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Cleaner Engineering and technology 2025年 27卷

作者： Ma, Shuaiyin Cao, Yichun Liu, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an 710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Department of Management and Engineering Linköping University Linköping SE-581 83 Sweden Industrial Engineering and Management University of Oulu Oulu 90570 Finland

Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. This architecture combines time-series generative adversarial network (TimeGAN) for data augmentation with the neural basis expansion analysis for time series (N-BEATS) for prediction, providing a robust solution for accurate energy consumption prediction. TimeGAN enhances the training dataset by generating high-quality synthetic time-series data, effectively mitigating issues of data sparsity and imbalance. N-BEATS, with its modular architecture and strong adaptability to temporal data, ensures precise predictions by capturing both global trends and local variations in energy usage. Experimental results demonstrate the effectiveness of the proposed architecture, exhibiting superior performance across key metrics when compared to traditional models. Specifically, the RMSE of TimeGAN-N-BEATS reduced by more than 8 %, the MSE decreased by over 18 %, and the R2 reached 97.31 %, outperforming baseline models such as long short-term memory and gated recurrent unit, and their attention-enhanced variants. This study highlights the potential of integrating generative and predictive models to optimize energy efficiency in liquid cooling systems, offering valuable insights for sustainable supercomputing operations. © 2025 The Authors

关键词： Edge computing Liquid cooling system N-BEATS Supercomputing centers TimeGAN

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Density PSO-based software module clustering algorithm

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The Journal of China Universities of Posts and Telecommunications 2018年第4期25卷 38-47页

作者： Sun Jiaze Ling Beilei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution,density PSO-based( DPSO) software module clustering algorithm is proposed. Firstly,the software system is converted into complex network diagram,and then the particle swarm optimization( PSO) algorithm is *** shortest path method is used to initialize the swarm,and the probability selection approach is used to update the particle positions. Furthermore,density-based modularization quality( DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of the DPSO algorithm. Hill climbing( HC) algorithm,genetic algorithm( GA),PSO and DPSO algorithm are compared in the modularization quality( MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than the other three traditional algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.

关键词： software module clustering complex network PSO MQ modularity density

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Measurements of differential and angle-integrated cross sections for the 10B（n, α）7Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

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Chinese Physics C 2019年第12期43卷 47-74页

作者： Haoyu Jiang Wei Jiang Huaiyong Bai Zengqi Cui Guohui Zhang Ruirui Fan HanYi Changjun Ning Liang Zhou Jingyu Tang Qi An Jie Bao Yu Bao Ping Cao Haolei Chen Qiping Chen Yonghao Chen Yukai Chen Zhen Chen Changqing Feng Keqing Gao Minhao Gu Changcai Han Zijie Han Guozhu He Yongcheng He Yang Hong Hanxiong Huang Weiling Huang Xiru Huang Xiaolu Ji Xuyang Ji Zhijie Jiang Hantao Jing Ling Kang Mingtao Kang Bo Li Chao Li Jiawen Li Lun Li Qiang Li Xiao Li Yang Li Rong Liu Shubin Liu Xingyan Liu Guangyuan Luan Qili Mu Binbin Qi Jie Ren Zhizhou Ren Xichao Ruan Zhaohui Song Yingpeng Song Hong Sun Kang Sun Xiaoyang Sun Zhijia Sun Zhixin Tan Hongqing Tang Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Qi Wang Taofeng Wang Zhaohui Wang Jie Wen Zhongwei Wen Qingbiao Wu Xiaoguang Wu Xuan Wu Likun Xie Yiwei Yang Li Yu Tao Yu Yongji Yu Linhao Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Yubin Zhao Luping Zhou Zuying Zhou Danyang Zhu Kejun Zhu Peng Zhu State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Particle Detection and Electronics Department of Modem Physics University of Science and Technology of ChinaHefei 230026China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Northwest Institute of Nuclear Technology Xi'an 710024China University of Chinese Academy of Sciences Beijing 100049China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China School of Physics Beihang UniversityBeijing 100083China

Differential and angle-integrated cross sections for the 10B(n,α)^7 Li,10B(n,α0)^7 Li and 10B(n,α1)^7 Li^*reactions have been measured at CSNS Back-n white neutron *** enriched(90%)10B samples 5.0 cm in diameter and^85.0μg/cm^2 in thickness each with an aluminum backing were prepared,and back-to-back mounted at the sample *** charged particles were detected using the silicon-detector array of the Light-charged Particle Detector Array(LPDA)*** neutron energy En was determined by TOF(time-of-flight)method,and the valid a events were extracted from the En-Amplitude two-dimensional *** 15 silicon detectors,the differential cross sections of a-particles were measured from 19.2°to 160.8°.Fitted with the Legendre polynomial series,the(n,a)cross sections were obtained through *** absolute cross sections were normalized using the standard cross sections of the 10B(n,α)^7 Li reaction in the 0.3-0.5 MeV neutron energy *** measurement neutron energy range for the 10B(n,α)^7 Li reaction is 1.0 eV≤En<2.5 Me V(67 energy points),and that for the 10B(n,α0)^7 Li and10B(n,α1)^7 Li^*reactions is 1.0 eV≤En<1.0 MeV(59 energy points).The present results have been analyzed by the resonance reaction mechanism and the level structure of the 11B compound system,and compared with existing measurements and evaluations.

