive normalization is actively being put forward. In parallel, by describing and understanding bodily illusions, researchers have suggested multisensory integration of bodily inputs within the peripersonal space as a key mechanism in bodily self-consciousness. Importantly, certain aspects of bodily self-consciousness, although still very much a minority, have been recently casted under the light of modern computational understandings of multisensory integration. In doing so, we argue, the field of bodily self-consciousness may borrow mechanistic descriptions regarding the neural implementation of inference computations outlined by the multisensory field. This computational approach, leveraged on the understanding of multisensory processes generally, promises to advance scientific comprehension regarding one of the most mysterious questions puzzling humankind, that is, how our brain creates the experience of a self in interaction with the environment. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

The impact of computer-based versus "traditional" textbook science instruction on selected student learning outcomes

NASA Astrophysics Data System (ADS)

Rothman, Alan H.

This study reports the results of research designed to examine the impact of computer-based science instruction on elementary school level students' science content achievement, their attitude about science learning, their level of critical thinking-inquiry skills, and their level of cognitive and English language development. The study compared these learning outcomes resulting from a computer-based approach compared to the learning outcomes from a traditional, textbook-based approach to science instruction. The computer-based approach was inherent in a curriculum titled The Voyage of the Mimi , published by The Bank Street College Project in Science and Mathematics (1984). The study sample included 209 fifth-grade students enrolled in three schools in a suburban school district. This sample was divided into three groups, each receiving one of the following instructional treatments: (a) Mixed-instruction primarily based on the use of a hardcopy textbook in conjunction with computer-based instructional materials as one component of the science course; (b) Non-Traditional, Technology-Based -instruction fully utilizing computer-based material; and (c) Traditional, Textbook-Based-instruction utilizing only the textbook as the basis for instruction. Pre-test, or pre-treatment, data related to each of the student learning outcomes was collected at the beginning of the school year and post-test data was collected at the end of the school year. Statistical analyses of pre-test data were used as a covariate to account for possible pre-existing differences with regard to the variables examined among the three student groups. This study concluded that non-traditional, computer-based instruction in science significantly improved students' attitudes toward science learning and their level of English language development. Non-significant, positive trends were found for the following student learning outcomes: overall science achievement and development of critical thinking-inquiry skills. These conclusions support the value of a non-traditional, computer-based approach to instruction, such as exemplified by The Voyage of the Mimi curriculum, and a recommendation for reform in science teaching that has recommended the use of computer technology to enhance learning outcomes from science instruction to assist in reversing the trend toward what has been perceived to be relatively poor science performance by American students, as documented by the 1996 Third International Mathematics and Science Study (TIMSS).

Community Science Workshops: A Powerful and Feasible Model for Serving Underserved Youth. An Evaluation Brief

ERIC Educational Resources Information Center

Inverness Research Associates, 2007

2007-01-01

The people at Inverness Research Associates spent 12 years studying Community Science Workshops (CSW) in California and in six other states. They gathered statistics on the scale, scope, and cost-efficiency of CSW services to youth. They observed youth at work in the shops--taking apart computers, repairing bikes, growing plants, and so on--and…

Mathematical and Computational Challenges in Population Biology and Ecosystems Science

NASA Technical Reports Server (NTRS)

Levin, Simon A.; Grenfell, Bryan; Hastings, Alan; Perelson, Alan S.

1997-01-01

Mathematical and computational approaches provide powerful tools in the study of problems in population biology and ecosystems science. The subject has a rich history intertwined with the development of statistics and dynamical systems theory, but recent analytical advances, coupled with the enhanced potential of high-speed computation, have opened up new vistas and presented new challenges. Key challenges involve ways to deal with the collective dynamics of heterogeneous ensembles of individuals, and to scale from small spatial regions to large ones. The central issues-understanding how detail at one scale makes its signature felt at other scales, and how to relate phenomena across scales-cut across scientific disciplines and go to the heart of algorithmic development of approaches to high-speed computation. Examples are given from ecology, genetics, epidemiology, and immunology.

African-American males in computer science---Examining the pipeline for clogs

NASA Astrophysics Data System (ADS)

Stone, Daryl Bryant

The literature on African-American males (AAM) begins with a statement to the effect that "Today young Black men are more likely to be killed or sent to prison than to graduate from college." Why are the numbers of African-American male college graduates decreasing? Why are those enrolled in college not majoring in the science, technology, engineering, and mathematics (STEM) disciplines? This research explored why African-American males are not filling the well-recognized industry need for Computer Scientist/Technologists by choosing college tracks to these careers. The literature on STEM disciplines focuses largely on women in STEM, as opposed to minorities, and within minorities, there is a noticeable research gap in addressing the needs and opportunities available to African-American males. The primary goal of this study was therefore to examine the computer science "pipeline" from the African-American male perspective. The method included a "Computer Science Degree Self-Efficacy Scale" be distributed to five groups of African-American male students, to include: (1) fourth graders, (2) eighth graders, (3) eleventh graders, (4) underclass undergraduate computer science majors, and (5) upperclass undergraduate computer science majors. In addition to a 30-question self-efficacy test, subjects from each group were asked to participate in a group discussion about "African-American males in computer science." The audio record of each group meeting provides qualitative data for the study. The hypotheses include the following: (1) There is no significant difference in "Computer Science Degree" self-efficacy between fourth and eighth graders. (2) There is no significant difference in "Computer Science Degree" self-efficacy between eighth and eleventh graders. (3) There is no significant difference in "Computer Science Degree" self-efficacy between eleventh graders and lower-level computer science majors. (4) There is no significant difference in "Computer Science Degree" self-efficacy between lower-level computer science majors and upper-level computer science majors. (5) There is no significant difference in "Computer Science Degree" self-efficacy between each of the five groups of students. Finally, the researcher selected African-American male students attending six primary schools, including the predominately African-American elementary, middle and high school that the researcher attended during his own academic career. Additionally, a racially mixed elementary, middle and high school was selected from the same county in Maryland. Bowie State University provided both the underclass and upperclass computer science majors surveyed in this study. Of the five hypotheses, the sample provided enough evidence to support the claim that there are significant differences in the "Computer Science Degree" self-efficacy between each of the five groups of students. ANOVA analysis by question and total self-efficacy scores provided more results of statistical significance. Additionally, factor analysis and review of the qualitative data provide more insightful results. Overall, the data suggest 'a clog' may exist in the middle school level and students attending racially mixed schools were more confident in their computer, math and science skills. African-American males admit to spending lots of time on social networking websites and emailing, but are 'dis-aware' of the skills and knowledge needed to study in the computing disciplines. The majority of the subjects knew little, if any, AAMs in the 'computing discipline pipeline'. The collegian African-American males, in this study, agree that computer programming is a difficult area and serves as a 'major clog in the pipeline'.

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Art in Science Competition invites artworks to the annual exhibition on ISMB 2018 in Chicago.

PubMed

Welch, Lonnie; Gaeta, Bruno; Kovats, Diane E; Frenkel Morgenstern, Milana

2018-01-01

The International Society of Computational Biology and Bioinformatics (ISCB) brings together scientists from a wide range of disciplines, including biology, medicine, computer science, mathematics and statistics. Practitioners in these fields are constantly dealing with information in visual form: from microscope images and photographs of gels to scatter plots, network graphs and phylogenetic trees, structural formulae and protein models to flow diagrams, visual aids for problem-solving are omnipresent. The ISCB Art in Science Competition 2017 at the ISCB/ECCB 2017 conference in Prague offered a way to show the beauty of science in art form. Past artworks in this annual exhibition at ISMB combined outstanding beauty and aesthetics with deep insight that perfectly validated the exhibit's approach or went beyond the problem's solution. Others were surprising and inspiring through the transition from science to art, opening eyes and minds to reflect on the work being undertaken.

Learning physical descriptors for materials science by compressed sensing

NASA Astrophysics Data System (ADS)

Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre; Ouyang, Runhai; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias

2017-02-01

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.

The Use of Fuzzy Theory in Grading of Students in Math

ERIC Educational Resources Information Center

Bjelica, Momcilo; Rankovic, Dragica

2010-01-01

The development of computer science, statistics and other technological fields, give us more opportunities to improve the process of evaluation of degree of knowledge and achievements in a learning process of our students. More and more we are relying on the computer software to guide us in the grading process. An improved way of grading can help…

The Application of Computer Technology to the Development of a Native American Planning and Information System.

ERIC Educational Resources Information Center

McKinley, Kenneth H.; Self, Burl E., Jr.

A study was conducted to determine the feasibility of using the computer-based Synagraphic Mapping Program (SYMAP) and the Statistical Package for the Social Sciences (SPSS) in formulating an efficient and accurate information system which Creek Nation tribal staff could implement and use in planning for more effective and precise delivery of…

Computing Science and Statistics: Proceedings of the Symposium on the Interface: Computationally Intensive Methods in Statistics (20th) Held in Fairfax, Virginia on April 20-23, 1988

DTIC Science & Technology

1989-03-15

essence of the idea ycessible mtho forunrtandig eth- Tis tand thP ra) rm guh ide propet oaes nd d of e aessie meh bsd fooesadng asymptoti- isthe for s...network? This of Such empirical parametric model fitting is of course depends heavily on the class of net- course the essence of much of applied...smaller problems is the essence of graphical modeling. A model hy- attributes. Let e be the discrete joint outcome space for those N pergraph, g

Statistical benchmark for BosonSampling

NASA Astrophysics Data System (ADS)

Walschaers, Mattia; Kuipers, Jack; Urbina, Juan-Diego; Mayer, Klaus; Tichy, Malte Christopher; Richter, Klaus; Buchleitner, Andreas

2016-03-01

Boson samplers—set-ups that generate complex many-particle output states through the transmission of elementary many-particle input states across a multitude of mutually coupled modes—promise the efficient quantum simulation of a classically intractable computational task, and challenge the extended Church-Turing thesis, one of the fundamental dogmas of computer science. However, as in all experimental quantum simulations of truly complex systems, one crucial problem remains: how to certify that a given experimental measurement record unambiguously results from enforcing the claimed dynamics, on bosons, fermions or distinguishable particles? Here we offer a statistical solution to the certification problem, identifying an unambiguous statistical signature of many-body quantum interference upon transmission across a multimode, random scattering device. We show that statistical analysis of only partial information on the output state allows to characterise the imparted dynamics through particle type-specific features of the emerging interference patterns. The relevant statistical quantifiers are classically computable, define a falsifiable benchmark for BosonSampling, and reveal distinctive features of many-particle quantum dynamics, which go much beyond mere bunching or anti-bunching effects.

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

This document comprises Pacific Northwest National Laboratory`s report for Fiscal Year 1996 on research and development programs. The document contains 161 project summaries in 16 areas of research and development. The 16 areas of research and development reported on are: atmospheric sciences, biotechnology, chemical instrumentation and analysis, computer and information science, ecological science, electronics and sensors, health protection and dosimetry, hydrological and geologic sciences, marine sciences, materials science and engineering, molecular science, process science and engineering, risk and safety analysis, socio-technical systems analysis, statistics and applied mathematics, and thermal and energy systems. In addition, this report provides an overview ofmore » the research and development program, program management, program funding, and Fiscal Year 1997 projects.« less

Active Learning with Statistical Models.

DTIC Science & Technology

1995-01-01

Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

Phase Transitions in Combinatorial Optimization Problems: Basics, Algorithms and Statistical Mechanics

NASA Astrophysics Data System (ADS)

Hartmann, Alexander K.; Weigt, Martin

2005-10-01

A concise, comprehensive introduction to the topic of statistical physics of combinatorial optimization, bringing together theoretical concepts and algorithms from computer science with analytical methods from physics. The result bridges the gap between statistical physics and combinatorial optimization, investigating problems taken from theoretical computing, such as the vertex-cover problem, with the concepts and methods of theoretical physics. The authors cover rapid developments and analytical methods that are both extremely complex and spread by word-of-mouth, providing all the necessary basics in required detail. Throughout, the algorithms are shown with examples and calculations, while the proofs are given in a way suitable for graduate students, post-docs, and researchers. Ideal for newcomers to this young, multidisciplinary field.

The emergence of modern statistics in agricultural science: analysis of variance, experimental design and the reshaping of research at Rothamsted Experimental Station, 1919-1933.

PubMed

Parolini, Giuditta

2015-01-01

During the twentieth century statistical methods have transformed research in the experimental and social sciences. Qualitative evidence has largely been replaced by quantitative results and the tools of statistical inference have helped foster a new ideal of objectivity in scientific knowledge. The paper will investigate this transformation by considering the genesis of analysis of variance and experimental design, statistical methods nowadays taught in every elementary course of statistics for the experimental and social sciences. These methods were developed by the mathematician and geneticist R. A. Fisher during the 1920s, while he was working at Rothamsted Experimental Station, where agricultural research was in turn reshaped by Fisher's methods. Analysis of variance and experimental design required new practices and instruments in field and laboratory research, and imposed a redistribution of expertise among statisticians, experimental scientists and the farm staff. On the other hand the use of statistical methods in agricultural science called for a systematization of information management and made computing an activity integral to the experimental research done at Rothamsted, permanently integrating the statisticians' tools and expertise into the station research programme. Fisher's statistical methods did not remain confined within agricultural research and by the end of the 1950s they had come to stay in psychology, sociology, education, chemistry, medicine, engineering, economics, quality control, just to mention a few of the disciplines which adopted them.

GASPRNG: GPU accelerated scalable parallel random number generator library

NASA Astrophysics Data System (ADS)

Gao, Shuang; Peterson, Gregory D.

2013-04-01

Graphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications. Catalogue identifier: AEOI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: UTK license. No. of lines in distributed program, including test data, etc.: 167900 No. of bytes in distributed program, including test data, etc.: 1422058 Distribution format: tar.gz Programming language: C and CUDA. Computer: Any PC or workstation with NVIDIA GPU (Tested on Fermi GTX480, Tesla C1060, Tesla M2070). Operating system: Linux with CUDA version 4.0 or later. Should also run on MacOS, Windows, or UNIX. Has the code been vectorized or parallelized?: Yes. Parallelized using MPI directives. RAM: 512 MB˜ 732 MB (main memory on host CPU, depending on the data type of random numbers.) / 512 MB (GPU global memory) Classification: 4.13, 6.5. Nature of problem: Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations are able to consume limitless random numbers for the computation as long as resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computational cost. The GASPRNG library presented here accelerates the generators of independent streams of random numbers using graphical processing units (GPUs). Solution method: Multiple copies of random number generators in GPUs allow a computational science application to consume large numbers of random numbers from independent, parallel streams. GASPRNG is a random number generators library to allow a computational science application to employ multiple copies of random number generators to boost performance. Users can interface GASPRNG with software code executing on microprocessors and/or GPUs. Running time: The tests provided take a few minutes to run.

ALCF Data Science Program: Productive Data-centric Supercomputing

NASA Astrophysics Data System (ADS)

Romero, Nichols; Vishwanath, Venkatram

The ALCF Data Science Program (ADSP) is targeted at big data science problems that require leadership computing resources. The goal of the program is to explore and improve a variety of computational methods that will enable data-driven discoveries across all scientific disciplines. The projects will focus on data science techniques covering a wide area of discovery including but not limited to uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, real-time data analysis, and complex and interactive workflows. Project teams will be among the first to access Theta, ALCFs forthcoming 8.5 petaflops Intel/Cray system. The program will transition to the 200 petaflop/s Aurora supercomputing system when it becomes available. In 2016, four projects have been selected to kick off the ADSP. The selected projects span experimental and computational sciences and range from modeling the brain to discovering new materials for solar-powered windows to simulating collision events at the Large Hadron Collider (LHC). The program will have a regular call for proposals with the next call expected in Spring 2017.http://www.alcf.anl.gov/alcf-data-science-program This research used resources of the ALCF, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

Supplementary Computer Generated Cueing to Enhance Air Traffic Controller Efficiency

DTIC Science & Technology

2013-03-01

assess the complexity of air traffic control (Mogford, Guttman, Morrow, & Kopardekar, 1995; Laudeman, Shelden, Branstrom, & Brasil , 1998). Controllers...Behaviorial Sciences: Volume 1: Methodological Issues Volume 2: Statistical Issues, 1, 257. Laudeman, I. V., Shelden, S. G., Branstrom, R., & Brasil

Maximum Kolmogorov-Sinai Entropy Versus Minimum Mixing Time in Markov Chains

NASA Astrophysics Data System (ADS)

Mihelich, M.; Dubrulle, B.; Paillard, D.; Kral, Q.; Faranda, D.

2018-01-01

We establish a link between the maximization of Kolmogorov Sinai entropy (KSE) and the minimization of the mixing time for general Markov chains. Since the maximisation of KSE is analytical and easier to compute in general than mixing time, this link provides a new faster method to approximate the minimum mixing time dynamics. It could be interesting in computer sciences and statistical physics, for computations that use random walks on graphs that can be represented as Markov chains.

An Extensible NetLogo Model for Visualizing Message Routing Protocols

DTIC Science & Technology

2017-08-01

the hard sciences to the social sciences to computer-generated art. NetLogo represents the world as a set of...describe the model is shown here; for the supporting methods , refer to the source code. Approved for public release; distribution is unlimited. 4 iv...if ticks - last-inject > time-to-inject [inject] if run# > #runs [stop] end Next, we present some basic statistics collected for the

Optimal Cost Avoidance Investment and Pricing Strategies for Performance-Based Post-Production Service Contracts

DTIC Science & Technology

2011-04-30

a BS degree in Mathematics and an MS degree in Statistics and Financial and Actuarial Mathematics from Kiev National Taras Shevchenko University...degrees from Rutgers University in Industrial Engineering (PhD and MS) and Statistics (MS) and from Universidad Nacional Autonoma de Mexico in Actuarial ...Science. His research efforts focus on developing mathematical models for the analysis, computation, and optimization of system performance with

An open-source textbook for teaching climate-related risk analysis using the R computing environment

NASA Astrophysics Data System (ADS)

Applegate, P. J.; Keller, K.

2015-12-01

Greenhouse gas emissions lead to increased surface air temperatures and sea level rise. In turn, sea level rise increases the risks of flooding for people living near the world's coastlines. Our own research on assessing sea level rise-related risks emphasizes both Earth science and statistics. At the same time, the free, open-source computing environment R is growing in popularity among statisticians and scientists due to its flexibility and graphics capabilities, as well as its large library of existing functions. We have developed a set of laboratory exercises that introduce students to the Earth science and statistical concepts needed for assessing the risks presented by climate change, particularly sea-level rise. These exercises will be published as a free, open-source textbook on the Web. Each exercise begins with a description of the Earth science and/or statistical concepts that the exercise teaches, with references to key journal articles where appropriate. Next, students are asked to examine in detail a piece of existing R code, and the exercise text provides a clear explanation of how the code works. Finally, students are asked to modify the existing code to produce a well-defined outcome. We discuss our experiences in developing the exercises over two separate semesters at Penn State, plus using R Markdown to interweave explanatory text with sample code and figures in the textbook.

Unperturbed Schelling Segregation in Two or Three Dimensions

NASA Astrophysics Data System (ADS)

Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew

2016-09-01

Schelling's models of segregation, first described in 1969 (Am Econ Rev 59:488-493, 1969) are among the best known models of self-organising behaviour. Their original purpose was to identify mechanisms of urban racial segregation. But his models form part of a family which arises in statistical mechanics, neural networks, social science, and beyond, where populations of agents interact on networks. Despite extensive study, unperturbed Schelling models have largely resisted rigorous analysis, prior results generally focusing on variants in which noise is introduced into the dynamics, the resulting system being amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory (Young in Individual strategy and social structure: an evolutionary theory of institutions, Princeton University Press, Princeton, 1998). A series of recent papers (Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012); Barmpalias et al. in: 55th annual IEEE symposium on foundations of computer science, Philadelphia, 2014, J Stat Phys 158:806-852, 2015), has seen the first rigorous analyses of 1-dimensional unperturbed Schelling models, in an asymptotic framework largely unknown in statistical mechanics. Here we provide the first such analysis of 2- and 3-dimensional unperturbed models, establishing most of the phase diagram, and answering a challenge from Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012).

A study of mathematics and science achievement scores among African American students and the impact of teacher-oriented variables on them through the Educational Longitudinal Study, 2002 (ELS: 2002) data

NASA Astrophysics Data System (ADS)

Walker, Valentine

The purpose of this dissertation was to utilize the ELS: 2002 longitudinal data to highlight the achievement of African American students relative to other racial sub-groups in mathematics and science and to highlight teacher oriented variables that might influence their achievement. Various statistical tools, including descriptive statistics, ANOVA, Multiple Regression were used to analyze data that was derived from the students', teachers' and administrations' questionnaires compiled in the base year of the study (2002) as well as the first follow-up transcript study (2006). The major findings are as follows: African American students performed lower than all other major racial subgroups in mathematics and science; Parental variables including SES and parental education were strong correlates of achievement in mathematics and science: The amount and type of mathematics and science courses students took were strong predictors of achievement in mathematics and science; Teachers' race, experience, certification status, graduate courses completed and professional development influenced African American students' achievement in mathematics and science; Aspects of classroom climate including teacher-pupil relationship, classroom management, students' perception of quality instructions, praise and rewards system might influence African American students' achievement in mathematics and science; Teachers' beliefs pertaining to students' background and intellectual ability might influence their educational expectation of African American students and subsequently student achievement in mathematics and science; Teaching strategies such as reviewing, lecturing and using graphing calculators had a positive influence on mathematics achievement while using computers, discussion and using other books than mathematics textbooks had negative influences on mathematics achievement; Computer use in science had positive influence on science achievement while homework had a positive influence on mathematics and science achievement among African American students. The application of these findings in settings populated with African American students might be important in increasing mathematics and science achievement among them.

The effects of computer-assisted instruction and locus of control upon preservice elementary teachers' acquisition of the integrated science process skills

NASA Astrophysics Data System (ADS)

Wesley, Beth Eddinger; Krockover, Gerald H.; Devito, Alfred

The purpose of this study was to determine the effects of computer-assisted instruction (CAI) versus a text mode of programmed instruction (PI), and the cognitive style of locus of control, on preservice elementary teachers' achievement of the integrated science process skills. Eighty-one preservice elementary teachers in six sections of a science methods class were classified as internally or externally controlled. The sections were randomly assigned to receive instruction in the integrated science process skills via a microcomputer or printed text. The study used a pretest-posttest control group design. Before assessing main and interaction effects, analysis of covariance was used to adjust posttest scores using the pretest scores. Statistical analysis revealed that main effects were not significant. Additionally, no interaction effects between treatments and loci of control were demonstrated. The results suggest that printed PI and tutorial CAI are equally effective modes of instruction for teaching internally and externally oriented preservice elementary teachers the integrated science process skills.

Translations on USSR Science and Technology, Physical Sciences and Technology, Number 49.

DTIC Science & Technology

1978-09-20

significant reduction in the times and now a reduction in the cost of the work), and data from the surveys of the incomes of families of workers...computer equipment, it provides comprehensive elaboration of the accounting and statistical material with a reduction in the cost of the work, and...themselves, while actively developing under conditons of space flight? We have already written about hydrogenous bacteria (TEKHNIKA — MOLODEZHI, No 4

First principles statistical mechanics of alloys and magnetism

NASA Astrophysics Data System (ADS)

Eisenbach, Markus; Khan, Suffian N.; Li, Ying Wai

Modern high performance computing resources are enabling the exploration of the statistical physics of phase spaces with increasing size and higher fidelity of the Hamiltonian of the systems. For selected systems, this now allows the combination of Density Functional based first principles calculations with classical Monte Carlo methods for parameter free, predictive thermodynamics of materials. We combine our locally selfconsistent real space multiple scattering method for solving the Kohn-Sham equation with Wang-Landau Monte-Carlo calculations (WL-LSMS). In the past we have applied this method to the calculation of Curie temperatures in magnetic materials. Here we will present direct calculations of the chemical order - disorder transitions in alloys. We present our calculated transition temperature for the chemical ordering in CuZn and the temperature dependence of the short-range order parameter and specific heat. Finally we will present the extension of the WL-LSMS method to magnetic alloys, thus allowing the investigation of the interplay of magnetism, structure and chemical order in ferrous alloys. This research was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division and it used Oak Ridge Leadership Computing Facility resources at Oak Ridge National Laboratory.

An analysis of science versus pseudoscience

NASA Astrophysics Data System (ADS)

Hooten, James T.

2011-12-01

This quantitative study identified distinctive features in archival datasets commissioned by the National Science Foundation (NSF) for Science and Engineering Indicators reports. The dependent variables included education level, and scores for science fact knowledge, science process knowledge, and pseudoscience beliefs. The dependent variables were aggregated into nine NSF-defined geographic regions and examined for the years 2004 and 2006. The variables were also examined over all years available in the dataset. Descriptive statistics were determined and tests for normality and homogeneity of variances were performed using Statistical Package for the Social Sciences. Analysis of Variance was used to test for statistically significant differences between the nine geographic regions for each of the four dependent variables. Statistical significance of 0.05 was used. Tukey post-hoc analysis was used to compute practical significance of differences between regions. Post-hoc power analysis using G*Power was used to calculate the probability of Type II errors. Tests for correlations across all years of the dependent variables were also performed. Pearson's r was used to indicate the strength of the relationship between the dependent variables. Small to medium differences in science literacy and education level were observed between many of the nine U.S. geographic regions. The most significant differences occurred when the West South Central region was compared to the New England and the Pacific regions. Belief in pseudoscience appeared to be distributed evenly across all U.S. geographic regions. Education level was a strong indicator of science literacy regardless of a respondent's region of residence. Recommendations for further study include more in-depth investigation to uncover the nature of the relationship between education level and belief in pseudoscience.

Structures and Statistics of Citation Networks

DTIC Science & Technology

2011-05-01

the citations among them. The papers are in the field of high- energy physics, and they were added to the online library between 1992-2003. Each paper... energy , physics:astrophysics, mathematics, computer science, statistics and many others. The value of the setSpec field can be any of these. However...the value of the categories field might contain multiple set names listed. For instance, a paper can primarily be considered as a high- energy physics

Plan Recognition using Statistical Relational Models

DTIC Science & Technology

2014-08-25

arguments. Section 4 describes several variants of MLNs for plan recognition. All MLN mod- els were implemented using Alchemy (Kok et al., 2010), an...For both MLN approaches, we used MC-SAT (Poon and Domingos, 2006) as implemented in the Alchemy system on both Monroe and Linux. Evaluation Metric We...Singla P, Poon H, Lowd D, Wang J, Nath A, Domingos P. The Alchemy System for Statistical Relational AI. Techni- cal Report; Department of Computer Science

Computer Series, 61: Bits & Pieces, 24.

ERIC Educational Resources Information Center

Moore, John W., Ed.

1985-01-01

Describes: (1) laboratory information science in the clinical chemistry curriculum; (2) testing Boyle's Law, a context for statistical methods in undergraduate laboratories; (3) acquiring chemical concepts using microcomputers as tutees; and (4) using Data Interchange Format files for Apple microcomputers. Includes feedback from a previous article…

A Glossary of Research Terms.

ERIC Educational Resources Information Center

Weatherup, Jim, Comp.

This glossary contains brief definitions of more than 550 special or technical terms used in scientific, technical, and social science research. Entries include various kinds of statistical measures, research variables, types of research tests, and research methodologies. Some computer terminology is also included. The glossary includes both…

Using and Evaluating Resampling Simulations in SPSS and Excel.

ERIC Educational Resources Information Center

Smith, Brad

2003-01-01

Describes and evaluates three computer-assisted simulations used with Statistical Package for the Social Sciences (SPSS) and Microsoft Excel. Designed the simulations to reinforce and enhance student understanding of sampling distributions, confidence intervals, and significance tests. Reports evaluations revealed improved student comprehension of…

Assessing Creative Problem-Solving with Automated Text Grading

ERIC Educational Resources Information Center

Wang, Hao-Chuan; Chang, Chun-Yen; Li, Tsai-Yen

2008-01-01

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit…

Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing.

PubMed

Devereux, Barry J; Taylor, Kirsten I; Randall, Billi; Geertzen, Jeroen; Tyler, Lorraine K

2016-03-01

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature-based conceptual account assumes that the statistical characteristics of concepts' features--the number of concepts they occur in (distinctiveness/sharedness) and likelihood of co-occurrence (correlational strength)--determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech-to-meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time-sensitive co-occurrence-driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general-to-specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation. Copyright © 2015 The Authors. Cognitive Science published by Cognitive Science Society, Inc.

What is biomedical informatics?

PubMed Central

Bernstam, Elmer V.; Smith, Jack W.; Johnson, Todd R.

2009-01-01

Biomedical informatics lacks a clear and theoretically grounded definition. Many proposed definitions focus on data, information, and knowledge, but do not provide an adequate definition of these terms. Leveraging insights from the philosophy of information, we define informatics as the science of information, where information is data plus meaning. Biomedical informatics is the science of information as applied to or studied in the context of biomedicine. Defining the object of study of informatics as data plus meaning clearly distinguishes the field from related fields, such as computer science, statistics and biomedicine, which have different objects of study. The emphasis on data plus meaning also suggests that biomedical informatics problems tend to be difficult when they deal with concepts that are hard to capture using formal, computational definitions. In other words, problems where meaning must be considered are more difficult than problems where manipulating data without regard for meaning is sufficient. Furthermore, the definition implies that informatics research, teaching, and service should focus on biomedical information as data plus meaning rather than only computer applications in biomedicine. PMID:19683067

Computer Based Instruction in the U.S. Army’s Entry Level Enlisted Training.

DTIC Science & Technology

1985-03-13

rosters with essential personal data, and graduation rosters with class standings and printed diplomas. The computer also managed the progress of the...discussion is presented in Chapter Three. Methods of Employment Course administration. In 1980 the US Army Research Center for Behaviorial and Social Studies...contained in Appendix C. Data Presentation All responses from the questionaires were coded for use by the Statistical Package for the Social Sciences

CoDA 2014 special issue: Exploring data-focused research across the department of energy: Editorial

DOE PAGES

Myers, Kary Lynn

2015-10-05

Here, this collection of papers, written by researchers at the national labs, in academia, and in industry present real problems, massive and complex datasets, and novel statistical approaches motivated by the challenges presented by experimental and computational science. You'll find explorations of the trajectories of aircraft and of the light curves of supernovae, of computer network intrusions and of nuclear forensics, of photovoltaics and overhead imagery.

Feature-Based Statistical Analysis of Combustion Simulation Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bennett, J; Krishnamoorthy, V; Liu, S

2011-11-18

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing andmore » reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.« less

Legendre modified moments for Euler's constant

NASA Astrophysics Data System (ADS)

Prévost, Marc

2008-10-01

Polynomial moments are often used for the computation of Gauss quadrature to stabilize the numerical calculation of the orthogonal polynomials, see [W. Gautschi, Computational aspects of orthogonal polynomials, in: P. Nevai (Ed.), Orthogonal Polynomials-Theory and Practice, NATO ASI Series, Series C: Mathematical and Physical Sciences, vol. 294. Kluwer, Dordrecht, 1990, pp. 181-216 [6]; W. Gautschi, On the sensitivity of orthogonal polynomials to perturbations in the moments, Numer. Math. 48(4) (1986) 369-382 [5]; W. Gautschi, On generating orthogonal polynomials, SIAM J. Sci. Statist. Comput. 3(3) (1982) 289-317 [4

Materials inspired by mathematics.

PubMed

Kotani, Motoko; Ikeda, Susumu

2016-01-01

Our world is transforming into an interacting system of the physical world and the digital world. What will be the materials science in the new era? With the rising expectations of the rapid development of computers, information science and mathematical science including statistics and probability theory, 'data-driven materials design' has become a common term. There is knowledge and experience gained in the physical world in the form of know-how and recipes for the creation of material. An important key is how we establish vocabulary and grammar to translate them into the language of the digital world. In this article, we outline how materials science develops when it encounters mathematics, showing some emerging directions.

Senior Computational Scientist | Center for Cancer Research

Cancer.gov

The Basic Science Program (BSP) pursues independent, multidisciplinary research in basic and applied molecular biology, immunology, retrovirology, cancer biology, and human genetics. Research efforts and support are an integral part of the Center for Cancer Research (CCR) at the Frederick National Laboratory for Cancer Research (FNLCR). The Cancer & Inflammation Program (CIP), Basic Science Program, HLA Immunogenetics Section, under the leadership of Dr. Mary Carrington, studies the influence of human leukocyte antigens (HLA) and specific KIR/HLA genotypes on risk of and outcomes to infection, cancer, autoimmune disease, and maternal-fetal disease. Recent studies have focused on the impact of HLA gene expression in disease, the molecular mechanism regulating expression levels, and the functional basis for the effect of differential expression on disease outcome. The lab’s further focus is on the genetic basis for resistance/susceptibility to disease conferred by immunogenetic variation. KEY ROLES/RESPONSIBILITIES The Senior Computational Scientist will provide research support to the CIP-BSP-HLA Immunogenetics Section performing bio-statistical design, analysis and reporting of research projects conducted in the lab. This individual will be involved in the implementation of statistical models and data preparation. Successful candidate should have 5 or more years of competent, innovative biostatistics/bioinformatics research experience, beyond doctoral training Considerable experience with statistical software, such as SAS, R and S-Plus Sound knowledge, and demonstrated experience of theoretical and applied statistics Write program code to analyze data using statistical analysis software Contribute to the interpretation and publication of research results

Data science for mental health: a UK perspective on a global challenge.

PubMed

McIntosh, Andrew M; Stewart, Robert; John, Ann; Smith, Daniel J; Davis, Katrina; Sudlow, Cathie; Corvin, Aiden; Nicodemus, Kristin K; Kingdon, David; Hassan, Lamiece; Hotopf, Matthew; Lawrie, Stephen M; Russ, Tom C; Geddes, John R; Wolpert, Miranda; Wölbert, Eva; Porteous, David J

2016-10-01

Data science uses computer science and statistics to extract new knowledge from high-dimensional datasets (ie, those with many different variables and data types). Mental health research, diagnosis, and treatment could benefit from data science that uses cohort studies, genomics, and routine health-care and administrative data. The UK is well placed to trial these approaches through robust NHS-linked data science projects, such as the UK Biobank, Generation Scotland, and the Clinical Record Interactive Search (CRIS) programme. Data science has great potential as a low-cost, high-return catalyst for improved mental health recognition, understanding, support, and outcomes. Lessons learnt from such studies could have global implications. Copyright © 2016 Elsevier Ltd. All rights reserved.

Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications

DTIC Science & Technology

2014-10-02

intervals (Neil, Tailor, Marquez, Fenton , & Hear, 2007). This is cumbersome, error prone and usually inaccurate. Even though a universal framework...Science. Neil, M., Tailor, M., Marquez, D., Fenton , N., & Hear. (2007). Inference in Bayesian networks using dynamic discretisation. Statistics

Fundamental Fortran for Social Scientists.

ERIC Educational Resources Information Center

Veldman, Donald J.

An introduction to Fortran programming specifically for social science statistical and routine data processing is provided. The first two sections of the manual describe the components of computer hardware and software. Topics include input, output, and mass storage devices; central memory; central processing unit; internal storage of data; and…

Graded SPSS Exercises.

ERIC Educational Resources Information Center

Allen, Mary J.

The attached materials have been developed for use on the CSU CYBER Computer's Statistical Package for the Social Sciences (SPSSONL). The assignments are graded in difficulty and gradually introduce new commands and require the practice of previously learned commands. The handouts begin with basic instructions for logging on; then XEDIT is taught…

Complex network problems in physics, computer science and biology

NASA Astrophysics Data System (ADS)

Cojocaru, Radu Ionut

There is a close relation between physics and mathematics and the exchange of ideas between these two sciences are well established. However until few years ago there was no such a close relation between physics and computer science. Even more, only recently biologists started to use methods and tools from statistical physics in order to study the behavior of complex system. In this thesis we concentrate on applying and analyzing several methods borrowed from computer science to biology and also we use methods from statistical physics in solving hard problems from computer science. In recent years physicists have been interested in studying the behavior of complex networks. Physics is an experimental science in which theoretical predictions are compared to experiments. In this definition, the term prediction plays a very important role: although the system is complex, it is still possible to get predictions for its behavior, but these predictions are of a probabilistic nature. Spin glasses, lattice gases or the Potts model are a few examples of complex systems in physics. Spin glasses and many frustrated antiferromagnets map exactly to computer science problems in the NP-hard class defined in Chapter 1. In Chapter 1 we discuss a common result from artificial intelligence (AI) which shows that there are some problems which are NP-complete, with the implication that these problems are difficult to solve. We introduce a few well known hard problems from computer science (Satisfiability, Coloring, Vertex Cover together with Maximum Independent Set and Number Partitioning) and then discuss their mapping to problems from physics. In Chapter 2 we provide a short review of combinatorial optimization algorithms and their applications to ground state problems in disordered systems. We discuss the cavity method initially developed for studying the Sherrington-Kirkpatrick model of spin glasses. We extend this model to the study of a specific case of spin glass on the Bethe lattice at zero temperature and then we apply this formalism to the K-SAT problem defined in Chapter 1. The phase transition which physicists study often corresponds to a change in the computational complexity of the corresponding computer science problem. Chapter 3 presents phase transitions which are specific to the problems discussed in Chapter 1 and also known results for the K-SAT problem. We discuss the replica method and experimental evidences of replica symmetry breaking. The physics approach to hard problems is based on replica methods which are difficult to understand. In Chapter 4 we develop novel methods for studying hard problems using methods similar to the message passing techniques that were discussed in Chapter 2. Although we concentrated on the symmetric case, cavity methods show promise for generalizing our methods to the un-symmetric case. As has been highlighted by John Hopfield, several key features of biological systems are not shared by physical systems. Although living entities follow the laws of physics and chemistry, the fact that organisms adapt and reproduce introduces an essential ingredient that is missing in the physical sciences. In order to extract information from networks many algorithm have been developed. In Chapter 5 we apply polynomial algorithms like minimum spanning tree in order to study and construct gene regulatory networks from experimental data. As future work we propose the use of algorithms like min-cut/max-flow and Dijkstra for understanding key properties of these networks.

On Muthen's Maximum Likelihood for Two-Level Covariance Structure Models

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Hayashi, Kentaro

2005-01-01

Data in social and behavioral sciences are often hierarchically organized. Special statistical procedures that take into account the dependence of such observations have been developed. Among procedures for 2-level covariance structure analysis, Muthen's maximum likelihood (MUML) has the advantage of easier computation and faster convergence. When…

Computer Techniques for Studying Coverage, Overlaps, and Gaps in Collections.

ERIC Educational Resources Information Center

White, Howard D.

1987-01-01

Describes techniques for using the Statistical Package for the Social Sciences (SSPS) to create tables for cooperative collection development across a number of libraries. Specific commands are given to generate holdings profiles focusing on collection coverage, overlaps, gaps, or other areas of interest, from a master bibliographic list. (CLB)

Company's Unusual Plan to Package Commercial Software with Business Textbooks Produces a Measure of Success.

ERIC Educational Resources Information Center

Watkins, Beverly T.

1992-01-01

Course Technology Inc. has developed 10 products combining textbooks with commercial software for college accounting, business, computer science, and statistics courses. Five of the products use Lotus 1-2-3 spreadsheet software. The products have been positively received by teachers and students. (DB)

Quality Improvement: Does the Air Force Systems Command Practice What It Preaches

DTIC Science & Technology

1990-03-01

without his assistance in getting supplies, computers, and plotters. Another special thanks goes to my committee chairman. Dr Stephen Blank. who provided...N.J.: Prentice-Hall. 1986). 166. 5. Ibid.. 181. 6. Sidney Siegel. Nonparametric Statistics for the Behavioral Sciences (New York: Mc- Graw -Hill. 1956

Agent-Based Models in Empirical Social Research

ERIC Educational Resources Information Center

Bruch, Elizabeth; Atwell, Jon

2015-01-01

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first…

Culturally Responsive Computing in Urban, After-School Contexts: Two Approaches

ERIC Educational Resources Information Center

Eglash, Ron; Gilbert, Juan E.; Taylor, Valerie; Geier, Susan R.

