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arXiv 2023年

作者： Liang, Zhen Wu, Taoran Zhao, Changyuan Liu, Wanwei Xue, Bai Yang, Wenjing Wang, Ji Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China

The increasing use of deep neural networks (DNNs) in safety-critical systems has raised concerns about their potential for exhibiting ill-behaviors. While DNN verification and testing provide post hoc conclusions regarding unexpected behaviors, they do not prevent the erroneous behaviors from occurring. To address this issue, DNN repair/patch aims to eliminate unexpected predictions generated by defective DNNs. Two typical DNN repair paradigms are retraining and fine-tuning. However, existing methods focus on the high-level abstract interpretation or inference of state spaces, ignoring the underlying neurons' outputs. This renders patch processes computationally prohibitive and limited to piecewise linear (PWL) activation functions to great extent. To address these shortcomings, we propose a behavior-imitation based repair framework, BIRDNN, which integrates the two repair paradigms for the first time. BIRDNN corrects incorrect predictions of negative samples by imitating the closest expected behaviors of positive samples during the retraining repair procedure. For the fine-tuning repair process, BIRDNN analyzes the behavior differences of neurons on positive and negative samples to identify the most responsible neurons for the erroneous behaviors. To tackle more challenging domain-wise repair problems (DRPs), we synthesize BIRDNN with a domain behavior characterization technique to repair buggy DNNs in a probably approximated correct style. We also implement a prototype tool based on BIRDNN and evaluate it on ACAS Xu DNNs. Our experimental results show that BIRDNN can successfully repair buggy DNNs with significantly higher efficiency than state-of-the-art repair tools. Additionally, BIRDNN is highly compatible with different activation *** Codes 68N99, , 68T99 Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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ParallelDualSPHysics: supporting efficient parallel fluid simulations through MPI-enabled SPH method 22

ParallelDualSPHysics: supporting efficient parallel fluid si...

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Proceedings of the 51st International Conference on Parallel Processing

作者： Sifan Long Xiao-Wei Guo Xiaokang Fan Chao Li Kelvin Wong Ran Zhao Yi Liu Sen Zhang Canqun Yang College of Computer National University of Defense Technology China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology China College of Computer Central South University China

ISBN: (纸本)9781450397339

Smoothed Particle Hydrodynamics (SPH) is a classical mesh-free particle method which has been successfully applied in the field of Computational Fluid Dynamics (CFD). Its advantages over traditional mesh-based methods have made it very popular in simulating problems involving large deformation and free-surface flow. The high computational cost of the SPH method has obstructed its vast application. A lot of research effort has been devoted to accelerating the SPH method using GPU and multi threading. However, developing efficient parallel SPH algorithms on modern high-performance computers (HPCs) remains significantly challenging, especially for simulating real-world engineering problems involving hundreds of millions of particles. In this paper, we proposed an MPI-enabled parallel SPH algorithm and developed the ParallelDualSPHysics1, an open-source software supporting efficient parallel fluid simulations. Based on an efficient domain decomposition scheme, the essential data structure and algorithms of DualSPHysics were refactored to build the parallel version. For collaborating with evenly distributed particles on a distributed-memory HPC system, the parallel particle interaction and particle update modules were introduced, which enabled the SPH solver to synchronize computations among multiple processors using MPI. In addition, the redesigned pre-processing and post-processing capabilities of the ParallelDualSPHysics supported the applications of this software in a wide range of areas. Real-life test cases with up to 120 million particles were simulated and analyzed on a modern HPC system. The results showed that the parallel efficiency of ParallelDualSPHysics exceeds 90 with up to 1024 CPU cores. It indicated that ParallelDualSPHysics has the potential for large-scale engineering applications.

