检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Yu, Bo Zhang, Jian-Chen Zhang, Tong-Jie Zhang, Tingting School of Mathematics and Big Data Dezhou University Dezhou253023 China Institute for Astronomical Science Dezhou University Dezhou253023 China Shandong Provincial Key Laboratory of Biophysics Institute of Biophysics Dezhou University Dezhou253023 China School of Computer and Information Dezhou University Dezhou253023 China Department of Astronomy Beijing Normal University Beijing100875 China School of Command and Control Engineering Army Engineering University Nanjing210017 China

By the use of homotopy perturbation method-Padé (HPM-Padé) technique, a new analytical approximation of luminosity distance in the flat universe is proposed, which has the advantage of significant improvement for accuracy in approximating luminosity distance over cosmological redshift range within 0 ≤ z ≤ 2.5. Then we confront the analytical expression of luminosity distance that is obtained by our new approach with the observational data, for the purpose of checking whether it works well. In order to probe the robustness of the proposed method, we also confront it to supernova type Ia and recent data on the Hubble expansion rate H(z). Markov Chain Monte Carlo (MCMC) code emcee is used in the data fitting. The result indicates that it works fairly *** Codes 32E30 Copyright © 2021, The Authors. All rights reserved.

关键词： Luminance

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Bacterial community characterization and relationship in the gut of deep-sea holothurians and sediment from the Northern Indian Ocean

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Deep Sea Research Part I: Oceanographic Research Papers 2025年 223卷

作者： Xinlong Li Anning Mou Yue Dong Jing Mo Xiaomei Liao Xiao Wang Zongling Wang Xuelei Zhang Qinzeng Xu Key Laboratory of Marine Eco-Environmental Science and Technology First Institute of Oceanography Ministry of Natural Resources Qingdao 266061 China Laboratory for Marine Ecology and Environmental Science Qingdao Marine Science and Technology Center Qingdao 266237 China College of Environmental Science and Engineering Ocean University of China Qingdao 266100 China School of Ocean Sciences China University of Geosciences (Beijing) Beijing 100083 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an 710129 China Qingdao Marine Engineering Survey Design and Research Institute Co. Ltd Qingdao 266061 China

Sea cucumbers, widely distributed benthic invertebrates in diverse deep-sea habitats, rely on their gut bacterial communities for essential functions including energy metabolism, biological adaptation, and health. However, the gut bacterial community of deep-sea holothurians in the Indian Ocean remains poorly understood. We used 16S rRNA gene sequencing to characterize bacterial communities in the guts of sea cucumbers and surrounding sediments from two distinct regions of the northern Indian Ocean: the Ninety-East Ridge and the Arabian Sea Basin. This study investigates the composition, diversity, predicted functions, co-occurrence networks, and assembly processes of gut bacterial communities. Alpha diversity of gut bacterial communities did not differ significantly between the two regions. Based on principal coordinate analysis, the bacterial communities of gut formed a distinct cluster from the sediment communities. Actinobacteriota, Proteobacteria, Firmicutes, Chloroflexi, and Bacteroidota were the dominant phyla in both gut and sediment bacterial communities. In the Ninety-East Ridge, the gut microbial network exhibited a higher proportion of positive correlation than the sediment network, suggesting a greater potential for cooperative interactions. Compared to sediment communities, functional predictions indicated that gut communities were enriched in metabolic pathways, including those for amino acid, glycan, vitamin, and lipid metabolism. Stochastic processes were predominant in gut bacterial community assembly, whereas deterministic processes dominated in sediment bacterial communities. This study provides critical insights into the gut microbiota of deep-sea holothurians, advancing our understanding of microbial ecology in extreme marine environments.

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A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

arXiv

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arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Vehicle re-identification with location and time stamps 32

Vehicle re-identification with location and time stamps

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32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019

作者： Lv, Kai Du, Heming Hou, Yunzhong Deng, Weijian Sheng, Hao Jiao, Jianbin Zheng, Liang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University University of Chinese Academy of Sciences Australian National University

ISBN: (纸本)9781728125060

This paper focuses on the problem of vehicle re-identification (Re-ID). In our attempt, we propose a re-identification framework by exploiting vehicle location and time stamps. The location and time information have the potential to cover the shortage of appearance-based feature representations. First, we introduce an ensemble technique to combine the informative cues of multiple Re-ID models effectively. To further improve the accuracy, we then build up a system to acquire the vehicle location and time stamps. Specifically, we utilize the detected results to obtain the needed information. With the help of the proposed system, we can remove irrelevant images from a given ranking list. Our system finished 3rd place in the 2019 AI-City challenge for city-scale multi-camera vehicle re-identification. © 2019 IEEE Computer Society. All rights reserved.

