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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, 13 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)

We investigate the scalar resonances in the processes of $D^{0} \rightarrow \pi^{0}\pi^{0}\pi^{0}, \pi^{0}\pi^{0}\eta, \pi^{0}\eta\eta$ decays based on the chiral unitary approach for the final state interaction. We start from singly Cabibbo-suppressed production diagrams which provide a primary quark pair to hadronize two pseudoscalar mesons in $D^{0}$ decays. The resonances $f_{0}(500)$, $f_{0}(980)$ and $a_{0}(980)$ are dynamically produced from the final state interactions of the meson pairs. In our results, the experimental data for the $\pi^{0}\eta$ invariant mass spectrum of the $D^{0} \rightarrow \pi^{0}\eta\eta$ decay can be described well. We also make the predictions for the $\pi^{0}\pi^{0}$ invariant mass spectrum of the $D^{0} \rightarrow \pi^{0}\pi^{0}\pi^{0}$, where the $f_{0}(980)$ can be found, and for the $\pi^{0}\pi^{0}$, $\pi^{0}\eta$ invariant mass spectrum of the $D^{0} \rightarrow \pi^{0}\pi^{0}\eta$, where the $f_{0}(500)$, $f_{0}(980)$ and $a_{0}(980)$ appear. Furthermore, the branching ratios of each decay channel are predicted. We expect more accurate measurements of these decays to better understand the nature of the states $f_{0}(500)$, $f_{0}(980)$ and $a_{0}(980)$.

The $k$-center problem for a point set~$P$ asks for a collection of $k$ congruent balls (that is, balls of equal radius) that together cover all the points in $P$ and whose radius is minimized. The $k$-center problem with outliers is defined similarly, except that $z$ of the points in $P$ do need not to be covered, for a given parameter $z$. We study the $k$-center problem with outliers in data streams in the sliding-window model. In this model we are given a possibly infinite stream $P=\langle p_1,p_2,p_3,\ldots\rangle$ of points and a time window of length $W$, and we want to maintain a small sketch of the set $P(t)$ of points currently in the window such that using the sketch we can approximately solve the problem on $P(t)$. We present the first algorithm for the $k$-center problem with outliers in the sliding-window model. The algorithm works for the case where the points come from a space of bounded doubling dimension and it maintains a set $S(t)$ such that an optimal solution on $S(t)$ gives a $(1+\varepsilon)$-approximate solution on $P(t)$. The algorithm is deterministic and uses $O((kz/\varepsilon^d)\log \sigma)$ storage, where $d$ is the doubling dimension of the underlying space and $\sigma$ is the spread of the points in the stream. Algorithms providing a $(1+\varepsilon)$-approximation were not even known in the setting without outliers or in the insertion-only setting with outliers. We also present a lower bound showing that any algorithm that provides a $(1+\varepsilon)$-approximation must use $\Omega((kz/\varepsilon)\log \sigma)$ storage., Comment: appeared in ESA 2021

We investigate the $D_{s}^{+} \rightarrow K^{+} K^{-} \pi^{+}$ decay theoretically with the final state interactions, which is based on the chiral unitary approach and takes into account the external and internal $W$-emission mechanisms at the quark level. Only considering three resonances contributions, the $f_0(980)$ in $S$-wave, the $\bar {K}^{*}(892)^{0}$ and $\phi(1020)$ in $P$-wave, one can make a good description of the recent experimental data from BESIII Collaboration, where the contribution from $S$-wave is found to be small. Besides, we also make a calculation of the corresponding branching fractions, which are consistent with the results of BESIII Collaboration and Particle Data Group., Comment: More discussions were made. Welcome for comments

Significant discrepancies have been observed and discussed on the lattice stability of Cr between the predictions from the ab initio calculations and the CALPHAD approach. In the current work, we carefully examined the possible structures for pure Cr and reviewed the history back from how Kaufman originally determined the Gibbs energy of FCC-Cr in the 1970s. The reliability of Cr lattice stability derived by the CALPHAD and ab initio approaches was systematically discussed. It is concluded that the Cr lattice stability based on the CALPHAD approach has large uncertainty. Meanwhile, we cannot claim that the ab initio HFCC-Cr is error-free as FCC-Cr is an unstable phase under ambient conditions. The present work shows that the ab initio HFCC-Cr can be a viable scientific approach. As both approaches have their limitations, the present work propose to integrate the ab initio results into the CALPHAD platform for the development of the next generation CALPHAD database. The Fe-Cr and Ni-Cr binary systems were chosen as two case studies demonstrating the capability to adopt the ab initio Cr lattice stability directly into the current CALPHAD database framework.

