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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Baohai Qu, Youyang Gao, Longxiang Li, Conggai Li, Lin Smith, David Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Data61 Commonwealth Science and Industry Research Organization CanberraVIC Australia College of Business and Law Royal Melbourne Institute of Technology MelbourneVIC Australia

ISBN: (纸本)9789819615445

In machine learning, data privacy and security has become an increasingly growing concern. The introduction of machine unlearning offers the ability to address this issue through the removal of personal and sensitive data from trained models to comply with laws, regulations, and user privacy requirements. However, despite the significant benefits of this technique, performing unlearning operations is only sometimes smooth in practice. When we attempt to remove specific data, the model may over-adjust, resulting in its performance on unlearned data. This phenomenon not only reduces the accuracy of the model on known data but also affects its ability to generalize new data, thus weakening the model’s overall performance. Therefore, in this paper, we analyze the phenomenon of over-unlearning;firstly, we explore how to mitigate the effects of over-unlearning by generating synthetic data to fill in the forgotten parts, using data synthesis-based techniques. Secondly, we combine the data synthesis-based compensation strategy with model fine-tuning to further improve the model’s adaptability and generalization capability by further training the model to accommodate synthetic data. Through comprehensive experimentation, we verify the effectiveness of the proposed data synthesis-based compensation strategy. The experimental results show that the data synthesis-based compensation strategy can effectively mitigate the effects of the over-unlearning phenomenon, as well as maintaining the stability and accuracy of the model after removing specific data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data assimilation

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High-frequency Voltage Signal Fault Diagnosis Based on Multi-channel Hierarchical Structure Capsule-Attention Network 8

High-frequency Voltage Signal Fault Diagnosis Based on Multi...

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8th International Conference on Big Data and Information Analytics, BigDIA 2022

作者： Miao, Qiucheng Xu, Chuanfu Zhan, Jun Zhu, Dong Wu, Chengkun Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

An important research area for covered conductors is partial discharge detection. However, the complex distribution of high-frequency signals with particular noise and the long tail problem create a considerable obstacle to accurate detection. This paper proposes a Capsule-Attention model with a Multi-channel Hierarchical structure, which separately utilize ResNet and BiGRU to extract short- and long-term high-frequency and pre-filter existing irrelevant information. The capsule layer is good at multidimensional and spatial information representation, and we utilize it to recognize and classify fault conditions. The attention mechanism's weight distribution quantifies the importance of features in the entire signal. Considering the class imbalance of the long-tail distribution of samples, We propose a Focal-MCC (FMCC) loss function. The proposed loss reshapes the typical cross-entropy loss, hence reducing the weights allocated to well-classified instances. Matthews Correlation Coefficient penalizes false positives/false negatives to prevent parameters from falling into local minimums. The experiments indicate that the model proposed is useful in detecting partial discharge faults and has application value. The code is available at https://***/qiumia030/capsule_attention. © 2022 IEEE.

关键词： Failure analysis

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GNN-Based Persistent K-core Community Search in Temporal Graphs

GNN-Based Persistent K-core Community Search in Temporal Gra...

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IEEE International Conference on Big Data

作者： Zongli Jiang Yirui Tan Guoxin Chen Fangda Guo Jinli Zhang Xiaolu Bai Computer Science and Technology Beijing University of Technology Beijing China Key Laboratory of AI Safety Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Key Laboratory of AI Safety Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The goal of community search is to provide effective solutions for real-time, high-quality community searches within large networks. In many practical applications, such as event organization and friend recommendations, discovering various community structures within a network is crucial for users. However, existing community search algorithms rarely address issues within temporal graphs, and those that do often have two main limitations: (1) traditional community search methods become inefficient and experience significant increases in computation time when scaled to large graphs; (2) while GNN-based community search methods for temporal graphs offer generalizability, they often focus solely on community connectivity and lack cohesiveness. Therefore, we propose a new model PK-GCN, based on Graph Neural Networks (GNNs), to identify persistent k-core communities in temporal networks. This model can handle dynamic changes in temporal graphs and identify communities that persist over time. Compared to existing community search methods, our model not only finds communities with tighter structures but also allows for dynamic queries based on user input without needing retraining. Specifically, our model constructs features by integrating k-core information from core decomposition, graph features, and query features, resulting in more expressive node representations. Additionally, we designed a flexible dynamic query mechanism that allows users to input time information to query communities. Experiments on multiple datasets demonstrate that our model outperforms other GNN-based community search algorithms in F1-score.

