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Abstract Understanding the magnetic structure of filament channels is difficult but essential for identifying the mechanism (s) responsible for solar eruptions. In this paper we characterize the magnetic field in a well-observed filament channel with two independent methods, prominence seismology and magnetohydrodynamics flux-rope modeling, and compare the results. In 2014 May and June, active region 12076 exhibited a complex of filaments undergoing repeated oscillations over the course of 12 days. We measure the oscillation periods in the region with both Global Oscillation Network Group Hαand Solar Dynamics Observatory (SDO) Advanced Imaging Assembly EUV images, and then utilize the pendulum model of large-amplitude longitudinal oscillations to calculate the radius of curvature of the fields supporting the oscillating plasma from the derived periods. We also employ the regularized Biot–Savart laws formalism to construct a flux-rope model of the field of the central filament in the region based on an SDO Helioseismic and Magnetic Imager magnetogram. We compare the estimated radius of curvature, location, and angle of the magnetic field in the plane of the sky derived from the observed oscillations with the corresponding magnetic-field properties extracted from the flux-rope model. We find that the two models are broadly consistent, but detailed comparisons of the model and specific oscillations often differ. Model observation comparisons such as these are important for advancing our understanding of the structure of filament channels. 

We estimate lexical Concreteness for millions of words across 77 languages. Using a simple regression framework, we combine vector-based models of lexical semantics with experimental norms of Concreteness in English and Dutch. By applying techniques to align vector-based semantics across distinct languages, we compute and release Concreteness esti- mates at scale in numerous languages for which experimental norms are not currently available. This paper lays out the technique and its efficacy. Although this is a difficult dataset to evaluate immediately, Concreteness estimates computed from English correlate with Dutch experimental norms at ρ = .75 in the vocabulary at large, increasing to ρ = .8 among Nouns. Our predictions also recapitulate attested relationships with word frequency. The approach we describe can be readily applied to numerous lexical measures beyond Concreteness. 

Do all languages convey semantic knowledge in the same way? If language simply mirrors the structure of the world, the answer should be a qualified “yes”. If, however, languages impose structure as much as reflecting it, then even ostensibly the “same” word in different languages may mean quite different things. We provide a first pass at a large-scale quantification of cross-linguistic semantic alignment of approximately 1000 meanings in 55 languages. We find that the translation equivalents in some domains (e.g., Time, Quantity, and Kinship) exhibit high alignment across languages while the structure of other domains (e.g., Politics, Food, Emotions, and Animals) exhibits substantial crosslinguistic variability. Our measure of semantic alignment correlates with known phylogenetic distances between languages: more phylogenetically distant languages have less semantic alignment. We also find semantic alignment to correlate with cultural distances between societies speaking the languages, suggesting a rich co-adaptation of language and culture even in domains of experience that appear most constrained by the natural world. 

What makes some words harder to learn than others in a second language? Although some robust factors have been identified based on small scale experimental studies, many relevant factors are difficult to study in such experiments due to the amount of data necessary to test them. Here, we investigate what factors affect the ease of learning of a word in a second language using a large data set of users learning English as a second language through the Duolingo mobile app. In a regression analysis, we test and confirm the well-studied effect of cognate status on word learning accuracy. Furthermore, we find significant effects for both cross-linguistic semantic alignment and English semantic density, two novel predictors derived from large scale distributional models of lexical semantics. Finally, we provide data on several other psycholinguistically plausible word level predictors. We conclude with a discussion of the limits, benefits and future research potential of using big data for investigating second language learning. 

New Mexico was at the forefront of the nuclear age, producing more uranium (U) than any other state in the U.S. for more than three decades until the early 1980s. The state is also unique because these historic activities have been studied and quantified over during this time, providing a unique opportunity to identify how historic uranium mining operations were influenced by economics and policy. In order to quantify these relationships, this study used a system dynamics approach to determine how these factors affected mining industry decisions and how those impacts varied based on mine size. The results of this work found that as the industry evolved over time, the influence of these factors changed and that they did not impact all mining operations equally. Results indicate that price guarantees for U concentrate and subsidies for mining and milling in the early years (1948–1964) of U mining encouraged mines of all size, although smaller mines opened and closed more quickly in response to changes in price. The economic environment created by these policies encouraged exploration and production. However, the latter led to an excess in supplies and declining prices when these incentives lapsed in the mid-1960s, which negatively impacted small- and medium-sized mines, neither of which opened after 1964. The presence of larger mines had more impact on the closing of small mines than closing of medium mines, possibly as a result of economies of scale for the medium mines or their ability to access milling resources after 1964. Lastly, medium and large mines that produced both uranium and vanadium may have had a slight historic advantage over mines that produced only uranium, as evidenced by longer delays in closing response to a unit change in average price. Quantification of these relationships assists in an improved understanding of the factors that influenced historic mining operational decisions and illustrates the complexity of the roles played by economics and policies in the boom and bust cycle manifested in the uranium industry. 

New Mexico was at the forefront of the nuclear age, producing more uranium (U) than any other state in the U.S. for more than three decades until the early 1980s. The state is also unique because these historic activities have been studied and quantified over during this time, providing a unique opportunity to identify how historic uranium mining operations were influenced by economics and policy. In order to quantify these relationships, this study used a system dynamics approach to determine how these factors affected mining industry decisions and how those impacts varied based on mine size. The results of this work found that as the industry evolved over time, the influence of these factors changed and that they did not impact all mining operations equally. Results indicate that price guarantees for U concentrate and subsidies for mining and milling in the early years (1948–1964) of U mining encouraged mines of all size, although smaller mines opened and closed more quickly in response to changes in price. The economic environment created by these policies encouraged exploration and production. However, the latter led to an excess in supplies and declining prices when these incentives lapsed in the mid-1960s, which negatively impacted small- and medium-sized mines, neither of which opened after 1964. The presence of larger mines had more impact on the closing of small mines than closing of medium mines, possibly as a result of economies of scale for the medium mines or their ability to access milling resources after 1964. Lastly, medium and large mines that produced both uranium and vanadium may have had a slight historic advantage over mines that produced only uranium, as evidenced by longer delays in closing response to a unit change in average price. Quantification of these relationships assists in an improved understanding of the factors that influenced historic mining operational decisions and illustrates the complexity of the roles played by economics and policies in the boom and bust cycle manifested in the uranium industry.