<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Fabbri, P.</style></author><author><style face="normal" font="default" size="100%">Gaetan, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">spMC: an R-package for 3D Lithological Reconstructions Based on Spatial Markov Chains</style></title><secondary-title><style face="normal" font="default" size="100%">Computers and Geosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0098300416301479</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">94</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil prediction/simulation in a plain site of the NE Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, in spMC package the most known estimation/simulation methods as indicator Kriging and CoKriging were implemented, but also most advanced methods such as path methods and Bayesian procedure exploiting the maximum entropy. Because the spMC package was thought for intensive geostatistical computations, part of the code is implemented with parallel computing via the OpenMP constructs, allowing to deal with more than five lithologies, but trying to keep a computational efficiency. A final analysis of this computational efficiency&amp;nbsp;of spMC compares the prediction/simulation results using different numbers of CPU cores, considering the example data set of the case study available in the package.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">40-47</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Susan Abbatiello</style></author><author><style face="normal" font="default" size="100%">Birgit Schilling</style></author><author><style face="normal" font="default" size="100%">D.R. Mani</style></author><author><style face="normal" font="default" size="100%">L.I. Shilling</style></author><author><style face="normal" font="default" size="100%">S.C. Hall</style></author><author><style face="normal" font="default" size="100%">B. McLean</style></author><author><style face="normal" font="default" size="100%">M. Albetolle</style></author><author><style face="normal" font="default" size="100%">S. Allen</style></author><author><style face="normal" font="default" size="100%">M. Burgess</style></author><author><style face="normal" font="default" size="100%">M.P. Cusack</style></author><author><style face="normal" font="default" size="100%">M Gosh</style></author><author><style face="normal" font="default" size="100%">V Hedrick</style></author><author><style face="normal" font="default" size="100%">J.M. Held</style></author><author><style face="normal" font="default" size="100%">H.D. Inerowicz</style></author><author><style face="normal" font="default" size="100%">A. Jackson</style></author><author><style face="normal" font="default" size="100%">H. Keshishian</style></author><author><style face="normal" font="default" size="100%">C.R. Kinsinger</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Makowski L</style></author><author><style face="normal" font="default" size="100%">Mesri M</style></author><author><style face="normal" font="default" size="100%">Rodriguez H</style></author><author><style face="normal" font="default" size="100%">Rudnick P</style></author><author><style face="normal" font="default" size="100%">Sadowski P</style></author><author><style face="normal" font="default" size="100%">Nell Sedransk</style></author><author><style face="normal" font="default" size="100%">Shaddox K</style></author><author><style face="normal" font="default" size="100%">Skates SJ</style></author><author><style face="normal" font="default" size="100%">Kuhn E</style></author><author><style face="normal" font="default" size="100%">Smith D</style></author><author><style face="normal" font="default" size="100%">Whiteaker, JR</style></author><author><style face="normal" font="default" size="100%">Whitwell C</style></author><author><style face="normal" font="default" size="100%">Zhang S</style></author><author><style face="normal" font="default" size="100%">Borchers CH</style></author><author><style face="normal" font="default" size="100%">Fisher SJ</style></author><author><style face="normal" font="default" size="100%">Gibson BW</style></author><author><style face="normal" font="default" size="100%">Liebler DC</style></author><author><style face="normal" font="default" size="100%">M.J. McCoss</style></author><author><style face="normal" font="default" size="100%">Neubert TA</style></author><author><style face="normal" font="default" size="100%">Paulovich AG</style></author><author><style face="normal" font="default" size="100%">Regnier FE</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma.</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular Cell Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">2357-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data, we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to subnanogram/ml sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and interlaboratory reproducibility was &amp;lt;20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy-isotope-labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an interlaboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality control measures, enables sensitive, specific, reproducible, and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Zou</style></author><author><style face="normal" font="default" size="100%">G. S. Datta</style></author><author><style face="normal" font="default" size="100%">S. Grannis</style></author><author><style face="normal" font="default" size="100%">J. Lynch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bayesian spatio-temporal approach for real-time detection of disease outbreaks: A case study</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Medical Informatics and Decision Making</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">108</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Forzani, E.</style></author><author><style face="normal" font="default" size="100%">Burlingame, C.</style></author><author><style face="normal" font="default" size="100%">Kulikowich, J.</style></author><author><style face="normal" font="default" size="100%">Coiro, J.</style></author><author><style face="normal" font="default" size="100%">Kennedy, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Neuman, S. B.</style></author><author><style face="normal" font="default" size="100%">Gambrell, L.B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The New Literacies of Online Research and Comprehension: Assessing and Preparing Students for the 21st Century with Common Core State Standards</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">International Reading Association</style></publisher><pages><style face="normal" font="default" size="100%">to appear</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">to appear</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abbatiello, S.