arXiv subjects

Zebin Wu

Publications and source records attributed to Zebin Wu.

16 recordsLinked to original sources

arXiv2025

Anisotropic electron damping and energy gap in Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$

The many body electron-electron interaction in cuprates causes the broadening of the electronic bands in \textit{\textbf{k}}-space, leading to a deviation from the standard Fermi liquid. While a \textit{\textbf{k}}-dependent anisotropic electronic scattering (\textit{\textbf{k}}-DAES) has been assessed by photoemission, its fingerprint in \textit{\textbf{Q}}-space has been scarcely considered. Here, we explore the \textit{\textbf{Q}}-dependent electron dynamics in optimally doped Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ through the evolution of low-energy charge excitations as measured by resonant inelastic x ray scattering (RIXS). In the normal state, the RIXS spectra display a continuum of excitations down to 0~meV, while the superconducting state features a spectral weight suppression below 80 meV without any enhancement at higher energies. To interpret the energy and \textit{\textbf{Q}}-evolution of our data, we introduce a phenomenological expression of the charge susceptibility by including the \textit{\textbf{k}}-DAES. We show that only the charge susceptibility with \textit{\textbf{k}}-DAES captures the RIXS data, highlighting the importance of \textit{\textbf{k}}-DAES when describing the \textit{\textbf{Q}}-dependence of charge excitations from 0 to few eV scale. Furthermore, we also find that the inclusion of \textit{\textbf{k}}-DAES is essential when quantitative parameters such as the electronic energy gap are extracted from RIXS data.

cond-mat.str-el↗

arXiv2025

Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model

Hyperspectral and multispectral image (HSI-MSI) fusion involves combining a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral image (HR-HSI). Most deep learning-based methods for HSI-MSI fusion rely on large amounts of hyperspectral data for supervised training, which is often scarce in practical applications. In this paper, we propose a self-learning Adaptive Residual Guided Subspace Diffusion Model (ARGS-Diff), which only utilizes the observed images without any extra training data. Specifically, as the LR-HSI contains spectral information and the HR-MSI contains spatial information, we design two lightweight spectral and spatial diffusion models to separately learn the spectral and spatial distributions from them. Then, we use these two models to reconstruct HR-HSI from two low-dimensional components, i.e, the spectral basis and the reduced coefficient, during the reverse diffusion process. Furthermore, we introduce an Adaptive Residual Guided Module (ARGM), which refines the two components through a residual guided function at each sampling step, thereby stabilizing the sampling process. Extensive experimental results demonstrate that ARGS-Diff outperforms existing state-of-the-art methods in terms of both performance and computational efficiency in the field of HSI-MSI fusion. Code is available at https://github.com/Zhu1116/ARGS-Diff.

cs.CV↗

arXiv2025

Imaging van Hove Singularity Heterogeneity in Overdoped Graphene

Tuning the chemical potential of a solid to the vicinity of a van Hove singularity (vHS) is a well-established route to discovering emergent quantum phases. In monolayer graphene, the use of electron-donating metal layers has recently emerged as a method to dope the chemical potential to the nearest vHS, as evidenced by Angle-Resolved Photoemission Spectroscopy (ARPES) measurements. In this work, we study the spatial uniformity of the doping from this process using spectroscopic imaging scanning tunneling microscopy (SI-STM). Using molecular beam epitaxy (MBE), we achieve electron doping of graphene on SiC using Ytterbium (Yb-Graphene). We show using in-situ ARPES that the chemical potential is shifted to within 250 meV of the vHS. Using in-situ SI-STM, we establish that there exists significant inhomogeneity in the vHS position in overdoped graphene. We find two separate reasons for this. First, the spatial inhomogeneity of the intercalated Yb leads to local variations in the doping, with a length scale of inhomogeneity set by the screening length of ~ 3 nm. Second, we observe the presence of substitutional Yb dopants in the graphene basal plane. These Yb dopants cause a strong local shift of the doping, along with a renormalization of the quasiparticle amplitude. Theoretical calculations confirm that the Yb impurities effectively change the local potential, thus energetically shifting the position of the van Hove singularity. Our results point to the importance of considering the spatial structure of doping and its inextricable link to electronic structure.

