Reciprocatory magnetic reconnection in a coronal bright point

Coronal bright points (CBPs) are small-scale and long-duration brightenings in the lower solar corona. They are often explained in terms of magnetic reconnection. We aim to study the sub-structures of a CBP and clarify the relationship among the brightenings of different patches inside the CBP. The event was observed by the X-ray Telescope (XRT) aboard the Hinode spacecraft on 2009 August 22$-$23. The CBP showed repetitive brightenings (or CBP flashes). During each of the two successive CBP flashes, i.e., weak and strong flashes which are separated by $\sim$2 hr, the XRT images revealed that the CBP was composed of two chambers, i.e., patches A and B. During the weak flash, patch A brightened first, and patch B brightened $\sim$2 min later. During the transition, the right leg of a large-scale coronal loop drifted from the right side of the CBP to the left side. During the strong flash, patch B brightened first, and patch A brightened $\sim$2 min later. During the transition, the right leg of the large-scale coronal loop drifted from the left side of the CBP to the right side. In each flash, the rapid change of the connectivity of the large-scale coronal loop is strongly suggestive of the interchange reconnection. For the first time we found reciprocatory reconnection in the CBP, i.e., reconnected loops in the outflow region of the first reconnection process serve as the inflow of the second reconnection process.Comment: 13 pages, 8 figure

Kosterlitz-Thouless transition of quantum XY model in two dimensions

The two-dimensional $S=1/2$ XY model is investigated with an extensive quantum Monte Carlo simulation. The helicity modulus is precisely estimated through a continuous-time loop algorithm for systems up to $128 \times 128$ near and below the critical temperature. The critical temperature is estimated as $T_{\rm KT} = 0.3427(2)J$. The obtained estimates for the helicity modulus are well fitted by a scaling form derived from the Kosterlitz renormalization group equation. The validity of the Kosterlitz-Thouless theory for this model is confirmed.Comment: 8 pages, 2 tables, 6 figure

Observation of non-Fermi liquid behavior in hole-doped LiFe1−x_{1-x}Vx_xAs

We synthesized a series of V-doped LiFe$_{1-x}$V$_x$As single crystals. The superconducting transition temperature $T_c$ of LiFeAs decreases rapidly at a rate of 7 K per 1\% V. The Hall coefficient of LiFeAs switches from negative to positive with 4.2\% V doping, showing that V doping introduces hole carriers. This observation is further confirmed by the evaluation of the Fermi surface volume measured by angle-resolved photoemission spectroscopy (ARPES), from which a 0.3 hole doping per V atom introduced is deduced. Interestingly, the introduction of holes does not follow a rigid band shift. We also show that the temperature evolution of the electrical resistivity as a function of doping is consistent with a crossover from a Fermi liquid to a non-Fermi liquid. Our ARPES data indicate that the non-Fermi liquid behavior is mostly enhanced when one of the hole $d_{xz}/d_{yz}$ Fermi surfaces is well nested by the antiferromagnetic wave vector to the inner electron Fermi surface pocket with the $d_{xy}$ orbital character. The magnetic susceptibility of LiFe$_{1-x}$V$_x$As suggests the presence of strong magnetic impurities following V doping, thus providing a natural explanation to the rapid suppression of superconductivity upon V doping.Comment: 7 pages, 5 figures. See published version for the latest updat

S4ND: Single-Shot Single-Scale Lung Nodule Detection

The state of the art lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature. The whole detection pipeline is designed as a single $3D$ Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publically available $888$ CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of $0.897$. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.Comment: Accepted for publication at MICCAI 2018 (21st International Conference on Medical Image Computing and Computer Assisted Intervention

Carbon-doped ZnO: A New Class of Room Temperature Dilute Magnetic Semiconductor

We report magnetism in carbon doped ZnO. Our first-principles calculations based on density functional theory predicted that carbon substitution for oxygen in ZnO results in a magnetic moment of 1.78 $\mu_B$ per carbon. The theoretical prediction was confirmed experimentally. C-doped ZnO films deposited by pulsed laser deposition with various carbon concentrations showed ferromagnetism with Curie temperatures higher than 400 K, and the measured magnetic moment based on the content of carbide in the films ($1.5 - 3.0 \mu_B$ per carbon) is in agreement with the theoretical prediction. The magnetism is due to bonding coupling between Zn ions and doped C atoms. Results of magneto-resistance and abnormal Hall effect show that the doped films are $n$-type semiconductors with intrinsic ferromagnetism. The carbon doped ZnO could be a promising room temperature dilute magnetic semiconductor (DMS) and our work demonstrates possiblity of produing DMS with non-metal doping.Comment: REVtex source with 4 figures in eps forma