检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Yang, Minghan Xu, Dong Chen, Hongyu Wen, Zaiwen Chen, Mengyun School of Mathematical Sciences Peking University China Beijing International Center for Mathematical Research Peking University China Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Huawei Technologies Co. Ltd China

In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propose a structured stochastic quasi-Newton method by using partial Hessian information as much as possible. By further exploiting either the low-rank structure or the kronecker-product properties of the quasi-Newton approximations, the computation of the quasi-Newton direction is affordable. Global convergence to stationary point and local superlinear convergence rate are established under some mild assumptions. Numerical results on logistic regression, deep autoencoder networks and deep convolutional neural networks show that our proposed method is quite competitive to the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Remote sensing monitoring of areca yellow leaf disease based on UAV multi-spectral images

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural engineering 2020年第8期36卷 54-61页

作者： Zhao, Jinling Jin, Yu Ye, Huichun Huang, Wenjiang Dong, Yingying Fan, Lingling Ma, Huiqin Jiang, Jing National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Hainan Key Laboratory of Earth Observation Sanya572029 China

Yellow leaf disease is a serious disease that endangers the growth of areca, it is urgent to monitor the infection severity and spatial distribution in time and accurately. However, the traditional monitoring methods are mainly depend on visual inspection and manual investigation, which affects the efficiency and spatial scope of monitoring. Low altitude UAV remote sensing technology can effectively solve the problems of insufficient optical satellite images acquisition caused by cloudy and rainy weather in areca planting area, and improve the real-time monitoring of areca yellow leaf disease. In this paper, in order to identify the severities and spatial distribution of areca yellow leaf disease, five band(including blue, green, red, near-infrared and red-edge wavebands) multispectral images were obtained by using the MicaSense RedEdge-M multispectral camera mounted on the DJI Phantom 4 Pro V2.0. Based on the Minimum Redundancy Maximum Relevance (mRMR), three sensitive features were selected from 15 potential vegetation indexes. Using Back Propagation Neural Network(BPNN), Random Forest(RF) and Support Vector Machine(SVM) classification algorithms respectively, a monitoring model of areca yellow leaf disease severity was constructed. A total sixty in-situ sampling points were selected and the disease index (DI) were obtained, according to the characteristics of the disease and the separability of remote sensing images, the severities of the disease were divided into three grades: health (DI © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural engineering. All right reserved.

关键词： Neural network models

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FPGA implementation of binary edwards curve point addition

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 107-116页

作者： Pan, Jeng-Shyang Song, Pei-Cheng Yang, Qing-Yong Liao, Lyu-Chao Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Universities Key Laboratory of Industrial Control and Data Analysis Fujian University of Technology Fuzhou350118 China

Currently, information security is becoming more and more important. Elliptic Curve Cryptography (ECC) has attracted more attention due to its high security performance and short key length, and has been more and more used in the process of information encryption. This article provides the corresponding FPGA hardware design scheme for the binary Edwards curve in ECC. At the same time, four schemes are analyzed and compared for the multiplier module in the finite field module. Finally, using the relevant parameters of GF(2163) in the NIST standard, the behavior simulation experiment of the Point Addition module of the binary Edwards curve is carried out to prove the feasibility of the scheme. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation

arXiv

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arXiv 2024年

作者： Guo, Yuhan Shao, Hanning Liu, Can Xu, Kai Yuan, Xiaoru Key Laboratory of Machine Perception Ministry of Education School of AI Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China University of Nottingham United Kingdom