关键词： Vehicles

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Multi-Level Chaotic Maps for 3D Textured Model Encryption 2nd

Multi-Level Chaotic Maps for 3D Textured Model Encryption

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Jin, Xin Zhu, Shuyun Wu, Le Zhao, Geng Li, Xiaodong Zhou, Quan Lu, Huimin Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (纸本)9783030177621

With the rapid progress of virtual reality and augmented reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often considers the point clouds, the meshes, and the textures individually. In this work, a multi-level chaotic maps model for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons, and textures. For vertices which make main contribution for recognizing, we use high-level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold’s cat map and 1D logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method and use the same high-level chaotic map for point cloud, polygons, and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, and correlation attack. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Textures

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A Predictive-Trend-Aware and Critical-Path-Estimation-Based Method for Workflow Scheduling Upon Cloud Services

A Predictive-Trend-Aware and Critical-Path-Estimation-Based ...

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IEEE International Conference on Services Computing (SCC)

作者： Yi Pan Xiaoning Sun Yunni Xia Wanbo Zheng Xin Luo College of Computer Science Chongqing University Chongqing China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.

关键词： Cloud computing Processor scheduling Computational modeling Quality of service Predictive models Dynamic scheduling Real-time systems

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Sea-land Segmentation of Synthetic Aperture Radar Imagery Using Deep Neural Network Models

Sea-land Segmentation of Synthetic Aperture Radar Imagery Us...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yinfei Zhou Gang Zheng Jingsong Yang Xiaofeng Li Bin Liu Jingliang Shao Han Jiang Ren Ren Peng Chen Lin Ren Bin Zhang Ocean College Zhejiang University Zhoushan China Ministry of Natural Resources State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Hangzhou China Chinese Academy of Sciences CAS Key Laboratory of Ocean Circulation and Waves Big Data Center Institute of Oceanology Qingdao China Chinese Academy of Sciences Center for Ocean Mega-Science Institute of Oceanology Qingdao China College of Marine Sciences Shanghai Ocean University Shanghai China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665409889

The waterline is the transient boundary between land and sea, and the waterline extraction is of great significance for coastal research and monitoring. Spaceborne synthetic aperture radar (SAR) has the ability to penetrate clouds and thus can image large coastal areas with high-resolution under all-weather and all-day conditions. These advantages make SAR well complement the waterline observations by optical satellites. However, SAR images of coastal areas usually have complex patterns because of the diverse topography and geomorphology, and the SAR-imaging mechanism also results in large coherent speckle noise in the images. The above poses challenges to the waterline extraction from SAR imagery. Traditional manual annotation is highly accurate, but is laborious and time-consuming. Therefore, a more efficient method is urgent. The procedure of waterline extraction is sea-land segmentation followed by boundary-extraction where accurate segmentation is critical. From the perspective of the computer vision, sea-land segmentation can be viewed as the image semantic segmentation task. In recent years, deep neural network (DNN) models have achieved great success in the semantic segmentation task. The U-Net, named for its U-shape architecture, is one of the most popular DNN models for the task. Compared with others, the U-Net has a lighter structure that efficiently extract and fuse the multi-scale image features. In this study, we applied the U-Net and its variants to sea-land segmentation and systematically investigated the models’ performances. All models can segment the sea and land efficiently, and the results are overall consistent with the manual annotation. Our study demonstrates the application potential of DNN technology in waterline monitoring from SAR imagery.

关键词： Deep learning Image segmentation Neural networks Semantics Sea measurements Manuals Radar polarimetry

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Improving The Biodegradability of Rice Straw by Electrochemical Pretreatment

SSRN

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SSRN 2022年

作者： Sun, Shaohua Zhang, Yuanyuan Yang, Zhaoyang Liu, Chunmei Zuo, Xiaoyu Tang, Yang Wan, Pingyu Liu, Yanping Li, Xiujin Coulon, Frederic Hu, Qing Yang, Xiao Jin State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical Technology 15 Beisanhuan East Road Chaoyang District Beijing100029 China Beijing City Environmental Pollution Control and Resource Reuse Engineering Center Beijing University of Chemical Technology 15 Beisanhuan East Road Chaoyang District Beijing100029 China National Fundamental Research Laboratory of New Hazardous Chemicals Assessment and Accident Analysis Beijing University of Chemical Technology Beijing100029 China School of Water Energy and Environment Cranfield University Cranfield MK43 0AL United Kingdom School of Environmental Science and Engineering South University of Science and Technology Guangdong Shenzhen518055 China Beijing Huanding Environmental Big Data Institute No. 1 Wangzhuang Road Beijing100083 China

