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Ferromagnetic side layers sandwiching a nonmagnetic spacer as a metallic trilayer has become a pivotal platform for achieving spintronic devices. Recent experiments demonstrate that manipulating the width or the nature of conducting spacer induces noncollinear magnetic alignment between the side layers. Our theoretical analysis reveals that altering the width of spacer significantly affects the interlayer exchange coupling (IEC), resulting in noncollinear alignment. Through analytic and first-principles methods, our study on the Fe/Ag/Fe trilayer shows that at a specific width of the Ag spacer, the magnetic moments of side layers tend to be perpendicular. This alignment is mediated by Ag quantum well states, exhibiting spin spirals across the trilayer. Our results reveal that the noncollinear IEC offers a degree of freedom to control magnetic devices and boot spintronic technology with improved transport capabilities., Comment: 14 pages and 5 figures, accept in Materials Today Physics

We theoretically demonstrate that carbon nanoscrolls -- spirally wrapped graphene layers with open endpoints -- can be characterized by a large positive magnetoconductance. We show that when a carbon nanoscroll is subject to an axial magnetic field of ~ 10T, the ballistic conductance at low carrier densities of the nanoscroll has an increase of about 200%. Importantly, we find that this positive magnetoconductance is not only preserved but can be even enhanced in the presence of on-site disorder. We prove that the positive magnetoconductance comes about the emergence of magnetic field-induced zero energy modes, specific of rolled-up geometries. Our results establish curved graphene systems as a new material platform displaying sizable magnetoresistive phenomena., Comment: 13 pages, 4 figures

In this study, we explore the interactions between mesons and baryons in the open heavy sectors to identify potential triple-heavy molecular pentaquarks. We derive the meson-baryon interaction potentials using the vector meson exchange mechanism within the extended local hidden gauge formalism. The scattering amplitudes are computed by solving the coupled-channel Bethe-Salpeter equation, revealing several bound systems. By analyzing the poles of these amplitudes in the complex plane, we determine the masses and widths of these bound states. Additionally, we evaluate the couplings and compositeness of different channels within each bound system to assess their molecular characteristics. Our predictions include four $\Omega_{ccc}$-like states, four $\Omega_{bbb}$-like states, fourteen $\Omega_{bcc}$-like states, and ten $\Omega_{bbc}$-like states, which could be targets for future experimental investigations., Comment: 14 pages, 3 figures, 8 tables, accepted by Phys. Rev. D

The transition of an impulsively excited kink oscillation of a solar coronal loop to an oscillation with a stationary amplitude, i.e., the damping pattern, is determined using the low-dimensional self-oscillation model. In the model, the decayless kink oscillations are sustained by the interaction of the oscillating loop with an external quasi-steady flow. The analytical solution is based on the assumption that the combined effect of the effective dissipation, for example, by resonant absorption, and interaction with an external flow, is weak. The effect is characterised by a dimensionless coupling parameter. The damping pattern is found to depend upon the initial amplitude and the coupling parameter. The approximate expression shows a good agreement with a numerical solution of the self-oscillation equation. The plausibility of the established damping pattern is demonstrated by an observational example. Notably, the damping pattern is not exponential, and the characteristic decay time is different from the time determined by the traditionally used exponential damping fit. Implications of this finding for seismology of the solar coronal plasmas are discussed. In particular, it is suggested that a very rapid, in less than the oscillation period, decay of the oscillation to the stationary level, achieved for larger values of the coupling parameter, can explain the relative rareness of the kink oscillation events., Comment: Accepted for publication in MNRAS, 8 pages, 5 figures

Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images. Recently, denoising diffusion probabilistic models have been gradually applied to visual tasks, enhancing controllable image generation through low-rank adaptation (LoRA). In this paper, we introduce a spatial-spectral integrated diffusion model for the remote sensing pansharpening task, called SSDiff, which considers the pansharpening process as the fusion process of spatial and spectral components from the perspective of subspace decomposition. Specifically, SSDiff utilizes spatial and spectral branches to learn spatial details and spectral features separately, then employs a designed alternating projection fusion module (APFM) to accomplish the fusion. Furthermore, we propose a frequency modulation inter-branch module (FMIM) to modulate the frequency distribution between branches. The two components of SSDiff can perform favorably against the APFM when utilizing a LoRA-like branch-wise alternative fine-tuning method. It refines SSDiff to capture component-discriminating features more sufficiently. Finally, extensive experiments on four commonly used datasets, i.e., WorldView-3, WorldView-2, GaoFen-2, and QuickBird, demonstrate the superiority of SSDiff both visually and quantitatively. The code will be made open source after possible acceptance.

