Unraveling the Structure and Bonding Evolution of the Newly Discovered Iron Oxide FeO2

Recently reported synthesis of FeO2 at high pressure has stimulated great interest in exploring this new iron oxide and elucidating its properties. Here, we present a systematic computational study of crystal structure, chemical bonding, and sound velocity of FeO2 in a wide range of pressure. Our results establish thermodynamic stability of the experimentally observed pyrite phase (P-phase) of FeO2 at pressures above 74 GPa and unveil two metastable FeO2 phases in Pbcn and P4(2)/mnm symmetry at lower pressures. Simulated x-ray diffraction (XRD) spectra of Pbcn and P4(2)/mnm FeO2 match well with measured XRD data of the decompression products of P-phase FeO2, providing compelling evidence for the presence of these metastable phases. Energetic calculations reveal unusually soft O-O bonds in P-phase FeO2 stemming from a low-frequency libration mode of FeO6 octahedra, rendering the O-O bond length highly sensitive to computational and physical environments. Calculated sound-velocity profiles of P-phase FeO2 are markedly different from those of the Pbcn and P4(2)/mnm phases, underscoring their distinct seismic signatures. Our findings offer insights for understanding the rich structural, bonding, and elastic behaviors of this newly discovered iron oxide

Fixed versus Dynamic Co-Occurrence Windows in TextRank Term Weights for Information Retrieval

TextRank is a variant of PageRank typically used in graphs that represent documents, and where vertices denote terms and edges denote relations between terms. Quite often the relation between terms is simple term co-occurrence within a fixed window of k terms. The output of TextRank when applied iteratively is a score for each vertex, i.e. a term weight, that can be used for information retrieval (IR) just like conventional term frequency based term weights. So far, when computing TextRank term weights over co- occurrence graphs, the window of term co-occurrence is al- ways ?xed. This work departs from this, and considers dy- namically adjusted windows of term co-occurrence that fol- low the document structure on a sentence- and paragraph- level. The resulting TextRank term weights are used in a ranking function that re-ranks 1000 initially returned search results in order to improve the precision of the ranking. Ex- periments with two IR collections show that adjusting the vicinity of term co-occurrence when computing TextRank term weights can lead to gains in early precision

Estimating spatial quantile regression with functional coefficients: A robust semiparametric framework

This paper considers an estimation of semiparametric functional (varying)-coefficient quantile regression with spatial data. A general robust framework is developed that treats quantile regression for spatial data in a natural semiparametric way. The local M-estimators of the unknown functional-coefficient functions are proposed by using local linear approximation, and their asymptotic distributions are then established under weak spatial mixing conditions allowing the data processes to be either stationary or nonstationary with spatial trends. Application to a soil data set is demonstrated with interesting findings that go beyond traditional analysis.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ480 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm