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International Joint Conference on Neural Networks (IJCNN)

作者： Jiang, Qin Wang, Qinglin Chi, Lihua Liu, Jie Sci & Technol Parallel & Distributed Proc Lab Paris France Lab Digitizing Software Frontier Equipment Changsha Peoples R China Hunan GuoKe Computil Technol Co Ltd Changsha Hunan Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time superresolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit Neural Alignment Network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.

关键词： Implicit Neural Representation Alignment Video Frame Interpolation Video Super-Resolution

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Evaluating mesh quality with graph neural networks

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ENGINEERING WITH COMPUTERS 2022年第5期38卷 4663-4673页

作者： Wang, Zhichao Chen, Xinhai Li, Tieju Gong, Chunye Pang, Yufei Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China

The quality of the finite element mesh has a considerable effect on the efficiency and accuracy of computational fluid dynamics (CFD) simulations. To ensure the generated mesh is of good quality, many quality metrics have been proposed to assess the generated mesh, such as aspect ratio, skewness, Jacobian ratio, etc. Such metrics, however, are primarily employed to detect locally distorted mesh elements. There are still no justifiable thresholds for determining whether the generated mesh is of sufficient quality for simulation. Consequently, it is necessary for the professionals to assess the generated mesh afterward which is time-consuming and labor-intensive work. With the ability to learn features on the graph, the graph neural networks have been successfully applied in many application areas to reduce human-computer interaction. In this paper, we define mesh quality evaluation as a graph classification problem. We first propose a novel and sparse-implemented algorithm to transform the mesh data into graph data. We then introduce a deep graph neural network, GMeshNet, to evaluate the mesh quality. Experimental results on the NACA-Market and NACA6510 mesh datasets demonstrate the effectiveness of our proposed network.

关键词： Computational fluid dynamics Mesh quality evaluation Graph neural network Deep learning application

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A self-attention based neural architecture for Chinese medical named entity recognition

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第4期17卷 3498-3511页

作者： Wan, Qian Liu, Jie Wei, Luona Ji, Bin Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

The combination of medical field and big data has led to an explosive growth in the volume of electronic medical records (EMRs), in which the information contained has guiding significance for diagnosis. And how to extract these information from EMRs has become a hot research topic. In this paper, we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records (CEMRs). Firstly, a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs. Then we use the encoder from Transformer (ET) as our model's encoder to alleviate the long context dependency problem, and the CRF is utilized as the decoder. At last, we compare the BiLSTM-CRF and ET-CRF model with word2vec and ELMo embeddings to CEMRs respectively to validate the effectiveness of ELMo-ET-CRF model. With the same training data and test data, the ELMo-ET-CRF outperforms all the other mentioned model architectures in this paper with 85.59% F1 -score, which indicates the effectiveness of the proposed model architecture, and the performance is also competitive on the CCKS2019 leaderboard.

关键词： self-attention ELMo named entity recognition Chinese electronic medical records natural language processing

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Enhancing Code Representation Learning for Code Search with Abstract Code Semantics

Enhancing Code Representation Learning for Code Search with ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Shaojie Ding, Yiwei Hu, Enrui Yu, Yue Zhang, Yu Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Huawei Distributed & Parallel Software Lab Shenzhen Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Code representation learning is an important way to encode the semantics of source code through pre-training. The learned representation supports a variety of downstream tasks, such as natural language code search and code defect detection. Inspired by pre-trained models for natural language representation learning, existing approaches often treat the source code or its structural information (e.g., Abstract Syntax Tree or AST) as a plain token sequence. Unlike natural language, programming language has its unique code unit information (e.g., identifiers and expressions) and logic information (e.g., the functionality of a code snippet). To further explore those properties, we propose Abstract Code Embedding (AbCE), a selfsupervised learning method that considers the abstract semantics of code logic. Instead of scattered tokens, AbCE treats an entire node or a subtree in an AST as a basic code unit during pre-training, which preserves the entirety of a coding unit. Moreover, AbCE learns the abstract semantics of AST nodes via a self-distillation way. Experimental results show that it achieves significant improvements over state-of-the-art baselines on code search tasks and comparable performance on code clone detection and defect detection tasks even without using contrastive learning or curriculum learning.

关键词： Natural Language Processing Code Comprehension Self-distillation Code Search

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Three-Dimensional Tradeoffs for Consensus Algorithms: A Review

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第2期19卷 1216-1228页

作者： Li, Linhui Shi, Peichang Fu, Xiang Chen, Peng Zhong, Tao Kong, Jinzhu Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China Kylin Software Co Ltd Changsha 410073 Peoples R China

Blockchain has been applied in many fields to solve the problems of trust, security, efficiency benefiting from its tamper-proof and traceability of data. However, it is still necessary to consider the technical constraints that limit the large-scale application of blockchain: scalability, security, and decentralization cannot be achieved altogether. Consensus algorithm is the core of blockchain, which determines the performance of blockchain system to a certain extent. The existing reviews or surveys mainly focus on processes of consensus algorithms, but fall short in covering the current trends and scenarios, thereby lacking intrinsic understanding of their design philosophy. In this paper, we propose a multi-dimensional tradeoff model and unearth various indicators of different dimensions to guide the construction of consensus algorithms. To summarize the existing efforts, we compare and analyze various classical consensus algorithms, and focus on the design principles of these algorithms under the multi-dimensional tradeoff model. According to different requirements, each algorithm has different tradeoffs. Furthermore, we provide different solutions for blockchain in different dimensions. Finally, we summarize the development trend of consensus design and the key technology prospects of blockchain. This is, to the best of our knowledge, the first survey that accomplishes such goals.

关键词： Blockchains Security Consensus algorithm Scalability Throughput Bitcoin Technological innovation Scalability decentralization security consensus

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Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking 21

Contrastive Learning of User Behavior Sequence for Context-A...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhu, Yutao Nie, Jian-Yun Dou, Zhicheng Ma, Zhengyi Zhang, Xinyu Du, Pan Zuo, Xiaochen Jiang, Hao Univ Montreal Montreal PQ Canada Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Huawei Distributed & Parallel Software Lab Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450384469

Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ranking. However, a user behavior sequence has often been viewed as a definite and exact signal reflecting a user's behavior. In reality, it is highly variable: user's queries for the same intent can vary, and different documents can be clicked. To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences. Specifically, we propose three data augmentation strategies to generate similar variants of user behavior sequences and contrast them with other sequences. In so doing, the model is forced to be more robust regarding the possible variations. The optimized sequence representation is incorporated into document ranking. Experiments on two real query log datasets show that our proposed model outperforms the state-of-the-art methods significantly, which demonstrates the effectiveness of our method for context-aware document ranking.

关键词： Context-aware Document Ranking Contrastive Learning User Behavior Sequence Data Augmentation

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Optimizing CSR-Based SpMV on a New MIMD Architecture Pezy-SC3s 1

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23rd International Conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Guo, Jihu Liu, Jie Wang, Qinglin Zhu, Xiaoxiong Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Sparse matrix-vector multiplication (SpMV) is extensively used in scientific computing and often accounts for a significant portion of the overall computational overhead. Therefore, improving the performance of SpMV is crucial. However, sparse matrices exhibit a sporadic and irregular distribution of non-zero elements, resulting in workload imbalance among threads and challenges in vectorization. To address these issues, numerous efforts have focused on optimizing SpMV based on the hardware characteristics of computing platforms. In this paper, we present an optimization on CSR-Based SpMV, since the CSR format is the most widely used and supported by various high-performance sparse computing libraries, on a novel MIMD computing platform Pezy-SC3s. Based on the hardware characteristics of Pezy-SC3s, we tackle poor data locality, workload imbalance, and vectorization challenges in CSRBased SpMV by employing matrix chunking, applying Atomic Cache for workload scheduling, and utilizing SIMD instructions during performing SpMV. As the first study to investigate SpMV optimization on Pezy-SC3s, we evaluate the performance of our work by comparing it with the CSR-Based SpMV and SpMV provided by Nvidia's CuSparse. Through experiments conducted on 2092 matrices obtained from SuiteSparse, we demonstrate that our optimization achieves a maximum speedup ratio of x17.63 and an average of x1.56 over CSR-Based SpMV and an average bandwidth utilization of 35.22% for large-scale matrices (nnz >= 10(6)) compared with 36.17% obtained using CuSparse. These results demonstrate that our optimization effectively harnesses the hardware resources of Pezy-SC3s, leading to improved performance of CSR-Based SpMV.