</style></author><author><style face="normal" font="default" size="100%">Feng, X.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author><author><style face="normal" font="default" size="100%">Mani, DR</style></author><author><style face="normal" font="default" size="100%">Schilling, B</style></author><author><style face="normal" font="default" size="100%">Maclean, B</style></author><author><style face="normal" font="default" size="100%">Zimmerman, LJ</style></author><author><style face="normal" font="default" size="100%">Cusack, MP</style></author><author><style face="normal" font="default" size="100%">Hall, SC</style></author><author><style face="normal" font="default" size="100%">Addona, T</style></author><author><style face="normal" font="default" size="100%">Allen, S</style></author><author><style face="normal" font="default" size="100%">Dodder, NG</style></author><author><style face="normal" font="default" size="100%">Ghosh, M</style></author><author><style face="normal" font="default" size="100%">Held, JM</style></author><author><style face="normal" font="default" size="100%">Hedrick, V</style></author><author><style face="normal" font="default" size="100%">Inerowicz, HD</style></author><author><style face="normal" font="default" size="100%">Jackson, A</style></author><author><style face="normal" font="default" size="100%">Keshishian, H</style></author><author><style face="normal" font="default" size="100%">Kim, JW</style></author><author><style face="normal" font="default" size="100%">Lyssand, JS</style></author><author><style face="normal" font="default" size="100%">Riley, CP</style></author><author><style face="normal" font="default" size="100%">Rudnick, P</style></author><author><style face="normal" font="default" size="100%">Sadowski, P</style></author><author><style face="normal" font="default" size="100%">Shaddox, K</style></author><author><style face="normal" font="default" size="100%">Smith, D</style></author><author><style face="normal" font="default" size="100%">Tomazela, D</style></author><author><style face="normal" font="default" size="100%">Wahlander, A</style></author><author><style face="normal" font="default" size="100%">Waldemarson, S</style></author><author><style face="normal" font="default" size="100%">Whitwell, CA</style></author><author><style face="normal" font="default" size="100%">You, J</style></author><author><style face="normal" font="default" size="100%">Zhang, S</style></author><author><style face="normal" font="default" size="100%">Kinsinger, CR</style></author><author><style face="normal" font="default" size="100%">Mesri, M</style></author><author><style face="normal" font="default" size="100%">Rodriguez, H</style></author><author><style face="normal" font="default" size="100%">Borchers, CH</style></author><author><style face="normal" font="default" size="100%">Buck, C</style></author><author><style face="normal" font="default" size="100%">Fisher, SJ</style></author><author><style face="normal" font="default" size="100%">Gibson, BW</style></author><author><style face="normal" font="default" size="100%">Liebler, D</style></author><author><style face="normal" font="default" size="100%">Maccoss, M</style></author><author><style face="normal" font="default" size="100%">Neubert, TA</style></author><author><style face="normal" font="default" size="100%">Paulovich, A</style></author><author><style face="normal" font="default" size="100%">Regnier, F</style></author><author><style face="normal" font="default" size="100%">Skates, SJ</style></author><author><style face="normal" font="default" size="100%">Tempst, P</style></author><author><style face="normal" font="default" size="100%">Wang, M</style></author><author><style face="normal" font="default" size="100%">Carr, SA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design, Implementation and Multisite Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS)</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular and Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">2623-2639</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation &amp;lt;0.15, peak width coefficient of variation &amp;lt;0.15, standard deviation of RT &amp;lt;0.15 min (9 s), and the RT drift &amp;lt;0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leu, D.</style></author><author><style face="normal" font="default" size="100%">Sedransk, N.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Neuman, S.</style></author><author><style face="normal" font="default" size="100%">Gambrell, L.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The New Literacies of Online Research and Comprehension: Assessing and Preparing Students for the 21st Century with Common Core State Standards</style></title><secondary-title><style face="normal" font="default" size="100%">Quality Reading Instruction in the Age of Common Core Standards</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">International Reading Association</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">16</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hughes-Oliver JM</style></author><author><style face="normal" font="default" size="100%">Brooks A</style></author><author><style face="normal" font="default" size="100%">Welch W</style></author><author><style face="normal" font="default" size="100%">Khaldei MG</style></author><author><style face="normal" font="default" size="100%">Hawkins DM</style></author><author><style face="normal" font="default" size="100%">Young SS</style></author><author><style face="normal" font="default" size="100%">Patil K</style></author><author><style face="normal" font="default" size="100%">Howell GW</style></author><author><style face="normal" font="default" size="100%">Ng RT</style></author><author><style face="normal" font="default" size="100%">Chu MT</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ChemModLab: A web-based cheminromates modeling laboratory</style></title><secondary-title><style face="normal" font="default" size="100%">Cheminformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">61-81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;ChemModLab, written by the ECCR @ NCSU consortium under NIH support, is a toolbox for fitting and assessing quantitative structure-activity relationships (QSARs). Its elements are: a cheminformatic front end used to supply molecular descriptors for use in modeling; a set of methods for fitting models; and methods for validating the resulting model. Compounds may be input as structures from which standard descriptors will be calculated using the freely available cheminformatic front end PowerMV; PowerMV also supports compound visualization. In addition, the user can directly input their own choices of descriptors, so the