In this study, the efficacy of three different pretreatments of rice straw including alkaline hydrogen peroxide (NaOH-H2O2), electrochemically produced NaOH-H2O2 (eNaOH-H2O2) and electrohydrolysis (EH) was investigated. All three pretreatments allowed to reach 80% of total biogas production within 23 and 25 days which was 3 to 5 days faster compared to conventional NaOH pretreatment. The cumulative biogas production was enhanced by 8%, 22% and 14% for NaOH-H2O2, eNaOH-H2O2 and EH, respectively compared to traditional treatment. The eNaOH-H2O2 pretreatment decreased the crystallinity of cellulose and created a porous surface vein, thus promoting microbial degradation of lignocellulose. The findings of this study demonstrate that electrochemical pretreatment provides a promising approach for efficiently utilizing rice straw biomass to produce renewable electricity. © 2022, The Authors. All rights reserved.

关键词： Oxidation

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Adaptive layer-wise personalized federated learning via dual delay update in future communication networks

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Computer Communications 2025年 239卷

作者： Xu, Haowen Mao, Yingchi Chen, Si Rong, Yi Muazu, Tasiu He, Xiaoming College of Computer Science And Software Engineering Hohai University No. 8 Focheng West Road Jiangning District Nanjing211100 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing210098 China China Huaneng Group Co. Ltd Beijing100000 China College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China

The future communication networks refers to large-scale mass-connected networks consisting of billions of cloud, edge, and end devices, which are expected to support the ever-growing communication demands. In the future communication networks, billions of end devices generate massive amount of data that needs to be processed and analyzed (e.g., model training). Artificial Intelligence of Things (AIoT) is a groundbreaking technology that leverages artificial intelligence models to process and analyze data generated by a large number of internet of things devices. As an emerging AIoT method, personalized Federated Learning (pFL) has emerged prominently in distributed model training using massive data from the future communication networks. However, it is challenging to accomplish high-performance and communication-efficient model training by existing pFL methods in the future communication networks, due to the following limitations. a) Dynamic role differences in each layer of a multi-layer model are neglected, leading to poor accuracy in customized models deployed on end devices. b) Owing to numerous end devices in the future communication networks, the communication frequency between a cloud server and end devices is extremely high in each communication round, resulting in expensive communication cost. To solve these two limitations, this paper presents a novel pFL framework for distributed model training in the future communication networks, called Adaptive Layer-wise personalized Federated Learning via Dual Delay Update (ALpFLDDU). First, in end devices, a layer-wise aggregation scheme based on an adaptive weight calculation mechanism is designed to capture the dynamic role differences of model layers. Second, in each communication round, we develop a dual delay update strategy to reduce communication frequency between a cloud server and end devices while ensuring model performance. Simulation experiments on text and image classification datasets are conducted. Th

关键词： Internet of things

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Influence of dextran solution and corneal collagen crosslinking on Corneal Biomechanical Parameters Evaluated by Corvis ST

Research Square

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Research Square 2021年

作者： Qin, Xiao Tian, Lei Zhang, Hui Zhang, Di Guo, Li-Li Zhang, Hai-Xia Jie, Ying Li, Lin Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing100069 China School of Biomedical Engineering Capital Medical University Beijing100069 China Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology & Visual Sciences Key Laboratory Beijing100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Capital Medical University Beijing Tongren Hospital Beijing100730 China

Purpose: To analyze the influence of dextran solution and corneal collagen crosslinking (CXL) on corneal biomechanical parameters evaluated by Corneal Visualization Scheimpflug Technology (Corvis ST). Materials and Methods: Forty porcine eyes were included in this study. Twenty porcine eyes were soaked in dextran solution for 30 minutes (10 eyes in 2% dextran solution and 10 eyes in 20% dextran solution). CXL treatment was performed in 10 porcine eyes, the other 10 porcine eyes were regarded as a control group. Each eye was fixed on an experimental inflation platform to carry out Corvis measurements at different IOPs. Corneal biomechanical parameters were calculated based on Corvis measurement. Statistical analysis was used to analyze the influence of dextran solution and CXL on corneal biomechanical parameters based on Corvis parameters. Results: Corneal energy absorbed area (Aabsorbed) decreased after being soaked in dextran solution under IOP of 15 mmHg;Corneal elastic modulus (E) decreased after being soaked in 2% dextran solution and increased after being soaked in 20% dextran solution;SP-A1 increased after CXL. Conclusion: Both dextran solution and CXL can change corneal biomechanical properties;SP-A1 may be used as an effective parameter for the evaluation of CXL. © 2021, CC BY.

关键词： Visualization

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