General condition of western forests

Presented at the Can forests meet our energy needs? The future of forest biomass in Colorado conference, February 21, 2008, Colorado State University, Fort Collins, Colorado.Dr. Wayne D. Shepperd retired in January, 2007 from a career as a Research Silviculturist, at the U.S. Forest Service Rocky Mountain Research Station in Fort Collins, Colorado. He has authored over 100 research publications on the ecology, growth, and management of Rocky Mountain Forests. He holds a B.S. in Outdoor Recreation, and M.S. and Ph.D. degrees in Silviculture from Colorado State University. In retirement, Wayne continues to work part time as a forestry consultant

The interpretation of sarcasm by typically developing children and children with LLD in the school age population

The present study was conducted to obtain information about the interpretation of sarcasm by typically developing children and children with language learning disabilities in the school age population. Prior research indicates sarcasm comprehension is a difficult semantic task for typically developing children to acquire, and thus it is likely that children with language learning disabilities, who have been shown to have significant semantic difficulties, are at risk for delayed acquisition of sarcasm comprehension. Participating children took a 24 question multiple-choice sarcasm test. Results demonstrated significant differences in sarcasm comprehension between children with language learning disabilities and their typically developing peers. Additionally, findings revealed a significant association between sarcasm comprehension and age, but no significant association with gender. Both groups of children (LLD vs. typical) deviated from the expected developmental sequence of sarcasm interpretation

Aspen mortality summit, December 18 and 19, 2006, Salt Lake City, Utah

The USDA Forest Service Rocky Mountain Research Station sponsored an aspen sum- mit meeting in Salt Lake City, Utah, on December 18 and19, 2006, to discuss the rapidly increasing mortality of aspen (Populus tremuloides) throughout the western United States. Selected scientists, university faculty, and managers from Federal, State, and non-profit agencies with experience working with aspen were invited. Participants were first asked to share information on recent aspen mortality. Subject matter working groups were then asked to determine factors associated with recent aspen mortality, recommend research needs, and organize those needs into testable questions and hypotheses. This report documents their findings, and will serve as a platform for Resource Managers to address the Sudden Aspen Decline issue

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Authors' Reply to “Comments on 'Researcher Bias: The Use of Machine Learning in Software Defect Prediction' ”

IEEE In 2014 we published a meta-analysis of software defect prediction studies [1]. This suggested that the most important factor in determining results was Research Group i.e., who conducts the experiment is more important than the classifier algorithms being investigated. A recent re-analysis [2] sought to argue that the effect is less strong than originally claimed since there is a relationship between Research Group and Dataset. In this response we show (i) the re-analysis is based on a small (21%) subset of our original data, (ii) using the same re-analysis approach with a larger subset shows that Research Group is more important than type of Classifier and (iii) however the data are analysed there is compelling evidence that who conducts the research has an effect on the results. This means that the problem of researcher bias remains. Addressing it should be seen as a matter of priority amongst those of us who conduct and publish experiments comparing the performance of competing software defect prediction systems

Silicon intercalation into the graphene-SiC interface

In this work we use LEEM, XPEEM and XPS to study how the excess Si at the graphene-vacuum interface reorders itself at high temperatures. We show that silicon deposited at room temperature onto multilayer graphene films grown on the SiC(000[`1]) rapidly diffuses to the graphene-SiC interface when heated to temperatures above 1020. In a sequence of depositions, we have been able to intercalate ~ 6 ML of Si into the graphene-SiC interface.Comment: 6 pages, 8 figures, submitted to PR

Accurate computation of quaternions from rotation matrices

The final publication is available at link.springer.comThe main non-singular alternative to 3×3 proper orthogonal matrices, for representing rotations in R3, is quaternions. Thus, it is important to have reliable methods to pass from one representation to the other. While passing from a quaternion to the corresponding rotation matrix is given by Euler-Rodrigues formula, the other way round can be performed in many different ways. Although all of them are algebraically equivalent, their numerical behavior can be quite different. In 1978, Shepperd proposed a method for computing the quaternion corresponding to a rotation matrix which is considered the most reliable method to date. Shepperd’s method, thanks to a voting scheme between four possible solutions, always works far from formulation singularities. In this paper, we propose a new method which outperforms Shepperd’s method without increasing the computational cost.Peer ReviewedPostprint (author's final draft

An external replication on the effects of test-driven development using a multi-site blind analysis approach

Context: Test-driven development (TDD) is an agile practice claimed to improve the quality of a software product, as well as the productivity of its developers. A previous study (i.e., baseline experiment) at the University of Oulu (Finland) compared TDD to a test-last development (TLD) approach through a randomized controlled trial. The results failed to support the claims. Goal: We want to validate the original study results by replicating it at the University of Basilicata (Italy), using a different design. Method: We replicated the baseline experiment, using a crossover design, with 21 graduate students. We kept the settings and context as close as possible to the baseline experiment. In order to limit researchers bias, we involved two other sites (UPM, Spain, and Brunel, UK) to conduct blind analysis of the data. Results: The Kruskal-Wallis tests did not show any significant difference between TDD and TLD in terms of testing effort (p-value = .27), external code quality (p-value = .82), and developers' productivity (p-value = .83). Nevertheless, our data revealed a difference based on the order in which TDD and TLD were applied, though no carry over effect. Conclusions: We verify the baseline study results, yet our results raises concerns regarding the selection of experimental objects, particularly with respect to their interaction with the order in which of treatments are applied. We recommend future studies to survey the tasks used in experiments evaluating TDD. Finally, to lower the cost of replication studies and reduce researchers' bias, we encourage other research groups to adopt similar multi-site blind analysis approach described in this paper.This research is supported in part by the Academy of Finland Project 278354

Caregiver perceptions of change in pediatric asthma control during the COVID-19 pandemic

PURPOSE: Although several indicators suggest that pediatric asthma control in the United States improved early in the pandemic, other indicators suggest not. Missing are reports from caregivers of the experiences of their children with asthma early in the pandemic. METHODS: Using the PP-ACT and other measures that we specifically constructed for our research, we conducted a cross-sectional national survey of US caregivers of children with asthma (N=595) to examine perceived change in their child\u27s asthma control and changes in reports of ED visits and use of emergency relief medicine and controller medicine pre-pandemic (January to March 2020) versus early-pandemic (June to September 2020). RESULTS: Caregivers fell into three groups: most caregivers perceived that their child\u27s asthma control was improved (50.3%) or unchanged (41.2%), and few reported worse control (8.5%). Surprisingly, all three groups of caregivers reported similar frequencies of early-pandemic and pre-pandemic ED visits and use of emergency relief medicine. Also surprising, caregivers who perceived their child\u27s asthma as more controlled (compared with the other two groups) reported more frequent ED visits and use of emergency relief medicine, yet also more use of controller medicine at both early-pandemic and pre-pandemic. CONCLUSION: The mismatch between caregivers\u27 perceptions of their child\u27s early-pandemic asthma control and their reports of ED visits and use of emergency relief medicine suggests that caregivers may rely on a gist (a global evaluation that can include nonbiomedical evidence) when estimating their child\u27s asthma control. Caregivers and their families could benefit from help from clinicians in understanding the discrepancy between subjective asthma control and asthma control indicators and in understanding what well-controlled asthma looks and feels like