649 research outputs found
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\%
- …