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Bamboo-like CoCu/Cu multilayer nanowires have been successfully fabricated into anodic aluminium oxide templates by using an electrodeposition method, and their chemistry and crystal structure characterised at the nanoscale. Energy-dispersive X-ray analysis indicated that the chemical composition of the regular periodic CoCu/Cu nanowires was Co 81 Cu 19 /Cu. Diffraction analysis revealed that Cu layers 10 and Co-rich layers exhibited polycrystalline fcc structure. A twin relationship of Cu layer {111} planes stacking on Co 81 Cu 19 layer {111} planes was observed at the lattice-resolution level. The magnetic properties analysed by experimental and theoretic simulations showed that there was a transition from a curling rotation mode to coherent rotation mode in our Co 81 Cu 19 /Cu multilayer nanowires when the angle between external field and nanowire length axis increased from 0° to 90°. This study highlights basic 15 morphological, chemical, structural information and magnetic reversal mechanism of CoCu/Cu nanowires, which are critical for the applications of multilayer nanowires in nanoscale sensors and electronics. 65 to the morphology, structure, chemistry and grain size of individual magnetic and non-magnetic layers. Besides, the performance of Co-Cu multilayer structures prepared by electrodeposition (ED) method [16][17][18] 22 is worse than that of physical deposition (PD) method. 26, 27 A number of published 70 work were devoted to figure out the intrinsic reasons between the GMR effect, magnetic properties of PD Co-Cu multilayer structures and their nanoscale characterisation, including morphology, chemistry, structure, interfaces and grain sizes. 26,27 However, the systemically nanoscale characterisation of 1D ED 75

With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Existing methods mostly adopt heuristic or attention-based preference aggregation strategies to synthesize group preferences. However, these models mainly focus on the pairwise connections of users and ignore the complex high-order interactions within and beyond groups. Besides, group recommendation suffers seriously from the problem of data sparsity due to severely sparse group-item interactions. In this paper, we propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals: (1) capturing the intraand inter-group interactions among users; (2) alleviating the data sparsity issue with the raw data itself. Technically, for (1), a hierarchical hypergraph convolutional network based on the userand group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups. For (2), we design a double-scale node dropout strategy to create selfsupervision signals that can regularize user representations with different granularities against the sparsity issue. The experimental analysis on multiple benchmark datasets demonstrates the superiority of the proposed model and also elucidates the rationality of the hypergraph modeling and the double-scale self-supervision. CCS CONCEPTS• Information systems → Recommender systems.

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