关键词： Computational modeling Heuristic algorithms Organizations Big Data Benchmark testing Search problems Graph neural networks Data models Real-time systems

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Performance Evaluation of Intelligent computing Platform for Air-Combat Decision Algorithm 24

Performance Evaluation of Intelligent Computing Platform for...

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xu, Kangfa Bai, Linting Wen, Pengcheng Niu, Wensheng Xi'an Aeronautics Computing Technique Research Institute Avic Aviation Key Laboratory of Science and Technology on Airborne and Missle-borne Computer Xi'an China

ISBN: (纸本)9798350319934

At present, some performance evaluation methods of intelligent computing platform oriented to air combat decision algorithm have problems such as complex evaluation index system construction, poor evaluation accuracy, difficult to verify the effectiveness of evaluation conclusions and lack of real-time performance. Based on this, this paper establishes the intelligent computing platform performance evaluation index set according to the intelligent decision class algorithm structure, platform compatibility and platform hardware characteristics. Cloud center of gravity method and consistent weighting method are selected as the evaluation algorithm and weighting algorithm respectively to carry out real-time evaluation and form the initial evaluation conclusion. Besides, the 'evaluation confidence' is used to judge the validity of the evaluation conclusion and whether a second evaluation is needed to form the final conclusion and the simulation environment of air combat is used to verify the accuracy of the evaluation results. This method avoids the randomness and uncertainty of the traditional evaluation methods, ensures the real-time and effectiveness of the evaluation results, significantly improves the accuracy, and can truly and effectively reflect the operational performance of the computing platform equipped with the air-combat decision algorithm. © 2022 IEEE.

关键词： Cloud computing

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Variational Clustering and Denoising of Spatial Transcriptomics

Variational Clustering and Denoising of Spatial Transcriptom...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Cuiyuan Zhang, Fa Hu, Kai Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Beijing Institute of Technology School of Medical Technology Beijing China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China

ISBN: (纸本)9798350337488

Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information to accurately identify spatial domains is difficult due to factors such as high-dimensionality, sparsity, noise, and dropout events. To address these issues, we introduce vGraphST, a novel graph-based deep learning approach tailored for spatial transcriptomics data. Our method combines auto-encoder and contrastive learning techniques to process high-dimensional data and generate meaningful low-dimensional embeddings. Additionally, we use continuous distributions instead of discrete values in both the latent space and the denoised gene expression space. Specifically, Gaussian distributions are used to model the latent space, while zero-inflated Poisson distributions are used to model the denoised gene expression space. Experimental results demonstrate the effectiveness of vGraphST in accurately representing and analyzing spatial transcriptomics data. When compared to other methods using the DLPFC dataset, vGraphST achieves an average Adjusted Rand Index (ARI) of 0.58, demonstrating its superiority in segmenting spatial domains and recognizing biologically relevant spatiotemporal patterns. © 2023 IEEE.

关键词： clustering contrastive learning denoising spatial transcriptomics variational auto-encoder

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Toward optimal circuit size for sparse quantum state preparation

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Physical Review A 2024年第3期110卷 032439页

作者： Rui Mao Guojing Tian Xiaoming Sun State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100049 China

Compared to general quantum states, the sparse states arise more frequently in the field of quantum computation. In this work we consider the preparation for n-qubit sparse quantum states with s nonzero amplitudes and propose two algorithms. The first algorithm uses O(ns/log2n+n) gates, improving upon previous methods by O(log2n). Moreover, the classical runtime of this algorithm is optimal. We further establish a matching lower bound for any algorithm that is not amplitude aware and employs at most poly(n) ancillary qubits. The second algorithm is tailored for binary strings that exhibit a short Hamiltonian path. An application is encoding the input data into a state with specified Hamming weight k in quantum machine learning, for which our algorithm constructs a circuit of size O(nklog2n). This surpasses previous results by O(k/log2n) and is close to the lower bound O(nk). The classical runtime is also nearly optimal. Both algorithms shrink the existing gap theoretically and provide increasing advantages numerically.

