A Comparative Study of the Valence Electronic Excitations of N_2 by Inelastic X-ray and Electron Scattering

Bound state, valence electronic excitation spectra of N_2 are probed by nonresonant inelastic x-ray and electron scattering. Within the usual theoretical treatments, dynamical structure factors derived from the two probes should be identical. However, we find strong disagreements outside the dipole scattering limit, even at high probe energies. This suggests an unexpectedly important contribution from intra-molecular multiple scattering of the probe electron from core electrons or the nucleus. These effects should grow progressively stronger as the atomic number of the target species increases.Comment: Submitted to Physical Review Letters April 27, 2010. 12 pages including 2 figure pages

Reexamining the Lyman-Birge-Hopfield band of N2

Motivated by fundamental molecular physics and by atmospheric and planetary sciences, the valence excitations of N2 gas have seen several decades of intensive study, especially by electron-energy-loss spectroscopy (EELS). It was consequently surprising when a comparison of nonresonant inelastic x-ray scattering (NIXS) and nonresonant EELS found strong evidence for violations of the first Born approximation for EELS when leaving the dipole scattering limit. Here we reassess the relative strengths of the constituent resonances of the lowest-energy excitations of N2, encompassed by the so-called Lyman-Birge-Hopfield (LBH) band, expanding on the prior, qualitative interpretation of the NIXS results for N2 by both quantifying the generalized oscillator strength of the lowest-energy excitations and also presenting a time-dependent density functional theory (TDDFT) calculation of the q dependence of the entire low-energy electronic excitation spectrum. At high q, we find that the LBH band has an unexpectedly large contribution from the octupolar w 1Δu resonance exactly in the regime where theory and EELS experiment for the presumed-dominant a 1Πg resonance have previously had substantial disagreement, and also where the EELS results must now be expected to show violations of the Born approximation. After correcting for this contamination, the a 1Πg generalized oscillator strength from the NIXS results is in good agreement with prior theory. The NIXS spectra, over their entire q range, also find satisfactory agreement with the TDDFT calculations for both bound and continuum excitations.This work was supported by the US Department of Energy, the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Australian Research Council, the Research Funds of the University of Helsinki, and the Academy of Finland (Contract No. 1127462, Centers of Excellence Program 2006-2011, and National Graduate School in Materials Physics). A.R. acknowledges support by MICINN (FIS2010-21282-C02-01),ACI-promociona (ACI2009-1036), Grupos Consolidados UPV/EHU del Gobierno Vasco (IT-319- 07), and the European Community through e-I3 ETSF project (Contract No. 211956).Peer Reviewe

Modeling concept drift: A probabilistic graphical model based approach

An often used approach for detecting and adapting to concept drift when doing classi cation is to treat the data as i.i.d. and use changes in classi cation accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure effcient inference and learning, we resort to a variational Bayes inference scheme. As a proof of concept, we demonstrate and analyze the proposed framework using synthetic data sets as well as a real fi nancial data set from a Spanish bank

AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition

A data stream is a flow of unbounded data that arrives continuously at high speed. In a dynamic streaming environment, the data changes over the time while stream evolves. The evolving nature of data causes essentially the appearance of new concepts. This novel concept could be abnormal such as fraud, network intrusion, or a sudden fall. It could also be a new normal concept that the system has not seen/trained on before. In this paper we propose, develop, and evaluate a technique for concept evolution in evolving data streams. The novel approach continuously monitors the movement of the streaming data to detect any emerging changes. The technique is capable of detecting the emergence of any novel concepts whether they are normal or abnormal. It also applies a continuous and active learning for assimilating the detected concepts in real time. We evaluate our approach on activity recognition domain as an application of evolving data streams. The study of the novel technique on benchmarked datasets showed its efficiency in detecting new concepts and continuous adaptation with low computational cost