78 research outputs found
Research on Regional Imbalance of Cross-Border E-Commerce --Take Fujian Free Trade Zone as an Example
This article takes the information data era, world trade, and the deep integration of the âBelt and Roadâ policy as the background. Using questionnaires and compare to analyze the cross-border e-commerce regional issues in Fujian Free Trade Area, based on the analysis of cross-border e-commerce differences between Fujian, Shanghai and Zhengzhou and the comparative analysis of crossborder e-commerce platforms, it establishes a cross-border e-commerce service quality gap model to further identify problems. This article points out the current development trend of cross-border e-commerce zone in Fujian Free Trade Area, and the problems and the development shortcomings. Utilizing the advantages of cross-border e-commerce and its platform in Shanghai and Zhengzhou Free Trade Area to improve the imbalance of cross-border e-commerce in the Fujian Free Trade Zone, It has come up with actions and recommendations for building a multi-level network, an open credit platform, and a service quality evaluation system
Solutions of stationary Kirchhoff equations involving nonlocal operators with critical nonlinearity in RN
In this paper, we consider the existence and multiplicity of solutions for fractional Schrödinger equations with critical nonlinearity in RN. We use the fractional version of Lions' second concentration-compactness principle and concentration-compactness principle at infinity to prove that (PSc) condition holds locally. Under suitable assumptions, we prove that it has at least one solution and, for any m â N, it has at least m pairs of solutions. Moreover, these solutions can converge to zero in some Sobolev space as Δ â 0
Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort
Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-II (NSGA-II), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the governmen
H2-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation
Constructing a high-quality dense map in real-time is essential for robotics,
AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly
improves the mapping performance, in this paper, we propose a NeRF-based
mapping method that enables higher-quality reconstruction and real-time
capability even on edge computers. Specifically, we propose a novel
hierarchical hybrid representation that leverages implicit multiresolution hash
encoding aided by explicit octree SDF priors, describing the scene at different
levels of detail. This representation allows for fast scene geometry
initialization and makes scene geometry easier to learn. Besides, we present a
coverage-maximizing keyframe selection strategy to address the forgetting issue
and enhance mapping quality, particularly in marginal areas. To the best of our
knowledge, our method is the first to achieve high-quality NeRF-based mapping
on edge computers of handheld devices and quadrotors in real-time. Experiments
demonstrate that our method outperforms existing NeRF-based mapping methods in
geometry accuracy, texture realism, and time consumption. The code will be
released at: https://github.com/SYSU-STAR/H2-MappingComment: Accepted by IEEE Robotics and Automation Letter
Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification
Occluded person re-identification (ReID) is a challenging problem due to
contamination from occluders, and existing approaches address the issue with
prior knowledge cues, eg human body key points, semantic segmentations and etc,
which easily fails in the presents of heavy occlusion and other humans as
occluders. In this paper, we propose a feature pruning and consolidation (FPC)
framework to circumvent explicit human structure parse, which mainly consists
of a sparse encoder, a global and local feature ranking module, and a feature
consolidation decoder. Specifically, the sparse encoder drops less important
image tokens (mostly related to background noise and occluders) solely
according to correlation within the class token attention instead of relying on
prior human shape information. Subsequently, the ranking stage relies on the
preserved tokens produced by the sparse encoder to identify k-nearest neighbors
from a pre-trained gallery memory by measuring the image and patch-level
combined similarity. Finally, we use the feature consolidation module to
compensate pruned features using identified neighbors for recovering essential
information while disregarding disturbance from noise and occlusion.
Experimental results demonstrate the effectiveness of our proposed framework on
occluded, partial and holistic Re-ID datasets. In particular, our method
outperforms state-of-the-art results by at least 8.6% mAP and 6.0% Rank-1
accuracy on the challenging Occluded-Duke dataset.Comment: 12 pages, 9 figure
Progressive Text-to-Image Diffusion with Soft Latent Direction
In spite of the rapidly evolving landscape of text-to-image generation, the
synthesis and manipulation of multiple entities while adhering to specific
relational constraints pose enduring challenges. This paper introduces an
innovative progressive synthesis and editing operation that systematically
incorporates entities into the target image, ensuring their adherence to
spatial and relational constraints at each sequential step. Our key insight
stems from the observation that while a pre-trained text-to-image diffusion
model adeptly handles one or two entities, it often falters when dealing with a
greater number. To address this limitation, we propose harnessing the
capabilities of a Large Language Model (LLM) to decompose intricate and
protracted text descriptions into coherent directives adhering to stringent
formats. To facilitate the execution of directives involving distinct semantic
operations-namely insertion, editing, and erasing-we formulate the Stimulus,
Response, and Fusion (SRF) framework. Within this framework, latent regions are
gently stimulated in alignment with each operation, followed by the fusion of
the responsive latent components to achieve cohesive entity manipulation. Our
proposed framework yields notable advancements in object synthesis,
particularly when confronted with intricate and lengthy textual inputs.
Consequently, it establishes a new benchmark for text-to-image generation
tasks, further elevating the field's performance standards.Comment: 14 pages, 15 figure
Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms
This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The modelâs performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patientsâ average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses
- âŠ