In the present work, we investigate several theoretical models of the pion vector form factor and aim at getting the best fit for the two-pion cross sections to reduce the uncertainties of the calculation of two-pion contribution to the muon anomalous magnetic moment. Combined with a polynomial description to the pion vector form factor, we obtain the best fit from the Gounaris-Sakurai (or K\"uhn-Santamaria) model for the experimental data up to 1 GeV. By product, the branching ratio of $\omega \to \pi\pi$ can be extracted as $\text{Br}(\omega\to\pi\pi)=(1.52\pm 0.06) \%$, which is consistent with the one of Particle Data Group. With the best fit of the data, we obtain the muon anomalous magnetic moment from two-pion contribution as $a_{\mu}^{\mathrm{HVP,\; LO}}(\pi^{+} \pi^{-} \leq 1\ \text{GeV}) = (497.76\pm3.15) \times 10^{-10}$. Our results are consistent with the other works., Comment: Updated with more references. More discussions can be referred to the published one

To understand the nature of two poles for the $\Lambda(1405)$ state, we revisit the interactions of $\bar{K}N$ and $\pi\Sigma$ with their coupled channels, where two-poles structure is found in the second Riemann sheet. We also dynamically generate two poles in the single channel interaction of $\bar{K}N$ and $\pi\Sigma$, respectively. Moreover, we make a further study of two poles' properties by evaluating the couplings, the compositeness, the wave functions, and the radii for the interactions of four coupled channels, two coupled channels and the single channel. Our results show that the nature of two poles is unique. The higher-mass pole is a pure $\bar{K} N$ molecule, and the lower-mass one is a compositeness of mainly $\pi \Sigma$ with tiny component $\bar{K} N$. From our results, one can conclude that the $\Lambda(1405)$ state would be overlapped with two different states of the same quantum number., Comment: Some tiny corrections have been made. Welcome for more comments

Empowered by large datasets, e.g., ImageNet, unsupervised learning on large-scale data has enabled significant advances for classification tasks. However, whether the large-scale unsupervised semantic segmentation can be achieved remains unknown. There are two major challenges: i) we need a large-scale benchmark for assessing algorithms; ii) we need to develop methods to simultaneously learn category and shape representation in an unsupervised manner. In this work, we propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to help the research progress. Building on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 50k high-quality semantic segmentation annotations for evaluation. Our benchmark has a high data diversity and a clear task objective. We also present a simple yet effective method that works surprisingly well for LUSS. In addition, we benchmark related un/weakly/fully supervised methods accordingly, identifying the challenges and possible directions of LUSS. The benchmark and source code is publicly available at https://github.com/LUSSeg., Comment: Benchmark and Source Code: https://github.com/LUSSeg

Radial superlattices are nanostructured materials obtained by rolling-up thin solid films into spiral-like tubular structures. The formation of these "high-order" superlattices from two-dimensional crystals or ultrathin films is expected to result in a transition of transport characteristics from two-dimensional to one-dimensional. Here, we show that a transport hallmark of radial superlattices is the appearance of magnetoconductance modulations in the presence of externally applied axial magnetic fields. This phenomenon critically relies on electronic interlayer tunneling processes that activates an unconventional Aharonov-Bohm-like effect. Using a combination of density functional theory calculations and low-energy continuum models, we determine the electronic states of a paradigmatic single-material radial superlattice -- a two-winding carbon nanoscroll -- and indeed show momentum-dependent oscillations of the magnetic states in axial configuration, which we demonstrate to be entirely due to hopping between the two windings of the spiral-shaped scroll., Comment: 21 pages, 4 figures. To appear in Nanoscale Horizons

Semantic segmentation is an extensively studied task in computer vision, with numerous methods proposed every year. Thanks to the advent of deep learning in semantic segmentation, the performance on existing benchmarks is close to saturation. A natural question then arises: Does the superior performance on the closed (and frequently re-used) test sets transfer to the open visual world with unconstrained variations? In this paper, we take steps toward answering the question by exposing failures of existing semantic segmentation methods in the open visual world under the constraint of very limited human labeling effort. Inspired by previous research on model falsification, we start from an arbitrarily large image set, and automatically sample a small image set by MAximizing the Discrepancy (MAD) between two segmentation methods. The selected images have the greatest potential in falsifying either (or both) of the two methods. We also explicitly enforce several conditions to diversify the exposed failures, corresponding to different underlying root causes. A segmentation method, whose failures are more difficult to be exposed in the MAD competition, is considered better. We conduct a thorough MAD diagnosis of ten PASCAL VOC semantic segmentation algorithms. With detailed analysis of experimental results, we point out strengths and weaknesses of the competing algorithms, as well as potential research directions for further advancement in semantic segmentation. The codes are publicly available at \url{https://github.com/QTJiebin/MAD_Segmentation}., Comment: 19 pages, 12 figures, 5 tables, accepted by IJCV

