Population Aging and Future Carbon Emissions in the United States

Changes in the age composition of U.S. households over the next several decades could affect energy use and carbon dioxide emissions. this article incorporates population age structure into and energy-economic growth model with multiple dynasties of heterogenous households. The model is used to estimate and compare effects of population aging and technical change on baseline paths of U.S. energy use and emissions. Results show that population aging reduces long-term carbon dioxide emissions, by almost 40% in low population scenario, and effects of aging on emissions can be as large, or larger than effects of technical change in some cases

3D shape of Orion A from Gaia DR2

Reproduced with permission from Astronomy & Astrophysics. © 2018 ESO.We use the Gaia DR2 distances of about 700 mid-infrared selected young stellar objects in the benchmark giant molecular cloud Orion A to infer its 3D shape and orientation. We find that Orion A is not the fairly straight filamentary cloud that we see in (2D) projection, but instead a cometary-like cloud oriented toward the Galactic plane, with two distinct components: a denser and enhanced star-forming (bent) Head, and a lower density and star-formation quieter ~75 pc long Tail. The true extent of Orion A is not the projected ~40 pc but ~90 pc, making it by far the largest molecular cloud in the local neighborhood. Its aspect ratio (~30:1) and high column-density fraction (~45%) make it similar to large-scale Milky Way filaments ("bones"), despite its distance to the galactic mid-plane being an order of magnitude larger than typically found for these structures.Peer reviewedFinal Accepted Versio

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

data mining, event history, life course, machine learning, transition to adulthood

In this paper we discuss and apply machine learning techniques, using ideas from a core research area in the artificial intelligence literature to analyse simultaneously timing, sequencing, and quantum of life course events from a comparative perspective. We outline the need for techniques which allow the adoption of a holistic approach to life course analysis, illustrating the specific case of the transition to adulthood. We briefly introduce machine learning algorithms to build decision trees and rule sets and then apply such algorithms to delineate the key features which distinguish Austrian and Italian pathways to adulthood, using Fertility and Family Survey data. The key role of sequencing and synchronization between events emerges clearly from the analysis

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster