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13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Zhang, Jie Hong, Jie Huang, Dafei Li, Meng Yan, Shiyu Gong, Helin School of Computing University of South China Hunan Hengyang China Engineering and Technology Research Center of Software Evaluation and Testing for Intellectual Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

Metamorphic testing can effectively alleviate the test oracle problem. However, the program under test needs to be executed one more time;it is particularly significant to select the metamorphic relation with the highly fault-detecting ability and improve the test cost-effectiveness. Due to the limitations of existing evaluation methods in engineering applications, a validity evaluation method for metamorphic relation is presented based on single factor and multi-factor mutation scores. It can guide their priority ranking and selection. Experiments show that this method can significantly reduce test costs. © 2022 IEEE.

关键词： Cost effectiveness

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Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder

arXiv

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arXiv 2023年

作者： Ma, Ruimin Xie, Ruitao Wang, Yanlin Meng, Jintao Wei, Yanjie Xi, Wenhui Pan, Yi Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Center for High Performance Computing Guangdong Shenzhen518055 China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Shenzhen518055 China

Autism spectrum disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural MRI (s-MRI). However, most studies involve with datasets where participants’ age are above 5. Few studies conduct machine classification of ASD for participants below 5-year-old, but, with mediocre predictive accuracy. In this paper, we push the boundary of predictive accuracy (above 0.97) of machine classification of ASD in children (age range: 0.92-4.83 years), based on s-MRI features extracted using contrastive variational autoencoder (CVAE). 78 s-MRI, collected from Shenzhen Children’s Hospital, are used for training CVAE, which consists of both ASD-specific feature channel and common shared feature channel. The ASD participants represented by ASD-specific features can be easily discriminated from TC participants represented by the common shared features, leading to high classification accuracy. In case of degraded predictive accuracy when data size is extremely small, a transfer learning strategy is proposed here as a potential solution. Finally, we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions, which discloses potential biomarkers that could help target treatments of ASD in the future. © 2023, CC BY-NC-ND.

关键词： Neuroimaging

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Progressive Perception Learning for Distribution Modulation in Siamese Tracking

Progressive Perception Learning for Distribution Modulation ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kun Hu Xianchen Zhou Mingyu Cao Mengzhu Wang Guangjie Gao Wenjing Yang Huibin Tan State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Sciences National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

We explore an innovative view on distribution modulation to boost Siamese trackers. Specially, we observed two cases of possible distribution inconsistency in Siamese tracking: 1) Two branches with different sizes may be in different distribution ranges after a shared backbone (including BN layers). 2) The background data may affect the total feature distribution of the search branch. To address these issues, we proposed a plug-and-play component named Progressive Perception Learning Module (P 2 LM) to modulate the distribution using three feature normalization blocks successively, i.e., Self-Aware Block (SAB), Target-Aware Block (TAB), and Region-Aware Block (RAB). SAB regulates the distribution of each branch independently for the first issue. TAB uses the target information to guide the distribution adjustments of the two branches. RAB divides the search image into foreground and background with a region mask and normalizes them separately to filter the background distractors for robust tracking. TAB and RAB synergistically alleviate the distribution shifts caused by environmental variance. Experiments on OTB100, UAV123, LaSOT, and GOT-10k verify the compelling effects of our module.

关键词： Target tracking Modulation Information filters Acoustics Speech processing Self-aware

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Towards Fast Large-scale Graph Analysis via Two-dimensional Balanced Partitioning 22

Towards Fast Large-scale Graph Analysis via Two-dimensional ...

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Proceedings of the 51st International Conference on Parallel Processing

作者： Shuai Lin Rui Wang Yongkun Li Yinlong Xu John C.S. Lui Fei Chen Pengcheng Wang Lei Han University of Science and Technology of China China Zhejiang University China Anhui Province Key Laboratory of High Performance Computing University of Science and Technology of China China The Chinese University of Hong Kong China Huawei China

ISBN: (纸本)9781450397339

Distributed graph systems often leverage a cluster of machines by partitioning a large graph into multiple small-size subgraphs. Thus, graph partition usually has a significant impact on the performance of distributed graph systems. However, existing widely used partition schemes in practical graph systems can realize a good balance only in one dimension, e.g., either the number of vertices or the number of edges, and they may also incur lots of edge cuts. To address the problem, we develop BPart, which adopts a two-phase partition scheme to realize two-dimensional balance for both vertices and edges. Its core idea is to first partition the original graph into more small pieces than the cluster scale, and combine the partition to realize desired properties, then selectively combine the small pieces to construct larger subgraphs to generate two-dimensional balanced partition. We implement BPart into two open-source distributed graph systems, Gemini [58] and KnightKing [57]. Results show that BPart realizes good balance in both dimensions, and also significantly reduces the number of edge cuts. As a result, BPart reduces the total running time of various graph applications by 5% - 70%, compared to multiple existing partition schemes, e.g., Chunk-V, Chunk-E, Fennel, and Hash.

