Miracle-dz/UniFAR-FTUs huggingface dataset:
Second Level Cluster Dependencies: A Comparison of Modeling Software and Missing Data Techniques

Second Level Cluster Dependencies: A Comparison of Modeling Software and Missing Data Techniques. (August 2010) Ross Allen Andrew Larsen, B.S., Brigham Young University; M.S., Brigham Young University Co–Chairs of Advisory Committee: Dr. Victor Willson Dr. Bob Hall Dependencies in multilevel models at the second level have never been thoroughly examined. For certain designs first-level subjects are independent over time, but the second level subjects may exhibit nonzero covariances over time. Following a review of revelant literature the first study investigated which widely used computer programs adequately take into account these dependencies in their analysis. This was accomplished through a simulation study with SAS, and examples of analyses with Mplus and LISREL. The second study investigated the impact of two different missing data techniques for such designs in the case where data is missing at the first level with a simulation study in SAS. The first study simulated data produced in a multiyear study varying the numbers of subjects in the first and second levels, the number of data waves, the magnitude of effects at both the first and second level, and the magnitude of the second level covariance. Results showed that SAS and the MULTILEV component in LISREL analyze such data well while Mplus does not. The second study compared two missing data techniques in the presence of a second level dependency, multiple imputation (MI) and full information maximum likelihood (FIML). They were compared in a SAS simulation study in which the data was simulated with all the factors of the first study and the addition of missing data
Oral reading fluency and prosody: A preliminary analysis of the Greek language

This article presents results from an initial investigation of Greek oral reading fluency and prosody. Although currently held perspectives consider reading the product of reading decoding and reading comprehension, there is enough evidence (both Greek and foreign) to suggest that other variables may affect reading, as well. Such variables include reading fluency and prosody. A small sample of 27 students from the 2nd and 5th grades of primary schools was examined using a variety of tests. All tests were computer-based. Data were collected and analyzed using Cognitive Workshop, a platform for presenting and recording of visually presented stimuli, and Praat, a specialized software for analyzing audio recordings and obtaining speech analysis measurements. Results suggested that differences in reading fluency might be attributed to automaticity acquired by students.
An Algorithm to Find Ecient Supported Solutions of Non-Convex Multiobjective Optimization Problems

This thesis presents an algorithm to find all efficient supported solutions of two-objective optimization problems. The algorithm is based on the first phase of the two-phase method, which has previously been studied as a technique to solve multiobjective combinatorial optimization problems. I give an implementation of the algorithm that can be used on non-convex problems, a class of problems that the original algorithm could not be used to solve. I also provide a test model that is based on previous work with the Single Commodity Allocation Problem (SCAP) for disaster recovery, and give test data for the model on benchmarks designed by Los Alamos National Laboratory. Finally, I discuss implications of the algorithm for specific use in humanitarian logistics work with the SCAP.
The Effect of Education on Medical Technology Adoption: Are the More Educated More Likely to Use New Drugs

There is a large body of work that documents a strong, positive correlation between education and measures of health, but little is known about the mechanisms by which education might affect health. One possibility is that more educated individuals are more likely to adopt new medical technologies. We investigate this theory by asking whether more educated people are more likely to use newer drugs, while controlling for other individual characteristics, such as income and insurance status. Using the 1997 MEPS, we find that more highly educated people are more likely to use drugs more recently approved by the FDA. We find that education only matters for individuals who repeatedly purchase drugs for a given condition, suggesting that the more educated are better able to learn from experience.
A Branch Predictor Directed Data Cache Prefetcher for Out-of-order and Multicore Processors

