An investigation of selected at risk factors in rural high schools in the Midwest

This study investigated the use of selected at risk factors to predict student high school success. In addition, academic and social viewpoints of students in grades 9-11 who had been retained were investigated, as were the perceptions of their parents. The sample was drawn from two public school districts which were representative of rural schools in a Midwestern state. A total of 373 students in grades 9-11 from both districts participated. Data on 26 selected at risk factors were gathered from school records and personnel to determine predictors of school success. Data analysis included descriptive statistics, step-wise multiple regression, and correlational analysis. Fifteen students who had been retained in grades K-4 participated in an interview dealing with views of school, while parents completed a mailed survey concerning their perceptions of how the students viewed school. Tabulations and frequency analyses were used to ascertain patterns of responses and whether parents and children shared similar viewpoints about school and retention. Findings indicated that combinations of at risk factors served as significant predictors of students\u27 success in high school. Self-concept score was predicted using a combination of grade point average, lack of participation in extracurricular activities, IQ score, and number of failed courses. Performance on Test Q (Quantitative) of the ITED was predicted using a combination of the Reading Total of the ITED, grade point average, IQ score, and number of failed courses. Performance on the Reading Total of the ITED was predicted using a combination of Test Q score, grade point average, lack of participation in extracurricular activities, IQ score, and being the youngest or only child in the family. Grade point average was predicted using a combination of Heading Total, number of failed courses, Test Q score, IQ score,attendance, number of sibling dropouts, and self-concept score. Findings also indicated that high school students who were retained and their parents showed positive agreement about academic, general, and social perceptions. Students viewed the effects of retention on current academic and social status more positively than did the parents

The Natural History of an Urban Creek: Waller Creek

Dr. Kevin M. Anderson details the history of Waller Creek in his 2012 presentation.Waller Creek Working Grou

Combined visible and near-infrared OPA for wavelength scaling experiments in strong-field physics

We report the operation of an optical parametric amplifier (OPA) capable of producing gigawatt peak-power laser pulses with tunable wavelength in either the visible or near-infrared spectrum. The OPA has two distinct operation modes (i) generation of >350 uJ, sub 100 fs pulses, tunable between 1250 - 1550 nm; (ii) generation of >190 uJ, sub 150 fs pulses tunable between 490 - 530 nm. We have recorded high-order harmonic spectra over a wide range of driving wavelengths. This flexible source of femtosecond pulses presents a useful tool for exploring the wavelength-dependence of strong-field phenomena, in both the multi-photon and tunnel ionization regimes.Comment: 14 pages, 9 figures, This paper was published in Proceedings of SPIE 10088, Nonlinear Frequency Generation and Conversion: Materials and Devices XVI, doi 10.1117/12.225077

How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

RAVE: Rapid Visualization Environment

Visualization is used in the process of analyzing large, multidimensional data sets. However, the selection and creation of visualizations that are appropriate for the characteristics of a particular data set and the satisfaction of the analyst's goals is difficult. The process consists of three tasks that are performed iteratively: generate, test, and refine. The performance of these tasks requires the utilization of several types of domain knowledge that data analysts do not often have. Existing visualization systems and frameworks do not adequately support the performance of these tasks. In this paper we present the RApid Visualization Environment (RAVE), a knowledge-based system that interfaces with commercial visualization frameworks and assists a data analyst in quickly and easily generating, testing, and refining visualizations. RAVE was used for the visualization of in situ measurement data captured by spacecraft

The SDSS-2MASS-WISE Ten Dimensional Stellar Color Locus

We present the fiducial main sequence stellar locus traced by 10 photometric colors observed by SDSS, 2MASS, and WISE. Median colors are determined using 1,052,793 stars with r-band extinction less than 0.125. We use this locus to measure the dust extinction curve relative to the r-band, which is consistent with previous measurements in the SDSS and 2MASS bands. The WISE band extinction coefficients are larger than predicted by standard extinction models. Using 13 lines of sight, we find variations in the extinction curve in H, Ks, and WISE bandpasses. Relative extinction decreases towards Galactic anti-center, in agreement with prior studies. Relative extinction increases with Galactic latitude, in contrast to previous observations. This indicates a universal mid-IR extinction law does not exist due to variations in dust grain size and chemistry with Galactocentric position. A preliminary search for outliers due to warm circumstellar dust is also presented, using stars with high signal-to-noise in the W3-band. We find 199 such outliers, identified by excess emission in Ks-W3. Inspection of SDSS images for these outliers reveals a large number of contaminants due to nearby galaxies. Six sources appear to be genuine dust candidates, yielding a fraction of systems with infrared excess of 0.12$\pm$0.05%.Comment: 11 pages, 10 figures, MNRAS Accepted. Tables 1 and 2 available online: https://github.com/jradavenport/wise_locu

Workplace Education Programs in Small- and Medium-Sized Michigan Firms

This paper presents a systematic, baseline picture of workplace education programs in small and medium-sized businesses (less than 500 employees) in Michigan. Specifically, it addresses why some firms are offering and other firms are not offering workplace education programs, what are the characteristics of the programs being provided, and what are the impacts of these programs on firms and employees. The paper draws upon two data sources. Case studies of 28 Michigan businesses were undertaken between May 1991 and July 1992 and a combination mail/telephone survey of small businesses in Michigan was conducted in early 1992. The paper finds that a significant share of the employed population, perhaps 25 to 40 percent of hourly workers, have basic skills difficulties that are reported to impair their productivity. Yet very few of the workers have an opportunity to receive education in basic skills through their workplace

Evaluating concentration estimation errors in ELISA microarray experiments

BACKGROUND: Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors. RESULTS: We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. CONCLUSIONS: This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process