The antiferroelectric (AFE) phase, in which nonpolar and polar states are switchable by an electric field, is a recent discovery in promising multiferroics of hexagonal rareearth manganites (ferrites), h-RMn(Fe)O 3 . However, this phase has so far only been observed at 60−160 K, which restricts key investigations into the microstructures and magnetoelectric behaviors. Herein, we report the successful expansion of the AFE temperature range (10−300 K) by preparing h-DyFeO 3 films through epitaxial stabilization. Room-temperature scanning transmission electron microscopy reveals that the AFE phase originates from a nanomosaic structure comprising AFE P3̅ c1 and ferroelectric P6 3 cm domains with small domain sizes of 1− 10 nm. The nanomosaic structure is stabilized by a low c/a ratio derived from the large ionic radius of Dy 3+ . Furthermore, weak ferromagnetism and magnetocapacitance behaviors are observed. Below 10 K, the film exhibits an M-shaped magnetocapacitance versus magnetic field curve, indicating unusual magnetoelectric coupling in the AFE phase.

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Automatic image captioning has many important applications, such as the depiction of visual contents for visually impaired people or the indexing of images on the internet. Recently, deep learning-based image captioning models have been researched extensively. For caption generation, they learn the relation between image features and words included in the captions. However, image features might not be relevant for certain words such as verbs. Therefore, our earlier reported method included the use of motion features along with image features for generating captions including verbs. However, all the motion features were used. Since not all motion features contributed positively to the captioning process, unnecessary motion features decreased the captioning accuracy. As described herein, we use experiments with motion features for thorough analysis of the reasons for the decline in accuracy. We propose a novel, end-to-end trainable method for image caption generation that alleviates the decreased accuracy of caption generation. Our proposed model was evaluated using three datasets: MSR-VTT2016-Image, MSCOCO, and several copyright-free images. Results demonstrate that our proposed method improves caption generation performance.