Wave propagation in one-dimensional nonlinear acoustic metamaterials

The propagation of waves in the nonlinear acoustic metamaterials (NAMs) is fundamentally different from that in the conventional linear ones. In this article we consider two one-dimensional NAM systems featuring respectively a diatomic and a tetratomic meta unit-cell. We investigate the attenuation of the wave, the band structure and the bifurcations to demonstrate novel nonlinear effects, which can significantly expand the bandwidth for elastic wave suppression and cause nonlinear wave phenomena. Harmonic averaging approach, continuation algorithm, Lyapunov exponents are combined to study the frequency responses, the nonlinear modes, bifurcations of periodic solutions and chaos. The nonlinear resonances are studied and the influence of damping on hyper-chaotic attractors is evaluated. Moreover, a "quantum" behavior is found between the low-energy and high-energy orbits. This work provides an important theoretical base for the further understandings and applications of NAMs

Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification

National Research Foundation (NRF) Singapor

Stress and Strength Analysis of Non-Right Angle H-section Beam

In this paper, according to the design requirements of a steel structural project, based on the principle of structural mechanics of thin-walled bar, the non-right angle H-section, which is subjected to bending moment and shear force, is taken as the object of study, the formulas of bending normal stress and shear stress are deduced. On this basis, the distribution of bending stress and shear stress and the location of dangerous stress are analyzed, the calculation method of section strength is discussed, and the FEA software ABAQUS is used to verify the above.&nbsp

UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning

Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train task-specific models for each task. However, application scenarios of ABSA tasks are often diverse. This solution usually requires a large amount of labeled data from each task to perform excellently. These dedicated models are separately trained and separately predicted, ignoring the relationship between tasks. To tackle these issues, we present UnifiedABSA, a general-purpose ABSA framework based on multi-task instruction tuning, which can uniformly model various tasks and capture the inter-task dependency with multi-task learning. Extensive experiments on two benchmark datasets show that UnifiedABSA can significantly outperform dedicated models on 11 ABSA tasks and show its superiority in terms of data efficiency