Two-dimensional(2D)magnets provide an ideal platform to explore new physical phenomena in fundamental magnetism and to realize the miniaturization of magnetic devices.The study on its domain structure evolution with t...Two-dimensional(2D)magnets provide an ideal platform to explore new physical phenomena in fundamental magnetism and to realize the miniaturization of magnetic devices.The study on its domain structure evolution with thickness is of great significance for better understanding the 2D magnetism.Here,we investigate the magnetization reversal and domain structure evolution in 2D ferromagnet Fe_(3)GeTe_(2)(FGT)with a thickness range of 11.2-112 nm.Three types of domain structures and their corresponding hysteresis loops can be obtained.The magnetic domain varies from a circular domain via a dendritic domain to a labyrinthian domain with increasing FGT thickness,which is accompanied by a transition from squared to slanted hysteresis loops with reduced coercive fields.These features can be ascribed to the total energy changes from exchange interaction-dominated to dipolar interaction-dominated with increasing FGT thickness.Our finding not only enriches the fundamental magnetism,but also paves a way towards spintronics based on 2D magnet.展开更多
This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited...This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited existing sentiment resources in Chinese. On the one hand, all available Chinese sentiment lexicons - individual and combined - are evaluated under our proposed framework. On the other hand, the domain dependent sentiment noise words are identified and removed using unlabeled data, to improve the classification performance. To the best of our knowledge, this is the first such attempt. Experiments have been conducted on three open datasets in two domains, and the results show that the proposed algorithm for sentiment noise words removal can improve the classification performance significantly.展开更多
基金Project supported by the National Key R&D Program of China(Grant Nos.2017YFA0206202 and 2019YFA0308000)the National Natural Science Foundation of China(Grant Nos.51871130,62022089,and 11874405)the Youth Innovation Promotion Association of Chinese Academy of Sciences(Grant No.2019007)。
文摘Two-dimensional(2D)magnets provide an ideal platform to explore new physical phenomena in fundamental magnetism and to realize the miniaturization of magnetic devices.The study on its domain structure evolution with thickness is of great significance for better understanding the 2D magnetism.Here,we investigate the magnetization reversal and domain structure evolution in 2D ferromagnet Fe_(3)GeTe_(2)(FGT)with a thickness range of 11.2-112 nm.Three types of domain structures and their corresponding hysteresis loops can be obtained.The magnetic domain varies from a circular domain via a dendritic domain to a labyrinthian domain with increasing FGT thickness,which is accompanied by a transition from squared to slanted hysteresis loops with reduced coercive fields.These features can be ascribed to the total energy changes from exchange interaction-dominated to dipolar interaction-dominated with increasing FGT thickness.Our finding not only enriches the fundamental magnetism,but also paves a way towards spintronics based on 2D magnet.
基金Supported by the National Natural Science Foundation of China(Nos.60405011,60575057,and 60875073)
文摘This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited existing sentiment resources in Chinese. On the one hand, all available Chinese sentiment lexicons - individual and combined - are evaluated under our proposed framework. On the other hand, the domain dependent sentiment noise words are identified and removed using unlabeled data, to improve the classification performance. To the best of our knowledge, this is the first such attempt. Experiments have been conducted on three open datasets in two domains, and the results show that the proposed algorithm for sentiment noise words removal can improve the classification performance significantly.