关键词： 10B(n,α)^7Li reaction cross sections LPDA CSNS Back-n white neutron source

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Some Results on Critical ( P 5 , H )-free Graphs

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Theoretical Computer science 2025年 1051卷

作者： Wen Xia Jorik Jooken Jan Goedgebeur Shenwei Huang School of Mathematics and Statistics Ankang University Ankang 725000 China College of Computer Science Nankai University Tianjin 300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin 300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk 8500 Kortrijk Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University 9000 Ghent Belgium School of Mathematical Sciences and LPMC Nankai University Tianjin 300071 China

Given two graphs H 1 and H 2 , a graph is ( H 1 , H 2 ) -free if it contains no induced subgraph isomorphic to H 1 or H 2 . A graph G is k -vertex-critical if every proper induced subgraph of G has chromatic number less than k , but G has chromatic number k . The study of k -vertex-critical graphs for specific graph classes is an important topic in algorithmic graph theory because if the number of such graphs that are in a given hereditary graph class is finite, then there exists a polynomial-time certifying algorithm to decide the k -colorability of a graph in the class. In this paper, we show that: (1) for k ≥ 1 , there are finitely many k -vertex-critical ( P 5 , K 1 , 4 + P 1 ) -free graphs; (2) for s ≥ 1 , there are finitely many 5-vertex-critical ( P 5 , K 1 , s + P 1 ) -free graphs; (3) for k ≥ 1 , there are finitely many k -vertex-critical ( P 5 , K 3 + 2 P 1 ‾ ) -free graphs. Moreover, we characterize all 5-vertex-critical ( P 5 , H ) -free graphs where H ∈ { K 1 , 3 + P 1 , K 1 , 4 + P 1 , K 3 + 2 P 1 ‾ } using an exhaustive graph generation algorithm.

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Signed graph attention networks

arXiv

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arXiv 2019年

作者： Huang, Junjie Shen, Huawei Hou, Liang Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Graph or network data is ubiquitous in the real world, including social networks, information networks, traffic networks, biological networks and various technical networks. The non-Euclidean nature of graph data poses the challenge for modeling and analyzing graph data. Recently, Graph Neural network (GNNs) are proposed as a general and powerful framework to handle tasks on graph data, e.g., node embedding, link prediction and node classification. As a representative implementation of GNNs, Graph Attention networks (GATs) are successfully applied in a variety of tasks on real datasets. However, GAT is designed to networks with only positive links and fails to handle signed networks which contain both positive and negative links. In this paper, we propose Signed Graph Attention networks (SiGATs), generalizing GAT to signed networks. SiGAT incorporates graph motifs into GAT to capture two well-known theories in signed network research, i.e., balance theory and status theory. In SiGAT, motifs offer us the flexible structural pattern to aggregate and propagate messages on the signed network to generate node embeddings. We evaluate the proposed SiGAT method by applying it to the signed link prediction task. Experimental results on three real datasets demonstrate that SiGAT outperforms feature-based method, network embedding method and state-of-the-art GNN-based methods like signed graph convolutional network (SGCN). Copyright © 2019, The Authors. All rights reserved.

关键词： Graph neural networks

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Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling

arXiv

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arXiv 2021年

作者： Deng, Xiumei Li, Jun Ma, Chuan Wei, Kang Shi, Long Ding, Ming Chen, Wen Poor, H. Vincent The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Hangzhou China Nanjing University of Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Data61 CSIRO KensingtonWA6152 Australia The Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear programming based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [O(1/V), O(√V)] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter V. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

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Integrating Appearance and Motion Cues for Visual Tracking via Spatio-Temporal Prompt

SSRN

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SSRN 2025年

作者： Jia, Xianxin Hou, ZhiQiang Wang, Yunlong Yue, Hao Hu, Shuai Ma, Sugang Yang, Xiaobao Pu, Lei School of Communications and Information Engineering School of Artificial Intelligence Xi’ an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Combat Support College Rocket Force University of Engineering Xi’an710025 China

