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Abstract The interplay between a multitude of electronic, spin, and lattice degrees of freedom underlies the complex phase diagrams of quantum materials. Layer stacking in van der Waals (vdW) heterostructures is responsible for exotic electronic and magnetic properties, which inspires stacking control of two-dimensional magnetism. Beyond the interplay between stacking order and interlayer magnetism, we discover a spin-shear coupling mechanism in which a subtle shear of the atomic layers can have a profound effect on the intralayer magnetic order in a family of vdW antiferromagnets. Using time-resolved X-ray diffraction and optical linear dichroism measurements, interlayer shear is identified as the primary structural degree of freedom that couples with magnetic order. The recovery times of both shear and magnetic order upon optical excitation diverge at the magnetic ordering temperature with the same critical exponent. The time-dependent Ginzburg-Landau theory shows that this concurrent critical slowing down arises from a linear coupling of the interlayer shear to the magnetic order, which is dictated by the broken mirror symmetry intrinsic to the monoclinic stacking. Our results highlight the importance of interlayer shear in ultrafast control of magnetic order via spin-mechanical coupling.

Sequential recommendation is the task of predicting the next items for users based on their interaction history. Modeling the dependence of the next action on the past actions accurately is crucial to this problem. Moreover, sequential recommendation often faces serious sparsity of item-to-item transitions in a user's action sequence, which limits the practical utility of such solutions. To tackle these challenges, we propose a Category-aware Collaborative Sequential Recommender. Our preliminary statistical tests demonstrate that the in-category item-to-item transitions are often much stronger indicators of the next items than the general item-to-item transitions observed in the original sequence. Our method makes use of item category in two ways. First, the recommender utilizes item category to organize a user's own actions to enhance dependency modeling based on her own past actions. It utilizes self-attention to capture in-category transition patterns, and determines which of the in-category transition patterns to consider based on the categories of recent actions. Second, the recommender utilizes the item category to retrieve users with similar in-category preferences to enhance collaborative learning across users, and thus conquer sparsity. It utilizes attention to incorporate in-category transition patterns from the retrieved users for the target user. Extensive experiments on two large datasetsmore »prove the effectiveness of our solution against an extensive list of state-of-the-art sequential recommendation models.« less

Moiré superlattices of twisted nonmagnetic two-dimensional (2D) materials are highly controllable platforms for the engineering of exotic correlated and topological states. Here, we report emerging magnetic textures in small-angle twisted 2D magnet chromium triiodide (CrI 3 ). Using single-spin quantum magnetometry, we directly visualized nanoscale magnetic domains and periodic patterns, a signature of moiré magnetism, and measured domain size and magnetization. In twisted bilayer CrI 3 , we observed the coexistence of antiferromagnetic (AFM) and ferromagnetic (FM) domains with disorder-like spatial patterns. In twisted double-trilayer CrI 3 , AFM and FM domains with periodic patterns appear, which is in good agreement with the calculated spatial magnetic structures that arise from the local stacking-dependent interlayer exchange interactions in CrI 3 moiré superlattices. Our results highlight magnetic moiré superlattices as a platform for exploring nanomagnetism.