检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Zhang, Xia Quan, Donghui Li, Runxia Esimbek, Jarken Chen, Long-Fei Zhao, Guoming Zhou, Yan Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Xinjiang Urumqi830011 China Xinjiang Key Laboratory of Radio Astrophysics 150 Science1-Street Urumqi830011 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Key Laboratory of Radio Astronomy Chinese Academy of Sciences Urumqi830011 China School of Science Jilin Institute of Chemical Technology Jilin Jilin132022 China BinZhou University Huanghe Road Shandong Binzhou256600 China

Cyanamide (NH2CN) and its isomer, carbodiimide (HNCNH), may form adenine in the interstellar medium (ISM) via a series of reactions. Therefore, they are considered key prebiotic molecules in the study of the origin of life. We used the three-phase NAUTILUS chemical code, which includes the gas, the dust surface, and the icy mantle, to investigate the formation and destruction of cyanamide and carbodiimide. We added over 200 new chemical reactions of the two isomers and related species, and established a relatively complete network. We applied cold core, hot corino/core and shock models to simulate the different physical environments, and found that the two isomers are mainly produced by the free radical reactions on grain surfaces. Our simulated results suggest that cyanamide and carbodiimide molecules come from surface chemistry at early evolutionary stages. Then they are released back to the gas phase, either by thermal process (in hot cores, hot corinos) or shock-induced desorption (in shock regions). We speculate that it is an inefficient route to form a tautomer of adenine by starting from molecules cyanoacetylene (C3NH), cyanamide and carbodiimide in ISM. Copyright © 2023, The Authors. All rights reserved.

关键词： Isomers

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Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review

arXiv

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arXiv 2022年

作者： Xu, Zikang Li, Jun Yao, Qingsong Li, Han Zhao, Mingyue Kevin Zhou, S. School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China

Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society. © 2022, CC BY.

关键词： Contrastive Learning

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Single-cell technologies:From research to application

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The Innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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Research on Rice Pest Classification and Identification Based on the Combination of Yolov5 and Transformer

SSRN

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SSRN 2023年

作者： Zha, Hainie Liu, Yuan Liu, Kui Liao, Jianfeng Zhang, Jing Zha, Pei Zhou, Jialong Ye, Zhangying University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing246011 China Institute of Agricultural Bio-Environmental Engineering College of Biosystems Engineering and Food Science Zhejiang University Hangzhou310058 China College of Electronic Information Engineering Anhui University Hefei230601 China Anhui Eagle Information Technology Co. Ltd Anqing246003 China

Pest trapping by pest detection lights for classification and identification is a standard method for agricultural pest early warning. However, the large variety of pests trapped by pest detection lights and the significant differences in the body size result in low recognition accuracy of existing models for rice pests. To address this issue, this paper proposes a classification and recognition model for small pest targets based on the combination of YOLO-v5 and Transformer models. First, the insect images captured by insect detection lights are filtered, the number of training samples is increased by data augmentation during training, and relevant noise data are added to improve the robustness and generalization ability of the model. Then, a sliding window detection technique is introduced to the YOLO-v5 detection head to determine the coordinate region of the pest and transfers the image data of this region to the Transformer model to obtain the classification results. Finally, YOLO-v5, Faster R-CNN, YOLO-v4, and the model proposed in this paper are trained on the same training set, and the average detection time and classification accuracy are used as evaluative metrics. The experimental results indicate that the proposed model combining YOLO-v5 and Transformer achieves the highest classification and recognition accuracy of 93.92%, and its average detection time is 40.36 ms, showing the best overall performance. The Faster R-CNN model achieves a recognition accuracy of 90.39%, with an average detection time of 29586.39 ms. The YOLO-v5 model achieves a recognition accuracy of 83.16%, with an average detection time of 18.21 ms. The YOLO-v4 model achieves a recognition accuracy of 82.15%, with an average detection time of 26.41 ms. Our proposed pest classification and recognition model can automatically identify 11 common pests in rice fields, with high accuracy and the shortest average recognition time, which well realizes rice pest recognition and provides a basi

关键词： Classification (of information)

