Periodic DOS modulation in underdoped Bi2223

In this report, we show the results of the STM/STS study on the Bi2223 single crystals whose doping levels were ranging from an optimally doped to an underdoped region. Bi2223 single crystals were grown by a TSFZ method and their doping levels were adjusted with annealing in an oxygen deficient atmosphere. Single crystals were cleaved in an UHV at around 77 K and STM/STS measurements were carried out in the same conditions. We successfully obtained the tunneling spectrum maps as well as topographic images. We found that the superconducting gap was much more homogeneous than in the case of the Bi2212 in optimal doping, but becomes inhomogeneous with decreasing a doping level. This suggests the decoupling of the three Cu-O layers in terms of the SC correlation. More importantly, we found a new periodic modulation in the LDOS map with periods about 2a0, which was almost dispersion-less and observed only in the underdoped TC=85 K sample. This modulation is possibly related to the charge/spin order in the inner plane, which is supposed to be highly undrerdoped.Comment: 4pages, 3 Figures. PD

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy

Domain Nucleation and Annihilation in Uniformly Magnetized State under Current Pulses in Narrow Ferromagnetic Wires

We investigate the current-driven magnetization dynamics in narrow Permalloy wires by means of Lorentz microscopy and electron holography. Current pulses are found to transform the magnetic structure in the uniformly magnetized state below the Curie temperature. A variety of magnetic states including reversed magnetic domains are randomly obtained in low probability. The dynamics of vortices found in most of observed magnetic states seems to play a key role in triggering the magnetization reversal.Comment: 11 pages, 3 figures, 1 video, to appear in Japanese Journal of Applied Physics (Express Letter

^{31}P and ^{75}As NMR evidence for a residual density of states at zero energy in superconducting BaFe_2(As_{0.67}P_{0.33})_2

^{31}P and ^{75}As NMR measurements were performed in superconducting BaFe_2(As_{0.67}P_{0.33})_2 with T_c = 30 K. The nuclear-spin-lattice relaxation rate T_1^{-1} and the Knight shift in the normal state indicate the development of antiferromagnetic fluctuations, and T_1^{-1} in the superconducting (SC) state decreases without a coherence peak just below T_c, as observed in (Ba_{1-x}K_{x})Fe_2As_2. In contrast to other iron arsenide superconductors, the T_1^{-1} \propto T behavior is observed below 4K, indicating the presence of a residual density of states at zero energy. Our results suggest that strikingly different SC gaps appear in BaFe_2(As_{1-x}P_{x})_2 despite a comparable T_c value, an analogous phase diagram, and similar Fermi surfaces to (Ba_{1-x}K_{x})Fe_2As_2.Comment: 4 pages, 5 figure

2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs

Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in two-dimensional X-ray images. We propose a supervised deep learning framework that achieves 2D/3D deformable image registration between 3D volumes and single-viewpoint 2D projected images. The proposed method learns the translation from the target 2D projection images and the initial 3D volume to 3D displacement fields. In experiments, we registered 3D-computed tomography (CT) volumes to digitally reconstructed radiographs generated from abdominal 4D-CT volumes. For validation, we used 4D-CT volumes of 35 cases and confirmed that the 3D-CT volumes reflecting the nonlinear and local respiratory organ displacement were reconstructed. The proposed method demonstrate the compatible performance to the conventional methods with a dice similarity coefficient of 91.6 \% for the liver region and 85.9 \% for the stomach region, while estimating a significantly more accurate CT values