检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Sun, Haichao Wang, Guoyin Liu, Qun Guo, Yike Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Engineering and Science Shanghai University Shanghai200444 China

Graph neural networks (GNNs) have gained significant attention and have been applied in various domain tasks. Currently, numerous pooling approaches have been proposed to aggregate node features and obtain node embeddings. However, current GNNs are almost black-box models that typically use a flat or single pooling step to aggregate nodes by only considering the similarity between nodes within the cluster. These approaches ignore the influence of relationships within and between clusters. To address this issue, we propose a novel multi-granular pooling method to aggregate nodes by simultaneously considering density and relationships among nodes and clusters. This method allows to obtain multi-granular node embedding clusters. The clusters in the current layer are built upon the previous layer, and these clusters change from fine to coarse as the number of clusters decreases, which is achieved by using multi-granular pooling (MgrPool for short). Additionally, the node representation of each layer is established through the ratio of node distance within clusters to that of between clusters. Finally, we conduct several experiments on node and graph classification tasks by this pooling approach. The results demonstrate that our GNN-MgrPool model outperforms the state-of-the-art similar algorithms and largely improves the interpretability of the learning process. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Quantifying gender imbalance in East Asian academia: Research career and citation practice

arXiv

引用

arXiv 2023年

作者： Nakajima, Kazuki Liu, Ruodan Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Gender imbalance in academia has been confirmed in terms of a variety of indicators, and its magnitude often varies from country to country. Europe and North America, which cover a large fraction of research workforce in the world, have been the main geographical regions for research on gender imbalance in academia. However, the academia in East Asia, which accounts for a substantial fraction of research, may be exposed to strong gender imbalance because Asia has been facing persistent and stronger gender imbalance in society at large than Europe and North America. Here we use publication data between 1950 and 2020 to analyze gender imbalance in academia in China, Japan, and South Korea in terms of the number of researchers, their career, and citation practice. We found that, compared to the average of the other countries, gender imbalance is larger in these three East Asian countries in terms of the number of researchers and their citation practice and additionally in Japan in terms of research career. Moreover, we found that Japan has been exposed to the larger gender imbalance than China and South Korea in terms of research career and citation practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Geographical regions

来源：评论

学校读者我要写书评

暂无评论

Metapopulation models imply non-Poissonian statistics of interevent times

引用

Physical Review Research 2022年第1期4卷 013050-013050页

作者： Elohim Fonseca dos Reis Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo 14260-2900 New York USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo 14260-5030 New York USA Faculty of Science and Engineering Waseda University Tokyo 169-8555 Japan

Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, which impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from one patch to another according to the simple random walk. Our results hold true irrespective of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics.

关键词： Continuous-time random walk Population dynamics Networks & random structures Social networks Diffusion & random walks Markovian processes Non-Markovian processes

来源：评论

学校读者我要写书评

暂无评论

Techno Economic Feasibility Analysis of Grid-Connected Microgrid by Using Solar PV for Residential Usage 9

Techno Economic Feasibility Analysis of Grid-Connected Micro...

引用

9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Mahmud, Dewan Mahnaaz Hasan, Sayeed Ahmed, S.M. Masum Zeyad, Mohammad Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Chattogram Bangladesh Erasmus+ Joint MSC In Smart Cities and Communities Faculty of Engineering Mons Belgium Erasmus+ Joint MSC In Smart Cities and Communities Department of Thermal Engineering Bilbao Spain

ISBN: (纸本)9781665432405

Renewable energy systems (RES) are becoming more prominent because of the depletion of natural resources. Optimum sizing of the hybrid RES is necessary to ensure that renewable energy sources penetrate. Improper sizing may result in higher initiating costs or an inability to meet load demand under various weather conditions. By analyzing with Hybrid Optimization Multiple Energy Resources (HOMER pro), this study shows the ideal hybrid energy system that provides the lowest cost of energy production, emissions, and so on. This study provides a grid-connected hybrid power system model based on metrological data collected from a residential building in Mohammadpur, Dhaka, Bangladesh. Solar energy is used as a sustainable energy source thus the cost of the system was decreased, which is also environmentally friendly. This renewable energy also helped to minimize the levelized cost of energy (COE). The conclusion estimates operational costs, production costs, net present cost, and energy cost for the two combinations evaluated for the proposed microgrid system. According to the simulation results, delivering power to the load via a microgrid would be a viable and cost-effective solution. © 2021 IEEE.