关键词： Computational Fluid Dynamics Smoothed Particle Hydrodynamics ParallelDualSPHysics Parallel computing

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Enhancing ASIC technology Mapping via Parallel Supergate computing

arXiv

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arXiv 2024年

作者： Cai, Ye Yang, Zonglin Ni, Liwei Xie, Biwei Li, Xingquan College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the development of large-scale integrated circuits, electronic design automation (EDA) tools are increasingly emphasizing efficiency, with parallel algorithms becoming a trend. The optimization of delay reduction is a crucial factor for ASIC technology mapping, and supergate technology proves to be an effective method for achieving this in EDA tools flow. However, we have observed that increasing the number of generated supergates can reduce delay, but this comes at the cost of an exponential increase in computation time. In this paper, we propose a parallel supergate computing method that addresses the tradeoff between time-consuming and delay optimization. The proposed method utilizes the input-constrained supergate pattern to parallelly generate the supergate candidates, and then filter the valid supergates as the results. Experiment results show the efficiency of the proposed method, for example, it can attain the improvement of 4× speedup in computation time and 10.1 in delay reduction with 32 threads. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Ethereum Phishing Detection Using Hyperbolic Neural Networks and Temporal Information

Ethereum Phishing Detection Using Hyperbolic Neural Networks...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Rong Xu Jun Zhang Xiaoyue Zhu Zhaoxiong Song Zhi Yu College of Computer Science Inner Mongolia University Hohhot China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural Networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness.

关键词： Attention mechanisms Phishing Neural networks Benchmark testing Feature extraction Real-time systems Blockchains Safety Security Tuning

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Enhancing ASIC technology Mapping via Parallel Supergate computing

Enhancing ASIC Technology Mapping via Parallel Supergate Com...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Ye Cai Zonglin Yang Liwei Ni Biwei Xie Xingquan Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China State Key Lab of Processors Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

With the development of large-scale integrated circuits, electronic design automation (EDA) tools are increasingly emphasizing efficiency, with parallel algorithms becoming a trend. The optimization of delay reduction is a crucial factor for ASIC technology mapping, and supergate technology proves to be an effective method for achieving this in EDA tools flow. However, we have observed that increasing the number of generated supergates can reduce delay, but this comes at the cost of an exponential increase in computation time. In this paper, we propose a parallel supergate computing method that addresses the tradeoff between time-consuming and delay optimization. The proposed method utilizes the input-constrained supergate pattern to parallelly generate the supergate candidates, and then filter the valid supergates as the results. Experiment results show the efficiency of the proposed method, for example, it can attain the improvement of 4x speedup in computation time and 10.1 in delay reduction with 32 threads.

关键词： Integrated circuits Design automation Costs Instruction sets Aerospace electronics Market research Delays

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GAUSS: GrAph-customized Universal Self-Supervised Learning 24

GAUSS: GrAph-customized Universal Self-Supervised Learning

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33rd ACM Web Conference, WWW 2024

作者： Yang, Liang Hu, Weixiao Xu, Jizhong Shi, Runjie He, Dongxiao Wang, Chuan Cao, Xiaochun Wang, Zhen Niu, Bingxin Guo, Yuanfang School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen China School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798400701719

To make Graph Neural Networks (GNNs) meet the requirements of the Web, the universality and the generalization become two important research directions. On one hand, many universal GNNs are presented for semi-supervised tasks on both homophilic and non-homophilic graphs by distinguishing homophilic and heterophilic edges with the help of labels. On the other hand, self-supervised learning (SSL) algorithms on graphs are presented by leveraging the self-supervised learning schemes from computer vision and natural language processing. Unfortunately, graph universal self-supervised learning remains resolved. Most existing SSL methods on graphs, which often employ two-layer GCN as the encoder and train the mapping functions, can't alter the low-passing filtering characteristic of GCN. Therefore, to be universal, SSL must becustomized for the graph, i.e., learning the graph. However, learning the graph via universal GNNs is disabled in SSL, since their distinguishability on homophilic and heterophilic edges disappears without the labels. To overcome this difficulty, this paper proposes novel GrAph-customized Universal Self-Supervised Learning (GAUSS) by exploiting local attribute distribution. The main idea is to replace the global parameters with locally learnable propagation. To make the propagation matrix demonstrate the affinity between the nodes, the self-representative learning framework is employed with k-block diagonal regularization. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness, universality and robustness to noises. © 2024 ACM.