cond-mat.mes-hall↗

arXiv2024

Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning

Hyperspectral and multispectral image fusion aims to generate high spectral and spatial resolution hyperspectral images (HR-HSI) by fusing high-resolution multispectral images (HR-MSI) and low-resolution hyperspectral images (LR-HSI). However, existing fusion methods encounter challenges such as unknown degradation parameters, incomplete exploitation of the correlation between high-dimensional structures and deep image features. To overcome these issues, in this article, an unsupervised blind fusion method for hyperspectral and multispectral images based on Tucker decomposition and spatial spectral manifold learning (DTDNML) is proposed. We design a novel deep Tucker decomposition network that maps LR-HSI and HR-MSI into a consistent feature space, achieving reconstruction through decoders with shared parameter. To better exploit and fuse spatial-spectral features in the data, we design a core tensor fusion network that incorporates a spatial spectral attention mechanism for aligning and fusing features at different scales. Furthermore, to enhance the capacity in capturing global information, a Laplacian-based spatial-spectral manifold constraints is introduced in shared-decoders. Sufficient experiments have validated that this method enhances the accuracy and efficiency of hyperspectral and multispectral fusion on different remote sensing datasets. The source code is available at https://github.com/Shawn-H-Wang/DTDNML.

cs.CV↗

arXiv2024

Shallow core levels, or how to determine the doping and $T_c$ of Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ and Bi$_{2}$Sr$_2$CuO$_{6+\delta}$ without cooling

Determining the doping level in high-temperature cuprate superconductors is crucial for understanding the origin of superconductivity in these materials and for unlocking their full potential. However, accurately determining the doping level remains a significant challenge due to a complex interplay of factors and limitations in various measurement techniques. In particular, in Bi$_{2}$Sr$_2$CuO$_{6+\delta}$ and Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, where the mobile carriers are introduced by non-stoichiometric oxygen $\delta$, the determination has been extremely problematic. Here, we study the doping dependence of the electronic structure of these materials in angle-resolved photoemission and find that both the doping level, $p$, and the superconducting transition temeprature, $T_c$ can be precisely determined from the binding energy of the Bi $5d$ core-levels. The measurements can be performed at room temperature, enabling the determination of $p$ and $T_c$ without cooling the samples. This should be very helpful for further studies of these materials.

cond-mat.supr-con↗

arXiv2023

A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution

Fusion-based hyperspectral image (HSI) super-resolution aims to produce a high-spatial-resolution HSI by fusing a low-spatial-resolution HSI and a high-spatial-resolution multispectral image. Such a HSI super-resolution process can be modeled as an inverse problem, where the prior knowledge is essential for obtaining the desired solution. Motivated by the success of diffusion models, we propose a novel spectral diffusion prior for fusion-based HSI super-resolution. Specifically, we first investigate the spectrum generation problem and design a spectral diffusion model to model the spectral data distribution. Then, in the framework of maximum a posteriori, we keep the transition information between every two neighboring states during the reverse generative process, and thereby embed the knowledge of trained spectral diffusion model into the fusion problem in the form of a regularization term. At last, we treat each generation step of the final optimization problem as its subproblem, and employ the Adam to solve these subproblems in a reverse sequence. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The code of the proposed approach will be available on https://github.com/liuofficial/SDP.

cs.CV↗

arXiv2023

Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images

Change detection has always been a concerned task in the interpretation of remote sensing images. It is essentially a unique binary classification task with two inputs, and there is a change relationship between these two inputs. At present, the mining of change relationship features is usually implicit in the network architectures that contain single-branch or two-branch encoders. However, due to the lack of artificial prior design for change relationship features, these networks cannot learn enough change semantic information and lose more accurate change detection performance. So we propose a network architecture NAME for the explicit mining of change relation features. In our opinion, the change features of change detection should be divided into pre-changed image features, post-changed image features and change relation features. In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively. In addition, we design the continuous change relation (CCR) branch to further obtain the continuous and detail change relation features to improve the change discrimination capability of the model. The experimental results show that our network performs better, in terms of F1, IoU, and OA, than those of the existing advanced networks for change detection on four public very high-resolution (VHR) remote sensing datasets. Our source code is available at https://github.com/DalongZ/NAME.

cs.CV↗

arXiv2023

SwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection

Among the current mainstream change detection networks, transformer is deficient in the ability to capture accurate low-level details, while convolutional neural network (CNN) is wanting in the capacity to understand global information and establish remote spatial relationships. Meanwhile, both of the widely used early fusion and late fusion frameworks are not able to well learn complete change features. Therefore, based on swin transformer V2 (Swin V2) and VGG16, we propose an end-to-end compounded dense network SwinV2DNet to inherit the advantages of both transformer and CNN and overcome the shortcomings of existing networks in feature learning. Firstly, it captures the change relationship features through the densely connected Swin V2 backbone, and provides the low-level pre-changed and post-changed features through a CNN branch. Based on these three change features, we accomplish accurate change detection results. Secondly, combined with transformer and CNN, we propose mixed feature pyramid (MFP) which provides inter-layer interaction information and intra-layer multi-scale information for complete feature learning. MFP is a plug and play module which is experimentally proven to be also effective in other change detection networks. Further more, we impose a self-supervision strategy to guide a new CNN branch, which solves the untrainable problem of the CNN branch and provides the semantic change information for the features of encoder. The state-of-the-art (SOTA) change detection scores and fine-grained change maps were obtained compared with other advanced methods on four commonly used public remote sensing datasets. The code is available at https://github.com/DalongZ/SwinV2DNet.

