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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Analysis and Treatment of Missing Data in Happiness Score Using Machine Learning 21

Analysis and Treatment of Missing Data in Happiness Score Us...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Touati, Wassim El Touati, Yamen Hadj-Alouane, Nejib Ben Jemai, Abderrazek Polytechnic School of Tunisia Carthage University SERCOM Laboratory Tunis Tunisia Northern Border University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia ENIT University of Tunis EI Manar OASIS Laboratory Tunisia American University in Dubai Electrical Engineering and Computer Engineering Department Dubai United Arab Emirates

ISBN: (纸本)9798350378078

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible. © 2024 IEEE.

关键词： Adversarial machine learning

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhou, Jieming Zhang, Tong Fang, Pengfei Petersson, Lars Harandi, Mehrtash Australian National University College of Engineering and Computer Science Canberra Australia Image and Visual Representation Laboratory Epfl Lausanne Switzerland Imaging and Computer Vision Group Csiro Canberra Australia Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350382204

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs' designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model's performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks. © 2023 IEEE.

关键词： Graph neural networks

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A Triple-mode Wideband Filtering Antenna with Planar Configuration by Combining One SIW cavity and Two Half-mode Patch Resonators

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IEEE Antennas and Wireless Propagation Letters 2025年第6期24卷 1512-1516页

作者： Liu, Qianwen Li, Chengyu Zhu, Lei Tang, Yiming Lu, Wen-Jun Lyu, Yun-Peng Huang, Feng Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China State Key Laboratory of Millimeter Waves Nanjing210003 China Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China

In this letter, a triple-mode wideband filtering antenna with planar configuration is presented. The designed antenna has a single substrate layer, which consists of one half-mode circular patch resonator, one half-mode rectangular patch resonator, and one rectangular SIW cavity, and it is fed through the conventional coaxial probe. On the basis of the field analysis, these three types of resonators are compactly integrated without disturbing the resonant properties of their dominant modes. Given this, three resonant modes of half-TM11, half-TM10, and TE11 are well generated, so as to form a widened operating band. Meanwhile, four radiation nulls are produced from far-field radiation cancellation with the increased radiation boundaries. For validation, a prototype antenna with the center frequency of 2.8 GHz is designed and fabricated. The measured results indicate that the proposed antenna achieves an improved fractional bandwidth of 15.7% under three in-band resonances. Besides, four radiation nulls created at 2.32, 3.22, 3.49, and 3.89 GHz in stopband effectively enhance the out-of-band suppression level. Moreover, low profile of 0.037λ0 is also implemented. © 2002-2011 IEEE.

关键词： Microstrip antennas

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Energy field intensity approach for probabilistic notch fatigue assessment under size effect

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Chinese Journal of Aeronautics 2025年第2期38卷 111-127页

作者： Ding LIAO Shunpeng ZHU Jiewei GAO Xuekang LI José CORREIA Rui CALÇADA Qingyuan WANG School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Civil Engineering Faculty of EngineeringUniversity of PortoPorto 4200-465Portugal School of Mechanical Engineering Xihua UniversityChengdu 610039China Kunming Shipborne Equipment Research and Test Center Kunming 650051China Failure Mechanics and Engineering Disaster Prevention and Mitigation Key Laboratory of Sichuan Province College of Architecture and EnvironmentSichuan UniversityChengdu 610065China Advanced Research Institute Chengdu UniversityChengdu 610106China

The development of modern engineering components and equipment features large size,intricate shape and long service life,which places greater demands on valid methods for fatigue performance *** a smooth transformation between small-scale laboratory specimens’fatigue properties and full-scale engineering components’fatigue strength has been a long-term *** this work,two dominant factors impeding the smooth transformation—notch and size effect were experimentally studied,in which fatigue tests on Al 7075-T6511(a very high-strength aviation alloy)notched specimens of different scales were carried *** analyses identified the evidence of the size effect on notch fatigue damage ***,the Energy Field Intensity(EFI)initially developed for multiaxial notch fatigue analysis was improved by utilizing the volume ratio of the Effective Damage Zones(EDZs)for size effect *** particular,it was extended to a probabilistic model considering the inherent variability of the fatigue *** experimental data of Al 7075-T6511 notched specimens and the model-predicted results were compared,indicating the high potential of the proposed approach in fatigue evaluation under combined notch and size effects.

关键词： Notch fatigue Size effect Energy field intensity Life prediction Probabilistic modelling

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Rapid stabilization of stochastic quantum systems in a unified framework

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Chinese Physics B 2023年第7期32卷 216-225页

作者：温杰王芳敏史元浩贾建芳曾建潮 School of Electrical and Control Engineering North University of ChinaTaiyuan 030051China State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing 100084China School of Computer Science and Technology North University of ChinaTaiyuan 030051China

Rapid stabilization of general stochastic quantum systems is investigated based on the rapid stability of stochastic differential *** introduce a Lyapunov-LaSalle-like theorem for a class of nonlinear stochastic systems first,based on which a unified framework of rapidly stabilizing stochastic quantum systems is *** to the proposed unified framework,we design the switching state feedback controls to achieve the rapid stabilization of singlequbit systems,two-qubit systems,and N-qubit *** the unified framework,the state space is divided into two state subspaces,and the target state is located in one state subspace,while the other system equilibria are located in the other state *** the designed state feedback controls,the system state can only transit through the boundary between the two state subspaces no more than two times,and the target state is globally asymptotically stable in *** particular,the system state can converge exponentially in(all or part of)the state subspace where the target state is ***,the effectiveness and rapidity of the designed state feedback controls are shown in numerical simulations by stabilizing GHZ states for a three-qubit system.

