Experimental Detection of Sign-Reversal Pairing in Iron-Based Superconductors

We propose a modified Josephson corner-junction experiment which can test whether the order parameter in the iron pnictides changes sign between the electron and hole pockets of the Fermi surface.Comment: 2 pages, 2 figures; After this paper was completed, we found a similar idea had been proposed by arXiv: 0812.4416: Published versio

Topological Spin Texture in a Quantum Anomalous Hall Insulator

The quantum anomalous Hall (QAH) effect has been recently discovered in experiment using thin-film topological insulator with ferromagnetic ordering and strong spin-orbit coupling. Here we investigate the spin degree of freedom of a QAH insulator and uncover a fundamental phenomenon that the edge states exhibit topologically stable spin texture in the boundary when a chiral-like symmetry is present. This result shows that edge states are chiral in both the orbital and spin degrees of freedom, and the chiral edge spin texture corresponds to the bulk topological states of the QAH insulator. We also study the potential applications of the edge spin texture in designing topological-state-based spin devices which might be applicable to future spintronic technologies.Comment: 5 pages manuscript, 8+ pages supplementary information, 8 figures; published versio

Multi-turn Dialogue Model Based on the Improved Hierarchical Recurrent Attention Network

When considering the multi-turn dialogue systems, the model needs to generate a natural and contextual response. At present, HRAN, one of the most advanced models for multi-turn dialogue problems, uses a hierarchical recurrent encoder-decoder combined with a hierarchical attention mechanism. However, for complex conversations, the traditional attention-based RNN does not fully understand the context, which results in attention to the wrong context that generates irrelevant responses. To solve this problem, we proposed an improved hierarchical recurrent attention network, a self-attention network (HSAN), instead of RNN, to learn word representations and utterances representations. Empirical studies on both Chinese and English datasets show that the proposed model has achieved significant improvement

Research on Rectal Tumor Identification Method by Convolutional Neural Network Based on Multi-Feature Fusion

Aiming at the obscure features of tumors in rectal CT images and their complexity, this paper proposes a multi-feature fusion-based convolutional neural network rectal tumor recognition method and uses it to model rectal tumors for classification experiments. This method extracts the convolutional features from rectal CT images using Alexnet, VGG16, ResNet, and DenseNet, respectively. At the same time, local features such as histogram of oriented gradient, local binary pattern, and HU moment invariants are extracted from this image. The above local features are fused linearly with the convolutional features. Then we put the new fused features into the fully connected layer for image classification. The experimental results finally reached the accuracy rates of 92.6 %, 93.1 %, 91.7 %, and 91.1 %, respectively. Comparative experiments show that this method improves the accuracy of rectal tumor recognition