2013-01-01

The academic performance and engagement of youth from under-represented ethnic groups (African American, Latino, and Indigenous) in science, technology, engineering, and mathematics (STEM) show statistically large gaps in comparison with their White and Asian peers. Some of these differences can be attributed to the direct impact of economic…

An Overview of R in Health Decision Sciences.

PubMed

Jalal, Hawre; Pechlivanoglou, Petros; Krijkamp, Eline; Alarid-Escudero, Fernando; Enns, Eva; Hunink, M G Myriam

2017-10-01

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R's popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.

ISMB 2016 offers outstanding science, networking, and celebration

PubMed Central

Fogg, Christiana

2016-01-01

The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community.  ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas. PMID:27347392

ISMB 2016 offers outstanding science, networking, and celebration.

PubMed

Fogg, Christiana

2016-01-01

The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community.  ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas.

Computer and Internet use among Undergraduate Medical Students in Iran

PubMed Central

Ayatollahi, Ali; Ayatollahi, Jamshid; Ayatollahi, Fatemeh; Ayatollahi, Reza; Shahcheraghi, Seyed Hossein

2014-01-01

Objective: Although computer technologies are now widely used in medicine, little is known about its use among medical students in Iran. The aim of this study was to determine the competence and access to computer and internet among the medical students. Methods: In this descriptive study, medical students of Shahid Sadoughi University of Medical Science, Yazd, Iran from the fifth years were asked to answer a questionnaire during a time-tabled lecture slot. The chi-square test was used to compare the frequency of computer and internet use between the two genders, and the level of statistical significance for all test was set at 0.05. Results: All the students have a personal computer and internet access. There were no statistically significant differences between men and women for the computer and internet access, use wireless device to access internet, having laptop and e-mail address and the difficulties encountered using internet. The main reason for less utilization of internet was slow speed of data transfer. Conclusions: Because of the wide range of computer skills and internet information among medical students in our institution, a single computer and internet course for all students would not be useful nor would it be accepted. PMID:25225525

Combining Statistics and Physics to Improve Climate Downscaling

NASA Astrophysics Data System (ADS)

Gutmann, E. D.; Eidhammer, T.; Arnold, J.; Nowak, K.; Clark, M. P.

2017-12-01

Getting useful information from climate models is an ongoing problem that has plagued climate science and hydrologic prediction for decades. While it is possible to develop statistical corrections for climate models that mimic current climate almost perfectly, this does not necessarily guarantee that future changes are portrayed correctly. In contrast, convection permitting regional climate models (RCMs) have begun to provide an excellent representation of the regional climate system purely from first principles, providing greater confidence in their change signal. However, the computational cost of such RCMs prohibits the generation of ensembles of simulations or long time periods, thus limiting their applicability for hydrologic applications. Here we discuss a new approach combining statistical corrections with physical relationships for a modest computational cost. We have developed the Intermediate Complexity Atmospheric Research model (ICAR) to provide a climate and weather downscaling option that is based primarily on physics for a fraction of the computational requirements of a traditional regional climate model. ICAR also enables the incorporation of statistical adjustments directly within the model. We demonstrate that applying even simple corrections to precipitation while the model is running can improve the simulation of land atmosphere feedbacks in ICAR. For example, by incorporating statistical corrections earlier in the modeling chain, we permit the model physics to better represent the effect of mountain snowpack on air temperature changes.

Magneto Caloric Effect in Ni-Mn-Ga alloys: First Principles and Experimental studies

NASA Astrophysics Data System (ADS)

Odbadrakh, Khorgolkhuu; Nicholson, Don; Brown, Gregory; Rusanu, Aurelian; Rios, Orlando; Hodges, Jason; Safa-Sefat, Athena; Ludtka, Gerard; Eisenbach, Markus; Evans, Boyd

2012-02-01

Understanding the Magneto-Caloric Effect (MCE) in alloys with real technological potential is important to the development of viable MCE based products. We report results of computational and experimental investigation of a candidate MCE materials Ni-Mn-Ga alloys. The Wang-Landau statistical method is used in tandem with Locally Self-consistent Multiple Scattering (LSMS) method to explore magnetic states of the system. A classical Heisenberg Hamiltonian is parametrized based on these states and used in obtaining the density of magnetic states. The Currie temperature, isothermal entropy change, and adiabatic temperature change are then calculated from the density of states. Experiments to observe the structural and magnetic phase transformations were performed at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) on alloys of Ni-Mn-Ga and Fe-Ni-Mn-Ga-Cu. Data from the observations are discussed in comparison with the computational studies. This work was sponsored by the Laboratory Directed Research and Development Program (ORNL), by the Mathematical, Information, and Computational Sciences Division; Office of Advanced Scientific Computing Research (US DOE), and by the Materials Sciences and Engineering Division; Office of Basic Energy Sciences (US DOE).

Opportunities and challenges of big data for the social sciences: The case of genomic data.

PubMed

Liu, Hexuan; Guo, Guang

2016-09-01

In this paper, we draw attention to one unique and valuable source of big data, genomic data, by demonstrating the opportunities they provide to social scientists. We discuss different types of large-scale genomic data and recent advances in statistical methods and computational infrastructure used to address challenges in managing and analyzing such data. We highlight how these data and methods can be used to benefit social science research. Copyright © 2016 Elsevier Inc. All rights reserved.

Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.

Computing Science and Statistics: Volume 24. Graphics and Visualization

DTIC Science & Technology

1993-03-20

r, is set to 3.569, the population examples include: kneading ingredients into a bread eventually oscillates about 16 fixed values. However the dough ...34fun statistics". My goal is to offer leagues I said in jest "After all, regression analysis is you the equivalent of a fortune cookie which clearly is... cookie of the night reads: One problem that statisticians traditionally seem to "You have good friends who will come to your aid in have is that they

From inverse problems to learning: a Statistical Mechanics approach

NASA Astrophysics Data System (ADS)

Baldassi, Carlo; Gerace, Federica; Saglietti, Luca; Zecchina, Riccardo

2018-01-01

We present a brief introduction to the statistical mechanics approaches for the study of inverse problems in data science. We then provide concrete new results on inferring couplings from sampled configurations in systems characterized by an extensive number of stable attractors in the low temperature regime. We also show how these result are connected to the problem of learning with realistic weak signals in computational neuroscience. Our techniques and algorithms rely on advanced mean-field methods developed in the context of disordered systems.

Materials inspired by mathematics

PubMed Central

Kotani, Motoko; Ikeda, Susumu

2016-01-01

Abstract Our world is transforming into an interacting system of the physical world and the digital world. What will be the materials science in the new era? With the rising expectations of the rapid development of computers, information science and mathematical science including statistics and probability theory, ‘data-driven materials design’ has become a common term. There is knowledge and experience gained in the physical world in the form of know-how and recipes for the creation of material. An important key is how we establish vocabulary and grammar to translate them into the language of the digital world. In this article, we outline how materials science develops when it encounters mathematics, showing some emerging directions. PMID:27877877

Thoth: Software for data visualization & statistics

NASA Astrophysics Data System (ADS)

Laher, R. R.

2016-10-01

Thoth is a standalone software application with a graphical user interface for making it easy to query, display, visualize, and analyze tabular data stored in relational databases and data files. From imported data tables, it can create pie charts, bar charts, scatter plots, and many other kinds of data graphs with simple menus and mouse clicks (no programming required), by leveraging the open-source JFreeChart library. It also computes useful table-column data statistics. A mature tool, having underwent development and testing over several years, it is written in the Java computer language, and hence can be run on any computing platform that has a Java Virtual Machine and graphical-display capability. It can be downloaded and used by anyone free of charge, and has general applicability in science, engineering, medical, business, and other fields. Special tools and features for common tasks in astronomy and astrophysical research are included in the software.

Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology⋆

PubMed Central

Fu, Wenjiang J.; Stromberg, Arnold J.; Viele, Kert; Carroll, Raymond J.; Wu, Guoyao

2009-01-01

Over the past two decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine fetal retardation). PMID:20233650

Philosophy of Data: Why?

ERIC Educational Resources Information Center

Furner, Jonathan

2017-01-01

Philosophy of data should not be dismissed as a cluster of scholastic puzzles whose solutions are of limited practical value. On the contrary, philosophy of data should be recognized as constituting the core of a field of data studies that is informed by, but far from equivalent to, statistics, computer science, and library and information studies.

A Biologically Informed Framework for the Analysis of the PPAR Signaling Pathway using a Bayesian Network

EPA Science Inventory

The US EPA’s ToxCastTM program seeks to combine advances in high-throughput screening technology with methodologies from statistics and computer science to develop high-throughput decision support tools for assessing chemical hazard and risk. To develop new methods of analysis of...

Research Methodologies Explored for a Paradigm Shift in University Teaching.

ERIC Educational Resources Information Center

Venter, I. M.; Blignaut, R. J.; Stoltz, D.

2001-01-01

Innovative teaching methods such as collaborative learning, teamwork, and mind maps were introduced to teach computer science and statistics courses at a South African university. Soft systems methodology was adapted and used to manage the research process of evaluating the effectiveness of the teaching methods. This research method provided proof…

The Effectiveness of Computer-Assisted Instruction to Teach Physical Examination to Students and Trainees in the Health Sciences Professions: A Systematic Review and Meta-Analysis.

PubMed

Tomesko, Jennifer; Touger-Decker, Riva; Dreker, Margaret; Zelig, Rena; Parrott, James Scott

2017-01-01

To explore knowledge and skill acquisition outcomes related to learning physical examination (PE) through computer-assisted instruction (CAI) compared with a face-to-face (F2F) approach. A systematic literature review and meta-analysis published between January 2001 and December 2016 was conducted. Databases searched included Medline, Cochrane, CINAHL, ERIC, Ebsco, Scopus, and Web of Science. Studies were synthesized by study design, intervention, and outcomes. Statistical analyses included DerSimonian-Laird random-effects model. In total, 7 studies were included in the review, and 5 in the meta-analysis. There were no statistically significant differences for knowledge (mean difference [MD] = 5.39, 95% confidence interval [CI]: -2.05 to 12.84) or skill acquisition (MD = 0.35, 95% CI: -5.30 to 6.01). The evidence does not suggest a strong consistent preference for either CAI or F2F instruction to teach students/trainees PE. Further research is needed to identify conditions which examine knowledge and skill acquisition outcomes that favor one mode of instruction over the other.

Cognitive biases, linguistic universals, and constraint-based grammar learning.

PubMed

Culbertson, Jennifer; Smolensky, Paul; Wilson, Colin

2013-07-01

According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology-the distribution of linguistic patterns across the world's languages-and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results of artificial grammar experiments on noun-phrase word order (Culbertson, Smolensky, & Legendre, 2012). Our proposal has several novel properties that distinguish it from prior work in the domains of linguistic theory, computational cognitive science, and machine learning. This study illustrates how ideas from these domains can be synthesized into a model of language learning in which biases range in strength from hard (absolute) to soft (statistical), and in which language-specific and domain-general biases combine to account for data from the macro-level scale of typological distribution to the micro-level scale of learning by individuals. Copyright © 2013 Cognitive Science Society, Inc.

An Empirical Investigation of Variance Design Parameters for Planning Cluster-Randomized Trials of Science Achievement.

PubMed

Westine, Carl D; Spybrook, Jessaca; Taylor, Joseph A

2013-12-01

Prior research has focused primarily on empirically estimating design parameters for cluster-randomized trials (CRTs) of mathematics and reading achievement. Little is known about how design parameters compare across other educational outcomes. This article presents empirical estimates of design parameters that can be used to appropriately power CRTs in science education and compares them to estimates using mathematics and reading. Estimates of intraclass correlations (ICCs) are computed for unconditional two-level (students in schools) and three-level (students in schools in districts) hierarchical linear models of science achievement. Relevant student- and school-level pretest and demographic covariates are then considered, and estimates of variance explained are computed. Subjects: Five consecutive years of Texas student-level data for Grades 5, 8, 10, and 11. Science, mathematics, and reading achievement raw scores as measured by the Texas Assessment of Knowledge and Skills. Results: Findings show that ICCs in science range from .172 to .196 across grades and are generally higher than comparable statistics in mathematics, .163-.172, and reading, .099-.156. When available, a 1-year lagged student-level science pretest explains the most variability in the outcome. The 1-year lagged school-level science pretest is the best alternative in the absence of a 1-year lagged student-level science pretest. Science educational researchers should utilize design parameters derived from science achievement outcomes. © The Author(s) 2014.

The physics of teams: Interdependence, measurable entropy and computational emotion

NASA Astrophysics Data System (ADS)

Lawless, William F.

2017-08-01

Most of the social sciences, including psychology, economics and subjective social network theory, are modeled on the individual, leaving the field not only a-theoretical, but also inapplicable to a physics of hybrid teams, where hybrid refers to arbitrarily combining humans, machines and robots into a team to perform a dedicated mission (e.g., military, business, entertainment) or to solve a targeted problem (e.g., with scientists, engineers, entrepreneurs). As a common social science practice, the ingredient at the heart of the social interaction, interdependence, is statistically removed prior to the replication of social experiments; but, as an analogy, statistically removing social interdependence to better study the individual is like statistically removing quantum effects as a complication to the study of the atom. Further, in applications of Shannon’s information theory to teams, the effects of interdependence are minimized, but even there, interdependence is how classical information is transmitted. Consequently, numerous mistakes are made when applying non-interdependent models to policies, the law and regulations, impeding social welfare by failing to exploit the power of social interdependence. For example, adding redundancy to human teams is thought by subjective social network theorists to improve the efficiency of a network, easily contradicted by our finding that redundancy is strongly associated with corruption in non-free markets. Thus, built atop the individual, most of the social sciences, economics and social network theory have little if anything to contribute to the engineering of hybrid teams. In defense of the social sciences, the mathematical physics of interdependence is elusive, non-intuitive and non-rational. However, by replacing determinism with bistable states, interdependence at the social level mirrors entanglement at the quantum level, suggesting the applicability of quantum tools for social science. We report how our quantum-like models capture some of the essential aspects of interdependence, a tool for the metrics of hybrid teams; as an example, we find additional support for our model of the solution to the open problem of team size. We also report on progress with the theory of computational emotion for hybrid teams, linking it qualitatively to the second law of thermodynamics. We conclude that the science of interdependence

Dynamic Modeling and Testing of MSRR-1 for Use in Microgravity Environments Analysis

NASA Technical Reports Server (NTRS)

Gattis, Christy; LaVerde, Bruce; Howell, Mike; Phelps, Lisa H. (Technical Monitor)

2001-01-01

Delicate microgravity science is unlikely to succeed on the International Space Station if vibratory and transient disturbers corrupt the environment. An analytical approach to compute the on-orbit acceleration environment at science experiment locations within a standard payload rack resulting from these disturbers is presented. This approach has been grounded by correlation and comparison to test verified transfer functions. The method combines the results of finite element and statistical energy analysis using tested damping and modal characteristics to provide a reasonable approximation of the total root-mean-square (RMS) acceleration spectra at the interface to microgravity science experiment hardware.

A review of small canned computer programs for survey research and demographic analysis.

PubMed

Sinquefield, J C

1976-12-01

A variety of small canned computer programs for survey research and demographic analysis appropriate for use in developing countries are reviewed in this article. The programs discussed are SPSS (Statistical Package for the Social Sciences); CENTS, CO-CENTS, CENTS-AID, CENTS-AIE II; MINI-TAB EDIT, FREQUENCIES, TABLES, REGRESSION, CLIENT RECORD, DATES, MULT, LIFE, and PREGNANCY HISTORY; FIVFIV and SINSIN; DCL (Demographic Computer Library); MINI-TAB Population Projection, Functional Population Projection, and Family Planning Target Projection. A description and evaluation for each program of uses, instruction manuals, computer requirements, and procedures for obtaining manuals and programs are provided. Such information is intended to facilitate and encourage the use of the computer by data processors in developing countries.

The image recognition based on neural network and Bayesian decision

NASA Astrophysics Data System (ADS)

Wang, Chugege

2018-04-01

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

Data mining: sophisticated forms of managed care modeling through artificial intelligence.

PubMed

Borok, L S

1997-01-01

Data mining is a recent development in computer science that combines artificial intelligence algorithms and relational databases to discover patterns automatically, without the use of traditional statistical methods. Work with data mining tools in health care is in a developmental stage that holds great promise, given the combination of demographic and diagnostic information.

Critical Thinking of Young Citizens towards News Headlines in Chile

ERIC Educational Resources Information Center

Vernier, Matthieu; Cárcamo, Luis; Scheihing, Eliana

2018-01-01

Strengthening critical thinking abilities of citizens in the face of news published on the web represents a key challenge for education. Young citizens appear to be vulnerable in the face of poor quality news or those containing non-explicit ideologies. In the field of data science, computational and statistical techniques have been developed to…

Computing the Average Square: An Agent-Based Introduction to Aspects of Current Psychometric Practice

ERIC Educational Resources Information Center

Stroup, Walter M.; Hills, Thomas; Carmona, Guadalupe

2011-01-01

This paper summarizes an approach to helping future educators to engage with key issues related to the application of measurement-related statistics to learning and teaching, especially in the contexts of science, mathematics, technology and engineering (STEM) education. The approach we outline has two major elements. First, students are asked to…

Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)

ERIC Educational Resources Information Center

Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed.

2009-01-01

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…

Proceedings of the International Conference on Educational Data Mining (EDM) (4th, Eindhoven, the Netherlands, July 6-8, 2011)

ERIC Educational Resources Information Center

Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John

2011-01-01

The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…

Computer networks for financial activity management, control and statistics of databases of economic administration at the Joint Institute for Nuclear Research

NASA Astrophysics Data System (ADS)

Tyupikova, T. V.; Samoilov, V. N.

2003-04-01

Modern information technologies urge natural sciences to further development. But it comes together with evaluation of infrastructures, to spotlight favorable conditions for the development of science and financial base in order to prove and protect legally new research. Any scientific development entails accounting and legal protection. In the report, we consider a new direction in software, organization and control of common databases on the example of the electronic document handling, which functions in some departments of the Joint Institute for Nuclear Research.

Role of cognitive assessment for high school graduates prior to choosing their college major.

PubMed

AlAbdulwahab, Sami S; Kachanathu, Shaji John; AlSaeed, Abdullah Saad

2018-02-01

[Purpose] Academic performance of college students can be impacted by the efficacy of students' ability and teaching methods. It is important to assess the progression of college students' cognitive abilities among different college majors and as they move from junior to senior levels. However, dearth of studies have been examined the role of cognitive ability tests as a tool to determine the aptitude of the perspective students. Therefore, this study assessed cognitive abilities of computer science and ART students. [Subjects and Methods] Participants were 130 college students (70 computer and 60 art students) in their first and final years of study at King Saud University. Cognitive ability was assessed using the Test of Nonverbal Intelligence, Third Edition. [Results] The cognitive ability of computer science students were statistically better than that of art students and were shown improvement from junior to senior levels, while the cognitive ability of art students did not. [Conclusion] The cognitive ability of computer science college students was superior compared to those in art, indicating the importance of cognitive ability assessment for high school graduates prior to choosing a college major. Cognitive scales should be included as an aptitude assessment tool for decision-makers and prospective students to determine an appropriate career, which might reduce the rate of university drop out.

Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python

USGS Publications Warehouse

Laura, Jason R.; Rey, Sergio J.

2017-01-01

Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.

Concerns and professional development needs of science faculty at Taibah University in adopting blended learning

NASA Astrophysics Data System (ADS)

Al-Sarrani, Nauaf

The purpose of this study was to obtain Science faculty concerns and professional development needs to adopt blended learning in their teaching at Taibah University. To answer these two research questions the survey instrument was designed to collect quantitative and qualitative data from close-ended and open-ended questions. The participants' general characteristics were first presented, then the quantitative measures were presented as the results of the null hypotheses. The data analysis for research question one revealed a statistically significant difference in the participants' concerns in adopting BL by their gender sig = .0015. The significances were found in stages one (sig = .000) and stage five (sig = .006) for female faculty. Therefore, null hypothesis 1.1 was rejected (There are no statistically significant differences between science faculty's gender and their concerns in adopting BL). The data analysis indicated also that there were no relationships between science faculty's age, academic rank, nationality, country of graduation and years of teaching experience and their concerns in adopting BL in their teaching, so the null hypotheses 1.2-7 were accepted (There are no statistically significant differences between Science faculty's age and their concerns in adopting BL, there are no statistically significant differences between Science faculty's academic rank and their concerns in adopting BL, there are no statistically significant differences between Science faculty's nationality and their concerns in adopting BL, there are no statistically significant differences between Science faculty's content area and their concerns in adopting BL, there are no statistically significant differences between Science faculty's country of graduation and their concerns in adopting BL and there are no statistically significant differences between Science faculty's years of teaching experience and their concerns in adopting BL). The data analyses for research question two revealed that there was a statistically significant difference between science faculty's use of technology in teaching by department and their attitudes towards technology integration in the Science curriculum. Lambda MANOVA test result was sig =.019 at the alpha = .05 level. Follow up ANOVA result indicated that Chemistry department was significant in the use of computer-based technology (sig =.049) and instructional technology use (sig =.041). Therefore, null hypothesis 2.1 was rejected (There are no statistically significant differences between science faculty's attitudes towards technology integration in the Science curriculum and faculty's use of technology in teaching by department). The data also revealed that there was no statistically significant difference (p<.05) between science faculty's use of technology in teaching by department and their instructional technology use on pedagogy. Therefore, null hypothesis 2.2 was accepted (There are no statistically significant differences between science faculty's perceptions of the effects of faculty IT use on pedagogy and faculty's use of technology in teaching by department). The data also revealed that there was a statistically significant difference between science faculty's use of technology in teaching by department and their professional development needs in adopting BL. Lambda MANOVA test result was .007 at the alpha = .05 level. The follow up ANOVA results showed that the value of significance of Science faculty's professional development needs for adopting BL was smaller than .05 in the Chemistry department with sig =.001 in instructional technology use. Therefore, null hypothesis 2.3 was rejected (There are no statistically significant differences between Science faculty's perceptions of technology professional development needs and faculty's use of technology in teaching by department). Qualitative measures included analyzing data based on answers to three open-ended questions, numbers thirty-six, seventy-four, and seventy-five. These three questions were on blended learning concerns comments (question 36, which had 10 units), professional development activities, support, or incentive requested (question 74, which had 28 units), and the most important professional development activities, support, or incentive (question 75, which had 37 units). These questions yielded 75 units, 23 categories and 8 themes that triangulated with the quantitative data. These 8 themes were then combined to obtain overall themes for all qualitative questions in the study. The two most important themes were "Professional development" with three categories; Professional development through workshops (10 units), Workshops (10 units), Professional development (5 units) and the second overall theme was "Technical support" with two categories: Internet connectivity (4 units), and Technical support (4 units). Finally, based on quantitative and qualitative data, the summary, conclusions, and recommendations for Taibah University regarding faculty adoption of BL in teaching were presented. The recommendations for future studies focused on Science faculty Level of Use and technology use in Saudi universities.

SciSpark: Highly Interactive and Scalable Model Evaluation and Climate Metrics

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Mattmann, C. A.; Waliser, D. E.; Kim, J.; Loikith, P.; Lee, H.; McGibbney, L. J.; Whitehall, K. D.

2014-12-01

Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We are developing a lightning fast Big Data technology called SciSpark based on ApacheTM Spark. Spark implements the map-reduce paradigm for parallel computing on a cluster, but emphasizes in-memory computation, "spilling" to disk only as needed, and so outperforms the disk-based ApacheTM Hadoop by 100x in memory and by 10x on disk, and makes iterative algorithms feasible. SciSpark will enable scalable model evaluation by executing large-scale comparisons of A-Train satellite observations to model grids on a cluster of 100 to 1000 compute nodes. This 2nd generation capability for NASA's Regional Climate Model Evaluation System (RCMES) will compute simple climate metrics at interactive speeds, and extend to quite sophisticated iterative algorithms such as machine-learning (ML) based clustering of temperature PDFs, and even graph-based algorithms for searching for Mesocale Convective Complexes. The goals of SciSpark are to: (1) Decrease the time to compute comparison statistics and plots from minutes to seconds; (2) Allow for interactive exploration of time-series properties over seasons and years; (3) Decrease the time for satellite data ingestion into RCMES to hours; (4) Allow for Level-2 comparisons with higher-order statistics or PDF's in minutes to hours; and (5) Move RCMES into a near real time decision-making platform. We will report on: the architecture and design of SciSpark, our efforts to integrate climate science algorithms in Python and Scala, parallel ingest and partitioning (sharding) of A-Train satellite observations from HDF files and model grids from netCDF files, first parallel runs to compute comparison statistics and PDF's, and first metrics quantifying parallel speedups and memory & disk usage.

The Statistical Package for the Social Sciences (SPSS) as an adjunct to pharmacokinetic analysis.

PubMed

Mather, L E; Austin, K L

1983-01-01

Computer techniques for numerical analysis are well known to pharmacokineticists. Powerful techniques for data file management have been developed by social scientists but have, in general, been ignored by pharmacokineticists because of their apparent lack of ability to interface with pharmacokinetic programs. Extensive use has been made of the Statistical Package for the Social Sciences (SPSS) for its data handling capabilities, but at the same time, techniques have been developed within SPSS to interface with pharmacokinetic programs of the users' choice and to carry out a variety of user-defined pharmacokinetic tasks within SPSS commands, apart from the expected variety of statistical tasks. Because it is based on a ubiquitous package, this methodology has all of the benefits of excellent documentation, interchangeability between different types and sizes of machines and true portability of techniques and data files. An example is given of the total management of a pharmacokinetic study previously reported in the literature by the authors.

Extending Landauer's bound from bit erasure to arbitrary computation

NASA Astrophysics Data System (ADS)

Wolpert, David

The minimal thermodynamic work required to erase a bit, known as Landauer's bound, has been extensively investigated both theoretically and experimentally. However, when viewed as a computation that maps inputs to outputs, bit erasure has a very special property: the output does not depend on the input. Existing analyses of thermodynamics of bit erasure implicitly exploit this property, and thus cannot be directly extended to analyze the computation of arbitrary input-output maps. Here we show how to extend these earlier analyses of bit erasure to analyze the thermodynamics of arbitrary computations. Doing this establishes a formal connection between the thermodynamics of computers and much of theoretical computer science. We use this extension to analyze the thermodynamics of the canonical ``general purpose computer'' considered in computer science theory: a universal Turing machine (UTM). We consider a UTM which maps input programs to output strings, where inputs are drawn from an ensemble of random binary sequences, and prove: i) The minimal work needed by a UTM to run some particular input program X and produce output Y is the Kolmogorov complexity of Y minus the log of the ``algorithmic probability'' of Y. This minimal amount of thermodynamic work has a finite upper bound, which is independent of the output Y, depending only on the details of the UTM. ii) The expected work needed by a UTM to compute some given output Y is infinite. As a corollary, the overall expected work to run a UTM is infinite. iii) The expected work needed by an arbitrary Turing machine T (not necessarily universal) to compute some given output Y can either be infinite or finite, depending on Y and the details of T. To derive these results we must combine ideas from nonequilibrium statistical physics with fundamental results from computer science, such as Levin's coding theorem and other theorems about universal computation. I would like to ackowledge the Santa Fe Institute, Grant No. TWCF0079/AB47 from the Templeton World Charity Foundation, Grant No. FQXi-RHl3-1349 from the FQXi foundation, and Grant No. CHE-1648973 from the U.S. National Science Foundation.

Collaborative Science Using Web Services and the SciFlo Grid Dataflow Engine

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Xing, Z.; Yunck, T.

2006-12-01

The General Earth Science Investigation Suite (GENESIS) project is a NASA-sponsored partnership between the Jet Propulsion Laboratory, academia, and NASA data centers to develop a new suite of Web Services tools to facilitate multi-sensor investigations in Earth System Science. The goal of GENESIS is to enable large-scale, multi-instrument atmospheric science using combined datasets from the AIRS, MODIS, MISR, and GPS sensors. Investigations include cross-comparison of spaceborne climate sensors, cloud spectral analysis, study of upper troposphere-stratosphere water transport, study of the aerosol indirect cloud effect, and global climate model validation. The challenges are to bring together very large datasets, reformat and understand the individual instrument retrievals, co-register or re-grid the retrieved physical parameters, perform computationally-intensive data fusion and data mining operations, and accumulate complex statistics over months to years of data. To meet these challenges, we have developed a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data access, subsetting, registration, mining, fusion, compression, and advanced statistical analysis. SciFlo leverages remote Web Services, called via Simple Object Access Protocol (SOAP) or REST (one-line) URLs, and the Grid Computing standards (WS-* &Globus Alliance toolkits), and enables scientists to do multi-instrument Earth Science by assembling reusable Web Services and native executables into a distributed computing flow (tree of operators). The SciFlo client &server engines optimize the execution of such distributed data flows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. In particular, SciFlo exploits the wealth of datasets accessible by OpenGIS Consortium (OGC) Web Mapping Servers & Web Coverage Servers (WMS/WCS), and by Open Data Access Protocol (OpenDAP) servers. The scientist injects a distributed computation into the Grid by simply filling out an HTML form or directly authoring the underlying XML dataflow document, and results are returned directly to the scientist's desktop. Once an analysis has been specified for a chunk or day of data, it can be easily repeated with different control parameters or over months of data. Recently, the Earth Science Information Partners (ESIP) Federation sponsored a collaborative activity in which several ESIP members advertised their respective WMS/WCS and SOAP services, developed some collaborative science scenarios for atmospheric and aerosol science, and then choreographed services from multiple groups into demonstration workflows using the SciFlo engine and a Business Process Execution Language (BPEL) workflow engine. For several scenarios, the same collaborative workflow was executed in three ways: using hand-coded scripts, by executing a SciFlo document, and by executing a BPEL workflow document. We will discuss the lessons learned from this activity, the need for standardized interfaces (like WMS/WCS), the difficulty in agreeing on even simple XML formats and interfaces, and further collaborations that are being pursued.

Factors that Influence the Success of Male and Female Computer Programming Students in College

NASA Astrophysics Data System (ADS)

Clinkenbeard, Drew A.

As the demand for a technologically skilled work force grows, experience and skill in computer science have become increasingly valuable for college students. However, the number of students graduating with computer science degrees is not growing proportional to this need. Traditionally several groups are underrepresented in this field, notably women and students of color. This study investigated elements of computer science education that influence academic achievement in beginning computer programming courses. The goal of the study was to identify elements that increase success in computer programming courses. A 38-item questionnaire was developed and administered during the Spring 2016 semester at California State University Fullerton (CSUF). CSUF is an urban public university comprised of about 40,000 students. Data were collected from three beginning programming classes offered at CSUF. In total 411 questionnaires were collected resulting in a response rate of 58.63%. Data for the study were grouped into three broad categories of variables. These included academic and background variables; affective variables; and peer, mentor, and role-model variables. A conceptual model was developed to investigate how these variables might predict final course grade. Data were analyzed using statistical techniques such as linear regression, factor analysis, and path analysis. Ultimately this study found that peer interactions, comfort with computers, computer self-efficacy, self-concept, and perception of achievement were the best predictors of final course grade. In addition, the analyses showed that male students exhibited higher levels of computer self-efficacy and self-concept compared to female students, even when they achieved comparable course grades. Implications and explanations of these findings are explored, and potential policy changes are offered.

Comparison of attitudes of non-science major students toward science and technology

NASA Astrophysics Data System (ADS)

Wick, Donald Gary

This study examines the attitudes of non-science major students who were enrolled in General Education Required (GER) science courses at three diverse Iowa post-secondary educational institutions: The University of Iowa, Cornell College, and Kirkwood Community College. The information was gathered using a survey instrument with the test subjects responding with a five-part Likert-scale to a series of statements regarding: (1) reasons for taking the science course, (2) views and attitudes toward science, and (3) the nature and implications of science and technology. The initial data gathered was analyzed using either chi-squared, analysis of variance (ANOVA), and/or Bonferroni tests. Responses to grouped statements were used to generate population indices related to: (1) experience, (2) attitude, (3) experimentation, and (4) technology. These indices were analyzed for statistically significant differences using Tukey's Studentized (HSD) and Tukey-Krammer tests. Statistically significant differences were found in the response means for some individual statements. When a population index was calculated for each school using the grouped responses related to attitude, experience, science/technology, multiple comparison testing determined significant differences with regards to attitude, experiences, and science/technology. No significant differences were found between the schools for the population index regarding experimentation. Demographic information gathered concerning the nature of the student populations included: (1) declared major, (2) classification, (3) previous number of science courses, (4) gender, and (5) use of computers for the science course. Analysis of demographic data also revealed statistically significant differences. The differences found in this study provide additional quantitative data to characterize the non-science major student. Recommendations based on this data are: (1) The University of Iowa strive for smaller GER class sizes and reevaluate current pedagogy, (2) Kirkwood Community College make class material more relevant and place more emphasis on research, (3) Cornell College utilize full professors in the non-major course and incorporate more technology, and (4) all reevaluate the science GERs course pedagogy, retain the science GERs, maintain the current number of GER science course choices, and, finally, reevaluate any GER science course credit reciprocity.

Open Science in the Cloud: Towards a Universal Platform for Scientific and Statistical Computing

NASA Astrophysics Data System (ADS)

Chine, Karim

The UK, through the e-Science program, the US through the NSF-funded cyber infrastructure and the European Union through the ICT Calls aimed to provide "the technological solution to the problem of efficiently connecting data, computers, and people with the goal of enabling derivation of novel scientific theories and knowledge".1 The Grid (Foster, 2002; Foster; Kesselman, Nick, & Tuecke, 2002), foreseen as a major accelerator of discovery, didn't meet the expectations it had excited at its beginnings and was not adopted by the broad population of research professionals. The Grid is a good tool for particle physicists and it has allowed them to tackle the tremendous computational challenges inherent to their field. However, as a technology and paradigm for delivering computing on demand, it doesn't work and it can't be fixed. On one hand, "the abstractions that Grids expose - to the end-user, to the deployers and to application developers - are inappropriate and they need to be higher level" (Jha, Merzky, & Fox), and on the other hand, academic Grids are inherently economically unsustainable. They can't compete with a service outsourced to the Industry whose quality and price would be driven by market forces. The virtualization technologies and their corollary, the Infrastructure-as-a-Service (IaaS) style cloud, hold the promise to enable what the Grid failed to deliver: a sustainable environment for computational sciences that would lower the barriers for accessing federated computational resources, software tools and data; enable collaboration and resources sharing and provide the building blocks of a ubiquitous platform for traceable and reproducible computational research.

Rtop - an R package for interpolation along the stream network

NASA Astrophysics Data System (ADS)

Skøien, J. O.

2009-04-01

Rtop - an R package for interpolation along the stream network Geostatistical methods have been used to a limited extent for estimation along stream networks, with a few exceptions(Gottschalk, 1993; Gottschalk, et al., 2006; Sauquet, et al., 2000; Skøien, et al., 2006). Interpolation of runoff characteristics are more complicated than the traditional random variables estimated by geostatistical methods, as the measurements have a more complicated support, and many catchments are nested. Skøien et al. (2006) presented the model Top-kriging which takes these effects into account for interpolation of stream flow characteristics (exemplified by the 100 year flood). The method has here been implemented as a package in the statistical environment R (R Development Core Team, 2004). Taking advantage of the existing methods in R for working with spatial objects, and the extensive possibilities for visualizing the result, this makes it considerably easier to apply the method on new data sets, in comparison to earlier implementation of the method. Gottschalk, L. 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., I. Krasovskaia, E. Leblois, and E. Sauquet. 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. R Development Core Team. 2004. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Sauquet, E., L. Gottschalk, and E. Leblois. 2000. Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J. O., R. Merz, and G. Blöschl. 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

e-Science platform for translational biomedical imaging research: running, statistics, and analysis

NASA Astrophysics Data System (ADS)

Wang, Tusheng; Yang, Yuanyuan; Zhang, Kai; Wang, Mingqing; Zhao, Jun; Xu, Lisa; Zhang, Jianguo

2015-03-01

In order to enable multiple disciplines of medical researchers, clinical physicians and biomedical engineers working together in a secured, efficient, and transparent cooperative environment, we had designed an e-Science platform for biomedical imaging research and application cross multiple academic institutions and hospitals in Shanghai and presented this work in SPIE Medical Imaging conference held in San Diego in 2012. In past the two-years, we implemented a biomedical image chain including communication, storage, cooperation and computing based on this e-Science platform. In this presentation, we presented the operating status of this system in supporting biomedical imaging research, analyzed and discussed results of this system in supporting multi-disciplines collaboration cross-multiple institutions.

Latent semantic analysis.

PubMed

Evangelopoulos, Nicholas E

2013-11-01

This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2013 John Wiley & Sons, Ltd.

Immersive Theater - a Proven Way to Enhance Learning Retention

NASA Astrophysics Data System (ADS)

Reiff, P. H.; Zimmerman, L.; Spillane, S.; Sumners, C.

2014-12-01

The portable immersive theater has gone from our first demonstration at fall AGU 2003 to a product offered by multiple companies in various versions to literally millions of users per year. As part of our NASA funded outreach program, we conducted a test of learning in a portable Discovery Dome as contrasted with learning the same materials (visuals and sound track) on a computer screen. We tested 200 middle school students (primarily underserved minorities). Paired t-tests and an independent t-test were used to compare the amount of learning that students achieved. Interest questionnaires were administered to participants in formal (public school) settings and focus groups were conducted in informal (museum camp and educational festival) settings. Overall results from the informal and formal educational setting indicated that there was a statistically significant increase in test scores after viewing We Choose Space. There was a statistically significant increase in test scores for students who viewed We Choose Space in the portable Discovery Dome (9.75) as well as with the computer (8.88). However, long-term retention of the material tested on the questionnaire indicated that for students who watched We Choose Space in the portable Discovery Dome, there was a statistically significant long-term increase in test scores (10.47), whereas, six weeks after learning on the computer, the improvements over the initial baseline (3.49) were far less and were not statistically significant. The test score improvement six weeks after learning in the dome was essentially the same as the post test immediately after watching the show, demonstrating virtually no loss of gained information in the six week interval. In the formal educational setting, approximately 34% of the respondents indicated that they wanted to learn more about becoming a scientist, while 35% expressed an interest in a career in space science. In the informal setting, 26% indicated that they were interested in pursuing a career in space science.

