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Automatic Mining of Security-Sensitive Functions from Source Code

Computers, Materials & Continua 2018年第8期56卷 199-210页

作者： Lin Chen Chunfang Yang Fenlin Liu Daofu Gong Shichang Ding Zhengzhou Science and Technology Institute Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China University of Göttingen Goldschmidtstr.737077 GöttingenGermany

When dealing with the large-scale program,many automatic vulnerability mining techniques encounter such problems as path explosion,state explosion,and low *** of large-scale programs based on safety-sensitive functions helps solve the above *** manual identification of security-sensitive functions is a tedious task,especially for the large-scale *** study proposes a method to mine security-sensitive functions the arguments of which need to be checked before they are *** argument-checking identification algorithms are proposed based on the analysis of two implementations of argument *** on these algorithms,security-sensitive functions are detected based on the ratio of invocation instances the arguments of which have been protected to the total number of *** results of experiments on three well-known open-source projects show that the proposed method can outperform competing methods in the literature.

关键词： Code mining vulnerabilities static analysis security-sensitive function source code.

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Space-variant Shack-Hartmann wavefront sensing based on affine transformation estimation

arXiv

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arXiv 2022年

作者： Feng, Fan Liang, Chen Chen, Dongdong Du, Ke Yang, Runjia Lu, Chang Chen, Shumin Chen, Liangyi Tao, Louis Mao, Heng Center for Bioinformatics National Laboratory of Protein Engineering and Plant Genetic Engineering School of Life Sciences Peking University Beijing100871 China LMAM School of Mathematical Sciences Peking University Beijing100871 China School of Software and Microelectronics Peking University Beijing100871 China State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular Medicine Institute of Molecular Medicine Center for Life Sciences College of Future Technology Peking University Beijing100871 China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China PKU-IDG/McGovern Institute for Brain Research Beijing100871 China Beijing Academy of Artificial Intelligence Beijing100871 China Center for Quantitative Biology Peking University Beijing100871 China Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Beijing100871 China

The space-variant wavefront reconstruction problem inherently exists in deep tissue imaging. In this paper, we propose a framework of Shack-Hartmann wavefront space-variant sensing with extended source illumination. The space-variant wavefront is modeled as a four-dimensional function where two dimensions are in the spatial domain and two in the Fourier domain with priors that both gently vary. Here, the affine transformation is used to characterize the wavefront space-variant function. Correspondingly, the zonal and modal methods are both escalated to adapt to four-dimensional representation and reconstruction. Experiments and simulations show double to quadruple improvements in space-variant wavefront reconstruction accuracy compared to the conventional space-invariant correlation method. Copyright © 2022, The Authors. All rights reserved.

关键词： Wavefronts

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Toward constraining QCD phase transitions in neutron star interiors: Bayesian inference with a Tolman-Oppenheimer-Volkof linear response analysis

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Physical Review D 2025年第7期111卷 074026-074026页

作者： Ronghao Li (李荣浩) Sophia Han (韩君) Zidu Lin (林子都) Lingxiao Wang (王凌霄) Kai Zhou (周凯) Shuzhe Shi (施舒哲) Department of Physics Tsinghua University Beijing 100084 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China University of Tennessee Knoxville Tennessee 37996 USA RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences(iTHEMS) Wako Saitama 351-0198 Japan School of Science and Engineering The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) Guangdong 518172 China Frankfurt Institute for Advanced Studies Ruth Moufang Strasse 1 D-60438 Frankfurt am Main Germany State Key Laboratory of Low-Dimensional Quantum Physics Tsinghua University Beijing 100084 China

The potential hadron-to-quark phase transition in neutron stars has not been fully understood as the property of cold, dense, and strongly interacting matter cannot be theoretically described by the first-principle perturbative calculations, nor have they been systematically measured through terrestrial low-to-intermediate energy heavy-ion experiments. Given the Tolman-Oppenheimer-Volkoff (TOV) equations, the equation of state (EoS) of the neutron star (NS) matter can be constrained by the observations of NS mass, radius, and tidal deformability. However, large observational uncertainties and the limited number of observations currently make it challenging to strictly reconstruct the EoS, especially to identify interesting features such as a strong first-order phase transition. In this work, we study the dependency of reconstruction quality of the phase transition on the number of NS observations of mass and radius as well as their uncertainty, based on a fiducial EoS. We conquer this challenging problem by constructing a neural network, which allows one to parametrize the EoS with minimum model-dependency, and by devising an algorithm of parameter optimization based on the analytical linear response analysis of the TOV equations. This work may pave the way for the understanding of the phase transition features in NSs using future x-ray and gravitational wave measurements.

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Investigation of the Ultra-High-Energy gamma-ray emission from the Northern Fermi Bubble with LHAASO-KM2A

Investigation of the Ultra-High-Energy gamma-ray emission fr...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Yi He, Jiayin Zhang, Rui Zhao, Shiping 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. Sun, X.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 analyze gamma-ray emission from the Northern Fermi bubble region at the ultra-high-energy range, using the data collected by LHAASO-KM2A from December 2019 to September 2022. Employing an improved gamma/hadron separation method, the median energy of the gamma rays is above 25 TeV. We perform the "direct integral method" in the background estimation;however, no significant excess is observed. Consequently, we present the expected upper limits for gamma-ray emissions within the Fermi bubble region at this energy range. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation

Cross-domain Object Detection through Coarse-to-Fine Feature...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yangtao Zheng Di Huang Songtao Liu Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.