In image fusion tasks, images from different sources possess distinct characteristics. This has driven the development of numerous methods to explore better ways of fusing them while preserving their respective characteristics.Mamba, as a state space model, has emerged in the field of natural language processing. Recently, many studies have attempted to extend Mamba to vision tasks. However, due to the nature of images different from causal language sequences, the limited state capacity of Mamba weakens its ability to model image information. Additionally, the sequence modeling ability of Mamba is only capable of spatial information and cannot effectively capture the rich spectral information in images. Motivated by these challenges, we customize and improve the vision Mamba network designed for the image fusion task. Specifically, we propose the local-enhanced vision Mamba block, dubbed as LEVM. The LEVM block can improve local information perception of the network and simultaneously learn local and global spatial information. Furthermore, we propose the state sharing technique to enhance spatial details and integrate spatial and spectral information. Finally, the overall network is a multi-scale structure based on vision Mamba, called LE-Mamba. Extensive experiments show the proposed methods achieve state-of-the-art results on multispectral pansharpening and multispectral and hyperspectral image fusion datasets, and demonstrate the effectiveness of the proposed approach. Codes can be accessed at \url{https://github.com/294coder/Efficient-MIF}.

Macroscopic wave and oscillatory phenomena ubiquitously detected in the plasma of the corona of the Sun are interpreted in terms of magnetohydrodynamic theory. Fast and slow magnetoacoustic waves are clearly distinguished in observations. Properties of coronal magnetohydrodynamic waves are determined by local parameters of the plasma, including the field-aligned filamentation typical for the corona. It makes coronal magnetohydrodynamic waves reliable probes of the coronal plasma structures by the method of magnetohydrodynamic seismology. For example, propagating slow waves indicate the local direction of the guiding magnetic field. Standing, sloshing and propagating slow waves can be used for probing the coronal heating function and the polytropic index. Kink oscillations of coronal plasma loops provide us with the estimations of the absolute value of the magnetic field in oscillating plasma loops. This tutorial introduces several techniques of magnetohydrodynamic seismology of solar coronal plasmas. It includes the description of practical steps in the data acquisition, pre-processing, and processing using the open-access data of the Atmospheric Imaging Assembly on the Solar Dynamics Observatory spacecraft, and elaborated data analysis techniques of motion magnification and Bayesian statistics., Comment: 39 pages, 12 figures, accepted for publication in Reviews of Modern Plasma Physics

The local atomic structure of NaF-ZrF$_4$ (53-47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements and compared with $ab-initio$ and Neural Network-based molecular dynamics (NNMD) simulations in the temperature range 515-700 {\deg}C. The machine-learning enhanced NNMD calculations offer improved efficiency while maintaining accuracy at higher distances compared to ab-initio calculations. Looking at the evolution of the Pair Distribution Function with increasing temperature, a fundamental change in the liquid structure within the selected temperature range, accompanied by a slight decrease in overall correlation is revealed. NNMD calculations indicate the co-existence of three different fluorozirconate complexes: [ZrF$_6$]$^{2-}$, [ZrF$_7$]$^{3-}$, and [ZrF$_8$]$^{4-}$, with a temperature-dependent shift in the dominant coordination state towards a 6-coordinated Zr ion at 700{\deg}C. The study also highlights the metastability of different coordination structures, with frequent interconversions between 6 and 7 coordinate states for the fluorozirconate complex from 525 {\deg}C to 700 {\deg}C. Analysis of the Zr-F-Zr angular distribution function reveals the presence of both $"$edge-sharing$"$ and $"$corner-sharing$"$ fluorozirconate complexes with specific bond angles and distances in accord with previous studies, while the next-nearest neighbor cation-cation correlations demonstrate a clear preference for unlike cations as nearest-neighbor pairs, emphasizing non-random arrangement. These findings contribute to a comprehensive understanding of the complex local structure of the molten salt, providing insights into temperature-dependent preferences and correlations within the molten system., Comment: 26 pages, 15 figures, 3 tables

