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SSRN 2023年

作者： Zhang, Zhi Ding, Xiang He, Xinyou Gong, Yang Xiao, Biaobiao Shen, Jixue Ou, Xing Engineering Research Center The Ministry of Education for Advanced Battery Materials School of Metallurgy and Environment Central South University Changsha410083 China Hunan Provincial Key Laboratory of Water Treatment Functional Materials Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraf College of Chemistry and Materials Engineering Hunan University of Arts and Science Changde415000 China Institute of Advanced Battery Materials and Devices Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China Tianmu Lake Institute of Advanced Energy Storage Technologies Co. Ltd Liyang213300 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China

关键词： Single crystals

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Operando Selenium Treatment Towards Enhanced Cyclic Stability for Single-Crystal Ni-Rich Cathode at Ultra-High Voltage of 4.7 V

SSRN

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SSRN 2023年

作者： Zhang, Zhi Ding, Xiang He, Xinyou Gong, Yang Xiao, Biaobiao Shen, Jixue Ou, Xing Hunan Provincial Key Laboratory of Water Treatment Functional Materials Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraf College of Chemistry and Materials Engineering Hunan University of Arts and Science Changde415000 China Engineering Research Center of the Ministry of Education for Advanced Battery Materials School of Metallurgy and Environment Central South University Changsha410083 China Institute of Advanced Battery Materials and Devices Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China Tianmu Lake Institute of Advanced Energy Storage Technologies Co. Ltd Liyang213300 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China

Developing high-voltage (≥4.3 V vs Li/Li+) single-crystal Ni-rich LiNixCoyMn1-x-yO2 offers an enticing strategy to achieve high energy density for lithium-ion batteries. However, at high-voltage operation, the cathode will be vulnerable to induce the intrinsic Oα- (α 0.6Co0.1Mn0.3O2 (SC-NCM) cathode materials by introducing the surface Se element, exhibiting an "anti-aging" effect of inhibiting the escape of oxygen on SC-NCM particles during the ultra-high voltage (4.7 V) cycling. Both theoretical calculation and experimental results confirm that the outside-in nanostructure would substitute some mobile Oα- at the surface of highly charged SC-NCM particle, stabilizing the oxygen lattice and suppressing O2 release during long-term cycling. Meanwhile, the surface modification of thin Se layer will alleviate the parasitic reactions and improve the electronic conductivity. Under the synergistic strategy of surface modification and interface doping, the obtained SC-NCM exhibits boosted cyclic stability in coin half-cell and pouch full-cell simultaneously. Therefore, the reversible capacity of LiNi0.6Co0.1Mn0.3O2 at high voltage is competitive with comercial LiNixCoyMn1-x-yO2 (x ≥ 0.8), demonstrating more superior safety ability and cyclic property. It provides an effective approach for improving the long-term performance of Ni-rich cathode materials for practical application under ultra-high working voltage. © 2023, The Authors. All rights reserved.

关键词： Single crystals

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Audio Tagging by Cross Filtering Noisy Labels

arXiv

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arXiv 2020年

作者： Zhu, Boqing Xu, Kele Kong, Qiuqiang Wang, Huaimin Peng, Yuxing Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha410073 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—High quality labeled datasets have allowed deep learning to achieve impressive results on many sound analysis tasks. Yet, it is labor-intensive to accurately annotate large amount of audio data, and the dataset may contain noisy labels in the practical settings. Meanwhile, the deep neural networks are susceptive to those incorrect labeled data because of their outstanding memorization ability. In this paper, we present a novel framework, named CrossFilter, to combat the noisy labels problem for audio tagging. Multiple representations (such as, Logmel and MFCC) are used as the input of our framework for providing more complementary information of the audio. Then, though the cooperation and interaction of two neural networks, we divide the dataset into curated and noisy subsets by incrementally pick out the possibly correctly labeled data from the noisy data. Moreover, our approach leverages the multitask learning on curated and noisy subsets with different loss function to fully utilize the entire dataset. The noisy-robust loss function is employed to alleviate the adverse effects of incorrect labels. On both the audio tagging datasets FSDKaggle2018 and FSDKaggle2019, empirical results demonstrate the performance improvement compared with other competing approaches. On FSDKaggle2018 dataset, our method achieves state-of-the-art performance and even surpasses the ensemble models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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An Effective Design to Improve the Efficiency of DPUs on FPGA

An Effective Design to Improve the Efficiency of DPUs on FPG...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Yutian Lei Qingyong Deng Saiqin Long Shaohui Liu Sangyoon Oh The Key Laboratory of Hunan Province for Internet of Things and Information Security The School of Computer Science Science and Technology on Parallel and Distributed Processing Laboratory Xiangtan University Xiangtan China The Artificial Intelligence Center PengCheng Laboratory shenzhen China Ajou University Suwon-si Republic of Korea

The following topics are dealt with: cloud computing; learning (artificial intelligence); resource allocation; security of data; mobile computing; neural nets; optimisation; parallel processing; scheduling; and storag... 详细信息

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

关键词： Deep learning Schedules Multicore processing Logic gates Throughput Acceleration Field programmable gate arrays

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Fast Nonlocal Diffusion by Stacking Local Filters for Image Denoising 5th

Fast Nonlocal Diffusion by Stacking Local Filters for Image ...