capability for comparing descriptors is effectively unlimited. The statistical methodologies comprise a comprehensive collection of approaches whose validity and utility have been accepted by experts in the fields. As far as possible, these tools are implemented in open-source software linked into the flexible R platform, giving the user the capability of applying many different QSAR modeling methods in a seamless way. As promising new QSAR methodologies emerge from the statistical and data-mining communities, they will be incorporated in the laboratory. The web site also incorporates links to public-domain data sets that can be used as test cases for proposed new modeling methods. The capabilities of ChemModLab are illustrated using a variety of biological responses, with different modeling methodologies being applied to each. These show clear differences in quality of the fitted QSAR model, and in computational requirements. The laboratory is web-based, and use is free. Researchers with new assay data, a new descriptor set, or a new modeling method may readily build QSAR models and benchmark their results against other findings. Users may also examine the diversity of the molecules identified by a QSAR model. Moreover, users have the choice of placing their data sets in a public area to facilitate communication with other researchers; or can keep them hidden to preserve confidentiality.&lt;/p&gt;
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Wang</style></author><author><style face="normal" font="default" size="100%">Gunning, P.</style></author><author><style face="normal" font="default" size="100%">Fleming, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Psychometric and Statistical Modeling for the Study of Retention and Graduation in Undergraduate Engineering</style></title><secondary-title><style face="normal" font="default" size="100%">Social Statistics and Higher Education Conference Volume</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fogel, P.</style></author><author><style face="normal" font="default" size="100%">Gobinet, C.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">Zugaj, D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of unmixing methods for the separation of Quantum Dot sources</style></title><secondary-title><style face="normal" font="default" size="100%">Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS ’09. First Workshop on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Bayesian positive source separation</style></keyword><keyword><style  face="normal" font="default" size="100%">BPSS</style></keyword><keyword><style  face="normal" font="default" size="100%">cadmium compounds</style></keyword><keyword><style  face="normal" font="default" size="100%">CdSe</style></keyword><keyword><style  face="normal" font="default" size="100%">consensus nonnegative matrix factorization</style></keyword><keyword><style  face="normal" font="default" size="100%">Fluorescence</style></keyword><keyword><style  face="normal" font="default" size="100%">hyperspectral images</style></keyword><keyword><style  face="normal" font="default" size="100%">Hyperspectral imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">hyperspectral system</style></keyword><keyword><style  face="normal" font="default" size="100%">ICA</style></keyword><keyword><style  face="normal" font="default" size="100%">II-VI semiconductors</style></keyword><keyword><style  face="normal" font="default" size="100%">independent component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanobioscience</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanocrystals</style></keyword><keyword><style  face="normal" font="default" size="100%">nanometer dimensions</style></keyword><keyword><style  face="normal" font="default" size="100%">NMF</style></keyword><keyword><style  face="normal" font="default" size="100%">Photonic crystals</style></keyword><keyword><style  face="normal" font="default" size="100%">Probes</style></keyword><keyword><style  face="normal" font="default" size="100%">quantum dot sources</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantum dots</style></keyword><keyword><style  face="normal" font="default" size="100%">semiconductor crystals</style></keyword><keyword><style  face="normal" font="default" size="100%">semiconductor quantum dots</style></keyword><keyword><style  face="normal" font="default" size="100%">Source separation</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial localization</style></keyword><keyword><style  face="normal" font="default" size="100%">ultraviolet spectra</style></keyword><keyword><style  face="normal" font="default" size="100%">unmixing methods</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">1-4</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-4686-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Quantum Dots (QDs) are semiconductor crystals with nanometer dimensions, which have fluorescence properties that can be adjusted through controlling their diameter. Under ultraviolet light excitation, these nanocrystals re-emit photons in the visible spectrum, with a wavelength ranging from red to blue as their size diminishes. We created an experiment to evaluate unmixing methods for hyperspectral images. The wells of a matrix [3 times 3] were filled with individual or up to three of five QDs. The matrix was imaged by a hyperspectral system (Photon Etc., Montreal, QC, CA) and a data ldquocuberdquo of 512 rows times 512 columns times 63 wavelengths was generated. For unmixing, we tested three approaches: independent component analysis (ICA), Bayesian positive source separation (BPSS) and our new consensus non-negative matrix factorization (CNFM) method. For each of these methods, we assessed the ability to separate the different sources from both spectral and spatial localization points of view. In this situation, we showed that BPSS and CNMF model estimates were very close to the original design of our experiment and were better than the ICA results. However, the time needed for the BPSS model to converge is substantially higher than CNMF. In addition, we show how the CNMF coefficients can be used to provide reasonable bounds for the number of sources, a key issue for unmixing methods, and allow for an effective segmentation of the spatial signal.