Foreign Military Sales Pricing Principles for Electronic Technical Manuals

DTIC Science & Technology

2004-06-01

companies provide benefits such as flexible hours, flexible days, and telecommuting . This information is useful because facilities costs and overhead can...personnel are listed below: Occupation Title Employment (1) Median Hourly Mean Hourly Mean Annual (2) Computer and Mathematical Science...be minimized or significantly reduced for companies providing this benefit . There was one disturbing statistic from this survey. Despite the

Citation Patterns of Engineering, Statistics, and Computer Science Researchers: An Internal and External Citation Analysis across Multiple Engineering Subfields

ERIC Educational Resources Information Center

Kelly, Madeline

2015-01-01

This study takes a multidimensional approach to citation analysis, examining citations in multiple subfields of engineering, from both scholarly journals and doctoral dissertations. The three major goals of the study are to determine whether there are differences between citations drawn from dissertations and those drawn from journal articles; to…

The Effectiveness of Computer-Assisted Instruction to Teach Physical Examination to Students and Trainees in the Health Sciences Professions: A Systematic Review and Meta-Analysis

PubMed Central

Tomesko, Jennifer; Touger-Decker, Riva; Dreker, Margaret; Zelig, Rena; Parrott, James Scott

2017-01-01

Purpose: To explore knowledge and skill acquisition outcomes related to learning physical examination (PE) through computer-assisted instruction (CAI) compared with a face-to-face (F2F) approach. Method: A systematic literature review and meta-analysis published between January 2001 and December 2016 was conducted. Databases searched included Medline, Cochrane, CINAHL, ERIC, Ebsco, Scopus, and Web of Science. Studies were synthesized by study design, intervention, and outcomes. Statistical analyses included DerSimonian-Laird random-effects model. Results: In total, 7 studies were included in the review, and 5 in the meta-analysis. There were no statistically significant differences for knowledge (mean difference [MD] = 5.39, 95% confidence interval [CI]: −2.05 to 12.84) or skill acquisition (MD = 0.35, 95% CI: −5.30 to 6.01). Conclusions: The evidence does not suggest a strong consistent preference for either CAI or F2F instruction to teach students/trainees PE. Further research is needed to identify conditions which examine knowledge and skill acquisition outcomes that favor one mode of instruction over the other. PMID:29349338

Supporting Regularized Logistic Regression Privately and Efficiently.

PubMed

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

Supporting Regularized Logistic Regression Privately and Efficiently

PubMed Central

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738

Consortium for Mathematics in the Geosciences (CMG++): Promoting the application of mathematics, statistics, and computational sciences to the geosciences

NASA Astrophysics Data System (ADS)

Mead, J.; Wright, G. B.

2013-12-01

The collection of massive amounts of high quality data from new and greatly improved observing technologies and from large-scale numerical simulations are drastically improving our understanding and modeling of the earth system. However, these datasets are also revealing important knowledge gaps and limitations of our current conceptual models for explaining key aspects of these new observations. These limitations are impeding progress on questions that have both fundamental scientific and societal significance, including climate and weather, natural disaster mitigation, earthquake and volcano dynamics, earth structure and geodynamics, resource exploration, and planetary evolution. New conceptual approaches and numerical methods for characterizing and simulating these systems are needed - methods that can handle processes which vary through a myriad of scales in heterogeneous, complex environments. Additionally, as certain aspects of these systems may be observable only indirectly or not at all, new statistical methods are also needed. This type of research will demand integrating the expertise of geoscientist together with that of mathematicians, statisticians, and computer scientists. If the past is any indicator, this interdisciplinary research will no doubt lead to advances in all these fields in addition to vital improvements in our ability to predict the behavior of the planetary environment. The Consortium for Mathematics in the Geosciences (CMG++) arose from two scientific workshops held at Northwestern and Princeton in 2011 and 2012 with participants from mathematics, statistics, geoscience and computational science. The mission of CMG++ is to accelerate the traditional interaction between people in these disciplines through the promotion of both collaborative research and interdisciplinary education. We will discuss current activities, describe how people can get involved, and solicit input from the broader AGU community.

A Disciplined Architectural Approach to Scaling Data Analysis for Massive, Scientific Data

NASA Astrophysics Data System (ADS)

Crichton, D. J.; Braverman, A. J.; Cinquini, L.; Turmon, M.; Lee, H.; Law, E.

2014-12-01

Data collections across remote sensing and ground-based instruments in astronomy, Earth science, and planetary science are outpacing scientists' ability to analyze them. Furthermore, the distribution, structure, and heterogeneity of the measurements themselves pose challenges that limit the scalability of data analysis using traditional approaches. Methods for developing science data processing pipelines, distribution of scientific datasets, and performing analysis will require innovative approaches that integrate cyber-infrastructure, algorithms, and data into more systematic approaches that can more efficiently compute and reduce data, particularly distributed data. This requires the integration of computer science, machine learning, statistics and domain expertise to identify scalable architectures for data analysis. The size of data returned from Earth Science observing satellites and the magnitude of data from climate model output, is predicted to grow into the tens of petabytes challenging current data analysis paradigms. This same kind of growth is present in astronomy and planetary science data. One of the major challenges in data science and related disciplines defining new approaches to scaling systems and analysis in order to increase scientific productivity and yield. Specific needs include: 1) identification of optimized system architectures for analyzing massive, distributed data sets; 2) algorithms for systematic analysis of massive data sets in distributed environments; and 3) the development of software infrastructures that are capable of performing massive, distributed data analysis across a comprehensive data science framework. NASA/JPL has begun an initiative in data science to address these challenges. Our goal is to evaluate how scientific productivity can be improved through optimized architectural topologies that identify how to deploy and manage the access, distribution, computation, and reduction of massive, distributed data, while managing the uncertainties of scientific conclusions derived from such capabilities. This talk will provide an overview of JPL's efforts in developing a comprehensive architectural approach to data science.

Citizen science: A new perspective to advance spatial pattern evaluation in hydrology.

PubMed

Koch, Julian; Stisen, Simon

2017-01-01

Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which inevitably gives benefits such as speed and the possibility to automatize processes. However, the human vision can be harnessed to evaluate the reliability of algorithms which are tailored to quantify similarity in spatial patterns. We established a citizen science project to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of several scenarios of a hydrological catchment model. In total, the turnout counts more than 2500 volunteers that provided over 43000 classifications of 1095 individual subjects. We investigate the capability of a set of advanced statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric. The obtained dataset can provide an insightful benchmark to the community to test novel spatial metrics.

Assessment of neck pain and cervical mobility among female computer workers at Hail University.

PubMed

Mohammad, Walaa S; Hamza, Hayat H; ElSais, Walaa M

2015-01-01

The aims of this study were to investigate the prevalence of neck pain among computer workers at Hail University, Saudi Arabia and to compare the cervical range of motion (ROM) of female computer workers suffering from neck pain to the cervical ROM of healthy female computer workers. One hundred and seventy-six female volunteers between 20 and 46 years of age were investigated. Fifty-six of these volunteers were staff members, 22 were administrators and 98 were students. The Cervical Range of Motion (CROM) instrument was used to measure the ROM of the cervical spine. A questionnaire was used to assess participants for the presence of neck pain. The data were analyzed using the Statistical Package for Social Sciences (SPSS) software, and the level of significant was set at p < .05 for all statistical tests. There was a high prevalence of neck pain (75%) among computer workers at Hail University, particularly among students. There were significant differences in cervical lateral flexion, rotation to the right side and protraction range between the pain and pain-free groups. Our results demonstrated that cervical ROM measurements, particularly cervical lateral flexion, rotation and protraction, could be useful for predicting changes in head and neck posture after long-term computer work.

Workflow based framework for life science informatics.

PubMed

Tiwari, Abhishek; Sekhar, Arvind K T

2007-10-01

Workflow technology is a generic mechanism to integrate diverse types of available resources (databases, servers, software applications and different services) which facilitate knowledge exchange within traditionally divergent fields such as molecular biology, clinical research, computational science, physics, chemistry and statistics. Researchers can easily incorporate and access diverse, distributed tools and data to develop their own research protocols for scientific analysis. Application of workflow technology has been reported in areas like drug discovery, genomics, large-scale gene expression analysis, proteomics, and system biology. In this article, we have discussed the existing workflow systems and the trends in applications of workflow based systems.

Evaluating Imaging and Computer-aided Detection and Diagnosis Devices at the FDA

PubMed Central

Gallas, Brandon D.; Chan, Heang-Ping; D’Orsi, Carl J.; Dodd, Lori E.; Giger, Maryellen L.; Gur, David; Krupinski, Elizabeth A.; Metz, Charles E.; Myers, Kyle J.; Obuchowski, Nancy A.; Sahiner, Berkman; Toledano, Alicia Y.; Zuley, Margarita L.

2017-01-01

This report summarizes the Joint FDA-MIPS Workshop on Methods for the Evaluation of Imaging and Computer-Assist Devices. The purpose of the workshop was to gather information on the current state of the science and facilitate consensus development on statistical methods and study designs for the evaluation of imaging devices to support US Food and Drug Administration submissions. Additionally, participants expected to identify gaps in knowledge and unmet needs that should be addressed in future research. This summary is intended to document the topics that were discussed at the meeting and disseminate the lessons that have been learned through past studies of imaging and computer-aided detection and diagnosis device performance. PMID:22306064

The Computer Student Worksheet Based Mathematical Literacy for Statistics

NASA Astrophysics Data System (ADS)

Manoy, J. T.; Indarasati, N. A.

2018-01-01

The student worksheet is one of media teaching which is able to improve teaching an activity in the classroom. Indicators in mathematical literacy were included in a student worksheet is able to help the students for applying the concept in daily life. Then, the use of computers in learning can create learning with environment-friendly. This research used developmental research which was Thiagarajan (Four-D) development design. There are 4 stages in the Four-D, define, design, develop, and disseminate. However, this research was finish until the third stage, develop stage. The computer student worksheet based mathematical literacy for statistics executed good quality. This student worksheet is achieving the criteria if able to achieve three aspects, validity, practicality, and effectiveness. The subject in this research was the students at The 1st State Senior High School of Driyorejo, Gresik, grade eleven of The 5th Mathematics and Natural Sciences. The computer student worksheet products based mathematical literacy for statistics executed good quality, while it achieved the aspects for validity, practical, and effectiveness. This student worksheet achieved the validity aspects with an average of 3.79 (94.72%), and practical aspects with an average of 2.85 (71.43%). Besides, it achieved the effectiveness aspects with a percentage of the classical complete students of 94.74% and a percentage of the student positive response of 75%.

Astro Data Science: The Next Generation

NASA Astrophysics Data System (ADS)

Mentzel, Chris

2018-01-01

Astronomers have been at the forefront of data-driven discovery since before the days of Kepler. Using data in the scientific inquiry into the workings of the the universe is the lifeblood of the field. This said, data science is considered a new thing, and researchers from every discipline are rushing to learn data science techniques, train themselves on data science tools, and even leaving academia to become data scientists. It is undeniable that our ability to harness new computational and statistical methods to make sense of today’s unprecedented size, complexity, and fast streaming data is helping scientists make new discoveries. The question now is how to ensure that researchers can employ these tools and use them appropriately. This talk will cover the state of data science as it relates to scientific research and the role astronomers play in its development, use, and training the next generation of astro-data scientists.

Reading Emotion From Mouse Cursor Motions: Affective Computing Approach.

PubMed

Yamauchi, Takashi; Xiao, Kunchen

2018-04-01

Affective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical. This study integrates mouse cursor motion analysis into affective computing and investigates the idea that movements of the computer cursor can provide information about emotion of the computer user. We extracted 16-26 trajectory features during a choice-reaching task and examined the link between emotion and cursor motions. Participants were induced for positive or negative emotions by music, film clips, or emotional pictures, and they indicated their emotions with questionnaires. Our 10-fold cross-validation analysis shows that statistical models formed from "known" participants (training data) could predict nearly 10%-20% of the variance of positive affect and attentiveness ratings of "unknown" participants, suggesting that cursor movement patterns such as the area under curve and direction change help infer emotions of computer users. Copyright © 2017 Cognitive Science Society, Inc.

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation

PubMed Central

Lee, Jae H.; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T.; Seo, Youngho

2014-01-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting. PMID:27081299

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation.

PubMed

Lee, Jae H; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T; Seo, Youngho

2014-11-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting.

Know Your Discipline: Teaching the Philosophy of Computer Science

ERIC Educational Resources Information Center

Tedre, Matti

2007-01-01

The diversity and interdisciplinarity of computer science and the multiplicity of its uses in other sciences make it hard to define computer science and to prescribe how computer science should be carried out. The diversity of computer science also causes friction between computer scientists from different branches. Computer science curricula, as…

Propagation of Statistical Noise Through a Two-Qubit Maximum Likelihood Tomography

DTIC Science & Technology

2018-04-01

University Daniel E Jones, Brian T Kirby, and Michael Brodsky Computational and Information Sciences Directorate, ARL Approved for...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources...gathering and maintaining the data needed, and completing and reviewing the collection information . Send comments regarding this burden estimate or any other

Space Mathematics: A Resource for Secondary School Teachers

NASA Technical Reports Server (NTRS)

Kastner, Bernice

1985-01-01

A collection of mathematical problems related to NASA space science projects is presented. In developing the examples and problems, attention was given to preserving the authenticity and significance of the original setting while keeping the level of mathematics within the secondary school curriculum. Computation and measurement, algebra, geometry, probability and statistics, exponential and logarithmic functions, trigonometry, matrix algebra, conic sections, and calculus are among the areas addressed.

Can the behavioral sciences self-correct? A social epistemic study.

PubMed

Romero, Felipe

2016-12-01

Advocates of the self-corrective thesis argue that scientific method will refute false theories and find closer approximations to the truth in the long run. I discuss a contemporary interpretation of this thesis in terms of frequentist statistics in the context of the behavioral sciences. First, I identify experimental replications and systematic aggregation of evidence (meta-analysis) as the self-corrective mechanism. Then, I present a computer simulation study of scientific communities that implement this mechanism to argue that frequentist statistics may converge upon a correct estimate or not depending on the social structure of the community that uses it. Based on this study, I argue that methodological explanations of the "replicability crisis" in psychology are limited and propose an alternative explanation in terms of biases. Finally, I conclude suggesting that scientific self-correction should be understood as an interaction effect between inference methods and social structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Statistical ecology comes of age.

PubMed

Gimenez, Olivier; Buckland, Stephen T; Morgan, Byron J T; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-12-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Statistical ecology comes of age

PubMed Central

Gimenez, Olivier; Buckland, Stephen T.; Morgan, Byron J. T.; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M.; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M.; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-01-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data. PMID:25540151

Why interdisciplinary research enriches the study of crime. Comment on "Statistical physics of crime: A review" by M.R. D'Orsogna and M. Perc

NASA Astrophysics Data System (ADS)

Donnay, Karsten

2015-03-01

The past several years have seen a rapidly growing interest in the use of advanced quantitative methodologies and formalisms adapted from the natural sciences to study a broad range of social phenomena. The research field of computational social science [1,2], for example, uses digital artifacts of human online activity to cast a new light on social dynamics. Similarly, the studies reviewed by D'Orsogna and Perc showcase a diverse set of advanced quantitative techniques to study the dynamics of crime. Methods used range from partial differential equations and self-exciting point processes to agent-based models, evolutionary game theory and network science [3].

Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science.

PubMed

Cavagnaro, Daniel R; Myung, Jay I; Pitt, Mark A; Kujala, Janne V

2010-04-01

Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention.

The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke.

PubMed

Jobe, Thomas H.; Helgason, Cathy M.

1998-04-01

Twentieth century medical science has embraced nineteenth century Boolean probability theory based upon two-valued Aristotelian logic. With the later addition of bit-based, von Neumann structured computational architectures, an epistemology based on randomness has led to a bivalent epidemiological methodology that dominates medical decision making. In contrast, fuzzy logic, based on twentieth century multi-valued logic, and computational structures that are content addressed and adaptively modified, has advanced a new scientific paradigm for the twenty-first century. Diseases such as stroke involve multiple concomitant causal factors that are difficult to represent using conventional statistical methods. We tested which paradigm best represented this complex multi-causal clinical phenomenon-stroke. We show that the fuzzy logic paradigm better represented clinical complexity in cerebrovascular disease than current probability theory based methodology. We believe this finding is generalizable to all of clinical science since multiple concomitant causal factors are involved in nearly all known pathological processes.

Cox process representation and inference for stochastic reaction-diffusion processes

NASA Astrophysics Data System (ADS)

Schnoerr, David; Grima, Ramon; Sanguinetti, Guido

2016-05-01

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.

Semantic Coherence Facilitates Distributional Learning.

PubMed

Ouyang, Long; Boroditsky, Lera; Frank, Michael C

2017-04-01

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. Copyright © 2016 Cognitive Science Society, Inc.

Citizen science: A new perspective to evaluate spatial patterns in hydrology.

NASA Astrophysics Data System (ADS)

Koch, J.; Stisen, S.

2016-12-01

Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning make humans often more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which is inevitable giving benefits such as speed and the possibility to automatize processes. This study highlights the integration of the generally underused human resource into hydrology. We established a citizen science project on the zooniverse platform entitled Pattern Perception. The aim is to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of a hydrological catchment model. In total, the turnout counts more than 2,800 users that provided over 46,000 classifications of 1,095 individual subjects within 64 days after the launch. Each subject displays simulated spatial patterns of land-surface variables of a baseline model and six modelling scenarios. The citizen science data discloses a numeric pattern similarity score for each of the scenarios with respect to the reference. We investigate the capability of a set of innovative statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide flexibility and auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric.

Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery.

PubMed

Metwally, Abdelkader A; Hathout, Rania M

2015-08-03

We hypothesize that, by using several chemo/bio informatics tools and statistical computational methods, we can study and then predict the behavior of several drugs in model nanoparticulate lipid and polymeric systems. Accordingly, two different matrices comprising tripalmitin, a core component of solid lipid nanoparticles (SLN), and PLGA were first modeled using molecular dynamics simulation, and then the interaction of drugs with these systems was studied by means of computing the free energy of binding using the molecular docking technique. These binding energies were hence correlated with the loadings of these drugs in the nanoparticles obtained experimentally from the available literature. The obtained relations were verified experimentally in our laboratory using curcumin as a model drug. Artificial neural networks were then used to establish the effect of the drugs' molecular descriptors on the binding energies and hence on the drug loading. The results showed that the used soft computing methods can provide an accurate method for in silico prediction of drug loading in tripalmitin-based and PLGA nanoparticulate systems. These results have the prospective of being applied to other nano drug-carrier systems, and this integrated statistical and chemo/bio informatics approach offers a new toolbox to the formulation science by proposing what we present as computer-assisted drug formulation design (CADFD).

Computing Models of M-type Host Stars and their Panchromatic Spectral Output

NASA Astrophysics Data System (ADS)

Linsky, Jeffrey; Tilipman, Dennis; France, Kevin

2018-06-01

We have begun a program of computing state-of-the-art model atmospheres from the photospheres to the coronae of M stars that are the host stars of known exoplanets. For each model we are computing the emergent radiation at all wavelengths that are critical for assessingphotochemistry and mass-loss from exoplanet atmospheres. In particular, we are computing the stellar extreme ultraviolet radiation that drives hydrodynamic mass loss from exoplanet atmospheres and is essential for determing whether an exoplanet is habitable. The model atmospheres are computed with the SSRPM radiative transfer/statistical equilibrium code developed by Dr. Juan Fontenla. The code solves for the non-LTE statistical equilibrium populations of 18,538 levels of 52 atomic and ion species and computes the radiation from all species (435,986 spectral lines) and about 20,000,000 spectral lines of 20 diatomic species.The first model computed in this program was for the modestly active M1.5 V star GJ 832 by Fontenla et al. (ApJ 830, 152 (2016)). We will report on a preliminary model for the more active M5 V star GJ 876 and compare this model and its emergent spectrum with GJ 832. In the future, we will compute and intercompare semi-empirical models and spectra for all of the stars observed with the HST MUSCLES Treasury Survey, the Mega-MUSCLES Treasury Survey, and additional stars including Proxima Cen and Trappist-1.This multiyear theory program is supported by a grant from the Space Telescope Science Institute.

Transactions of The Army Conference on Applied Mathematics and Computing (5th) Held in West Point, New York on 15-18 June 1987

DTIC Science & Technology

1988-03-01

29 Statistical Machine Learning for the Cognitive Selection of Nonlinear Programming Algorithms in Engineering Design Optimization Toward...interpolation and Interpolation by Box Spline Surfaces Charles K. Chui, Harvey Diamond, Louise A. Raphael. 301 Knot Selection for Least Squares...West Virginia University, Morgantown, West Virginia; and Louise Raphael, National Science Foundation, Washington, DC Knot Selection for Least

Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction

DTIC Science & Technology

2007-05-01

Texas at Austin Austin, TX 78712 lilyanam@cs.utexas.edu Doctoral Dissertation Proposal Supervising Professor: Raymond J. Mooney Abstract Statistical...maxima and plateaus. It is therefore an important research problem to develop learning algorithms that improve the speed and accuracy of this process. The...of Texas at Austin,Department of Computer Sciences,Austin,TX,78712 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S

Aerial Refueling Simulator Validation Using Operational Experimentation and Response Surface Methods with Time Series Responses

DTIC Science & Technology

2013-03-21

10 2.3 Time Series Response Data ................................................................................. 12 2.4 Comparison of Response...to 12 evaluating the efficiency of the parameter estimates. In the past, the most popular form of response surface design used the D-optimality...as well. A model can refer to almost anything in math , statistics, or computer science. It can be any “physical, mathematical, or logical

The Study Team for Early Life Asthma Research (STELAR) consortium ‘Asthma e-lab’: team science bringing data, methods and investigators together

PubMed Central

Custovic, Adnan; Ainsworth, John; Arshad, Hasan; Bishop, Christopher; Buchan, Iain; Cullinan, Paul; Devereux, Graham; Henderson, John; Holloway, John; Roberts, Graham; Turner, Steve; Woodcock, Ashley; Simpson, Angela

2015-01-01

We created Asthma e-Lab, a secure web-based research environment to support consistent recording, description and sharing of data, computational/statistical methods and emerging findings across the five UK birth cohorts. The e-Lab serves as a data repository for our unified dataset and provides the computational resources and a scientific social network to support collaborative research. All activities are transparent, and emerging findings are shared via the e-Lab, linked to explanations of analytical methods, thus enabling knowledge transfer. eLab facilitates the iterative interdisciplinary dialogue between clinicians, statisticians, computer scientists, mathematicians, geneticists and basic scientists, capturing collective thought behind the interpretations of findings. PMID:25805205

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update

PubMed Central

Afgan, Enis; Baker, Dannon; van den Beek, Marius; Blankenberg, Daniel; Bouvier, Dave; Čech, Martin; Chilton, John; Clements, Dave; Coraor, Nate; Eberhard, Carl; Grüning, Björn; Guerler, Aysam; Hillman-Jackson, Jennifer; Von Kuster, Greg; Rasche, Eric; Soranzo, Nicola; Turaga, Nitesh; Taylor, James; Nekrutenko, Anton; Goecks, Jeremy

2016-01-01

High-throughput data production technologies, particularly ‘next-generation’ DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale. PMID:27137889

AGENT-BASED MODELS IN EMPIRICAL SOCIAL RESEARCH*

PubMed Central

Bruch, Elizabeth; Atwell, Jon

2014-01-01

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research. PMID:25983351

Life sciences research in space: The requirement for animal models

NASA Technical Reports Server (NTRS)

Fuller, C. A.; Philips, R. W.; Ballard, R. W.

1987-01-01

Use of animals in NASA space programs is reviewed. Animals are needed because life science experimentation frequently requires long-term controlled exposure to environments, statistical validation, invasive instrumentation or biological tissue sampling, tissue destruction, exposure to dangerous or unknown agents, or sacrifice of the subject. The availability and use of human subjects inflight is complicated by the multiple needs and demands upon crew time. Because only living organisms can sense, integrate and respond to the environment around them, the sole use of tissue culture and computer models is insufficient for understanding the influence of the space environment on intact organisms. Equipment for spaceborne experiments with animals is described.

Citizen science: A new perspective to advance spatial pattern evaluation in hydrology

PubMed Central

Stisen, Simon

2017-01-01

Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which inevitably gives benefits such as speed and the possibility to automatize processes. However, the human vision can be harnessed to evaluate the reliability of algorithms which are tailored to quantify similarity in spatial patterns. We established a citizen science project to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of several scenarios of a hydrological catchment model. In total, the turnout counts more than 2500 volunteers that provided over 43000 classifications of 1095 individual subjects. We investigate the capability of a set of advanced statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric. The obtained dataset can provide an insightful benchmark to the community to test novel spatial metrics. PMID:28558050

Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis

NASA Astrophysics Data System (ADS)

Benakli, Nadia; Kostadinov, Boyan; Satyanarayana, Ashwin; Singh, Satyanand

2017-04-01

The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data analysis. We present nine computer experiments and suggest a few more, with applications to calculus, probability and data analysis, which engage computational thinking through simulations, visualizations and data analysis. We are using the free (open-source) statistical programming language R. Our goal is to give a taste of what R offers rather than to present a comprehensive tutorial on the R language. In our experience, these kinds of interactive computer activities can be easily integrated into a smart classroom. Furthermore, these activities do tend to keep students motivated and actively engaged in the process of learning, problem solving and developing a better intuition for understanding complex mathematical concepts.

The clinical value of large neuroimaging data sets in Alzheimer's disease.

PubMed

Toga, Arthur W

2012-02-01

Rapid advances in neuroimaging and cyberinfrastructure technologies have brought explosive growth in the Web-based warehousing, availability, and accessibility of imaging data on a variety of neurodegenerative and neuropsychiatric disorders and conditions. There has been a prolific development and emergence of complex computational infrastructures that serve as repositories of databases and provide critical functionalities such as sophisticated image analysis algorithm pipelines and powerful three-dimensional visualization and statistical tools. The statistical and operational advantages of collaborative, distributed team science in the form of multisite consortia push this approach in a diverse range of population-based investigations. Copyright © 2012 Elsevier Inc. All rights reserved.

High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

PubMed

Majda, Andrew J; Abramov, Rafail; Gershgorin, Boris

2010-01-12

Climate change science focuses on predicting the coarse-grained, planetary-scale, longtime changes in the climate system due to either changes in external forcing or internal variability, such as the impact of increased carbon dioxide. The predictions of climate change science are carried out through comprehensive, computational atmospheric, and oceanic simulation models, which necessarily parameterize physical features such as clouds, sea ice cover, etc. Recently, it has been suggested that there is irreducible imprecision in such climate models that manifests itself as structural instability in climate statistics and which can significantly hamper the skill of computer models for climate change. A systematic approach to deal with this irreducible imprecision is advocated through algorithms based on the Fluctuation Dissipation Theorem (FDT). There are important practical and computational advantages for climate change science when a skillful FDT algorithm is established. The FDT response operator can be utilized directly for multiple climate change scenarios, multiple changes in forcing, and other parameters, such as damping and inverse modelling directly without the need of running the complex climate model in each individual case. The high skill of FDT in predicting climate change, despite structural instability, is developed in an unambiguous fashion using mathematical theory as guidelines in three different test models: a generic class of analytical models mimicking the dynamical core of the computer climate models, reduced stochastic models for low-frequency variability, and models with a significant new type of irreducible imprecision involving many fast, unstable modes.

University of Washington's eScience Institute Promotes New Training and Career Pathways in Data Science

NASA Astrophysics Data System (ADS)

Stone, S.; Parker, M. S.; Howe, B.; Lazowska, E.

2015-12-01

Rapid advances in technology are transforming nearly every field from "data-poor" to "data-rich." The ability to extract knowledge from this abundance of data is the cornerstone of 21st century discovery. At the University of Washington eScience Institute, our mission is to engage researchers across disciplines in developing and applying advanced computational methods and tools to real world problems in data-intensive discovery. Our research team consists of individuals with diverse backgrounds in domain sciences such as astronomy, oceanography and geology, with complementary expertise in advanced statistical and computational techniques such as data management, visualization, and machine learning. Two key elements are necessary to foster careers in data science: individuals with cross-disciplinary training in both method and domain sciences, and career paths emphasizing alternative metrics for advancement. We see persistent and deep-rooted challenges for the career paths of people whose skills, activities and work patterns don't fit neatly into the traditional roles and success metrics of academia. To address these challenges the eScience Institute has developed training programs and established new career opportunities for data-intensive research in academia. Our graduate students and post-docs have mentors in both a methodology and an application field. They also participate in coursework and tutorials to advance technical skill and foster community. Professional Data Scientist positions were created to support research independence while encouraging the development and adoption of domain-specific tools and techniques. The eScience Institute also supports the appointment of faculty who are innovators in developing and applying data science methodologies to advance their field of discovery. Our ultimate goal is to create a supportive environment for data science in academia and to establish global recognition for data-intensive discovery across all fields.

Factors influencing exemplary science teachers' levels of computer use

NASA Astrophysics Data System (ADS)

Hakverdi, Meral

This study examines exemplary science teachers' use of technology in science instruction, factors influencing their level of computer use, their level of knowledge/skills in using specific computer applications for science instruction, their use of computer-related applications/tools during their instruction, and their students' use of computer applications/tools in or for their science class. After a relevant review of the literature certain variables were selected for analysis. These variables included personal self-efficacy in teaching with computers, outcome expectancy, pupil-control ideology, level of computer use, age, gender, teaching experience, personal computer use, professional computer use and science teachers' level of knowledge/skills in using specific computer applications for science instruction. The sample for this study includes middle and high school science teachers who received the Presidential Award for Excellence in Science Teaching Award (sponsored by the White House and the National Science Foundation) between the years 1997 and 2003 from all 50 states and U.S. territories. Award-winning science teachers were contacted about the survey via e-mail or letter with an enclosed return envelope. Of the 334 award-winning science teachers, usable responses were received from 92 science teachers, which made a response rate of 27.5%. Analysis of the survey responses indicated that exemplary science teachers have a variety of knowledge/skills in using computer related applications/tools. The most commonly used computer applications/tools are information retrieval via the Internet, presentation tools, online communication, digital cameras, and data collection probes. Results of the study revealed that students' use of technology in their science classroom is highly correlated with the frequency of their science teachers' use of computer applications/tools. The results of the multiple regression analysis revealed that personal self-efficacy related to the exemplary science teachers' level of computer use suggesting that computer use is dependent on perceived abilities at using computers. The teachers' use of computer-related applications/tools during class, and their personal self-efficacy, age, and gender are highly related with their level of knowledge/skills in using specific computer applications for science instruction. The teachers' level of knowledge/skills in using specific computer applications for science instruction and gender related to their use of computer-related applications/tools during class and the students' use of computer-related applications/tools in or for their science class. In conclusion, exemplary science teachers need assistance in learning and using computer-related applications/tool in their science class.

Cosmic shear measurements with Dark Energy Survey Science Verification data

DOE PAGES

Becker, M. R.

2016-07-06

Here, we present measurements of weak gravitational lensing cosmic shear two-point statistics using Dark Energy Survey Science Verification data. We demonstrate that our results are robust to the choice of shear measurement pipeline, either ngmix or im3shape, and robust to the choice of two-point statistic, including both real and Fourier-space statistics. Our results pass a suite of null tests including tests for B-mode contamination and direct tests for any dependence of the two-point functions on a set of 16 observing conditions and galaxy properties, such as seeing, airmass, galaxy color, galaxy magnitude, etc. We use a large suite of simulationsmore » to compute the covariance matrix of the cosmic shear measurements and assign statistical significance to our null tests. We find that our covariance matrix is consistent with the halo model prediction, indicating that it has the appropriate level of halo sample variance. We also compare the same jackknife procedure applied to the data and the simulations in order to search for additional sources of noise not captured by the simulations. We find no statistically significant extra sources of noise in the data. The overall detection significance with tomography for our highest source density catalog is 9.7σ. Cosmological constraints from the measurements in this work are presented in a companion paper.« less

Statistics and Informatics in Space Astrophysics

NASA Astrophysics Data System (ADS)

Feigelson, E.

2017-12-01

The interest in statistical and computational methodology has seen rapid growth in space-based astrophysics, parallel to the growth seen in Earth remote sensing. There is widespread agreement that scientific interpretation of the cosmic microwave background, discovery of exoplanets, and classifying multiwavelength surveys is too complex to be accomplished with traditional techniques. NASA operates several well-functioning Science Archive Research Centers providing 0.5 PBy datasets to the research community. These databases are integrated with full-text journal articles in the NASA Astrophysics Data System (200K pageviews/day). Data products use interoperable formats and protocols established by the International Virtual Observatory Alliance. NASA supercomputers also support complex astrophysical models of systems such as accretion disks and planet formation. Academic researcher interest in methodology has significantly grown in areas such as Bayesian inference and machine learning, and statistical research is underway to treat problems such as irregularly spaced time series and astrophysical model uncertainties. Several scholarly societies have created interest groups in astrostatistics and astroinformatics. Improvements are needed on several fronts. Community education in advanced methodology is not sufficiently rapid to meet the research needs. Statistical procedures within NASA science analysis software are sometimes not optimal, and pipeline development may not use modern software engineering techniques. NASA offers few grant opportunities supporting research in astroinformatics and astrostatistics.

Coalescent: an open-science framework for importance sampling in coalescent theory.

PubMed

Tewari, Susanta; Spouge, John L

2015-01-01

Background. In coalescent theory, computer programs often use importance sampling to calculate likelihoods and other statistical quantities. An importance sampling scheme can exploit human intuition to improve statistical efficiency of computations, but unfortunately, in the absence of general computer frameworks on importance sampling, researchers often struggle to translate new sampling schemes computationally or benchmark against different schemes, in a manner that is reliable and maintainable. Moreover, most studies use computer programs lacking a convenient user interface or the flexibility to meet the current demands of open science. In particular, current computer frameworks can only evaluate the efficiency of a single importance sampling scheme or compare the efficiencies of different schemes in an ad hoc manner. Results. We have designed a general framework (http://coalescent.sourceforge.net; language: Java; License: GPLv3) for importance sampling that computes likelihoods under the standard neutral coalescent model of a single, well-mixed population of constant size over time following infinite sites model of mutation. The framework models the necessary core concepts, comes integrated with several data sets of varying size, implements the standard competing proposals, and integrates tightly with our previous framework for calculating exact probabilities. For a given dataset, it computes the likelihood and provides the maximum likelihood estimate of the mutation parameter. Well-known benchmarks in the coalescent literature validate the accuracy of the framework. The framework provides an intuitive user interface with minimal clutter. For performance, the framework switches automatically to modern multicore hardware, if available. It runs on three major platforms (Windows, Mac and Linux). Extensive tests and coverage make the framework reliable and maintainable. Conclusions. In coalescent theory, many studies of computational efficiency consider only effective sample size. Here, we evaluate proposals in the coalescent literature, to discover that the order of efficiency among the three importance sampling schemes changes when one considers running time as well as effective sample size. We also describe a computational technique called "just-in-time delegation" available to improve the trade-off between running time and precision by constructing improved importance sampling schemes from existing ones. Thus, our systems approach is a potential solution to the "2(8) programs problem" highlighted by Felsenstein, because it provides the flexibility to include or exclude various features of similar coalescent models or importance sampling schemes.

Computational Material Processing in Microgravity

NASA Technical Reports Server (NTRS)

2005-01-01

Working with Professor David Matthiesen at Case Western Reserve University (CWRU) a computer model of the DPIMS (Diffusion Processes in Molten Semiconductors) space experiment was developed that is able to predict the thermal field, flow field and concentration profile within a molten germanium capillary under both ground-based and microgravity conditions as illustrated. These models are coupled with a novel nonlinear statistical methodology for estimating the diffusion coefficient from measured concentration values after a given time that yields a more accurate estimate than traditional methods. This code was integrated into a web-based application that has become a standard tool used by engineers in the Materials Science Department at CWRU.

Attitudes and gender differences of high school seniors within one-to-one computing environments in South Dakota

NASA Astrophysics Data System (ADS)

Nelson, Mathew

In today's age of exponential change and technological advancement, awareness of any gender gap in technology and computer science-related fields is crucial, but further research must be done in an effort to better understand the complex interacting factors contributing to the gender gap. This study utilized a survey to investigate specific gender differences relating to computing self-efficacy, computer usage, and environmental factors of exposure, personal interests, and parental influence that impact gender differences of high school students within a one-to-one computing environment in South Dakota. The population who completed the One-to-One High School Computing Survey for this study consisted of South Dakota high school seniors who had been involved in a one-to-one computing environment for two or more years. The data from the survey were analyzed using descriptive and inferential statistics for the determined variables. From the review of literature and data analysis several conclusions were drawn from the findings. Among them are that overall, there was very little difference in perceived computing self-efficacy and computing anxiety between male and female students within the one-to-one computing initiative. The study supported the current research that males and females utilized computers similarly, but males spent more time using their computers to play online games. Early exposure to computers, or the age at which the student was first exposed to a computer, and the number of computers present in the home (computer ownership) impacted computing self-efficacy. The results also indicated parental encouragement to work with computers also contributed positively to both male and female students' computing self-efficacy. Finally the study also found that both mothers and fathers encouraged their male children more than their female children to work with computing and pursue careers in computing science fields.

Reusable Software Component Retrieval via Normalized Algebraic Specifications

DTIC Science & Technology

1991-12-01

outputs. In fact, this method of query is simpler for matching since it relieves the system from the burden of generating a test set. Eichmann [Eich9l...September 1991. [Eich9l] Eichmann , David A., "Selecting Reusable Components Using Algebraic Specifications", Proceedings of the Second International...Technology Atlanta, Georgia 30332-0800 12. Dr. David Eichmann 1 Department of Statistics and Computer Science Knapp Hall West Virginia University Morgantown, West Virginia 26506 226

Computing Science and Statistics. Volume 24. Graphics and Visualization

DTIC Science & Technology

1993-03-01

the dough , turbulent fluid flow, the time between drips of behavior changes radically when the population growth water from a faucet, Brownian motion... cookie which clearly is the discrete parameter analogue of continuous param- appropriate as after dinner fun. eter time series analysis". I strongly...methods. Your fortune cookie of the night reads: One problem that statisticians traditionally seem to "uYou have good friends who will come to your aid in

Examining the Reproducibility of 6 Published Studies in Public Health Services and Systems Research.

PubMed

Harris, Jenine K; B Wondmeneh, Sarah; Zhao, Yiqiang; Leider, Jonathon P

2018-02-23

Research replication, or repeating a study de novo, is the scientific standard for building evidence and identifying spurious results. While replication is ideal, it is often expensive and time consuming. Reproducibility, or reanalysis of data to verify published findings, is one proposed minimum alternative standard. While a lack of research reproducibility has been identified as a serious and prevalent problem in biomedical research and a few other fields, little work has been done to examine the reproducibility of public health research. We examined reproducibility in 6 studies from the public health services and systems research subfield of public health research. Following the methods described in each of the 6 papers, we computed the descriptive and inferential statistics for each study. We compared our results with the original study results and examined the percentage differences in descriptive statistics and differences in effect size, significance, and precision of inferential statistics. All project work was completed in 2017. We found consistency between original and reproduced results for each paper in at least 1 of the 4 areas examined. However, we also found some inconsistency. We identified incorrect transcription of results and omitting detail about data management and analyses as the primary contributors to the inconsistencies. Increasing reproducibility, or reanalysis of data to verify published results, can improve the quality of science. Researchers, journals, employers, and funders can all play a role in improving the reproducibility of science through several strategies including publishing data and statistical code, using guidelines to write clear and complete methods sections, conducting reproducibility reviews, and incentivizing reproducible science.

State-Transition Structures in Physics and in Computation

NASA Astrophysics Data System (ADS)

Petri, C. A.

1982-12-01

In order to establish close connections between physical and computational processes, it is assumed that the concepts of “state” and of “transition” are acceptable both to physicists and to computer scientists, at least in an informal way. The aim of this paper is to propose formal definitions of state and transition elements on the basis of very low level physical concepts in such a way that (1) all physically possible computations can be described as embedded in physical processes; (2) the computational aspects of physical processes can be described on a well-defined level of abstraction; (3) the gulf between the continuous models of physics and the discrete models of computer science can be bridged by simple mathematical constructs which may be given a physical interpretation; (4) a combinatorial, nonstatistical definition of “information” can be given on low levels of abstraction which may serve as a basis to derive higher-level concepts of information, e.g., by a statistical or probabilistic approach. Conceivable practical consequences are discussed.

