Induced Technological Change: Exploring its Implications for the Economics of Atmospheric Stabilization: Synthesis Report from the Innovation Modeling Comparison Project

This paper summarizes results from ten global economy-energy-environment models implementing mechanisms of endogenous technological change (ETC). Climate policy goals represented as different CO2 stabilization levels are imposed, and the contribution of induced technological change (ITC) to meeting the goals is assessed. Findings indicate that climate policy induces additional technological change, in some models substantially. Its effect is a reduction of abatement costs in all participating models. The majority of models calculate abatement costs below 1 percent of present value aggregate gross world product for the period 2000-2100. The models predict different dynamics for rising carbon costs, with some showing a decline in carbon costs towards the end of the century. There are a number of reasons for differences in results between models; however four major drivers of differences are identified. First, the extent of the necessary CO2 reduction which depends mainly on predicted baseline emissions, determines how much a model is challenged to comply with climate policy. Second, when climate policy can offset market distortions, some models show that not costs but benefits accrue from climate policy. Third, assumptions about long-term investment behavior, e.g. foresight of actors and number of available investment options, exert a major influence. Finally, whether and how options for carbon-free energy are implemented (backstop and end-of-the-pipe technologies) strongly affects both the mitigation strategy and the abatement costs

Brain-derived neurotrophic factor signaling in the HVC is required for testosterone-induced song of female canaries

Testosterone-induced singing in songbirds is thought to involve testosterone-dependent morphological changes that include angiogenesis and neuronal recruitment into the HVC, a central part of the song control circuit. Previous work showed that testosterone induces the production of vascular endothelial growth factor (VEGF) and its receptor (VEGFR2 tyrosine kinase), which in turn leads to an upregulation of brain-derived neurotrophic factor (BDNF) production in HVC endothelial cells. Here we report for the first time that systemic inhibition of the VEGFR2 tyrosine kinase is sufficient to block testosterone-induced song in adult female canaries, despite sustained androgen exposure and the persistence of the effects of testosterone on HVC morphology. Expression of exogenous BDNF in HVC, induced locally by in situ transfection, reversed the VEGFR2 inhibition-mediated blockade of song development, thereby restoring the behavioral phenotype associated with androgen-induced song. The VEGFR2-inhibited, BDNF-treated females developed elaborate malelike song that included large syllable repertoires and high syllable repetition rates, features known to attract females. Importantly, although functionally competent new neurons were recruited to HVC after testosterone treatment, the time course of neuronal addition appeared to follow BDNF-induced song development. These findings indicate that testosterone-associated VEGFR2 activity is required for androgen-induced song in adult songbirds and that the behavioral effects of VEGFR2 inhibition can be rescued by BDNF within the adult HVC. Copyright © 2009 Society for Neuroscience

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

A Metric Framework for quantifying Data Concentration

Poor performance of artificial neural nets when applied to credit-related classification problems is investigated and contrasted with logistic regression classification. We propose that artificial neural nets are less successful because of the inherent structure of credit data rather than any particular aspect of the neural net structure. Three metrics are developed to rationalise the result with such data. The metrics exploit the distributional properties of the data to rationalise neural net results. They are used in conjunction with a variant of an established concentration measure that differentiates between class characteristics. The results are contrasted with those obtained using random data, and are compared with results obtained using logistic regression. We find, in general agreement with previous studies, that logistic regressions out-perform neural nets in the majority of cases. An approximate decision criterion is developed in order to explain adverse results

Lumbar spine segmentation in MR images: a dataset and a public benchmark

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain. It was collected from four different hospitals and was divided into a training (179 patients) and validation (39 patients) set. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. We set up a continuous segmentation challenge to allow for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation, and improve the diagnostic value of lumbar spine MRI