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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Yue Wang, Li Chen, Yanyan Liu, Qi Pan, Jingshan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor's storage structure;combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency;optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication;employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation. © 2023 IEEE.

关键词： Communication-avoid Heterogeneous System Jacobi preconditioner PCG algorithm SW26010-Pro

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CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks 40

CPDG: A Contrastive Pre-Training Method for Dynamic Graph Ne...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Bei, Yuanchen Xu, Hao Zhou, Sheng Chi, Huixuan Wang, Haishuai Zhang, Mengdi Li, Zhao Bu, Jiajun Meituan China College of Computer Science and Technology Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University China School of Software Technology Zhejiang University China Institute of Computing Technology Chinese Academy of Science China

ISBN: (纸本)9798350317152

Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks (DGNNs), the rich information and diverse downstream tasks have posed significant difficulties for the practical application of DGNNs in industrial scenarios. To this end, in this paper, we propose to address them by pre-training and present the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG). CPDG tackles the challenges of pre-training for DGNNs, including generalization capability and long-short term modeling capability, through a flexible structural-temporal subgraph sampler along with structural-temporal contrastive pre-training schemes. Extensive experiments conducted on both large-scale research and industrial dynamic graph datasets show that CPDG outperforms existing methods in dynamic graph pre-training for various downstream tasks under three transfer settings. © 2024 IEEE.

关键词： Graph neural networks

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PancakeFS: A Write Efficiently and Read Optimized Filesystem 4

PancakeFS: A Write Efficiently and Read Optimized Filesystem

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4th International Conference on Consumer Electronics and computer Engineering, ICCECE 2024

作者： Wang, Yongkang Liu, Yang Chen, Zhiguang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Institute for Quantum Information College of Computer National University of Defense Technology State Key Laboratory of High-Performance Computing Changsha China

ISBN: (纸本)9798350384062

With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance. © 2024 IEEE.

关键词： Data handling

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Image Generation Method for Addressing Class Imbalance in Small-Sample Pulsar Candidates 21st

Image Generation Method for Addressing Class Imbalance in ...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhang, Maoyu Xu, Hai Yan, Fanfan Ding, Haoran Guo, Meng Shandong Jinan250013 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Shandong Jinan250013 China Jinan Institute of Supercomputing Technology Shandong Jinan250013 China

ISBN: (纸本)9789819601219

Pulsars, rapidly rotating neutron stars, have been crucial topics in astrophysics since their discovery. The advent of deep learning offers a novel method for the efficient classification of pulsar candidates. However, the extreme imbalance between positive and negative classes in the dataset severely affects the performance of the classification model. Therefore, a network enhancing detail perception and stability (EDPSNet) is proposed to address class imbalance. The model combines Deep Convolutional Generative Adversarial Networks (DCGAN) with multi-scale Channel-Spatial Attention (CSA) modules and Spectral Normalization (SN). To enhance local details and global features, thereby enhancing the overall quality and detail of generated images, we introduced a self-designed CSA module. To enhance the stability of the model, SN stabilizes training and prevents mode collapse by constraining the weight matrices of the network. In addition, we proposed the Pixel Moving Segmentation (PMS) technique to address the overfitting issue in generative models caused by insufficient samples. This method expands the dataset while preserving the original features, thus increasing the sample diversity. Experimental results indicate that the EDPSNet model and PMS technique offer significant advantages in handling class imbalanced, small-sample pulsar candidate datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pulsars

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Application of Autonomous Control for Quadruped Robots Based on Deep Reinforcement Learning 6

Application of Autonomous Control for Quadruped Robots Based...

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6th International Conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Qi, Yonghui Yu, Bo Xu, Sen Yang, Xin School of Computer Science and Technology Shenyang University of Chemical Technology Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (纸本)9798350353174

To endow the simulated quadruped robot with autonomous action control capabilities, this paper proposes the use of the PPO (Proximal Policy Optimization) algorithm, a deep reinforcement learning technique, to enable the quadruped robot to make autonomous decisions during walking tasks. Since walking tasks involve continuous actions that depend not only on the current state but also on past actions and states, this paper improves the PPO algorithm with LSTM (Long Short-Term Memory) to enhance the autonomous decision-making ability of the quadruped robot during walking. Using PyBullet to simulate the walking environment of the quadruped robot, experiments have verified that the improved PPO algorithm significantly outperforms the unimproved PPO algorithm in terms of performance. © 2024 IEEE.

关键词： Deep reinforcement learning

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NDANN: Efficient SSD-Based Approximate Nearest Neighbor Search through Navigation

NDANN: Efficient SSD-Based Approximate Nearest Neighbor Sear...