Gender differences in the use of computers, programming, and peer interactions in computer science classrooms

NASA Astrophysics Data System (ADS)

Stoilescu, Dorian; Egodawatte, Gunawardena

2010-12-01

Research shows that female and male students in undergraduate computer science programs view computer culture differently. Female students are interested more in the use of computers than in doing programming, whereas male students see computer science mainly as a programming activity. The overall purpose of our research was not to find new definitions for computer science culture but to see how male and female students see themselves involved in computer science practices, how they see computer science as a successful career, and what they like and dislike about current computer science practices. The study took place in a mid-sized university in Ontario. Sixteen students and two instructors were interviewed to get their views. We found that male and female views are different on computer use, programming, and the pattern of student interactions. Female and male students did not have any major issues in using computers. In computing programming, female students were not so involved in computing activities whereas male students were heavily involved. As for the opinions about successful computer science professionals, both female and male students emphasized hard working, detailed oriented approaches, and enjoying playing with computers. The myth of the geek as a typical profile of successful computer science students was not found to be true.

CARMA: Software for continuous affect rating and media annotation

PubMed Central

Girard, Jeffrey M

2017-01-01

CARMA is a media annotation program that collects continuous ratings while displaying audio and video files. It is designed to be highly user-friendly and easily customizable. Based on Gottman and Levenson's affect rating dial, CARMA enables researchers and study participants to provide moment-by-moment ratings of multimedia files using a computer mouse or keyboard. The rating scale can be configured on a number of parameters including the labels for its upper and lower bounds, its numerical range, and its visual representation. Annotations can be displayed alongside the multimedia file and saved for easy import into statistical analysis software. CARMA provides a tool for researchers in affective computing, human-computer interaction, and the social sciences who need to capture the unfolding of subjective experience and observable behavior over time. PMID:29308198

New measures for new roles: defining and measuring the current practices of health sciences librarians

PubMed Central

Scherrer, Carol S.; Jacobson, Susan

2002-01-01

The roles of academic health sciences librarians are continually evolving as librarians initiate new programs and services in response to developments in computer technology and user demands. However, statistics currently collected by libraries do not accurately reflect or measure these new roles. It is essential for librarians to document, measure, and evaluate these new activities to continue to meet the needs of users and to ensure the viability of their professional role. To determine what new measures should be compiled, the authors examined current statistics, user demands, professional literature, and current activities of librarians as reported in abstracts of poster sessions at Medical Library Association annual meetings. Three new categories of services to be measured are proposed. The first, consultation, groups activities such as quality filtering and individual point-of-need instruction. The second, outreach, includes activities such as working as liaisons, participating in grand rounds or morning report, and providing continuing education. The third area, Web authoring, encompasses activities such as designing Web pages, creating online tutorials, and developing new products. Adding these three measures to those already being collected will provide a more accurate and complete depiction of the services offered by academic health sciences librarians. PMID:11999174

Engaging with the Art & Science of Statistics

ERIC Educational Resources Information Center

Peters, Susan A.

2010-01-01

How can statistics clearly be mathematical and yet distinct from mathematics? The answer lies in the reality that statistics is both an art and a science, and both aspects are important for teaching and learning statistics. Statistics is a mathematical science in that it applies mathematical theories and techniques. Mathematics provides the…

Exploring the Relationships between Self-Efficacy and Preference for Teacher Authority among Computer Science Majors

ERIC Educational Resources Information Center

Lin, Che-Li; Liang, Jyh-Chong; Su, Yi-Ching; Tsai, Chin-Chung

2013-01-01

Teacher-centered instruction has been widely adopted in college computer science classrooms and has some benefits in training computer science undergraduates. Meanwhile, student-centered contexts have been advocated to promote computer science education. How computer science learners respond to or prefer the two types of teacher authority,…

Tracing technology in the Association of Academic Health Sciences Libraries.

PubMed

Guard, J Roger; Peay, Wayne J

2003-04-01

From the beginning of the association, technology and the Association of Academic Health Sciences Libraries (AAHSL) have been intertwined. Technology was the focus of one of the first committees. Innovative applications of technology have been employed in the operations of the association. Early applications of mini-computers were used in preparing the Annual Statistics. The association's use of network communications was among the first in the country and later applications of the Web have enhanced association services. For its members, technology has transformed libraries. The association's support of the early development of Integrated Advanced Information Management Systems (IAIMS) and of its recent reconceptualization has contributed to the intellectual foundation for this revolution.

Oxford dictionary of Physics

NASA Astrophysics Data System (ADS)

Isaacs, Alan

The dictionary is derived from the Concise Science Dictionary, first published by Oxford University Press in 1984 (third edition, 1996). It consists of all the entries relating to physics in that dictionary, together with some of those entries relating to astronomy that are required for an understanding of astrophysics and many entries that relate to physical chemistry. It also contains a selection of the words used in mathematics that are relevant to physics, as well as the key words in metal science, computing, and electronics. For this third edition a number of words from quantum field physics and statistical mechanics have been added. Cosmology and particle physics have been updated and a number of general entries have been expanded.

Using genetic data to strengthen causal inference in observational research.

PubMed

Pingault, Jean-Baptiste; O'Reilly, Paul F; Schoeler, Tabea; Ploubidis, George B; Rijsdijk, Frühling; Dudbridge, Frank

2018-06-05

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

Academic computer science and gender: A naturalistic study investigating the causes of attrition

NASA Astrophysics Data System (ADS)

Declue, Timothy Hall

Far fewer women than men take computer science classes in high school, enroll in computer science programs in college, or complete advanced degrees in computer science. The computer science pipeline begins to shrink for women even before entering college, but it is at the college level that the "brain drain" is the most evident numerically, especially in the first class taken by most computer science majors called "Computer Science 1" or CS-I. The result, for both academia and industry, is a pronounced technological gender disparity in academic and industrial computer science. The study revealed the existence of several factors influencing success in CS-I. First, and most clearly, the effect of attribution processes seemed to be quite strong. These processes tend to work against success for females and in favor of success for males. Likewise, evidence was discovered which strengthens theories related to prior experience and the perception that computer science has a culture which is hostile to females. Two unanticipated themes related to the motivation and persistence of successful computer science majors. The findings did not support the belief that females have greater logistical problems in computer science than males, or that females tend to have a different programming style than males which adversely affects the females' ability to succeed in CS-I.

Manual vs. computer-assisted sperm analysis: can CASA replace manual assessment of human semen in clinical practice?

PubMed

Talarczyk-Desole, Joanna; Berger, Anna; Taszarek-Hauke, Grażyna; Hauke, Jan; Pawelczyk, Leszek; Jedrzejczak, Piotr

2017-01-01

The aim of the study was to check the quality of computer-assisted sperm analysis (CASA) system in comparison to the reference manual method as well as standardization of the computer-assisted semen assessment. The study was conducted between January and June 2015 at the Andrology Laboratory of the Division of Infertility and Reproductive Endocrinology, Poznań University of Medical Sciences, Poland. The study group consisted of 230 men who gave sperm samples for the first time in our center as part of an infertility investigation. The samples underwent manual and computer-assisted assessment of concentration, motility and morphology. A total of 184 samples were examined twice: manually, according to the 2010 WHO recommendations, and with CASA, using the program set-tings provided by the manufacturer. Additionally, 46 samples underwent two manual analyses and two computer-assisted analyses. The p-value of p < 0.05 was considered as statistically significant. Statistically significant differences were found between all of the investigated sperm parameters, except for non-progressive motility, measured with CASA and manually. In the group of patients where all analyses with each method were performed twice on the same sample we found no significant differences between both assessments of the same probe, neither in the samples analyzed manually nor with CASA, although standard deviation was higher in the CASA group. Our results suggest that computer-assisted sperm analysis requires further improvement for a wider application in clinical practice.

System Architecture Development for Energy and Water Infrastructure Data Management and Geovisual Analytics

NASA Astrophysics Data System (ADS)

Berres, A.; Karthik, R.; Nugent, P.; Sorokine, A.; Myers, A.; Pang, H.

2017-12-01

Building an integrated data infrastructure that can meet the needs of a sustainable energy-water resource management requires a robust data management and geovisual analytics platform, capable of cross-domain scientific discovery and knowledge generation. Such a platform can facilitate the investigation of diverse complex research and policy questions for emerging priorities in Energy-Water Nexus (EWN) science areas. Using advanced data analytics, machine learning techniques, multi-dimensional statistical tools, and interactive geovisualization components, such a multi-layered federated platform is being developed, the Energy-Water Nexus Knowledge Discovery Framework (EWN-KDF). This platform utilizes several enterprise-grade software design concepts and standards such as extensible service-oriented architecture, open standard protocols, event-driven programming model, enterprise service bus, and adaptive user interfaces to provide a strategic value to the integrative computational and data infrastructure. EWN-KDF is built on the Compute and Data Environment for Science (CADES) environment in Oak Ridge National Laboratory (ORNL).

Charting the Replica Symmetric Phase

NASA Astrophysics Data System (ADS)

Coja-Oghlan, Amin; Efthymiou, Charilaos; Jaafari, Nor; Kang, Mihyun; Kapetanopoulos, Tobias

2018-02-01

Diluted mean-field models are spin systems whose geometry of interactions is induced by a sparse random graph or hypergraph. Such models play an eminent role in the statistical mechanics of disordered systems as well as in combinatorics and computer science. In a path-breaking paper based on the non-rigorous `cavity method', physicists predicted not only the existence of a replica symmetry breaking phase transition in such models but also sketched a detailed picture of the evolution of the Gibbs measure within the replica symmetric phase and its impact on important problems in combinatorics, computer science and physics (Krzakala et al. in Proc Natl Acad Sci 104:10318-10323, 2007). In this paper we rigorise this picture completely for a broad class of models, encompassing the Potts antiferromagnet on the random graph, the k-XORSAT model and the diluted k-spin model for even k. We also prove a conjecture about the detection problem in the stochastic block model that has received considerable attention (Decelle et al. in Phys Rev E 84:066106, 2011).

Current Developments in Machine Learning Techniques in Biological Data Mining.

PubMed

Dumancas, Gerard G; Adrianto, Indra; Bello, Ghalib; Dozmorov, Mikhail

2017-01-01

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Exploring Human Cognition Using Large Image Databases.

PubMed

Griffiths, Thomas L; Abbott, Joshua T; Hsu, Anne S

2016-07-01

Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how some of the challenges of using natural images as stimuli in experiments can be addressed through increased sample sizes, using representations from computer vision, and developing new experimental methods. Finally, we illustrate these points by summarizing recent work using large image databases to explore questions about human cognition in four different domains: modeling subjective randomness, defining a quantitative measure of representativeness, identifying prior knowledge used in word learning, and determining the structure of natural categories. Copyright © 2016 Cognitive Science Society, Inc.

Computing Science and Statistics: Proceedings of the Symposium on Interface, 21-24 Apr 1991, Seattle, WA

DTIC Science & Technology

1991-01-01

Auvt’r discordaint lairs . p’ f~~~Ilit’ trtthailitY if ;i Ittili x’ ii IHit’ s,;tilct sIpac.’ is fujr s~,re ’’uristartt A1 𔃺 and 0t < 1, A ’tark...reconstruction algorithms, usually of the filtered back-projection type, do 99mTcIIMPAO Thallium-201 not correct for nonuniform photon attenuation and depth

Annual Progress Report for July 1, 1980 through June 30, 1981,

DTIC Science & Technology

1981-08-01

71 14.4 Directory of Computer-Readable Bibliographic Databases .......... 73 14.5 University of Illinois Online Search Service...34Aeasures of Human Performance in Fault Diagnosis Tasks," M.S.I.E. Thesis (July 1931). 13.35 ). R. Morehead, "Models of Human Behavior in Online Searching...1981 , to appear). 1 Journal Articles 14.7 A. E. Williams, Databases and Online Statistics for 1979," Bul. Amer. Soc. for Information Science 7(2

Spatio-Temporal Nonlinear Filtering With Applications to Information Assurance and Counter Terrorism

DTIC Science & Technology

2011-11-14

2009): 279. doi: 10.1016/j.comnet.2008.10.001 2011/11/07 14:36:13 44 Jelena Mirkovic, Peter Reiher, Christos Papadopoulos, Alefiya Hussain, Marla Shepard ...Applications to Remote Sensing,” Department of Statistics and Department of Computer Sciences, University of Chicago , September, 2011 (Invited). 2. A.G... Chicago , IL April 13, 2010, Edward H. Bosch Organizer. 58. Andrea Bertozzi, Invited talk, Plenary talk, Joint SIAM/RSME-SCM-SEMA Meeting on Emerging

Computer-Game Construction: A Gender-Neutral Attractor to Computing Science

ERIC Educational Resources Information Center

Carbonaro, Mike; Szafron, Duane; Cutumisu, Maria; Schaeffer, Jonathan

2010-01-01

Enrollment in Computing Science university programs is at a dangerously low level. A major reason for this is the general lack of interest in Computing Science by females. In this paper, we discuss our experience with using a computer game construction environment as a vehicle to encourage female participation in Computing Science. Experiments…

Statistical Inference of a RANS closure for a Jet-in-Crossflow simulation

NASA Astrophysics Data System (ADS)

Heyse, Jan; Edeling, Wouter; Iaccarino, Gianluca

2016-11-01

The jet-in-crossflow is found in several engineering applications, such as discrete film cooling for turbine blades, where a coolant injected through hols in the blade's surface protects the component from the hot gases leaving the combustion chamber. Experimental measurements using MRI techniques have been completed for a single hole injection into a turbulent crossflow, providing full 3D averaged velocity field. For such flows of engineering interest, Reynolds-Averaged Navier-Stokes (RANS) turbulence closure models are often the only viable computational option. However, RANS models are known to provide poor predictions in the region close to the injection point. Since these models are calibrated on simple canonical flow problems, the obtained closure coefficient estimates are unlikely to extrapolate well to more complex flows. We will therefore calibrate the parameters of a RANS model using statistical inference techniques informed by the experimental jet-in-crossflow data. The obtained probabilistic parameter estimates can in turn be used to compute flow fields with quantified uncertainty. Stanford Graduate Fellowship in Science and Engineering.

OceanXtremes: Scalable Anomaly Detection in Oceanographic Time-Series

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Armstrong, E. M.; Chin, T. M.; Gill, K. M.; Greguska, F. R., III; Huang, T.; Jacob, J. C.; Quach, N.

2016-12-01

The oceanographic community must meet the challenge to rapidly identify features and anomalies in complex and voluminous observations to further science and improve decision support. Given this data-intensive reality, we are developing an anomaly detection system, called OceanXtremes, powered by an intelligent, elastic Cloud-based analytic service backend that enables execution of domain-specific, multi-scale anomaly and feature detection algorithms across the entire archive of 15 to 30-year ocean science datasets.Our parallel analytics engine is extending the NEXUS system and exploits multiple open-source technologies: Apache Cassandra as a distributed spatial "tile" cache, Apache Spark for in-memory parallel computation, and Apache Solr for spatial search and storing pre-computed tile statistics and other metadata. OceanXtremes provides these key capabilities: Parallel generation (Spark on a compute cluster) of 15 to 30-year Ocean Climatologies (e.g. sea surface temperature or SST) in hours or overnight, using simple pixel averages or customizable Gaussian-weighted "smoothing" over latitude, longitude, and time; Parallel pre-computation, tiling, and caching of anomaly fields (daily variables minus a chosen climatology) with pre-computed tile statistics; Parallel detection (over the time-series of tiles) of anomalies or phenomena by regional area-averages exceeding a specified threshold (e.g. high SST in El Nino or SST "blob" regions), or more complex, custom data mining algorithms; Shared discovery and exploration of ocean phenomena and anomalies (facet search using Solr), along with unexpected correlations between key measured variables; Scalable execution for all capabilities on a hybrid Cloud, using our on-premise OpenStack Cloud cluster or at Amazon. The key idea is that the parallel data-mining operations will be run "near" the ocean data archives (a local "network" hop) so that we can efficiently access the thousands of files making up a three decade time-series. The presentation will cover the architecture of OceanXtremes, parallelization of the climatology computation and anomaly detection algorithms using Spark, example results for SST and other time-series, and parallel performance metrics.

PREFACE: New trends in Computer Simulations in Physics and not only in physics

NASA Astrophysics Data System (ADS)

Shchur, Lev N.; Krashakov, Serge A.

2016-02-01

In this volume we have collected papers based on the presentations given at the International Conference on Computer Simulations in Physics and beyond (CSP2015), held in Moscow, September 6-10, 2015. We hope that this volume will be helpful and scientifically interesting for readers. The Conference was organized for the first time with the common efforts of the Moscow Institute for Electronics and Mathematics (MIEM) of the National Research University Higher School of Economics, the Landau Institute for Theoretical Physics, and the Science Center in Chernogolovka. The name of the Conference emphasizes the multidisciplinary nature of computational physics. Its methods are applied to the broad range of current research in science and society. The choice of venue was motivated by the multidisciplinary character of the MIEM. It is a former independent university, which has recently become the part of the National Research University Higher School of Economics. The Conference Computer Simulations in Physics and beyond (CSP) is planned to be organized biannually. This year's Conference featured 99 presentations, including 21 plenary and invited talks ranging from the analysis of Irish myths with recent methods of statistical physics, to computing with novel quantum computers D-Wave and D-Wave2. This volume covers various areas of computational physics and emerging subjects within the computational physics community. Each section was preceded by invited talks presenting the latest algorithms and methods in computational physics, as well as new scientific results. Both parallel and poster sessions paid special attention to numerical methods, applications and results. For all the abstracts presented at the conference please follow the link http://csp2015.ac.ru/files/book5x.pdf

Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies.

PubMed

Boulesteix, Anne-Laure; Wilson, Rory; Hapfelmeier, Alexander

2017-09-09

The goal of medical research is to develop interventions that are in some sense superior, with respect to patient outcome, to interventions currently in use. Similarly, the goal of research in methodological computational statistics is to develop data analysis tools that are themselves superior to the existing tools. The methodology of the evaluation of medical interventions continues to be discussed extensively in the literature and it is now well accepted that medicine should be at least partly "evidence-based". Although we statisticians are convinced of the importance of unbiased, well-thought-out study designs and evidence-based approaches in the context of clinical research, we tend to ignore these principles when designing our own studies for evaluating statistical methods in the context of our methodological research. In this paper, we draw an analogy between clinical trials and real-data-based benchmarking experiments in methodological statistical science, with datasets playing the role of patients and methods playing the role of medical interventions. Through this analogy, we suggest directions for improvement in the design and interpretation of studies which use real data to evaluate statistical methods, in particular with respect to dataset inclusion criteria and the reduction of various forms of bias. More generally, we discuss the concept of "evidence-based" statistical research, its limitations and its impact on the design and interpretation of real-data-based benchmark experiments. We suggest that benchmark studies-a method of assessment of statistical methods using real-world datasets-might benefit from adopting (some) concepts from evidence-based medicine towards the goal of more evidence-based statistical research.

Central Computer Science Concepts to Research-Based Teacher Training in Computer Science: An Experimental Study

ERIC Educational Resources Information Center

Zendler, Andreas; Klaudt, Dieter

2012-01-01

The significance of computer science for economics and society is undisputed. In particular, computer science is acknowledged to play a key role in schools (e.g., by opening multiple career paths). The provision of effective computer science education in schools is dependent on teachers who are able to properly represent the discipline and whose…

VERCE: a productive e-Infrastructure and e-Science environment for data-intensive seismology research

NASA Astrophysics Data System (ADS)

Vilotte, Jean-Pierre; Atkinson, Malcolm; Carpené, Michele; Casarotti, Emanuele; Frank, Anton; Igel, Heiner; Rietbrock, Andreas; Schwichtenberg, Horst; Spinuso, Alessandro

2016-04-01

Seismology pioneers global and open-data access -- with internationally approved data, metadata and exchange standards facilitated worldwide by the Federation of Digital Seismic Networks (FDSN) and in Europe the European Integrated Data Archives (EIDA). The growing wealth of data generated by dense observation and monitoring systems and recent advances in seismic wave simulation capabilities induces a change in paradigm. Data-intensive seismology research requires a new holistic approach combining scalable high-performance wave simulation codes and statistical data analysis methods, and integrating distributed data and computing resources. The European E-Infrastructure project "Virtual Earthquake and seismology Research Community e-science environment in Europe" (VERCE) pioneers the federation of autonomous organisations providing data and computing resources, together with a comprehensive, integrated and operational virtual research environment (VRE) and E-infrastructure devoted to the full path of data use in a research-driven context. VERCE delivers to a broad base of seismology researchers in Europe easily used high-performance full waveform simulations and misfit calculations, together with a data-intensive framework for the collaborative development of innovative statistical data analysis methods, all of which were previously only accessible to a small number of well-resourced groups. It balances flexibility with new integrated capabilities to provide a fluent path from research innovation to production. As such, VERCE is a major contribution to the implementation phase of the ``European Plate Observatory System'' (EPOS), the ESFRI initiative of the solid-Earth community. The VRE meets a range of seismic research needs by eliminating chores and technical difficulties to allow users to focus on their research questions. It empowers researchers to harvest the new opportunities provided by well-established and mature high-performance wave simulation codes of the community. It enables active researchers to invent and refine scalable methods for innovative statistical analysis of seismic waveforms in a wide range of application contexts. The VRE paves the way towards a flexible shared framework for seismic waveform inversion, lowering the barriers to uptake for the next generation of researchers. The VRE can be accessed through the science gateway that puts together computational and data-intensive research into the same framework, integrating multiple data sources and services. It provides a context for task-oriented and data-streaming workflows, and maps user actions to the full gamut of the federated platform resources and procurement policies, activating the necessary behind-the-scene automation and transformation. The platform manages and produces domain metadata, coupling them with the provenance information describing the relationships and the dependencies, which characterise the whole workflow process. This dynamic knowledge base, can be explored for validation purposes via a graphical interface and a web API. Moreover, it fosters the assisted selection and re-use of the data within each phase of the scientific analysis. These phases can be identified as Simulation, Data Access, Preprocessing, Misfit and data processing, and are presented to the users of the gateway as dedicated and interactive workspaces. By enabling researchers to share results and provenance information, VERCE steers open-science behaviour, allowing researchers to discover and build on prior work and thereby to progress faster. A key asset is the agile strategy that VERCE deployed in a multi-organisational context, engaging seismologists, data scientists, ICT researchers, HPC and data resource providers, system administrators into short-lived tasks each with a goal that is a seismology priority, and intimately coupling research thinking with technical innovation. This changes the focus from HPC production environments and community data services to user-focused scenario, avoiding wasteful bouts of technology centricity where technologists collect requirements and develop a system that is not used because the ideas of the planned users have moved on. As such the technologies and concepts developed in VERCE are relevant to many other disciplines in computational and data driven Earth Sciences and can provide the key technologies for a European wide computational and data intensive framework in Earth Sciences.

Young adolescents' engagement in dietary behaviour - the impact of gender, socio-economic status, self-efficacy and scientific literacy. Methodological aspects of constructing measures in nutrition literacy research using the Rasch model.

PubMed

Guttersrud, Øystein; Petterson, Kjell Sverre

2015-10-01

The present study validates a revised scale measuring individuals' level of the 'engagement in dietary behaviour' aspect of 'critical nutrition literacy' and describes how background factors affect this aspect of Norwegian tenth-grade students' nutrition literacy. Data were gathered electronically during a field trial of a standardised sample test in science. Test items and questionnaire constructs were distributed evenly across four electronic field-test booklets. Data management and analysis were performed using the RUMM2030 item analysis package and the IBM SPSS Statistics 20 statistical software package. Students responded on computers at school. Seven hundred and forty tenth-grade students at twenty-seven randomly sampled public schools were enrolled in the field-test study. The engagement in dietary behaviour scale and the self-efficacy in science scale were distributed to 178 of these students. The dietary behaviour scale and the self-efficacy in science scale came out as valid, reliable and well-targeted instruments usable for the construction of measurements. Girls and students with high self-efficacy reported higher engagement in dietary behaviour than other students. Socio-economic status and scientific literacy - measured as ability in science by applying an achievement test - did not correlate significantly different from zero with students' engagement in dietary behaviour.

Using spatial principles to optimize distributed computing for enabling the physical science discoveries

PubMed Central

Yang, Chaowei; Wu, Huayi; Huang, Qunying; Li, Zhenlong; Li, Jing

2011-01-01

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century. PMID:21444779

Using spatial principles to optimize distributed computing for enabling the physical science discoveries.

PubMed

Yang, Chaowei; Wu, Huayi; Huang, Qunying; Li, Zhenlong; Li, Jing

2011-04-05

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.

A Financial Technology Entrepreneurship Program for Computer Science Students

ERIC Educational Resources Information Center

Lawler, James P.; Joseph, Anthony

2011-01-01

Education in entrepreneurship is becoming a critical area of curricula for computer science students. Few schools of computer science have a concentration in entrepreneurship in the computing curricula. The paper presents Technology Entrepreneurship in the curricula at a leading school of computer science and information systems, in which students…

Computer Science Teacher Professional Development in the United States: A Review of Studies Published between 2004 and 2014

ERIC Educational Resources Information Center

Menekse, Muhsin

2015-01-01

While there has been a remarkable interest to make computer science a core K-12 academic subject in the United States, there is a shortage of K-12 computer science teachers to successfully implement computer sciences courses in schools. In order to enhance computer science teacher capacity, training programs have been offered through teacher…

Computer-Assisted Instruction in Statistics. Technical Report.

ERIC Educational Resources Information Center

Cooley, William W.

A paper given at a conference on statistical computation discussed teaching statistics with computers. It concluded that computer-assisted instruction is most appropriately employed in the numerical demonstration of statistical concepts, and for statistical laboratory instruction. The student thus learns simultaneously about the use of computers…

ALISE Library and Information Science Education Statistical Report, 1999.

ERIC Educational Resources Information Center

Daniel, Evelyn H., Ed.; Saye, Jerry D., Ed.

This volume is the twentieth annual statistical report on library and information science (LIS) education published by the Association for Library and Information Science Education (ALISE). Its purpose is to compile, analyze, interpret, and report statistical (and other descriptive) information about library/information science programs offered by…

The 6th International Conference on Computer Science and Computational Mathematics (ICCSCM 2017)

NASA Astrophysics Data System (ADS)

2017-09-01

The ICCSCM 2017 (The 6th International Conference on Computer Science and Computational Mathematics) has aimed to provide a platform to discuss computer science and mathematics related issues including Algebraic Geometry, Algebraic Topology, Approximation Theory, Calculus of Variations, Category Theory; Homological Algebra, Coding Theory, Combinatorics, Control Theory, Cryptology, Geometry, Difference and Functional Equations, Discrete Mathematics, Dynamical Systems and Ergodic Theory, Field Theory and Polynomials, Fluid Mechanics and Solid Mechanics, Fourier Analysis, Functional Analysis, Functions of a Complex Variable, Fuzzy Mathematics, Game Theory, General Algebraic Systems, Graph Theory, Group Theory and Generalizations, Image Processing, Signal Processing and Tomography, Information Fusion, Integral Equations, Lattices, Algebraic Structures, Linear and Multilinear Algebra; Matrix Theory, Mathematical Biology and Other Natural Sciences, Mathematical Economics and Financial Mathematics, Mathematical Physics, Measure Theory and Integration, Neutrosophic Mathematics, Number Theory, Numerical Analysis, Operations Research, Optimization, Operator Theory, Ordinary and Partial Differential Equations, Potential Theory, Real Functions, Rings and Algebras, Statistical Mechanics, Structure Of Matter, Topological Groups, Wavelets and Wavelet Transforms, 3G/4G Network Evolutions, Ad-Hoc, Mobile, Wireless Networks and Mobile Computing, Agent Computing & Multi-Agents Systems, All topics related Image/Signal Processing, Any topics related Computer Networks, Any topics related ISO SC-27 and SC- 17 standards, Any topics related PKI(Public Key Intrastructures), Artifial Intelligences(A.I.) & Pattern/Image Recognitions, Authentication/Authorization Issues, Biometric authentication and algorithms, CDMA/GSM Communication Protocols, Combinatorics, Graph Theory, and Analysis of Algorithms, Cryptography and Foundation of Computer Security, Data Base(D.B.) Management & Information Retrievals, Data Mining, Web Image Mining, & Applications, Defining Spectrum Rights and Open Spectrum Solutions, E-Comerce, Ubiquitous, RFID, Applications, Fingerprint/Hand/Biometrics Recognitions and Technologies, Foundations of High-performance Computing, IC-card Security, OTP, and Key Management Issues, IDS/Firewall, Anti-Spam mail, Anti-virus issues, Mobile Computing for E-Commerce, Network Security Applications, Neural Networks and Biomedical Simulations, Quality of Services and Communication Protocols, Quantum Computing, Coding, and Error Controls, Satellite and Optical Communication Systems, Theory of Parallel Processing and Distributed Computing, Virtual Visions, 3-D Object Retrievals, & Virtual Simulations, Wireless Access Security, etc. The success of ICCSCM 2017 is reflected in the received papers from authors around the world from several countries which allows a highly multinational and multicultural idea and experience exchange. The accepted papers of ICCSCM 2017 are published in this Book. Please check http://www.iccscm.com for further news. A conference such as ICCSCM 2017 can only become successful using a team effort, so herewith we want to thank the International Technical Committee and the Reviewers for their efforts in the review process as well as their valuable advices. We are thankful to all those who contributed to the success of ICCSCM 2017. The Secretary

Computer Science | Classification | College of Engineering & Applied

Science.gov Websites

EMS 1011 profile photo Adrian Dumitrescu, Ph.D.ProfessorComputer Science(414) 229-4265Eng & Math @uwm.eduEng & Math Sciences 919 profile photo Hossein Hosseini, Ph.D.ProfessorComputer Science(414) 229 -5184hosseini@uwm.eduEng & Math Sciences 1091 profile photo Amol Mali, Ph.D.Associate ProfessorComputer

Computers in Science Education: Can They Go Far Enough? Have We Gone Too Far?

ERIC Educational Resources Information Center

Schrock, John Richard

1984-01-01

Indicates that although computers may churn out creative research, science is still dependent on science education, and that science education consists of increasing human experience. Also considers uses and misuses of computers in the science classroom, examining Edgar Dale's "cone of experience" related to laboratory computer and "extended…

Computer and Software Use in Teaching the Beginning Statistics Course.

ERIC Educational Resources Information Center

Bartz, Albert E.; Sabolik, Marisa A.

2001-01-01

Surveys the extent of computer usage in the beginning statistics course and the variety of statistics software used. Finds that 69% of the psychology departments used computers in beginning statistics courses and 90% used computer-assisted data analysis in statistics or other courses. (CMK)

Crossing over...Markov meets Mendel.

PubMed

Mneimneh, Saad

2012-01-01

Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the mathematically more elaborate parts. I kept those as separate sections in the exposition.

Crossing Over…Markov Meets Mendel

PubMed Central

Mneimneh, Saad

2012-01-01

Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage—one of the first efforts towards a computational approach to biology—relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the mathematically more elaborate parts. I kept those as separate sections in the exposition. PMID:22629235

Short-term Temperature Prediction Using Adaptive Computing on Dynamic Scales

NASA Astrophysics Data System (ADS)

Hu, W.; Cervone, G.; Jha, S.; Balasubramanian, V.; Turilli, M.

2017-12-01

When predicting temperature, there are specific places and times when high accuracy predictions are harder. For example, not all the sub-regions in the domain require the same amount of computing resources to generate an accurate prediction. Plateau areas might require less computing resources than mountainous areas because of the steeper gradient of temperature change in the latter. However, it is difficult to estimate beforehand the optimal allocation of computational resources because several parameters play a role in determining the accuracy of the forecasts, in addition to orography. The allocation of resources to perform simulations can become a bottleneck because it requires human intervention to stop jobs or start new ones. The goal of this project is to design and develop a dynamic approach to generate short-term temperature predictions that can automatically determines the required computing resources and the geographic scales of the predictions based on the spatial and temporal uncertainties. The predictions and the prediction quality metrics are computed using a numeric weather prediction model, Analog Ensemble (AnEn), and the parallelization on high performance computing systems is accomplished using Ensemble Toolkit, one component of the RADICAL-Cybertools family of tools. RADICAL-Cybertools decouple the science needs from the computational capabilities by building an intermediate layer to run general ensemble patterns, regardless of the science. In this research, we show how the ensemble toolkit allows generating high resolution temperature forecasts at different spatial and temporal resolution. The AnEn algorithm is run using NAM analysis and forecasts data for the continental United States for a period of 2 years. AnEn results show that temperature forecasts perform well according to different probabilistic and deterministic statistical tests.

Federated data storage system prototype for LHC experiments and data intensive science

NASA Astrophysics Data System (ADS)

Kiryanov, A.; Klimentov, A.; Krasnopevtsev, D.; Ryabinkin, E.; Zarochentsev, A.

2017-10-01

Rapid increase of data volume from the experiments running at the Large Hadron Collider (LHC) prompted physics computing community to evaluate new data handling and processing solutions. Russian grid sites and universities’ clusters scattered over a large area aim at the task of uniting their resources for future productive work, at the same time giving an opportunity to support large physics collaborations. In our project we address the fundamental problem of designing a computing architecture to integrate distributed storage resources for LHC experiments and other data-intensive science applications and to provide access to data from heterogeneous computing facilities. Studies include development and implementation of federated data storage prototype for Worldwide LHC Computing Grid (WLCG) centres of different levels and University clusters within one National Cloud. The prototype is based on computing resources located in Moscow, Dubna, Saint Petersburg, Gatchina and Geneva. This project intends to implement a federated distributed storage for all kind of operations such as read/write/transfer and access via WAN from Grid centres, university clusters, supercomputers, academic and commercial clouds. The efficiency and performance of the system are demonstrated using synthetic and experiment-specific tests including real data processing and analysis workflows from ATLAS and ALICE experiments, as well as compute-intensive bioinformatics applications (PALEOMIX) running on supercomputers. We present topology and architecture of the designed system, report performance and statistics for different access patterns and show how federated data storage can be used efficiently by physicists and biologists. We also describe how sharing data on a widely distributed storage system can lead to a new computing model and reformations of computing style, for instance how bioinformatics program running on supercomputers can read/write data from the federated storage.

The Complexity of Primary Care Psychology: Theoretical Foundations.

PubMed

Smit, E H; Derksen, J J L

2015-07-01

How does primary care psychology deal with organized complexity? Has it escaped Newtonian science? Has it, as Weaver (1991) suggests, found a way to 'manage problems with many interrelated factors that cannot be dealt by statistical techniques'? Computer simulations and mathematical models in psychology are ongoing positive developments in the study of complex systems. However, the theoretical development of complex systems in psychology lags behind these advances. In this article we use complexity science to develop a theory on experienced complexity in the daily practice of primary care psychologists. We briefly answer the ontological question of what we see (from the perspective of primary care psychology) as reality, the epistemological question of what we can know, the methodological question of how to act, and the ethical question of what is good care. Following our empirical study, we conclude that complexity science can describe the experienced complexity of the psychologist and offer room for personalized client-centered care. Complexity science is slowly filling the gap between the dominant reductionist theory and complex daily practice.

Building the biomedical data science workforce.

PubMed

Dunn, Michelle C; Bourne, Philip E

2017-07-01

This article describes efforts at the National Institutes of Health (NIH) from 2013 to 2016 to train a national workforce in biomedical data science. We provide an analysis of the Big Data to Knowledge (BD2K) training program strengths and weaknesses with an eye toward future directions aimed at any funder and potential funding recipient worldwide. The focus is on extramurally funded programs that have a national or international impact rather than the training of NIH staff, which was addressed by the NIH's internal Data Science Workforce Development Center. From its inception, the major goal of BD2K was to narrow the gap between needed and existing biomedical data science skills. As biomedical research increasingly relies on computational, mathematical, and statistical thinking, supporting the training and education of the workforce of tomorrow requires new emphases on analytical skills. From 2013 to 2016, BD2K jump-started training in this area for all levels, from graduate students to senior researchers.

Building the biomedical data science workforce

PubMed Central

Dunn, Michelle C.; Bourne, Philip E.

2017-01-01

This article describes efforts at the National Institutes of Health (NIH) from 2013 to 2016 to train a national workforce in biomedical data science. We provide an analysis of the Big Data to Knowledge (BD2K) training program strengths and weaknesses with an eye toward future directions aimed at any funder and potential funding recipient worldwide. The focus is on extramurally funded programs that have a national or international impact rather than the training of NIH staff, which was addressed by the NIH’s internal Data Science Workforce Development Center. From its inception, the major goal of BD2K was to narrow the gap between needed and existing biomedical data science skills. As biomedical research increasingly relies on computational, mathematical, and statistical thinking, supporting the training and education of the workforce of tomorrow requires new emphases on analytical skills. From 2013 to 2016, BD2K jump-started training in this area for all levels, from graduate students to senior researchers. PMID:28715407

Proceedings of the Symposium on the Interface of Computer Science and Statistics (17th) Held in Lexington, Kentucky on 17-19 March 1985.

DTIC Science & Technology

1986-03-04

satisfied, but the availability of the machinery will entice developments to appear over the next few years. TABULATION AND DISPLAY To gain access to...independent, identicallyof the optimal predictor and the mean square distributed random variables with mean 0 and difference between the optimal forecast... optimal forecast (the conditional mean of YT+, given qute approximation to a2 1 j For the% Yl ~ ~ ~ ~ ~ ~ ~ . ... ,FT) thehtm-[ ]# rn-1 Y1...;,YT) and

Computer Science and Statistics. Proceedings of the Symposium on the Interface (18th) Held on March 19-21, 1986 in Fort Collins, Colorado.

DTIC Science & Technology

1987-08-26

example, expert systems research would benefit examples are the Acute Renal Failure [15] system, the if it could attract statisticians to assist in...research projects including the Acute Renal Failure [15] system, the 6. EXPLAINING COMPLEX REASONING INTERNIST-] [22] system for diagnosis within the...the MEDAS and Acute Renal Failure systems. task at any point in reasoning about a case is constrained to Entropy-discriminate makes use of a measure

An interdisciplinary school project using a Nintendo Wii controller for measuring car speed

NASA Astrophysics Data System (ADS)

Hansen, Nils Kristian; Mitchell, James Robert

2013-03-01

This work examines the feasibility of employing a Nintendo Wii game controller for measuring car speed in an interdisciplinary school project. It discusses the physical characteristics of the controller and of vehicle headlights. It suggests how an experiment may be linked to topics in mathematics, statistics, physics and computer science. An algorithm for calculating speed from repeated recordings of car headlights is provided. Finally the results of repeated experiments with an approaching car are provided.

Modeling of Pedestrian Flows Using Hybrid Models of Euler Equations and Dynamical Systems

NASA Astrophysics Data System (ADS)

Bärwolff, Günter; Slawig, Thomas; Schwandt, Hartmut

2007-09-01

In the last years various systems have been developed for controlling, planning and predicting the traffic of persons and vehicles, in particular under security aspects. Going beyond pure counting and statistical models, approaches were found to be very adequate and accurate which are based on well-known concepts originally developed in very different research areas, namely continuum mechanics and computer science. In the present paper, we outline a continuum mechanical approach for the description of pedestrain flow.

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science

NASA Technical Reports Server (NTRS)

1987-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period April, 1986 through September 30, 1986 is summarized.

78 FR 10180 - Annual Computational Science Symposium; Conference

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-13

...] Annual Computational Science Symposium; Conference AGENCY: Food and Drug Administration, HHS. ACTION... Computational Science Symposium.'' The purpose of the conference is to help the broader community align and share experiences to advance computational science. At the conference, which will bring together FDA...