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Simulating a Chern Insulator with C = ±2 on Synthetic Floquet Lattice

arXiv

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arXiv 2024年

作者： Lei, Lingxiao Wang, Weichen Huang, Guangyao Hu, Shun Cao, Xi Zhang, Xinfang Deng, Mingtang Chen, Ping-Xing Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Greatwall Quantum Laboratory Changsha410006 China Hefei National Laboratory Hefei230088 China

The synthetic Floquet lattice, generated by multiple strong drives with mutually incommensurate frequencies, provides a powerful platform for the quantum simulation of topological phenomena. In this study, we propose a 4-band tight-binding model of the Chern insulator with a Chern number C = ±2 by coupling two layers of the half Bernevig-Hughes-Zhang lattice and subsequently mapping it onto the Floquet lattice to simulate its topological properties. To determine the Chern number of our Floquet-version model, we extend the energy pumping method proposed by Martin et al. [Phys. Rev. X 7, 041008 (2017)] and the topological oscillation method introduced by Boyers et al. [Phys. Rev. Lett. 125, 160505 (2020)], followed by numerical simulations for both methodologies. The simulation results demonstrate the successful extraction of the Chern number using either of these methods, providing an excellent prediction of the phase diagram that closely aligns with the theoretical one derived from the original bilayer half Bernevig-Hughes-Zhang model. Finally, we briefly discuss a potential experimental implementation for our model. Our work demonstrates significant potential for simulating complex topological matter using quantum computing platforms, thereby paving the way for constructing a more universal simulator for non-interacting topological quantum states and advancing our understanding of these intriguing phenomena. © 2024, CC BY.

关键词： Quantum computers

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A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization 27

A First-Order Multi-Gradient Algorithm for Multi-Objective B...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Ye, Feiyang Lin, Baijiong Cao, Xiaofeng Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence Jilin University China Shanghai Artificial Intelligence Laboratory China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781643685489

In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational inefficient problem. To address this, we propose an efficient first-order multi-gradient method for MOBLO, called FORUM. Specifically, we reformulate MOBLO problems as a constrained multi-objective optimization (MOO) problem via the value-function approach. Then we propose a novel multi-gradient aggregation method to solve the challenging constrained MOO problem. Theoretically, we provide the complexity analysis to show the efficiency of the proposed method and a non-asymptotic convergence result. Empirically, extensive experiments demonstrate the effectiveness and efficiency of the proposed FORUM method in different learning problems. In particular, it achieves state-of-the-art performance on three multi-task learning benchmark datasets. © 2024 The Authors.

关键词： Optimization algorithms

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DynamicNet: Efficient Federated Learning for Mobile Edge computing With Dynamic Privacy Budget and Aggregation Weights

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Li, Zheng Duan, Mingxing Yu, Siyang Yang, Wenjing School of Information Science and Engineering Hunan University Changsha China School of Computer Science and Electronic Engineering Hunan University Hunan China Hunan University of Finance and Economics Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

Mobile Edge computing (MEC) has been successfully applied to smart homes, mobile devices, and other electronic products, offering reduced communication latency and alleviating pressure on cloud services. The integration of Federated Learning (FL) and MEC provides customized services for enhancing user experiences. However, there are still gaps in combining these technologies: FL is susceptible to membership inference attacks, risking user privacy;MEC faces limitations in energy and computational resources, restricting FL performance. In order to address these issues, we propose a novel approach, named DynamicNet, which incorporates Privacy Budget Regulation (PBR) and Dynamic Aggregation Weights (DAW). PBR addresses privacy leaks in edge devices by assessing local model privacy, dynamically adjusting privacy budget for subsequent use, ensuring desired level of privacy protection while minimizing noise impact on global model. DAW tackles MEC-related performance limitations by generating synthetic data, computing optimal weights for edge devices using this synthetic data, and aggregating parameters accordingly. Experimental results on two mainstream datasets demonstrate: 1) PBR preserves participant data privacy while enhancing global model convergence speed and accuracy;2) DAW adapts well to heterogeneous federated environments, ensuring high global model accuracy even when some edge device datasets are of inferior quality. IEEE

关键词： Data privacy

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SPM-GCN: An adaptive reordering algorithm for sparse LU factorization via GCN

SPM-GCN: An adaptive reordering algorithm for sparse LU fact...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Huazeng Liu Junjie Wang Qi Liu Wei Du Zanjun Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it’s difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU.

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Decision mamba: reinforcement learning via hybrid selective sequence modeling 24

Decision mamba: reinforcement learning via hybrid selective ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Sili Huang Jifeng Hu Zhejian Yang Liwei Yang Tao Luo Hechang Chen Lichao Sun Bo Yang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and School of Artificial Intelligence Jilin University China School of Artificial Intelligence Jilin University China Institute of High Performance Computing Agency for Science Technology and Research Singapore Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

ISBN: (纸本)9798331314385

Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online environments by providing task contexts, such as multiple trajectories, called in-context RL. However, due to the quadratic computation complexity of attention in transformers, current in-context RL methods suffer from huge computational costs as the task horizon increases. In contrast, the Mamba model is renowned for its efficient ability to process long-term dependencies, which provides an opportunity for in-context RL to solve tasks that require long-term memory. To this end, we first implement Decision Mamba (DM) by replacing the backbone of Decision Transformer (DT). Then, we propose a Decision Mamba-Hybrid (DM-H) with the merits of transformers and Mamba in high-quality prediction and long-term memory. Specifically, DM-H first generates high-value sub-goals from long-term memory through the Mamba model. Then, we use sub-goals to prompt the transformer, establishing high-quality predictions. Experimental results demonstrate that DM-H achieves state-of-the-art in long and short-term tasks, such as D4RL, Grid World, and Tmaze benchmarks. Regarding efficiency, the online testing of DM-H in the long-term task is 28× times faster than the transformer-based baselines.

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th International Conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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