Neutron Scattering Studies of Cuprates and Iron Pnictides

Presented within are neutron scattering studies of several different high temperature superconducting materials: BaFe1.9Ni0.1As2 [Barium Iron Nickel Arsenic], BaFe1.85Ni0.15As2 [Barium Iron Nickel Arsenic], Ba0.67K0.33Fe2As2 [Barium Potassium Iron Arsenic], and Pr0.88LaCe0.12CuO4-y [Praseodymium Lanthanum Cerium Copper Oxide]. The main focus is on the magnetic excitations within the systems. For BaFe1.9Ni0.1As2 [Barium Iron Nickel Arsenic], we measured the intensity of its magnetic excitations and compared the results with excitations in antiferromagnetic non-superconducting BaFe2As2 [Barium Iron Arsenic]. We find electron-doping only affects spin excitations below 100 meV while the total size of the magnetic moment and the energy distribution do not change much. It shows that the magnetic moments in both materials are similar to insulating copper oxides, an indicator of the importance of strong electron correlations in high temperature superconductivity. For both BaFe1.85Ni0.15As2 [Barium Iron Nickel Arsenic] and Ba0.67K0.33Fe2As2 [Barium Potassium Iron Arsenic], we use polarized inelastic neutron scattering to study their low-energy spin excitations and their spatial anisotropy. Our neutron polarization analysis reveals that magnetic excitations are isotropic for the in-plane and out-of-plane components in both the normal and superconducting states for BaFe1.85Ni0.15As2 [Barium Iron Nickel Arsenic], while in Ba0.67K0.33Fe2As2 [Barium Potassium Iron Arsenic] large difference in spin gaps were found. A comparison of these results with those of undoped BaFe2As2 [Barium Iron Arsenic] and optimally electron-doped BaFe1.9Ni0.1As2 [Barium Iron Nickel Arsenic] suggests that the spin anisotropy observed Ba0.67K0.33Fe2As2 [Barium Potassium Iron Arsenic] are likely due to their proximity to their parent compound, where spin anisotropy exists below TN [Neel Temperature], while the neutron spin resonance is isotropic in the overdoped regime, consistent with a singlet to triplet excitation. For as-grown and optimal superconducting Pr0.88LaCe0.12CuO4-y [Praseodymium Lanthanum Cerium Copper Oxide] (PLCCO), we measured their magnetic excitations over a wide energy range, and compared their corresponding results. The spectra is considerably larger throughout the whole zone in as-grown PLCCO, than in the optimal superconducting PLCCO, which is very different from the BaFe2As2 [Barium Iron Arsenic] system

Magnetic anisotropy in hole-doped superconducting Ba 0.67K 0.33Fe 2As2 probed by polarized inelastic neutron scattering

We use polarized inelastic neutron scattering (INS) to study spin excitations of optimally hole-doped superconductor Ba$_{0.67}$K$_{0.33}$Fe$_2$As$_{2}$ ($T_c=38$ K). In the normal state, the imaginary part of the dynamic susceptibility, $\chi^{\prime\prime}(Q,\omega)$, shows magnetic anisotropy for energies below $\sim$7 meV with c-axis polarized spin excitations larger than that of the in-plane component. Upon entering into the superconducting state, previous unpolarized INS experiments have shown that spin gaps at $\sim$5 and 0.75 meV open at wave vectors $Q=(0.5,0.5,0)$ and $(0.5,0.5,1)$, respectively, with a broad neutron spin resonance at $E_r=15$ meV. Our neutron polarization analysis reveals that the large difference in spin gaps is purely due to different spin gaps in the c-axis and in-plane polarized spin excitations, resulting resonance with different energy widths for the c-axis and in-plane spin excitations. The observation of spin anisotropy in both opitmally electron and hole-doped BaFe$_2$As$_2$ is due to their proximity to the AF ordered BaFe$_2$As$_2$ where spin anisotropy exists below $T_N$.Comment: 5 pages, 4 figure

Polarized neutron scattering studies of magnetic excitations in electron-overdoped superconducting BaFe1.85_{1.85}Ni0.15_{0.15}As2_{2}

We use polarized inelastic neutron scattering to study low-energy spin excitations and their spatial anisotropy in electron-overdoped superconducting BaFe$_{1.85}$Ni$_{0.15}$As$_{2}$ ($T_c=14$ K). In the normal state, the imaginary part of the dynamic susceptibility, $\chi^{\prime\prime}(Q,\omega)$, at the antiferromagnetic (AF) wave vector $Q=(0.5,0.5,1)$ increases linearly with energy for $E\le 13$ meV. Upon entering the superconducting state, a spin gap opens below $E\approx 3$ meV and a broad neutron spin resonance appears at $E\approx 7$ meV. Our careful neutron polarization analysis reveals that $\chi^{\prime\prime}(Q,\omega)$ is isotropic for the in-plane and out-of-plane components in both the normal and superconducting states. A comparison of these results with those of undoped BaFe$_2$As$_2$ and optimally electron-doped BaFe$_{1.9}$Ni$_{0.1}$As$_{2}$ ($T_c=20$ K) suggests that the spin anisotropy observed in BaFe$_{1.9}$Ni$_{0.1}$As$_{2}$ is likely due to its proximity to the undoped BaFe$_2$As$_2$. Therefore, the neutron spin resonance is isotropic in the overdoped regime, consistent with a singlet to triplet excitation

RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices

Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped with powerful, high-end mobile CPUs and GPUs. However, it is still a challenging task to execute 3D Convolutional Neural Networks (CNNs) targeting for real-time performance, besides high inference accuracy. The reason is more complex model structure and higher model dimensionality overwhelm the available computation/storage resources on mobile devices. A natural way may be turning to deep learning weight pruning techniques. However, the direct generalization of existing 2D CNN weight pruning methods to 3D CNNs is not ideal for fully exploiting mobile parallelism while achieving high inference accuracy. This paper proposes RT3D, a model compression and mobile acceleration framework for 3D CNNs, seamlessly integrating neural network weight pruning and compiler code generation techniques. We propose and investigate two structured sparsity schemes i.e., the vanilla structured sparsity and kernel group structured (KGS) sparsity that are mobile acceleration friendly. The vanilla sparsity removes whole kernel groups, while KGS sparsity is a more fine-grained structured sparsity that enjoys higher flexibility while exploiting full on-device parallelism. We propose a reweighted regularization pruning algorithm to achieve the proposed sparsity schemes. The inference time speedup due to sparsity is approaching the pruning rate of the whole model FLOPs (floating point operations). RT3D demonstrates up to 29.1$\times$ speedup in end-to-end inference time comparing with current mobile frameworks supporting 3D CNNs, with moderate 1%-1.5% accuracy loss. The end-to-end inference time for 16 video frames could be within 150 ms, when executing representative C3D and R(2+1)D models on a cellphone. For the first time, real-time execution of 3D CNNs is achieved on off-the-shelf mobiles.Comment: To appear in Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21