Role of inter-particle friction in granular materials under three dimensional conditions

The inter-particle friction is known to be an important contributor to the strength and deformation characteristics in granular materials. The mechanism of inter-particle friction to the macroscopic responses can be explained by microscopic investigations. Based on the discrete element method (DEM), a series of true triaxial tests for the cubic granular assembly are carried out and the effects of inter-particle friction coefficient (μ) on the evolutions of macro- and micromechanical parameters of granular materials are studied. The macroscopic stress, the distribution of coordination numbers and contact force with regard to strong and weak contact networks are concerned, as well as the corresponding fabric tensor and anisotropies. Findings indicate that increasing inter-particle friction sharpens the peak value of deviatoric stress and enhances the degree of dilatancy of the granular assembly at the macroscopic level. From the microscopic perspective, the distribution of the coordination number of the weak contact system varies dramatically, while the number of particles with smaller coordination number in the strong contact system changes little with different μ. Besides, the difference between strong and weak contact networks is enlarged, and anisotropy indicators are significantly enhanced, which strengthen the bearing ability of anisotropic stresses in granular materials

I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset

A simile is a figure of speech that compares two different things (called the tenor and the vehicle) via shared properties. The tenor and the vehicle are usually connected with comparator words such as "like" or "as". The simile phenomena are unique and complex in a real-life dialogue scene where the tenor and the vehicle can be verbal phrases or sentences, mentioned by different speakers, exist in different sentences, or occur in reversed order. However, the current simile research usually focuses on similes in a triplet tuple (tenor, property, vehicle) or a single sentence where the tenor and vehicle are usually entities or noun phrases, which could not reflect complex simile phenomena in real scenarios. In this paper, we propose a novel and high-quality multilingual simile dialogue (MSD) dataset to facilitate the study of complex simile phenomena. The MSD is the largest manually annotated simile data ($\sim$20K) and it contains both English and Chinese data. Meanwhile, the MSD data can also be used on dialogue tasks to test the ability of dialogue systems when using similes. We design 3 simile tasks (recognition, interpretation, and generation) and 2 dialogue tasks (retrieval and generation) with MSD. For each task, we provide experimental results from strong pre-trained or state-of-the-art models. The experiments demonstrate the challenge of MSD and we have released the data/code on GitHub.Comment: 13 Pages, 1 Figure, 12 Tables, ACL 2023 finding