Modern superscalar pipelines have tremendous capacity to consume the instruction stream. This has been possible owing to improvements in process technology, technology scaling and microarchitectural design improvements that allow programs to speculate past control and data dependencies in the superscalar architecture. However, the speed of the memory subsystem lags behind due to physical constraints in bringing in huge amounts of data to the processor core. Cache hierarchies have subdued the impact of this speed gap, however, there is much that can be still done in improving microarchitecture. Data prefetching techniques bring in memory content significantly before the instruction stream actually witnesses demand misses. However, a majority of the techniques proposed so far depend upon an initial demand miss that initiates a stream of previously identified prefetches. In this thesis, we propose a novel prefetching algorithm, which leverages branch prediction to facilitate deep memory system speculation. The branch predictor directed lookahead mechanism builds a speculative control flow path for the instruction stream about to be fetched by the main superscalar pipeline. Prefetches are generated along this speculative path from a condensed representation of the memory instructions, leveraging register index based correlation. The technique integrates eloquently with the main pipeline’s branch predictor to filter out prefetches along invalid speculative paths. Impact of the prefetching scheme is analyzed using outof-order model of the Gem5 cycle accurate simulator. Evaluation shows that on a set of 13 memory intensive SPEC CPU2006 benchmarks, our prefetching technique improves performance by an average of 5.6% over the baseline out-of-order processor.
Mobile Learning: A Handbook for Developers, Educators, and Learners

Explore the game-changing technology that allows mobile learning to effectively reach K-12 students Mobile Learning: A Handbook for Developers, Educators and Learners provides research-based foundations for developing, evaluating, and integrating effective mobile learning pedagogy. Twenty-first century students require twenty-first century technology, and mobile devices provide new and effective ways to educate children. But with new technologies come new challenges therefore, this handbook presents a comprehensive look at mobile learning by synthesizing relevant theories and drawing practical conclusions for developers, educators, and students. Mobile devices in ways that the laptop, the personal computer, and netbook computers have not present the opportunity to make learning more engaging, interactive, and available in both traditional classroom settings and informal learning environments. From theory to practice, Mobile Learning explores how mobile devices are different than their technological predecessors, makes the case for developers, teachers, and parents to invest in the technology, and illustrates the many ways in which it is innovative, exciting, and effective in educating K-12 students. * Explores how mobile devices can support the needs of students * Provides examples, screenshots, graphics, and visualizations to enhance the material presented in the book * Provides developers with the background necessary to create the apps their audience requires * Presents the case for mobile learning in and out of classrooms as early as preschool * Discusses how mobile learning enables better educational opportunities for the visually impaired, students with Autism, and adult learners. If you're a school administrator, teacher, app developer, or parent, this topical book provides a theoretical, well-researched discussion of the pedagogical theory and mobile learning, as well as practical advice in setting up a mobile learning strategy.
Figures of Speech in Strategic Communication

We provide a class of tractable communication games where each sender type chooses a possibly truth-distorting …gure of speech, which the receiver interprets before choosing an action. Because language is inherently vague, a …gure of speech mapping determines informativeness of communication: Ex- aggeration results in better information transmission than understatement. A sender's …gure of speech optimal choice trades othe distribution of actions he wants to induce and the conformity of the …gure of speech to his ideal for accu- racy. There can be between one and …ve equilibria, all of them fully separating, yet only partially revealing. Their informativeness is unambiguously ranked. At most two of these equilibria, the less informative ones, are ironic. The other (between one and three) equilibria are straight-talking, either exaggerating or understated. We …nd that a receiver may prefer a sender who is either more dissimilar to him, less honest or less competent. He may also prefer to com- municate in a vaguer language. Finally, we study the limit of the equilibria as either (i) the language vagueness vanishes or (ii) the ideal for accuracy vanishes.
Analysis of Landuse/Landcover Change in Damaturu Town of Yobe State, Nigeria

Landuse/landcover change detection is an essential process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial pattern, distribution, and variation of the physical and cultural landscape features. Damaturu town became the capital of Yobe State when it was created in 1991. Since then, Damaturu has been experiencing rapid changes in the landuse/landcover types due to urban expansion, economic development, and social transformation in the town. Therefore, there is a need to examine the trend and pattern of these changes for proper planning and development of the town. This study examined the dynamics of landuse/landcover change of Damaturu town from 1986 to 2009 using Remote Sensing and Geographic Information System techniques. The following satellite imageries of Damaturu Township were obtained and processed for the analysis: LandSat MSS – 1985 and 1991; Landsat ETM – 1999, and Nig-sat1 ETM of 2009. The images were resampled, rectified and georeferenced to the UTM map projection. A supervised classification approach and maximum likelihood algorithm was used for landuse evaluation and mapping from Landsat data with spatial
Cephalic Arch Stenosis in Autogenous Brachiocephalic Hemodialysis Fistulas: Results of Cutting Balloon Angioplasty