The Effect of Problem-Solving Instruction on the Programming Self-efficacy and Achievement of Introductory Computer Science Students

NASA Astrophysics Data System (ADS)

Maddrey, Elizabeth

Research in academia and industry continues to identify a decline in enrollment in computer science. One major component of this decline in enrollment is a shortage of female students. The primary reasons for the gender gap presented in the research include lack of computer experience prior to their first year in college, misconceptions about the field, negative cultural stereotypes, lack of female mentors and role models, subtle discriminations in the classroom, and lack of self-confidence (Pollock, McCoy, Carberry, Hundigopal, & You, 2004). Male students are also leaving the field due to misconceptions about the field, negative cultural stereotypes, and a lack of self-confidence. Analysis of first year attrition revealed that one of the major challenges faced by students of both genders is a lack of problem-solving skills (Beaubouef, Lucas & Howatt, 2001; Olsen, 2005; Paxton & Mumey, 2001). The purpose of this study was to investigate whether specific, non-mathematical problem-solving instruction as part of introductory programming courses significantly increased computer programming self-efficacy and achievement of students. The results of this study showed that students in the experimental group had significantly higher achievement than students in the control group. While this shows statistical significance, due to the effect size and disordinal nature of the data between groups, care has to be taken in its interpretation. The study did not show significantly higher programming self-efficacy among the experimental students. There was not enough data collected to statistically analyze the effect of the treatment on self-efficacy and achievement by gender. However, differences in means were observed between the gender groups, with females in the experimental group demonstrating a higher than average degree of self-efficacy when compared with males in the experimental group and both genders in the control group. These results suggest that the treatment from this study may provide a gender-based increase in self-efficacy and future research should focus on exploring this possibility.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hules, John

This 1998 annual report from the National Scientific Energy Research Computing Center (NERSC) presents the year in review of the following categories: Computational Science; Computer Science and Applied Mathematics; and Systems and Services. Also presented are science highlights in the following categories: Basic Energy Sciences; Biological and Environmental Research; Fusion Energy Sciences; High Energy and Nuclear Physics; and Advanced Scientific Computing Research and Other Projects.

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.

PubMed

Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M; Norman, Kenneth A

2017-01-05

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway-the pathway connecting entorhinal cortex directly to region CA1-was able to support statistical learning, while the trisynaptic pathway-connecting entorhinal cortex to CA1 through dentate gyrus and CA3-learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Enduring Influence of Stereotypical Computer Science Role Models on Women's Academic Aspirations

ERIC Educational Resources Information Center

Cheryan, Sapna; Drury, Benjamin J.; Vichayapai, Marissa

2013-01-01

The current work examines whether a brief exposure to a computer science role model who fits stereotypes of computer scientists has a lasting influence on women's interest in the field. One-hundred undergraduate women who were not computer science majors met a female or male peer role model who embodied computer science stereotypes in appearance…

A Web of Resources for Introductory Computer Science.

ERIC Educational Resources Information Center

Rebelsky, Samuel A.

As the field of Computer Science has grown, the syllabus of the introductory Computer Science course has changed significantly. No longer is it a simple introduction to programming or a tutorial on computer concepts and applications. Rather, it has become a survey of the field of Computer Science, touching on a wide variety of topics from digital…

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science

NASA Technical Reports Server (NTRS)

1988-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period April l, 1988 through September 30, 1988.

Summary of research in applied mathematics, numerical analysis and computer science at the Institute for Computer Applications in Science and Engineering

NASA Technical Reports Server (NTRS)

1984-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science during the period October 1, 1983 through March 31, 1984 is summarized.

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science

NASA Technical Reports Server (NTRS)

1987-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period October 1, 1986 through March 31, 1987 is summarized.

Spectral analysis of time series of categorical variables in earth sciences

NASA Astrophysics Data System (ADS)

Pardo-Igúzquiza, Eulogio; Rodríguez-Tovar, Francisco J.; Dorador, Javier

2016-10-01

Time series of categorical variables often appear in Earth Science disciplines and there is considerable interest in studying their cyclic behavior. This is true, for example, when the type of facies, petrofabric features, ichnofabrics, fossil assemblages or mineral compositions are measured continuously over a core or throughout a stratigraphic succession. Here we deal with the problem of applying spectral analysis to such sequences. A full indicator approach is proposed to complement the spectral envelope often used in other disciplines. Additionally, a stand-alone computer program is provided for calculating the spectral envelope, in this case implementing the permutation test to assess the statistical significance of the spectral peaks. We studied simulated sequences as well as real data in order to illustrate the methodology.

High school computer science education paves the way for higher education: the Israeli case

NASA Astrophysics Data System (ADS)

Armoni, Michal; Gal-Ezer, Judith

2014-07-01

The gap between enrollments in higher education computing programs and the high-tech industry's demands is widely reported, and is especially prominent for women. Increasing the availability of computer science education in high school is one of the strategies suggested in order to address this gap. We look at the connection between exposure to computer science in high school and pursuing computing in higher education. We also examine the gender gap, in the context of high school computer science education. We show that in Israel, students who took the high-level computer science matriculation exam were more likely to pursue computing in higher education. Regarding the issue of gender, we will show that, in general, in Israel the difference between males and females who take computer science in high school is relatively small, and a larger, though still not very large difference exists only for the highest exam level. In addition, exposing females to high-level computer science in high school has more relative impact on pursuing higher education in computing.

International Conference on Intelligent Systems for Molecular Biology (ISMB)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldberg, Debra; Hibbs, Matthew; Kall, Lukas

The Intelligent Systems for Molecular Biology (ISMB) conference has provided a general forum for disseminating the latest developments in bioinformatics on an annual basis for the past 13 years. ISMB is a multidisciplinary conference that brings together scientists from computer science, molecular biology, mathematics and statistics. The goal of the ISMB meeting is to bring together biologists and computational scientists in a focus on actual biological problems, i.e., not simply theoretical calculations. The combined focus on "intelligent systems" and actual biological data makes ISMB a unique and highly important meeting, and 13 years of experience in holding the conference hasmore » resulted in a consistently well organized, well attended, and highly respected annual conference. The ISMB 2005 meeting was held June 25-29, 2005 at the Renaissance Center in Detroit, Michigan. The meeting attracted over 1,730 attendees. The science presented was exceptional, and in the course of the five-day meeting, 56 scientific papers, 710 posters, 47 Oral Abstracts, 76 Software demonstrations, and 14 tutorials were presented. The attendees represented a broad spectrum of backgrounds with 7% from commercial companies, over 28% qualifying for student registration, and 41 countries were represented at the conference, emphasizing its important international aspect. The ISMB conference is especially important because the cultures of computer science and biology are so disparate. ISMB, as a full-scale technical conference with refereed proceedings that have been indexed by both MEDLINE and Current Contents since 1996, bridges this cultural gap.« less

Defining Computational Thinking for Mathematics and Science Classrooms

NASA Astrophysics Data System (ADS)

Weintrop, David; Beheshti, Elham; Horn, Michael; Orton, Kai; Jona, Kemi; Trouille, Laura; Wilensky, Uri

2016-02-01

Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include "computational thinking" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.

Utah Virtual Lab: JAVA interactivity for teaching science and statistics on line.

PubMed

Malloy, T E; Jensen, G C

2001-05-01

The Utah on-line Virtual Lab is a JAVA program run dynamically off a database. It is embedded in StatCenter (www.psych.utah.edu/learn/statsampler.html), an on-line collection of tools and text for teaching and learning statistics. Instructors author a statistical virtual reality that simulates theories and data in a specific research focus area by defining independent, predictor, and dependent variables and the relations among them. Students work in an on-line virtual environment to discover the principles of this simulated reality: They go to a library, read theoretical overviews and scientific puzzles, and then go to a lab, design a study, collect and analyze data, and write a report. Each student's design and data analysis decisions are computer-graded and recorded in a database; the written research report can be read by the instructor or by other students in peer groups simulating scientific conventions.

GENESIS SciFlo: Choreographing Interoperable Web Services on the Grid using a Semantically-Enabled Dataflow Execution Environment

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Xing, Z.

2007-12-01

The General Earth Science Investigation Suite (GENESIS) project is a NASA-sponsored partnership between the Jet Propulsion Laboratory, academia, and NASA data centers to develop a new suite of Web Services tools to facilitate multi-sensor investigations in Earth System Science. The goal of GENESIS is to enable large-scale, multi-instrument atmospheric science using combined datasets from the AIRS, MODIS, MISR, and GPS sensors. Investigations include cross-comparison of spaceborne climate sensors, cloud spectral analysis, study of upper troposphere-stratosphere water transport, study of the aerosol indirect cloud effect, and global climate model validation. The challenges are to bring together very large datasets, reformat and understand the individual instrument retrievals, co-register or re-grid the retrieved physical parameters, perform computationally-intensive data fusion and data mining operations, and accumulate complex statistics over months to years of data. To meet these challenges, we have developed a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data access, subsetting, registration, mining, fusion, compression, and advanced statistical analysis. SciFlo leverages remote Web Services, called via Simple Object Access Protocol (SOAP) or REST (one-line) URLs, and the Grid Computing standards (WS-* & Globus Alliance toolkits), and enables scientists to do multi- instrument Earth Science by assembling reusable Web Services and native executables into a distributed computing flow (tree of operators). The SciFlo client & server engines optimize the execution of such distributed data flows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. In particular, SciFlo exploits the wealth of datasets accessible by OpenGIS Consortium (OGC) Web Mapping Servers & Web Coverage Servers (WMS/WCS), and by Open Data Access Protocol (OpenDAP) servers. SciFlo also publishes its own SOAP services for space/time query and subsetting of Earth Science datasets, and automated access to large datasets via lists of (FTP, HTTP, or DAP) URLs which point to on-line HDF or netCDF files. Typical distributed workflows obtain datasets by calling standard WMS/WCS servers or discovering and fetching data granules from ftp sites; invoke remote analysis operators available as SOAP services (interface described by a WSDL document); and merge results into binary containers (netCDF or HDF files) for further analysis using local executable operators. Naming conventions (HDFEOS and CF-1.0 for netCDF) are exploited to automatically understand and read on-line datasets. More interoperable conventions, and broader adoption of existing converntions, are vital if we are to "scale up" automated choreography of Web Services beyond toy applications. Recently, the ESIP Federation sponsored a collaborative activity in which several ESIP members developed some collaborative science scenarios for atmospheric and aerosol science, and then choreographed services from multiple groups into demonstration workflows using the SciFlo engine and a Business Process Execution Language (BPEL) workflow engine. We will discuss the lessons learned from this activity, the need for standardized interfaces (like WMS/WCS), the difficulty in agreeing on even simple XML formats and interfaces, the benefits of doing collaborative science analysis at the "touch of a button" once services are connected, and further collaborations that are being pursued.

Nicholas Brunhart-Lupo | NREL

Science.gov Websites

. Education Ph.D., Computer Science, Colorado School of Mines M.S., Computer Science, University of Queensland B.S., Computer Science, Colorado School of Mines Brunhart-Lupo Nicholas Brunhart-Lupo Computational Science Nicholas.Brunhart-Lupo@nrel.gov

The Need for Computer Science

ERIC Educational Resources Information Center

Margolis, Jane; Goode, Joanna; Bernier, David

2011-01-01

Broadening computer science learning to include more students is a crucial item on the United States' education agenda, these authors say. Although policymakers advocate more computer science expertise, computer science offerings in high schools are few--and actually shrinking. In addition, poorly resourced schools with a high percentage of…

Summary of research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science

NASA Technical Reports Server (NTRS)

1989-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period October 1, 1988 through March 31, 1989 is summarized.

Alliance for Computational Science Collaboration HBCU Partnership at Fisk University. Final Report 2001

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collins, W. E.

2004-08-16

Computational Science plays a big role in research and development in mathematics, science, engineering and biomedical disciplines. The Alliance for Computational Science Collaboration (ACSC) has the goal of training African-American and other minority scientists in the computational science field for eventual employment with the Department of Energy (DOE). The involvements of Historically Black Colleges and Universities (HBCU) in the Alliance provide avenues for producing future DOE African-American scientists. Fisk University has been participating in this program through grants from the DOE. The DOE grant supported computational science activities at Fisk University. The research areas included energy related projects, distributed computing,more » visualization of scientific systems and biomedical computing. Students' involvement in computational science research included undergraduate summer research at Oak Ridge National Lab, on-campus research involving the participation of undergraduates, participation of undergraduate and faculty members in workshops, and mentoring of students. These activities enhanced research and education in computational science, thereby adding to Fisk University's spectrum of research and educational capabilities. Among the successes of the computational science activities are the acceptance of three undergraduate students to graduate schools with full scholarships beginning fall 2002 (one for master degree program and two for Doctoral degree program).« less

Curricular Influences on Female Afterschool Facilitators' Computer Science Interests and Career Choices

NASA Astrophysics Data System (ADS)

Koch, Melissa; Gorges, Torie

2016-10-01

Underrepresented populations such as women, African-Americans, and Latinos/as often come to STEM (science, technology, engineering, and mathematics) careers by less traditional paths than White and Asian males. To better understand how and why women might shift toward STEM, particularly computer science, careers, we investigated the education and career direction of afterschool facilitators, primarily women of color in their twenties and thirties, who taught Build IT, an afterschool computer science curriculum for middle school girls. Many of these women indicated that implementing Build IT had influenced their own interest in technology and computer science and in some cases had resulted in their intent to pursue technology and computer science education. We wanted to explore the role that teaching Build IT may have played in activating or reactivating interest in careers in computer science and to see whether in the years following implementation of Build IT, these women pursued STEM education and/or careers. We reached nine facilitators who implemented the program in 2011-12 or shortly after. Many indicated that while facilitating Build IT, they learned along with the participants, increasing their interest in and confidence with technology and computer science. Seven of the nine participants pursued further STEM or computer science learning or modified their career paths to include more of a STEM or computer science focus. Through interviews, we explored what aspects of Build IT influenced these facilitators' interest and confidence in STEM and when relevant their pursuit of technology and computer science education and careers.

Students perception on the usage of PowerPoint in learning calculus

NASA Astrophysics Data System (ADS)

Othman, Zarith Sofiah; Tarmuji, Nor Habibah; Hilmi, Zulkifli Ab Ghani

2017-04-01

Mathematics is a core subject in most of the science and technology courses and in some social sciences programs. However, the low achievement of students in the subject especially in topics such as Differentiation and Integration is always an issue. Many factors contribute to the low performance such as motivation, environment, method of learning, academic background and others. The purpose of this paper is to determine the perception of learning mathematics using PowerPoint on Integration concepts at the undergraduate level with respect to mathematics anxiety, learning enjoyment, mobility and learning satisfaction. The main content of the PowerPoint presentation focused on the integration method with historical elements as an added value. The study was conducted on 48 students randomly selected from students in computer and applied sciences program as experimental group. Questionnaires were distributed to students to explore their learning experiences. Another 51 students who were taught using the traditional chalkboard method were used as the control group. Both groups were given a test on Integration. The statistical methods used were descriptive statistics and independent sample t-test between the experimental and the control group. The finding showed that most students perceived positively to the PowerPoint presentations with respect to mobility and learning satisfaction. The experimental group performed better than the control group.

Real-world visual statistics and infants' first-learned object names.

PubMed

Clerkin, Elizabeth M; Hart, Elizabeth; Rehg, James M; Yu, Chen; Smith, Linda B

2017-01-05

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present-a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

The NASA computer science research program plan

NASA Technical Reports Server (NTRS)

1983-01-01

A taxonomy of computer science is included, one state of the art of each of the major computer science categories is summarized. A functional breakdown of NASA programs under Aeronautics R and D, space R and T, and institutional support is also included. These areas were assessed against the computer science categories. Concurrent processing, highly reliable computing, and information management are identified.

On teaching computer ethics within a computer science department.

PubMed

Quinn, Michael J

2006-04-01

The author has surveyed a quarter of the accredited undergraduate computer science programs in the United States. More than half of these programs offer a 'social and ethical implications of computing' course taught by a computer science faculty member, and there appears to be a trend toward teaching ethics classes within computer science departments. Although the decision to create an 'in house' computer ethics course may sometimes be a pragmatic response to pressure from the accreditation agency, this paper argues that teaching ethics within a computer science department can provide students and faculty members with numerous benefits. The paper lists topics that can be covered in a computer ethics course and offers some practical suggestions for making the course successful.

In silico environmental chemical science: properties and processes from statistical and computational modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tratnyek, Paul G.; Bylaska, Eric J.; Weber, Eric J.

2017-01-01

Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs usingmore » descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.« less

Application of the Statistical ICA Technique in the DANCE Data Analysis

NASA Astrophysics Data System (ADS)

Baramsai, Bayarbadrakh; Jandel, M.; Bredeweg, T. A.; Rusev, G.; Walker, C. L.; Couture, A.; Mosby, S.; Ullmann, J. L.; Dance Collaboration

2015-10-01

The Detector for Advanced Neutron Capture Experiments (DANCE) at the Los Alamos Neutron Science Center is used to improve our understanding of the neutron capture reaction. DANCE is a highly efficient 4 π γ-ray detector array consisting of 160 BaF2 crystals which make it an ideal tool for neutron capture experiments. The (n, γ) reaction Q-value equals to the sum energy of all γ-rays emitted in the de-excitation cascades from the excited capture state to the ground state. The total γ-ray energy is used to identify reactions on different isotopes as well as the background. However, it's challenging to identify contribution in the Esum spectra from different isotopes with the similar Q-values. Recently we have tested the applicability of modern statistical methods such as Independent Component Analysis (ICA) to identify and separate different (n, γ) reaction yields on different isotopes that are present in the target material. ICA is a recently developed computational tool for separating multidimensional data into statistically independent additive subcomponents. In this conference talk, we present some results of the application of ICA algorithms and its modification for the DANCE experimental data analysis. This research is supported by the U. S. Department of Energy, Office of Science, Nuclear Physics under the Early Career Award No. LANL20135009.

A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education.

PubMed

Christou, Nicolas; Dinov, Ivo D

2010-09-01

Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources.

A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education

PubMed Central

Christou, Nicolas; Dinov, Ivo D.

2011-01-01

Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources. PMID:21603097

Computational Science News | Computational Science | NREL

Science.gov Websites

-Cooled High-Performance Computing Technology at the ESIF February 28, 2018 NREL Launches New Website for High-Performance Computing System Users The National Renewable Energy Laboratory (NREL) Computational Science Center has launched a revamped website for users of the lab's high-performance computing (HPC

Stability Analysis of Finite Difference Approximations to Hyperbolic Systems, and Problems in Applied and Computational Matrix Theory

DTIC Science & Technology

1988-07-08

Marcus and C. Baczynski), Computer Science Press, Rockville, Maryland, 1986. 3. An Introduction to Pascal and Precalculus , Computer Science Press...Science Press, Rockville, Maryland, 1986. 35. An Introduction to Pascal and Precalculus , Computer Science Press, Rockville, Maryland, 1986. 36

Empirical Determination of Competence Areas to Computer Science Education

ERIC Educational Resources Information Center

Zendler, Andreas; Klaudt, Dieter; Seitz, Cornelia

2014-01-01

The authors discuss empirically determined competence areas to K-12 computer science education, emphasizing the cognitive level of competence. The results of a questionnaire with 120 professors of computer science serve as a database. By using multi-dimensional scaling and cluster analysis, four competence areas to computer science education…

Factors Influencing Exemplary Science Teachers' Levels of Computer Use

ERIC Educational Resources Information Center

Hakverdi, Meral; Dana, Thomas M.; Swain, Colleen

2011-01-01

The purpose of this study was to examine exemplary science teachers' use of technology in science instruction, factors influencing their level of computer use, their level of knowledge/skills in using specific computer applications for science instruction, their use of computer-related applications/tools during their instruction, and their…

Preparing Future Secondary Computer Science Educators

ERIC Educational Resources Information Center

Ajwa, Iyad

2007-01-01

Although nearly every college offers a major in computer science, many computer science teachers at the secondary level have received little formal training. This paper presents details of a project that could make a significant contribution to national efforts to improve computer science education by combining teacher education and professional…

OPENING REMARKS: SciDAC: Scientific Discovery through Advanced Computing

NASA Astrophysics Data System (ADS)

Strayer, Michael

2005-01-01

Good morning. Welcome to SciDAC 2005 and San Francisco. SciDAC is all about computational science and scientific discovery. In a large sense, computational science characterizes SciDAC and its intent is change. It transforms both our approach and our understanding of science. It opens new doors and crosses traditional boundaries while seeking discovery. In terms of twentieth century methodologies, computational science may be said to be transformational. There are a number of examples to this point. First are the sciences that encompass climate modeling. The application of computational science has in essence created the field of climate modeling. This community is now international in scope and has provided precision results that are challenging our understanding of our environment. A second example is that of lattice quantum chromodynamics. Lattice QCD, while adding precision and insight to our fundamental understanding of strong interaction dynamics, has transformed our approach to particle and nuclear science. The individual investigator approach has evolved to teams of scientists from different disciplines working side-by-side towards a common goal. SciDAC is also undergoing a transformation. This meeting is a prime example. Last year it was a small programmatic meeting tracking progress in SciDAC. This year, we have a major computational science meeting with a variety of disciplines and enabling technologies represented. SciDAC 2005 should position itself as a new corner stone for Computational Science and its impact on science. As we look to the immediate future, FY2006 will bring a new cycle to SciDAC. Most of the program elements of SciDAC will be re-competed in FY2006. The re-competition will involve new instruments for computational science, new approaches for collaboration, as well as new disciplines. There will be new opportunities for virtual experiments in carbon sequestration, fusion, and nuclear power and nuclear waste, as well as collaborations with industry and virtual prototyping. New instruments of collaboration will include institutes and centers while summer schools, workshops and outreach will invite new talent and expertise. Computational science adds new dimensions to science and its practice. Disciplines of fusion, accelerator science, and combustion are poised to blur the boundaries between pure and applied science. As we open the door into FY2006 we shall see a landscape of new scientific challenges: in biology, chemistry, materials, and astrophysics to name a few. The enabling technologies of SciDAC have been transformational as drivers of change. Planning for major new software systems assumes a base line employing Common Component Architectures and this has become a household word for new software projects. While grid algorithms and mesh refinement software have transformed applications software, data management and visualization have transformed our understanding of science from data. The Gordon Bell prize now seems to be dominated by computational science and solvers developed by TOPS ISIC. The priorities of the Office of Science in the Department of Energy are clear. The 20 year facilities plan is driven by new science. High performance computing is placed amongst the two highest priorities. Moore's law says that by the end of the next cycle of SciDAC we shall have peta-flop computers. The challenges of petascale computing are enormous. These and the associated computational science are the highest priorities for computing within the Office of Science. Our effort in Leadership Class computing is just a first step towards this goal. Clearly, computational science at this scale will face enormous challenges and possibilities. Performance evaluation and prediction will be critical to unraveling the needed software technologies. We must not lose sight of our overarching goal—that of scientific discovery. Science does not stand still and the landscape of science discovery and computing holds immense promise. In this environment, I believe it is necessary to institute a system of science based performance metrics to help quantify our progress towards science goals and scientific computing. As a final comment I would like to reaffirm that the shifting landscapes of science will force changes to our computational sciences, and leave you with the quote from Richard Hamming, 'The purpose of computing is insight, not numbers'.

Enabling Wide-Scale Computer Science Education through Improved Automated Assessment Tools

NASA Astrophysics Data System (ADS)

Boe, Bryce A.

There is a proliferating demand for newly trained computer scientists as the number of computer science related jobs continues to increase. University programs will only be able to train enough new computer scientists to meet this demand when two things happen: when there are more primary and secondary school students interested in computer science, and when university departments have the resources to handle the resulting increase in enrollment. To meet these goals, significant effort is being made to both incorporate computational thinking into existing primary school education, and to support larger university computer science class sizes. We contribute to this effort through the creation and use of improved automated assessment tools. To enable wide-scale computer science education we do two things. First, we create a framework called Hairball to support the static analysis of Scratch programs targeted for fourth, fifth, and sixth grade students. Scratch is a popular building-block language utilized to pique interest in and teach the basics of computer science. We observe that Hairball allows for rapid curriculum alterations and thus contributes to wide-scale deployment of computer science curriculum. Second, we create a real-time feedback and assessment system utilized in university computer science classes to provide better feedback to students while reducing assessment time. Insights from our analysis of student submission data show that modifications to the system configuration support the way students learn and progress through course material, making it possible for instructors to tailor assignments to optimize learning in growing computer science classes.

Increasing Diversity in Computer Science: Acknowledging, yet Moving Beyond, Gender

NASA Astrophysics Data System (ADS)

Larsen, Elizabeth A.; Stubbs, Margaret L.

Lack of diversity within the computer science field has, thus far, been examined most fully through the lens of gender. This article is based on a follow-on to Margolis and Fisher's (2002) study and includes interviews with 33 Carnegie Mellon University students from the undergraduate senior class of 2002 in the School of Computer Science. We found evidence of similarities among the perceptions of these women and men on definitions of computer science, explanations for the notoriously low proportion of women in the field, characterizations of a typical computer science student, impressions of recent curricular changes, a sense of the atmosphere/culture in the program, views of the Women@SCS campus organization, and suggestions for attracting and retaining well-rounded students in computer science. We conclude that efforts to increase diversity in the computer science field will benefit from a more broad-based approach that considers, but is not limited to, notions of gender difference.

Democratizing Computer Science

ERIC Educational Resources Information Center

Margolis, Jane; Goode, Joanna; Ryoo, Jean J.

2015-01-01

Computer science programs are too often identified with a narrow stratum of the student population, often white or Asian boys who have access to computers at home. But because computers play such a huge role in our world today, all students can benefit from the study of computer science and the opportunity to build skills related to computing. The…

Implementing an Affordable High-Performance Computing for Teaching-Oriented Computer Science Curriculum

ERIC Educational Resources Information Center

Abuzaghleh, Omar; Goldschmidt, Kathleen; Elleithy, Yasser; Lee, Jeongkyu

2013-01-01

With the advances in computing power, high-performance computing (HPC) platforms have had an impact on not only scientific research in advanced organizations but also computer science curriculum in the educational community. For example, multicore programming and parallel systems are highly desired courses in the computer science major. However,…

Computer Science and the Liberal Arts

ERIC Educational Resources Information Center

Shannon, Christine

2010-01-01

Computer science and the liberal arts have much to offer each other. Yet liberal arts colleges, in particular, have been slow to recognize the opportunity that the study of computer science provides for achieving the goals of a liberal education. After the precipitous drop in computer science enrollments during the first decade of this century,…

Marrying Content and Process in Computer Science Education

ERIC Educational Resources Information Center

Zendler, A.; Spannagel, C.; Klaudt, D.

2011-01-01

Constructivist approaches to computer science education emphasize that as well as knowledge, thinking skills and processes are involved in active knowledge construction. K-12 computer science curricula must not be based on fashions and trends, but on contents and processes that are observable in various domains of computer science, that can be…

Computing Whether She Belongs: Stereotypes Undermine Girls' Interest and Sense of Belonging in Computer Science

ERIC Educational Resources Information Center

Master, Allison; Cheryan, Sapna; Meltzoff, Andrew N.

2016-01-01

Computer science has one of the largest gender disparities in science, technology, engineering, and mathematics. An important reason for this disparity is that girls are less likely than boys to enroll in necessary "pipeline courses," such as introductory computer science. Two experiments investigated whether high-school girls' lower…

Approaching Gender Parity: Women in Computer Science at Afghanistan's Kabul University

ERIC Educational Resources Information Center

Plane, Jandelyn

2010-01-01

This study explores the representation of women in computer science at the tertiary level through data collected about undergraduate computer science education at Kabul University in Afghanistan. Previous studies have theorized reasons for underrepresentation of women in computer science, and while many of these reasons are indeed present in…

Some Hail 'Computational Science' as Biggest Advance Since Newton, Galileo.

ERIC Educational Resources Information Center

Turner, Judith Axler

1987-01-01

Computational science is defined as science done on a computer. A computer can serve as a laboratory for researchers who cannot experiment with their subjects, and as a calculator for those who otherwise might need centuries to solve some problems mathematically. The National Science Foundation's support of supercomputers is discussed. (MLW)

Journal news

USGS Publications Warehouse

Conroy, M.J.; Samuel, M.D.; White, Joanne C.

1995-01-01

Statistical power (and conversely, Type II error) is often ignored by biologists. Power is important to consider in the design of studies, to ensure that sufficient resources are allocated to address a hypothesis under examination. Deter- mining appropriate sample size when designing experiments or calculating power for a statistical test requires an investigator to consider the importance of making incorrect conclusions about the experimental hypothesis and the biological importance of the alternative hypothesis (or the biological effect size researchers are attempting to measure). Poorly designed studies frequently provide results that are at best equivocal, and do little to advance science or assist in decision making. Completed studies that fail to reject Ho should consider power and the related probability of a Type II error in the interpretation of results, particularly when implicit or explicit acceptance of Ho is used to support a biological hypothesis or management decision. Investigators must consider the biological question they wish to answer (Tacha et al. 1982) and assess power on the basis of biologically significant differences (Taylor and Gerrodette 1993). Power calculations are somewhat subjective, because the author must specify either f or the minimum difference that is biologically important. Biologists may have different ideas about what values are appropriate. While determining biological significance is of central importance in power analysis, it is also an issue of importance in wildlife science. Procedures, references, and computer software to compute power are accessible; therefore, authors should consider power. We welcome comments or suggestions on this subject.

Enabling Efficient Climate Science Workflows in High Performance Computing Environments

NASA Astrophysics Data System (ADS)

Krishnan, H.; Byna, S.; Wehner, M. F.; Gu, J.; O'Brien, T. A.; Loring, B.; Stone, D. A.; Collins, W.; Prabhat, M.; Liu, Y.; Johnson, J. N.; Paciorek, C. J.

2015-12-01

A typical climate science workflow often involves a combination of acquisition of data, modeling, simulation, analysis, visualization, publishing, and storage of results. Each of these tasks provide a myriad of challenges when running on a high performance computing environment such as Hopper or Edison at NERSC. Hurdles such as data transfer and management, job scheduling, parallel analysis routines, and publication require a lot of forethought and planning to ensure that proper quality control mechanisms are in place. These steps require effectively utilizing a combination of well tested and newly developed functionality to move data, perform analysis, apply statistical routines, and finally, serve results and tools to the greater scientific community. As part of the CAlibrated and Systematic Characterization, Attribution and Detection of Extremes (CASCADE) project we highlight a stack of tools our team utilizes and has developed to ensure that large scale simulation and analysis work are commonplace and provide operations that assist in everything from generation/procurement of data (HTAR/Globus) to automating publication of results to portals like the Earth Systems Grid Federation (ESGF), all while executing everything in between in a scalable environment in a task parallel way (MPI). We highlight the use and benefit of these tools by showing several climate science analysis use cases they have been applied to.

Girls in computer science: A female only introduction class in high school

NASA Astrophysics Data System (ADS)

Drobnis, Ann W.

This study examined the impact of an all girls' classroom environment in a high school introductory computer science class on the student's attitudes towards computer science and their thoughts on future involvement with computer science. It was determined that an all girls' introductory class could impact the declining female enrollment and female students' efficacy towards computer science. This research was conducted in a summer school program through a regional magnet school for science and technology which these students attend during the school year. Three different groupings of students were examined for the research: female students in an all girls' class, female students in mixed-gender classes and male students in mixed-gender classes. A survey, Attitudes about Computers and Computer Science (ACCS), was designed to obtain an understanding of the students' thoughts, preconceptions, attitude, knowledge of computer science, and future intentions around computer science, both in education and career. Students in all three groups were administered the ACCS prior to taking the class and upon completion of the class. In addition, students in the all girls' class wrote in a journal throughout the course, and some of those students were also interviewed upon completion of the course. The data was analyzed using quantitative and qualitative techniques. While there were no major differences found in the quantitative data, it was determined that girls in the all girls' class were truly excited by what they had learned and were more open to the idea of computer science being a part of their future.

Bringing computational science to the public.

PubMed

McDonagh, James L; Barker, Daniel; Alderson, Rosanna G

2016-01-01

The increasing use of computers in science allows for the scientific analyses of large datasets at an increasing pace. We provided examples and interactive demonstrations at Dundee Science Centre as part of the 2015 Women in Science festival, to present aspects of computational science to the general public. We used low-cost Raspberry Pi computers to provide hands on experience in computer programming and demonstrated the application of computers to biology. Computer games were used as a means to introduce computers to younger visitors. The success of the event was evaluated by voluntary feedback forms completed by visitors, in conjunction with our own self-evaluation. This work builds on the original work of the 4273π bioinformatics education program of Barker et al. (2013, BMC Bioinform. 14:243). 4273π provides open source education materials in bioinformatics. This work looks at the potential to adapt similar materials for public engagement events. It appears, at least in our small sample of visitors (n = 13), that basic computational science can be conveyed to people of all ages by means of interactive demonstrations. Children as young as five were able to successfully edit simple computer programs with supervision. This was, in many cases, their first experience of computer programming. The feedback is predominantly positive, showing strong support for improving computational science education, but also included suggestions for improvement. Our conclusions are necessarily preliminary. However, feedback forms suggest methods were generally well received among the participants; "Easy to follow. Clear explanation" and "Very easy. Demonstrators were very informative." Our event, held at a local Science Centre in Dundee, demonstrates that computer games and programming activities suitable for young children can be performed alongside a more specialised and applied introduction to computational science for older visitors.

Computer Science and Telecommunications Board summary of activities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blumenthal, M.S.

1992-03-27

The Computer Science and Telecommunications Board (CSTB) considers technical and policy issues pertaining to computer science, telecommunications, and associated technologies. CSTB actively disseminates the results of its completed projects to those in a position to help implement their recommendations or otherwise use their insights. It provides a forum for the exchange of information on computer science, computing technology, and telecommunications. This report discusses the major accomplishments of CSTB.

Hispanic women overcoming deterrents to computer science: A phenomenological study

NASA Astrophysics Data System (ADS)

Herling, Lourdes

The products of computer science are important to all aspects of society and are tools in the solution of the world's problems. It is, therefore, troubling that the United States faces a shortage in qualified graduates in computer science. The number of women and minorities in computer science is significantly lower than the percentage of the U.S. population which they represent. The overall enrollment in computer science programs has continued to decline with the enrollment of women declining at a higher rate than that of men. This study addressed three aspects of underrepresentation about which there has been little previous research: addressing computing disciplines specifically rather than embedding them within the STEM disciplines, what attracts women and minorities to computer science, and addressing the issues of race/ethnicity and gender in conjunction rather than in isolation. Since women of underrepresented ethnicities are more severely underrepresented than women in general, it is important to consider whether race and ethnicity play a role in addition to gender as has been suggested by previous research. Therefore, this study examined what attracted Hispanic women to computer science specifically. The study determines whether being subjected to multiple marginalizations---female and Hispanic---played a role in the experiences of Hispanic women currently in computer science. The study found five emergent themes within the experiences of Hispanic women in computer science. Encouragement and role models strongly influenced not only the participants' choice to major in the field, but to persist as well. Most of the participants experienced a negative atmosphere and feelings of not fitting in while in college and industry. The interdisciplinary nature of computer science was the most common aspect that attracted the participants to computer science. The aptitudes participants commonly believed are needed for success in computer science are the Twenty-First Century skills problem solving, creativity, and critical thinking. While not all the participants had experience with computers or programming prior to attending college, experience played a role in the self-confidence of those who did.

Research in applied mathematics, numerical analysis, and computer science

NASA Technical Reports Server (NTRS)

1984-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering (ICASE) in applied mathematics, numerical analysis, and computer science is summarized and abstracts of published reports are presented. The major categories of the ICASE research program are: (1) numerical methods, with particular emphasis on the development and analysis of basic numerical algorithms; (2) control and parameter identification; (3) computational problems in engineering and the physical sciences, particularly fluid dynamics, acoustics, and structural analysis; and (4) computer systems and software, especially vector and parallel computers.

Science-Driven Computing: NERSC's Plan for 2006-2010

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simon, Horst D.; Kramer, William T.C.; Bailey, David H.

NERSC has developed a five-year strategic plan focusing on three components: Science-Driven Systems, Science-Driven Services, and Science-Driven Analytics. (1) Science-Driven Systems: Balanced introduction of the best new technologies for complete computational systems--computing, storage, networking, visualization and analysis--coupled with the activities necessary to engage vendors in addressing the DOE computational science requirements in their future roadmaps. (2) Science-Driven Services: The entire range of support activities, from high-quality operations and user services to direct scientific support, that enable a broad range of scientists to effectively use NERSC systems in their research. NERSC will concentrate on resources needed to realize the promise ofmore » the new highly scalable architectures for scientific discovery in multidisciplinary computational science projects. (3) Science-Driven Analytics: The architectural and systems enhancements and services required to integrate NERSC's powerful computational and storage resources to provide scientists with new tools to effectively manipulate, visualize, and analyze the huge data sets derived from simulations and experiments.« less

Gender Differences in the Use of Computers, Programming, and Peer Interactions in Computer Science Classrooms

ERIC Educational Resources Information Center

Stoilescu, Dorian; Egodawatte, Gunawardena

2010-01-01

Research shows that female and male students in undergraduate computer science programs view computer culture differently. Female students are interested more in the use of computers than in doing programming, whereas male students see computer science mainly as a programming activity. The overall purpose of our research was not to find new…

Interval versions of statistical techniques with applications to environmental analysis, bioinformatics, and privacy in statistical databases

NASA Astrophysics Data System (ADS)

Kreinovich, Vladik; Longpre, Luc; Starks, Scott A.; Xiang, Gang; Beck, Jan; Kandathi, Raj; Nayak, Asis; Ferson, Scott; Hajagos, Janos

2007-02-01

In many areas of science and engineering, it is desirable to estimate statistical characteristics (mean, variance, covariance, etc.) under interval uncertainty. For example, we may want to use the measured values x(t) of a pollution level in a lake at different moments of time to estimate the average pollution level; however, we do not know the exact values x(t)--e.g., if one of the measurement results is 0, this simply means that the actual (unknown) value of x(t) can be anywhere between 0 and the detection limit (DL). We must, therefore, modify the existing statistical algorithms to process such interval data. Such a modification is also necessary to process data from statistical databases, where, in order to maintain privacy, we only keep interval ranges instead of the actual numeric data (e.g., a salary range instead of the actual salary). Most resulting computational problems are NP-hard--which means, crudely speaking, that in general, no computationally efficient algorithm can solve all particular cases of the corresponding problem. In this paper, we overview practical situations in which computationally efficient algorithms exist: e.g., situations when measurements are very accurate, or when all the measurements are done with one (or few) instruments. As a case study, we consider a practical problem from bioinformatics: to discover the genetic difference between the cancer cells and the healthy cells, we must process the measurements results and find the concentrations c and h of a given gene in cancer and in healthy cells. This is a particular case of a general situation in which, to estimate states or parameters which are not directly accessible by measurements, we must solve a system of equations in which coefficients are only known with interval uncertainty. We show that in general, this problem is NP-hard, and we describe new efficient algorithms for solving this problem in practically important situations.

