Two-dimensional topological superconducting phases emerged from d-wave superconductors in proximity to antiferromagnets

Motivated by the recent observations of nodeless superconductivity in the monolayer CuO$_{2}$ grown on the Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+\delta }$ substrates, we study the two-dimensional superconducting (SC) phases described by the two-dimensional $t$-$J$ model in proximity to an antiferromagnetic (AF) insulator. We found that (i) the nodal d-wave SC state can be driven via a continuous transition into a nodeless d-wave pairing state by the proximity induced AF field. (ii) The energetically favorable pairing states in the strong field regime have extended s-wave symmetry and can be nodal or nodeless. (iii) Between the pure d-wave and s-wave paired phases, there emerge two topologically distinct SC phases with ($s+$i$d$) symmetry, i.e., the weak and strong pairing phases, and the weak pairing phase is found to be a $Z_{2}$ topological superconductor protected by valley symmetry, exhibiting robust gapless non-chiral edge modes. These findings strongly suggest that the high-$T_{c}$ superconductors in proximity to antiferromagnets can realize fully gapped symmetry protected topological SC.Comment: 7 pages, 4 figures; revised versio

Kramers Fulde-Ferrell state and superconducting spin diode effect

We study a novel Fulde-Ferrell equal-spin pairing state with the opposite center of mass Cooper pair momentum for each spin polarization. This state respects time-reversal symmetry and can be realized in a one-dimensional system with spin-orbit coupling and nearest neighbor attraction. We find this state can have nonreciprocal spin transport for both the bulk superconductor and the Josephson junction structure when a hidden inversion symmetry is broken. In addition to the spin Josephson diode effect, we find that the charge transport in such Josephson junctions is controlled by intriguing dynamics of bound states whose transitions can be manipulated by the length of the superconducting chain.Comment: 6 pages of main text + 7 pages of appendi

Digital Transformation, Operating Capacity and Firm Performance: Empirical Evidence from the Chinese Stock Market

In the context of the digital economy, digital technologies such as big data, artificial intelligence, cloud computing and so on continue to promote enterprise value creation. This paper takes the annual reports of listed companies in Shanghai and Shenzhen A-shares from 2015 to 2021 as the object, constructs digital transformation indexes based on Word2Vec machine learning technology, and empirically analyzes the relationship between digital transformation, enterprise operating capacity and company performance. The study finds that: the higher the degree of digital transformation, the stronger the operating capacity of enterprises; the improvement of enterprise operating capacity can significantly enhance company performance; digital transformation will strengthen the positive effect of operating capacity on company performance. The study's findings not only reveal the moderating role of digital transformation in the relationship between enterprise operating capacity and company performance but also provide theoretical and empirical evidence for a comprehensive and objective evaluation of the implementation effect of digital transformation. Keywords: digital transformation, operating capacity, company performance, text analysis DOI: 10.7176/EJBM/15-16-01 Publication date:September 30th 202

FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs

Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since data augmentation strategies have largely alleviated the training instability, how to further improve the generative performance of DE-GANs becomes a hotspot. Recently, contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs, yet related principles are not well explored. In this paper, we revisit and compare different contrastive learning strategies in DE-GANs, and identify (i) the current bottleneck of generative performance is the discontinuity of latent space; (ii) compared to other contrastive learning strategies, Instance-perturbation works towards latent space continuity, which brings the major improvement to DE-GANs. Based on these observations, we propose FakeCLR, which only applies contrastive learning on perturbed fake samples, and devises three related training techniques: Noise-related Latent Augmentation, Diversity-aware Queue, and Forgetting Factor of Queue. Our experimental results manifest the new state of the arts on both few-shot generation and limited-data generation. On multiple datasets, FakeCLR acquires more than 15% FID improvement compared to existing DE-GANs. Code is available at https://github.com/iceli1007/FakeCLR.Comment: Accepted by ECCV202

Spatial-Contextual Discrepancy Information Compensation for GAN Inversion

Most existing GAN inversion methods either achieve accurate reconstruction but lack editability or offer strong editability at the cost of fidelity. Hence, how to balance the distortioneditability trade-off is a significant challenge for GAN inversion. To address this challenge, we introduce a novel spatial-contextual discrepancy information compensationbased GAN-inversion method (SDIC), which consists of a discrepancy information prediction network (DIPN) and a discrepancy information compensation network (DICN). SDIC follows a "compensate-and-edit" paradigm and successfully bridges the gap in image details between the original image and the reconstructed/edited image. On the one hand, DIPN encodes the multi-level spatial-contextual information of the original and initial reconstructed images and then predicts a spatial-contextual guided discrepancy map with two hourglass modules. In this way, a reliable discrepancy map that models the contextual relationship and captures finegrained image details is learned. On the other hand, DICN incorporates the predicted discrepancy information into both the latent code and the GAN generator with different transformations, generating high-quality reconstructed/edited images. This effectively compensates for the loss of image details during GAN inversion. Both quantitative and qualitative experiments demonstrate that our proposed method achieves the excellent distortion-editability trade-off at a fast inference speed for both image inversion and editing tasks