Generative text-to-image models, which allow users to create appealing images through a text prompt, have seen a dramatic increase in popularity in recent years. However, most users have a limited understanding of how such models work and it often requires many trials and errors to achieve satisfactory results. The prompt history contains a wealth of information that could provide users with insights into what have been explored and how the prompt changes impact the output image, yet little research attention has been paid to the visual analysis of such process to support users. We propose the Image Variant Graph, a novel visual representation designed to support comparing prompt-image pairs and exploring the editing history. The Image Variant Graph models prompt differences as edges between corresponding images and presents the distances between images through projection. Based on the graph, we developed the PrompTHis system through co-design with artists. Besides Image Variant Graph, PrompTHis also incorporates a detailed prompt-image history and a navigation mini-map. Based on the review and analysis of the prompting history, users can better understand the impact of prompt changes and have a more effective control of image generation. A quantitative user study with eleven amateur participants and qualitative interviews with five professionals and one amateur user were conducted to evaluate the effectiveness of PrompTHis. The results demonstrate PrompTHis can help users review the prompt history, make sense of the model, and plan their creative process. © 2024, CC BY.

关键词： Image analysis

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Molecular epidemiology and phylogenetic analysis of influenza viruses A (H3N2) and B/ Victoria during the COVID-19 pandemic in Guangdong, China

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Infectious Diseases of Poverty 2024年第4期13卷 99-100页

作者： Zhiqi Zeng Yong Liu Wenxiang Jin Jingyi Liang Jinbin Chen Ruihan Chen Qianying Li Wenda Guan Lixi Liang Qiubao Wu Yuanfang Lai Xiaoyan Deng Zhengshi Lin Chitin Hon Zifeng Yang State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory Health the First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong 510180 P.R. China Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory Medicine Guangzhou Medical University Guangzhou People’s Republic of China Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative Engineering Macau University of Science and Technology Macau SAR China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa Macau China Guangzhou Laboratory Guangzhou China Kingmed Virology Diagnostic and Translational Center Guangzhou Kingmed Center for Clinical Laboratory Co. Ltd. Guangzhou China

Background Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019(COVID-19)and ***,with COVID-19 restrictions lifted,an unanticipated outbreak of the influ-enza B/Nictoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong,southern *** mechanism underlying this phenomenon remains *** better prepare for potential influenza out-breaks during COVID-19 pandemic,we studied the molecular epidemiology and phylogenetics of influenza A(H3N2)and BNictoria that circulated during the COVID-19 pandemic in this *** From January 1,2018 to December 31,2022,we collected throat swabs from 173,401 patients in Guang-dong who had acute respiratory tract *** viruses in the samples were tested using reverse tran-scription-polymerase chain reaction,followed by subtype identification and sequencing of hemagglutinin(HA)and neuraminidase(NA)*** and genetic diversity analyses were performed on both genes from 403 samples.A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the *** During the early period of COVID-19 control,various influenza viruses were nearly undetectable in respiratory *** control measures were relaxed in January 2020,the influenza infection rate peaked at 4.94%(39/789)in December 2021,with the influenza B/Victoria accounting for 87.18%(34/39)of the total influenza *** months later,the influenza infection rate again increased and peaked at 11.34%(255/2248)in June 2022;influenza A/H3N2 accounted for 94.51%(241/255)of the total influenza cases in autumn *** diverse geographic distribution of HA genes of B/Nictoria and A/H3N2 had drastically reduced,and most strains originated from *** rate of B/Victoria HA e

关键词： Insidious transmission Influenza COVID-19 control Genetic diversity Evolution

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PropNet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans

arXiv

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arXiv 2023年

作者： Chen, Zifan Li, Jiazheng Zhao, Jie Liu, Yiting Li, Hongfeng Dong, Bin Tang, Lei Zhang, Li Center for Data Science Peking University Beijing China Radiology Department Peking University Cancer Hospital & Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Center for Health and Medical Data Science Peking University Beijing China Peking University Beijing China

