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research on key technologies of edge cache in virtual data space across WAN

Frontiers of computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing center in Guangzhou, National Supercomputing center in Jinan, National Supercomputing center in Changsha, Shanghai Supercomputing center, and computer Network Information center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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Video Stabilization via Prediction with Time-Series Network and Image Inpainting with Pyramid Fusion

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Chinese Journal of Electronics 2021年第6期30卷 1103-1110页

作者： CHENG Keyang LI Shichao RONG Lan WANG Wenshan SHI Wenxi ZHAN Yongzhao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center Cyber Space Security Academy of Jiangsu University National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Xinjiang Lianhaichuangzhi Information Technology Co. Ltd.

Due to the poor filling effect of the video image defect commonly used in the video stabilization field, the video is seemed still unstable after the image stabilization process, which seriously affects the visual effect. To solve this problem, we improve a video stabilization method based on time-series network prediction and pyramid fusion restoration is proposed to optimize the visual effect after image *** flow of the proposed method is as follows: First,it is adaptive to determine whether the defect of the corresponding frame at the current time needs padding inpainting. Then, for the frame that needs to be inpainting,the frames generated before the current moment are sent to the model combining the convolutional neural networks and the gate recurrent unit to predict the part to be filled. Next the current defect image and the complete image to be filled are brought into the Laplacian pyramid reconstruction, and the improved weighted optimal suture is introduced for splicing during the fusion. Finally, the video frame is cut after reconstruction. The method is tested on a data set composed of videos commonly used in the field of video stabilization. The experimental results show that the average peak signal to noise ratio of the method is 2 to 5 d B higher than that of the comparison algorithm, and the average structural similarity index is improved by about 2% to 7% compared with the contrast algorithm.

关键词： image restoration image sequences video frame cutting improved weighted optimal suture gate recurrent unit video signal processing video stabilization method image fusion Laplacian pyramid reconstruction padding inpainting image resolution neural nets convolutional neural networks video image defect pyramid fusion restoration image inpainting visual effect optimization image reconstruction time-series network prediction image stabilization process

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STFormer: Learning to Explore Spot Relationships for Spatial Transcriptomics Prediction from Histology of Colorectal Cancer 46

STFormer: Learning to Explore Spot Relationships for Spatial...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhan, Gan Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or failing to well explore inter-spot correlations in scenarios with limited Whole Slide Image (WSI) data. In response, we present STFormer, a deep learning approach that incorporates the Style-Aug module to enhance feature generalization through medically-irrelevant style transfer augmentation. Additionally, the Cross-WSI Transformer module is introduced to capture Cross-WSI spot relationships efficiently. Our experimental results, conducted on both internal and external datasets, demonstrate that STFormer surpasses existing methods by a substantial margin. This showcases its potential as a powerful tool for spatial transcriptomics predictions, addressing critical gaps in current methodologies. © 2024 IEEE.

关键词： WSI circuits

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques

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Information Processing in Agriculture 2025年第2期12卷 151-168页

作者： Simhadri, Chinna Gopi Kondaveeti, Hari Kishan Vatsavayi, Valli Kumari Mitra, Alakananda Ananthachari, Preethi School of Computer Science Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India Andhra University Andhra Pradesh Visakhapatnam530003 India Nebraska Water Center Institute of Agriculture and Natural Resources University of Nebraska-Lincoln LincolnNE United States AI & Big Data Endicott College of International Studies Woosong University Endicott Building Daejeon Korea Republic of

Rice is an essential food crop that is cultivated in many countries. Rice leaf diseases can cause significant damage to crop cultivation, leading to reduced yields and economic losses. Traditional disease detection approaches are often time-consuming, labor-intensive, and require expertise. Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference. Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques. Image processing techniques are used to extract features from diseased leaf images, such as the color, texture, vein patterns, and shape of lesions. Machine learning techniques are used to detect diseases based on the extracted features. In contrast, deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks. This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection, such as Transfer Learning, Ensemble Learning, and Hybrid approaches. This review also discusses the effectiveness of these approaches in addressing various challenges. This review discusses the details of various models and hyperparameter settings used, model fine-tuning techniques followed, and performance evaluation metrics utilized in various studies. This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques. © 2024 The Author(s)

