239 research outputs found
Hybrid Base Complex: Extract and Visualize Structure of Hex-dominant Meshes
Hex-dominant mesh generation has received significant attention in recent
research due to its superior robustness compared to pure hex-mesh generation
techniques. In this work, we introduce the first structure for analyzing
hex-dominant meshes. This structure builds on the base complex of pure
hex-meshes but incorporates the non-hex elements for a more comprehensive and
complete representation. We provide its definition and describe its
construction steps. Based on this structure, we present an extraction and
categorization of sheets using advanced graph matching techniques to handle the
non-hex elements. This enables us to develop an enhanced visual analysis of the
structure for any hex-dominant meshes.We apply this structure-based visual
analysis to compare hex-dominant meshes generated by different methods to study
their advantages and disadvantages. This complements the standard quality
metric based on the non-hex element percentage for hex-dominant meshes.
Moreover, we propose a strategy to extract a cleaned (optimized) valence-based
singularity graph wireframe to analyze the structure for both mesh and sheets.
Our results demonstrate that the proposed hybrid base complex provides a coarse
representation for mesh element, and the proposed valence singularity graph
wireframe provides a better internal visualization of hex-dominant meshes.Comment: accepted by IEEE Transactions on Visualization and Computer Graphic
The combined effect of foreign direct investment on firm productivity
This paper attempts to answer the economic implications of combining
inward foreign direct investment (IFDI) and outward foreign
direct investment (OFDI) by constructing a panel fixed
effects model using Chinese industrial firm-level data for the
period 1998–2013. Specifically, we focus on the impact of combining
IFDI and OFDI on firm productivity in China. We also introduce
interactive terms into the model to explore the direct and
indirect mechanisms through which IFDI and OFDI affect productivity
growth. The results show that IFDI and OFDI work together
to contribute to productivity growth by acting directly on the
level of technology, thereby increasing productivity. IFDI intensifies
market concentration, which in turn positively moderates the
relationship between OFDI and productivity. Furthermore, IFDI
moderates the financing constraints of firms, but has a weaker
effect; the easing of financing constraints facilitates the positive
impact of OFDI on productivity. Absorptive capacity favours IFDI
spillover, but OFDI inhibits absorptive capacity improvements. Our
in-depth analysis of the mechanism of the combined impact of
IFDI and OFDI on productivity reveals the objectives of using this
combination, thereby providing theoretical support and policy
recommendations for the implementation of this strategy
Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer
entity in a knowledge base which is several hops from the topic entity
mentioned in the question. Existing Retrieval-based approaches first generate
instructions from the question and then use them to guide the multi-hop
reasoning on the knowledge graph. As the instructions are fixed during the
whole reasoning procedure and the knowledge graph is not considered in
instruction generation, the model cannot revise its mistake once it predicts an
intermediate entity incorrectly. To handle this, we propose KBIGER(Knowledge
Base Iterative Instruction GEnerating and Reasoning), a novel and efficient
approach to generate the instructions dynamically with the help of reasoning
graph. Instead of generating all the instructions before reasoning, we take the
(k-1)-th reasoning graph into consideration to build the k-th instruction. In
this way, the model could check the prediction from the graph and generate new
instructions to revise the incorrect prediction of intermediate entities. We do
experiments on two multi-hop KBQA benchmarks and outperform the existing
approaches, becoming the new-state-of-the-art. Further experiments show our
method does detect the incorrect prediction of intermediate entities and has
the ability to revise such errors.Comment: Accepted by NLPCC 2022(oral
Chinese Teachers’ Perceptions on Implementation of CLT in College Business English Class
This qualitative study investigates teachers’ perceptions and challenges of the implementation of Communicative Language Teaching. The participants were nine Business English teachers at a private college in Chengdu, China. The data was collected through semi-structured interviews. The findings revealed that the majority of participants have favourable perceptions of CLT. However, participants mentioned teacher-related challenges, student-related challenges, and policy-related challenges that hinder their implementation of CLT in Business English classes. The findings of this study are beneficial to the field of CLT in China, especially in the English for Specific Purpose context. The recommendations for future studies are discussed
Constructing Carrollian Field Theories from Null Reduction
In this paper, we propose a novel way to construct off-shell actions of
-dimensional Carrollian field theories by considering the null-reduction of
the Bargmann invariant actions in dimensions. This is based on the fact
that -dimensional Carrollian symmetry is the restriction of the
-dimensional Bargmann symmetry to a null hyper-surface. We focus on free
scalar field theory and electromagnetic field theory, and show that the
electric and magnetic sectors of these theories originate from different
Bargmann invariant actions in one higher dimension. In the cases of the
massless free scalar field and electromagnetic field, we verify
Carrollian conformal invariance of the resulting theories, and find that there
appear naturally chain representations and staggered modules of Carrollian
conformal algebra.Comment: 59 pages, major revisions, results unchange
CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement
Visual geolocalization is a cost-effective and scalable task that involves
matching one or more query images, taken at some unknown location, to a set of
geo-tagged reference images. Existing methods, devoted to semantic features
representation, evolving towards robustness to a wide variety between query and
reference, including illumination and viewpoint changes, as well as scale and
seasonal variations. However, practical visual geolocalization approaches need
to be robust in appearance changing and extreme viewpoint variation conditions,
while providing accurate global location estimates. Therefore, inspired by
curriculum design, human learn general knowledge first and then delve into
professional expertise. We first recognize semantic scene and then measure
geometric structure. Our approach, termed CurriculumLoc, involves a delicate
design of multi-stage refinement pipeline and a novel keypoint detection and
description with global semantic awareness and local geometric verification. We
rerank candidates and solve a particular cross-domain perspective-n-point (PnP)
problem based on these keypoints and corresponding descriptors, position
refinement occurs incrementally. The extensive experimental results on our
collected dataset, TerraTrack and a benchmark dataset, ALTO, demonstrate that
our approach results in the aforementioned desirable characteristics of a
practical visual geolocalization solution. Additionally, we achieve new high
recall@1 scores of 62.6% and 94.5% on ALTO, with two different distances
metrics, respectively. Dataset, code and trained models are publicly available
on https://github.com/npupilab/CurriculumLoc.Comment: 14 pages, 15 figure
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