关键词： Battery storage

来源：评论

学校读者我要写书评

暂无评论

Privacy computing using deep compression learning techniques for neural decoding

引用

Smart Health 2022年 23卷

作者： Li, Huining Chen, Huan Xu, Chenhan Das, Anarghya Chen, Xingyu Li, Zhengxiong Xiao, Jian Huang, Ming-Chun Xu, Wenyao Department of Computer Science and Engineering University at Buffalo United States Department of Electrical Computer and Systems Engineering Case Western Reserve University United States Department of Computer Science and Engineering University of Colorado Denver United States School of Electronic and Control Engineering Chang'an University China Department of Data and Computational Science Duke Kunshan University China

The brain–computer interface supports a variety of applications with the help of machine learning technology. However, existing edge-cloud infrastructure requires subjects to send their sensitive neural signals to the cloud for training the model which brings privacy concerns. Although the recent distributed learning technology is used to help protect subjects’ privacy, it brings high communication costs and cannot avoid privacy reconstruction attacks. In this paper, we propose deep compression learning techniques in the privacy computing infrastructure that can be used for neural decoding while preserving privacy and largely reducing the communication cost. Specifically, we first perform heterogeneous neural signals processing and convert them to resized functional brain connectivity images. Then, a semantics structure-based unsupervised deep compression learning network is trained and generates a neural hash locally for each image. Each hash value is irreversible that cannot be used to reconstruct the user's original neural signal. After that, the cloud end receives the uploaded neural hashes and corresponding labels and filters the abnormal ones. Finally, these neural hashes are used for training neural decoding models. Since a single hash value can correspond to different types of labels, it only needs to be uploaded once with a very small size and then reused for different training tasks, which largely reduce communication cost. Our experiment results show that the proposed privacy computing techniques can be applied to heterogeneous neural signals for training different neural decoding models where the relative accuracy can achieve above 83%. © 2021

关键词： Brain–computer interface Neural decoding Privacy computing Unsupervised deep learning

来源：评论

学校读者我要写书评

暂无评论

FINANCIAL VISION BASED DIFFERENTIAL PRIVACY APPLICATIONS

arXiv

引用

arXiv 2021年

作者： Chen, Jun-Hao Tsai, Yun-Cheng Wang, Yi-Jen Chen, Samuel Yen-Chi Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Data Science Soochow University China Computational Science Initiative Brookhaven National Laboratory

The importance of deep learning data privacy has gained significant attention in recent years. It is probably to suffer data breaches when applying deep learning to cryptocurrency that lacks supervision of financial regulatory agencies. However, there is little relative research in the financial area to our best knowledge. We apply two representative deep learning privacy-privacy frameworks proposed by Google to financial trading data. We designed the experiments with several different parameters suggested from the original studies. In addition, we refer the degree of privacy to Google and Apple companies to estimate the results more reasonably. The results show that DP-SGD performs better than the PATE framework in financial trading data. The tradeoff between privacy and accuracy is low in DP-SGD. The degree of privacy also is in line with the actual case. Therefore, we can obtain a strong privacy guarantee with precision to avoid potential financial loss. © 2021, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Optimization of Microgrid System for Community Electrification by using HOMER Pro 9

Optimization of Microgrid System for Community Electrificati...

引用

9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Islam, Md.Kamrul Akanto, Jannatul Mawa Zeyad, Mohammad Ahmed, S.M. Masum Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Advanced Bioinformatics Chattogram Bangladesh Erasmus+ Joint MSC In Smart Cities and Communities Department of Thermal Engineering Bilbao Spain Erasmus+ Joint MSC In Smart Cities and Communities Faculty of Engineering Mons Belgium

ISBN: (纸本)9781665432405

A microgrid included multiple energy sources such as a diesel generator, photovoltaic system (PV), wind turbines (WTs), and so on. The key reason for the growth of hybrid energy technology or microgrid is the reliability, abundance of renewable energy (RE) and it can use the resources in an efficient manner. This research aims to incorporate a stand-alone community microgrid in Cumilla, Bangladesh to deliver electricity to a residential community and determine the optimum solution to satisfy total demand. The load profile is considered in this study for a residential community situated in Cumilla. Moreover, three case studies are taken into account to analyze the optimum result. The simulation analysis is executed in the optimization software that is the Hybrid Optimization Model for Multiple Energy Resources (HOMER pro). Several types of analysis are conducted such as NPV, COE, emission, energy production, and operating cost. The simulation results reveal that case 3 (solar, wind and diesel generator) is the optimum solution as it has the lowest NPV, COE, and operating cost. © 2021 IEEE.

