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Outlier Robust Feature Correspondence by Learning Based Matching Process

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Journal of Systems Science & Complexity 2024年第4期37卷 1351-1367页

作者： YANG Xu ZENG Shaofeng LIU Zhiyong State Key Laboratory of Multimodal artificial intelligence Systems Institute of Automation of Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Feature correspondence is a crucial aspect of various computer vision and robot vision *** traditional optimization-based matching techniques,researchers have recently adopted a learning-based approach for matching,but these methods face challenges in dealing with outlier *** paper presents an outlier robust feature correspondence method that employs a pruned attentional graph neural network and a matching layer to address the outlier ***,the authors introduce a modified cross-entropy matching loss to handle the outlier *** a result,the proposed method significantly enhances the performance of learning-based matching algorithms in the presence of outlier *** experiments confirm the effectiveness of the proposed approach.

关键词： Feature correspondence graph neural network optimal transport problem pointmatching

Two Nonmonotone Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold

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Acta Mathematicae Applicatae Sinica 2024年

作者： Jin-chao ZHANG Juan GAO Ya-kui HUANG Xin-wei LIU School of artificial intelligence Hebei University of Technology institute of Mathematics Hebei University of Technology

We propose two nonmonotone retraction-based proximal gradient methods for solving a class of nonconvex nonsmooth optimization problems over the Stiefel manifold. The proposed methods are equipped with the descent direction obtained by a proximal mapping restricted in tangent space of the manifold and the BarzilaiBorwein stepsizes determined by two recent iteration points and the corresponding descent directions. By employing, respectively, the Grippo-Lampariello-Lucidi nonmonotone line search strategy and the Dai-Fletcher nonmonotone line search strategy, our proposed methods are proved to be globally convergent. Analysis on the iteration complexity of NPGL for obtaining an?-stationary solution is provided. Numerical results on the sparse principle component analysis problems demonstrate the efficiency of our methods.

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Photocatalytic vinyl radical-mediated multicomponent 1,4-/1,8-carboimination across alkynes and olefins/(hetero)arenes

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Science China Chemistry 2024年第2期67卷 558-567页

作者： Shan-Shan Li Yu-Shi Jiang Xue-Ling Luo Xuming Ran Yuqiang Li Dong Wu Cheng-Xue Pan Peng-Ju Xia School of Chemistry and Pharmaceutical Sciences Guangxi Normal UniversityGuilin541004China Shanghai artificial intelligence Laboratory Shanghai200433China The institute for Advanced Studies Wuhan UniversityWuhan430064China

We developed a highly selective and efficient multicomponent transformation by utilizing alkynes and olefins/(hetero)arenes through photoinduced energy-transfer *** reaction involves the formation of three distinct chemical bonds,namely C(sp~3)–C(sp~2),C(sp~2)–C(sp~3),and C(sp~3)–N,in a single coordinated *** strategy used a vinyl radical-mediated radical relay approach under mild conditions,exhibiting a broad substrate scope(>70 examples),excellent functional-group tolerance,and remarkable regio-and *** the utilization of a combination of experimental techniques and density functional theory(DFT),we delved deeper into the mechanistic intricacies of this distinctive *** revealed that the selective radical addition to electron-deficient alkynes,rather than olefins,was governed by the inherent reactivity of alkyl *** discovery presented a highly effective approach for the synthesis of stereodefined multisubstituted alkenes.

关键词： alkyne-olefin coupling DFT studies regio-and anti-stereoselectivity multisubstituted alkenes

Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 301-328页

作者： Yuchuang Tong Haotian Liu Zhengtao Zhang IEEE CAS Engineering Laboratory for Intelligent Industrial Vision Institute of AutomationChinese Academy of SciencesBeijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China

This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure,control and decision-making, and perception and interaction, a holistic overview of the current state of humanoid robot research is presented. Furthermore, emerging challenges in the field are identified, emphasizing the necessity for a deeper understanding of biological motion mechanisms, improved structural design,enhanced material applications, advanced drive and control methods, and efficient energy utilization. The integration of bionics, brain-inspired intelligence, mechanics, and control is underscored as a promising direction for the development of advanced humanoid robotic systems. This paper serves as an invaluable resource, offering insightful guidance to researchers in the field,while contributing to the ongoing evolution and potential of humanoid robots across diverse domains.

