Mechanism of metal-insulator transition (MIT) in strained VO2 thin films is very complicated and incompletely understood despite three scenarios with potential explanations including electronic correlation (Mott mechanism), structural transformation (Peierls theory) and collaborative Mott-Peierls transition. Herein, we have decoupled coactions of structural and electronic phase transitions across the MIT by implementing epitaxial strain on 13-nm-thick (001)-VO2 films in comparison to thicker films. The structural evolution during MIT characterized by temperature-dependent synchrotron radiation high-resolution X-ray diffraction reciprocal space mapping and Raman spectroscopy suggested that the structural phase transition in the temperature range of vicinity of the MIT is suppressed by epitaxial strain. Furthermore, temperature-dependent Ultraviolet Photoelectron Spectroscopy (UPS) revealed the changes in electron occupancy near the Fermi energy EF of V 3d orbital, implying that the electronic transition triggers the MIT in the strained films. Thus the MIT in the bi-axially strained VO2 thin films should be only driven by electronic transition without assistance of structural phase transition. Density functional theoretical calculations further confirmed that the tetragonal phase across the MIT can be both in insulating and metallic states in the strained (001)-VO2/TiO2 thin films. This work offers a better understanding of the mechanism of MIT in the strained VO2 films.

RGBT tracking usually suffers from various challenge factors, such as fast motion, scale variation, illumination variation, thermal crossover and occlusion, to name a few. Existing works often study fusion models to solve all challenges simultaneously, and it requires fusion models complex enough and training data large enough, which are usually difficult to be constructed in real-world scenarios. In this work, we disentangle the fusion process via the challenge attributes, and thus propose a novel Attribute-based Progressive Fusion Network (APFNet) to increase the fusion capacity with a small number of parameters while reducing the dependence on large-scale training data. In particular, we design five attribute-specific fusion branches to integrate RGB and thermal features under the challenges of thermal crossover, illumination variation, scale variation, occlusion and fast motion respectively. By disentangling the fusion process, we can use a small number of parameters for each branch to achieve robust fusion of different modalities and train each branch using the small training subset with the corresponding attribute annotation. Then, to adaptive fuse features of all branches, we design an aggregation fusion module based on SKNet. Finally, we also design an enhancement fusion transformer to strengthen the aggregated feature and modality-specific features. Experimental results on benchmark datasets demonstrate the effectiveness of our APFNet against other state-of-the-art methods.

Insulating antiferromagnets have recently emerged as efficient and robust conductors of spin current. Element-specific and phase-resolved x-ray ferromagnetic resonance has been used to probe the injection and transmission of ac spin current through thin epitaxial NiO(001) layers. The spin current is found to be mediated by coherent evanescent spin waves of GHz frequency, rather than propagating magnons of THz frequency, paving the way towards coherent control of the phase and amplitude of spin currents within an antiferromagnetic insulator at room temperature.