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2022 International Conference on Mechanisms and Robotics, ICMAR 2022

作者： Yang, Kaixiang Wang, Hongya Tan, Zongyuan Zhang, Jie College of Computer Science and Technology Donghua University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Institute of Artificial Intelligence Donghua University Shanghai China

ISBN: (纸本)9781510657328

Graph-based Approximate Nearest Neighbor Search (ANNS) algorithms have attracted more attention due to their better performance. Numerous ANNS optimization methods have been proposed by researchers. However, Current graph-based ANNS algorithms still cannot index billion-scale datasets on a single server with 256GB RAM. Although several researchers have investigated on this problem, we believe there is still an improvement in reducing disk accesses. In this paper, we provide a fast navigation layer to decrease the number of disk accesses by assisting query points in swiftly reaching the range of strongly connected components. Compared with the state-of-the-art ANNS algorithms, NDANN reduces the mean latency by about 20% under the same recall. © 2022 SPIE.

关键词： Graphic methods

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Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information

Extractive Medical Entity Disambiguation with Memory Mechani...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Guobiao Peng, Xueping Shen, Tao Long, Guodong Si, Jiasheng Qin, Libo Lu, Wenpeng China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Computer Science and Engineering Central South University Changsha China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing China

ISBN: (纸本)9798891761681

Medical entity disambiguation (MED) aims to ground medical mentions in text with ontological entities in knowledge bases (KBs). A notable challenge of MED is the long medical text usually contains multiple entities' mentions with intricate correlations. However, limited by computation overhead, many existing methods consider only a single candidate entity mention during the disambiguation process. As such, they focus only on local MED optimal while ignoring the sole-mention disambiguation possibly boosted by richer context from other mentions' disambiguating processes - missing global optimal on entity combination in the text. Motivated by this, we propose a new approach called Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (M3E). Specifically, we reformulate MED as a text extraction task, which simultaneously accepts the context of medical mentions, all possible candidate entities, and entity definitions, and it is then trained to extract the text span corresponding to the correct entity. Upon our new formulation, 1) to alleviate the computation overhead from the enriched context, we devise a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual;and 2) to utilize the disambiguation clues from other mentions, we design an auxiliary disambiguation module that employs a gating mechanism to assist the disambiguation of remaining mentions. Extensive experiments on two benchmark datasets demonstrate the superiority of M3E over the state-of-the-art MED methods on all metrics. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Expressive Speech Synthesis Enhancement with Conditional Embeddings 21st

Expressive Speech Synthesis Enhancement with Conditional Em...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Yan, Fanfan Zhang, Maoyu Xu, Hai Ding, Haoran Guo, Meng Shandong Jinan250013 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Shandong Jinan250013 China Jinan Institute of Supercomputing Technology Shandong Jinan250013 China

ISBN: (纸本)9789819601240

In recent years, emotional speech synthesis techniques have attracted considerable interest because of their wide-ranging potential applications. However, when confronted with datasets containing emotional attributes, speech synthesized by traditional methods frequently encounters difficulties, such as a mismatch with the text content or an unnatural expression of emotions. To solve these problems, we developed a straightforward emotional speech synthesis model. This model builds upon the VITS framework and incorporates an emotion prediction module, a prosody prediction module, and a conditional encoder. It can automatically predict emotion labels based on the input text, or manually specify emotions for precise control of emotions. Experimental outcomes indicate improved naturalness and expressiveness in synthesized speech, thus enhancing the overall audio quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech enhancement

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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Bandwidth-tunable silicon nitride microring resonators

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Chinese Physics B 2022年第1期31卷 263-266页

作者： Jiacheng Liu Chao Wu Gongyu Xia Qilin Zheng Zhihong Zhu Ping Xu Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Device Institute for Quantum Information and State Key Laboratory of High Performance ComputingCollege of Advanced Interdisciplinary Studies&College of ComputerNational University of Defense TechnologyChangsha 410073China

We designed a reconfigurable dual-interferometer coupled silicon nitride microring *** tuning the integrated heater on interferometer's arms,the"critical coupling"bandwidth of resonant mode is continuously adjustable whose quality factor varies from 7.9×10^(4) to 1.9×10^(5) with the extinction ratio keeping higher than 25 *** a variety of coupling spanning from"under-coupling"to"over-coupling"were achieved,showing the ability to tune the quality factor from 6.0×10^(3) to 2.3×10^(5).Our design can provide an adjustable filtering method on silicon nitride photonic chip and contribute to optimize the nonlinear process for quantum photonics and all-optical signal processing.

关键词： silicon nitride microring resonators Mach-Zehnder interferometer ring filter nonlinear optics

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