Temporal receptive fields of models play an important role in action segmentation. Large receptive fields facilitate the long-term relations among video clips while small receptive fields help 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 combination patterns 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 global-to-local search can be plugged into existing action segmentation methods to achieve state-of-the-art performance., Comment: Accepted by CVPR 2021. Source code: https://github.com/ShangHua-Gao/G2L-search

In the present work, we investigate the decays of $B^{0}_{s} \rightarrow \phi\pi^{+}\pi^{-}$ and $B^{0} \rightarrow \phi\pi^{+}\pi^{-}$ with the final state interactions based on the chiral unitary approach. In the final state interactions of the $\pi^+\pi^-$ with its coupled channels, we study the effects of the $\eta\eta$ channel in the two-body interactions for the reproduction of the $f_{0}(980)$ state. Our results for the $\pi^+\pi^-$ invariant mass distributions of the decay $B^{0}_{s} \rightarrow \phi\pi^{+}\pi^{-}$ describe the experimental data up to 1 GeV well, with the resonance contributions from the $f_{0}(980)$ and $\rho$. For the predicted invariant mass distributions of the $B^{0} \rightarrow \phi\pi^{+}\pi^{-}$ decay, we found that the contributions from the $f_{0}(500)$ are significant except for the ones from the $f_{0}(980)$ state. With some experimental branching ratios as input to determine the production vertex factors, we make some predictions for the branching ratios of the other final decay channels, including the vector mesons, in the $B^0_{(s)}$ decays, where some of them are consistent with the experimental ones within the uncertainties., Comment: More comments are added

Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from the data on which they are trained. In this paper, we present evidence of such undesirable biases towards mentions of disability in two different English language models: toxicity prediction and sentiment analysis. Next, we demonstrate that the neural embeddings that are the critical first step in most NLP pipelines similarly contain undesirable biases towards mentions of disability. We end by highlighting topical biases in the discourse about disability which may contribute to the observed model biases; for instance, gun violence, homelessness, and drug addiction are over-represented in texts discussing mental illness., Comment: ACL 2020 short paper. 5 pages

We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples. Our method requires only a single noisy realization of each training example and a statistical model of the noise distribution, and is applicable to a wide variety of noise models, including spatially structured noise. Our model produces results which are competitive with other learned methods which require richer training data, and outperforms traditional non-learned denoising methods. We present derivations of our method for arbitrary additive noise, an improvement specific to Gaussian additive noise, and an extension to multiplicative Bernoulli noise.

Eddy current testing (ECT) is one of the most popular Nondestructive Testing (NDT) techniques, especially for conductive materials. Reconstructing the crack profile from measured EC signals is one of the main goals of ECT. This task is highly challenging, as the EC signals are nonlinear responses resulted from the presence of cracks, and reconstructing the crack profile requires establishing the forward model of the nonlinear electromagnetic dynamics and solving its inverse problem, which is an ill-posed numerical optimization problem. Instead of solving the inverse problem numerically, we propose to directly learn the inverse mapping from EC signals to crack profiles with a deep encoder-decoder convolutional neural network named EddyNet. EddyNet is trained on a set of randomly generated crack profiles and the corresponding simulated EC responses generated from a realistic forward model. On the held-out test data, EddyNet achieved a mean absolute error of 0.198 between predicted profiles and ground truth ones. Qualitatively, the geometries of predicted profiles are visually similar to the ground truth profiles. Our method greatly reduces the usual reliance on domain experts, and the reconstruction is extremely fast both on GPUs and on CPUs. The source code of EddyNet is released on https://github.com/askerlee/EddyNet., Comment: Accepted by Second Workshop on Machine Learning and the Physical Sciences, NeurIPS 2019