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FAST Observations of an Extremely Active Episode of FRB 20201124A.Ⅲ.Polarimetry

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Research in Astronomy and Astrophysics 2022年第12期22卷 81-98页

作者： Jin-Chen Jiang Wei-Yang Wang Heng Xu Jiang-Wei Xu Chun-Feng Zhang Bo-Jun Wang De-Jiang Zhou Yong-Kun Zhang Jia-Rui Niu Ke-Jia Lee Bing Zhang Jin-Lin Han Di Li Wei-Wei Zhu Zi-Gao Dai Yi Feng Wei-Cong Jing Dong-Zi Li Rui Luo Chen-Chen Miao Chen-Hui Niu Chao-Wei Tsai Fa-Yin Wang Pei Wang Ren-Xin Xu Yuan-Pei Yang Zong-Lin Yang Ju-Mei Yao Mao Yuan Department of Astronomy Peking UniversityBeijing 100871China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China School of Astronomy University of Chinese Academy of SciencesBeijing 100049China Department of Physics and Astronomy University of NevadaLas VegasNV 89154USA CAS Key Laboratory of FAST NAOCChinese Academy of SciencesBeijing 100101China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa Department of Astronomy University of Science and Technology of ChinaHefei 230026China Cahill Center for Astronomy and Astrophysics MC 249-17California Institute of TechnologyPasadenaCA 91125USA CSIRO Space and Astronomy P.O.Box 76EppingNSW 1710Australia School of Astronomy and Space Science Nanjing UniversityNanjing 210093China Key Laboratory of Modern Astronomy and Astrophysics(Nanjing University) Ministry of EducationChina South-Western Institute For Astronomy Research Yunnan UniversityYunnan 650504China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China

As the third paper in the multiple-part series,we report the statistical properties of radio bursts detected from the repeating fast radio burst(FRB)source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an extremely active episode between the 25th and 28th of September 2021(UT).We focus on the polarization properties of536 bright bursts with S/N>*** found that the Faraday rotation measures(RMs)monotonically dropped from-579to-605 rad m^(-2)in the 4 day *** RM values were compatible with the values(-300 to-900 rad m^(-2))reported 4 months ***,the RM evolution rate in the current observation window was at least an order of magnitude smaller than the one(~500 rad m^(-2)day^(-1))previously reported during the rapid RM-variation phase,but is still higher than the one(≤1 rad m^(-2)day^(-1))during the later RM no-evolution *** bursts of FRB 20201124A were highly polarized with the total degree of polarization(circular plus linear)greater than 90%for more than 90%of all *** distribution of linear polarization position angles(PAs),degree of linear polarization(L/I)and degree of circular polarization(V/I)can be characterized with unimodal distribution *** the observation window,the distributions became wider with time,i.e.,with larger scatter,but the centroids of the distribution functions remained nearly *** individual bursts,significant PA variations(confidence level 5σ)were observed in 33%of all *** polarization of single pulses seems to follow certain complex trajectories on the Poincarésphere,which may shed light on the radiation mechanism at the source or the plasma properties along the path of FRB propagation.

关键词： (stars:)pulsars:general stars:magnetars radio continuum:general polarization

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

引用

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Characterising abundance-age relations of GALAH stars using oxygen-enhanced stellar models

arXiv

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arXiv 2023年

作者： Sun, Tiancheng Chen, Xunzhou Bi, Shaolan Ge, Zhishuai Xiang, Maosheng Wu, Yaqian Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Beijing Planetarium Beijing Academy of Science and Technology Beijing100044 China Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences A20 Datun Rd. Chaoyang District Beijing100101 China

