725 research outputs found
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 grown on the BiSrCaCuO
substrates, we study the two-dimensional superconducting (SC) phases described
by the two-dimensional - 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 (i) symmetry, i.e., the weak and
strong pairing phases, and the weak pairing phase is found to be a
topological superconductor protected by valley symmetry, exhibiting robust
gapless non-chiral edge modes. These findings strongly suggest that the
high- 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
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
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
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