This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for JD.com, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step ('response modeling') fits a user-level model for purchase of a product as a function of the user's exposure to ads. The second ('credit allocation') uses the fitted model to allocate the incremental part of the observed purchase due to advertising, to the ads the user is exposed to over the previous T days. To implement step one, we train a Recurrent Neural Network (RNN) on user-level conversion and exposure data. The RNN has the advantage of flexibly handling the sequential dependence in the data in a semi-parametric way. The specific RNN formulation we implement captures the impact of advertising intensity, timing, competition, and user-heterogeneity, which are known to be relevant to ad-response. To implement step two, we compute Shapley Values, which have the advantage of having axiomatic foundations and satisfying fairness considerations. The specific formulation of the Shapley Value we implement respects incrementality by allocating the overall incremental improvement in conversion to the exposed ads, while handling the sequence-dependence of exposures on the observed outcomes. The system is under production at JD.com, and scales to handle the high dimensionality of the problem on the platform (attribution of the orders of about 300M users, for roughly 160K brands, across 200+ ad-types, served about 80B ad-impressions over a typical 15-day period)., Comment: arXiv admin note: text overlap with arXiv:1506.00019 by other authors

Deep learning techniques have demonstrated significant capacity in modeling some of the most challenging real world problems of high complexity. Despite the popularity of deep models, we still strive to better understand the underlying mechanism that drives their success. Motivated by observations that neurons in trained deep nets predict attributes indirectly related to the training tasks, we recognize that a deep network learns representations more general than the task at hand to disentangle impacts of multiple confounding factors governing the data, in order to isolate the effects of the concerning factors and optimize a given objective. Consequently, we propose a general framework to augment training of deep models with information on auxiliary explanatory data variables, in an effort to boost this disentanglement and train deep networks that comprehend the data interactions and distributions more accurately, and thus improve their generalizability. We incorporate information on prominent auxiliary explanatory factors of the data population into existing architectures as secondary objective/loss blocks that take inputs from hidden layers during training. Once trained, these secondary circuits can be removed to leave a model with the same architecture as the original, but more generalizable and discerning thanks to its comprehension of data interactions. Since pose is one of the most dominant confounding factors for object recognition, we apply this principle to instantiate a pose-aware deep convolutional neural network and demonstrate that auxiliary pose information indeed improves the classification accuracy in our experiments on SAR target classification tasks.

Efficient indexing and searching of high dimensional data has been an area of active research due to the growing exploitation of high dimensional data and the vulnerability of traditional search methods to the curse of dimensionality. This paper presents a new approach for fast and effective searching and indexing of high dimensional features using random partitions of the feature space. Experiments on both handwritten digits and 3-D shape descriptors have shown the proposed algorithm to be highly effective and efficient in indexing and searching real data sets of several hundred dimensions. We also compare its performance to that of the state-of-the-art locality sensitive hashing algorithm.

The low-lying isovector dipole strengths in neutron rich nuclei $^{26}$Ne and $^{28}$Ne are investigated in the quasiparticle relativistic random phase approximation. Nuclear ground state properties are calculated in an extended relativistic mean-field theory plus BCS method where the contribution of the resonant continuum to pairing correlations is properly treated. Numerical calculations are tested in the case of isovector dipole and isoscalar quadrupole modes in the neutron rich nucleus $^{22}$O. It is found that in present calculation low-lying isovector dipole strengths at $E_x < 10$ MeV in nuclei $^{26}$Ne and $^{28}$Ne exhaust about 4.9% and 5.8% of the Thomas-Reiche-Kuhn dipole sum rule, respectively. The centroid energy of the low-lying dipole excitation is located at 8.3 MeV in $^{26}$Ne and 7.9 MeV in $^{28}$Ne., Comment: 20pages 5 figures. Accepted for publication in Physical Review C

The soft dipole modes in neutron rich even-even Ni-isotopes are investigated in the quasiparticle relativistic random phase approximation. We study the evolution of strengths distribution, centroid energies of dipole excitation in low-lying and normal GDR regions with the increase of the neutron excess. It is found in the present study that the centroid energies of the soft dipole strengths strongly depend on the thickness of neutron skin along with the neutron rich even-even Ni-isotopes., Comment: 14 pages, 7 figures

The isospin dependence of the nucleon effective mass is investigated in the framework of the Dirac Brueckner-Hartree-Fock (DBHF) approach. The definition of nucleon scalar and vector effective masses in the relativistic approach is clarified. Only the vector effective mass is the quantity related to the empirical value extracted from the analysis in the nonrelatiistic shell and optical potentials. In the relativistic mean field theory, where the nucleon scalar and vector potentials are both energy independent, the neutron vector potential is stronger than that of proton in the neutron rich nuclear matter, which produces a smaller neutron vector effective mass than that of proton. It is pointed out that the energy dependence of nucleon potentials has to be considered in the analysis of the isospin dependence of the nucleon effective mass. In the DBHF the neutron vector effective mass is larger than that of proton once the energy dependence of nucleon potentials is considered. The results are consistent with the analysis of phenomenological isospin dependent optical potentials., Comment: 4 pages, 3 Postscript figures