关键词： Graph neural networks

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Towards determining the presence of barren plateaus in some chemically inspired variational quantum algorithms

arXiv

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arXiv 2023年

作者： Rui, Mao Guojing, Tian Xiaoming, Sun State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China

In quantum chemistry, the variational quantum eigensolver (VQE) is a promising algorithm for molecular simulations on near-term quantum computers. However, VQEs using hardware-efficient circuits face scaling challenges due to the barren plateau problem. This raises the question of whether chemically inspired circuits from unitary coupled cluster (UCC) methods can avoid this issue. Here we provide theoretical evidence indicating they may not. By examining alternated dUCC ansätzes and relaxed Trotterized UCC ansätzes, we find that in the infinite depth limit, a separation occurs between particle-hole one- and two-body unitary operators. While one-body terms yield a polynomially concentrated energy landscape, adding two-body terms leads to exponential concentration. Numerical simulations support these findings, suggesting that popular 1-step Trotterized unitary coupled-cluster with singles and doubles (UCCSD) ansätze may not scale. Our results emphasize the link between trainability and circuit expressiveness, raising doubts about VQEs' ability to surpass classical methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum computers

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Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices

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Journal of computer Science & technology 2020年第1期35卷 179-193页

作者： Wen-Li Zhang Ke Liu Yi-Fan Shen Ya-Zhu Lan Hui Song Ming-Yu Chen Yuan-Fei Chen State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518000China Zhongke Zhicheng Electronic Technology Company Limited Jining 272000China

Internet of Things(IoT)applications have massive client connections to cloud servers,and the number of networked IoT devices is remarkably *** services require both low-tail latency and high concurrency in *** study aims to determine whether an order of magnitude improvement is possible in tail latency and concurrency in mainstream systems by proposing a hardware-software codesigned labeled network stack(LNS)for future *** key innovation is a cross-layered payload labeling mechanism that distinguishes different requests by payload across the full network stack,including application,TCP/IP,and Ethernet *** type of design enables prioritized data packet processing and forwarding along the full datapath,such that latency-insensitive requests cannot significantly interfere with high-priority *** build a prototype datacenter server to evaluate the LNS design against a commercial X86 server and the mTCP research,using a cloud-supported IoT application *** results show that the LNS design can provide an order of magnitude improvement in tail latency and concurrency.A single datacenter server node can support over 2 million concurrent long-living connections for IoT devices as a 99-percentile tail latency of 50 ms is *** addition,the hardware-software codesign approach remarkably reduces the labeling and prioritization overhead and constrains the interference of high-priority requests to low-priority requests.

关键词： tail latency high concurrency network stack cloud server Internet of Things(IoT)service

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DAPO-QAOA: An algorithm for solving combinatorial optimization problems by dynamically constructing phase operators

arXiv

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arXiv 2025年

作者： Wang, Yukun Li, ZeYang Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Computer and Information Science Southwest University Chongqing400715 China

The Quantum Approximate Optimization Algorithm (QAOA) is a well-known hybrid quantum-classical algorithm for combinatorial optimization problems. Improving QAOA involves enhancing its approximation ratio while addressing practical constraints of Noisy Intermediate Scale Quantum (NISQ) devices, such as minimizing the number of two-qubit gates and reducing circuit depth. Although existing research has optimized designs for phase and mixer operators to improve performance, challenges remain, particularly concerning the excessive use of two-qubit gates in the construction of phase operators. To address these issues, we introduce a Dynamic Adaptive Phase Operator (DAPO) algorithm, which dynamically constructs phase operators based on the output of previous layers and neighborhood search approach, optimizing the problem Hamiltonian more efficiently. By using solutions generated by QAOA itself to simplify the problem Hamiltonian at each layer, the algorithm captures the problem’s structural properties more effectively, progressively steering the solution closer to the optimal target. Experimental results on MaxCut and NAE3SAT problems show that DAPO achieves higher approximation ratios and significantly reduces two-qubit RZZ gates, especially in dense graphs. Compared to vanilla QAOA, DAPO uses only 66% of RZZ gates at the same depth while delivering better results, demonstrating its potential for efficient combinatorial optimization in the NISQ era. © 2025, CC BY.

关键词： Hamiltonians

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