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Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains. However, constructing an end-to-end graph structure learning model poses a challenge due to the impediment of gradient flow caused by the nearest neighbor sampling strategy. In this paper, we construct a differential graph structure learning model by replacing the non-differentiable nearest neighbor sampling with a differentiable sampling using the reparameterization trick. Under this framework, we argue that the act of sampling \mbox{nearest} neighbors may not invariably be essential, particularly in instances where node features exhibit a significant degree of similarity. To alleviate this issue, the bell-shaped Gaussian Similarity (GauSim) modeling is proposed to sample non-nearest neighbors. To adaptively model the similarity, we further propose Neural Gaussian Similarity (NeuralGauSim) with learnable parameters featuring flexible sampling behaviors. In addition, we develop a scalable method by transferring the large-scale graph to the transition graph to significantly reduce the complexity. Experimental results demonstrate the effectiveness of the proposed methods., Comment: Accepted by AAAI 2024

We propose a new framework that formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation. During training stage, object transformation diffuses from ground-truth transformation to random distribution, and the model learns to reverse this noising process. In sampling stage, the model refines randomly generated transformation to the output result in a progressive way. We derive the variational bound in closed form for training and provide implementations of the model. Our work provides the following crucial findings: (i) In contrast to most existing methods, our framework, Diffusion Probabilistic Models for Point Cloud Registration (PCRDiffusion) does not require repeatedly update source point cloud to refine the predicted transformation. (ii) Point cloud registration, one of the representative discriminative tasks, can be solved by a generative way and the unified probabilistic formulation. Finally, we discuss and provide an outlook on the application of diffusion model in different scenarios for point cloud registration. Experimental results demonstrate that our model achieves competitive performance in point cloud registration. In correspondence-free and correspondence-based scenarios, PCRDifussion can both achieve exceeding 50\% performance improvements.

The damping of La0.67Sr0.33MnO3 (LSMO) epitaxial films as a function of thickness at different temperatures was studied. The competition between two scattering types (\r{ho}-like and {\sigma}-like) with entirely distinct thickness and temperature dependencies resulted in complicated damping behavior. The behavior of {\sigma}-like damping in LSMO films is consistent with the behavior in magnetic metal films. However, because \r{ho}-like damping is sensitive to the fine electron structure near the Fermi surface, the distortion of the oxygen octahedra controlled by the film thickness is an important factor in controlling the damping. Our study demonstrates that the complexity of damping in LSMO epitaxial films is a consequence of strong-correlation effects, which are characteristic of complex transition-metal oxides.

The interplay between dimensionality and topology manifests in magnetism via both exotic texture morphology and novel dynamics. A free magnetic skyrmion exhibits the skyrmion Hall effect under electric currents. Once it is confined in one-dimensional (1D) channels, the skyrmion Hall effect would be suppressed, and the current-driven skyrmion speed should be boosted by the non-adiabatic spin transfer torque \b{eta}. Here, we experimentally demonstrate that stripes of a spatially modulated spin helix serve as natural 1D channels to restrict skyrmion. Using FeGe as a benchmark, an interstitial skyrmion is created by geometry notch and further moves steadily without the skyrmion Hall effect. The slope of the current-velocity curve for 1D skyrmion is enhanced almost by an order of magnitude owing to a large \b{eta} in FeGe. This feature is also observed in other topological defects. Utilizing the 1D skyrmion dynamics would be a highly promising route to implement topological spintronic devices.

The empirical studies of Graph Neural Networks (GNNs) broadly take the original node feature and adjacency relationship as singleview input, ignoring the rich information of multiple graph views. To circumvent this issue, the multiview graph analysis framework has been developed to fuse graph information across views. How to model and integrate shared (i.e. consistency) and view-specific (i.e. complementarity) information is a key issue in multiview graph analysis. In this paper, we propose a novel Multiview Variational Graph Information Bottleneck (MVGIB) principle to maximize the agreement for common representations and the disagreement for view-specific representations. Under this principle, we formulate the common and view-specific information bottleneck objectives across multiviews by using constraints from mutual information. However, these objectives are hard to directly optimize since the mutual information is computationally intractable. To tackle this challenge, we derive variational lower and upper bounds of mutual information terms, and then instead optimize variational bounds to find the approximate solutions for the information objectives. Extensive experiments on graph benchmark datasets demonstrate the superior effectiveness of the proposed method.