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. Chen</style></author><author><style face="normal" font="default" size="100%">L. Brandt</style></author><author><style face="normal" font="default" size="100%">V. Gregg</style></author><author><style face="normal" font="default" size="100%">R. Traunmüller</style></author><author><style face="normal" font="default" size="100%">S. Dawes</style></author><author><style face="normal" font="default" size="100%">E. Hovy</style></author><author><style face="normal" font="default" size="100%">A. Macintosh</style></author><author><style face="normal" font="default" size="100%">C. A. Larson</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Citizen access to government statistical information</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">503-529</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern electronic technologies have dramatically increased the volume of information collected and assembled by government agencies at all levels. This chapter describes digital government research aimed at keeping government data warehouses from turning into data cemeteries. The products of the research exploit modern electronic technologies in order to allow “ordinary citizens” and researchers access to government-assembled information. The goal is to help ensure that more data also means better and more useful data. Underlying the chapter are three tensions. The first is between comprehensiveness and understandability of information available to non-technically oriented “private citizens.” The second is between ensuring usefulness of detailed statistical information and protecting confidentiality of data subjects. The third tension is between the need to analyze “global” data sets and the reality that government data are distributed among both levels of government and agencies (typically, by the “domain” of data, such as education, health, or transportation).&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">25</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephen E. Fienberg</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Chen, Hsinchun</style></author><author><style face="normal" font="default" size="100%">Reid, Edna</style></author><author><style face="normal" font="default" size="100%">Sinai, Joshua</style></author><author><style face="normal" font="default" size="100%">Silke, Andrew</style></author><author><style face="normal" font="default" size="100%">Ganor, Boaz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Homeland Insecurity</style></title><secondary-title><style face="normal" font="default" size="100%">Terrorism Informatics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Integrated Series In Information Systems</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-0-387-71613-8_10</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">197-218</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-71612-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. There have also been claims that prospective datamining could be used to find the “signature” of terrorist cells embedded in larger networks. We present an overview of why the public has concerns about such activities and describe some proposals for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literatures on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of “selective revelation” and their confidentiality implications.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M.J. Bayarri</style></author><author><style face="normal" font="default" size="100%">J. Berger</style></author><author><style face="normal" font="default" size="100%">Garcia-Donato, G.</style></author><author><style face="normal" font="default" size="100%">Liu, F.</style></author><author><style face="normal" font="default" size="100%">R. Paulo</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Palomo, J.</style></author><author><style face="normal" font="default" size="100%">Walsh, D.</style></author><author><style face="normal" font="default" size="100%">J. Cafeo</style></author><author><style face="normal" font="default" size="100%">Parthasarathy, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer Model Validation with Functional Output</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of  Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">1874-190</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">J. Ghosh</style></author><author><style face="normal" font="default" size="100%">J. P. Reiter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure computation with horizontally partitioned data using adaptive regression splines</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational Statistics and Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">12</style></number><volume><style face="normal" font="default" size="100%">51</style></volume><pages><style face="normal" font="default" size="100%">5813-5820</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;When several data owners possess data on different records but the same variables, known as horizontally partitioned data, the owners can improve statistical inferences by sharing their data with each other. Often, however, the owners are unwilling or unable to share because the data are confidential or proprietary. Secure computation protocols enable the owners to compute parameter estimates for some statistical models, including linear regressions, without sharing individual records’ data. A drawback to these techniques is that the model must be specified in advance of initiating the protocol, and the usual exploratory strategies for determining good-fitting models have limited usefulness since the individual records are not shared. In this paper, we present a protocol for secure adaptive regression splines that allows for flexible, semi-automatic regression modeling. This reduces the risk of model mis-specification inherent in secure computation settings. We illustrate the protocol with air pollution data.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shanti Gomatam</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Swapping as a Decision Problem</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Official Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">categorical data</style></keyword><keyword><style  face="normal" font="default" size="100%">data confidentiality</style></keyword><keyword><style  face="normal" font="default" size="100%">Data swapping</style></keyword><keyword><style  face="normal" font="default" size="100%">data utility</style></keyword><keyword><style  face="normal" font="default" size="100%">disclosure risk</style></keyword><keyword><style  face="normal" font="default" size="100%">risk-utility frontier</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">635–655</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We construct a decision-theoretic formulation of data swapping in which quantitative measures of disclosure risk and data utility are employed to select one release from a possibly large set of candidates. The decision variables are the swap rate, swap attribute(s) and, possibly, constraints on the unswapped attributes. Risk–utility frontiers, consisting of those candidates not dominated in (risk, utility) space by any other candidate, are a principal tool for reducing the scale of the decision problem. Multiple measures of disclosure risk and data utility, including utility measures based directly on use of the swapped data for statistical inference, are introduced. Their behavior and resulting insights into the decision problem are illustrated using data from the U.S. Current Population Survey, the well-studied “Czech auto worker data” and data on schools and administrators generated by the U.S. National Center for Education Statistics.