Opportunities for Computational Discovery in Basic Energy Sciences

NASA Astrophysics Data System (ADS)

Pederson, Mark

2011-03-01

An overview of the broad-ranging support of computational physics and computational science within the Department of Energy Office of Science will be provided. Computation as the third branch of physics is supported by all six offices (Advanced Scientific Computing, Basic Energy, Biological and Environmental, Fusion Energy, High-Energy Physics, and Nuclear Physics). Support focuses on hardware, software and applications. Most opportunities within the fields of~condensed-matter physics, chemical-physics and materials sciences are supported by the Officeof Basic Energy Science (BES) or through partnerships between BES and the Office for Advanced Scientific Computing. Activities include radiation sciences, catalysis, combustion, materials in extreme environments, energy-storage materials, light-harvesting and photovoltaics, solid-state lighting and superconductivity.~ A summary of two recent reports by the computational materials and chemical communities on the role of computation during the next decade will be provided. ~In addition to materials and chemistry challenges specific to energy sciences, issues identified~include a focus on the role of the domain scientist in integrating, expanding and sustaining applications-oriented capabilities on evolving high-performance computing platforms and on the role of computation in accelerating the development of innovative technologies. ~~

Research | Computational Science | NREL

Science.gov Websites

Research Research NREL's computational science experts use advanced high-performance computing (HPC technologies, thereby accelerating the transformation of our nation's energy system. Enabling High-Impact Research NREL's computational science capabilities enable high-impact research. Some recent examples

Academic reading format preferences and behaviors among university students worldwide: A comparative survey analysis

PubMed Central

Kurbanoglu, Serap; Boustany, Joumana

2018-01-01

This study reports the descriptive and inferential statistical findings of a survey of academic reading format preferences and behaviors of 10,293 tertiary students worldwide. The study hypothesized that country-based differences in schooling systems, socioeconomic development, culture or other factors might have an influence on preferred formats, print or electronic, for academic reading, as well as the learning engagement behaviors of students. The main findings are that country of origin has little to no relationship with or effect on reading format preferences of university students, and that the broad majority of students worldwide prefer to read academic course materials in print. The majority of participants report better focus and retention of information presented in print formats, and more frequently prefer print for longer texts. Additional demographic and post-hoc analysis suggests that format preference has a small relationship with academic rank. The relationship between task demands, format preferences and reading comprehension are discussed. Additional outcomes and implications for the fields of education, psychology, computer science, information science and human-computer interaction are considered. PMID:29847560

Large Eddy Simulation of Turbulent Flow in a Ribbed Pipe

NASA Astrophysics Data System (ADS)

Kang, Changwoo; Yang, Kyung-Soo

2011-11-01

Turbulent flow in a pipe with periodically wall-mounted ribs has been investigated by large eddy simulation with a dynamic subgrid-scale model. The value of Re considered is 98,000, based on hydraulic diameter and mean bulk velocity. An immersed boundary method was employed to implement the ribs in the computational domain. The spacing of the ribs is the key parameter to produce the d-type, intermediate and k-type roughness flows. The mean velocity profiles and turbulent intensities obtained from the present LES are in good agreement with the experimental measurements currently available. Turbulence statistics, including budgets of the Reynolds stresses, were computed, and analyzed to elucidate turbulence structures, especially around the ribs. In particular, effects of the ribs are identified by comparing the turbulence structures with those of smooth pipe flow. The present investigation is relevant to the erosion/corrosion that often occurs around a protruding roughness in a pipe system. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0008457).

Overview of Aro Program on Network Science for Human Decision Making

NASA Astrophysics Data System (ADS)

West, Bruce J.

This program brings together researchers from disparate disciplines to work on a complex research problem that defies confinement within any single discipline. Consequently, not only are new and rewarding solutions sought and obtained for a problem of importance to society and the Army, that is, the human dimension of complex networks, but, in addition, collaborations are established that would not otherwise have formed given the traditional disciplinary compartmentalization of research. This program develops the basic research foundation of a science of networks supporting the linkage between the physical and human (cognitive and social) domains as they relate to human decision making. The strategy is to extend the recent methods of non-equilibrium statistical physics to non-stationary, renewal stochastic processes that appear to be characteristic of the interactions among nodes in complex networks. We also pursue understanding of the phenomenon of synchronization, whose mathematical formulation has recently provided insight into how complex networks reach accommodation and cooperation. The theoretical analyses of complex networks, although mathematically rigorous, often elude analytic solutions and require computer simulation and computation to analyze the underlying dynamic process.

A Delphi study: Practitioners' perceptions of how the science curriculum is differentiated for academically gifted students at the middle school level

NASA Astrophysics Data System (ADS)

Kelley, Jean Mary

The purpose of this study was to identify, analyze, and compare the perceptions of selected district science educators and teachers of middle school science students regarding the following issues: (1) Current methods of differentiating science instruction for gifted middle school students. (2) Strengths of the current methods of differentiating science instruction for gifted middle school students. (3) Weaknesses of the current methods of differentiating science instruction for gifted middle school students. (4) The types of training/experience needed to prepare teachers to effectively differentiate science instruction for gifted middle school students. (5) The steps need to develop an effective differentiated science program at the middle school level. (6) Trends for the future development of differentiated science programs at the middle school level. The panel of educators was identified using the Delphi technique and asked to participate in the study by responding to the research questions. The responses to the first round were condensed into two lists of discrete statements, and in the second round, each group of panelists was asked to rank each statement on a Likert scale. A third round was sent to each group of panel members showing the median and interquartile ranges of the second round. Panelists could adjust their responses based on the results of the second round. The analysis of the data was computed using the computer program Statistics Package for the Social Sciences. Based on the data obtained, the following results and conclusions were determined. The coordinators and the teachers both considered training of teachers, strategies for differentiation, and future trends to be the most important considerations. The areas with the most differences were those dealing with the current methods of differentiating science instruction at the middle school level. There were several limitations identified in this study. Among them were the makeup of the sample of panelists and different definitions of the same term(s). If we are to address the needs of middle school students who are academically gifted in science, teachers and coordinators need to communicate more about expectations in the classroom and what is really happening.

Rational approximations to rational models: alternative algorithms for category learning.

PubMed

Sanborn, Adam N; Griffiths, Thomas L; Navarro, Daniel J

2010-10-01

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.

Computational algorithms dealing with the classical and statistical mechanics of celestial scale polymers in space elevator technology

NASA Astrophysics Data System (ADS)

Knudsen, Steven; Golubovic, Leonardo

Prospects to build Space Elevator (SE) systems have become realistic with ultra-strong materials such as carbon nano-tubes and diamond nano-threads. At cosmic length-scales, space elevators can be modeled as polymer like floppy strings of tethered mass beads. A new venue in SE science has emerged with the introduction of the Rotating Space Elevator (RSE) concept supported by novel algorithms discussed in this presentation. An RSE is a loopy string reaching into outer space. Unlike the classical geostationary SE concepts of Tsiolkovsky, Artsutanov, and Pearson, our RSE exhibits an internal rotation. Thanks to this, objects sliding along the RSE loop spontaneously oscillate between two turning points, one of which is close to the Earth whereas the other one is in outer space. The RSE concept thus solves a major problem in SE technology which is how to supply energy to the climbers moving along space elevator strings. The investigation of the classical and statistical mechanics of a floppy string interacting with objects sliding along it required development of subtle computational algorithms described in this presentation

The Undergraduate Statistics Major--A Prelude to Actuarial Science Training.

ERIC Educational Resources Information Center

Ratliff, Michael I.; Williams, Raymond E.

Recently there has been increased interest related to the Actuarial Science field. An actuary is a business professional who uses mathematical skills to define, analyze, and solve financial and social problems. This paper examines: (1) the interface between Statistical and Actuarial Science training; (2) statistical courses corresponding to…

SciSpark: Highly Interactive and Scalable Model Evaluation and Climate Metrics

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Palamuttam, R. S.; Mogrovejo, R. M.; Whitehall, K. D.; Mattmann, C. A.; Verma, R.; Waliser, D. E.; Lee, H.

2015-12-01

Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We are developing a lightning fast Big Data technology called SciSpark based on ApacheTM Spark under a NASA AIST grant (PI Mattmann). Spark implements the map-reduce paradigm for parallel computing on a cluster, but emphasizes in-memory computation, "spilling" to disk only as needed, and so outperforms the disk-based ApacheTM Hadoop by 100x in memory and by 10x on disk. SciSpark will enable scalable model evaluation by executing large-scale comparisons of A-Train satellite observations to model grids on a cluster of 10 to 1000 compute nodes. This 2nd generation capability for NASA's Regional Climate Model Evaluation System (RCMES) will compute simple climate metrics at interactive speeds, and extend to quite sophisticated iterative algorithms such as machine-learning based clustering of temperature PDFs, and even graph-based algorithms for searching for Mesocale Convective Complexes. We have implemented a parallel data ingest capability in which the user specifies desired variables (arrays) as several time-sorted lists of URL's (i.e. using OPeNDAP model.nc?varname, or local files). The specified variables are partitioned by time/space and then each Spark node pulls its bundle of arrays into memory to begin a computation pipeline. We also investigated the performance of several N-dim. array libraries (scala breeze, java jblas & netlib-java, and ND4J). We are currently developing science codes using ND4J and studying memory behavior on the JVM. On the pyspark side, many of our science codes already use the numpy and SciPy ecosystems. The talk will cover: the architecture of SciSpark, the design of the scientific RDD (sRDD) data structure, our efforts to integrate climate science algorithms in Python and Scala, parallel ingest and partitioning of A-Train satellite observations from HDF files and model grids from netCDF files, first parallel runs to compute comparison statistics and PDF's, and first metrics quantifying parallel speedups and memory & disk usage.

Are X-rays the key to integrated computational materials engineering?

DOE PAGES

Ice, Gene E.

2015-11-01

The ultimate dream of materials science is to predict materials behavior from composition and processing history. Owing to the growing power of computers, this long-time dream has recently found expression through worldwide excitement in a number of computation-based thrusts: integrated computational materials engineering, materials by design, computational materials design, three-dimensional materials physics and mesoscale physics. However, real materials have important crystallographic structures at multiple length scales, which evolve during processing and in service. Moreover, real materials properties can depend on the extreme tails in their structural and chemical distributions. This makes it critical to map structural distributions with sufficient resolutionmore » to resolve small structures and with sufficient statistics to capture the tails of distributions. For two-dimensional materials, there are high-resolution nondestructive probes of surface and near-surface structures with atomic or near-atomic resolution that can provide detailed structural, chemical and functional distributions over important length scales. Furthermore, there are no nondestructive three-dimensional probes with atomic resolution over the multiple length scales needed to understand most materials.« less

NASA's computer science research program

NASA Technical Reports Server (NTRS)

Larsen, R. L.

1983-01-01

Following a major assessment of NASA's computing technology needs, a new program of computer science research has been initiated by the Agency. The program includes work in concurrent processing, management of large scale scientific databases, software engineering, reliable computing, and artificial intelligence. The program is driven by applications requirements in computational fluid dynamics, image processing, sensor data management, real-time mission control and autonomous systems. It consists of university research, in-house NASA research, and NASA's Research Institute for Advanced Computer Science (RIACS) and Institute for Computer Applications in Science and Engineering (ICASE). The overall goal is to provide the technical foundation within NASA to exploit advancing computing technology in aerospace applications.

Girls Save the World through Computer Science

ERIC Educational Resources Information Center

Murakami, Christine

2011-01-01

It's no secret that fewer and fewer women are entering computer science fields. Attracting high school girls to computer science is only part of the solution. Retaining them while they are in higher education or the workforce is also a challenge. To solve this, there is a need to show girls that computer science is a wide-open field that offers…

The Assessment of Taiwanese College Students' Conceptions of and Approaches to Learning Computer Science and Their Relationships

ERIC Educational Resources Information Center

Liang, Jyh-Chong; Su, Yi-Ching; Tsai, Chin-Chung

2015-01-01

The aim of this study was to explore Taiwanese college students' conceptions of and approaches to learning computer science and then explore the relationships between the two. Two surveys, Conceptions of Learning Computer Science (COLCS) and Approaches to Learning Computer Science (ALCS), were administered to 421 college students majoring in…

Hispanic Women Overcoming Deterrents to Computer Science: A Phenomenological Study

ERIC Educational Resources Information Center

Herling, Lourdes

2011-01-01

The products of computer science are important to all aspects of society and are tools in the solution of the world's problems. It is, therefore, troubling that the United States faces a shortage in qualified graduates in computer science. The number of women and minorities in computer science is significantly lower than the percentage of the…

The Effects of Integrating Service Learning into Computer Science: An Inter-Institutional Longitudinal Study

ERIC Educational Resources Information Center

Payton, Jamie; Barnes, Tiffany; Buch, Kim; Rorrer, Audrey; Zuo, Huifang

2015-01-01

This study is a follow-up to one published in computer science education in 2010 that reported preliminary results showing a positive impact of service learning on student attitudes associated with success and retention in computer science. That paper described how service learning was incorporated into a computer science course in the context of…

Non-Determinism: An Abstract Concept in Computer Science Studies

ERIC Educational Resources Information Center

Armoni, Michal; Gal-Ezer, Judith

2007-01-01

Non-determinism is one of the most important, yet abstract, recurring concepts of Computer Science. It plays an important role in Computer Science areas such as formal language theory, computability theory, distributed computing, and operating systems. We conducted a series of studies on the perception of non-determinism. In the current research,…

An Investigation of Primary School Science Teachers' Use of Computer Applications

ERIC Educational Resources Information Center

Ocak, Mehmet Akif; Akdemir, Omur

2008-01-01

This study investigated the level and frequency of science teachers' use of computer applications as an instructional tool in the classroom. The manner and frequency of science teachers' use of computer, their perceptions about integration of computer applications, and other factors contributed to changes in their computer literacy are…

Methodical Approaches to Teaching of Computer Modeling in Computer Science Course

ERIC Educational Resources Information Center

Rakhimzhanova, B. Lyazzat; Issabayeva, N. Darazha; Khakimova, Tiyshtik; Bolyskhanova, J. Madina

2015-01-01

The purpose of this study was to justify of the formation technique of representation of modeling methodology at computer science lessons. The necessity of studying computer modeling is that the current trends of strengthening of general education and worldview functions of computer science define the necessity of additional research of the…

Learning from Massive Distributed Data Sets (Invited)

NASA Astrophysics Data System (ADS)

Kang, E. L.; Braverman, A. J.

2013-12-01

Technologies for remote sensing and ever-expanding computer experiments in climate science are generating massive data sets. Meanwhile, it has been common in all areas of large-scale science to have these 'big data' distributed over multiple different physical locations, and moving large amounts of data can be impractical. In this talk, we will discuss efficient ways for us to summarize and learn from distributed data. We formulate a graphical model to mimic the main characteristics of a distributed-data network, including the size of the data sets and speed of moving data. With this nominal model, we investigate the trade off between prediction accurate and cost of data movement, theoretically and through simulation experiments. We will also discuss new implementations of spatial and spatio-temporal statistical methods optimized for distributed data.

Climate Modeling Computing Needs Assessment

NASA Astrophysics Data System (ADS)

Petraska, K. E.; McCabe, J. D.

2011-12-01

This paper discusses early findings of an assessment of computing needs for NASA science, engineering and flight communities. The purpose of this assessment is to document a comprehensive set of computing needs that will allow us to better evaluate whether our computing assets are adequately structured to meet evolving demand. The early results are interesting, already pointing out improvements we can make today to get more out of the computing capacity we have, as well as potential game changing innovations for the future in how we apply information technology to science computing. Our objective is to learn how to leverage our resources in the best way possible to do more science for less money. Our approach in this assessment is threefold: Development of use case studies for science workflows; Creating a taxonomy and structure for describing science computing requirements; and characterizing agency computing, analysis, and visualization resources. As projects evolve, science data sets increase in a number of ways: in size, scope, timelines, complexity, and fidelity. Generating, processing, moving, and analyzing these data sets places distinct and discernable requirements on underlying computing, analysis, storage, and visualization systems. The initial focus group for this assessment is the Earth Science modeling community within NASA's Science Mission Directorate (SMD). As the assessment evolves, this focus will expand to other science communities across the agency. We will discuss our use cases, our framework for requirements and our characterizations, as well as our interview process, what we learned and how we plan to improve our materials after using them in the first round of interviews in the Earth Science Modeling community. We will describe our plans for how to expand this assessment, first into the Earth Science data analysis and remote sensing communities, and then throughout the full community of science, engineering and flight at NASA.

Careers in Data Science: A Berkeley Perspective

NASA Astrophysics Data System (ADS)

Koy, K.

2015-12-01

Last year, I took on an amazing opportunity to serve as the Executive Director of the new Berkeley Institute for Data Science (BIDS). After a 15-year career working with geospatial data to advance our understanding of the environment, I have been presented with a unique opportunity through BIDS to work with talented researchers from a wide variety of backgrounds. Founded in 2013, BIDS is a central hub of research and education at UC Berkeley designed to facilitate and nurture data-intensive science. We are building a community centered on a cohort of talented data science fellows and senior fellows who are representative of the world-class researchers from across our campus and are leading the data science revolution within their disciplines. Our initiatives are designed to bring together broad constituents of the data science community, including domain experts from the life, social, and physical sciences and methodological experts from computer science, statistics, and applied mathematics. While many of these individuals rarely cross professional paths, BIDS actively seeks new and creative ways to engage and foster collaboration across these different research fields. In this presentation, I will share my own story, along with some insights into how BIDS is supporting the careers of data scientists, including graduate students, postdocs, faculty, and research staff. I will also describe how these individuals we are helping support are working to address a number of data science-related challenges in scientific research.

Kenny Gruchalla | NREL

Science.gov Websites

feature extraction, human-computer interaction, and physics-based modeling. Professional Experience 2009 ., computer science, University of Colorado at Boulder M.S., computer science, University of Colorado at Boulder B.S., computer science, New Mexico Institute of Mining and Technology

Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry

PubMed Central

Staples, Patrick; Onnela, Jukka-Pekka

2015-01-01

Smartphones are now ubiquitous and can be harnessed to offer psychiatry a wealth of real-time data regarding patient behavior, self-reported symptoms, and even physiology. The data collected from smartphones meet the three criteria of big data: velocity, volume, and variety. Although these data have tremendous potential, transforming them into clinically valid and useful information requires using new tools and methods as a part of assessment in psychiatry. In this paper, we introduce and explore numerous analytical methods and tools from the computational and statistical sciences that appear readily applicable to psychiatric data collected using smartphones. By matching smartphone data with appropriate statistical methods, psychiatry can better realize the potential of mobile mental health and empower both patients and providers with novel clinical tools. PMID:26073363

Computer-aided design and computer science technology

NASA Technical Reports Server (NTRS)

Fulton, R. E.; Voigt, S. J.

1976-01-01

A description is presented of computer-aided design requirements and the resulting computer science advances needed to support aerospace design. The aerospace design environment is examined, taking into account problems of data handling and aspects of computer hardware and software. The interactive terminal is normally the primary interface between the computer system and the engineering designer. Attention is given to user aids, interactive design, interactive computations, the characteristics of design information, data management requirements, hardware advancements, and computer science developments.

The effects of integrating service learning into computer science: an inter-institutional longitudinal study

NASA Astrophysics Data System (ADS)

Payton, Jamie; Barnes, Tiffany; Buch, Kim; Rorrer, Audrey; Zuo, Huifang

2015-07-01

This study is a follow-up to one published in computer science education in 2010 that reported preliminary results showing a positive impact of service learning on student attitudes associated with success and retention in computer science. That paper described how service learning was incorporated into a computer science course in the context of the Students & Technology in Academia, Research, and Service (STARS) Alliance, an NSF-supported broadening participation in computing initiative that aims to diversify the computer science pipeline through innovative pedagogy and inter-institutional partnerships. The current paper describes how the STARS Alliance has expanded to diverse institutions, all using service learning as a vehicle for broadening participation in computing and enhancing attitudes and behaviors associated with student success. Results supported the STARS model of service learning for enhancing computing efficacy and computing commitment and for providing diverse students with many personal and professional development benefits.

ICASE Computer Science Program

NASA Technical Reports Server (NTRS)

1985-01-01

The Institute for Computer Applications in Science and Engineering computer science program is discussed in outline form. Information is given on such topics as problem decomposition, algorithm development, programming languages, and parallel architectures.

Applications of Out-of-Domain Knowledge in Students' Reasoning about Computer Program State

ERIC Educational Resources Information Center

Lewis, Colleen Marie

2012-01-01

To meet a growing demand and a projected deficit in the supply of computer professionals (NCWIT, 2009), it is of vital importance to expand students' access to computer science. However, many researchers in the computer science education community unproductively assume that some students lack an innate ability for computer science and…

On mechanics and material length scales of failure in heterogeneous interfaces using a finite strain high performance solver

NASA Astrophysics Data System (ADS)

Mosby, Matthew; Matouš, Karel

2015-12-01

Three-dimensional simulations capable of resolving the large range of spatial scales, from the failure-zone thickness up to the size of the representative unit cell, in damage mechanics problems of particle reinforced adhesives are presented. We show that resolving this wide range of scales in complex three-dimensional heterogeneous morphologies is essential in order to apprehend fracture characteristics, such as strength, fracture toughness and shape of the softening profile. Moreover, we show that computations that resolve essential physical length scales capture the particle size-effect in fracture toughness, for example. In the vein of image-based computational materials science, we construct statistically optimal unit cells containing hundreds to thousands of particles. We show that these statistically representative unit cells are capable of capturing the first- and second-order probability functions of a given data-source with better accuracy than traditional inclusion packing techniques. In order to accomplish these large computations, we use a parallel multiscale cohesive formulation and extend it to finite strains including damage mechanics. The high-performance parallel computational framework is executed on up to 1024 processing cores. A mesh convergence and a representative unit cell study are performed. Quantifying the complex damage patterns in simulations consisting of tens of millions of computational cells and millions of highly nonlinear equations requires data-mining the parallel simulations, and we propose two damage metrics to quantify the damage patterns. A detailed study of volume fraction and filler size on the macroscopic traction-separation response of heterogeneous adhesives is presented.

Geoscience in the Big Data Era: Are models obsolete?

NASA Astrophysics Data System (ADS)

Yuen, D. A.; Zheng, L.; Stark, P. B.; Morra, G.; Knepley, M.; Wang, X.

2016-12-01

In last few decades, the velocity, volume, and variety of geophysical data have increased, while the development of the Internet and distributed computing has led to the emergence of "data science." Fitting and running numerical models, especially based on PDEs, is the main consumer of flops in geoscience. Can large amounts of diverse data supplant modeling? Without the ability to conduct randomized, controlled experiments, causal inference requires understanding the physics. It is sometimes possible to predict well without understanding the system—if (1) the system is predictable, (2) data on "important" variables are available, and (3) the system changes slowly enough. And sometimes even a crude model can help the data "speak for themselves" much more clearly. For example, Shearer (1991) used a 1-dimensional velocity model to stack long-period seismograms, revealing upper mantle discontinuities. This was a "big data" approach: the main use of computing was in the data processing, rather than in modeling, yet the "signal" became clear. In contrast, modelers tend to use all available computing power to fit even more complex models, resulting in a cycle where uncertainty quantification (UQ) is never possible: even if realistic UQ required only 1,000 model evaluations, it is never in reach. To make more reliable inferences requires better data analysis and statistics, not more complex models. Geoscientists need to learn new skills and tools: sound software engineering practices; open programming languages suitable for big data; parallel and distributed computing; data visualization; and basic nonparametric, computationally based statistical inference, such as permutation tests. They should work reproducibly, scripting all analyses and avoiding point-and-click tools.

Scientific Computing Strategic Plan for the Idaho National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whiting, Eric Todd

Scientific computing is a critical foundation of modern science. Without innovations in the field of computational science, the essential missions of the Department of Energy (DOE) would go unrealized. Taking a leadership role in such innovations is Idaho National Laboratory’s (INL’s) challenge and charge, and is central to INL’s ongoing success. Computing is an essential part of INL’s future. DOE science and technology missions rely firmly on computing capabilities in various forms. Modeling and simulation, fueled by innovations in computational science and validated through experiment, are a critical foundation of science and engineering. Big data analytics from an increasing numbermore » of widely varied sources is opening new windows of insight and discovery. Computing is a critical tool in education, science, engineering, and experiments. Advanced computing capabilities in the form of people, tools, computers, and facilities, will position INL competitively to deliver results and solutions on important national science and engineering challenges. A computing strategy must include much more than simply computers. The foundational enabling component of computing at many DOE national laboratories is the combination of a showcase like data center facility coupled with a very capable supercomputer. In addition, network connectivity, disk storage systems, and visualization hardware are critical and generally tightly coupled to the computer system and co located in the same facility. The existence of these resources in a single data center facility opens the doors to many opportunities that would not otherwise be possible.« less

A Cognitive Model for Problem Solving in Computer Science

ERIC Educational Resources Information Center

Parham, Jennifer R.

2009-01-01

According to industry representatives, computer science education needs to emphasize the processes involved in solving computing problems rather than their solutions. Most of the current assessment tools used by universities and computer science departments analyze student answers to problems rather than investigating the processes involved in…

Approaches to Classroom-Based Computational Science.

ERIC Educational Resources Information Center

Guzdial, Mark

Computational science includes the use of computer-based modeling and simulation to define and test theories about scientific phenomena. The challenge for educators is to develop techniques for implementing computational science in the classroom. This paper reviews some previous work on the use of simulation alone (without modeling), modeling…

Defining Computational Thinking for Mathematics and Science Classrooms

ERIC Educational Resources Information Center

Weintrop, David; Beheshti, Elham; Horn, Michael; Orton, Kai; Jona, Kemi; Trouille, Laura; Wilensky, Uri

2016-01-01

Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include "computational thinking" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new…

Computer Aided Statistics Instruction Protocol (CASIP) Restructuring Undergraduate Statistics in Psychology: An Integration of Computers into Instruction and Evaluation.

ERIC Educational Resources Information Center

Rah, Ki-Young; Scuello, Michael

As a result of the development of two computer statistics laboratories in the psychology department at New York's Brooklyn College, a project was undertaken to develop and implement computer program modules in undergraduate and graduate statistics courses. Rather than use the technology to merely make course presentations more exciting, the…

NASA Center for Computational Sciences: History and Resources

NASA Technical Reports Server (NTRS)

2000-01-01

The Nasa Center for Computational Sciences (NCCS) has been a leading capacity computing facility, providing a production environment and support resources to address the challenges facing the Earth and space sciences research community.

Institute for Computer Applications in Science and Engineering (ICASE)

NASA Technical Reports Server (NTRS)

1984-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science during the period April 1, 1983 through September 30, 1983 is summarized.

Computers in Science: Thinking Outside the Discipline.

ERIC Educational Resources Information Center

Hamilton, Todd M.

2003-01-01

Describes the Computers in Science course which integrates computer-related techniques into the science disciplines of chemistry, physics, biology, and Earth science. Uses a team teaching approach and teaches students how to solve chemistry problems with spreadsheets, identify minerals with X-rays, and chemical and force analysis. (Contains 14…

78 FR 64255 - Advisory Committee for Computer and Information Science and Engineering; Cancellation of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-28

... NATIONAL SCIENCE FOUNDATION Advisory Committee for Computer and Information Science and Engineering; Cancellation of Meeting SUMMARY: As a result of the impact of the recent government shutdown, the... Committee for Computer and Information Science and Engineering meeting. The public notice for this committee...

Exemplary Science Teachers' Use of Technology

ERIC Educational Resources Information Center

Hakverdi-Can, Meral; Dana, Thomas M.

2012-01-01

The purpose of this study is to examine exemplary science teachers' level of computer use, their knowledge/skills in using specific computer applications for science instruction, their use of computer-related applications/tools during their instruction, how often they required their students to use those applications in or for their science class…

What is bioinformatics? A proposed definition and overview of the field.

PubMed

Luscombe, N M; Greenbaum, D; Gerstein, M

2001-01-01

The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

Software Reviews.

ERIC Educational Resources Information Center

Science and Children, 1990

1990-01-01

Reviewed are seven computer software packages for IBM and/or Apple Computers. Included are "Windows on Science: Volume 1--Physical Science"; "Science Probe--Physical Science"; "Wildlife Adventures--Grizzly Bears"; "Science Skills--Development Programs"; "The Clean Machine"; "Rock Doctor";…

Generalized entropies and the similarity of texts

NASA Astrophysics Data System (ADS)

Altmann, Eduardo G.; Dias, Laércio; Gerlach, Martin

2017-01-01

We show how generalized Gibbs-Shannon entropies can provide new insights on the statistical properties of texts. The universal distribution of word frequencies (Zipf’s law) implies that the generalized entropies, computed at the word level, are dominated by words in a specific range of frequencies. Here we show that this is the case not only for the generalized entropies but also for the generalized (Jensen-Shannon) divergences, used to compute the similarity between different texts. This finding allows us to identify the contribution of specific words (and word frequencies) for the different generalized entropies and also to estimate the size of the databases needed to obtain a reliable estimation of the divergences. We test our results in large databases of books (from the google n-gram database) and scientific papers (indexed by Web of Science).

Machine learning bandgaps of double perovskites

PubMed Central

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; Ramprasad, R.; Gubernatis, J. E.; Lookman, T.

2016-01-01

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance. PMID:26783247

Advancements in RNASeqGUI towards a Reproducible Analysis of RNA-Seq Experiments

PubMed Central

Russo, Francesco; Righelli, Dario

2016-01-01

We present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interface that helps biologists to handle and analyse large data collected in RNA-Seq experiments. This work focuses on the concept of reproducible research and shows how it has been incorporated in RNASeqGUI to provide reproducible (computational) results. The novel version of RNASeqGUI combines graphical interfaces with tools for reproducible research, such as literate statistical programming, human readable report, parallel executions, caching, and interactive and web-explorable tables of results. These features allow the user to analyse big datasets in a fast, efficient, and reproducible way. Moreover, this paper represents a proof of concept, showing a simple way to develop computational tools for Life Science in the spirit of reproducible research. PMID:26977414

Attitudes toward science among grades 3 through 12 Arab students in Qatar: findings from a cross-sectional national study

NASA Astrophysics Data System (ADS)

Said, Ziad; Summers, Ryan; Abd-El-Khalick, Fouad; Wang, Shuai

2016-03-01

This study assessed students' attitudes toward science in Qatar. A cross-sectional, nationwide probability sample representing all students enrolled in grades 3 through 12 in the various types of schools in Qatar completed the 'Arabic Speaking Students' Attitudes toward Science Survey' (ASSASS). The validity and reliability of the 32-item instrument, encompassing five sub-scales, have already been shown to be robust. The present analysis focused on responses from 1978 participants representing the students who completed the ASSASS in Arabic. Descriptive statistics were computed and a competing pair of multiple indicators multiple causes models is presented that attempt to link patterns in students' responses to the ASSASS with a set of indicators. The final model retained student age, gender, nationality (i.e. Qatari vs. Non-Qatari Arab), and school type as indicators. Findings from this study suggest that participants' attitudes toward science decrease with age, and that these attitudes and related preferences are influenced by students' nationality and the type of school they attend. Equally important, the often-reported advantages for male over female precollege students in terms of attitudes toward science were much less prominent in the present study.

An Overview of NASA's Intelligent Systems Program

NASA Technical Reports Server (NTRS)

Cooke, Daniel E.; Norvig, Peter (Technical Monitor)

2001-01-01

NASA and the Computer Science Research community are poised to enter a critical era. An era in which - it seems - that each needs the other. Market forces, driven by the immediate economic viability of computer science research results, place Computer Science in a relatively novel position. These forces impact how research is done, and could, in worst case, drive the field away from significant innovation opting instead for incremental advances that result in greater stability in the market place. NASA, however, requires significant advances in computer science research in order to accomplish the exploration and science agenda it has set out for itself. NASA may indeed be poised to advance computer science research in this century much the way it advanced aero-based research in the last.

A Review of Models for Teacher Preparation Programs for Precollege Computer Science Education.

ERIC Educational Resources Information Center

Deek, Fadi P.; Kimmel, Howard

2002-01-01

Discusses the need for adequate precollege computer science education and focuses on the issues of teacher preparation programs and requirements needed to teach high school computer science. Presents models of teacher preparation programs and compares state requirements with Association for Computing Machinery (ACM) recommendations. (Author/LRW)

A DDC Bibliography on Computers in Information Sciences. Volume II. Information Sciences Series.

ERIC Educational Resources Information Center

Defense Documentation Center, Alexandria, VA.

The unclassified and unlimited bibliography compiles references dealing specifically with the role of computers in information sciences. The volume contains 239 annotated references grouped under three major headings: Artificial and Programming Languages, Computer Processing of Analog Data, and Computer Processing of Digital Data. The references…

Making Advanced Computer Science Topics More Accessible through Interactive Technologies

ERIC Educational Resources Information Center

Shao, Kun; Maher, Peter

2012-01-01

Purpose: Teaching advanced technical concepts in a computer science program to students of different technical backgrounds presents many challenges. The purpose of this paper is to present a detailed experimental pedagogy in teaching advanced computer science topics, such as computer networking, telecommunications and data structures using…

ASCR Workshop on Quantum Computing for Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aspuru-Guzik, Alan; Van Dam, Wim; Farhi, Edward

This report details the findings of the DOE ASCR Workshop on Quantum Computing for Science that was organized to assess the viability of quantum computing technologies to meet the computational requirements of the DOE’s science and energy mission, and to identify the potential impact of quantum technologies. The workshop was held on February 17-18, 2015, in Bethesda, MD, to solicit input from members of the quantum computing community. The workshop considered models of quantum computation and programming environments, physical science applications relevant to DOE's science mission as well as quantum simulation, and applied mathematics topics including potential quantum algorithms formore » linear algebra, graph theory, and machine learning. This report summarizes these perspectives into an outlook on the opportunities for quantum computing to impact problems relevant to the DOE’s mission as well as the additional research required to bring quantum computing to the point where it can have such impact.« less

BIOCOMPUTATION: some history and prospects.

PubMed

Cull, Paul

2013-06-01

At first glance, biology and computer science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing. We discuss several models of computation which have arisen from various biological studies. We show what these have in common, and conjecture how biology can still suggest answers and models for the next generation of computing problems. We discuss computation and argue that these biologically inspired models do not extend the theoretical limits on computation. We suggest that, in practice, biological models may give more succinct representations of various problems, and we mention a few cases in which biological models have proved useful. We also discuss the reciprocal impact of computer science on biology and cite a few significant contributions to biological science. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Linking Science and Statistics: Curriculum Expectations in Three Countries

ERIC Educational Resources Information Center

Watson, Jane M.

2017-01-01

This paper focuses on the curriculum links between statistics and science that teachers need to understand and apply in order to be effective teachers of the two fields of study. Meaningful statistics does not exist without context and science is the context for this paper. Although curriculum documents differ from country to country, this paper…

A Case Study of the Introduction of Computer Science in NZ Schools

ERIC Educational Resources Information Center

Bell, Tim; Andreae, Peter; Robins, Anthony

2014-01-01

For many years computing in New Zealand schools was focused on teaching students how to use computers, and there was little opportunity for students to learn about programming and computer science as formal subjects. In this article we review a series of initiatives that occurred from 2007 to 2009 that led to programming and computer science being…

Implementation and evaluation of an efficient secure computation system using ‘R’ for healthcare statistics

PubMed Central

Chida, Koji; Morohashi, Gembu; Fuji, Hitoshi; Magata, Fumihiko; Fujimura, Akiko; Hamada, Koki; Ikarashi, Dai; Yamamoto, Ryuichi

2014-01-01

Background and objective While the secondary use of medical data has gained attention, its adoption has been constrained due to protection of patient privacy. Making medical data secure by de-identification can be problematic, especially when the data concerns rare diseases. We require rigorous security management measures. Materials and methods Using secure computation, an approach from cryptography, our system can compute various statistics over encrypted medical records without decrypting them. An issue of secure computation is that the amount of processing time required is immense. We implemented a system that securely computes healthcare statistics from the statistical computing software ‘R’ by effectively combining secret-sharing-based secure computation with original computation. Results Testing confirmed that our system could correctly complete computation of average and unbiased variance of approximately 50 000 records of dummy insurance claim data in a little over a second. Computation including conditional expressions and/or comparison of values, for example, t test and median, could also be correctly completed in several tens of seconds to a few minutes. Discussion If medical records are simply encrypted, the risk of leaks exists because decryption is usually required during statistical analysis. Our system possesses high-level security because medical records remain in encrypted state even during statistical analysis. Also, our system can securely compute some basic statistics with conditional expressions using ‘R’ that works interactively while secure computation protocols generally require a significant amount of processing time. Conclusions We propose a secure statistical analysis system using ‘R’ for medical data that effectively integrates secret-sharing-based secure computation and original computation. PMID:24763677

Implementation and evaluation of an efficient secure computation system using 'R' for healthcare statistics.

PubMed

Chida, Koji; Morohashi, Gembu; Fuji, Hitoshi; Magata, Fumihiko; Fujimura, Akiko; Hamada, Koki; Ikarashi, Dai; Yamamoto, Ryuichi

2014-10-01

While the secondary use of medical data has gained attention, its adoption has been constrained due to protection of patient privacy. Making medical data secure by de-identification can be problematic, especially when the data concerns rare diseases. We require rigorous security management measures. Using secure computation, an approach from cryptography, our system can compute various statistics over encrypted medical records without decrypting them. An issue of secure computation is that the amount of processing time required is immense. We implemented a system that securely computes healthcare statistics from the statistical computing software 'R' by effectively combining secret-sharing-based secure computation with original computation. Testing confirmed that our system could correctly complete computation of average and unbiased variance of approximately 50,000 records of dummy insurance claim data in a little over a second. Computation including conditional expressions and/or comparison of values, for example, t test and median, could also be correctly completed in several tens of seconds to a few minutes. If medical records are simply encrypted, the risk of leaks exists because decryption is usually required during statistical analysis. Our system possesses high-level security because medical records remain in encrypted state even during statistical analysis. Also, our system can securely compute some basic statistics with conditional expressions using 'R' that works interactively while secure computation protocols generally require a significant amount of processing time. We propose a secure statistical analysis system using 'R' for medical data that effectively integrates secret-sharing-based secure computation and original computation. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

USGS Publications Warehouse

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

Research in Applied Mathematics, Fluid Mechanics and Computer Science

NASA Technical Reports Server (NTRS)

1999-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period October 1, 1998 through March 31, 1999.

[Research activities in applied mathematics, fluid mechanics, and computer science

NASA Technical Reports Server (NTRS)

1995-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period April 1, 1995 through September 30, 1995.

Activities of the Institute for Computer Applications in Science and Engineering

NASA Technical Reports Server (NTRS)

1985-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period April 1, 1985 through October 2, 1985 is summarized.