A method was proposed for the point cloud-based registration and image fusion between cardiac single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) and cardiac computed tomography angiograms (CTA). Firstly, the left ventricle (LV) epicardial regions (LVERs) in SPECT and CTA images were segmented by using different U-Net neural networks trained to generate the point clouds of the LV epicardial contours (LVECs). Secondly, according to the characteristics of cardiac anatomy, the special points of anterior and posterior interventricular grooves (APIGs) were manually marked in both SPECT and CTA image volumes. Thirdly, we developed an in-house program for coarsely registering the special points of APIGs to ensure a correct cardiac orientation alignment between SPECT and CTA images. Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images. Finally, the image fusion between SPECT and CTA was realized after the fine registration. The experimental results showed that the cardiac orientation was aligned well and the mean distance error of the optimal registration method (CPD with affine transform) was consistently less than 3 mm. The proposed method could effectively fuse the structures from cardiac CTA and SPECT functional images, and demonstrated a potential in assisting in accurate diagnosis of cardiac diseases by combining complementary advantages of the two imaging modalities.

In this work, we investigate the double-heavy molecular pentaquark states with the quark contents $ccss\bar{s}$, $bbss\bar{s}$, and $bcss\bar{s}$ by using the coupled channel approach. The extended local hidden gauge Lagrangians are used to obtain the meson-baryon interactions by exchanging the vector mesons. We predict some candidates for the molecular states with the quantum numbers $I(J^{P}) = 0(1/2^{-}, 3/2^{-}, 5/2^{-})$, whose binding energies are of the order of $20-30$ MeV and whose widths are all less than $8$ MeV. These predicted exotic double-heavy molecular pentaquark states may be accessible in future experiments such as LHCb., Comment: Minor corrections are made

Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions. However, complementarity between such heterogeneous architectures has not been well exploited in self-supervised learning. Thus, we propose Heterogeneous Self-Supervised Learning (HSSL), which enforces a base model to learn from an auxiliary head whose architecture is heterogeneous from the base model. In this process, HSSL endows the base model with new characteristics in a representation learning way without structural changes. To comprehensively understand the HSSL, we conduct experiments on various heterogeneous pairs containing a base model and an auxiliary head. We discover that the representation quality of the base model moves up as their architecture discrepancy grows. This observation motivates us to propose a search strategy that quickly determines the most suitable auxiliary head for a specific base model to learn and several simple but effective methods to enlarge the model discrepancy. The HSSL is compatible with various self-supervised methods, achieving superior performances on various downstream tasks, including image classification, semantic segmentation, instance segmentation, and object detection. Our source code will be made publicly available.

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak using only a few discharges. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak, called physics-guided feature extraction (PGFE). The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamak (source domain) based on a domain adaptation algorithm called CORrelation ALignment (CORAL). It is the first attempt at applying domain adaptation in the task of disruption prediction. PGFE has been successfully applied in J-TEXT to predict disruption with excellent performance. PGFE can also reduce the data volume requirements due to extracting the less device-specific features, thereby establishing a solid foundation for cross-tokamak disruption prediction. We have further improved CORAL (supervised CORAL, S-CORAL) to enhance its appropriateness in feature alignment for the disruption prediction task. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak, which has a large gap in the ranges of plasma parameters. The utilization of the S-CORAL improves the disruption prediction performance on future tokamak. Through interpretable analysis, we discovered that the learned knowledge of the disruption prediction model through this approach exhibits more similarities to the model trained on large data volumes of future tokamak., Comment: 17 pages, 9 figures

