PriorPose: Reference-Guided Joint Deformation and Alignment for Category-Level Object Pose Estimation
本文提出PriorPose，通过参考引导的对应框架联合解决类别级物体姿态估计中的规范化和对齐问题，提高在形状变化和域迁移下的鲁棒性。
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Generics-Aware Fuzz Target Generation for Rust Libraries via Structured API Analysis
Existing fuzzing tools struggle to generate compilable API call sequences that satisfy Rust’s ownership, generic, and trait constraints, resulting in low coverage. This work proposes a novel approach that constructs a generic- and trait-aware API dependency graph through structured parsing of Rust documentation, then combines topological-guided traversal with large language model–based code synthesis, iteratively refining test cases under compiler feedback. To the best of our knowledge, this is the first method to systematically model generic and trait constraints for fuzzing. Evaluated on 13 real-world crates, it achieves an average API coverage of 80.75% and a compilation success rate of 96.19%, outperforming RULF, RPG, and deepSURF by factors of 4.76×, 2.43×, and 1.41× in coverage, respectively.
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DTMC-Based Analysis and Scheduling for Periodic Flows with Proactive HARQ
This work addresses the challenge of schedulability analysis for heterogeneous periodic traffic in ultra-reliable low-latency communication (URLLC) systems, where proactive HARQ introduces slot-level timing effects—such as feedback delay—that complicate resource allocation. To tackle this, the paper proposes a discrete-time Markov chain (DTMC)-based modeling framework that accurately captures cross-slot dynamics, including HARQ round-trip latency, through an expanded state space. Coupled with a two-stage genetic algorithm, the approach optimizes offset scheduling to meet diverse reliability and latency requirements. This study is the first to apply DTMCs to timing modeling of periodic flows under proactive HARQ, enabling precise schedulability analysis that explicitly accounts for feedback delay. Simulations demonstrate that the proposed method significantly improves schedulability compared to reactive HARQ, K-repetition schemes, and non-guaranteed proactive HARQ, while maintaining manageable computational overhead.
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PriorPose: Reference-Guided Joint Deformation and Alignment for Category-Level Object Pose Estimation
本文提出PriorPose，通过参考引导的对应框架联合解决类别级物体姿态估计中的规范化和对齐问题，提高在形状变化和域迁移下的鲁棒性。
0 citationsRead paper
Generics-Aware Fuzz Target Generation for Rust Libraries via Structured API Analysis
Existing fuzzing tools struggle to generate compilable API call sequences that satisfy Rust’s ownership, generic, and trait constraints, resulting in low coverage. This work proposes a novel approach that constructs a generic- and trait-aware API dependency graph through structured parsing of Rust documentation, then combines topological-guided traversal with large language model–based code synthesis, iteratively refining test cases under compiler feedback. To the best of our knowledge, this is the first method to systematically model generic and trait constraints for fuzzing. Evaluated on 13 real-world crates, it achieves an average API coverage of 80.75% and a compilation success rate of 96.19%, outperforming RULF, RPG, and deepSURF by factors of 4.76×, 2.43×, and 1.41× in coverage, respectively.
0 citationsRead paper
DTMC-Based Analysis and Scheduling for Periodic Flows with Proactive HARQ
This work addresses the challenge of schedulability analysis for heterogeneous periodic traffic in ultra-reliable low-latency communication (URLLC) systems, where proactive HARQ introduces slot-level timing effects—such as feedback delay—that complicate resource allocation. To tackle this, the paper proposes a discrete-time Markov chain (DTMC)-based modeling framework that accurately captures cross-slot dynamics, including HARQ round-trip latency, through an expanded state space. Coupled with a two-stage genetic algorithm, the approach optimizes offset scheduling to meet diverse reliability and latency requirements. This study is the first to apply DTMCs to timing modeling of periodic flows under proactive HARQ, enabling precise schedulability analysis that explicitly accounts for feedback delay. Simulations demonstrate that the proposed method significantly improves schedulability compared to reactive HARQ, K-repetition schemes, and non-guaranteed proactive HARQ, while maintaining manageable computational overhead.
0 citationsRead paper