A Quantitative Model for Assessing Visual Simulation Software Architecture

DTIC Science & Technology

2011-09-01

Software Engineering Arnold Buss Research Associate Professor of MOVES LtCol Jeff Boleng, PhD Associate Professor of Computer Science U.S. Air Force Academy... science (operating and programming systems series). New York, NY, USA: Elsevier Science Ltd. Henry, S., & Kafura, D. (1984). The evaluation of software...Rudy Darken Professor of Computer Science Dissertation Supervisor Ted Lewis Professor of Computer Science Richard Riehle Professor of Practice

K-16 Computationally Rich Science Education: A Ten-Year Review of the "Journal of Science Education and Technology" (1998-2008)

ERIC Educational Resources Information Center

Wofford, Jennifer

2009-01-01

Computing is anticipated to have an increasingly expansive impact on the sciences overall, becoming the third, crucial component of a "golden triangle" that includes mathematics and experimental and theoretical science. However, even more true with computing than with math and science, we are not preparing our students for this new reality. It is…

Interactive Synthesis of Code Level Security Rules

DTIC Science & Technology

2017-04-01

Interactive Synthesis of Code-Level Security Rules A Thesis Presented by Leo St. Amour to The Department of Computer Science in partial fulfillment...of the requirements for the degree of Master of Science in Computer Science Northeastern University Boston, Massachusetts April 2017 DISTRIBUTION...Abstract of the Thesis Interactive Synthesis of Code-Level Security Rules by Leo St. Amour Master of Science in Computer Science Northeastern University

Approaching gender parity: Women in computer science at Afghanistan's Kabul University

NASA Astrophysics Data System (ADS)

Plane, Jandelyn

This study explores the representation of women in computer science at the tertiary level through data collected about undergraduate computer science education at Kabul University in Afghanistan. Previous studies have theorized reasons for underrepresentation of women in computer science, and while many of these reasons are indeed present in Afghanistan, they appear to hinder advancement to degree to a lesser extent. Women comprise at least 36% of each graduating class from KU's Computer Science Department; however, in 2007 women were 25% of the university population. In the US, women comprise over 50% of university populations while only graduating on average 25% women in undergraduate computer science programs. Representation of women in computer science in the US is 50% below the university rate, but at KU, it is 50% above the university rate. This mixed methods study of KU was conducted in the following three stages: setting up focus groups with women computer science students, distributing surveys to all students in the CS department, and conducting a series of 22 individual interviews with fourth year CS students. The analysis of the data collected and its comparison to literature on university/department retention in Science, Technology, Engineering and Mathematics gender representation and on women's education in underdeveloped Islamic countries illuminates KU's uncharacteristic representation of women in its Computer Science Department. The retention of women in STEM through the education pipeline has several characteristics in Afghanistan that differ from countries often studied in available literature. Few Afghan students have computers in their home and few have training beyond secretarial applications before considering studying CS at university. University students in Afghanistan are selected based on placement exams and are then assigned to an area of study, and financially supported throughout their academic career, resulting in a low attrition rate from the program. Gender and STEM literature identifies parental encouragement, stereotypes and employment perceptions as influential characteristics. Afghan women in computer science received significant parental encouragement even from parents with no computer background. They do not seem to be influenced by any negative "geek" stereotypes, but they do perceive limitations when considering employment after graduation.

Science-Technology Coupling: The Case of Mathematical Logic and Computer Science.

ERIC Educational Resources Information Center

Wagner-Dobler, Roland

1997-01-01

In the history of science, there have often been periods of sudden rapprochements between pure science and technology-oriented branches of science. Mathematical logic as pure science and computer science as technology-oriented science have experienced such a rapprochement, which is studied in this article in a bibliometric manner. (Author)

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Assessment of the Mars Science Laboratory Entry, Descent, and Landing Simulation

NASA Technical Reports Server (NTRS)

Way, David W.; Davis, J. L.; Shidner, Jeremy D.

2013-01-01

On August 5, 2012, the Mars Science Laboratory rover, Curiosity, successfully landed inside Gale Crater. This landing was only the seventh successful landing and fourth rover to be delivered to Mars. Weighing nearly one metric ton, Curiosity is the largest and most complex rover ever sent to investigate another planet. Safely landing such a large payload required an innovative Entry, Descent, and Landing system, which included the first guided entry at Mars, the largest supersonic parachute ever flown at Mars, and a novel and untested Sky Crane landing system. A complete, end-to-end, six degree-of-freedom, multi-body computer simulation of the Mars Science Laboratory Entry, Descent, and Landing sequence was developed at the NASA Langley Research Center. In-flight data gathered during the successful landing is compared to pre-flight statistical distributions, predicted by the simulation. These comparisons provide insight into both the accuracy of the simulation and the overall performance of the vehicle.

A new course and textbook on Physical Models of Living Systems, for science and engineering undergraduates

NASA Astrophysics Data System (ADS)

Nelson, Philip

2015-03-01

I'll describe an intermediate-level course on ``Physical Models of Living Systems.'' The only prerequisite is first-year university physics and calculus. The course is a response to rapidly growing interest among undergraduates in a broad range of science and engineering majors. Students acquire several research skills that are often not addressed in traditional courses: Basic modeling skills Probabilistic modeling skills Data analysis methods Computer programming using a general-purpose platform like MATLAB or Python Dynamical systems, particularly feedback control. These basic skills, which are relevant to nearly any field of science or engineering, are presented in the context of case studies from living systems, including: Virus dynamics Bacterial genetics and evolution of drug resistance Statistical inference Superresolution microscopy Synthetic biology Naturally evolved cellular circuits. Work supported by NSF Grants EF-0928048 and DMR-0832802.

Preliminary assessment of the Mars Science Laboratory entry, descent, and landing simulation

NASA Astrophysics Data System (ADS)

Way, David W.

On August 5, 2012, the Mars Science Laboratory rover, Curiosity, successfully landed inside Gale Crater. This landing was the seventh successful landing and fourth rover to be delivered to Mars. Weighing nearly one metric ton, Curiosity is the largest and most complex rover ever sent to investigate another planet. Safely landing such a large payload required an innovative Entry, Descent, and Landing system, which included the first guided entry at Mars, the largest supersonic parachute ever flown at Mars, and the novel Sky Crane landing system. A complete, end-to-end, six degree-of-freedom, multi-body computer simulation of the Mars Science Laboratory Entry, Descent, and Landing sequence was developed at the NASA Langley Research Center. In-flight data gathered during the successful landing is compared to pre-flight statistical distributions, predicted by the simulation. These comparisons provide insight into both the accuracy of the simulation and the overall performance of the Entry, Descent, and Landing system.

Aurorasaurus Database of Real-Time, Soft-Sensor Sourced Aurora Data for Space Weather Research

NASA Astrophysics Data System (ADS)

Kosar, B.; MacDonald, E.; Heavner, M.

2017-12-01

Aurorasaurus is an innovative citizen science project focused on two fundamental objectives i.e., collecting real-time, ground-based signals of auroral visibility from citizen scientists (soft-sensors) and incorporating this new type of data into scientific investigations pertaining to aurora. The project has been live since the Fall of 2014, and as of Summer 2017, the database compiled approximately 12,000 observations (5295 direct reports and 6413 verified tweets). In this presentation, we will focus on demonstrating the utility of this robust science quality data for space weather research needs. These data scale with the size of the event and are well-suited to capture the largest, rarest events. Emerging state-of-the-art computational methods based on statistical inference such as machine learning frameworks and data-model integration methods can offer new insights that could potentially lead to better real-time assessment and space weather prediction when citizen science data are combined with traditional sources.

Preliminary Assessment of the Mars Science Laboratory Entry, Descent, and Landing Simulation

NASA Technical Reports Server (NTRS)

Way, David W.

2013-01-01

On August 5, 2012, the Mars Science Laboratory rover, Curiosity, successfully landed inside Gale Crater. This landing was only the seventh successful landing and fourth rover to be delivered to Mars. Weighing nearly one metric ton, Curiosity is the largest and most complex rover ever sent to investigate another planet. Safely landing such a large payload required an innovative Entry, Descent, and Landing system, which included the first guided entry at Mars, the largest supersonic parachute ever flown at Mars, and a novel and untested Sky Crane landing system. A complete, end-to-end, six degree-of-freedom, multibody computer simulation of the Mars Science Laboratory Entry, Descent, and Landing sequence was developed at the NASA Langley Research Center. In-flight data gathered during the successful landing is compared to pre-flight statistical distributions, predicted by the simulation. These comparisons provide insight into both the accuracy of the simulation and the overall performance of the vehicle.

The effects of hands-on-science instruction on the science achievement of middle school students

NASA Astrophysics Data System (ADS)

Wiggins, Felita

Student achievement in the Twenty First Century demands a new rigor in student science knowledge, since advances in science and technology require students to think and act like scientists. As a result, students must acquire proficient levels of knowledge and skills to support a knowledge base that is expanding exponentially with new scientific advances. This study examined the effects of hands-on-science instruction on the science achievement of middle school students. More specifically, this study was concerned with the influence of hands-on science instruction versus traditional science instruction on the science test scores of middle school students. The subjects in this study were one hundred and twenty sixth-grade students in six classes. Instruction involved lecture/discussion and hands-on activities carried out for a three week period. Specifically, the study ascertained the influence of the variables gender, ethnicity, and socioeconomic status on the science test scores of middle school students. Additionally, this study assessed the effect of the variables gender, ethnicity, and socioeconomic status on the attitudes of sixth grade students toward science. The two instruments used to collect data for this study were the Prentice Hall unit ecosystem test and the Scientific Work Experience Programs for Teachers Study (SWEPT) student's attitude survey. Moreover, the data for the study was treated using the One-Way Analysis of Covariance and the One-Way Analysis of Variance. The following findings were made based on the results: (1) A statistically significant difference existed in the science performance of middle school students exposed to hands-on science instruction. These students had significantly higher scores than the science performance of middle school students exposed to traditional instruction. (2) A statistically significant difference did not exist between the science scores of male and female middle school students. (3) A statistically significant difference did not exist between the science scores of African American and non-African American middle school students. (4) A statistically significant difference existed in the socioeconomic status of students who were not provided with assisted lunches. Students with unassisted lunches had significantly higher science scores than those middle school students who were provided with assisted lunches. (5) A statistically significant difference was not found in the attitude scores of middle school students who were exposed to hands-on or traditional science instruction. (6) A statistically significant difference was not found in the observed attitude scores of middle school students who were exposed to either hands-on or traditional science instruction by their socioeconomic status. (7) A statistically significant difference was not found in the observed attitude scores of male and female students. (8) A statistically significant difference was not found in the observed attitude scores of African American and non African American students.

78 FR 61870 - Advisory Committee for Computer and Information Science and Engineering; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-04

... NATIONAL SCIENCE FOUNDATION Advisory Committee for Computer and Information Science and Engineering; Notice of Meeting In accordance with Federal Advisory Committee Act (Pub. L. 92-463, as amended... Committee for Computer and Information Science and Engineering (1115). Date/Time: Oct 31, 2013: 12:30 p.m...

Use of handheld computers in clinical practice: a systematic review.

PubMed

Mickan, Sharon; Atherton, Helen; Roberts, Nia Wyn; Heneghan, Carl; Tilson, Julie K

2014-07-06

Many healthcare professionals use smartphones and tablets to inform patient care. Contemporary research suggests that handheld computers may support aspects of clinical diagnosis and management. This systematic review was designed to synthesise high quality evidence to answer the question; Does healthcare professionals' use of handheld computers improve their access to information and support clinical decision making at the point of care? A detailed search was conducted using Cochrane, MEDLINE, EMBASE, PsycINFO, Science and Social Science Citation Indices since 2001. Interventions promoting healthcare professionals seeking information or making clinical decisions using handheld computers were included. Classroom learning and the use of laptop computers were excluded. Two authors independently selected studies, assessed quality using the Cochrane Risk of Bias tool and extracted data. High levels of data heterogeneity negated statistical synthesis. Instead, evidence for effectiveness was summarised narratively, according to each study's aim for assessing the impact of handheld computer use. We included seven randomised trials investigating medical or nursing staffs' use of Personal Digital Assistants. Effectiveness was demonstrated across three distinct functions that emerged from the data: accessing information for clinical knowledge, adherence to guidelines and diagnostic decision making. When healthcare professionals used handheld computers to access clinical information, their knowledge improved significantly more than peers who used paper resources. When clinical guideline recommendations were presented on handheld computers, clinicians made significantly safer prescribing decisions and adhered more closely to recommendations than peers using paper resources. Finally, healthcare professionals made significantly more appropriate diagnostic decisions using clinical decision making tools on handheld computers compared to colleagues who did not have access to these tools. For these clinical decisions, the numbers need to test/screen were all less than 11. Healthcare professionals' use of handheld computers may improve their information seeking, adherence to guidelines and clinical decision making. Handheld computers can provide real time access to and analysis of clinical information. The integration of clinical decision support systems within handheld computers offers clinicians the highest level of synthesised evidence at the point of care. Future research is needed to replicate these early results and to identify beneficial clinical outcomes.

Use of handheld computers in clinical practice: a systematic review

PubMed Central

2014-01-01

Background Many healthcare professionals use smartphones and tablets to inform patient care. Contemporary research suggests that handheld computers may support aspects of clinical diagnosis and management. This systematic review was designed to synthesise high quality evidence to answer the question; Does healthcare professionals’ use of handheld computers improve their access to information and support clinical decision making at the point of care? Methods A detailed search was conducted using Cochrane, MEDLINE, EMBASE, PsycINFO, Science and Social Science Citation Indices since 2001. Interventions promoting healthcare professionals seeking information or making clinical decisions using handheld computers were included. Classroom learning and the use of laptop computers were excluded. Two authors independently selected studies, assessed quality using the Cochrane Risk of Bias tool and extracted data. High levels of data heterogeneity negated statistical synthesis. Instead, evidence for effectiveness was summarised narratively, according to each study’s aim for assessing the impact of handheld computer use. Results We included seven randomised trials investigating medical or nursing staffs’ use of Personal Digital Assistants. Effectiveness was demonstrated across three distinct functions that emerged from the data: accessing information for clinical knowledge, adherence to guidelines and diagnostic decision making. When healthcare professionals used handheld computers to access clinical information, their knowledge improved significantly more than peers who used paper resources. When clinical guideline recommendations were presented on handheld computers, clinicians made significantly safer prescribing decisions and adhered more closely to recommendations than peers using paper resources. Finally, healthcare professionals made significantly more appropriate diagnostic decisions using clinical decision making tools on handheld computers compared to colleagues who did not have access to these tools. For these clinical decisions, the numbers need to test/screen were all less than 11. Conclusion Healthcare professionals’ use of handheld computers may improve their information seeking, adherence to guidelines and clinical decision making. Handheld computers can provide real time access to and analysis of clinical information. The integration of clinical decision support systems within handheld computers offers clinicians the highest level of synthesised evidence at the point of care. Future research is needed to replicate these early results and to identify beneficial clinical outcomes. PMID:24998515

The Role of Statistics in Kosovo Enterprises

ERIC Educational Resources Information Center

Gjonbalaj, Muje; Dema, Marjan; Miftari, Iliriana

2009-01-01

Considering science as the main contributor to contemporary developments has encouraged us to raise a scientific discussion regarding the role of statistics in business decision-making and economic development. Statistics, as an applicative science, is growing and being widely applied in different fields and professions. Statistical thinking is…

Activities of the Institute for Computer Applications in Science and Engineering (ICASE)

NASA Technical Reports Server (NTRS)

1985-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period October 1, 1984 through March 31, 1985 is summarized.

[Research Conducted at the Institute for Computer Applications in Science and Engineering

NASA Technical Reports Server (NTRS)

1997-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period 1 Oct. 1996 - 31 Mar. 1997.

Activities of the Institute for Computer Applications in Science and Engineering (ICASE)

NASA Technical Reports Server (NTRS)

1988-01-01

This report summarizes research conducted at the Institute for Computer Applications Science and Engineering in applied mathematics, numerical analysis, and computer science during the period October 2, 1987 through March 31, 1988.

[Activities of Institute for Computer Applications in Science and Engineering (ICASE)

NASA Technical Reports Server (NTRS)

1999-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics. fluid mechanics, and computer science during the period April 1, 1999 through September 30. 1999.

Practical Measurement of Complexity In Dynamic Systems

DTIC Science & Technology

2012-01-01

policies that produce highly complex behaviors , yet yield no benefit. 21Jason B. Clark and David R. Jacques / Procedia Computer Science 8 (2012) 14... Procedia Computer Science 8 (2012) 14 – 21 1877-0509 © 2012 Published by Elsevier B.V. doi:10.1016/j.procs.2012.01.008 Available online at...www.sciencedirect.com Procedia Computer Science Procedia Computer Science 00 (2012) 000–000 www.elsevier.com/locate/ procedia Available online at

The role of physicality in rich programming environments

NASA Astrophysics Data System (ADS)

Liu, Allison S.; Schunn, Christian D.; Flot, Jesse; Shoop, Robin

2013-12-01

Computer science proficiency continues to grow in importance, while the number of students entering computer science-related fields declines. Many rich programming environments have been created to motivate student interest and expertise in computer science. In the current study, we investigated whether a recently created environment, Robot Virtual Worlds (RVWs), can be used to teach computer science principles within a robotics context by examining its use in high-school classrooms. We also investigated whether the lack of physicality in these environments impacts student learning by comparing classrooms that used either virtual or physical robots for the RVW curriculum. Results suggest that the RVW environment leads to significant gains in computer science knowledge, that virtual robots lead to faster learning, and that physical robots may have some influence on algorithmic thinking. We discuss the implications of physicality in these programming environments for learning computer science.

Path Not Found: Disparities in Access to Computer Science Courses in California High Schools

ERIC Educational Resources Information Center

Martin, Alexis; McAlear, Frieda; Scott, Allison

2015-01-01

"Path Not Found: Disparities in Access to Computer Science Courses in California High Schools" exposes one of the foundational causes of underrepresentation in computing: disparities in access to computer science courses in California's public high schools. This report provides new, detailed data on these disparities by student body…

Developing Oral and Written Communication Skills in Undergraduate Computer Science and Information Systems Curriculum

ERIC Educational Resources Information Center

Kortsarts, Yana; Fischbach, Adam; Rufinus, Jeff; Utell, Janine M.; Yoon, Suk-Chung

2010-01-01

Developing and applying oral and written communication skills in the undergraduate computer science and computer information systems curriculum--one of the ABET accreditation requirements - is a very challenging and, at the same time, a rewarding task that provides various opportunities to enrich the undergraduate computer science and computer…

EOS MLS Science Data Processing System: A Description of Architecture and Capabilities

NASA Technical Reports Server (NTRS)

Cuddy, David T.; Echeverri, Mark D.; Wagner, Paul A.; Hanzel, Audrey T.; Fuller, Ryan A.

2006-01-01

This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components--the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products.

RANDOMNESS of Numbers DEFINITION(QUERY:WHAT? V HOW?) ONLY Via MAXWELL-BOLTZMANN CLASSICAL-Statistics(MBCS) Hot-Plasma VS. Digits-Clumping Log-Law NON-Randomness Inversion ONLY BOSE-EINSTEIN QUANTUM-Statistics(BEQS) .

NASA Astrophysics Data System (ADS)

Siegel, Z.; Siegel, Edward Carl-Ludwig

2011-03-01

RANDOMNESS of Numbers cognitive-semantics DEFINITION VIA Cognition QUERY: WHAT???, NOT HOW?) VS. computer-``science" mindLESS number-crunching (Harrel-Sipser-...) algorithmics Goldreich "PSEUDO-randomness"[Not.AMS(02)] mea-culpa is ONLY via MAXWELL-BOLTZMANN CLASSICAL-STATISTICS(NOT FDQS!!!) "hot-plasma" REPULSION VERSUS Newcomb(1881)-Weyl(1914;1916)-Benford(1938) "NeWBe" logarithmic-law digit-CLUMPING/ CLUSTERING NON-Randomness simple Siegel[AMS Joint.Mtg.(02)-Abs. # 973-60-124] algebraic-inversion to THE QUANTUM and ONLY BEQS preferentially SEQUENTIALLY lower-DIGITS CLUMPING/CLUSTERING with d = 0 BEC, is ONLY VIA Siegel-Baez FUZZYICS=CATEGORYICS (SON OF TRIZ)/"Category-Semantics"(C-S), latter intersection/union of Lawvere(1964)-Siegel(1964)] category-theory (matrix: MORPHISMS V FUNCTORS) "+" cognitive-semantics'' (matrix: ANTONYMS V SYNONYMS) yields Siegel-Baez FUZZYICS=CATEGORYICS/C-S tabular list-format matrix truth-table analytics: MBCS RANDOMNESS TRUTH/EMET!!!

Sensitivity to the Sampling Process Emerges From the Principle of Efficiency.

PubMed

Jara-Ettinger, Julian; Sun, Felix; Schulz, Laura; Tenenbaum, Joshua B

2018-05-01

Humans can seamlessly infer other people's preferences, based on what they do. Broadly, two types of accounts have been proposed to explain different aspects of this ability. The first account focuses on spatial information: Agents' efficient navigation in space reveals what they like. The second account focuses on statistical information: Uncommon choices reveal stronger preferences. Together, these two lines of research suggest that we have two distinct capacities for inferring preferences. Here we propose that this is not the case, and that spatial-based and statistical-based preference inferences can be explained by the assumption that agents are efficient alone. We show that people's sensitivity to spatial and statistical information when they infer preferences is best predicted by a computational model of the principle of efficiency, and that this model outperforms dual-system models, even when the latter are fit to participant judgments. Our results suggest that, as adults, a unified understanding of agency under the principle of efficiency underlies our ability to infer preferences. Copyright © 2018 Cognitive Science Society, Inc.

Photo-Realistic Statistical Skull Morphotypes: New Exemplars for Ancestry and Sex Estimation in Forensic Anthropology.

PubMed

Caple, Jodi; Stephan, Carl N

2017-05-01

Graphic exemplars of cranial sex and ancestry are essential to forensic anthropology for standardizing casework, training analysts, and communicating group trends. To date, graphic exemplars have comprised hand-drawn sketches, or photographs of individual specimens, which risks bias/subjectivity. Here, we performed quantitative analysis of photographic data to generate new photo-realistic and objective exemplars of skull form. Standardized anterior and left lateral photographs of skulls for each sex were analyzed in the computer graphics program Psychomorph for the following groups: South African Blacks, South African Whites, American Blacks, American Whites, and Japanese. The average cranial form was calculated for each photographic view, before the color information for every individual was warped to the average form and combined to produce statistical averages. These mathematically derived exemplars-and their statistical exaggerations or extremes-retain the high-resolution detail of the original photographic dataset, making them the ideal casework and training reference standards. © 2016 American Academy of Forensic Sciences.

Women in computer science: An interpretative phenomenological analysis exploring common factors contributing to women's selection and persistence in computer science as an academic major

NASA Astrophysics Data System (ADS)

Thackeray, Lynn Roy

The purpose of this study is to understand the meaning that women make of the social and cultural factors that influence their reasons for entering and remaining in study of computer science. The twenty-first century presents many new challenges in career development and workforce choices for both men and women. Information technology has become the driving force behind many areas of the economy. As this trend continues, it has become essential that U.S. citizens need to pursue a career in technologies, including the computing sciences. Although computer science is a very lucrative profession, many Americans, especially women, are not choosing it as a profession. Recent studies have shown no significant differences in math, technical and science competency between men and women. Therefore, other factors, such as social, cultural, and environmental influences seem to affect women's decisions in choosing an area of study and career choices. A phenomenological method of qualitative research was used in this study, based on interviews of seven female students who are currently enrolled in a post-secondary computer science program. Their narratives provided meaning into the social and cultural environments that contribute to their persistence in their technical studies, as well as identifying barriers and challenges that are faced by female students who choose to study computer science. It is hoped that the data collected from this study may provide recommendations for the recruiting, retention and support for women in computer science departments of U.S. colleges and universities, and thereby increase the numbers of women computer scientists in industry. Keywords: gender access, self-efficacy, culture, stereotypes, computer education, diversity.

Computer programs for computing particle-size statistics of fluvial sediments

USGS Publications Warehouse

Stevens, H.H.; Hubbell, D.W.

1986-01-01

Two versions of computer programs for inputing data and computing particle-size statistics of fluvial sediments are presented. The FORTRAN 77 language versions are for use on the Prime computer, and the BASIC language versions are for use on microcomputers. The size-statistics program compute Inman, Trask , and Folk statistical parameters from phi values and sizes determined for 10 specified percent-finer values from inputed size and percent-finer data. The program also determines the percentage gravel, sand, silt, and clay, and the Meyer-Peter effective diameter. Documentation and listings for both versions of the programs are included. (Author 's abstract)

Introduction to multivariate discrimination

NASA Astrophysics Data System (ADS)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

77 FR 38630 - Open Internet Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-28

... Computer Science and Co-Founder of the Berkman Center for Internet and Society, Harvard University, is... of Technology Computer Science and Artificial Intelligence Laboratory, is appointed vice-chairperson... Jennifer Rexford, Professor of Computer Science, Princeton University Dennis Roberson, Vice Provost...

Research in progress at the Institute for Computer Applications in Science and Engineering

NASA Technical Reports Server (NTRS)

1987-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period April 1, 1987 through October 1, 1987.

A parallel-processing approach to computing for the geographic sciences; applications and systems enhancements

USGS Publications Warehouse

Crane, Michael; Steinwand, Dan; Beckmann, Tim; Krpan, Greg; Liu, Shu-Guang; Nichols, Erin; Haga, Jim; Maddox, Brian; Bilderback, Chris; Feller, Mark; Homer, George

2001-01-01

The overarching goal of this project is to build a spatially distributed infrastructure for information science research by forming a team of information science researchers and providing them with similar hardware and software tools to perform collaborative research. Four geographically distributed Centers of the U.S. Geological Survey (USGS) are developing their own clusters of low-cost, personal computers into parallel computing environments that provide a costeffective way for the USGS to increase participation in the high-performance computing community. Referred to as Beowulf clusters, these hybrid systems provide the robust computing power required for conducting information science research into parallel computing systems and applications.

Enabling Earth Science Through Cloud Computing

NASA Technical Reports Server (NTRS)

Hardman, Sean; Riofrio, Andres; Shams, Khawaja; Freeborn, Dana; Springer, Paul; Chafin, Brian

2012-01-01

Cloud Computing holds tremendous potential for missions across the National Aeronautics and Space Administration. Several flight missions are already benefiting from an investment in cloud computing for mission critical pipelines and services through faster processing time, higher availability, and drastically lower costs available on cloud systems. However, these processes do not currently extend to general scientific algorithms relevant to earth science missions. The members of the Airborne Cloud Computing Environment task at the Jet Propulsion Laboratory have worked closely with the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) mission to integrate cloud computing into their science data processing pipeline. This paper details the efforts involved in deploying a science data system for the CARVE mission, evaluating and integrating cloud computing solutions with the system and porting their science algorithms for execution in a cloud environment.

On sufficient statistics of least-squares superposition of vector sets.

PubMed

Konagurthu, Arun S; Kasarapu, Parthan; Allison, Lloyd; Collier, James H; Lesk, Arthur M

2015-06-01

The problem of superposition of two corresponding vector sets by minimizing their sum-of-squares error under orthogonal transformation is a fundamental task in many areas of science, notably structural molecular biology. This problem can be solved exactly using an algorithm whose time complexity grows linearly with the number of correspondences. This efficient solution has facilitated the widespread use of the superposition task, particularly in studies involving macromolecular structures. This article formally derives a set of sufficient statistics for the least-squares superposition problem. These statistics are additive. This permits a highly efficient (constant time) computation of superpositions (and sufficient statistics) of vector sets that are composed from its constituent vector sets under addition or deletion operation, where the sufficient statistics of the constituent sets are already known (that is, the constituent vector sets have been previously superposed). This results in a drastic improvement in the run time of the methods that commonly superpose vector sets under addition or deletion operations, where previously these operations were carried out ab initio (ignoring the sufficient statistics). We experimentally demonstrate the improvement our work offers in the context of protein structural alignment programs that assemble a reliable structural alignment from well-fitting (substructural) fragment pairs. A C++ library for this task is available online under an open-source license.

Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts

PubMed Central

2017-01-01

Background Data concerning patients originates from a variety of sources on social media. Objective The aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients’ perspectives on a given medical prescription. Methods To shed light on patients’ behavior and concerns, we focused on Crohn’s disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn’s disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen’s kappa coefficient method. Results The first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen’s kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was found between the experts’ opinion and the software tool. Conclusions We show how data posted on Facebook by Crohn’s disease patients are a useful dataset to understand the patient’s perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients’ opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients’ sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients’ perspective on a specific medical therapy. PMID:28793981

Teaching and Learning Methodologies Supported by ICT Applied in Computer Science

ERIC Educational Resources Information Center

Capacho, Jose

2016-01-01

The main objective of this paper is to show a set of new methodologies applied in the teaching of Computer Science using ICT. The methodologies are framed in the conceptual basis of the following sciences: Psychology, Education and Computer Science. The theoretical framework of the research is supported by Behavioral Theory, Gestalt Theory.…

76 FR 61118 - Advisory Committee for Computer and Information Science and Engineering; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-03

... Engineering; Notice of Meeting In accordance with the Federal Advisory Committee Act (Pub. L. 92- 463, as... Computer and Information Science and Engineering (1115). Date and Time: November 1, 2011 from 12 p.m.-5:30... Computer and Information Science and Engineering, National Science Foundation, 4201 Wilson Blvd., Suite...

Computer Science in High School Graduation Requirements. ECS Education Trends (Updated)

ERIC Educational Resources Information Center

Zinth, Jennifer

2016-01-01

Allowing high school students to fulfill a math or science high school graduation requirement via a computer science credit may encourage more student to pursue computer science coursework. This Education Trends report is an update to the original report released in April 2015 and explores state policies that allow or require districts to apply…

A Review of Computer Science Resources for Learning and Teaching with K-12 Computing Curricula: An Australian Case Study

ERIC Educational Resources Information Center

Falkner, Katrina; Vivian, Rebecca

2015-01-01

To support teachers to implement Computer Science curricula into classrooms from the very first year of school, teachers, schools and organisations seek quality curriculum resources to support implementation and teacher professional development. Until now, many Computer Science resources and outreach initiatives have targeted K-12 school-age…

Technology, Pedagogy, and Epistemology: Opportunities and Challenges of Using Computer Modeling and Simulation Tools in Elementary Science Methods

ERIC Educational Resources Information Center

Schwarz, Christina V.; Meyer, Jason; Sharma, Ajay

2007-01-01

This study infused computer modeling and simulation tools in a 1-semester undergraduate elementary science methods course to advance preservice teachers' understandings of computer software use in science teaching and to help them learn important aspects of pedagogy and epistemology. Preservice teachers used computer modeling and simulation tools…

Prospective Students' Reactions to the Presentation of the Computer Science Major

ERIC Educational Resources Information Center

Weaver, Daniel Scott

2010-01-01

The number of students enrolling in Computer Science in colleges and Universities has declined since its peak in the early 2000s. Some claim contributing factors that intimate that prospective students fear the lack of employment opportunities if they study computing in college. However, the lack of understanding of what Computer Science is and…

Exposing the Science in Citizen Science: Fitness to Purpose and Intentional Design.

PubMed

Parrish, Julia K; Burgess, Hillary; Weltzin, Jake F; Fortson, Lucy; Wiggins, Andrea; Simmons, Brooke

2018-05-21

Citizen science is a growing phenomenon. With millions of people involved and billions of in-kind dollars contributed annually, this broad extent, fine grain approach to data collection should be garnering enthusiastic support in the mainstream science and higher education communities. However, many academic researchers demonstrate distinct biases against the use of citizen science as a source of rigorous information. To engage the public in scientific research, and the research community in the practice of citizen science, a mutual understanding is needed of accepted quality standards in science, and the corresponding specifics of project design and implementation when working with a broad public base. We define a science-based typology focused on the degree to which projects deliver the type(s) and quality of data/work needed to produce valid scientific outcomes directly useful in science and natural resource management. Where project intent includes direct contribution to science and the public is actively involved either virtually or hands-on, we examine the measures of quality assurance (methods to increase data quality during the design and implementation phases of a project) and quality control (post hoc methods to increase the quality of scientific outcomes). We suggest that high quality science can be produced with massive, largely one-off, participation if data collection is simple and quality control includes algorithm voting, statistical pruning and/or computational modeling. Small to mid-scale projects engaging participants in repeated, often complex, sampling can advance quality through expert-led training and well-designed materials, and through independent verification. Both approaches - simplification at scale and complexity with care - generate more robust science outcomes.

PREPARING FOR EXASCALE: ORNL Leadership Computing Application Requirements and Strategy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joubert, Wayne; Kothe, Douglas B; Nam, Hai Ah

2009-12-01

In 2009 the Oak Ridge Leadership Computing Facility (OLCF), a U.S. Department of Energy (DOE) facility at the Oak Ridge National Laboratory (ORNL) National Center for Computational Sciences (NCCS), elicited petascale computational science requirements from leading computational scientists in the international science community. This effort targeted science teams whose projects received large computer allocation awards on OLCF systems. A clear finding of this process was that in order to reach their science goals over the next several years, multiple projects will require computational resources in excess of an order of magnitude more powerful than those currently available. Additionally, for themore » longer term, next-generation science will require computing platforms of exascale capability in order to reach DOE science objectives over the next decade. It is generally recognized that achieving exascale in the proposed time frame will require disruptive changes in computer hardware and software. Processor hardware will become necessarily heterogeneous and will include accelerator technologies. Software must undergo the concomitant changes needed to extract the available performance from this heterogeneous hardware. This disruption portends to be substantial, not unlike the change to the message passing paradigm in the computational science community over 20 years ago. Since technological disruptions take time to assimilate, we must aggressively embark on this course of change now, to insure that science applications and their underlying programming models are mature and ready when exascale computing arrives. This includes initiation of application readiness efforts to adapt existing codes to heterogeneous architectures, support of relevant software tools, and procurement of next-generation hardware testbeds for porting and testing codes. The 2009 OLCF requirements process identified numerous actions necessary to meet this challenge: (1) Hardware capabilities must be advanced on multiple fronts, including peak flops, node memory capacity, interconnect latency, interconnect bandwidth, and memory bandwidth. (2) Effective parallel programming interfaces must be developed to exploit the power of emerging hardware. (3) Science application teams must now begin to adapt and reformulate application codes to the new hardware and software, typified by hierarchical and disparate layers of compute, memory and concurrency. (4) Algorithm research must be realigned to exploit this hierarchy. (5) When possible, mathematical libraries must be used to encapsulate the required operations in an efficient and useful way. (6) Software tools must be developed to make the new hardware more usable. (7) Science application software must be improved to cope with the increasing complexity of computing systems. (8) Data management efforts must be readied for the larger quantities of data generated by larger, more accurate science models. Requirements elicitation, analysis, validation, and management comprise a difficult and inexact process, particularly in periods of technological change. Nonetheless, the OLCF requirements modeling process is becoming increasingly quantitative and actionable, as the process becomes more developed and mature, and the process this year has identified clear and concrete steps to be taken. This report discloses (1) the fundamental science case driving the need for the next generation of computer hardware, (2) application usage trends that illustrate the science need, (3) application performance characteristics that drive the need for increased hardware capabilities, (4) resource and process requirements that make the development and deployment of science applications on next-generation hardware successful, and (5) summary recommendations for the required next steps within the computer and computational science communities.« less

Using the Tower of Hanoi puzzle to infuse your mathematics classroom with computer science concepts

NASA Astrophysics Data System (ADS)

Marzocchi, Alison S.

2016-07-01

This article suggests that logic puzzles, such as the well-known Tower of Hanoi puzzle, can be used to introduce computer science concepts to mathematics students of all ages. Mathematics teachers introduce their students to computer science concepts that are enacted spontaneously and subconsciously throughout the solution to the Tower of Hanoi puzzle. These concepts include, but are not limited to, conditionals, iteration, and recursion. Lessons, such as the one proposed in this article, are easily implementable in mathematics classrooms and extracurricular programmes as they are good candidates for 'drop in' lessons that do not need to fit into any particular place in the typical curriculum sequence. As an example for readers, the author describes how she used the puzzle in her own Number Sense and Logic course during the federally funded Upward Bound Math/Science summer programme for college-intending low-income high school students. The article explains each computer science term with real-life and mathematical examples, applies each term to the Tower of Hanoi puzzle solution, and describes how students connected the terms to their own solutions of the puzzle. It is timely and important to expose mathematics students to computer science concepts. Given the rate at which technology is currently advancing, and our increased dependence on technology in our daily lives, it has become more important than ever for children to be exposed to computer science. Yet, despite the importance of exposing today's children to computer science, many children are not given adequate opportunity to learn computer science in schools. In the United States, for example, most students finish high school without ever taking a computing course. Mathematics lessons, such as the one described in this article, can help to make computer science more accessible to students who may have otherwise had little opportunity to be introduced to these increasingly important concepts.

Computers as an Instrument for Data Analysis. Technical Report No. 11.

ERIC Educational Resources Information Center

Muller, Mervin E.

A review of statistical data analysis involving computers as a multi-dimensional problem provides the perspective for consideration of the use of computers in statistical analysis and the problems associated with large data files. An overall description of STATJOB, a particular system for doing statistical data analysis on a digital computer,…

Resilience Among Students at the Basic Enlisted Submarine School

DTIC Science & Technology

2016-12-01

reported resilience. The Hayes’ Macro in the Statistical Package for the Social Sciences (SSPS) was used to uncover factors relevant to mediation analysis... Statistical Package for the Social Sciences (SPSS) was used to uncover factors relevant to mediation analysis. Findings suggest that the encouragement of...to Stressful Experiences Scale RTC Recruit Training Command SPSS Statistical Package for the Social Sciences SS Social Support SWB Subjective Well

Utilization of computer technology by science teachers in public high schools and the impact of standardized testing

NASA Astrophysics Data System (ADS)

Priest, Richard Harding

A significant percentage of high school science teachers are not using computers to teach their students or prepare them for standardized testing. A survey of high school science teachers was conducted to determine how they are having students use computers in the classroom, why science teachers are not using computers in the classroom, which variables were relevant to their not using computers, and what are the effects of standardized testing on the use of technology in the high school science classroom. A self-administered questionnaire was developed to measure these aspects of computer integration and demographic information. A follow-up telephone interview survey of a portion of the original sample was conducted in order to clarify questions, correct misunderstandings, and to draw out more holistic descriptions from the subjects. The primary method used to analyze the quantitative data was frequency distributions. Multiple regression analysis was used to investigate the relationships between the barriers and facilitators and the dimensions of instructional use, frequency, and importance of the use of computers. All high school science teachers in a large urban/suburban school district were sent surveys. A response rate of 58% resulted from two mailings of the survey. It was found that contributing factors to why science teachers do not use computers were not enough up-to-date computers in their classrooms and other educational commitments and duties do not leave them enough time to prepare lessons that include technology. While a high percentage of science teachers thought their school and district administrations were supportive of technology, they also believed more inservice technology training and follow-up activities to support that training are needed and more software needs to be created. The majority of the science teachers do not use the computer to help students prepare for standardized tests because they believe they can prepare students more efficiently without a computer. Nearly half of the teachers, however, gave lack of time to prepare instructional materials and lack of a means to project a computer image to the whole class as reasons they do not use computers. A significant percentage thought science standardized testing was having a negative effect on computer use.

Educational NASA Computational and Scientific Studies (enCOMPASS)

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess

2013-01-01

Educational NASA Computational and Scientific Studies (enCOMPASS) is an educational project of NASA Goddard Space Flight Center aimed at bridging the gap between computational objectives and needs of NASA's scientific research, missions, and projects, and academia's latest advances in applied mathematics and computer science. enCOMPASS achieves this goal via bidirectional collaboration and communication between NASA and academia. Using developed NASA Computational Case Studies in university computer science/engineering and applied mathematics classes is a way of addressing NASA's goals of contributing to the Science, Technology, Education, and Math (STEM) National Objective. The enCOMPASS Web site at http://encompass.gsfc.nasa.gov provides additional information. There are currently nine enCOMPASS case studies developed in areas of earth sciences, planetary sciences, and astrophysics. Some of these case studies have been published in AIP and IEEE's Computing in Science and Engineering magazines. A few university professors have used enCOMPASS case studies in their computational classes and contributed their findings to NASA scientists. In these case studies, after introducing the science area, the specific problem, and related NASA missions, students are first asked to solve a known problem using NASA data and past approaches used and often published in a scientific/research paper. Then, after learning about the NASA application and related computational tools and approaches for solving the proposed problem, students are given a harder problem as a challenge for them to research and develop solutions for. This project provides a model for NASA scientists and engineers on one side, and university students, faculty, and researchers in computer science and applied mathematics on the other side, to learn from each other's areas of work, computational needs and solutions, and the latest advances in research and development. This innovation takes NASA science and engineering applications to computer science and applied mathematics university classes, and makes NASA objectives part of the university curricula. There is great potential for growth and return on investment of this program to the point where every major university in the U.S. would use at least one of these case studies in one of their computational courses, and where every NASA scientist and engineer facing a computational challenge (without having resources or expertise to solve it) would use enCOMPASS to formulate the problem as a case study, provide it to a university, and get back their solutions and ideas.