cs.CV↗

arXiv2022

ReAFFPN: Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection

This paper proposes a Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection named ReAFFPN. ReAFFPN aims at improving the effect of rotation-equivariant features fusion between adjacent layers which suffers from the semantic and scale discontinuity. Due to the particularity of rotational equivariant convolution, general methods are unable to achieve their original effect while ensuring rotation equivariance of the network. To solve this problem, we design a new Rotation-equivariant Channel Attention which has the ability to both generate channel attention and keep rotation equivariance. Then we embed a new channel attention function into Iterative Attentional Feature Fusion (iAFF) module to realize Rotation-equivariant Attention Feature Fusion. Experimental results demonstrate that ReAFFPN achieves a better rotation-equivariant feature fusion ability and significantly improve the accuracy of the Rotation-equivariant Convolutional Networks.

cs.CV↗

arXiv2022

Enhanced Curie temperature and skyrmion stability in room temperature ferromagnetic semiconductor CrISe monolayer

We report CrISe monolayer as a room temperature ferromagnetic semiconductor with the Curie temperature ($T_C$), magnetic anisotropy energy (MAE) and band gap being 322 K, 113 $\mu$eV and 0.67 eV, respectively. The $T_C$ and MAE can be further enhanced up to 385 K and 313 $\mu$eV by tensile strain. More interestingly, the magnetic easy axis can be switched between off-plane and in-plane by compressive strain. Particularly, due to the broken inversion symmetry and strong spin-orbit coupling of Se atoms, a large Dzyaloshinskii-Moriya interaction (DMI) of 2.40 meV is obtained. More importantly, by micromagnetic simulations, stable skyrmions with sub-10 nm radius are stabilized by the large DMI above room temperature in a wide range of strain from $-2\%$ to $6\%$. Our work demonstrates CrISe as a promising candidate for next-generation skyrmion-based information storage devices and provides guidance for the research of DMI and skyrmions in room temperature ferromagnetic semiconductors.

cond-mat.mtrl-sci↗

arXiv2022

High-Order Coupled Fully-Connected Tensor Network Decomposition for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution addresses the problem of fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to produce a high-resolution hyperspectral image (HR-HSI). Tensor analysis has been proven to be an efficient method for hyperspectral image processing. However, the existing tensor-based methods of hyperspectral image super-resolution like the tensor train and tensor ring decomposition only establish an operation between adjacent two factors and are highly sensitive to the permutation of tensor modes, leading to an inadequate and inflexible representation. In this paper, we propose a novel method for hyperspectral image super-resolution by utilizing the specific properties of high-order tensors in fully-connected tensor network decomposition. The proposed method first tensorizes the target HR-HSI into a high-order tensor that has multiscale spatial structures. Then, a coupled fully-connected tensor network decomposition model is proposed to fuse the corresponding high-order tensors of LR-HSI and HR-MSI. Moreover, a weighted-graph regularization is imposed on the spectral core tensors to preserve spectral information. In the proposed model, the superiorities of the fully-connected tensor network decomposition lie in the outstanding capability for characterizing adequately the intrinsic correlations between any two modes of tensors and the essential invariance for transposition. Experimental results on three data sets show the effectiveness of the proposed approach as compared to other hyperspectral image super-resolution methods.

math.NA↗

arXiv2022

Strongly-overdoped La$_{2-x}$Sr$_x$CuO$_4$: Evidence for Josephson-coupled grains of strongly-correlated superconductor

The interpretation of how superconductivity disappears in cuprates at large hole doping has been controversial. To address this issue, we present an experimental study of single-crystal and thin film samples of La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) with $x\ge0.25$. In particular, measurements of bulk susceptibility on LSCO crystals with $x=0.25$ indicate an onset of diamagnetism at $T_{c1}=38.5$ K, with a sharp transition to a phase with full bulk shielding at $T_{c2}=18$ K, independent of field direction. Strikingly, the in-plane resistivity only goes to zero at $T_{c2}$. Inelastic neutron scattering on $x=0.25$ crystals confirms the presence of low-energy incommensurate magnetic excitations with reduced strength compared to lower doping levels. The ratio of the spin gap to $T_{c2}$ is anomalously large. Our results are consistent with a theoretical prediction for strongly overdoped cuprates by Spivak, Oreto, and Kivelson, in which superconductivity initially develops within disconnected self-organized grains characterized by a reduced hole concentration, with bulk superconductivity occurring only after superconductivity is induced by proximity effect in the surrounding medium of higher hole concentration. Beyond the superconducting-to-metal transition, local differential conductance measurements on an LSCO thin film suggest that regions with pairing correlations survive, but are too dilute to support superconducting order. Future experiments will be needed to test the degree to which these results apply to overdoped cuprates in general.