关键词： Quantum algorithms Quantum algorithms & computation Quantum circuits

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Statistical static timing analysis for circuit aging prediction

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 14-23页

作者： Duan Shengyu Zhai Dongyao Lu Yue Shanghai Engineering Research Center of Intelligent Computing System Shanghai UniversityShanghai 200444China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

关键词： bias temperature instability(BTI) reliability prediction statistical static timing analysis(SSTA)

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Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection

Knowledge-Enhanced Self-Supervised Prototypical Network for ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Kailin Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences China

Prototypical network based joint methods have attracted much attention in few-shot event detection, which carry out event detection in a unified sequence tagging framework. However, these methods suffer from the inaccurate prototype representation problem, due to two main reasons: the number of instances for calculating prototypes is limited;And, they do not well capture the relationships among event prototypes. To deal with this problem, we propose a Knowledge-Enhanced self-supervised Prototypical Network, called KE-PN, for few-shot event detection. KE-PN adopts hybrid rules, which can automatically align event types to an external knowledge base, i.e., FrameNet, to obtain more instances. It proposes a self-supervised learning method to filter out noisy data from enhanced instances. KE-PN is further equipped with an auxiliary event type relationship classification module, which injects the relationship information into representations of event prototypes. Extensive experiments on three benchmark datasets, i.e., FewEvent, MAVEN, and ACE2005 demonstrate the state-of-the-art performance of KE-PN. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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Quantum circuit synthesis on noisy intermediate-scale quantum devices

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Physical Review A 2024年第1期109卷 012602-012602页

作者： Shuai Yang Guojing Tian Jialin Zhang Xiaoming Sun State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China and School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100049 China

In the near future of the noisy intermediate-scale quantum (NISQ) era, almost all quantum computing devices will be restricted to a specific fixed qubits connectivity architecture. Thus, the synthesis of quantum circuits with limited connectivity is urgent. We design quantum circuit synthesis algorithms for basic and essential synthesis problems, such as quantum state preparation, general unitary synthesis, and quantum isometries. For any architecture, the controlled not (cnot) count is at most 5/3 times the state-of-the-art result on complete-graph architecture. For some specific architectures, such as square-grid ones, the ratio is reduced to 1.126. The numerical simulation result is confirmatory of theoretical conclusions. Our algorithms significantly reduce by more than 50% additional cnot count compared to mapping algorithms. These algorithms help to implement the larger-scale algorithm in the physics device. Our results illustrate that well-designed synthesis algorithms can mitigate the problem of limited qubit connectivity in the NISQ era and may suggest the design of large-scale quantum devices.

关键词： Quantum algorithms & computation Quantum circuits Quantum computation Quantum gates

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94LVI Algorithms of OIpU(One-Iteration-per-Update) Type Handling Time-Dependent QP(Quadratic Programming) with Two Constraints 41

94LVI Algorithms of OIpU(One-Iteration-per-Update) Type Hand...

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第41届中国控制会议

作者： Yunong Zhang Zhiwen Yuan School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Research Institute of Sun Yat-sen University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents three relatively effective numerical algorithms,i.e.,94 LVI algorithms of OIpU(one-iteration-perupdate) type,to handle time-dependent QP(quadratic programming) problems subject to two constraints respectively(i.e.,equality and inequality constraints,equality and bound constraints,or inequality and bound constraints).Specifically,two "bridges" play essential parts of the methods(i.e.,the first "bridge" makes a QP problem convert to an LVI [linear variational inequality],and the second "bridge" makes the LVI convert to a PLPE [piecewise-linear projection equation]).On the basis of the standard94 LVI algorithm,the methods only update once through the solution of the previous sampling time ***,we conduct a number of experiments to substantiate the three algorithms respectively,which use exponential functions,log functions,and trigonometric functions in corresponding coefficients of the experimental examples.

关键词： Quadratic programming Time-dependent Numerical algorithms Linear variational inequalities(LVI)

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