Multiplet resonance lifetimes in resonant inelastic X-ray scattering involving shallow core levels

Resonant inelastic X-ray scattering (RIXS) spectra of model copper- and nickel-based transition metal oxides are measured over a wide range of energies near the M-edge (h$\nu$=60-80eV) to better understand the properties of resonant scattering involving shallow core levels. Standard multiplet RIXS calculations are found to deviate significantly from the observed spectra. However, by incorporating the self consistently calculated decay lifetime for each intermediate resonance state within a given resonance edge, we obtain dramatically improved agreement between data and theory. Our results suggest that these textured lifetime corrections can enable a quantitative correspondence between first principles predictions and RIXS data on model multiplet systems. This accurate model is also used to analyze resonant elastic scattering, which displays the elastic Fano effect and provides a rough upper bound for the core hole shake-up response time.Comment: 6 pages, 3 figure

Multi-Context Attention for Human Pose Estimation

In this paper, we propose to incorporate convolutional neural networks with a multi-context attention mechanism into an end-to-end framework for human pose estimation. We adopt stacked hourglass networks to generate attention maps from features at multiple resolutions with various semantics. The Conditional Random Field (CRF) is utilized to model the correlations among neighboring regions in the attention map. We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts. Hence our model has the ability to focus on different granularity from local salient regions to global semantic-consistent spaces. Additionally, we design novel Hourglass Residual Units (HRUs) to increase the receptive field of the network. These units are extensions of residual units with a side branch incorporating filters with larger receptive fields, hence features with various scales are learned and combined within the HRUs. The effectiveness of the proposed multi-context attention mechanism and the hourglass residual units is evaluated on two widely used human pose estimation benchmarks. Our approach outperforms all existing methods on both benchmarks over all the body parts.Comment: The first two authors contribute equally to this wor