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2020国际计算机前沿大会

作者： Qian Wang Yue Wu Jianxin Zhang Hengbo Zhang Chao Che Lin Shan Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University School of Computer Science and Engineering Dalian Minzu University School of Economic and Management Dalian University of Science and Technology

Recently, deep hashing methods play a pivotal role in image retrieval tasks by combining advanced convolutional neural networks(CNNs) with efficient hashing. Meanwhile, second-order representations of deep convolutional activations have been established to effectively improve network performance in various computer vision applications. In this work, to obtain more compact hash codes, we propose a supervised deep second-order covariance hashing(SDSo CH) method by combining deep hashing with second-order statistic model. SDSo CH utilizes a powerful covariance pooling to model the second-order statistics of convolutional features, which is naturally integrated into the existing point-wise hashing network in an end-to-end manner. The embedded covariance pooling operation well captures the interaction of convolutional features and produces global feature representations with more discriminant capability, leading to the more informative hash codes. Extensive experiments conducted on two benchmarks demonstrate that the proposed SDSoCH outperforms its first-order counterparts and achieves superior retrieval performance.

关键词： Deephashing Point-wisemanner Covariancepooling Imageretrieval

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Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

arXiv

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arXiv 2023年

作者： Teng, Siyu Hu, Xuemin Deng, Peng Li, Bai Li, Yuchen Yang, Dongsheng Ai, Yunfeng Li, Lingxi Xuanyuan, Zhe Zhu, Fenghua Chen, Long BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon 999077 Hong Kong The School of Computer Science and Information Engineering Hubei University Wuhan430062 China The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body China The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China University of Chinese Academy of Sciences Beijing100049 China The School of Public Management/Emergency Management Jinan University Guangzhou510632 China Indianapolis United States Guangdong provincial key lab of IRADS BNU-HKBU United International College Zhuhai519087 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Ion replacement to construct coral-like montmorillonite/manganese-cobalt bimetallic oxide for high-performance supercapacitors with aqueous electrolyte

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Electrochimica Acta 2025年 533卷

作者： Yuan, Yu-Han Li, Gui-Fang Cai, Shuang-Lin Yan, Jin-Wei Liang, Jia Huang, Hai Jin, Long-Long Zeng, Ye Weng, Jian-Chun Fan, Xiao-Hong Li, Yi Deng, Ding-Rong Wu, Qi-Hui Jimei University College of Marine Equipment and Mechanical Engineering Key Laboratory of Energy Cleaning Utilization Development Cleaning Combustion and Energy Utilization Research Center of Fujian Province Xiamen Key Laboratory of Marine Corrosion and Smart Protective Materials Fujian Xiamen361021 China Jiangsu Key Lab of Advanced Functional Polymer Design and Application Department of Polymer Science and Engineering College of Chemistry Chemical Engineering and Materials Science Soochow University Suzhou215123 China

With the growth demand of new energies and the limitations of traditional fossil fuels, the development of efficient and environmentally friendly energy storage technologies for the new energies have become crucial. In this study, montmorillonite (MON) was used as the substrate to prepare a novel composite electrode material with manganese-cobalt oxide (HSMnCo), which is in-situ grown on the surface of MON. The manganese-cobalt oxide grown on the surface of MON fills the voids between the MON lamellae, preventing the agglomeration of MON lamellae and keeping it in a relatively loose structure. Meanwhile, MON has a high ion exchange capacity, and the Si4+ between its layers can undergo exchange reactions with Mn2+ and Co2+. Moreover, there is a synergistic and competitive effect between Mn2+ and Co2+, which changes the nucleation sites, nucleation number and growth process of manganese-cobalt oxide on the surface of MON. As a result, HSMnCo with a unique coral-like structure with multiple wrinkles and layers is obtained, significantly increasing the specific surface area (212 m2∙g-1) and porosity of the composite electrode material. Electrochemical tests showed that HSMnCo has a specific capacitance as high as 1235 F∙g-1 at a current density of 0.5 A∙g-1 and still retains 91.6 % of its initial capacity after 5000 cycles, demonstrating excellent cycling stability. The synergistic effect of manganese-cobalt bimetals optimizes the electron transport path, reduces the charge transfer resistance (Rct = 3.58Ω), and improves the power density (279.83 W∙kg⁻¹) and energy density (43.53 Wh∙kg-1). In addition, when it was applied to zinc-ion supercapacitors, the long cycle capacity retention rate is further increased to 94.7 %, and when the power density is 374.78 W∙kg-1, the energy density reaches 177.50 Wh∙kg-1. This study not only provides new ideas for the high value utilization of waste resources such as silicate minerals but also promotes the development of green and low

关键词： Coal storage

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Unveiling the drivers of atmospheric methane variability in Iran: A 20-year exploration using spatiotemporal modeling and machine learning