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Xiaodong Lin</style></author><author><style face="normal" font="default" size="100%">Ashish P. Sanil</style></author><author><style face="normal" font="default" size="100%">Jerome P. Reiter</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">D. Olwell</style></author><author><style face="normal" font="default" size="100%">A. G.Wilson</style></author><author><style face="normal" font="default" size="100%">G. Wilson</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure statistical analysis of distributed databases using partially trusted third parties. Manuscript in preparation</style></title><secondary-title><style face="normal" font="default" size="100%">In Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer–Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">New York</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. Gomatam</style></author><author><style face="normal" font="default" size="100%">C. Liu</style></author><author><style face="normal" font="default" size="100%">A. P. Sanil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data swapping: A risk–utility framework and Web service implementation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. dg.o 2003, National Conference on Digital Government Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Digital Government Research Center</style></publisher><pages><style face="normal" font="default" size="100%">245-248</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ashish Sanil</style></author><author><style face="normal" font="default" size="100%">Shanti Gomatam</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">NISS WebSwap: A Web Service for Data Swapping</style></title><secondary-title><style face="normal" font="default" size="100%">J. Statist. Software</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">2003</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">T.L. Graves</style></author><author><style face="normal" font="default" size="100%">A. Mockus</style></author><author><style face="normal" font="default" size="100%">P. Schuster</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variability of travel times on arterial streets: effects of signals and volume</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Record C</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">000-000</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephen G. Eick</style></author><author><style face="normal" font="default" size="100%">Paul Schuster</style></author><author><style face="normal" font="default" size="100%">Audris Mockus</style></author><author><style face="normal" font="default" size="100%">Todd L. Graves</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualizing Software Changes</style></title><secondary-title><style face="normal" font="default" size="100%">INTERACTIONS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">29–31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephen G. Eick</style></author><author><style face="normal" font="default" size="100%">Todd L. Graves</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">J. S. Marron</style></author><author><style face="normal" font="default" size="100%">Audris Mockus</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Does code decay? Assessing the evidence from change management data</style></title><secondary-title><style face="normal" font="default" size="100%">In IEEE Transactions on Software Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">1–12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A central feature of the evolution of large software systems is that changeÐwhich is necessary to add new functionality, accommodate new hardware, and repair faultsÐbecomes increasingly difficult over time. In this paper, we approach this phenomenon, which we term code decay, scientifically and statistically. We define code decay and propose a number of measurements (code decay indices) on software and on the organizations that produce it, that serve as symptoms, risk factors, and predictors of decay. Using an unusually rich data set (the fifteen-plus year change history of the millions of lines of software for a telephone switching system), we find mixed, but on the whole persuasive, statistical evidence of code decay, which is corroborated by developers of the code. Suggestive indications that perfective maintenance can retard code decay are also discussed. Index TermsÐSoftware maintenance, metrics, statistical analysis, fault potential, span of changes, effort modeling.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Young, S.Stanley</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gundertofte, Klaus</style></author><author><style face="normal" font="default" size="100%">Jørgensen, Flemming Steen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of a Large, High-Throughput Screening Data Using Recursive Partitioning</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular Modeling and Prediction of Bioactivity</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4615-4141-7_17</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">149-156</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4613-6857-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;As biological drug targets multiply through the human genome project and as the number of chemical compounds available for screening becomes very large, the expense of screening every compound against every target becomes prohibitive. We need to improve the efficiency of the drug screening process so that active compounds can be found for more biological targets and turned over to medicinal chemists for atom-by-atom optimization. We create a method for analysis of the very large, complex data sets coming from high throughput screening, and then integrate the analysis with the selection of compounds for screening so that the structure-activity rules derived from an initial compound set can be used to suggest additional compounds for screening. Cycles of screening and analysis become sequential screening rather than the mass screening of all available compounds. We extend the analysis method to deal with multivariate responses. Previously, a screening campaign might screen hundreds of thousands of compounds; sequential screening can cut the number of compounds screened by up to eighty percent. Sequential screening also gives SAR rules that can be used to mathematically screen compound collections or virtual chemical libraries.