Uniaxially anisotropic antiferromagnets in a field along the easy axis
Uniaxially anisotropic antiferromagnets in a field along the easy axis are studied with the help of ground state considerations and Monte Carlo simulations. For classical models, the XXZ model as well as variants, we analyze the role of non-collinear spin configurations of biconical or bidirectional type interpolating between the well-known antiferromagnetic and spin-flop structures. Possible experimental applications to layered cuprate antiferromagnets are discussed. Finally, results of quantum Monte Carlo simulations for the S=1/2 XXZ model on a square lattice are presented, and compared with previous findings.
Frustrated honeycomb-lattice bilayer quantum antiferromagnet in a magnetic field
Abstract Frustrated bilayer quantum magnets have attracted attention as flat-band spin systems with unconventional thermodynamic properties. We study the low-temperature properties of a frustrated honeycomb-lattice bilayer spin- 1 2 isotropic ( XXX ) Heisenberg antiferromagnet in a magnetic field by means of an effective low-energy theory using exact diagonalizations and quantum Monte Carlo simulations. Our main focus is on the magnetization curve and the temperature dependence of the specific heat indicating a finite-temperature phase transition in high magnetic fields.
Automatic classification of TV news articles based on telop character recognition
The purpose of this study is to develop a multimedia database system for TV news video data. TV news video data consist of speech, characters and images. In this study, telop characters are recognized and given to the news articles as indices for classification. At first, telop frames which include telop characters are detected and then the telop characters are extracted and recognized. Through morphological analysis of the recognized telop characters, keywords are extracted which consist of more than two characters. Their keywords are used as indices to classify the TV news articles. We carried out experiments for 30 days of NHK 5 minutes news and obtained 95.4% telop character extraction rate, 81.4% character recognition rate and 83.8% article classification rate. We improved the article classification rate by 43.4% through character recognition improvement from 44.1% to 81.4%.