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DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

Frontiers of Computer Science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

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Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic Optimization

arXiv

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arXiv 2025年

作者： Chang, Yuanyuan Yao, Yinghua Qin, Tao Wang, Mengmeng Tsang, Ivor Dai, Guang MOE Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University China Center for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Zhejiang University of Technology China SGIT AI Lab State Grid Corporation of China China

Text-to-image diffusion models have emerged as powerful tools for high-quality image generation and editing. Many existing approaches rely on text prompts as editing guidance. However, these methods are constrained by the need for manual prompt crafting, which can be time-consuming, introduce irrelevant details, and significantly limit editing performance. In this work, we propose optimizing semantic embeddings guided by attribute classifiers to steer text-to-image models toward desired edits, without relying on text prompts or requiring any training or fine-tuning of the diffusion model. We utilize classifiers to learn precise semantic embeddings at the dataset level. The learned embeddings are theoretically justified as the optimal representation of attribute semantics, enabling disentangled and accurate edits. Experiments further demonstrate that our method achieves high levels of disentanglement and strong generalization across different domains of data. Code is available at https://***/Chang-yuanyuan/CASO. Copyright © 2025, The Authors. All rights reserved.

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Ideal Quadratic Nodal Point with Large Surface Arc States in Half-Heusler Material Scbipt

SSRN

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SSRN 2022年

作者： Yang, Tie Liu, Ying Tan, Xingwen Zhang, Xiaoming Wu, Zhimin Zhang, Gang State Key Lab for Reliability and Intelligence of Electrical Equipment China School of Physical Science and Technology Southwest University Chongqing400715 China School of Physics and Electronic Engineering Chongqing Normal University Chongqing401331 China School of Materials Science and Engineering Hebei University of Technology Tianjin300130 China Institute of High Performance Computing Agency for Science Technology and Research Connexis 138632 Singapore

Recently, the research focus of topological states in electronic systems has been shifted from the conventional linear type to high order dispersion, in which more exotic physical properties are exhibited, such as high topological charge, large magnetoresponse and unusual transport behavior. In this work, based on first principles calculations, we proposed a quadratic nodal point semimetal, the half-Heusler material ScBiPt. Detailed band structures reveal that an ideal nodal point is present exactly at the Fermi level and, more importantly, it exhibits quadratic dispersion along all directions, which has also been confirmed from the symmetry analyzation and effective model analysis. Moreover, when the spin orbital coupling effect is considered, the nodal point changes from triple to quadruple degeneracy but the quadratic dispersion is still maintained. Two prominent arc surface states are emanated from the nodal point and they are well separated from the bulk band, making the future experimental verification extremely beneficial. Overall, the current quadratic nodal point material with multiple advantages can serve as an ideal platform and the relative studies can be immediately inspired and advanced. © 2022, The Authors. All rights reserved.

关键词： Topology

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Multi-UAV surveillance implementation under hierarchical dynamic task scheduling architecture

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Journal of Central South University 2020年第9期27卷 2614-2627页

作者： WU Wen-di WU Yun-long LI Jing-hua REN Xiao-guang SHI Dian-xi TANG Yu-hua State Key Laboratory of High Performance Computing(HPCL) College of ComputerNational University of Defense TechnologyChangsha 410073China Artificial Intelligence Research Center(AIRC) National Innovation Institute of Defense Technology(NIIDT)Beijing 100071China Tianjin Artificial Intelligence Innovation Center(TAIIC) Tianjin 300457China

In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground *** this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory ***,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation ***,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling *** this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin ***,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task *** order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.

关键词： prediction-based synchronization dynamic task scheduling hierarchical software architecture

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An integrated framework for accelerating reactive flow simulation using GPU and machine learning models

arXiv

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arXiv 2023年

作者： Mao, Runze Wang, Yingrui Zhang, Min Li, Han Xu, Jiayang Dong, Xinyu Zhang, Yan Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Beijing100080 China Shanghai SenseTime Intelligent Technology Co. LTD Shanghai200233 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU. © 2023, CC BY.

关键词： Graphics processing unit

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Optimal subsystem approach to multi-qubit quantum state discrimination and experimental investigation

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Science China(Physics,Mechanics & Astronomy) 2018年第2期61卷 26-34页

作者： ShiChuan Xue JunJie Wu Ping Xu XueJun Yang State Key Laboratory of High Performance Computing National University of Defense Technology College of Computer National University of Defense Technology

Quantum computing is a significant computing capability which is superior to classical computing because of its superposition feature. Distinguishing several quantum states from quantum algorithm outputs is often a vital computational task. In most cases, the quantum states tend to be non-orthogonal due to superposition; quantum mechanics has proved that perfect outcomes could not be achieved by measurements, forcing repetitive measurement. Hence, it is important to determine the optimum measuring method which requires fewer repetitions and a lower error rate. However, extending current measurement approaches mainly aiming at quantum cryptography to multi-qubit situations for quantum computing confronts challenges, such as conducting global operations which has considerable costs in the experimental realm. Therefore, in this study, we have proposed an optimum subsystem method to avoid these difficulties. We have provided an analysis of the comparison between the reduced subsystem method and the global minimum error method for two-qubit problems; the conclusions have been verified experimentally. The results showed that the subsystem method could effectively discriminate non-orthogonal two-qubit states, such as separable states, entangled pure states, and mixed states; the cost of the experimental process had been significantly reduced, in most circumstances, with acceptable error rate. We believe the optimal subsystem method is the most valuable and promising approach for multi-qubit quantum computing applications.

