Interplay between Kitaev interaction and single ion anisotropy in ferromagnetic CrI3_3 and CrGeTe3_3 monolayers

Magnetic anisotropy is crucially important for the stabilization of two-dimensional (2D) magnetism, which is rare in nature but highly desirable in spintronics and for advancing fundamental knowledge. Recent works on CrI$_3$ and CrGeTe$_3$ monolayers not only led to observations of the long-time-sought 2D ferromagnetism, but also revealed distinct magnetic anisotropy in the two systems, namely Ising behavior for CrI$_3$ versus Heisenberg behavior for CrGeTe$_3$. Such magnetic difference strongly contrasts with structural and electronic similarities of these two materials, and understanding it at a microscopic scale should be of large benefits. Here, first-principles calculations are performed and analyzed to develop a simple Hamiltonian, to investigate magnetic anisotropy of CrI$_3$ and CrGeTe$_3$ monolayers. The anisotropic exchange coupling in both systems is surprisingly determined to be of Kitaev-type. Moreover, the interplay between this Kitaev interaction and single ion anisotropy (SIA) is found to naturally explain the different magnetic behaviors of CrI$_3$ and CrGeTe$_3$. Finally, both the Kitaev interaction and SIA are further found to be induced by spin-orbit coupling of the heavy ligands (I of CrI$_3$ or Te of CrGeTe$_3$) rather than the commonly believed 3d magnetic Cr ions

Sumei Jia,

Abstract: Compared with UV embossing and micro-contact imprinting, hot embossing technology is the first to be used in nano-imprint lithography, and is access to copying the parallel structure in micro-nano-scale at low cost and relatively faster speed. This paper explores which factors influence some pattern transferring accuracy appearing in the experiment: the adhesion between mold and polymethyl methacrylate, the main factors of affecting embossing plastic flow including imprinting pressure, temperature, time and the plastic filling effect affected by mold pattern, the effect on the viscosity of embossing adhesive by temperature and the effect on the viscosity of embossing adhesive by embossing pressure and time. The parameters affecting the accuracy of pattern transfer are optimized via the IntelliSuite simulation designed specifically for Micro-electro-mechanical systems. A micro-level pattern with high-precision by the use of nano-imprint Obduct machine is eventually made. Copyright © 2014 IFSA Publishing, S. L

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score

Research on the impact mechanism of scientific and technological innovation on the high-quality development of the marine economy

Promoting the high-quality development of the marine economy is an inevitable choice for building a maritime power, and technological innovation can provide strong impetus for the high-quality development of the marine economy. Therefore, it is urgent to clarify the impact mechanism of technological innovation on the high-quality development of the marine economy, and promote the high-quality development of the marine economy. This study employs panel data from 11 coastal provinces and municipalities (autonomous regions) in China, spanning the years 2006 to 2020. The objective is to empirically evaluate the mechanism through which scientific and technical innovation impacts the high-quality development of the marine economy. This is achieved by utilizing the PVAR model and the mediation effect model. The research findings indicate that there is a noteworthy impact of enhancing scientific and technological innovation on the marine economy of China. Moreover, there exists a significant reciprocal relationship between scientific and technological innovation and the pursuit of high-quality development in the marine economy. It is observed that scientific and technological innovation not only has a positive influence on the high-quality development of the marine economy by enhancing green total factor productivity and optimizing the industrial structure, but it also facilitates the advancement of the marine economy through the chain mediation path of “improving green total factor productivity and optimizing industrial structure”

Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion

Wound image segmentation is a critical component for the clinical diagnosis and in-time treatment of wounds. Recently, deep learning has become the mainstream methodology for wound image segmentation. However, the pre-processing of the wound image, such as the illumination correction, is required before the training phase as the performance can be greatly improved. The correction procedure and the training of deep models are independent of each other, which leads to sub-optimal segmentation performance as the fixed illumination correction may not be suitable for all images. To address aforementioned issues, an end-to-end dual-view segmentation approach was proposed in this paper, by incorporating a learn-able illumination correction module into the deep segmentation models. The parameters of the module can be learned and updated during the training stage automatically, while the dual-view fusion can fully employ the features from both the raw images and the enhanced ones. To demonstrate the effectiveness and robustness of the proposed framework, the extensive experiments are conducted on the benchmark datasets. The encouraging results suggest that our framework can significantly improve the segmentation performance, compared to the state-of-the-art methods