Selected work

Representative Papers

NIDAR: NIR-Guided Intrinsic Decomposition for Scalable Scene-Agnostic LiDAR Intensity Reconstruction

Sep 29, 2026

This study addresses the absence of intensity data in LiDAR simulation and the high computational cost and poor reusability of conventional per-scene optimization. We propose a feedforward LiDAR intensity generation framework that integrates pseudo-near-infrared conversion with hierarchical intrinsic decomposition, alongside geometry-aware modulation and source-domain distribution calibration, to synthesize dense intensity maps directly from RGB and geometric inputs. Crucially, the method achieves cross-scene generalization using fixed weights, eliminating the need for target-scene labels or gradient-based fitting. Experiments demonstrate that our framework surpasses existing baselines in accuracy and fidelity on the Waymo and nuScenes datasets. Furthermore, its practical utility has been validated through downstream SLAM tasks implemented within the Unreal Engine 5 environment.

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FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion

Jun 30, 2026

This work addresses the instability in value estimation and degradation of policy plasticity commonly observed in high-throughput sampling scenarios with frequent data updates. To mitigate these issues, the authors propose FastDSAC, an algorithm built upon a distributed Actor-Critic framework that models the policy using a truncated Gaussian distribution to simultaneously respect action constraints and preserve exploratory stochasticity. The method incorporates an adaptive variance modulation mechanism to enhance the accuracy of value estimation and employs implicit regularization to maintain the adaptability of the policy network. Experimental results demonstrate that FastDSAC achieves more stable training dynamics, faster convergence, and superior asymptotic performance compared to existing approaches on both the MuJoCo Playground and HumanoidBench benchmarks.

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UAV Trajectory and Bandwidth Allocation for Efficient Data Collection in Low-Altitude Intelligent IoT: A Hierarchical DRL Approach

Apr 25, 2026

This work addresses the challenges of unknown interference, dynamic data volumes, and limited onboard computational resources in unmanned aerial vehicle (UAV)-based data collection within low-altitude intelligent Internet of Things (IoT) systems. To tackle these issues, the authors propose a lightweight hierarchical deep reinforcement learning framework, termed TBH-DDPG, which jointly optimizes data collection efficiency through coarse-grained trajectory planning at the upper level and fine-grained bandwidth allocation at the lower level. Evaluated in realistic environments featuring interference sources, time-varying data demands, and diverse obstacles, the proposed method achieves rapid convergence and low computational overhead. Compared to non-hierarchical approaches, it improves convergence speed by 44.44% and reduces computational cost by 58.05%, significantly enhancing total data throughput and overall system performance.

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Revisiting and Expanding the IPv6 Network Periphery: Global-Scale Measurement and Security Analysis

Apr 21, 2026

This study addresses structural security risks at the IPv6 network edge—such as service exposure and routing loops—for which global-scale systematic assessment has been lacking. The authors present the first comprehensive IPv6 edge security measurement covering 73 countries, introducing a Response-Guided Prefix Selection (RGPS) strategy to efficiently scan high-value targets. They further develop a Hierarchical Large Language Model Exposure Verification (HLEV) framework to analyze unauthorized access risks. Their analysis identifies 281.9 million active IPv6 edge nodes, with a service exposure rate of 2.5%, and detects 4.5 million routing loop responses. The work also uncovers multiple security vulnerabilities in widely used LLM deployment tools, stemming from missing authentication mechanisms due to insecure default configurations.

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Position-Prior-Guided Network for System Matrix Super-Resolution in Magnetic Particle Imaging

Nov 08, 2025

To address the time-consuming and frequently repeated system matrix (SM) calibration in magnetic particle imaging (MPI), this paper proposes a physics-informed deep super-resolution method. The core innovation lies in the first incorporation of SM’s intrinsic spatial symmetry and positional prior into the network architecture, realized via a position-guided convolutional module that jointly enforces physical constraints and data-driven learning. The method enables 2D and 3D SM reconstruction without additional hardware or measurements. Experiments demonstrate that, at identical downsampling ratios, our approach achieves ≥2.1 dB higher PSNR and ≥0.03 higher SSIM than purely data-driven baselines, reduces calibration time by 68%, and exhibits strong generalization and reconstruction stability. This work establishes a new paradigm for efficient, interpretable MPI system calibration.

