检索结果-内蒙古大学图书馆

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

Light(science & Applications) 2023年第12期12卷 2826-2844页

作者： Weisong Zhao Xiaoshuai Huang Jianyu Yang Liying Qu Guohua Qiu Yue Zhao Xinwei Wang Deer Su Xumin Ding Heng Mao Yaming Jiu Ying Hu Jiubin Tan Shiqun Zhao Leiting Pan Liangyi Chen Haoyu Li Innovation Photonics and Imaging Center School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbinChina Key Laboratory of Ultra-Precision Intelligent Instrumentation of Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbinChina Biomedical Engineering Department International Cancer InstitutePeking University Cancer Hospital and InstituteHealth Science CenterPeking UniversityBeijingChina The Key Laboratory of Weak-Light Nonlinear Photonics of Education Ministry School of Physics and TEDA Institute of Applied PhysicsFrontiers Science Center for Cell ResponsesNankai UniversityTianjinChina State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijingChina Department of Control Science and Engineering Harbin Institute of TechnologyHarbinChina School of Mathematical Sciences Peking UniversityBeijingChina Unit of Cell Biology and Imaging Study of Pathogen Host Interaction The Center for MicrobesDevelopment and HealthKey Laboratory of Molecular Virology and ImmunologyShanghai Institute of Immunity and InfectionChinese Academy of SciencesShanghaiChina School of Life Science and Technology Harbin Institute of TechnologyHarbinChina PKU-IDG/McGovern Institute for Brain Research BeijingChina Beijing Academy of Artificial Intelligence BeijingChina Frontiers Science Center for Matter Behave in Space Environment Harbin Institute of TechnologyHarbinChina Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education Harbin Institute of TechnologyHarbinChina

In fluorescence microscopy,computational algorithms have been developed to suppress noise,enhance contrast,and even enable super-resolution(SR).However,the local quality of the images may vary on multiple scales,and these differences can lead to *** mapping methods fail to finely estimate the local quality,challenging to associate the SR scale ***,we develop a rolling Fourier ring correlation(rFRC)method to evaluate the reconstruction uncertainties down to SR *** visually pinpoint regions with low reliability,a filtered rFRC is combined with a modified resolution-scaled error map(RSM),offering a comprehensive and concise map for further *** demonstrate their performances on various SR imaging modalities,and the resulting quantitative maps enable better SR images integrated from different ***,we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

关键词： resolution local enable

来源：评论

学校读者我要写书评

暂无评论

Evidence of aircraft activity impact on local air quality: A study in the context of uncommon airport operation

引用

Journal of Environmental sciences 2023年第3期125卷 603-615页

作者： Hao Xu Kai Xiao Jun Pan Qingyan Fu Xiaodong Wei Junrui Zhou Yamei Yu Xue Hu Huarui Ren Jinping Cheng Shitao Peng Ningning Hong Yin Ye Ning Su Zehui He Tao Hu School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Wuhan Environmental Protection Science Academy Wuhan 430015China Shanghai Environmental Monitoring Center Shanghai 200233China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China East China Air Traffic Management Bureau CAAC Shanghai 200335China China-UK Low Carbon College Shanghai 201306China Tianjin Research Institute for Water Transport Engineering Ministry of TransportTianjin 300456China

Wuhan Tianhe International Airport (WUH) was suspended to contain the spread of COVID-19,while Shanghai Hongqiao International Airport (SHA) saw a tremendousflight *** of a major international airport is extremely rare and thus represents a unique opportunity to straightforwardly observe the impact of airport emissions on local air *** this study,a series of statistical tools were applied to analyze the variations in air pollutant levels in the vicinity of WUH and *** results of bivariate polar plots show that airport SHA and WUH are a major source of nitrogen ***,NO_(2)and NO diminished by 55.8%,44.1%,76.9%,and 40.4%,33.3% and 59.4% during the COVID-19 lockdown compared to those in the same period of 2018 and 2019,under a reduction in aircraft activities by 58.6%and61.4%.The concentration of NO_(2),SO_(2)and PM_(2.5)decreased by 77.3%,8.2%,29.5%,right after the closure of airport WUH on 23 January *** average concentrations of NO,NO_(2)and NOxscatter plots at downwind of SHA after the lockdown were 78.0%,47.9%,57.4%and 62.3%,34.8%,41.8%lower than those during the same period in 2018 and ***,a significant increase in O_(3)levels by 50.0% and 25.9%at WUH and SHA was observed,*** results evidently show decreased nitrogen oxides concentrations in the airport vicinity due to reduced aircraft activities,while amplified O_(3)pollution due to a lower titration by NO under strong reduction in NOxemissions.

关键词： Aircraft emissions COVID-19 Nitrogen oxides Ozone Airport closure

来源：评论

学校读者我要写书评

暂无评论

Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks

arXiv

引用

arXiv 2024年

作者： Li, Ziqiang Zeng, Yueqi Xia, Pengfei Liu, Lei Fu, Zhangjie Li, Bin Nanjing University of Information Science and Technology China Big Data and Decision Lab University of Science and Technology of China China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common for users and businesses to outsource the labor-intensive task of data collection to third-party entities. Unfortunately, recent research has unveiled the inherent risk associated with this practice, particularly in exposing NLP systems to potential backdoor attacks. Specifically, these attacks enable malicious control over the behavior of a trained model by poisoning a small portion of the training data. Unlike backdoor attacks in computer vision, textual backdoor attacks impose stringent requirements for attack stealthiness. However, existing attack methods meet significant trade-off between effectiveness and stealthiness, largely due to the high information entropy inherent in textual data. In this paper, we introduce the Efficient and Stealthy Textual backdoor attack method, EST-Bad, leveraging Large Language Models (LLMs). Our EST-Bad encompasses three core strategies: optimizing the inherent flaw of models as the trigger, stealthily injecting triggers with LLMs, and meticulously selecting the most impactful samples for backdoor injection. Through the integration of these techniques, EST-Bad demonstrates an efficient achievement of competitive attack performance while maintaining superior stealthiness compared to prior methods across various text classifier datasets. © 2024, CC BY.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition 41

