Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning
本文提出基于Transformer的量子状态特征化模型(TQSC),用于在复杂噪声环境下准确估计远程状态准备(RSP)的目标状态,通过深度学习方法提高了状态估计的精度和鲁棒性。
本文提出基于Transformer的量子状态特征化模型(TQSC),用于在复杂噪声环境下准确估计远程状态准备(RSP)的目标状态,通过深度学习方法提高了状态估计的精度和鲁棒性。
该研究通过引入多教师在线策略蒸馏方法Flow3D-OPD,解决了3D几何生成中定义奖励困难和梯度干扰问题,提升了3D模型的质量。
研究通过全球引用网络分析,使用网络重排零模型方法,发现中国科研的本土引用偏好低于普遍认知,并逐渐减少,且其颠覆性影响正在接近美国。
This study addresses the lack of CVE trigger assessment and configuration redundancy in Linux kernels by proposing FCC, a framework that automatically infers 1-minimal trigger configurations satisfying both build-time and runtime constraints. Integrating Kconfig constraint solving, implicit dependency completion, and topology-guided minimization, FCC is the first to generate auditable trigger boundaries validated via olddefconfig. Experimental results demonstrate that the configuration success rate increases from 62.5% to 96.6%, while the average candidate size is reduced by 78.7%. These improvements significantly lower evaluation overhead and effectively enable vendors to precisely determine CVE triggerability within specific deployments.
This work addresses the challenges of semantic-control misalignment, action inconsistency, and unreliable termination in language-guided drone navigation within target-visible environments. To this end, the authors propose DBFly, a novel framework that introduces an explicit vision-guided spatial reasoning chain prior to waypoint generation. This chain comprises target-direction anchoring, spatial diagnosis, and maneuver decision-making, complemented by implicit flight corridor modeling and a terminal convergence-aware stopping strategy to reliably bridge high-level linguistic intent with continuous low-level control. Experimental results demonstrate that DBFly achieves a 25.07 percentage point improvement in average success rate over the strongest baseline across both seen and unseen objects and scenes, significantly enhancing navigation stability and reliability.
本文提出基于Transformer的量子状态特征化模型(TQSC),用于在复杂噪声环境下准确估计远程状态准备(RSP)的目标状态,通过深度学习方法提高了状态估计的精度和鲁棒性。
该研究通过引入多教师在线策略蒸馏方法Flow3D-OPD,解决了3D几何生成中定义奖励困难和梯度干扰问题,提升了3D模型的质量。
研究通过全球引用网络分析,使用网络重排零模型方法,发现中国科研的本土引用偏好低于普遍认知,并逐渐减少,且其颠覆性影响正在接近美国。
This study addresses the lack of CVE trigger assessment and configuration redundancy in Linux kernels by proposing FCC, a framework that automatically infers 1-minimal trigger configurations satisfying both build-time and runtime constraints. Integrating Kconfig constraint solving, implicit dependency completion, and topology-guided minimization, FCC is the first to generate auditable trigger boundaries validated via olddefconfig. Experimental results demonstrate that the configuration success rate increases from 62.5% to 96.6%, while the average candidate size is reduced by 78.7%. These improvements significantly lower evaluation overhead and effectively enable vendors to precisely determine CVE triggerability within specific deployments.
This work addresses the challenges of semantic-control misalignment, action inconsistency, and unreliable termination in language-guided drone navigation within target-visible environments. To this end, the authors propose DBFly, a novel framework that introduces an explicit vision-guided spatial reasoning chain prior to waypoint generation. This chain comprises target-direction anchoring, spatial diagnosis, and maneuver decision-making, complemented by implicit flight corridor modeling and a terminal convergence-aware stopping strategy to reliably bridge high-level linguistic intent with continuous low-level control. Experimental results demonstrate that DBFly achieves a 25.07 percentage point improvement in average success rate over the strongest baseline across both seen and unseen objects and scenes, significantly enhancing navigation stability and reliability.