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Graham, Jinko</style></author><author><style face="normal" font="default" size="100%">Curran, James</style></author><author><style face="normal" font="default" size="100%">Weir, Bruce</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conditional Genotypic Probabilities for Microsatellite Loci</style></title><secondary-title><style face="normal" font="default" size="100%">Genetics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">155</style></volume><pages><style face="normal" font="default" size="100%">1973-1980</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modern forensic DNA profiles are constructed using microsatellites, short tandem repeats of 2-5 bases. In the absence of genetic data on a crime-specific subpopulation, one tool for evaluating profile evidence is the match probability. The match probability is the conditional probability that a random person would have the profile of interest given that the suspect has it and that these people are different members of the same subpopulation. One issue in evaluating the match probability is population differentiation, which can induce coancestry among subpopulation members. Forensic assessments that ignore coancestry typically overstate the strength of evidence against the suspect. Theory has been developed to account for coancestry; assumptions include a steady-state population and a mutation model in which the allelic state after a mutation event is independent of the prior state. Under these assumptions, the joint allelic probabilities within a subpopulation may be approximated by the moments of a Dirichlet distribution. We investigate the adequacy of this approximation for profiled loci that mutate according to a generalized stepwise model. Simulations suggest that the Dirichlet theory can still overstate the evidence against a suspect with a common microsatellite genotype. However, Dirichlet-based estimators were less biased than the product-rule estimator, which ignores coancestry.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">M. Ghandehari</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of water flow through cracked concrete under load</style></title><secondary-title><style face="normal" font="default" size="100%">ACI Materials Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">97</style></volume><pages><style face="normal" font="default" size="100%">567?575</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This research studied the relationship between cracking and water permeability of normal-strength concrete under load and compared the experimental results with theoretical models. A feedback-controlled wedge splitting test was used to generate width-controlled cracks. Speckle interferometry was used to record the cracking history. Water permeability of the loaded specimens was evaluated by a low-pressure water permeability test at the designed crack mouth opening displacements (CMODs). Water permeability results were compared with those previously obtained for unloaded specimens for which cracks were induced by a feedback-controlled splitting tension test. The experimental results indicate that water permeability of cracked material significantly increases with increasing crack width. The flow for the same cracking level is repeatable regardless of the procedure used for inducing the cracks. No direct relationship between water flow and crack length was observed, whereas clear relationships existed between CMOD or crack area and flow characteristics. Experimentally measured flow was compared with theoretical models of flow through cracked rocks with parallel walls and a correction factor accounting for the tortuosity of the crack was determined. Calculated flow through cracks induced by a wedge-splitting test provided an acceptable approximation of the measured flow.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">S. G. Eick</style></author><author><style face="normal" font="default" size="100%">T.L. Graves</style></author><author><style face="normal" font="default" size="100%">J. S. Marron</style></author><author><style face="normal" font="default" size="100%">H. Siy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting fault incidence using software change history</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transportation Software Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">aging</style></keyword><keyword><style  face="normal" font="default" size="100%">change history</style></keyword><keyword><style  face="normal" font="default" size="100%">degradation</style></keyword><keyword><style  face="normal" font="default" size="100%">management of change</style></keyword><keyword><style  face="normal" font="default" size="100%">software fault tolerance</style></keyword><keyword><style  face="normal" font="default" size="100%">software maintenance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">653?661</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper is an attempt to understand the processes by which software ages. We define code to be aged or decayed if its structure makes it unnecessarily difficult to understand or change and we measure the extent of decay by counting the number of faults in code in a period of time. Using change management data from a very large, long-lived software system, we explore the extent to which measurements from the change history are successful in predicting the distribution over modules of these incidences of faults. In general, process measures based on the change history are more useful in predicting fault rates than product metrics of the code: For instance, the number of times code has been changed is a better indication of how many faults it will contain than is its length. We also compare the fault rates of code of various ages, finding that if a module is, on the average, a year older than an otherwise similar module, the older module will have roughly a third fewer faults. Our most successful model measures the fault potential of a module as the sum of contributions from all of the times the module has been changed, with large, recent changes receiving the most weight&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Todd L. Graves</style></author><author><style face="normal" font="default" size="100%">Harrold, Mary Jean</style></author><author><style face="normal" font="default" size="100%">Kim, Jung-Min</style></author><author><style face="normal" font="default" size="100%">Adam Porter</style></author><author><style