The pursuit of room-temperature ambient-pressure superconductivity in novel materials has sparked interest, with recent reports suggesting such properties in Cu-substituted lead apatite, known as LK-99. However, these claims lack comprehensive experimental and theoretical support. In this study, we address this gap by conducting ab initio calculations to explore the impact of varying doping concentrations (x = 0, 1, 2) on the stability and electronic properties of five compounds in the LK-99 family. Our investigations confirm the isolated flat bands that intersect the Fermi level in LK-99 (Pb9Cu(PO4)6O:Cu). In contrast, the other four parent compounds exhibit insulating behavior with wide band gaps. X-ray diffraction spectra based on the DFT simulations at 0K confirm the presence of Cu substitution on Pb(1) sites in the originally synthesized LK-99 sample, while an extra peak suggests potential alternative like Pb8Cu2(PO4)6 phases due to compositional variations in the original LK-99 samples. Furthermore, the LK-99 structure undergoes substantial lattice constriction, resulting in a significant 5.5% reduction in volume and 6.8% in area of two mutually inverted triangles formed by Pb(2) atoms. Meanwhile, energy calculations reveal a marginal energy preference for substituting Cu on Pb(2) sites over Pb(1) sites, with a difference of approximately 0.010 eV per atom (roughly 0.9645 k/mol). Intriguingly, at pressures exceeding 73 GPa, stability shifts towards LK-99 containing Cu substitutions on Pb(1) sites. Despite exhibiting higher electronic conductivity than parent compounds, Pb9Cu(PO4)6O:Cu falls short of the conductivity levels observed in metals or advanced oxide conductors with the simulation based on the Boltzmann transport theory.

With the chiral unitary approach, we evaluate the hidden strange $B_{c}$-like molecular states of $b\bar{c}s\bar{s}$ systems $\bar{B}_{s}\bar{D}_{s}$, $\bar{B}_{s}^{*}\bar{D}_{s}$, $\bar{B}_{s}\bar{D}_{s}^{*}$, and $\bar{B}_{s}^{*}\bar{D}_{s}^{*}$ coupled to the non-strange channels. The $S$-wave scattering amplitudes are calculated based on the vector meson exchange, four pseudoscalar mesons contact interactions, and four vector mesons contact interactions obtained from the extended local hidden gauge approach. We find six states below the threshold of the most relevant channel. The binding energies of these states are around $1-10$ MeV and the widths are around $0.2-0.7$ MeV. Our research is a supplement to the mass spectra of $B_{c}$-like states, which may be useful for the experimental search in the future., Comment: 15 pages, 8 figures

The stock segment of China's time-honored brand enterprises has an important position in our securities stock market. The holiday effect is one of the market anomalies that occur in the securities market, which refers to the phenomenon that the stock market has significantly different returns than other trading days around festivals. The study of the holiday effect of China's time-honored brand enterprises can provide fresh ideas for the revitalization of our time-honored brands and the revitalization of time-honored enterprises. This paper takes listed companies of China's time-honored brand enterprises as the research object and focuses on the impact of the holiday effect on listed companies of China's time-honored brands with the help of the event study, and empirically analyses the changes in the return of listed companies of China time-honored brands during the Spring Festival period from 2012 to 2021. The empirical results reveal that: the time-honored brand concept stocks have a significant post-holiday effect during the Chinese New Year period, the time-honored alcoholic beverage enterprises are more sensitive to the Chinese New Year reflection, while the holiday effect of the time-honored pharmaceutical manufacturing enterprises is not significant., Comment: 24pages

The BESIII Collaboration claimed that a new $a_{0}(1817)$ resonance was found in the recent results of the $D_{s}^{+} \rightarrow K_{S}^{0}K^{+}\pi^{0}$ decay. For this decay process, we perform a unitary amplitude to analyze the contributions of the states $a_{0}(980)^{+}$ and $a_{0}(1710)^{+}$ with the final state interactions. Considering the Cabibbo-favored external and internal $W$-emission mechanisms at the quark level, and the contributions of the resonances $a_{0}(980)^{+}$, $a_{0}(1710)^{+}$ in the $S$-wave and $\bar{K}^{*}(892)^{0}$, ${K}^{*}(892)^{+}$ in the $P$-wave, the recent experimental data of the $K_{S}^{0}K^{+}$ invariant mass spectrum from the BESIII Collaboration can be described well. In our results, the states $a_{0}(980)$ and $a_{0}(1710)$ are dynamically generated from the final state interactions of $K\bar{K}$ and $K^{*}\bar{K}^{*}$, respectively, which support the molecular nature for them. Moreover, some obtained branching fractions are in agreement with the experimental measurements.