关键词： Learning systems

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Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer 46

Biologically Interpretable Model for Precise Recurrence Pred...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ai, Yang Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinical trustworthiness. In this paper, we propose a novel Biologically Informed Pathway-Aware Neural Network (BioPAN). By automatically extracting biological prior knowledge to guide the architecture of DNN models, we design a unified architecture of gene-pathway-biological process-disease. This approach endows each neuron with entity meaning and learns a multi-level view of biological pathways and processes related to recurrence for fully interpretable NSCLC recurrence prediction. We demonstrated that the proposed model well explains the molecular mechanisms linking genes to NSCLC recurrence, identifies several genes that significantly promote and inhibit recurrence, and elucidates the pivotal roles of various gene pathways at the biological process level. Moreover, it outperforms classical machine learning methods, provides fully interpretable biological information, and requires fewer parameters. Broadly, BioPAN enables clinical discovery and prediction, and may have general applicability across cancer types. © 2024 IEEE.

关键词： Lung cancer

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POINE2: Improving Poincaré Embeddings for Hierarchy-Aware Complex Query Reasoning over Knowledge Graphs 26

POINE2: Improving Poincaré Embeddings for Hierarchy-Aware C...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Junnan Mao, Qianren Li, Jianxin Fu, Xingcheng Wang, Zheng School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China Zhongguancun Laboratory Beijing China The School of Computing University of Leeds United Kingdom

ISBN: (纸本)9781643684369

Reasoning complex logical queries on incomplete and massive knowledge graphs (KGs) remains a significant challenge. The prevailing method for this problem is query embedding, which embeds KG units (i.e., entities and relations) and complex queries into low-dimensional space. Recent developments in the field show that embedding queries as geometric shapes is a viable means for modeling entity set and logical relationships between them. Despite being promising, current geometric-based methods face challenges in capturing hierarchical structures of complex queries, which leaves considerable room for improvement. This paper presents POINE2, a geometric-based query embedding framework based on hyperbolic geometry to handle complex queries on knowledge graphs. POINE2 maps entities and queries as geometric shapes on a Cartesian product space of Poincaré ball spaces. To capture the hierarchical structures of complex queries, we use the Poincaré radius to represent the different levels of the hierarchy, and we use the aperture of the shape to indicate semantic differences at the same level of the hierarchy. Additionally, POINE2 offers a flexible and expressive definition of logical operations. Experimental results show that POINE2 outperforms existing salient geometric-based embedding methods and significantly improves these methods on evaluation datasets. © 2023 The Authors.

关键词： Knowledge graph

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research on Tensor Multi-Clustering Distributed Incremental Updating Method for big data

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computers, Materials & Continua 2024年第10期81卷 1409-1432页

作者： Hongjun Zhang Zeyu Zhang Yilong Ruan Hao Ye Peng Li Desheng Shi School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China Ministry of Science and Technology Innovation China Communications Services Corporation LimitedBeijing100071China School of Artificial Intelligence The University of ManchesterManchesterM139PLUK Ministry of Technology Innovation China Telecom Artificial Intelligence Technology(Beijing)Corporation LimitedBeijing100032China Ministry of Science and Technology Innovation Zhongbo Information Technology Research Institute Corporation LimitedNanjing210012China Jiangsu Postal Big Data Technology and Application Engineering Research Center Nanjing University of Posts and TelecommunicationsNanjing210003China

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering *** address this issue,this paper introduces a new method named big data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering ***,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing ***,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary *** new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in ***,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic *** experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most ***,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering *** not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through *** method shows great potential in real-world applications where dynamic data growth is common,and it is of significant imp

关键词： Tensor incremental update distributed clustering processing big data

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Ripple Sparse Self-Attention for Monaural Speech Enhancement 48

Ripple Sparse Self-Attention for Monaural Speech Enhancement

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Qiquan Zhu, Hongxu Song, Qi Qian, Xinyuan Ni, Zhaoheng Li, Haizhou University of New South Wales School of Electrical Engineering and Telecommunications Australia National University of Singapore Department of Electrical and Computer Engineering Singapore Alibaba Group China Meta United States The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility. © 2023 IEEE.

关键词： Speech enhancement

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