Effects of the computational domain on the secondary flow in turbulent plane Couette flow

NASA Astrophysics Data System (ADS)

Gai, Jie; Xia, Zhen-Hua; Cai, Qing-Dong

2015-10-01

A series of direct numerical simulations of the fully developed plane Couette flow at a Reynolds number of 6000 (based on the relative wall speed and half the channel height h) with different streamwise and spanwise lengths are conducted to investigate the effects of the computational box sizes on the secondary flow (SF). Our focuses are the number of counter-rotating vortex pairs and its relationship to the statistics of the mean flow and the SF in the small and moderate computational box sizes. Our results show that the number of vortex pairs is sensitive to the computational box size, and so are the slope parameter, the rate of the turbulent kinetic energy contributed by the SF, and the ratio of the kinetic energy of the SF to the total kinetic energy. However, the averaged spanwise width of each counter-rotating vortex pair in the plane Couette flow is found, for the first time, within 4(1 ± 0.25)h despite the domain sizes. Project supported by the National Natural Science Foundation of China (Grant Nos. 11221061, 11272013, and 11302006).

A Science Cloud: OneSpaceNet

NASA Astrophysics Data System (ADS)

Morikawa, Y.; Murata, K. T.; Watari, S.; Kato, H.; Yamamoto, K.; Inoue, S.; Tsubouchi, K.; Fukazawa, K.; Kimura, E.; Tatebe, O.; Shimojo, S.

2010-12-01

Main methodologies of Solar-Terrestrial Physics (STP) so far are theoretical, experimental and observational, and computer simulation approaches. Recently "informatics" is expected as a new (fourth) approach to the STP studies. Informatics is a methodology to analyze large-scale data (observation data and computer simulation data) to obtain new findings using a variety of data processing techniques. At NICT (National Institute of Information and Communications Technology, Japan) we are now developing a new research environment named "OneSpaceNet". The OneSpaceNet is a cloud-computing environment specialized for science works, which connects many researchers with high-speed network (JGN: Japan Gigabit Network). The JGN is a wide-area back-born network operated by NICT; it provides 10G network and many access points (AP) over Japan. The OneSpaceNet also provides with rich computer resources for research studies, such as super-computers, large-scale data storage area, licensed applications, visualization devices (like tiled display wall: TDW), database/DBMS, cluster computers (4-8 nodes) for data processing and communication devices. What is amazing in use of the science cloud is that a user simply prepares a terminal (low-cost PC). Once connecting the PC to JGN2plus, the user can make full use of the rich resources of the science cloud. Using communication devices, such as video-conference system, streaming and reflector servers, and media-players, the users on the OneSpaceNet can make research communications as if they belong to a same (one) laboratory: they are members of a virtual laboratory. The specification of the computer resources on the OneSpaceNet is as follows: The size of data storage we have developed so far is almost 1PB. The number of the data files managed on the cloud storage is getting larger and now more than 40,000,000. What is notable is that the disks forming the large-scale storage are distributed to 5 data centers over Japan (but the storage system performs as one disk). There are three supercomputers allocated on the cloud, one from Tokyo, one from Osaka and the other from Nagoya. One's simulation job data on any supercomputers are saved on the cloud data storage (same directory); it is a kind of virtual computing environment. The tiled display wall has 36 panels acting as one display; the pixel (resolution) size of it is as large as 18000x4300. This size is enough to preview or analyze the large-scale computer simulation data. It also allows us to take a look of multiple (e.g., 100 pictures) on one screen together with many researchers. In our talk we also present a brief report of the initial results using the OneSpaceNet for Global MHD simulations as an example of successful use of our science cloud; (i) Ultra-high time resolution visualization of Global MHD simulations on the large-scale storage and parallel processing system on the cloud, (ii) Database of real-time Global MHD simulation and statistic analyses of the data, and (iii) 3D Web service of Global MHD simulations.

Research in progress and other activities of the Institute for Computer Applications in Science and Engineering

NASA Technical Reports Server (NTRS)

1993-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics and computer science during the period April 1, 1993 through September 30, 1993. The major categories of the current ICASE research program are: (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest to LaRC, including acoustic and combustion; (3) experimental research in transition and turbulence and aerodynamics involving LaRC facilities and scientists; and (4) computer science.

Research in progress in applied mathematics, numerical analysis, fluid mechanics, and computer science

NASA Technical Reports Server (NTRS)

1994-01-01

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period October 1, 1993 through March 31, 1994. The major categories of the current ICASE research program are: (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest to LaRC, including acoustics and combustion; (3) experimental research in transition and turbulence and aerodynamics involving LaRC facilities and scientists; and (4) computer science.

77 FR 52704 - Notice of Submission for OMB Review; Institute of Education Sciences; Early Childhood...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-30

... DEPARTMENT OF EDUCATION Notice of Submission for OMB Review; Institute of Education Sciences... for Education Statistics (NCES) within the Institute of Education Sciences (IES) of the U.S... Statistics (NCES) within the Institute of Education Sciences (IES) of the U.S. Department of Education (ED...

Introducing Students to the Application of Statistics and Investigative Methods in Political Science

ERIC Educational Resources Information Center

Wells, Dominic D.; Nemire, Nathan A.

2017-01-01

This exercise introduces students to the application of statistics and its investigative methods in political science. It helps students gain a better understanding and a greater appreciation of statistics through a real world application.

Creating Science Simulations through Computational Thinking Patterns

ERIC Educational Resources Information Center

Basawapatna, Ashok Ram

2012-01-01

Computational thinking aims to outline fundamental skills from computer science that everyone should learn. As currently defined, with help from the National Science Foundation (NSF), these skills include problem formulation, logically organizing data, automating solutions through algorithmic thinking, and representing data through abstraction.…

77 FR 65417 - Proposal Review Panel for Computing Communication Foundations; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-26

...: To assess the progress of the EIC Award, ``Collaborative Research: Computational Behavioral Science... NATIONAL SCIENCE FOUNDATION Proposal Review Panel for Computing Communication Foundations; Notice... National Science Foundation announces the following meeting: Name: Site Visit, Proposal Panel Review for...

History of Science and Statistical Education: Examples from Fisherian and Pearsonian Schools

ERIC Educational Resources Information Center

Yu, Chong Ho

2004-01-01

Many students share a popular misconception that statistics is a subject-free methodology derived from invariant and timeless mathematical axioms. It is proposed that statistical education should include the aspect of history/philosophy of science. This article will discuss how statistical methods developed by Karl Pearson and R. A. Fisher are…

Crosscut report: Exascale Requirements Reviews, March 9–10, 2017 – Tysons Corner, Virginia. An Office of Science review sponsored by: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard; Hack, James; Riley, Katherine

The mission of the U.S. Department of Energy Office of Science (DOE SC) is the delivery of scientific discoveries and major scientific tools to transform our understanding of nature and to advance the energy, economic, and national security missions of the United States. To achieve these goals in today’s world requires investments in not only the traditional scientific endeavors of theory and experiment, but also in computational science and the facilities that support large-scale simulation and data analysis. The Advanced Scientific Computing Research (ASCR) program addresses these challenges in the Office of Science. ASCR’s mission is to discover, develop, andmore » deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to DOE. ASCR supports research in computational science, three high-performance computing (HPC) facilities — the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory and Leadership Computing Facilities at Argonne (ALCF) and Oak Ridge (OLCF) National Laboratories — and the Energy Sciences Network (ESnet) at Berkeley Lab. ASCR is guided by science needs as it develops research programs, computers, and networks at the leading edge of technologies. As we approach the era of exascale computing, technology changes are creating challenges for science programs in SC for those who need to use high performance computing and data systems effectively. Numerous significant modifications to today’s tools and techniques will be needed to realize the full potential of emerging computing systems and other novel computing architectures. To assess these needs and challenges, ASCR held a series of Exascale Requirements Reviews in 2015–2017, one with each of the six SC program offices,1 and a subsequent Crosscut Review that sought to integrate the findings from each. Participants at the reviews were drawn from the communities of leading domain scientists, experts in computer science and applied mathematics, ASCR facility staff, and DOE program managers in ASCR and the respective program offices. The purpose of these reviews was to identify mission-critical scientific problems within the DOE Office of Science (including experimental facilities) and determine the requirements for the exascale ecosystem that would be needed to address those challenges. The exascale ecosystem includes exascale computing systems, high-end data capabilities, efficient software at scale, libraries, tools, and other capabilities. This effort will contribute to the development of a strategic roadmap for ASCR compute and data facility investments and will help the ASCR Facility Division establish partnerships with Office of Science stakeholders. It will also inform the Office of Science research needs and agenda. The results of the six reviews have been published in reports available on the web at http://exascaleage.org/. This report presents a summary of the individual reports and of common and crosscutting findings, and it identifies opportunities for productive collaborations among the DOE SC program offices.« less

High School Students Learning University Level Computer Science on the Web: A Case Study of the "DASK"-Model

ERIC Educational Resources Information Center

Grandell, Linda

2005-01-01

Computer science is becoming increasingly important in our society. Meta skills, such as problem solving and logical and algorithmic thinking, are emphasized in every field, not only in the natural sciences. Still, largely due to gaps in tuition, common misunderstandings exist about the true nature of computer science. These are especially…

Prognocean Plus: the Science-Oriented Sea Level Prediction System as a Tool for Public Stakeholders

NASA Astrophysics Data System (ADS)

Świerczyńska, M. G.; Miziński, B.; Niedzielski, T.

2015-12-01

The novel real-time system for sea level prediction, known as Prognocean Plus, has been developed as a new generation service available through the Polish supercomputing grid infrastructure. The researchers can access the service at https://prognocean.plgrid.pl/. Although the system is science-oriented, we wish to discuss herein its potentials to enhance ocean management studies carried out routinely by public stakeholders. The system produces the short- and medium-term predictions of global altimetric gridded Sea Level Anomaly (SLA) time series, updated daily. The spatial resolution of the SLA forecasts is 1/4° x 1/4°, while the temporal resolution of prognoses is equal to 1 day. The system computes the predictions of time-variable ocean topography using five data-based models, which are not computationally demanding, enabling us to compare their skillfulness in respect to physically-based approaches commonly used by different sea level prediction systems. However, the aim of the system is not only to compute the predictions for science purposes, but primarily to build a user-oriented platform that serves the prognoses and their statistics to a broader community. Thus, we deliver the SLA forecasts as a rapid service available online. In order to provide potential users with the access to science results the Web Map Service (WMS) for Prognocean Plus is designed. We regularly publish the forecasts, both in the interactive graphical WMS service, available from the browser, as well as through the Web Coverage Service (WCS) standard. The Prognocean Plus system, as an early-response system, may be interesting for public stakeholders. It may be used for marine navigation as well as for climate risk management (delineate areas vulnerable to local sea level rise), marine management (advise offered for offshore activities) and coastal management (early warnings against coastal floodings).

Computational data sciences for assessment and prediction of climate extremes

NASA Astrophysics Data System (ADS)

Ganguly, A. R.

2011-12-01

Climate extremes may be defined inclusively as severe weather events or large shifts in global or regional weather patterns which may be caused or exacerbated by natural climate variability or climate change. This area of research arguably represents one of the largest knowledge-gaps in climate science which is relevant for informing resource managers and policy makers. While physics-based climate models are essential in view of non-stationary and nonlinear dynamical processes, their current pace of uncertainty reduction may not be adequate for urgent stakeholder needs. The structure of the models may in some cases preclude reduction of uncertainty for critical processes at scales or for the extremes of interest. On the other hand, methods based on complex networks, extreme value statistics, machine learning, and space-time data mining, have demonstrated significant promise to improve scientific understanding and generate enhanced predictions. When combined with conceptual process understanding at multiple spatiotemporal scales and designed to handle massive data, interdisciplinary data science methods and algorithms may complement or supplement physics-based models. Specific examples from the prior literature and our ongoing work suggests how data-guided improvements may be possible, for example, in the context of ocean meteorology, climate oscillators, teleconnections, and atmospheric process understanding, which in turn can improve projections of regional climate, precipitation extremes and tropical cyclones in an useful and interpretable fashion. A community-wide effort is motivated to develop and adapt computational data science tools for translating climate model simulations to information relevant for adaptation and policy, as well as for improving our scientific understanding of climate extremes from both observed and model-simulated data.

Experience with Data Science as an Intern with the Jet Propulsion Laboratory

NASA Astrophysics Data System (ADS)

Whittell, J.; Mattmann, C. A.; Whitehall, K. D.; Ramirez, P.; Goodale, C. E.; Boustani, M.; Hart, A. F.; Kim, J.; Waliser, D. E.; Joyce, M. J.

2013-12-01

The Regional Climate Model Evaluation System (RCMES, http://rcmes.jpl.nasa.gov) at NASA's Jet Propulsion Laboratory seeks to improve regional climate model output by comparing past model predictions with Earth-orbiting satellite data (Mattmann et al. 2013). RCMES ingests satellite and RCM data and processes these data into a common format; as needed, the software queries the RCMES database for these datasets, on which it runs a series of statistical metrics including model-satellite comparisons. The development of the RCMES software relies on collaboration between climatologists and computer scientists, as evinced by RCMES longstanding work with CORDEX (Kim et al. 2012). Over a total of 17 weeks in 2011, 2012, and 2013, I worked as an intern at NASA's Jet Propulsion Laboratory in a supportive capacity for RCMES. A high school student, I had no formal background in either Earth science or computer technology, but was immersed in both fields. In 2011, I researched three earth-science data management projects, producing a high-level explanation of these endeavors. The following year, I studied Python, contributing a command-line user interface to the RCMES project code. In 2013, I assisted with data acquisition, wrote a file header information plugin, and the visualization tool GrADS. The experience demonstrated the importance of an interdisciplinary approach to data processing: to streamline data ingestion and processing, scientists must understand, at least on a high-level, any programs they might utilize while to best serve the needs of earth scientists, software engineers must understand the science behind the data they handle.

Report on Computing and Networking in the Space Science Laboratory by the SSL Computer Committee

NASA Technical Reports Server (NTRS)

Gallagher, D. L. (Editor)

1993-01-01

The Space Science Laboratory (SSL) at Marshall Space Flight Center is a multiprogram facility. Scientific research is conducted in four discipline areas: earth science and applications, solar-terrestrial physics, astrophysics, and microgravity science and applications. Representatives from each of these discipline areas participate in a Laboratory computer requirements committee, which developed this document. The purpose is to establish and discuss Laboratory objectives for computing and networking in support of science. The purpose is also to lay the foundation for a collective, multiprogram approach to providing these services. Special recognition is given to the importance of the national and international efforts of our research communities toward the development of interoperable, network-based computer applications.

A review of Computer Science resources for learning and teaching with K-12 computing curricula: an Australian case study

NASA Astrophysics Data System (ADS)

Falkner, Katrina; Vivian, Rebecca

2015-10-01

To support teachers to implement Computer Science curricula into classrooms from the very first year of school, teachers, schools and organisations seek quality curriculum resources to support implementation and teacher professional development. Until now, many Computer Science resources and outreach initiatives have targeted K-12 school-age children, with the intention to engage children and increase interest, rather than to formally teach concepts and skills. What is the educational quality of existing Computer Science resources and to what extent are they suitable for classroom learning and teaching? In this paper, an assessment framework is presented to evaluate the quality of online Computer Science resources. Further, a semi-systematic review of available online Computer Science resources was conducted to evaluate resources available for classroom learning and teaching and to identify gaps in resource availability, using the Australian curriculum as a case study analysis. The findings reveal a predominance of quality resources, however, a number of critical gaps were identified. This paper provides recommendations and guidance for the development of new and supplementary resources and future research.

Ambient belonging: how stereotypical cues impact gender participation in computer science.

PubMed

Cheryan, Sapna; Plaut, Victoria C; Davies, Paul G; Steele, Claude M

2009-12-01

People can make decisions to join a group based solely on exposure to that group's physical environment. Four studies demonstrate that the gender difference in interest in computer science is influenced by exposure to environments associated with computer scientists. In Study 1, simply changing the objects in a computer science classroom from those considered stereotypical of computer science (e.g., Star Trek poster, video games) to objects not considered stereotypical of computer science (e.g., nature poster, phone books) was sufficient to boost female undergraduates' interest in computer science to the level of their male peers. Further investigation revealed that the stereotypical broadcast a masculine stereotype that discouraged women's sense of ambient belonging and subsequent interest in the environment (Studies 2, 3, and 4) but had no similar effect on men (Studies 3, 4). This masculine stereotype prevented women's interest from developing even in environments entirely populated by other women (Study 2). Objects can thus come to broadcast stereotypes of a group, which in turn can deter people who do not identify with these stereotypes from joining that group.

Relationship between teacher preparedness and inquiry-based instructional practices to students' science achievement: Evidence from TIMSS 2007

NASA Astrophysics Data System (ADS)

Martin, Lynn A.

The purpose of this study was to examine the relationship between teachers' self-reported preparedness for teaching science content and their instructional practices to the science achievement of eighth grade science students in the United States as demonstrated by TIMSS 2007. Six hundred eighty-seven eighth grade science teachers in the United States representing 7,377 students responded to the TIMSS 2007 questionnaire about their instructional preparedness and their instructional practices. Quantitative data were reported. Through correlation analysis, the researcher found statistically significant positive relationships emerge between eighth grade science teachers' main area of study and their self-reported beliefs about their preparedness to teach that same content area. Another correlation analysis found a statistically significant negative relationship existed between teachers' self-reported use of inquiry-based instruction and preparedness to teach chemistry, physics and earth science. Another correlation analysis discovered a statistically significant positive relationship existed between physics preparedness and student science achievement. Finally, a correlation analysis found a statistically significant positive relationship existed between science teachers' self-reported implementation of inquiry-based instructional practices and student achievement. The data findings support the conclusion that teachers who have feelings of preparedness to teach science content and implement more inquiry-based instruction and less didactic instruction produce high achieving science students. As science teachers obtain the appropriate knowledge in science content and pedagogy, science teachers will feel prepared and will implement inquiry-based instruction in science classrooms.

Snatching Defeat from the Jaws of Victory: When Good Projects Go Bad. Girls and Computer Science.

ERIC Educational Resources Information Center

Sanders, Jo

In week-long semesters in the summers of 1997, 1998, and 1999, the 6APT (Summer Institute in Computer Science for Advanced Placement Teachers) project taught 240 high school teachers of Advanced Placement Computer Science (APCS) about gender equity in computers. Teachers were then followed through 2000. Results indicated that while teachers, did…

PaleoMac: A Macintosh™ application for treating paleomagnetic data and making plate reconstructions

NASA Astrophysics Data System (ADS)

Cogné, J. P.

2003-01-01

This brief note provides an overview of a new Macintosh™ application, PaleoMac, (MacOS 8.0 or later, 15Mb RAM required) which permits rapid processing of paleomagnetic data, from the demagnetization data acquired in the laboratory, to the treatment of paleomagnetic poles, plate reconstructions, finite rotation computations on a sphere, and characterization of relative plate motions. Capabilities of PaleoMac include (1) high interactivity between the user and data displayed on screen which provides a fast and easy way to handle, add and remove data or contours, perform computations on subsets of points, change projections, sizes, etc.; (2) performance of all standard principal component analysis and statistical processing on a sphere [, 1953] etc.); (3) output of high quality plots, compatible with graphic programs such as Adobe Illustrator, and output of numerical results as ASCII files. Beyond its usefulness in treating paleomagnetic data, its ability to handle plate motion computations should be of large interest to the Earth science community.

Algorithmic-Reducibility = Renormalization-Group Fixed-Points; ``Noise''-Induced Phase-Transitions (NITs) to Accelerate Algorithmics (``NIT-Picking'') Replacing CRUTCHES!!!: Gauss Modular/Clock-Arithmetic Congruences = Signal X Noise PRODUCTS..

NASA Astrophysics Data System (ADS)

Siegel, J.; Siegel, Edward Carl-Ludwig

2011-03-01

Cook-Levin computational-"complexity"(C-C) algorithmic-equivalence reduction-theorem reducibility equivalence to renormalization-(semi)-group phase-transitions critical-phenomena statistical-physics universality-classes fixed-points, is exploited with Gauss modular/clock-arithmetic/model congruences = signal X noise PRODUCT reinterpretation. Siegel-Baez FUZZYICS=CATEGORYICS(SON of ``TRIZ''): Category-Semantics(C-S) tabular list-format truth-table matrix analytics predicts and implements "noise"-induced phase-transitions (NITs) to accelerate versus to decelerate Harel [Algorithmics(1987)]-Sipser[Intro. Theory Computation(1997) algorithmic C-C: "NIT-picking" to optimize optimization-problems optimally(OOPO). Versus iso-"noise" power-spectrum quantitative-only amplitude/magnitude-only variation stochastic-resonance, this "NIT-picking" is "noise" power-spectrum QUALitative-type variation via quantitative critical-exponents variation. Computer-"science" algorithmic C-C models: Turing-machine, finite-state-models/automata, are identified as early-days once-workable but NOW ONLY LIMITING CRUTCHES IMPEDING latter-days new-insights!!!

Big Data & Datamining: Using APIs to computationally determine who follows space science, & what do they care about?

NASA Astrophysics Data System (ADS)

Gay, Pamela L.; Bakerman, Maya; Graziano, Nancy; Murph, Susan; Reiheld, Alison; CosmoQuest

2017-10-01

In today's connected world, scientists & space science projects are turning to social media outlets like Twitter to share our achievements, request aid, & discuss the issues of our profession. Maintaining these disparate feeds requires time & resources that are already in short supply. To justify these efforts, we must examine the data to determine: are we speaking to our intended audiences; are our varied efforts needed; & what types of messages achieve the greatest interactions. The software used to support this project is available on GitHub.Previously, it has been unclear if our day-to-day social media efforts have been merely preaching to one homogeneous choir from which we have all drawn our audiences, or if our individual efforts have been able to reach into different communities to multiply our impact. In this preliminary study, we examine the social media audiences of several space science Twitter feeds that relate to: podcasting; professional societies; individual programs; & individuals. This study directly measures the overlap in audiences & the diversity of interests held by these audiences. Through statistical analysis, we can discern if these audiences are all drawn from one single population, or if we are sampling different base populations with different feeds.The data generated in this project allow us to look beyond how our audiences interact with space science, with the added benefit of revealing their other interests. These interests are reflected by the non-space science accounts they follow on Twitter. This information will allow us to effectively recruit new people from space science adjacent interests.After applying large data analytics & statistics to social media interactions, we can model online communications, audience population types, & the causal relationships between how we tweet &how our audiences interact. With this knowledge, we are then able to institute reliable communications & effective interactions with our target audience.This work is supported through NASA cooperative agreement NNX17AD20A.

C-MORE Science Kits: Putting Technology in the Hands of K-12 Teachers and Students

NASA Astrophysics Data System (ADS)

Achilles, K.; Weersing, K.; Daniels, C.; Puniwai, N.; Matsuzaki, J.; Bruno, B. C.

2008-12-01

The Center for Microbial Oceanography: Research and Education (C-MORE) is a NSF Science and Technology Center based at the University of Hawaii. The C-MORE education and outreach program offers a variety of resources and professional development opportunities for science educators, including online resources, participation in oceanography research cruises, teacher-training workshops, mini-grants to incorporate microbial oceanography-related content and activities into their classroom and, most recently, C- MORE science kits. C-MORE science kits provide hands-on classroom, field, and laboratory activities related to microbial oceanography for K-12 students. Each kit comes with complete materials and instructions, and is available free of charge to Hawaii's public school teachers. Several kits are available nationwide. C-MORE science kits cover a range of topics and technologies and are targeted at various grade levels. Here is a sampling of some available kits: 1) Marine Murder Mystery: The Case of the Missing Zooxanthellae. Students learn about the effect of climate change and other environmental threats on coral reef destruction through a murder-mystery experience. Participants also learn how to use DNA to identify a suspect. Grades levels: 3-8. 2) Statistical sampling. Students learn basic statistics through an exercise in random sampling, with applications to microbial oceanography. The laptops provided with this kit enable students to enter, analyze, and graph their data using EXCEL. Grades levels: 6-12. 3) Chlorophyll Lab. A research-quality fluorometer is used to measure the chlorophyll content in marine and freshwater systems. This enables students to compare biomass concentrations in samples collected from various locations. Grades levels: 9-12. 4) Conductivity-Temperature-Depth (CTD). Students predict how certain variables (e.g., temperature, pressure, chlorophyll, oxygen) vary with depth. A CTD, attached to a laptop computer, is deployed into deep water off a dock or a ship to collect real-time data and test their hypotheses. Grades levels: 9-12.

77 FR 12823 - Advanced Scientific Computing Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-02

... Exascale ARRA projects--Magellan final report, Advanced Networking update Status from Computer Science COV Early Career technical talks Summary of Applied Math and Computer Science Workshops ASCR's new SBIR..., Office of Science. ACTION: Notice of Open Meeting. SUMMARY: This notice announces a meeting of the...

75 FR 18407 - Investing in Innovation Fund

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-12

... include computer science rather than science. To correct this error, the Department makes the following..., in footnote number eight, in line six, ``including science'' is replaced with ``including computer... obtain this document in an accessible format (e.g., Braille, large print, audiotape, or computer diskette...

Innovative Science Experiments Using Phoenix

ERIC Educational Resources Information Center

Kumar, B. P. Ajith; Satyanarayana, V. V. V.; Singh, Kundan; Singh, Parmanand

2009-01-01

A simple, flexible and very low cost hardware plus software framework for developing computer-interfaced science experiments is presented. It can be used for developing computer-interfaced science experiments without getting into the details of electronics or computer programming. For developing experiments this is a middle path between…

The Metamorphosis of an Introduction to Computer Science.

ERIC Educational Resources Information Center

Ben-Jacob, Marion G.

1997-01-01

Introductory courses in computer science at colleges and universities have undergone significant changes in 20 years. This article provides an overview of the history of introductory computer science (FORTRAN, ANSI flowchart symbols, BASIC, data processing concepts, and PASCAL) and its future (robotics and C++). (PEN)

All Roads Lead to Computing: Making, Participatory Simulations, and Social Computing as Pathways to Computer Science

ERIC Educational Resources Information Center

Brady, Corey; Orton, Kai; Weintrop, David; Anton, Gabriella; Rodriguez, Sebastian; Wilensky, Uri

2017-01-01

Computer science (CS) is becoming an increasingly diverse domain. This paper reports on an initiative designed to introduce underrepresented populations to computing using an eclectic, multifaceted approach. As part of a yearlong computing course, students engage in Maker activities, participatory simulations, and computing projects that…

Methodological approach to crime scene investigation: the dangers of technology

NASA Astrophysics Data System (ADS)

Barnett, Peter D.

1997-02-01

The visitor to any modern forensic science laboratory is confronted with equipment and processes that did not exist even 10 years ago: thermocyclers to allow genetic typing of nanogram amounts of DNA isolated from a few spermatozoa; scanning electron microscopes that can nearly automatically detect submicrometer sized particles of molten lead, barium and antimony produced by the discharge of a firearm and deposited on the hands of the shooter; and computers that can compare an image of a latent fingerprint with millions of fingerprints stored in the computer memory. Analysis of populations of physical evidence has permitted statistically minded forensic scientists to use Bayesian inference to draw conclusions based on a priori assumptions which are often poorly understood, irrelevant, or misleading. National commissions who are studying quality control in DNA analysis propose that people with barely relevant graduate degrees and little forensic science experience be placed in charge of forensic DNA laboratories. It is undeniable that high- tech has reversed some miscarriages of justice by establishing the innocence of a number of people who were imprisoned for years for crimes that they did not commit. However, this papers deals with the dangers of technology in criminal investigations.

PREFACE: 3rd International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE 2014)

NASA Astrophysics Data System (ADS)

2015-01-01

The third International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE) took place at Madrid, Spain, from Thursday 28 to Sunday 31 August 2014. The Conference was attended by more than 200 participants and hosted about 350 oral, poster, and virtual presentations. More than 600 pre-registered authors were also counted. The third IC-MSQUARE consisted of different and diverging workshops and thus covered various research fields where Mathematical Modeling is used, such as Theoretical/Mathematical Physics, Neutrino Physics, Non-Integrable Systems, Dynamical Systems, Computational Nanoscience, Biological Physics, Computational Biomechanics, Complex Networks, Stochastic Modeling, Fractional Statistics, DNA Dynamics, Macroeconomics etc. The scientific program was rather heavy since after the Keynote and Invited Talks in the morning, three parallel oral sessions and one poster session were running every day. However, according to all attendees, the program was excellent with high level of talks and the scientific environment was fruitful, thus all attendees had a creative time. We would like to thank the Keynote Speaker and the Invited Speakers for their significant contribution to IC-MSQUARE. We also would like to thank the Members of the International Advisory and Scientific Committees as well as the Members of the Organizing Committee.

PREFACE: 4th International Conference on Mathematical Modeling in Physical Sciences (IC-MSquare2015)

NASA Astrophysics Data System (ADS)

Vlachos, Dimitrios; Vagenas, Elias C.

2015-09-01

The 4th International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE) took place in Mykonos, Greece, from Friday 5th June to Monday 8th June 2015. The Conference was attended by more than 150 participants and hosted about 200 oral, poster, and virtual presentations. There were more than 600 pre-registered authors. The 4th IC-MSQUARE consisted of different and diverging workshops and thus covered various research fields where Mathematical Modeling is used, such as Theoretical/Mathematical Physics, Neutrino Physics, Non-Integrable Systems, Dynamical Systems, Computational Nanoscience, Biological Physics, Computational Biomechanics, Complex Networks, Stochastic Modeling, Fractional Statistics, DNA Dynamics, Macroeconomics etc. The scientific program was rather intense as after the Keynote and Invited Talks in the morning, three parallel oral and one poster session were running every day. However, according to all attendees, the program was excellent with a high quality of talks creating an innovative and productive scientific environment for all attendees. We would like to thank the Keynote Speaker and the Invited Speakers for their significant contribution to IC-MSQUARE. We also would like to thank the Members of the International Advisory and Scientific Committees as well as the Members of the Organizing Committee.

After-Hours Science: Microchips and Onion Dip.

ERIC Educational Resources Information Center

Brugger, Steve

1984-01-01

Computer programs were developed for a science center nutrition exhibit. The exhibit was recognized by the National Science Teachers Association Search for Excellence in Science Education as an outstanding science program. The computer programs (Apple II) and their use in the exhibit are described. (BC)

Joint Data Assimilation and Parameter Calibration in on-line groundwater modelling using Sequential Monte Carlo techniques

NASA Astrophysics Data System (ADS)

Ramgraber, M.; Schirmer, M.

2017-12-01

As computational power grows and wireless sensor networks find their way into common practice, it becomes increasingly feasible to pursue on-line numerical groundwater modelling. The reconciliation of model predictions with sensor measurements often necessitates the application of Sequential Monte Carlo (SMC) techniques, most prominently represented by the Ensemble Kalman Filter. In the pursuit of on-line predictions it seems advantageous to transcend the scope of pure data assimilation and incorporate on-line parameter calibration as well. Unfortunately, the interplay between shifting model parameters and transient states is non-trivial. Several recent publications (e.g. Chopin et al., 2013, Kantas et al., 2015) in the field of statistics discuss potential algorithms addressing this issue. However, most of these are computationally intractable for on-line application. In this study, we investigate to what extent compromises between mathematical rigour and computational restrictions can be made within the framework of on-line numerical modelling of groundwater. Preliminary studies are conducted in a synthetic setting, with the goal of transferring the conclusions drawn into application in a real-world setting. To this end, a wireless sensor network has been established in the valley aquifer around Fehraltorf, characterized by a highly dynamic groundwater system and located about 20 km to the East of Zürich, Switzerland. By providing continuous probabilistic estimates of the state and parameter distribution, a steady base for branched-off predictive scenario modelling could be established, providing water authorities with advanced tools for assessing the impact of groundwater management practices. Chopin, N., Jacob, P.E. and Papaspiliopoulos, O. (2013): SMC2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75 (3), p. 397-426. Kantas, N., Doucet, A., Singh, S.S., Maciejowski, J., and Chopin, N. (2015): On Particle Methods for Parameter Estimation in State-Space Models. Statistical Science, 30 (3), p. 328.-351.

Statistical reporting inconsistencies in experimental philosophy

PubMed Central

Colombo, Matteo; Duev, Georgi; Nuijten, Michèle B.; Sprenger, Jan

2018-01-01

Experimental philosophy (x-phi) is a young field of research in the intersection of philosophy and psychology. It aims to make progress on philosophical questions by using experimental methods traditionally associated with the psychological and behavioral sciences, such as null hypothesis significance testing (NHST). Motivated by recent discussions about a methodological crisis in the behavioral sciences, questions have been raised about the methodological standards of x-phi. Here, we focus on one aspect of this question, namely the rate of inconsistencies in statistical reporting. Previous research has examined the extent to which published articles in psychology and other behavioral sciences present statistical inconsistencies in reporting the results of NHST. In this study, we used the R package statcheck to detect statistical inconsistencies in x-phi, and compared rates of inconsistencies in psychology and philosophy. We found that rates of inconsistencies in x-phi are lower than in the psychological and behavioral sciences. From the point of view of statistical reporting consistency, x-phi seems to do no worse, and perhaps even better, than psychological science. PMID:29649220

Knowledge-Based Environmental Context Modeling

NASA Astrophysics Data System (ADS)

Pukite, P. R.; Challou, D. J.

2017-12-01

As we move from the oil-age to an energy infrastructure based on renewables, the need arises for new educational tools to support the analysis of geophysical phenomena and their behavior and properties. Our objective is to present models of these phenomena to make them amenable for incorporation into more comprehensive analysis contexts. Starting at the level of a college-level computer science course, the intent is to keep the models tractable and therefore practical for student use. Based on research performed via an open-source investigation managed by DARPA and funded by the Department of Interior [1], we have adapted a variety of physics-based environmental models for a computer-science curriculum. The original research described a semantic web architecture based on patterns and logical archetypal building-blocks (see figure) well suited for a comprehensive environmental modeling framework. The patterns span a range of features that cover specific land, atmospheric and aquatic domains intended for engineering modeling within a virtual environment. The modeling engine contained within the server relied on knowledge-based inferencing capable of supporting formal terminology (through NASA JPL's Semantic Web for Earth and Environmental Technology (SWEET) ontology and a domain-specific language) and levels of abstraction via integrated reasoning modules. One of the key goals of the research was to simplify models that were ordinarily computationally intensive to keep them lightweight enough for interactive or virtual environment contexts. The breadth of the elements incorporated is well-suited for learning as the trend toward ontologies and applying semantic information is vital for advancing an open knowledge infrastructure. As examples of modeling, we have covered such geophysics topics as fossil-fuel depletion, wind statistics, tidal analysis, and terrain modeling, among others. Techniques from the world of computer science will be necessary to promote efficient use of our renewable natural resources. [1] C2M2L (Component, Context, and Manufacturing Model Library) Final Report, https://doi.org/10.13140/RG.2.1.4956.3604

Modern Physics Simulations

NASA Astrophysics Data System (ADS)

Brandt, Douglas; Hiller, John R.; Moloney, Michael J.

1995-10-01

The Consortium for Upper Level Physics Software (CUPS) has developed a comprehensive series of Nine Book/Software packages that Wiley will publish in FY `95 and `96. CUPS is an international group of 27 physicists, all with extensive backgrounds in the research, teaching, and development of instructional software. The project is being supported by the National Science Foundation (PHY-9014548), and it has received other support from the IBM Corp., Apple Computer Corp., and George Mason University. The Simulations being developed are: Astrophysics, Classical Mechanics, Electricity & Magnetism, Modern Physics, Nuclear and Particle Physics, Quantum Mechanics, Solid State, Thermal and Statistical, and Wave and Optics.

A cost and utility analysis of NIM/CAMAC standards and equipment for shuttle payload data acquisition and control systems. Volume 2: Tasks 1 and 2

NASA Technical Reports Server (NTRS)

1976-01-01

A representative set of payloads for both science and applications disciplines were selected that would ensure a realistic and statistically significant estimate of equipment utilization. The selected payloads were analyzed to determine the applicability of Nuclear Instrumentation Modular (NIM)/Computer Automated Measurement Control (CAMAC) equipment in satisfying their data acquisition and control requirements. The analyses results were combined with the comparable results from related studies to arrive at an overall assessment of the applicability and commonality of NIM/CAMAC equipment usage across the spectrum of payloads.

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics

PubMed Central

2016-01-01

Background We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. Objective To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. Methods The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Results Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. Conclusions IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise. PMID:27729304

eSACP - a new Nordic initiative towards developing statistical climate services

NASA Astrophysics Data System (ADS)

Thorarinsdottir, Thordis; Thejll, Peter; Drews, Martin; Guttorp, Peter; Venälainen, Ari; Uotila, Petteri; Benestad, Rasmus; Mesquita, Michel d. S.; Madsen, Henrik; Fox Maule, Cathrine

2015-04-01

The Nordic research council NordForsk has recently announced its support for a new 3-year research initiative on "statistical analysis of climate projections" (eSACP). eSACP will focus on developing e-science tools and services based on statistical analysis of climate projections for the purpose of helping decision-makers and planners in the face of expected future challenges in regional climate change. The motivation behind the project is the growing recognition in our society that forecasts of future climate change is associated with various sources of uncertainty, and that any long-term planning and decision-making dependent on a changing climate must account for this. At the same time there is an obvious gap between scientists from different fields and between practitioners in terms of understanding how climate information relates to different parts of the "uncertainty cascade". In eSACP we will develop generic e-science tools and statistical climate services to facilitate the use of climate projections by decision-makers and scientists from all fields for climate impact analyses and for the development of robust adaptation strategies, which properly (in a statistical sense) account for the inherent uncertainty. The new tool will be publically available and include functionality to utilize the extensive and dynamically growing repositories of data and use state-of-the-art statistical techniques to quantify the uncertainty and innovative approaches to visualize the results. Such a tool will not only be valuable for future assessments and underpin the development of dedicated climate services, but will also assist the scientific community in making more clearly its case on the consequences of our changing climate to policy makers and the general public. The eSACP project is led by Thordis Thorarinsdottir, Norwegian Computing Center, and also includes the Finnish Meteorological Institute, the Norwegian Meteorological Institute, the Technical University of Denmark and the Bjerknes Centre for Climate Research, Norway. This poster will present details of focus areas in the project and show some examples of the expected analysis tools.

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics.

PubMed

Hoyt, Robert Eugene; Snider, Dallas; Thompson, Carla; Mantravadi, Sarita

2016-10-11

We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

Users guide for information retrieval using APL

NASA Technical Reports Server (NTRS)

Shapiro, A.

1974-01-01

A Programming Language (APL) is a precise, concise, and powerful computer programming language. Several features make APL useful to managers and other potential computer users. APL is interactive; therefore, the user can communicate with his program or data base in near real-time. This, coupled with the fact that APL has excellent debugging features, reduces program checkout time to minutes or hours rather than days or months. Of particular importance is the fact that APL can be utilized as a management science tool using such techniques as operations research, statistical analysis, and forecasting. The gap between the scientist and the manager could be narrowed by showing how APL can be used to do what the scientists and the manager each need to do, retrieve information. Sometimes, the information needs to be retrieved rapidly. In this case APL is ideally suited for this challenge.

Machine learning bandgaps of double perovskites

DOE PAGES

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; ...

2016-01-19

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the mostmore » crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.« less