关键词： Feature extraction Subspace constraints Object detection Detectors Task analysis Semantics Prototypes

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Self-embedding Image Watermarking based on Combined Decision Using Pre-offset and Post-offset Blocks

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Computers, Materials & Continua 2018年第11期57卷 243-260页

作者： Daofu Gong Yan Chen Haoyu Lu Zhenyu Li Yibing Han Zhengzhou Science and Technology Institute Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Department of Computer Science University of YorkYork YO105GHUK

To detect and recover random tampering areas,a combined-decision-based self-embedding watermarking scheme is proposed *** this scheme,the image is first partitioned into 2×2 size ***,the high 5 bits of a block’s average value is embedded into its offset *** tampering type of block is detected by comparing the watermarks of its pre-offset and post-offset *** theoretical analysis and experiments demonstrate that the proposed scheme not only has a lower ratio of false detection but also better performance with regard to avoiding random tampering.

关键词： Fragile watermarking self-embedding offset block tamper recovery random tampering

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SURE-based Stopping Strategy for Fine-tunable Supervised PET Image Denoising

SURE-based Stopping Strategy for Fine-tunable Supervised PET...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Jianan Cui Kuang Gong Ning Guo Scott Wollenweber Floris Jansen Huafeng Liu Quanzheng Li State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Center for Advanced Medical Computing and Analysis Massachusetts General Hospital/Harvard Medical School Boston MA USA Gordon Center for Medical Imaging Massachusetts General Hospital/Harvard Medical School Boston MA USA GE Healthcare Waukesha USA

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Previously, based on the unsupervised deep learning method, conditional deep image prior (CDIP), we showed the possibility of finetuning during the testing phase after supervised learning. However, one barrier for CDIP finetuning is that it needs early stopping before the network output converges to the noise which is hard to determine. In this work, we proposed to construct the finetuning loss based on Stein’s unbiased risk estimator (SURE), which can provide an unbiased estimation of the true mean square error (MSE). We demonstrated that using SURE-based finetuning loss, the network output can be close to the ground truth while not converging to the noise. Quantification results based on seven real patient datasets show that the proposed method is more stable during finetuning than CDIP.

关键词： Training Deep learning Supervised learning Estimation Mean square error methods Testing Image denoising

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An Image Steganography Algorithm Based on Quantization Index Modulation Resisting Scaling Attacks and Statistical Detection

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Computers, Materials & Continua 2018年第7期56卷 151-167页

作者： Yue Zhang Dengpan Ye Junjun Gan Zhenyu Li Qingfeng Cheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Computer School of Wuhan University Wuhan 430072China Department of Computer Science University of YorkYork YO105GHUK

In view of the fact that the current adaptive steganography algorithms are difficult to resist scaling attacks and that a method resisting scaling attack is only for the nearest neighbor interpolation method,this paper proposes an image steganography algorithm based on quantization index modulation resisting both scaling attacks and statistical *** the spatial image,this paper uses the watermarking algorithm based on quantization index modulation to extract the embedded *** construct the embedding distortion function of the new embedded domain based on S-UNIWARD steganography,and use the minimum distortion coding to realize the embedding of the secret ***,according to the embedding modification amplitude of secret messages in the new embedded domain,the quantization index modulation algorithm is applied to realize the final embedding of secret messages in the original embedded *** experimental results show that the algorithm proposed is robust to the three common interpolation attacks including the nearest neighbor interpolation,the bilinear interpolation and the bicubic *** the average correct extraction rate of embedded messages increases from 50%to over 93% after 0.5 times-fold scaling attack using the bicubic interpolation method,compared with the classical steganography algorithm *** the algorithm proposed has higher detection resistance than the original watermarking algorithm based on quantization index modulation.

关键词： Image steganography anti-scaling attack anti-statistical detection quantization index modulation

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Author Correction: An optoelectrochemical synapse based on a single-component n-type mixed conductor

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Nature communications 2025年第1期16卷 5045页

作者： Yazhou Wang Wentao Shan Hanrui Li Yizhou Zhong Shofarul Wustoni Johana Uribe Tianrui Chang Valentina E Musteata Tania Cecilia Hidalgo Castillo Wan Yue Haifeng Ling Nazek El-Atab Sahika Inal Organic Bioelectronics Laboratory Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. Smart Advanced Memory Devices and Applications (SAMA) Laboratory Computer Electrical Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. Imaging and Characterization Core Lab King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. School of Materials Science and Engineering State Key Laboratory of Optoelectronic Materials and Technologies Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices Sun Yat-sen University Guangzhou 510275 China. State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM) Nanjing University of Posts & Telecommunications Nanjing 210023 China. Organic Bioelectronics Laboratory Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia. sahika.inal@kaust.edu.sa.

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A Pipelining Loop Optimization Method for Dataflow Architecture

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Journal of Computer Science & Technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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