Glass transitions in two-dimensional suspensions of colloidal ellipsoids

We observed a two-step glass transition in monolayers of colloidal ellipsoids by video microscopy. The glass transition in the rotational degree of freedom was at a lower density than that in the translational degree of freedom. Between the two transitions, ellipsoids formed an orientational glass. Approaching the respective glass transitions, the rotational and translational fastest-moving particles in the supercooled liquid moved cooperatively and formed clusters with power-law size distributions. The mean cluster sizes diverge in power law as approaching the glass transitions. The clusters of translational and rotational fastest-moving ellipsoids formed mainly within pseudo-nematic domains, and around the domain boundaries, respectively

Exploration of the Application of Electronic Circuit Simulation Technology in Integrated Circuit Design

In the current era of continuously improving technological level, various electronic products have emerged one after another, bringing great convenience to people’s lives. Electronic circuit simulation technology is also widely used in circuit design, which has an important effect on improving the overall quality of circuit design. In addition, the widespread application of electronic circuit simulation technology has further reduced the development time of similar products, ensuring circuit performance. Based on this, this article mainly explores and analyzes the application practice of electronic circuit simulation technology in integrated circuit design, for reference by relevant personnel

Management Power and Corporate Risk Taking: Evidence from China

Abstract Corporate risk-taking is the degree to which a firm demonstrates its tolerance for the risks it faces in order to achieve high returns in the course of its business. Management power influences the choice of business strategy and the preference for risk taking. Corporate risk-taking is not only a matter of improving corporate performance and long-term growth, but also of accumulating social capital. Based on it, Chinese A-share listed firms from 2016 to 2020 are the research objects in this paper. Through both theoretical research and empirical study, this paper investigates the impact of the combined management power and six different dimensions of management power on corporate risk-taking. This provides an in-depth analysis of the mechanism of the role of managerial power on corporate risk-taking behavior. The results of the study show that combined management power, organization power, ownership power, expert power and reputation power have a significant inhibitory effect on the level of corporate risk-taking. This implies that in order to promote a positive level of corporate risk-taking and to accelerate the accumulation of corporate capital, the different latitudes of management power should be appropriately allocated. Different incentive schemes could be used to set up for management. Companies also need to choose their management with a dialectical perspective. This will enhance the level of corporate risk-taking and help to increase the value of the company in the long term

Spontaneous Bending of Hydra Tissue Fragments Driven by Supracellular Actomyosin Bundles

Hydra tissue fragments excised freshly from Hydra body bend spontaneously to some quasi-stable shape in several minutes. We propose that the spontaneous bending is driven mechanically by supracellular actomyosin bundles inherited from parent Hydra. An active-laminated-plate model is constructed, from which we predict that the fragment shape characterized by spontaneous curvature is determined by its anisotropy in contractility and elasticity. The inward bending to endoderm side is ensured by the presence of a soft intermediate matrix (mesoglea) layer. The bending process starts diffusively from the edges and relaxes exponentially to the final quasi-stable shape. Two characteristic time scales are identified from the dissipation due to viscous drag and interlayer frictional sliding, respectively. The former is about 0.01 seconds, but the latter is much larger, about several minutes, consistent with experiments.Comment: 26 pages, 10 figure

Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning

Recent knowledge enhanced pre-trained language models have shown remarkable performance on downstream tasks by incorporating structured knowledge from external sources into language models. However, they usually suffer from a heterogeneous information alignment problem and a noisy knowledge injection problem. For complex reasoning, the contexts contain rich knowledge that typically exists in complex and sparse forms. In order to model structured knowledge in the context and avoid these two problems, we propose to unify structure reasoning and language model pre-training. It identifies four types of elementary knowledge structures from contexts to construct structured queries, and utilizes the box embedding method to conduct explicit structure reasoning along queries during language modeling. To fuse textual and structured semantics, we utilize contextual language representations of knowledge structures to initialize their box embeddings for structure reasoning. We conduct experiments on complex language reasoning and knowledge graph (KG) reasoning tasks. The results show that our model can effectively enhance the performance of complex reasoning of both language and KG modalities.Comment: 10 pages, 4 figures, 6 table

Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation

When applying a pre-trained 2D-to-3D human pose lifting model to a target unseen dataset, large performance degradation is commonly encountered due to domain shift issues. We observe that the degradation is caused by two factors: 1) the large distribution gap over global positions of poses between the source and target datasets due to variant camera parameters and settings, and 2) the deficient diversity of local structures of poses in training. To this end, we combine \textbf{global adaptation} and \textbf{local generalization} in \textit{PoseDA}, a simple yet effective framework of unsupervised domain adaptation for 3D human pose estimation. Specifically, global adaptation aims to align global positions of poses from the source domain to the target domain with a proposed global position alignment (GPA) module. And local generalization is designed to enhance the diversity of 2D-3D pose mapping with a local pose augmentation (LPA) module. These modules bring significant performance improvement without introducing additional learnable parameters. In addition, we propose local pose augmentation (LPA) to enhance the diversity of 3D poses following an adversarial training scheme consisting of 1) a augmentation generator that generates the parameters of pre-defined pose transformations and 2) an anchor discriminator to ensure the reality and quality of the augmented data. Our approach can be applicable to almost all 2D-3D lifting models. \textit{PoseDA} achieves 61.3 mm of MPJPE on MPI-INF-3DHP under a cross-dataset evaluation setup, improving upon the previous state-of-the-art method by 10.2\%