Out-of-plane spin polarization {\sigma}_z has attracted increasing interests of researchers recently, due to its potential in high-density and low-power spintronic devices. Noncollinear antiferromagnet (AFM), which has unique 120{\deg} triangular spin configuration, has been discovered to possess {\sigma}_z. However, the physical origin of {\sigma}_z in noncollinear AFM is still not clear, and the external magnetic field-free switching of perpendicular magnetic layer using the corresponding {\sigma}_z has not been reported yet. Here, we use the cluster magnetic octupole in antiperovskite AFM Mn3SnN to demonstrate the generation of {\sigma}_z. {\sigma}_z is induced by the precession of carrier spins when currents flow through the cluster magnetic octupole, which also relies on the direction of the cluster magnetic octupole in conjunction with the applied current. With the aid of {\sigma}_z, current induced spin-orbit torque (SOT) switching of adjacent perpendicular ferromagnet is realized without external magnetic field. Our findings present a new perspective to the generation of out-of-plane spin polarizations via noncollinear AFM spin structure, and provide a potential path to realize ultrafast high-density applications., Comment: 23 pages, 4 figures, accepted by Nat. Commun

Current-induced spin torques provide efficient data writing approaches for magnetic memories. Recently, the spin splitting torque (SST) was theoretically predicted (R. Gonz\'alez-Hern\'andez et al. Phys. Rev. Lett. 126, 127701 (2021)), which combines advantages of conventional spin transfer torque (STT) and spin-orbit torque (SOT) as well as enables controllable spin polarization. Here we provide the experimental evidence of SST in collinear antiferromagnet RuO2 films. The spin current direction is found to be correlated to the crystal orientation of RuO2 and the spin polarization direction is dependent on (parallel to) the N\'eel vector. These features are quite characteristic for the predicted SST. Our finding not only present a new member for the spin torques besides traditional STT and SOT, but also proposes a promising spin source RuO2 for spintronics., Comment: 18 pages, 4 figures, accepted by Phys. Rev. Lett

Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture information from the topology view but ignore the feature view. To circumvent this issue, we propose a novel approach by exploiting mutual information maximization across feature and topology views. Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we then develop a common representation learning module using mutual information maximization and reconstruction loss minimization. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, our proposed method can achieve comparable or even better performance under the unsupervised representation and linear evaluation protocol.

We systematically studied the magnetoresistance effect in a Pt/(CoNi)n multilayer system with perpendicular magnetic anisotropy and the fcc (111) texture. The angular dependence of magnetoresistance, including high-order cosine terms, was observed in a plane perpendicular to the electrical current; this was attributed to the geometrical-size effects caused by crystal symmetry, the ordered arrangement of grains, and the anisotropic interface magnetoresistance effect caused by the breaking of the symmetry at interfaces. Based on the accuracy of our experimental results, the magnitude of spin Hall magnetoresistance (SMR) in Pt/(CoNi)n was expected to be below $1\times10^{-4}$. However, on evaluating the spin Hall angle of $\geq$ 0.07 for Pt using spin-torque ferromagnetic resonance measurements, the theoretical magnitude of SMR in our samples was estimated to exceed $7\times10^{-4}$. This absence of SMR in the experimental results can be explained by the Elliott-Yafet spin relaxation of itinerant electrons in the ferromagnetic metal, which indicates that the boundary conditions of the spin current in the heavy metal/ferromagnetic insulator may not be applicable to all-metallic heterostructures., Comment: 16 pages,5 figures

In this paper, we report the observation of spin-orbit magnetoresistance (SOMR) in ferromagnetic metal/heavy metal/MgO system. We measure the magnetoresistance as the function of the thickness of heavy metal (HM) for CoFeB/HM/MgO and CoFeB/HM films where HM = Pt and Ta. Besides the conventional spin Hall magnetoresistance (SMR) peak, the evidence of the SOMR is indicated by another peak of the MR ratio when the thickness of HM is around 1 ~ 2 nm for CoFeB/HM/MgO films, which is absent for CoFeB/HM films. We speculate the SOMR observed in our experiment originates from the spin-orbit coupling at the HM/MgO interface. We give the boundary conditions of our samples and calculate the theoretical magnetoresistance based on spin diffusion equation. Based on the theoretical results, we can explain the two peaks we observe separately comes from the spin current generated by spin Hall effect and by Edelstein effect., Comment: 11 pages, 3 figures