关键词： rapid stabilization state feedback stochastic quantum systems switching control

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Evaluating Machine Learning Classifiers for Predicting Liver Disease Outcomes

Evaluating Machine Learning Classifiers for Predicting Liver...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Khaled Mohamad AlMustafa Juliano Katrib Department of Electrical and Computer Engineering College of Engineering and Architecture Gulf University for Science and Technology (GUST)

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning classifiers, Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), and Logistic Regression, in predicting liver disease outcomes based on clinical and biochemical features. Each model's performance was rigorously evaluated using multiple metrics, including F1-score, accuracy, root mean square error (RMSE), mean absolute error (MAE), precision, recall, and cross-validation ROC-AUC scores. The Random Forest Classifier emerged as the most effective model, achieving a perfect accuracy of 100% and an F1-score of 1.00, suggesting its robustness in handling complex, high-dimensional data. The Gradient Boosting Classifier also demonstrated commendable performance with an accuracy of 89% and an F1-score of 0.85, indicating its efficacy in capturing the underlying patterns of liver disease. Conversely, Logistic Regression and SVM exhibited lower performance metrics, pointing to potential limitations in their ability to model the intricacies of liver disease data effectively. The insights from this study not only enhance our understanding of liver disease prediction using machine learning but also emphasize the need for further research to validate these models in diverse clinical populations. This study contributes to the growing body of literature advocating for the integration of machine learning techniques in clinical decision-making processes, ultimately aiming to improve patient outcomes in liver disease management.

关键词： Support vector machines Measurement Logistic regression Accuracy Liver diseases Biological system modeling Predictive models Boosting Root mean square Random forests

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Free-electron crystals for enhanced X-ray radiation

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Light(science & Applications) 2024年第7期13卷 1299-1309页

作者： Lee Wei Wesley Wong Xihang Shi Aviv Karnieli Jeremy Lim Suraj Kumar Sergio Carbajo Ido Kaminer Liang Jie Wong School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore. Solid State Institute and Faculty of Electrical and Computer Engineering Technion–Israel Institute of TechnologyHaifa 3200003Israel. School of Electrical Engineering Fleischman Faculty of EngineeringTel Aviv UniversityTel Aviv 69978Israel Department of Applied Physics Stanford UniversityStanfordCA 94305USA. Science Mathematics and TechnologySingapore University of Technology and Design8 Somapah RoadSingapore 487372Singapore. Electrical and Computer Engineering Department UCLA420 WestwoodLos AngelesCA 90095USA. Physics and Astronomy Department UCLA475 Portola PlazaLos AngelesCA 90095USA. SLAC National Accelerator Laboratory Stanford University2575 Sand Hill RoadMenlo ParkCA 94025USA

Bremsstrahlung—the spontaneous emission of broadband radiation from free electrons that are deflected by atomic nuclei—contributes to the majority of X-rays emitted from X-ray tubes and used in applications ranging from medical imaging to semiconductor chip ***,we show that the bremsstrahlung intensity can be enhanced significantly—by more than three orders of magnitude—through shaping the electron wavefunction to periodically overlap with atoms in crystalline ***,we show how to shape the bremsstrahlung X-ray emission pattern into arbitrary angular emission profiles for purposes such as unidirectionality and ***,we find that these enhancements and shaped emission profiles cannot be attributed solely to the spatial overlap between the electron probability distribution and the atomic centers,as predicted by the paraxial and nonrecoil theory for free electron light *** work highlights an unprecedented regime of free electron light emission where electron waveshaping provides multi-dimensional control over practical radiation processes like *** results pave the way towards greater versatility in table-top X-ray sources and improved fundamental understanding of quantum electron-light interactions.

关键词： overlap attributed arbitrary

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Behavior of multiple modes before and during minor disruption with the external resonant magnetic perturbations on J-TEXT tokamak

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Plasma science and Technology 2022年第6期24卷 88-95页

作者： Mingxiang HUANG Zhengkang REN Feiyue MAO Zhoujun YANG Yuan GAO Zhichao ZHANG Shunfan HE Guoliang LI Jinrong FAN Wei TIAN Nengchao WANG Zhipeng CHEN Yonghua DING Yuan PAN Zhongyong CHEN College of Computer Science South-Central Minzu UniversityWuhan 430074People's Republic of China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China

The behavior of multiple modes before and during minor disruption with the external resonant magnetic perturbations(RMPs)has been studied on a J-TEXT *** main component of RMPs is m/n=2/1,where m and n are the numbers of the poloidal and toroidal modes,*** the mode-locking caused by RMPs,it is found that before a minor or a major disruption(if there is no minor disruption),strong oscillations in both electron temperature and density occur if the edge safety factor q_(a)>*** analysis shows that the oscillations are caused by the m/n=3/1 *** addition,using the ECE,Mirnov coils and 2D electron cyclotron emission imaging diagnostic systems,it is found that a thermal collapse occurs on the inner side of the 2/1magnetic island during the minor disruption,and before the collapse,a 3/2 island increases,after the collapse,the 3/2 island may ***,the study also shows that these 3/1,2/1 and 3/2modes play roles in the thermal collapse of disruptions.

关键词： oscillation multiple modes disruption

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： Knowledge graph

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