Background: Accurate segmentation of gastric tumors on 3D CT scans is essential for diagnosis and treatment planning. However, irregular shapes and blurred boundaries of tumors make automatic segmentation challenging. Existing 2D-based methods do not model 3D morphology effectively, and 3D-based methods are computationally expensive and require annotated samples. Some studies have used few-shot learning (FSL) to train models with prior knowledge, but these methods cannot handle irregular shapes and blurred boundaries. Purpose: In this pilot study, we anticipate that the human-guided prior knowledge and specially designed modules will address the challenges of 3D segmentation of gastric tumors described above. Drawing inspiration from FSL, we aim to enable the model to learn from information propagation between slices rather than from specific segmentation annotation. Methods: We propose the PropNet framework to achieve 3D segmentation of gastric tumors by propagating the radiologist's prior knowledge from the 2D annotated slice into the entire 3D CT space. The proposing stage uses an inter-slice context module to extract contextual information between adjacent slices and generate a coarse segmentation. The refining stage then corrects difficult-to-segment pixels for improved segmentation. We use two-way branches for boundary and region in each stage to enhance the segmentation performance at tumor boundaries. Furthermore, we adopt an up-down parallel propagation strategy to speed up our model's predictions. Results: Our study utilizes 98 patients (173 CT scans) as a training set and 30 patients (59 CT scans) as a validation set. The proposed method achieves a comparable agreement with manual annotation (Dice of 0.803 with a p-value of 0.563) and radiologists (Dice of 0.797) on the validation set while significantly improving efficiency with only 40 seconds for annotation and segmentation of a tumor. Furthermore, we test our proposed method on an independent external

关键词： Refining

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Sketchy empirical natural gradient methods for deep learning

arXiv

引用

arXiv 2020年

作者： Yang, Minghan Xu, Dong Wen, Zaiwen Chen, Mengyun Xu, Pengxiang School of Mathematical Sciences Peking University China Beijing International Center for Mathematical Research Peking University China Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Huawei Technologies Co. Ltd China Peng Cheng Laboratory Shenzhen China

In this paper, we develop an efficient sketchy empirical natural gradient method (SENG) for large-scale deep learning problems. The empirical Fisher information matrix is usually low-rank since the sampling is only practical on a small amount of data at each iteration. Although the corresponding natural gradient direction lies in a small subspace, both the computational cost and memory requirement are still not tractable due to the high dimensionality. We design randomized techniques for different neural network structures to resolve these challenges. For layers with a reasonable dimension, sketching can be performed on a regularized least squares subproblem. Otherwise, since the gradient is a vectorization of the product between two matrices, we apply sketching on the low-rank approximations of these matrices to compute the most expensive parts. A distributed version of SENG is also developed for extremely large-scale applications. Global convergence to stationary points is established under some mild assumptions and a fast linear convergence is analyzed under the neural tangent kernel (NTK) case. Extensive experiments on convolutional neural networks show the competitiveness of SENG compared with the state-of-the-art methods. On the task ResNet50 with ImageNet-1k, SENG achieves 75.9% Top-1 testing accuracy within 41 epochs. Experiments on the distributed large-batch training show that the scaling efficiency is quite reasonable. Copyright © 2020, The Authors. All rights reserved.

关键词： Fisher information matrix

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, Shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

引用

IEEE Annual International Computer Software and applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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An Optimal Power System Multi-objective Environmental-Economic Planning Based on Moth-Flame Optimization 1

引用

18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Chu, Shu-Chuan Nguyen, Trong-The Nguyen, Vinh-Tiep Nguyen, Trinh-Dong Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

A power system’s optimization under economic planning concerns scheduling sectors as a challenging reality task with its characteristics with non-convex, high-dimensional, nonlinear problems. This study introduces an optimal power system environmental and economic scheduling based on a multi-moth-flame optimizer (MMFO). A multi-objective programming mathematical model uses the Pareto optimal frontier to obtain the Pareto non-inferiority optimal solution, generate the optimal configuration scheme, and search for the optimal global solution in the feasible region. Calculation results of a power system with IEEE test case of 15 and 40 generator sets demonstrate the high convergence and strong adaptability of the algorithm suggested in addressing the environmental-economic dispatch of the power system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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