C$^{2}$-INR: Customized Convolutional Implicit Neural Representation
本文提出C²-INR方法，通过定制卷积核和激活函数选择机制解决图像表示中空间相关性利用不足的问题。
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为解决Verilog RTL调试基准不足的问题，通过实证驱动的故障构建、基于LLM的测试增强和执行保留方法，构建了VeriBugBench框架。
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EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition
针对现有模型在实际传感变化中的局限，EdgeHAR通过将传感器信号分解为活动语义、运动动态和采集上下文三个代码来学习可迁移表示，实现高效适应并满足边缘计算约束。
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Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration
Existing diffusion-based image restoration methods struggle with spatially non-uniform degradation due to their reliance on fixed data constraints and uniform sampling steps, often leading to structural distortions, detail loss, and computational redundancy. This work proposes the LEADer framework, which introduces local epistemic uncertainty into diffusion-based image restoration for the first time. In the spatial domain, it dynamically modulates the strength of null-space priors based on pixel-wise uncertainty, enabling adaptive data consistency enforcement. In the temporal domain, it leverages the trace of uncertainty to prune the sampling trajectory, achieving efficient and adaptive inference. Evaluated across multiple state-of-the-art diffusion-based image restoration models, LEADer significantly improves restoration quality while substantially reducing sampling time, incurs negligible memory overhead, and guarantees strict data consistency along with deterministic error bounds.
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ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds
This study addresses the under-segmentation of ground points in sparse long-range point clouds, which is exacerbated by terrain undulations and interference from non-ground structures. To tackle this challenge, the authors propose a two-stage ground segmentation method based on an adaptive concentric zone model. In the coarse segmentation stage, dynamic sector partitioning balances local point density, and plane fitting is guided by a minimum-height seed constraint combined with height-decay weighting. The fine segmentation stage incorporates reflectance intensity consistency to select high-confidence ground points and refines ambiguous regions using neighborhood height stability. By innovatively integrating geometric and intensity features, the proposed approach achieves F1 scores of 97.66% on SemanticKITTI and 99.36% on RUBY-PLUS, significantly enhancing both segmentation accuracy and robustness.
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C$^{2}$-INR: Customized Convolutional Implicit Neural Representation
本文提出C²-INR方法，通过定制卷积核和激活函数选择机制解决图像表示中空间相关性利用不足的问题。
0 citationsRead paper
VeriBugBench: An Empirically Grounded Framework for Constructing Verilog RTL Debugging Benchmarks
为解决Verilog RTL调试基准不足的问题，通过实证驱动的故障构建、基于LLM的测试增强和执行保留方法，构建了VeriBugBench框架。
0 citationsRead paper
EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition
针对现有模型在实际传感变化中的局限，EdgeHAR通过将传感器信号分解为活动语义、运动动态和采集上下文三个代码来学习可迁移表示，实现高效适应并满足边缘计算约束。
0 citationsRead paper
Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration
Existing diffusion-based image restoration methods struggle with spatially non-uniform degradation due to their reliance on fixed data constraints and uniform sampling steps, often leading to structural distortions, detail loss, and computational redundancy. This work proposes the LEADer framework, which introduces local epistemic uncertainty into diffusion-based image restoration for the first time. In the spatial domain, it dynamically modulates the strength of null-space priors based on pixel-wise uncertainty, enabling adaptive data consistency enforcement. In the temporal domain, it leverages the trace of uncertainty to prune the sampling trajectory, achieving efficient and adaptive inference. Evaluated across multiple state-of-the-art diffusion-based image restoration models, LEADer significantly improves restoration quality while substantially reducing sampling time, incurs negligible memory overhead, and guarantees strict data consistency along with deterministic error bounds.
0 citationsRead paper
ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds
This study addresses the under-segmentation of ground points in sparse long-range point clouds, which is exacerbated by terrain undulations and interference from non-ground structures. To tackle this challenge, the authors propose a two-stage ground segmentation method based on an adaptive concentric zone model. In the coarse segmentation stage, dynamic sector partitioning balances local point density, and plane fitting is guided by a minimum-height seed constraint combined with height-decay weighting. The fine segmentation stage incorporates reflectance intensity consistency to select high-confidence ground points and refines ambiguous regions using neighborhood height stability. By innovatively integrating geometric and intensity features, the proposed approach achieves F1 scores of 97.66% on SemanticKITTI and 99.36% on RUBY-PLUS, significantly enhancing both segmentation accuracy and robustness.
0 citationsRead paper