Main Sequence Turn-off stars (MSTO) and subgiant stars are good tracers of galactic populations. We present a study of 41,034 MSTO and subgiant stars from the GALAH survey. Using a grid of stellar models that accounts for the variation of O abundances, we determine their ages with a median age uncertainty of ∼9.4 per cent. Our analysis reveals that the ages of high-O stars based on O-enhanced models (OEM models) are smaller than those determined with α-enhanced models, resulting in a mean fractional age difference of -5.3 per cent at [O/α] = 0.2 and -11.0 per cent at [O/α] = 0.4. This age difference significantly impacts the age distribution of thick disc and halo stars, leading to a steeper downward trend in the [Fe/H]-age plane from 8 Gyr to 14 Gyr, indicating a shorter formation time-scale and a faster chemical-enhanced history for these populations. We confirm the V-shape of the normalized age–metallicity distribution p(τ|[Fe/H]) of thin disc stars, which is presumably a consequence of the second gas infall. Additionally, we find that the halo stars in our sample can be divided into two sequences, a metal-rich sequence (Splash stars) and a metal-poor sequence (accreted stars), with the Splash stars predominantly older than 9 Gyr and the accreted halo stars older than 10 Gyr. Finally, we observe two distinct sequences in the relations between various chemical abundances and age for disc stars, namely a young sequence with ages ∼8 Gyr. Copyright © 2023, The Authors. All rights reserved.

关键词： Stars

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A Proposed Radio Environment Monitoring System of Ali Observatory

A Proposed Radio Environment Monitoring System of Ali Observ...

引用

General Assembly and Scientific Symposium, URSI

作者： Haiyan Zhang Yu Wang Shijie Huang Hao Hu Chuanjiang Zhao Guifang Wu National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing China Hebei Key Laboratory of Radio Astronomy Technology Shijiazhuang Hebei China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Zhongtai Street Yuhang District Hangzhou Zhejiang China Beijing Boer Communication Technology Co. Ltd Zhongguancun Software Park No. 8 Dongbei Wangxi Road Haidian District Beijing China China Electric Power Research Institute Co. Ltd 15 Qinghe Xiaoying East Road Haidian District Beijing China

With its extremely dry air and high altitude above 5000 meters, the Ail Observatory located in the Tibet plateau has been a promising site for several astronomical instruments working in the optical or millimeter wave bands. In addition to protect the dark sky around the Ali Observatory, the measures to protect local radio environment have also been discussed. Based on the requirements of radio astronomical instruments, a radio environment monitoring system has been proposed. This paper describes the preliminary design of the system, including the technical specification and the system composition. In the near future, this system will be put into use.

关键词：

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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Ed-Tgan: A Methodology on Temporal Generative Gan for Imbalanced Data for Predicting Academic Performance of Students

SSRN

引用

SSRN 2022年

作者： Waheed, Hajra Zhou, Jia Aljohani, Naif R. Tuarob, Suppawong Nawaz, Raheel Herrera, Francisco Hassan, Saeed-Ul Information Technology University 346-B Ferozepur Road Lahore Pakistan Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Faculty of Information and Communication Technology Mahidol University Thailand Staffordshire University Stoke-on-Trent United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Manchester Metropolitan University Manchester United Kingdom

With the increase in the self-paced learning phenomenon, class imbalance in the learning analytics community has become more prevalent, with lesser students dropping out than students who intend to pursue. Similarly, an imbalance in the behavioral performances of students is also observed. This imbalance problem becomes more complicated for temporal data, where each time sequence represents a particular student, and several sequences formulate the overall engagement of students throughout a course. The conventional methods for generating synthetic data do not account for this association of sequences, where each synthetically generated student is associated with its previous sequence. Generative Adversarial Networks (GANs) have been rapidly adopted in the machine learning discipline for synthetic data generation to reduce the class imbalance. In this study, we propose a deep learning-powered methodology called Educational Temporal Generative Adversarial Networks (Ed-TGANs) methodology comprising the following steps: a) Smart Data Processing for Actionable Data, b) GANs for Temporal Data Generation, and c) Learning Approach. Furthermore, the Open University Learning Analytics dataset was deployed as a case study comprising 69% pass and 31% fail students to validate the effectiveness of our proposed methodology, encompassing discrete and continuous features. The results are compared with GAN-based and non-GAN upsampling approaches to demonstrate the proposed methodology's effectiveness, which shows an increase in accuracy of up to 5.33% compared to classic GANs, resulting in improved understating of student behavior. We believe that the presented methodology can be used as a starting point for various areas for upsampling jobs with temporal longitudinal data. © 2022, The Authors. All rights reserved.

关键词： Students

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