Graphene-based spintronic components

A major challenge of spintronics is in generating, controlling and detecting spin-polarized current. Manipulation of spin-polarized current, in particular, is difficult. We demonstrate here, based on calculated transport properties of graphene nanoribbons, that nearly +-100% spin-polarized current can be generated in zigzag graphene nanoribbons (ZGNRs) and tuned by a source-drain voltage in the bipolar spin diode, in addition to magnetic configurations of the electrodes. This unusual transport property is attributed to the intrinsic transmission selection rule of the spin subbands near the Fermi level in ZGNRs. The simultaneous control of spin current by the bias voltage and the magnetic configurations of the electrodes provides an opportunity to implement a whole range of spintronics devices. We propose theoretical designs for a complete set of basic spintronic devices, including bipolar spin diode, transistor and logic gates, based on ZGNRs.Comment: 14 pages, 4 figure

A GAN-based Tunable Image Compression System

The method of importance map has been widely adopted in DNN-based lossy image compression to achieve bit allocation according to the importance of image contents. However, insufficient allocation of bits in non-important regions often leads to severe distortion at low bpp (bits per pixel), which hampers the development of efficient content-weighted image compression systems. This paper rethinks content-based compression by using Generative Adversarial Network (GAN) to reconstruct the non-important regions. Moreover, multiscale pyramid decomposition is applied to both the encoder and the discriminator to achieve global compression of high-resolution images. A tunable compression scheme is also proposed in this paper to compress an image to any specific compression ratio without retraining the model. The experimental results show that our proposed method improves MS-SSIM by more than 10.3% compared to the recently reported GAN-based method to achieve the same low bpp (0.05) on the Kodak dataset

The Kagome Antiferromagnet: A Schwinger-Boson Mean-Field Theory Study

The Heisenberg antiferromagnet on the Kagom\'{e} lattice is studied in the framework of Schwinger-boson mean-field theory. Two solutions with different symmetries are presented. One solution gives a conventional quantum state with $\mathbf{q}=0$ order for all spin values. Another gives a gapped spin liquid state for spin $S=1/2$ and a mixed state with both $\mathbf{q}=0$ and $\sqrt{3}\times \sqrt{3}$ orders for spin $S>1/2$. We emphasize that the mixed state exhibits two sets of peaks in the static spin structure factor. And for the case of spin $S=1/2$, the gap value we obtained is consistent with the previous numerical calculations by other means. We also discuss the thermodynamic quantities such as the specific heat and magnetic susceptibility at low temperatures and show that our result is in a good agreement with the Mermin-Wagner theorem.Comment: 9 pages, 5 figure

C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer

Human video motion transfer (HVMT) aims to synthesize videos that one person imitates other persons' actions. Although existing GAN-based HVMT methods have achieved great success, they either fail to preserve appearance details due to the loss of spatial consistency between synthesized and exemplary images, or generate incoherent video results due to the lack of temporal consistency among video frames. In this paper, we propose Coarse-to-Fine Flow Warping Network (C2F-FWN) for spatial-temporal consistent HVMT. Particularly, C2F-FWN utilizes coarse-to-fine flow warping and Layout-Constrained Deformable Convolution (LC-DConv) to improve spatial consistency, and employs Flow Temporal Consistency (FTC) Loss to enhance temporal consistency. In addition, provided with multi-source appearance inputs, C2F-FWN can support appearance attribute editing with great flexibility and efficiency. Besides public datasets, we also collected a large-scale HVMT dataset named SoloDance for evaluation. Extensive experiments conducted on our SoloDance dataset and the iPER dataset show that our approach outperforms state-of-art HVMT methods in terms of both spatial and temporal consistency. Source code and the SoloDance dataset are available at https://github.com/wswdx/C2F-FWN.Comment: This work is accepted by AAAI202