Multi-scale 3D Convolution Network for Video Based Person Re-Identification

This paper proposes a two-stream convolution network to extract spatial and temporal cues for video based person Re-Identification (ReID). A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network. The resulting M3D convolution network introduces a fraction of parameters into the 2D CNN, but gains the ability of multi-scale temporal feature learning. With this compact architecture, M3D convolution network is also more efficient and easier to optimize than existing 3D convolution networks. The temporal stream further involves Residual Attention Layers (RAL) to refine the temporal features. By jointly learning spatial-temporal attention masks in a residual manner, RAL identifies the discriminative spatial regions and temporal cues. The other stream in our network is implemented with a 2D CNN for spatial feature extraction. The spatial and temporal features from two streams are finally fused for the video based person ReID. Evaluations on three widely used benchmarks datasets, i.e., MARS, PRID2011, and iLIDS-VID demonstrate the substantial advantages of our method over existing 3D convolution networks and state-of-art methods.Comment: AAAI, 201

Neutron powder diffraction study on the iron-based nitride superconductor ThFeAsN

We report neutron diffraction and transport results on the newly discovered superconducting nitride ThFeAsN with $T_c=$ 30 K. No magnetic transition, but a weak structural distortion around 160 K, is observed cooling from 300 K to 6 K. Analysis on the resistivity, Hall transport and crystal structure suggests this material behaves as an electron optimally doped pnictide superconductors due to extra electrons from nitrogen deficiency or oxygen occupancy at the nitrogen site, which together with the low arsenic height may enhance the electron itinerancy and reduce the electron correlations, thus suppress the static magnetic order.Comment: 4 pages, 4 figures, Accepted by EP

Surface ozone and its precursors at Summit, Greenland: Comparison between observations and model simulations

Recent studies have shown significant challenges for atmospheric models to simulate tropospheric ozone (O3/and its precursors in the Arctic. In this study, ground-based data were combined with a global 3-D chemical transport model (GEOS-Chem) to examine the abundance and seasonal variations of O3 and its precursors at Summit, Greenland (72.34° N, 38.29° W; 3212 ma.s.l.). Model simulations for atmospheric nitrogen oxides (NOx/, peroxyacetyl nitrate (PAN), ethane (C2H6/, propane (C3H8/, carbon monoxide (CO), and O3 for the period July 2008-June 2010 were compared with observations. The model performed well in simulating certain species (such as CO and C3H8/, but some significant discrepancies were identified for other species and further investigated. The model generally underestimated NOx and PAN (by ∼50 and 30 %, respectively) for March-June. Likely contributing factors to the low bias include missing NOx and PAN emissions from snowpack chemistry in the model. At the same time, the model overestimated NOx mixing ratios by more than a factor of 2 in wintertime, with episodic NOx mixing ratios up to 15 times higher than the typical NOx levels at Summit. Further investigation showed that these simulated episodic NOx spikes were always associated with transport events from Europe, but the exact cause remained unclear. The model systematically overestimated C2H6 mixing ratios by approximately 20% relative to observations. This discrepancy can be resolved by decreasing anthropogenic C2H6 emissions over Asia and the US by ∼20 %, from 5.4 to 4.4 Tg year-1. GEOS-Chem was able to reproduce the seasonal variability of O3 and its spring maximum. However, compared with observations, it underestimated surface O3 by approximately 13% (6.5 ppbv) from April to July. This low bias appeared to be driven by several factors including missing snowpack emissions of NOx and nitrous acid in the model, the weak simulated stratosphereto-troposphere exchange flux of O3 over the summit, and the coarse model resolution

First-order magnetic and structural phase transitions in Fe1+y_{1+y}Sex_xTe1−x_{1-x}

We use bulk magnetic susceptibility, electronic specific heat, and neutron scattering to study structural and magnetic phase transitions in Fe$_{1+y}$Se% $_x$Te$_{1-x}$. Fe$_{1.068}$Te exhibits a first order phase transition near 67 K with a tetragonal to monoclinic structural transition and simultaneously develops a collinear antiferromagnetic (AF) order responsible for the entropy change across the transition. Systematic studies of FeSe$$Te$_x$ system reveal that the AF structure and lattice distortion in these materials are different from those of FeAs-based pnictides. These results call into question the conclusions of present density functional calculations, where FeSe$_{1-x}$Te$_x$ and FeAs-based pnictides are expected to have similar Fermi surfaces and therefore the same spin-density-wave AF order.Comment: 5 pages, 3 figure