cond-mat.supr-con↗

arXiv2021

Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution

This paper focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on hand-craft priors. Inspired by the specific properties of model, we make the first attempt to design a model inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs respectively. In the encoding stage, we present a pixel-wise fusion model to estimate hidden outputs directly, and then reformulate and unfold the model's algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model, and can be trained patch by patch rather than the entire image. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach.

eess.IV↗

arXiv2021

Theoretical prediction of Curie temperature in two-dimensional ferromagnetic monolayer

Theoretical prediction of Curie temperature (TC) is of vital importance for designing the spintronic devices in two-dimensional (2D) ferromagnetic materials. Herein, based on the extensive investigation of Monte Carlo simulations, we summary and propose an improved method to estimate TC more precisely, which includes the different contributions of multiple near-neighbor interactions. Taking monolayer CrI3 as an example, the trends of TC with biaxial strain are investigated via Monte Carlo simulations, mean-field formulas and our method. Besides, our method is not only accurate and convenient to predicting the TC in 2D ferromagnetic honeycomb lattice CrI3 but it can be extended for predicting the TC of other 2D lattices. Our work paves the way to accelerate the prediction and discovery of novel 2D ferromagnets for spintronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Strain-tunable Dzyaloshinskii--Moriya interaction and skyrmions in two-dimensional Janus Cr$_{2}$X$_{3}$Y$_{3}$ (X, Y = Cl, Br, I, X $\neq$ Y) trihalide monolayers

Recently, great effort has been devoted to the search for two-dimensional (2D) ferromagnetic materials with inherent strong Dzyaloshinskii--Moriya interaction (DMI). Here, through a first-principles approach, we systematically investigate the effect of biaxial strain on the DMI, the Heisenberg exchange interaction, and the magnetic anisotropy energy (MAE) of Janus Cr$_{2}$X$_{3}$Y$_{3}$ (X, Y = Cl, Br, I, X $\neq$ Y) monolayers. Both DMI and MAE can be significantly enhanced by tensile strain, while a reversal of the chirality of DMI in Cr$_{2}$Cl$_{3}$Br$_{3}$ and a switch of MAE from off-plane to in-plane in Cr$_{2}$I$_{3}$Cl$_{3}$ are induced by a compressive strain of $2\%$. Microscopically, DMI and MAE are associated mainly with the large spin--orbit coupling of the heavy nonmagnetic halogen atoms rather than that of the magnetic Cr atoms. In particular, the peculiar magnetic transition of Cr$_{2}$I$_{3}$Cl$_{3}$ is explained by competition between direct exchange and superexchange interactions. Micromagnetic simulations show that a small external magnetic field of 65~mT stabilizes a skyrmion with a diameter of 9.8~nm in the Cr$_2$I$_3$Cl$_3$ monolayer. Our results will provide guidance for further research on DMI and skyrmions in 2D Janus materials, as well as a basis for the potential applications in spintronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Hole-Like Fermi Surface in the Overdoped Non-Superconducting Bi$_{1.8}$Pb$_{0.4}$Sr$_2$CuO$_{6+\delta}$

In high-temperature cuprate superconductors, the anti-ferromagnetic spin fluctuations are thought to have a very important role in naturally producing an attractive interaction between the electrons in the $d$-wave channel. The connection between superconductivity and spin fluctuations is expected to be especially consequential at the overdoped end point of the superconducting dome. In some materials, that point seems to coincide with a Lifshitz transition, where the Fermi surface changes from the hole-like centered at ($\pi, \pi$) to the electron-like, centered at the $\Gamma$ point causing a loss of large momentum anti-ferromagnetic fluctuations. Here, we study the doping dependence of the electronic structure of Bi$_{1.8}$Pb$_{0.4}$Sr$_2$CuO$_{6+\delta}$ in angle-resolved photoemission and find that the superconductivity vanishes at lower doping than at which the Lifshitz transition occurs. This requires a more detailed re-examination of a spin-fluctuation scenario.

cond-mat.supr-con↗