face="normal" font="default" size="100%">Rothermel, Gregg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Empirical Study of Regression Test Selection Techniques</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 20th International Conference on Software Engineering</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">ICSE ’98</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=302163.302182</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pub-location><style face="normal" font="default" size="100%">Washington, DC, USA</style></pub-location><pages><style face="normal" font="default" size="100%">188–197</style></pages><isbn><style face="normal" font="default" size="100%">0-8186-8368-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T.L. Graves</style></author><author><style face="normal" font="default" size="100%">A. Mockus</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferring change effort from configuration management databases</style></title><secondary-title><style face="normal" font="default" size="100%">Software Metrics Symposium, 1998. Metrics 1998. Proceedings. Fifth International</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">267-273</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we describe a methodology and algorithm for historical analysis of the effort necessary for developers to make changes to software. The algorithm identifies factors which have historically increased the difficulty of changes. This methodology has implications for research into cost drivers. As an example of a research finding, we find that a system under study was “decaying” in that changes grew more difficult to implement at a rate of 20% per year. We also quantify the difference in costs between changes that fix faults and additions of new functionality: fixes require 80% more effort after accounting for size. Since our methodology adds no overhead to the development process, we also envision it being used as a project management tool: for example, developers can identify code modules which have grown more difficult to change than previously, and can match changes to developers with appropriate expertise. The methodology uses data from a change management system, supported by monthly time sheet data if available. The method’s performance does not degrade much when the quality of the time sheet data is limited. We validate our results using a survey of the developers under study: the change efforts resulting from the algorithm match the developers’ opinions. Our methodology includes a technique based on the jackknife to determine factors that contribute significantly to change effort&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">G. Eick</style></author><author><style face="normal" font="default" size="100%">A. Mockus</style></author><author><style face="normal" font="default" size="100%">T.L. Graves</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Badilla</style></author><author><style face="normal" font="default" size="100%">F. Faulbaum</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">SoftStat ?97: Advances in Statistical Software 6</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Lucius &amp; Lucius</style></publisher><pages><style face="normal" font="default" size="100%">3-10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">Web-based text visualization</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">G. Eick</style></author><author><style face="normal" font="default" size="100%">A. Mockus</style></author><author><style face="normal" font="default" size="100%">T.L. Graves</style></author><author><style face="normal" font="default" size="100%">A. F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Web laboratory for software data analysis</style></title><secondary-title><style face="normal" font="default" size="100%">World Wide Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">55-60</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We describe two prototypical elements of a World Wide Web?based system for visualization and analysis of data produced in the software development process. Our system incorporates interactive applets and visualization techniques into Web pages. A particularly powerful example of such an applet, SeeSoftTM, can display thousands of lines of text on a single screen, allowing detection of patterns not discernible directly from the text. In our system, Live Documents replace static statistical tables in ordinary documents by dynamic Web?based documents, in effect allowing the ?reader? to customize the document as it is read. Use of the Web provides several advantages. The tools access data from a very large central data base, instead of requiring that it be downloaded; this ensures that readers are always working with the most up?to?date version of the data, and relieves readers of the responsibility of preparing data for their use. The tools encourage collaborative research, as one researcher’s observations can easily be replicated and studied in greater detail by other team members. We have found this particularly useful while studying software data as part of a team that includes researchers in computer science, software engineering, and statistics, as well as development managers. Live documents will also help the Web revolutionize scientific publication, as papers published on the Web can contain Java applets that permit readers to confirm the conclusions reached by the authors’ statistical analyses.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nobile, Agostino</style></author><author><style face="normal" font="default" size="100%">Bhat, Chandra R.</style></author><author><style face="normal" font="default" size="100%">Pas, Eric I.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gatsonis, Constantine</style></author><author><style face="normal" font="default" size="100%">Hodges, JamesS.</style></author><author><style face="normal" font="default" size="100%">Kass, RobertE.</style></author><author><style face="normal" font="default" size="100%">McCulloch, Robert</style></author><author><style face="normal" font="default" size="100%">Rossi, Peter</style></author><author><style face="normal" font="default" size="100%">Singpurwalla, NozerD.