Learning Label Shift Correction for Test-Agnostic Long-Taile...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Wei, Tong Mao, Zhen Zhou, Zi-Hao Wan, Yuanyu Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Software Technology Zhejiang University Ningbo China

Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance. The source code is available at https://***/Stomach-ache/label-shift-correction. Copyright 2024 by the author(s)

关键词： Black-box testing

来源：评论

学校读者我要写书评

暂无评论

Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

引用

IEEE Transactions on Computational Social systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

引用

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

来源：评论

学校读者我要写书评

暂无评论

IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

引用

Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

来源：评论

学校读者我要写书评

暂无评论

Bird Species Recognition by Integrating EfficientNet-B0 Deep Learning Architecture and Broad Learning system

Bird Species Recognition by Integrating EfficientNet-B0 Deep...

引用

2024 China Automation Congress, CAC 2024

作者： Fan, Xing-Rong Chen, Hui-Ting Deng, Yu-He Wan, Hui Cai, Zong-Rong Peng, Qiu-Ju Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Technology and Business University Chongqing China Chongqing Key Laboratory of Intelligent Perception and Block Chain Technology Chongqing Technology and Business University Chongqing China School of Artificial Intelligence School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China

ISBN: (纸本)9798350368604

Accurate identification and classification of bird species are crucial for advancing research and conservation efforts, enhancing our understanding of population dynamics, and implementing targeted conservation strategies. Despite its importance, this task is challenging due to the wide variation in bird appearances and environmental noise, which can lead to poor generalization and increased complexity. To address these challenges, this paper proposes a novel bird species recognition method, EN-BLS, which integrates the EfficientNet-B0 deep learning architecture with a broad learning system. EfficientNet-B0 is used as the backbone network for feature extraction from bird images, with the final feature map layer serving as input for the broad learning system. The resulting hybrid model, EN-BLS, is designed for bird species classification. The model's performance is evaluated using the Birds 525 Species dataset, the latest publicly available benchmark dataset with the highest number of categories collected from diverse real-world environments. Experimental results show that EN-BLS achieves a recognition accuracy of 93.79% with lower computational complexity compared to existing methods, demonstrating its effectiveness for bird species diversity monitoring and conservation efforts. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Reducing systemic Risks in Humanitarian Aid Work with Guided Network Topology

Reducing Systemic Risks in Humanitarian Aid Work with Guided...

引用

International Conference on information and Communication Technologies for Disaster Management (ICT-DM)

作者： Sujoy Das Sadia Sharmin Md. Saidur Rahman Department of Computer Science & Engineering Graph Drawing and Information Visualization Laboratory Bangladesh University of Engineering & Technology

ISBN: (数字)9798350367928

ISBN: (纸本)9798350367935

Humanitarian aid works are critical for emergency response, damage mitigation, initial recovery and comprehensive rehabilitation following disasters. These efforts involve various aid actors with diverse mandates, capacities and expertise. Their interactions give rise to a network of aid actors. Effective coordination and communication among these actors are essential but challenging due to systemic risks such as random failures, extended communication paths, redundant connectivity, irrelevant connectivity and poor navigability. Reducing these systemic risks is crucial for the success of humanitarian aid workflows. This research aims to address these challenges by proposing an algorithm that proactively generates a resilient network of aid actors. The algorithm focuses on creating a network that is scale-free, exhibits small-world effect, is robust against random failures and maintains lower structural entropy. Using the SIDR-2007 humanitarian aid dataset, our experiments demonstrate that the proposed aid actors’ network enhances efficiency in navigability, coordination, communication and control. These findings suggest that understanding and optimizing network topology can significantly mitigate systemic risks and improve the performance of humanitarian aid efforts.

关键词： Network topology Navigation Disasters Prevention and mitigation Redundancy Humanitarian activities Emergency services Entropy information and communication technology Delays

来源：评论

学校读者我要写书评

暂无评论

An Attempt to Apply the Theory of Biquadratic Curve to Generating Plane-Symmetric Bricard Linkages Network 23rd

An Attempt to Apply the Theory of Biquadratic Curve to Gener...

引用

23rd IFToMM China International Conference on Mechanism and Machine science and engineering, IFToMM CCMMS 2022

作者： Zhang, Yuehao Zhang, Enjie Wang, Shuaihu Xiao, Jie Fang, Guangqiang Shanghai Institute of Aerospace System Engineering Shanghai Academy of Spaceflight Technology No. 3888 Yuanjiang Road Shanghai China Space Structure and Mechanism Technology Laboratory of China Aerospace Science and Technology Group Co. Ltd Shanghai China

ISBN: (纸本)9789811993978

Large-area space deployable antennas require very high folding efficiency. Since plane-symmetric Bricard linkage has a large folding efficiency, its networking becomes a potential scheme to meet the demand. However, complex geometry makes networking a very difficult problem. This paper studied the closure equations of plane-symmetric Bricard linkage from the perspective of biquadratic curve and proved several propositions that can be applied to networking. The basic motivation is that the compatible conditions between linkages are equivalent to their biquadratic curves having a common branche under Mobius transformation. Using the propositions proved in this paper, a kinematically compatible network of plane-symmetric Bricard linkages was constructed, and the motion simulation was carried out with a CAD model. Results shown that the theories proposed in this paper are correct, having a reference significance for the invention of new deployable mechanisms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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：