Gamow-Teller(GT) resonances in finite nuclei are studied in a fully consistent relativistic random phase approximation (RPA) framework. A relativistic form of the Landau-Migdal contact interaction in the spin-isospin channel is adopted. This choice ensures that the GT excitation energy in nuclear matter is correctly reproduced in the non-relativistic limit. The GT response functions of doubly magic nuclei $^{48}$Ca, $^{90}$Zr and $^{208}$Pb are calculated using the parameter set NL3 and $g_0'$=0.6 . It is found that effects related to Dirac sea states account for a reduction of 6-7 % in the GT sum rule., Comment: 9 pages, 1 figure

A proper treatment of the resonant continuum is to take account of not only the energy of the resonant state, but also its width. The effect of the resonant states on pairing correlations is presented based on the relativistic mean field theory plus Bardeen-Cooper-Schrieffer(BCS) approximation with a constant pairing strength. The study is performed in an effective Lagrangian with the parameter set NL3 for neutron rich even-even Ni isotopes. The results show that the contribution of the proper treatment of the resonant continuum to pairing correlations for those nuclei close to neutron drip line is important. The pairing gaps, Fermi energies, pairing correlation energies, and binding energies are considerably affected with a proper consideration of the width of resonant states. The problem of an unphysical particle gas, which may appear in the calculation of the traditional mean field plus BCS method for nuclei in the vicinity of drip line could be well overcome when the pairing correlation is performed by using the resonant states instead of the discretized states in the continuum., Comment: 19 pages, 8 Postscript figure

A fully consistent relativistic random phase approximation is applied to study the systematic behavior of the isovector giant dipole resonance of nuclei along the $\beta$-stability line in order to test the effective Lagrangians recently developed. The centroid energies of response functions of the isovector giant dipole resonance for stable nuclei are compared with the corresponding experimental data and the good agreement is obtained. It is found that the effective Lagrangian with an appropriate nuclear symmetry energy, which can well describe the ground state properties of nuclei, could also reproduce the isovector giant dipole resonance of nuclei along the $\beta$-stability line., Comment: 4 pages, 1 Postscript figure, to be submitted to Chin.Phys.Lett

The matrix model with mass term has a nontrivially classical solution which is known to represent a noncommutative fuzzy sphere. The fuzzy sphere has a lower energy then that of the trivial solution. In this letter we investigate the quantum correction of the energy of the fuzzy sphere by using the Gaussian variational technique, in contrast to the other studying in which only the small fluctuation was considered. Our result, which only considering the boson part, shows that the quantum correction does not change the stability of the fuzzy sphere., Comment: Short summary of the Master-thesis

Single-particle resonant-states in the continuum are determined by solving scattering states of the Dirac equation with proper asymptotic conditions in the relativistic mean field theory (RMF). The regular and irregular solutions of the Dirac equation at a large radius where the nuclear potentials vanish are relativistic Coulomb wave functions, which are calculated numerically. Energies, widths and wave functions of single-particle resonance states in the continuum for ^{120}Sn are studied in the RMF with the parameter set of NL3. The isoscalar giant octupole resonance of ^{120}Sn is investigated in a fully consistent relativistic random phase approximation. Comparing the results with including full continuum states and only those single-particle resonances we find that the contributions from those resonant-states dominate in the nuclear giant resonant processes., Comment: 16 pages, 2 figures

A new decomposition of the Dirac structure of nucleon self-energies in the Dirac Brueckner-Hartree-Fock (DBHF) approach is adopted to investigate the equation of state for asymmetric nuclear matter. The effective coupling constants of $\sigma $, $\omega $, $\delta $ and $\rho $ mesons with a density dependence in the relativistic mean field approach are deduced by reproducing the nucleon self-energy resulting from the DBHF at each density for symmetric and asymmetric nuclear matter. With these couplings the properties of finite nuclei are investigated. The agreement of charge radii and binding energies of finite nuclei with the experimental data are improved simultaneously in comparison with the projection method. It seems that the properties of finite nuclei are sensitive to the scheme used for the DBHF self-energy extraction. We may conclude that the properties of the asymmetric nuclear matter and finite nuclei could be well described by the new decomposition approach of the G matrix., Comment: 16 pages, 5 figures