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Recent publications

Latest Papers

NIDAR: NIR-Guided Intrinsic Decomposition for Scalable Scene-Agnostic LiDAR Intensity Reconstruction

Sep 29, 2026

This study addresses the absence of intensity data in LiDAR simulation and the high computational cost and poor reusability of conventional per-scene optimization. We propose a feedforward LiDAR intensity generation framework that integrates pseudo-near-infrared conversion with hierarchical intrinsic decomposition, alongside geometry-aware modulation and source-domain distribution calibration, to synthesize dense intensity maps directly from RGB and geometric inputs. Crucially, the method achieves cross-scene generalization using fixed weights, eliminating the need for target-scene labels or gradient-based fitting. Experiments demonstrate that our framework surpasses existing baselines in accuracy and fidelity on the Waymo and nuScenes datasets. Furthermore, its practical utility has been validated through downstream SLAM tasks implemented within the Unreal Engine 5 environment.

0 citationsRead paper

FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion

Jun 30, 2026

This work addresses the instability in value estimation and degradation of policy plasticity commonly observed in high-throughput sampling scenarios with frequent data updates. To mitigate these issues, the authors propose FastDSAC, an algorithm built upon a distributed Actor-Critic framework that models the policy using a truncated Gaussian distribution to simultaneously respect action constraints and preserve exploratory stochasticity. The method incorporates an adaptive variance modulation mechanism to enhance the accuracy of value estimation and employs implicit regularization to maintain the adaptability of the policy network. Experimental results demonstrate that FastDSAC achieves more stable training dynamics, faster convergence, and superior asymptotic performance compared to existing approaches on both the MuJoCo Playground and HumanoidBench benchmarks.

0 citationsRead paper

UAV Trajectory and Bandwidth Allocation for Efficient Data Collection in Low-Altitude Intelligent IoT: A Hierarchical DRL Approach

Apr 25, 2026

This work addresses the challenges of unknown interference, dynamic data volumes, and limited onboard computational resources in unmanned aerial vehicle (UAV)-based data collection within low-altitude intelligent Internet of Things (IoT) systems. To tackle these issues, the authors propose a lightweight hierarchical deep reinforcement learning framework, termed TBH-DDPG, which jointly optimizes data collection efficiency through coarse-grained trajectory planning at the upper level and fine-grained bandwidth allocation at the lower level. Evaluated in realistic environments featuring interference sources, time-varying data demands, and diverse obstacles, the proposed method achieves rapid convergence and low computational overhead. Compared to non-hierarchical approaches, it improves convergence speed by 44.44% and reduces computational cost by 58.05%, significantly enhancing total data throughput and overall system performance.

0 citationsRead paper

Revisiting and Expanding the IPv6 Network Periphery: Global-Scale Measurement and Security Analysis

Apr 21, 2026

This study addresses structural security risks at the IPv6 network edge—such as service exposure and routing loops—for which global-scale systematic assessment has been lacking. The authors present the first comprehensive IPv6 edge security measurement covering 73 countries, introducing a Response-Guided Prefix Selection (RGPS) strategy to efficiently scan high-value targets. They further develop a Hierarchical Large Language Model Exposure Verification (HLEV) framework to analyze unauthorized access risks. Their analysis identifies 281.9 million active IPv6 edge nodes, with a service exposure rate of 2.5%, and detects 4.5 million routing loop responses. The work also uncovers multiple security vulnerabilities in widely used LLM deployment tools, stemming from missing authentication mechanisms due to insecure default configurations.

0 citationsRead paper

Position-Prior-Guided Network for System Matrix Super-Resolution in Magnetic Particle Imaging

Nov 08, 2025

To address the time-consuming and frequently repeated system matrix (SM) calibration in magnetic particle imaging (MPI), this paper proposes a physics-informed deep super-resolution method. The core innovation lies in the first incorporation of SM’s intrinsic spatial symmetry and positional prior into the network architecture, realized via a position-guided convolutional module that jointly enforces physical constraints and data-driven learning. The method enables 2D and 3D SM reconstruction without additional hardware or measurements. Experiments demonstrate that, at identical downsampling ratios, our approach achieves ≥2.1 dB higher PSNR and ≥0.03 higher SSIM than purely data-driven baselines, reduces calibration time by 68%, and exhibits strong generalization and reconstruction stability. This work establishes a new paradigm for efficient, interpretable MPI system calibration.

0 citationsRead paper