We investigated the electronic structure of multiferroic hexagonal RMnO 3 (R = Gd, Tb, Dy, and Ho) thin films using both optical spectroscopy and first-principles calculations. One of the difficulties in explaining the electronic structures of hexagonal RMnO 3 is that they exist in nature with limited rare earth ions (i.e., R = Sc, Y, Ho -Lu), so a systematic study in terms of the different R ions has been lacking. Recently, our group succeeded in fabricating hexagonal RMnO 3 (R = Gd, Tb, and Dy) using the epitaxial stabilization technique [Adv. Mater. 18, 3125 (2006)]. Using artificially stabilized hexagonal RMnO 3 , we extended the optical spectroscopic studies on the hexagonal multiferroic manganite system. We observed two optical transitions located near 1.7 eV and 2.3 eV, in addition to the predominant absorption above 5 eV. With the help of first-principles calculations, we attribute the low-lying optical absorption peaks to inter-site transitions from the oxygen states hybridized strongly with different Mn orbital symmetries to the Mn 3d 3z 2 -r 2 state. As the ionic radius of the rare earth ion increased, the lowest peak showed a systematic increase in its peak position. We explained this systematic change in terms of a flattening of the MnO 5 triangular bipyramid.
Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) children, the stability of such procedures can be disrupted by limitations pertaining to time expenses and the subjectivity of clinicians. Consequently, automated diagnostic methods have been developed for acquiring objective measures of autism, and in various fields of research, vocal characteristics have not only been reported as distinctive characteristics by clinicians, but have also shown promising performance in several studies utilizing deep learning models based on the automated discrimination of children with ASD from children with TD. However, difficulties still exist in terms of the characteristics of the data, the complexity of the analysis, and the lack of arranged data caused by the low accessibility for diagnosis and the need to secure anonymity. In order to address these issues, we introduce a pre-trained feature extraction auto-encoder model and a joint optimization scheme, which can achieve robustness for widely distributed and unrefined data using a deep-learning-based method for the detection of autism that utilizes various models. By adopting this auto-encoder-based feature extraction and joint optimization in the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) speech feature data set, we acquire improved performance in the detection of ASD in infants compared to the raw data set.
We investigated the effects of temperature and magnetic field on the electronic structure of hexagonal RMnO 3 (R = Gd, Tb, Dy, and Ho) thin films using optical spectroscopy. As the magnetic ordering of the system was disturbed, a systematic change in the electronic structure was commonly identified in this series. The optical absorption peak near 1.7 eV showed an unexpectedly large shift of more than 150 meV from 300 K to 15 K, accompanied by an anomaly of the shift at the Néel temperature. The magnetic field dependent measurement clearly revealed a sizable shift of the corresponding peak when a high magnetic field was applied. Our findings indicated strong coupling between the magnetic ordering and the electronic structure in the multiferroic hexagonal RMnO 3 compounds. PACS number(s): 78.20.Ci, 78.66.Nk, 77.90.+k,
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