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Qiao, Peng Sun, Weichu Dou, Yong Li, Rongchun Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology ChangshaHunan410073 China University of South China HengyangHunan421000 China

ISBN: (纸本)9789811501203

Many image denoising methods achieve state-of-the-art performance with little consideration of computation efficiency. With the popularity of high-definition imaging devices, these denoising methods do not scale well to the high-definition images. Therefore, a fast image denoising method is proposed to meet the demand of processing high-definition images. Based on the analysis of the distribution of the distance distrs between the similar patches and their reference patches from a semantic aspect, the large distrs s was found to occur while their contribution to the overall distribution was small. Therefore, the nonlocal filters was replaced in trainable non-local reaction diffusion (TNLRD) with local filters. For image with 4096 X 4096 resolution, the proposed method runs about 6 times faster than TNLRD via a single-thread CPU implementation. And the GPU implementation of the proposed method is about 10 times faster than the CPU implementation. Furthermore, the proposed model achieves competing denoising performance compared with TNLRD in terms of PSNR and SSIM. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Image denoising

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Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data 2nd

Secure and Fast Decision Tree Evaluation on Outsourced Cloud...

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2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019

作者： Liu, Lin Su, Jinshu Chen, Rongmao Chen, Jinrong Sun, Guangliang Li, Jie School of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (纸本)9783030306182

Decision trees are famous machine learning classifiers which have been widely used in many areas, such as healthcare, text classification and remote diagnostics, etc. The service providers usually host a decision tree model on the cloud server and provide some classification service for clients to use such a model remotely. In such a scenario, the model is a valuable asset to the cloud which should not be disclosed to the clients, while the query data and classification results are private to the client. To solve such a problem, we propose several building blocks, i.e., secure comparison and secure polynomial calculation, in a two-cloud model. Based on these building blocks, we design a privacy-preserving decision tree evaluation scheme. Compared with the most recent works, our scheme can fully protect the tree model and clients’ data privacy simultaneously. Besides, our scheme also supports offline service users which is essential to the system’s scalability. Moreover, through theoretical analysis and real-world experimental test, it is oblivious that our scheme is quite efficient. © 2019, Springer Nature Switzerland AG.

关键词： Decision trees

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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Balancing communication overhead and accuracy in compression integration: a survey

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Journal of Supercomputing 2025年第8期81卷

作者： Yang, Aiqiang Liu, Jie Yang, Bo Mo, Zeyao Li, Keqin College of Computer Science and Technology National University of Defense Technology Changsha410000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China China Academy of Engineering Physics Beijing China Department of Computer Science State University of New York New PaltzNY12561 United States

In large-scale distributed training, communication compression techniques are widely used to reduce the significant communication overhead caused by the frequent exchange of model parameters or gradients between training nodes. However, these techniques often introduce additional computational complexity and may lead to data loss, thereby affecting model convergence and performance. This review examines key optimization methods in communication compression, including pruning techniques that remove irrelevant weights, quantization techniques that convert floating-point parameters to low-precision representations, and sparsification techniques that transmit only critical gradients. Low-rank approximation techniques, which compress parameters through matrix factorization, are particularly useful for large-scale models. These techniques have also been widely applied in various application scenarios, demonstrating their effectiveness in different environments. Application scenarios include distributed training, federated learning, and edge computing, where bottlenecks are carefully identified and evaluated in common scenarios, providing a basis for further optimization. Future development directions emphasize co-design of hardware and algorithms, dynamic strategies, and cross-layer optimization. This study provides valuable comparisons of key methods and theoretical analysis for efficient communication compression in distributed systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Edge computing

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CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer Topology 33rd

CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer T...

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33rd International Conference on Advanced Information Networking and Applications, AINA 2019

作者： Zheng, Li-ming Long, Wen-feng Liu, Yu-Jia Sun, Wei-dong Department of Message Communication Armed Police Officer Academy Chengdu China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030150341

Data distribution is a key technology for resources convergence and sharing in distributed environment. To better meet the requirement for real time data distribution in the dynamic network, a trace routing algorithm named CRAWL based on the hybrid two-layered topology is put forward. The algorithm contains an overlay topology named CBDLO, upper of which consists of multiple distributed balanced binary trees corresponding to different properties and the lower of which is an unstructured topology. CRAWL forwards the data on the lower unstructured topology in the form of random walk, so that the data can be sent to the corresponding upper topology entry. It also includes a matching algorithm named CDM for the parallel matching data properties on the upper distributed and balanced binary tree and transmitting the matched data to the nodes that are interested in the data. The experimental results show that the algorithm can effectively support large-scale data distribution in a dynamical network, reduce distribution overhead and matching delays. © 2019, Springer Nature Switzerland AG.

关键词： Routing algorithms

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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