The fabrication of so-called ghost-leg sheets and their electronic properties is reported. This unique sheet structure is composed of one-dimensional mixed-valence nickel chains, which are linked with one another by bis(azamacrocycle) ligands. They are also topologically unique Ni /Ni mixed-valence complexes, as confirmed by X-ray and optical measurements. Moreover, their magnetic susceptibilities indicated two-dimensional antiferromagnetic behavior following the Fisher 1D chain model with interchain interactions, where spins on Ni sites mutually interact antiferromagnetically in the sheets.

We propose Multi-head Self/Cross-Attention (MSCA), which introduces a temporal cross-attention mechanism for action recognition, based on the structure of the Multi-head Self-Attention (MSA) mechanism of the Vision Transformer (ViT). Simply applying ViT to each frame of a video frame can capture frame features, but cannot model temporal features. However, simply modeling temporal information with CNN or Transfomer is computationally expensive. TSM that perform feature shifting assume a CNN and cannot take advantage of the ViT structure. The proposed model captures temporal information by shifting the Query, Key, and Value in the calculation of MSA of ViT. This is efficient without additional coinformationmputational effort and is a suitable structure for extending ViT over temporal. Experiments on Kineitcs400 show the effectiveness of the proposed method and its superiority over previous methods.

A novel triangle-shaped macrocyclic complex, [(tmeda)Pt(azpy)]3(PF6)6·13H2O (tmeda: tetramethylethylenediamine, azpy: 4,4′-azopyridine) has been successfully synthesized via self-assembly of [(tmeda)PtCl2] with a bulky capping ligand, tmeda and flexible organic linker, azpy. 1H NMR results show the realization of a triangle-shaped complex as a major component of the self-assembly. The single-crystal X-ray study shows its shape to be an equilateral triangle with ca. 2.1 nm sides.

The fabrication of so-called ghost-leg sheets and their electronic properties is reported. This unique sheet structure is composed of one-dimensional mixed-valence nickel chains,w hicha re linked with one another by bis(azamacrocycle) ligands.T hey are also topologically unique Ni II / Ni III mixed-valence complexes,a sc onfirmed by X-ray and optical measurements.Moreover,their magnetic susceptibilities indicated two-dimensional antiferromagnetic behavior following the Fisher 1D chain model with interchain interactions, where spins on Ni III sites mutually interact antiferromagnetically in the sheets. and Institute for Integrated Cell-MaterialS ciences (iCeMS), Kyoto University Yoshida, Sakyo-ku, Kyoto 606-8501 (Japan)Supportinginformation for this article (including details of the synthetic methods, X-ray crystallographic information, IR spectroscopy,Raman spectroscopy)c an be found under: http://dx.

In the design of action recognition models, the quality of videos in the dataset is an important issue, however the trade-off between the quality and performance is often ignored. In general, action recognition models are trained and tested on high-quality videos, but in actual situations where action recognition models are deployed, sometimes it might not be assumed that the input videos are of high quality. In this study, we report qualitative evaluations of action recognition models for the quality degradation associated with transcoding by JPEG and H.264/AVC. Experimental results are shown for evaluating the performance of pre-trained models on the transcoded validation videos of Kinetics400. The models are also trained on the transcoded training videos. From these results, we quantitatively show the degree of degradation of the model performance with respect to the degradation of the video quality.