关键词： SpMV Optimization CSR-Based SpMV Pezy-SC3s

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Fast and Accurate Poisson Denoising With Trainable Nonlinear Diffusion

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第6期48卷 1708-1719页

作者： Feng, Wensen Qiao, Peng Chen, Yunjin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518061 Peoples R China Natl Univ Def Technol Sch Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China ULSee Inc Hangzhou 310020 Zhejiang Peoples R China

The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision, and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this paper we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly developed trainable nonlinear reaction diffusion (TNRD) model which has proven an extremely fast image restoration approach with performance surpassing recent state-of-the-arts. However, the straightforward direct gradient descent employed in the original TNRD-based denoising task is not applicable in this paper. To solve this problem, we resort to the proximal gradient descent method. We retrain the model parameters, including the linear filters and influence functions by taking into account the Poisson noise statistics, and end up with a well-trained nonlinear diffusion model specialized for Poisson denoising. The trained model provides strongly competitive results against state-of-the-art approaches, meanwhile bearing the properties of simple structure and high efficiency. Furthermore, our proposed model comes along with an additional advantage, that the diffusion process is well-suited for parallel computation on graphics processing units (GPUs). For images of size 512 x 512, our GPU implementation takes less than 0.1 s to produce state-of-the-art Poisson denoising performance.

关键词： Convolutional neural networks (CNNs) Poisson denoising trainable nonlinear reaction diffusion model trained activation functions

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ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

ST-PINN: A Self-Training Physics-Informed Neural Network for...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan, Junjun Chen, Xinhai Wang, Zhichao Zhoui, Enqiang Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China

ISBN: (纸本)9781665488679

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x.

关键词： partial differential equations physics-informed neural networks pseudo label self-training

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Eunomia: Scaling Concurrent Index Structures Under Contention Using HTM

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2018年第8期29卷 1837-1850页

作者： Zhang, Weihua Wang, Xin Ji, Shiyu Wei, Ziyun Wang, Zhaoguo Chen, Haibo Fudan Univ Software Sch Shanghai Key Lab Data Sci Shanghai 200433 Peoples R China Fudan Univ Parallel Proc Inst Shanghai 200433 Peoples R China NYU Dept Comp Sci 550 1St Ave New York NY 10003 USA Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai 200240 Peoples R China

Hardware transactional memory (HTM) is an emerging hardware feature. HTM simplifies the programming model of concurrent programs while preserving high and scalable performance. With the commercial availability of HTM-capable processors, HTM has recently been adopted to construct efficient concurrent index structures. However, with the expansion of data volume and user amount, data management systems have to process workloads exhibiting high contention;meanwhile, according to our experiments, the conventional HTM-base concurrent index structures fail to provide scalable performance under highly-contented workloads. Such performance pathology strictly constrains the usage of HTM on data management systems. In this paper, we first conduct a thorough analysis on HTM-based concurrent index structures, and uncover several reasons for excessive HTM aborts incurred by both false and true conflicts under contention. Based on the analysis, we advocate Eunomia, a design pattern for HTM-based concurrent index structure which contains several principles to improve HTM performance, including splitting HTM regions with version-based concurrency control to reduce HTM working sets, partitioned data layout to reduce false conflicts, proactively detecting and avoiding conflicting requests, and adaptive concurrency control strategy. To validate their effectiveness, we apply such design principles to construct a scalable concurrent B+ Tree and a skip list using HTM. Evaluation using key-value store and database benchmarks on a 20-core HTM-capable multi-core machine shows that Eunomia leads to substantial speedup under high contention, while incurring small overhead under low contention.

关键词： Hardware transactional memory concurrent index structure data conflicts

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