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Random-Effects Multinomial Probit Model of Car Ownership Choice</style></title><secondary-title><style face="normal" font="default" size="100%">Case Studies in Bayesian Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">car ownership</style></keyword><keyword><style  face="normal" font="default" size="100%">longitudinal data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-2290-3_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">121</style></volume><pages><style face="normal" font="default" size="100%">419-434</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-94990-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The number of cars in a household has an important effect on its travel behavior (e.g., choice of number of trips, mode to work and non-work destinations), hence car ownership modeling is an essential component of any travel demand forecasting effort. In this paper we report on a random effects multinomial probit model of car ownership level, estimated using longitudinal data collected in the Netherlands. A Bayesian approach is taken and the model is estimated by means of a modification of the Gibbs sampling with data augmentation algorithm considered by McCulloch and Rossi (1994). The modification consists in performing, after each Gibbs sampling cycle, a Metropolis step along a direction of constant likelihood. An examination of the simulation output illustrates the improved performance of the resulting sampler.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xie, Minge</style></author><author><style face="normal" font="default" size="100%">Simpson, Douglas G</style></author><author><style face="normal" font="default" size="100%">Carroll, Raymond J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gregoire, Timothy G.</style></author><author><style face="normal" font="default" size="100%">Brillinger, David R.</style></author><author><style face="normal" font="default" size="100%">Diggle, PeterJ.</style></author><author><style face="normal" font="default" size="100%">Russek-Cohen, Estelle</style></author><author><style face="normal" font="default" size="100%">Warren, William G.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, Russell D.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Scaled Link Functions for Heterogeneous Ordinal Response Data*</style></title><secondary-title><style face="normal" font="default" size="100%">Modelling Longitudinal and Spatially Correlated Data</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Statistics</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aggregated observations</style></keyword><keyword><style  face="normal" font="default" size="100%">Generalized likelihood inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Marginal modeling approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Ordinal regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4612-0699-6_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York</style></publisher><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">23-36</style></pages><isbn><style face="normal" font="default" size="100%">978-0-387-98216-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper describes a class ordinal regression models in which the link function has scale parameters that may be estimated along with the regression parameters. One motivation is to provide a plausible model for group level categorical responses. In this case a natural class of scaled link functions is obtained by treating the group level responses as threshold averages of possible correlated latent individual level variables. We find scaled link functions also arise naturally in other circumstances. Our methodology is illustrated through environmental risk assessment data where (correlated) individual level responses and group level responses are mixed.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gao, Feng</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Welch, William</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting ozone levels and trends with semiparametric modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural, Biological, and Environmental Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">404-425</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">404</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Styer</style></author><author><style face="normal" font="default" size="100%">McMillan, N</style></author><author><style face="normal" font="default" size="100%">Gao, F</style></author><author><style face="normal" font="default" size="100%">Davis, J</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of outdoor airborne particulate matter on daily death count</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental Health Perspectives</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">103</style></volume><pages><style face="normal" font="default" size="100%">490–497</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To investigate the possible relationship between airborne particulate matter and mortality, we developed regression models of daily mortality counts using meteorological covariates and measures of outdoor PM10. Our analyses included data from Cook County, Illinois, and Salt Lake County, Utah. We found no evidence that particulate matter &amp;lt; or = 10 microns (PM10) contributes to excess mortality in Salt Lake County, Utah. In Cook County, Illinois, we found evidence of a positive PM10 effect in spring and autumn, but not in winter and summer. We conclude that the reported effects of particulates on mortality are unconfirmed.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gough, William A.</style></author><author><style face="normal" font="default" size="100%">Welch, William J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parameter space exploration of an ocean general circulation model using an isopycnal mixing parameterization</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Marine Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><volume><style face="normal" font="default" size="100%">52</style></volume><pages><style face="normal" font="default" size="100%">773-796</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this study we have employed statistical methods to efficiently design experiments and analyze output of an ocean general circulation model that uses an isopycnal mixing parameterization. Full ranges of seven inputs are explored using 51 numerical experiments. Fifteen of the cases fail to reach satisfactory equilibria. These are attributable to numerical limitations specific to the isopycnal model. Statistical approximating functions are evaluated using the remaining cases to determine the dependency of each of the six scalar outputs on the inputs. With the exception of one output, the approximating functions perform well. Known sensitivities, particularly the importance of diapycnal (vertical) eddy diffusivity and wind stress, are reproduced. The sensitivities of the model to two numerical constraints specific to the isopycnal parameterization, maximum allowable isopycnal slope and horizontal background eddy diffusivity, are explored. Isopycnal modelling issues, convection reduction and the Veronis effect, are examined and found to depend crucially on the isopycnal modelling constraints.</style></abstract></record></records></xml>