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels., Comment: 21 pages, 11 figures

Practical applications of microwave imaging often require the solution of inverse scattering problems with inhomogeneous backgrounds. Towards this end, a novel inversion strategy, which combines the multi-scaling (MS) regularization scheme and the Difference Contraction Integral Equation (DCIE) formulation, is proposed. Such an integrated approach mitigates the non-linearity and the ill-posedness of the problem to obtain reliable high-resolution reconstructions of the unknown scattering profiles. The arising algorithmic implementation, denoted as MS-DCIE, does not require the computation of the Green's function of the inhomogeneous background, thus it provides an efficient and effective way to deal with complex scenarios. The performance of the MS-DCIE are assessed by means of numerical and experimental tests, in comparison with competitive state-of-the-art inversion strategies, as well.

The decay process $\Xi_{b}^{-} \rightarrow pK^{-}K^{-}$ is studied with the final state interaction approach by considering the contributions from the $S$-wave meson-baryon interactions, and also the intermediate state $\Lambda(1520)$ in the $D$-wave. The low-lying resonances $\Lambda(1405)$ and $\Lambda(1670)$ have significant contributions, which are both dynamically generated from the $S$-wave final state interactions with isospin $I=0$. Furthermore, the $\Lambda(1520)$ state also has important contributions from $D$-wave. With these resonances contributions, the experimental data of the lower $pK^{-}$ invariant mass distributions are well described. We also discuss the contribution of another resonance in the $S$-wave with isospoin $I=1$, which cannot be ignored. Moreover, some of the branching fractions obtained for the corresponding decay channels are consistent with the experimental measurements., Comment: 18 pages, 6 figures, 3 tables, published in Phys. Rev. D

We report an opto-fluidic method that enables to efficiently measure the enantiomeric excess of chiral molecules at low concentration. The approach is to monitor the optical activity induced by a Kagome-lattice hollow-core photonic crystal fiber filled with a sub-ul volume of chiral compound. The technique also allows monitoring the enzymatic racemization of R mandelic acid.

Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors. The purpose of most disruption predictors is accuracy or cross-machine capability. However, if a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption and gives us insight into the mechanism of disruption. This paper designs a disruption predictor called Interpretable Disruption Predictor based On Physics-guided feature extraction (IDP-PGFE) on J-TEXT. The prediction performance of the model is effectively improved by extracting physics-guided features. A high-performance model is required to ensure the validity of the interpretation results. The interpretability study of IDP-PGFE provides an understanding of J-TEXT disruption and is generally consistent with existing comprehension of disruption. IDP-PGFE has been applied to the disruption due to continuously increasing density towards density limit experiments on J-TEXT. The time evolution of the PGFE features contribution demonstrates that the application of ECRH triggers radiation-caused disruption, which lowers the density at disruption. While the application of RMP indeed raises the density limit in J-TEXT. The interpretability study guides intuition on the physical mechanisms of density limit disruption that RMPs affect not only the MHD instabilities but also the radiation profile, which delays density limit disruption., Comment: 17 pages, 15 figures

Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation-guided iterative local search scheme to refine combinations effectively. Our RF-Next models, plugging receptive field search to various models, boost the performance on many tasks, e.g., temporal action segmentation, object detection, instance segmentation, and speech synthesis. The source code is publicly available on http://mmcheng.net/rfnext., Comment: Accepted by TPAMI. This paper is a journal extension of our CVPR 2021 paper (arXiv:2101.00910)

Learning representations with self-supervision for convolutional networks (CNN) has been validated to be effective for vision tasks. As an alternative to CNN, vision transformers (ViT) have strong representation ability with spatial self-attention and channel-level feedforward networks. Recent works reveal that self-supervised learning helps unleash the great potential of ViT. Still, most works follow self-supervised strategies designed for CNN, e.g., instance-level discrimination of samples, but they ignore the properties of ViT. We observe that relational modeling on spatial and channel dimensions distinguishes ViT from other networks. To enforce this property, we explore the feature SElf-RElation (SERE) for training self-supervised ViT. Specifically, instead of conducting self-supervised learning solely on feature embeddings from multiple views, we utilize the feature self-relations, i.e., spatial/channel self-relations, for self-supervised learning. Self-relation based learning further enhances the relation modeling ability of ViT, resulting in stronger representations that stably improve performance on multiple downstream tasks. Our source code is publicly available at: https://github.com/MCG-NKU/SERE., Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)