275 research outputs found
A fourth-order unfitted characteristic finite element method for solving the advection-diffusion equation on time-varying domains
We propose a fourth-order unfitted characteristic finite element method to
solve the advection-diffusion equation on time-varying domains. Based on a
characteristic-Galerkin formulation, our method combines the cubic MARS method
for interface tracking, the fourth-order backward differentiation formula for
temporal integration, and an unfitted finite element method for spatial
discretization. Our convergence analysis includes errors of discretely
representing the moving boundary, tracing boundary markers, and the spatial
discretization and the temporal integration of the governing equation.
Numerical experiments are performed on a rotating domain and a severely
deformed domain to verify our theoretical results and to demonstrate the
optimal convergence of the proposed method
MDFL: Multi-domain Diffusion-driven Feature Learning
High-dimensional images, known for their rich semantic information, are
widely applied in remote sensing and other fields. The spatial information in
these images reflects the object's texture features, while the spectral
information reveals the potential spectral representations across different
bands. Currently, the understanding of high-dimensional images remains limited
to a single-domain perspective with performance degradation. Motivated by the
masking texture effect observed in the human visual system, we present a
multi-domain diffusion-driven feature learning network (MDFL) , a scheme to
redefine the effective information domain that the model really focuses on.
This method employs diffusion-based posterior sampling to explicitly consider
joint information interactions between the high-dimensional manifold structures
in the spectral, spatial, and frequency domains, thereby eliminating the
influence of masking texture effects in visual models. Additionally, we
introduce a feature reuse mechanism to gather deep and raw features of
high-dimensional data. We demonstrate that MDFL significantly improves the
feature extraction performance of high-dimensional data, thereby providing a
powerful aid for revealing the intrinsic patterns and structures of such data.
The experimental results on three multi-modal remote sensing datasets show that
MDFL reaches an average overall accuracy of 98.25%, outperforming various
state-of-the-art baseline schemes. The code will be released, contributing to
the computer vision community
Finite-time lag projective synchronization of delayed fractional-order quaternion-valued neural networks with parameter uncertainties
This paper discusses a class issue of finite-time lag projective synchronization (FTLPS) of delayed fractional-order quaternion-valued neural networks (FOQVNNs) with parameter uncertainties, which is solved by a non-decomposition method. Firstly, a new delayed FOQVNNs model with uncertain parameters is designed. Secondly, two types of feedback controller and adaptive controller without sign functions are designed in the quaternion domain. Based on the Lyapunov analysis method, the non-decomposition method is applied to replace the decomposition method that requires complex calculations, combined with some quaternion inequality techniques, to accurately estimate the settling time of FTLPS. Finally, the correctness of the obtained theoretical results is testified by a numerical simulation example
Trust in China: A Cross-Regional Analysis
Using the cross-regional data, this paper shows that trust has a strong effect on uneven development of economy in China. As is discovered in many studies, it is found that trust affects the growth of economy, size distribution of enterprise, and FDI inflow and so on. We also find that cross-regional differences of trust in China are reflections of the regional diversities of education, marketization of economies, urbanization, population density and transportation facilities. Although not statistically significant, “too many officials” may damage social trust. The paper demonstrates that trust cannot simply be taken as a cultural heritage. The paper also argues that sustainability of further economic development of China much depends on how fast China can build trust-facilitating institution, and that the most fundamental institution for trust is the property right.http://deepblue.lib.umich.edu/bitstream/2027.42/39972/3/wp586.pd
Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching
Considering the computation complexity, we propose a Guided Hybrid
Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely,
we first design a structure called guided quantization self-distillation
(GQSD), which is an innovative idea for realizing lightweight through the
synergy of quantization and distillation. The training process of the
quantization model is guided by its full-precision model, which is time-saving
and cost-saving without preparing a huge pre-trained model in advance. Second,
we put forward a hybrid quantization (HQ) module to obtain the optimal bit
width automatically under a constrained condition where a threshold for
distribution distance between the center and samples is applied in the weight
value search space. Third, in order to improve information transformation, we
propose a one-to-one self-teaching (OST) module to give the student network a
ability of self-judgment. A switch control machine (SCM) builds a bridge
between the student network and teacher network in the same location to help
the teacher to reduce wrong guidance and impart vital knowledge to the student.
This distillation method allows a model to learn from itself and gain
substantial improvement without any additional supervision. Extensive
experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA,
NWPU, and DIOR) show that object detection based on GHOST outperforms the
existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs)
(<2158 G) compared with any remote sensing-based, lightweight or
distillation-based algorithms demonstrate the superiority in the lightweight
design domain. Our code and model will be released at
https://github.com/icey-zhang/GHOST.Comment: This article has been delivered to TRGS and is under revie
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