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Gigahertz Surface Acoustic Wave Topological Rainbow in Nanoscale Phononic Crystals.

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December 2024

Nanjing University, National Laboratory of Solid State Microstructures & Department of Materials Science and Engineering, Nanjing 210093, China.

Precisely engineered gigahertz surface acoustic wave (SAW) trapping enables diverse and controllable interconnections with various quantum systems, which are crucial to unlocking the full potential of phonons. The topological rainbow based on synthetic dimension presents a promising avenue for facile and precise localization of SAWs. In this study, we successfully developed a monolithic gigahertz SAW topological rainbow by utilizing a nanoscale translational deformation as a synthetic dimension.

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Analytical Model for Atomic Relaxation in Twisted Moiré Materials.

Phys Rev Lett

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National University of Singapore, Department of Materials Science and Engineering, 9 Engineering Drive 1, Singapore 117575.

By virtue of being atomically thin, the electronic properties of heterostructures built from two-dimensional materials are strongly influenced by atomic relaxation. The atomic layers behave as flexible membranes rather than rigid crystals. Here we develop an analytical theory of lattice relaxation in twisted moiré materials.

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Z boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from standard model predictions. All previous measurements of Z boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins.

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scHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.

Bioinformatics

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School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.

Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.

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