关键词： quantum computing quantum measurement quantum state discrimination entanglement reduced density operators

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Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

arXiv

引用

arXiv 2024年

作者： Lee, Jae Yong Schotthöfer, Steffen Xiao, Tianbai Krumscheid, Sebastian Frank, Martin Center for Artificial Intelligence and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States State Key Laboratory of High Temperature Gas Dynamics Centre for Interdisciplinary Research in Fluids Institute of Mechanics Chinese Academy of Sciences Beijing China School of Engineering Science University of Chinese Academy of Sciences Beijing China Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany

This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensional collision operator of the linear kinetic equation. This integral operator has crucial analytical structures that a surrogate model, e.g., a DeepONet, needs to preserve to enable meaningful physical simulation. We propose several DeepONet modifications to encapsulate essential structural properties of this integral operator in a DeepONet model. To be precise, we adapt the architecture of the trunk-net so the DeepONet has the same collision invariants as the theoretical kinetic collision operator, thus preserving conserved quantities, e.g., mass, of the modeled many-particle system. Further, we propose an entropy-inspired data-sampling method tailored to train the modified DeepONet surrogates without requiring an excessive expensive simulation-based data generation. © 2024, CC BY-NC-SA.

关键词： Kinetic energy

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Resilience of Quantum key Distribution Source Against Laser-Damage Attack by a Variety of Lasers

Resilience of Quantum Key Distribution Source Against Laser-...

引用

Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Daria Ruzhitskaya Irina Zhluktova Anastasiya Ponosova Daniil Trefilov Poompong Chaiwongkhot Anqi Huang Vladimir Kamynin Vadim Makarov Russian Quantum Center Skolkovo Moscow Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow Russia Prokhorov General Physics Institute of Russian Academy of Sciences Moscow Russia MIREA - Russian Technological University Moscow Russia Vigo Quantum Communication Center University of Vigo Vigo Spain Department of Physics Faculty of Science Mahidol University Bangkok Thailand State Key Laboratory of High Performance Computing Institute for Quantum Information College of Computer Science and Technology National University of Defense Technology Changsha China Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai People's Republic of China

ISBN: (纸本)9798350345995

Quantum key distribution (QKD) systems provide quantum-safe key exchange. Therefore, complete security analysis of implementations of QKD protocols is in the focus of interest of a worldwide information-security community. For today, a number of QKD loopholes are closed by countermeasures, which are also considered in emerging QKD security evaluation and certification [1]–[3]. However, new threats to practical QKD implementations are still found, such as the laser-damage attack, which is a powerful hacking strategy. In investigations, CW laser radiation is most often used, but in contrast to it, the interaction of pulsed laser radiation with optical materials may lead to a wide range of effects, like nonlinear effects, dielectric breakdown, etc.

关键词： Fiber optic components high power lasers Nonlinear effects Optical materials Quantum key distribution Quantum key distribution networks

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Non-Contact Actuated Snap-Through Buckling of a Pre-Buckled Bistable Hard-Magnetic Elastica

SSRN

引用

SSRN 2023年

作者： Zhang, Yingchao Ma, Yinji Yu, Jing Gao, Huajian School of Materials Science and Engineering Nanyang Technological University 637553 Singapore AML Department of Engineering Mechanics Tsinghua University Beijing100084 China Laboratory of Flexible Electronics Technology Tsinghua University Beijing100084 China Institute for Digital Molecular Analytics and Science Nanyang Technological University 637553 Singapore School of Mechanical and Aerospace Engineering Nanyang Technological University 637553 Singapore Institute of High Performance Computing A*STAR 138632 Singapore

Snap-through buckling of bistable structures is a classic topic in mechanics which has been widely studied and applied in various fields such as mechanical meta-materials and soft robotics. Obstacles that hinder broader applications of conventional bistable structures include the requirement of contact actuation to trigger instability and difficulty to control post-buckling configurations. In contrast, hard magnetic elastica (HME), a composite made of hard ferromagnetic particles and soft elastomer that deforms in response to an externally applied magnetic field, exhibits great potential to bring major advances in this field by allowing non-contact actuation and programmable control of snap-through buckling via magnetization distribution. Here, we develop a theoretical framework to trace the instability and post-buckling evolution process of snap-through buckling of a bistable HME. In contrast to the conventional snapping through end-end shortening, the design space for bistable HME includes two key parameters: the remanent magnetization density after pre-magnetization and the external magnetic field. We focus on two simple yet practical cases: a fixed amplitude of magnetization density along the HME with direction reversed at the magnetization interface (M-interface), and a uniform magnetic field with varied direction. We identify an optimal position for the single M-interface and direction for the uniform actuation field for pre-buckled beams with two-ends fixed, which can reduce the required actuation field for snapping to nearly half in comparison with the symmetric cases. Experiments and finite element analysis are performed to validate the model predictions. Our work may stimulate further studies on utilizing snap-through buckling in applications where fast and large shape transitions from one stable state to another can be actuated in a low-energy, non-contact mode through a remotely applied stimulus field. © 2023, The Authors. All rights reserved.

关键词： Magnetization

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Observation of Large-Scale Anisotropy of Proton above Tens of TeV with LHAASO-KM2A 38

Observation of Large-Scale Anisotropy of Proton above Tens o...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： He, Jiayin Zhao, Shiping Guo, Yingying Zhang, Yi Yuan, Qiang Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 9 Jiangsu Nanjing210023 China University of Science and Technology of China Anhui Hefei230026 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Cor

We present a measurement for large-scale anisotropy of pure protons with the Large high Altitude Air Shower Observatory (LHAASO). We analyze the data collected from the full array of KM2A in one year’s operation. To select the proton data set, we employed a technique similar to the gamma/background discrimination method in KM2A, using a cut on the ratio of muonic and electron magnetic components. The purity of the proton sample is up to 90%, and the reconstructed energy is from 10 TeV to 270 TeV. We first perform the energy evolution analysis of the dipole anisotropy of protons at this energy range. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Anisotropy

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