A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and att...
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A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarization encoding, a qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and postprocessing. We analyze the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure.
We develop a second-order continuousfinite element method for solving the static Eikonal *** is based on the vanishing viscosity approach with a homotopy method for solving the discretized nonlinear *** specifically,t...
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We develop a second-order continuousfinite element method for solving the static Eikonal *** is based on the vanishing viscosity approach with a homotopy method for solving the discretized nonlinear *** specifically,the homotopy method is utilized to decrease the viscosity coefficient gradually,while Newton’s method is applied to compute the solution for each viscosity ***’s method alone converges for just big enough viscosity coefficients on very coarse grids and for simple 1D examples,but the proposed method is much more robust and guarantees the convergence of the nonlinear solver for all viscosity coefficients and for all examples over all *** experiments from 1D to 3D are presented to confirm the second-order convergence and the effectiveness of the proposed method on both structured or unstructured meshes.
Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based reasoning, a complex task requiring phy...
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In-Band Network Telemetry (INT) and sketch algorithms are two representative methodologies for measuring network traffics in real time. To combine sketch with INT and to keep their advantages, the "reconstructing...
Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face ch...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatiotemporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset.
Array constraint solving is widely adopted by the existing symbolic execution engines for encoding programs precisely. The counterexample-guided abstraction refinement (CEGAR) based method is state-of-the-art for arra...
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ISBN:
(纸本)9781665455381
Array constraint solving is widely adopted by the existing symbolic execution engines for encoding programs precisely. The counterexample-guided abstraction refinement (CEGAR) based method is state-of-the-art for array constraint solving. However, we observed that the CEGAR-based method may need many refinements to solve the array constraints produced by the symbolic executor, which decreases the performance of constraint solving. Based on the observation, we propose a machine learning-based method to improve the efficiency of the CEGAR-based array constraint solving. Our method adaptively turns on or off the CEGAR loop according to different solving problems. We have implemented our method on the symbolic executor KLEE and its underlying CEGAR-based constraint solver STP. We have conducted an extensive experiment on 55 real-world programs. On average, our method increases the number of explored paths by 21%. The results of the extensive experiments on real-world C programs show the effectiveness of our method.
Grasping a specified object from multi-object scenes is an essential ability for intelligent *** ability depends on the affiliation between the grasp position and the object category. Most existing multi-object grasp ...
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Grasping a specified object from multi-object scenes is an essential ability for intelligent *** ability depends on the affiliation between the grasp position and the object category. Most existing multi-object grasp detection methods considering the affiliation rely on object detection results, thus limiting the improvement of robotic grasp detection accuracy. This paper proposes a decoupled single-stage multitask robotic grasp detection method based on the Faster R-CNN framework for multi-object scenes. The designed network independently detects the category of an object and its possible grasp positions by using one loss function. A new grasp matching strategy is designed to determine the relationship between object categories and predicted grasp positions. The VMRD grasp dataset is used to test the performance of the proposed method. Compared with other grasp detection methods, the proposed method achieves higher object detection accuracy and grasp detection accuracy.
Floating-point programs are challenging for symbolic execution due to the constraint solving problem. To investigate the effectiveness and limitations of the existing methods, we conduct the first empirical study in t...
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ISBN:
(纸本)9781665455381
Floating-point programs are challenging for symbolic execution due to the constraint solving problem. To investigate the effectiveness and limitations of the existing methods, we conduct the first empirical study in this paper on five existing symbolic execution methods for floating-point programs. We have implemented the existing methods on the state-of-the-art symbolic execution KLEE and use the real-world representative floatingpoint programs as the benchmarks, which are used to evaluate the existing methods with respect to code coverage and bug finding. The results indicate that the existing methods complement each other in bug finding. Based on the findings of the experimental results, we propose synergizing the existing methods to improve symbolic execution‘s effectiveness. The experimental results demonstrate that our synergic method can detect more bugs.
Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called ...
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Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called Heterogeneous Redundant Proactive Defense Framework(HRPDF).We propose a heterogeneous PLC architecture in HRPDF,including multiple heterogeneous,equivalent,and synchronous runtimes,which can thwart multiple types of attacks against PLC without the need of external *** ensure the availability of PLC,we also design an inter-process communication algorithm that minimizes the overhead of *** implement a prototype system of HRPDF and test it in a real-world PLC and an OpenPLC-based device,*** results show that HRPDF can defend against multiple types of attacks with 10.22%additional CPU and 5.56%additional memory overhead,and about 0.6 ms additional time overhead.
Dear editor,Modern semantic segmentation, which has important applications such as medical image analysis, image editing, and video surveillance, has made remarkable progress using deep convolution neural network mode...
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Dear editor,Modern semantic segmentation, which has important applications such as medical image analysis, image editing, and video surveillance, has made remarkable progress using deep convolution neural network models. Recently, an efficient real-time semantic segmentation method has received considerable attention, as intelligent edge devices not only have faster inference speed requirements for semantic segmentation models but also cannot rely on the cloud services of data centers. There are two feasible approaches to develop an efficient semantic segmentation model.
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