关键词： Future trends and challenges humanoid robots human-robot interaction key technologies potential applications

A CSP-based retraining framework for motor imagery based brain-computer interfaces

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Science China(Information Sciences) 2024年第8期67卷 349-350页

作者： Xue JIANG Lubin MENG Xinru CHEN Dongrui WU Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research institute

Motor imagery(MI) is a classic paradigm of electroencephalogram(EEG)-based brain-computer interfaces(BCIs). It entails individuals mentally imagining the movement of a body part without physically performing it. EEG s... 详细信息

关键词： Classification (of information)

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A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

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Research 2024年第2期2023卷 703-718页

作者： Minjie Mou Ziqi Pan Zhimeng Zhou Lingyan Zheng Hanyu Zhang Shuiyang Shi Fengcheng Li Xiuna Sun Feng Zhu College of Pharmaceutical Sciences The Second Affliated HospitalZhejiang University School of MedicineNational Key Laboratory of Advanced Drug Delivery and Release SystemsZhejiang UniversityHangzhou 310058China Innovation institute for artificial intelligence in Medicine of Zhejiang University Alibaba-Zhejiang University Joint Research Center of Future Digital HealthcareHangzhou 330110China

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://***/ensemppis.

关键词： residue extracting ensemble

Proposing a machine learning approach to analyze and predict basic high-temperature properties of iron ore fines and its factors

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Journal of Iron and Steel Research(International) 2024年第5期31卷 1082-1094页

作者： Qing-ke Sun Yao-zu Wang Jian-liang Zhang Zheng-jian Liu Le-le Niu Chang-dong Shan Yun-fei Ma institute of artificial intelligence University of Science and Technology BeijingBeijing100083China School of intelligence Science and Technology University of Science and Technology BeijingBeijing100083China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China

The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending,but the testing process for these properties is complex and has significant lag time,which cannot meet the actual needs of ore blending.A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic ***,the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was ***,a random forest model optimized by genetic algorithms was built,further improving the prediction accuracy of the *** test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore,with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after *** trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.

关键词： Iron ore Basic high-temperature property Machine learning Random forest Genetic algorithm

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

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Light(Science & Applications) 2024年第3期13卷 466-477页

作者： Xinyuan Fang Xiaonan Hu Baoli Li Hang Su Ke Cheng Haitao Luan Min Gu institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for artificial-intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词： polarization mode momentum

Ultrahigh performance passive radiative cooling by hybrid polar dielectric metasurface thermal emitters

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Opto-Electronic Advances 2024年第4期7卷 17-25页

作者： Yinan Zhang Yinggang Chen Tong Wang Qian Zhu Min Gu institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for artificial-intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Real-world passive radiative cooling requires highly emissive,selective,and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling *** various selective thermal emitters have been demonstrated,it is still challenging to achieve these conditions sim-ultaneously because of the extreme difficulty in controlling thermal emission of photonic structures in *** we demonstrated hybrid polar dielectric metasurface thermal emitters with machine learning inverse design,en-abling a high emissivity of~0.92 within the atmospheric transparency window 8-13μm,a large spectral selectivity of~1.8 and a wide emission angle up to 80 degrees,*** selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as~15.4°C under strong solar irradiation of~800 W/m2,signific-antly surpassing the state-of-the-art *** designed structures also show great potential in tackling the urban heat island effect,with modelling results suggesting a large energy saving and deployment area *** research will make significant impact on passive radiative cooling,thermal energy photonics and tackling global climate change.

关键词： radiative cooling dielectric metasurfaces machine learning thermal emitters