关键词： Operating costs

来源：评论

学校读者我要写书评

暂无评论

EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning

arXiv

引用

arXiv 2024年

作者： Pei, Wenbin Dai, Ruohao Xue, Bing Zhang, Mengjie Zhang, Qiang Cheung, Yiu-Ming Xia, Shuyin The School of Computer Science and Technology Dalian University of Technology Dalian116024 China Ministry of Education Dalian116024 China The School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand The Department of Computer Science Hong Kong Baptist University Hong Kong The Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class is of importance in many real-life applications. Hybrid sampling methods address this by oversampling the minority class to increase the number of its instances, followed by undersampling to remove low-quality instances. However, most existing sampling methods face difficulties in generating diverse high-quality instances and often fail to remove noise or low-quality instances on a larger scale effectively. This paper therefore proposes an evolutionary multi-granularity hybrid sampling method, called EvoSampling. During the oversampling process, genetic programming (GP) is used with multi-task learning to effectively and efficiently generate diverse high-quality instances. During the undersampling process, we develop a granular ball-based undersampling method that removes noise in a multi-granular fashion, thereby enhancing data quality. Experiments on 20 imbalanced datasets demonstrate that EvoSampling effectively enhances the performance of various classification algorithms by providing better datasets than existing sampling methods. Besides, ablation studies further indicate that allowing knowledge transfer accelerates the GP's evolutionary learning process. Copyright © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

在天文学中基于机器学习的流体力学问题加速求解

在天文学中基于机器学习的流体力学问题加速求解

引用

第十三届全国流体力学学术会议

作者：毛顺元董若冰王炜琦 Kwang Moo Yi 陆路王思凡 Paris Perdikaris Department of Physics and Astronomy University of Victoria Department of Computer Science University of British Columbia Department of Statistics and Data Science Yale University Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania Department ofMechanical Engineering and Applied Mechanics University of Pennsylvania

探索行星形成的过程不仅揭示了地球及其生命的起源,还为寻找地外生命提供了关键线索。行星在原行星盘中形成。原行星盘是由气体和尘埃构成的扁平盘状系统,围绕在新生恒星的周围,其跨度可达数千亿公里。近年来,通过分析行星对原行星盘的... 详细信息

探索行星形成的过程不仅揭示了地球及其生命的起源,还为寻找地外生命提供了关键线索。行星在原行星盘中形成。原行星盘是由气体和尘埃构成的扁平盘状系统,围绕在新生恒星的周围,其跨度可达数千亿公里。近年来,通过分析行星对原行星盘的引力扰动,并比较观测和数值模拟,天文学家得以推断正在盘中形成的行星的质量和轨道。然而,这种方法依赖于复杂的流体力学数值模拟,因计算成本高昂而难以普及。为了克服这一挑战,我们开发了一款基于机器学习的工具。该工具应用数据驱动的深度算子网络(DeepONet),将系统的参数映射到流体力学偏微分方程的解,从而无需昂贵的数值模拟即可实现两项功能:1)快速求解正问题,即从行星参数预测盘中物质分布和速度场;2)高效求解反问题,即从观测到的扰动图像中推断行星参数。我们展示了该工具在求解速度、精确性和鲁棒性方面的显著优势,并讨论了其在理论研究和观测中的潜在应用。我们还探索了物理信息神经网络(PINN)在这一问题的应用,特别是针对原初行星盘中旋转吸积流的时间依赖性二维可压缩Navier-Stokes方程。行星引力扰动引入显著的时间依赖性空间特征,使问题复杂化。通过对物理定律而非标注数据的训练,PINN在捕捉系统演化的空间高频特征方面展现潜力。标准PINN在此任务中存在收敛困难、预测误差随时间累积、长时间解偏离物理定律以及捕捉空间高频特征能力不足的问题。为此,我们的研究引入了自适应激活函数、时间推进策略和网络权重继承方法,以提升PINN性能。改进后的PINN模型在与标准PINN的比较中表现出显著更低的预测误差,成功再现了大部分空间高频特征,无需标注数据。这项研究为将PINN应用于类似的时间依赖性流体力学问题提供了重要参考。

关键词：天文行星形成深度学习深度算子网络物理信息神经网络

来源：评论

学校读者我要写书评

暂无评论

Continuous-in-time Limit for Bayesian Bandits

arXiv

引用

arXiv 2022年

作者： Zhu, Yuhua Izzo, Zach Ying, Lexing Department of Mathematics Halıcıoglu Data Science Institute University of California-San Diego United States Department of Mathematics Stanford University United States Department of Mathematics Institute for Computational and Mathematical Engineering Stanford University United States

This paper revisits the bandit problem in the Bayesian setting. The Bayesian approach formulates the bandit problem as an optimization problem, and the goal is to find the optimal policy which minimizes the Bayesian regret. One of the main challenges facing the Bayesian approach is that computation of the optimal policy is often intractable, especially when the length of the problem horizon or the number of arms is large. In this paper, we first show that under a suitable rescaling, the Bayesian bandit problem converges toward a continuous Hamilton-Jacobi-Bellman (HJB) equation. The optimal policy for the limiting HJB equation can be explicitly obtained for several common bandit problems, and we give numerical methods to solve the HJB equation when an explicit solution is not available. Based on these results, we propose an approximate Bayes-optimal policy for solving Bayesian bandit problems with large horizons. Our method has the added benefit that its computational cost does not increase as the horizon increases. © 2022, CC BY.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：