Rich appearance cues in the template frame and dynamic motion cues between consecutive frames are essential for achieving robust visual tracking. However, most existing tracking methods exhibit limitations in their architectural design, often relying on a single cue while neglecting the other. This lack of integration hinders the effective interaction between different cues, thereby restricting the model’s ability to comprehensively interpret the target characteristics. To address this challenge, we propose a unified spatio-temporal prompt learning framework, ICSPTrack, which systematically captures target appearance and motion information across consecutive ***,theAppearancecueEncoderfirstextractsappearance-relatedfeatures, followed by the Motion Cue Encoder, which derives motion-related representations. These cues are subsequently integrated within the Spatio-Temporal Prompt Decoder to facilitate future reasoning by unifying different information sources. The proposed tracker was trained in an end-to-end manner and rigorously evaluated on seven benchmark datasets, including LaSOT, TNL2K, and TrackingNet. The framework achieved success rates of 72.0%, 58.5%, and 84.4% on LaSOT, TNL2K, and TrackingNet, respectively, while attaining an average overlap rate of 74.7% on GOT-10K. Despite its low computational complexity of only 28.8 GFLOPs, the model demonstrated efficient inference at 56 FPS. Experimental results indicate that the proposed method effectively handles complex tracking scenarios and achieves excellent tracking performance. © 2025, The Authors. All rights reserved.

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Full-Duplex ISCC for Low-Altitude networks: Resource Allocation and Coordinated Beamforming

arXiv

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arXiv 2025年

作者： Chen, Yiyang Liu, Wenchao Zhang, Xuhui Ren, Jinke Xing, Huijun Wang, Shuqiang Shen, Yanyan Southern University of Science and Technology Guangdong518055 China Guangdong518055 China Shenzhen Future Network of Intelligence Institute the School of Science and Engineering Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom SIAT CAS Guangdong518055 China Shenzhen University of Advanced Technology Guangdong518055 China

This paper investigates an integrated sensing, communication, and computing system deployed over low-altitude networks for enabling applications within the low-altitude economy. In the considered system, a full-duplex enabled unmanned aerial vehicle (UAV) is dispatched in the airspace, functioning as a UAV-enabled low-altitude platform (ULAP). The ULAP is capable of achieving simultaneous information transmission, target sensing, and mobile edge computing services. To reduce the overall energy consumption of the system while meeting the sensing beampattern threshold and user computation requirements, we formulate an energy consumption minimization problem by jointly optimizing the task allocation, computation resource allocation, transmit beamforming, and receive beamforming. Since the problem is non-convex and involves highly coupled variables, we propose an efficient algorithm based on alternation optimization, which decomposes the original problem into tractable convex subproblems. Moreover, we analyze the convergence and complexity of the proposed algorithm. Numerical results demonstrate that the proposed scheme saves up to 54.12% energy consumption performance compared to the benchmark schemes. Copyright © 2025, The Authors. All rights reserved.

关键词： Signaling

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SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT

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science Bulletin 2020年第10期65卷 842-853,M0004页

作者： Zhonglin Jiang Lina Lu Yuwei Liu Si Zhang Shuxian Li Guanyu Wang Peng Wang Luonan Chen Key Laboratory of Systems Biology Center for Excellence in Molecular Cell ScienceInstitute of Biochemistry and Cell BiologyShanghai Institutes for Biological SciencesChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghai 200031China Bio-med Big Data Center Key Laboratory of Computational BiologyCAS-MPG Partner Institute of Computational BiologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200031China Institute of Biomedical Sciences and School of Life Sciences East China Normal UniversityShanghai 200241China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunming 650223China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research Guangdong Provincial Key Laboratory of Computational Science and Material DesignDepartment of BiologySouthern University of Science and TechnologyShenzhen 518055China Laboratory of Systems Biology Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 200031China

Epithelial–mesenchymal transition(EMT) is a complex nonlinear biological process that plays essential roles in fundamental biological processes such as embryogenesis, wounding healing, tissue regeneration,and cancer metastasis. A hallmark of EMT is the switch-like behavior during state transition, which is characteristic of phase transitions. Hence, detecting the tipping point just before mesenchymal state transition is critical for understanding molecular mechanism of EMT. Through dynamic network biomarkers(DNB) model, a DNB group with 37 genes was identified which can provide the early-warning signals of EMT. Particularly, we found that two DNB genes, i.e., SMAD7 and SERPINE1 promoted EMT by switching their regulatory network which was further validated by biological experiments. Survival analyses revealed that SMAD7 and SERPINE1 as DNB genes further acted as prognostic biomarkers for lung adenocarcinoma.

关键词： Dynamic network biomarker Tipping point Epithelial–mesenchymal transition Bistability Quantitative real-time PCR

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Fair and autonomous sharing of federate learning models in mobile Internet of Things

arXiv

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arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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