Purpose Cephalic arch stenosis is a known cause of hemodialysis access failure in patients with brachiocephalic fistulas (BCFs). Outcomes of endovascular treatment are affected by resistance of the stenosis to balloon dilation, a high vein rupture rate and the development of early restenosis. The purpose of this retrospective study was to report outcomes after cutting balloon angioplasty (CBA) of cephalic arch stenosis. Methods In our vascular access database we identified 74 dysfunctional BcFs of which 30 (41%) were caused by cephalic arch stenosis. Seventeen fistulas in 17 patients (13 males and four females; median age 62 yrs; range 52–86) were treated with CBA (June 2005 to January 2008). Twenty-five procedures were performed. In 15 procedures, a cutting balloon was used alone and in 10 procedures CBA was followed by standard or high-pressure balloon angioplasty. Restenosis rates were calculated and patency rates were estimated with the Kaplan-Meier method. Results Primary patency rates (±SEE) at 3, 6, 12 and 15 months were 94% (±6%), 81% (±10%), 38% (±14%) and 22% (±15%), respectively. Assisted primary patency rates (±SEE) at the same intervals were 100% (±0%), 94% (±6%), 77% (±12%) and 63% (±13%), respectively. The mean interval between radiological interventions was 13 months (SD=8) and the mean number of interventions required per patient-year of dialysis was 0.9. Conclusion Treatment of cephalic arch stenosis with CBA did not improve patency compared to published results of conventional PTA, but our results indicate that CBA may lower the frequency of required re-interventions.
Bridging the Literacy Gap: Teaching the Skills of Reading and Writing as They Apply in School Science

Whereas science teachers in the last century were trained to place empirical activities at the heart of school science (Yore, Bisanz & Hand, 2003) and give relatively less attention to language issues, fundamental literacy (as defined by Norris & Phillips, 2003) is now recognised as having a crucial role in learning science. However, there have been few research reports detailing just how experienced secondary science teachers go about teaching the language and literacies necessary for school science, especially for students who have low literacy skills. This paper explores the literacy-teaching practices of a teacher of "learning support" students during a double-period Earth science class. While the focus was on the science content, many reading and writing skills were taught either as part of the lesson plan or incidentally, thus ensuring that all students could participate more fully. Implications for science teaching and teacher professional development are discussed.
Doing Data Science: Straight Talk from the Frontline

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field thats so clouded in hype? This insightful book, based on Columbia Universitys Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If youre familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include:Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy ONeil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
The Health of Nations: Infectious Disease, Environmental Change, and Their Effects on National Security and Development

Andrew T Price-Smith ! [Graphic][1] MIT Press, £15.95, pp 220 ISBN 0 262 66123 3 Rating:! [Graphic][2] ! [Graphic][3] Health matters have increasingly been discussed in recent years within the higher echelons of international policy making, largely as a result of rapidly emerging concerns about health security. Health security is a post cold war concept that recognises the need to address non-military threats to national security. Other non-military threats, such as environmental degradation, population movements, economic instability, and terrorism, are now among the concerns that keep strategic planners awake at night. The growing literature on health security has encouraged non-health specialists to enter the health field and offer the … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif [3]: /embed/inline-graphic-3.gif
Sustained Learning in 4th and 5th Graders but not 7th Graders: Two Experiments with a Talking Pedagogical Agent

In this chapter, agent movement and temporal speech cueing were designated for empirical study. In Experiment 1 an agent presented students in grades 4 and 5 (n = 133) with instruction, practice, and feedback on the proper usage of the apostrophe to show singular and plural ownership. Analyses of the data in Experiment 1 showed that modality effects favoured speech cueing over text cueing but agent animation had no effect. In Experiment 2, a different agent presented students in grade 7 (n = 91) with examples and practice questions on multiplying and dividing fractions. Experiment 2 data showed no effects for modality or agent animation. The data reflects previous findings of inconsistent effects in modality research.
Wiki use that increases communication and collaboration motivation: A reflection several semesters later

This article gave an account of how wikis were used as a platform to provide an opportunity for students to work on group projects without such barriers as communication and collaboration issues that arise from traditional group work. The original motivation to write the article was to share an innovative teaching experience so that others may be encouraged to try using wikis in the classroom to facilitate group work. It is pleasing to note the surprising number of downloads the paper received. I expect it has generated interest in those wanting to see how technology is being used and those considering adopting similar ideas for themselves. It would be interesting to hear accounts of others’ experiences that were prompted by the paper.
Coarse Grained Modeling and Approaches to Protein Folding

The theoretical prediction of protein structures has become a field of increasing importance in both biology and physics. Reliable prediction methods in fact, would spare time consuming experimental X-ray and NMR techniques and they would represent a challenge for computational protein modeling as well. The well known limitations of all-atom models call for the development of coarse-grained protein descriptions including a minimal number of protein-like fea- tures, while being capable of mimicking the essence of protein folding mechanisms. In this paper we review the most im- portant classes of coarse-grained protein models in order of increasing complexity, starting from (over simplified) binary models, to models with one or two reaction centers per residue. We discuss how, despite their simplification, coarse- grained models constitute a viable approach to structure prediction and they shed light on many aspects of protein-folding problem.
Students’ Use of Information and Communication Technology on English as a Foreign Language Learning: A Study at the English Department Mataram University

This thesis dealt with the use of Information and Communication Technology on English as foreign language learning. This study aimed to map the utilization of ICT among the students of English Department Mataram University. A total of 72 respondents, chosen using stratified random sampling technique, participated within the current study. The data were gathered by using close-ended questionnaire and open ended-interview. The gathered data indicated that the utilization of ICT among them was above the average level. Moreover, the most ICT used by the students were social media, presentation software, word processing program, e-dictionary, and online library resources. The study also revealed some activities done by English Department students by using ICT: surfing internet, downloading or listening to music or video, sending e-mails, and doing classroom activities.
Using channelized Hotelling observers to quantify temporal effects of medical liquid crystal displays on detection performance

Clinical practice is rapidly moving in the direction of volumetric imaging. Often, radiologists interpret these images in liquid crystal displays at browsing rates of 30 frames per second or higher. However, recent studies suggest that the slow response of the display can compromise image quality. In order to quantify the temporal effect of medical displays on detection performance, we investigate two designs of a multi-slice channelized Hotelling observer (msCHO) model in the task of detecting a single-slice signal in multi-slice simulated images. The design of msCHO models is inspired by simplifying assumptions about how humans observe while viewing in the stack-browsing mode. For comparison, we consider a standard CHO applied only on the slice where the signal is located, recently used in a similar study. We refer to it as a single-slice CHO (ssCHO). Overall, our results confirm previous findings that the slow response of displays degrades the detection performance of the observers. More specifically, the observed performance range of msCHO designs is higher compared to the ssCHO suggesting that the extent and rate of degradation, though significant, may be less drastic than previously estimated by the ssCHO. Especially, the difference between msCHO and ssCHO is more significant for higher browsing speeds than for slow image sequences or static images. This, together with their design criteria driven by the assumptions about humans, makes the msCHO models promising candidates for further studies aimed at building anthropomorphic observer models for the stack-mode image presentation.
Bayesian Gaussian Graphical models using sparse selection priors and their mixtures

Bayesian Gaussian Graphical Models Using Sparse Selection Priors and Their Mixtures. (August 2011) Rajesh Talluri, B.Tech., Indian Institute of Technology, Guwahati Co-Chairs of Advisory Committee: Dr. Bani K. Mallick Dr. Veerabhadran Baladandayuthapani We propose Bayesian methods for estimating the precision matrix in Gaussian graphical models. The methods lead to sparse and adaptively shrunk estimators of the precision matrix, and thus conduct model selection and estimation simultaneously. Our methods are based on selection and shrinkage priors leading to parsimonious parameterization of the precision (inverse covariance) matrix, which is essential in several applications in learning relationships among the variables. In Chapter I, we employ the Laplace prior on the off-diagonal element of the precision matrix, which is similar to the lasso model in a regression context. This type of prior encourages sparsity while providing shrinkage estimates. Secondly we introduce a novel type of selection prior that develops a sparse structure of the precision matrix by making most of the elements exactly zero, ensuring positive-definiteness. In Chapter II we extend the above methods to perform classification. Reversephase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limits the potential of this technology is the lack of methods that allows for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation, and provide insight into the distinct biological relationships underlying different cancers. We propose a Bayesian sparse graphical modeling