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Environmental Challenges 2024年 15卷

作者： Mousavi, Seyed Mohsen Dinan, Naghmeh Mobarghaee Ansarifard, Saeed Borhani, Faezeh Darvishi, Asef Mustafa, Farhan Naghibi, Amir Department of Environmental Planning and Design Shahid Beheshti University Tehran *** Iran School of Physics Institute for Research in Fundamental Sciences (IPM) P.O. Box 19395-5531 Tehran Iran Department of Civil & Environmental Engineering Faculty of Environment University of Tehran Tehran *** Iran Policy and Governance Laboratory (UT GOV/LAB) Faculty of Governance University of Tehran Tehran *** Iran Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam-Bornim Max-Eyth-Allee 100 Potsdam 14469 Germany Guangdong-Hong Kong Joint Laboratory for Carbon Neutrality Jiangmen Laboratory of Carbon Science and Technology Guangdong Province Jiangmen 529199 China Guangzhou HKUST Fok Ying Tung Research Institute (FYTRI) Nansha Guangzhou 511458 China Department of Water Resources Engineering & Center for Advanced Middle Eastern Studies Lund University SE Lund 221 00 Sweden

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH4) is crucial for mitigating its impacts and implementing emission reduction strategies. This study compr... 详细信息

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH₄) is crucial for mitigating its impacts and implementing emission reduction strategies. This study comprehensively investigates XCH4 and its driving factors (environmental, meteorological, and anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, and machine learning algorithms. The spatiotemporal analysis showed notable spatial variation, with high XCH₄ levels in central, southern, and eastern Iran and lower levels in the northwest and north. Moreover, distinct seasonal cycles emerged, with maximum XCH₄ occurring during summer (August-September) and minimum levels in spring (April-May). Correlation analysis and variable importance assessment were developed to elucidate the key drivers governing XCH₄ dynamics. Correlation analysis revealed that vegetation cover, precipitation, and soil moisture were negatively correlated with XCH₄, while temperature indices showed a positive correlation, exhibiting the highest correlation in time dispersion and quantity among the studied variables. The Permutation Importance technique, used with a Random Forest classifier, a machine learning-based approach that considers the role of all variables together, showed that land surface temperature, wind speed, soil moisture, and vegetation cover are the dominant controls, with their importance ranked respectively. Surprisingly, anthropogenic emissions played a relatively minor role in shaping XCH₄ distributions at the regional scale. These findings highlight the significant influence of meteorological variables and ecosystem processes on XCH₄ modulation, revealing intricate Earth system feedbacks that inform targeted mitigation strategies and predictive models for curbing

关键词： Climate change Methane Remote sensing Temperature Variable importance

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Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy

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Physical Review B 2021年第9期104卷 094310-094310页

作者： Hongliang Yang Yifan Zhu Erting Dong Yabei Wu Jiong Yang Wenqing Zhang State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai 200050 China University of Chinese Academy of Sciences Beijing 100049 China Department of Physics and Shenzhen Institute for Quantum Science & Engineering Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Lab for Computational Science and Materials Design and Shenzhen Municipal Key-Lab for Advanced Quantum Materials and Devices Southern University of Science and Technology Shenzhen 518055 China Materials Genome Institute Shanghai University Shanghai 200444 China

The development of reliable and flexible machine learning based interatomic potentials (ML-IPs) is becoming increasingly important in studying the physical properties of complex condensed matter systems. Besides the structure descriptor model for total energy decomposition, the trial-and-error approach used in the design of the training dataset makes the ML-IP hardly improvable and reliable for modeling materials with chemical bond hierarchy. In this work, a dual adaptive sampling (DAS) method with an on the fly ambiguity threshold was developed to automatically generate an effective training dataset covering a wide temperature range or a wide spectrum of thermodynamic conditions. The DAS method consists of an inner loop for exploring the local configuration space and an outer loop for covering a wide temperature range. We validated the developed DAS method by simulating thermal transport of complex materials. The simulation results show that even with a substantially small dataset, our approach not only accurately reproduces the energies and forces but also predicts reliably effective high-order force constants to at least fourth order. The lattice thermal conductivity and its temperature dependence were evaluated using the Green-Kubo simulations with ML-IP for CoSb3 with up to third-order phonon scattering, and those for Mg3Sb2 with up to fourth-order phonon scattering, and all show good agreements with experiments. Our work provides an avenue to effectively construct a training dataset for ML-IP of complex materials with chemical bond hierarchy.

关键词： Interatomic & molecular potentials Lattice thermal conductivity Machine learning

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A Chiral COFs Membrane for Enantioselective Amino Acid Separation

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Angewandte Chemie 2024年第5期137卷

作者： Narmadha Manoranjan Wangxi Fang Yuzhang Zhu Prof. Dr. Jian Jin i-Lab Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Suzhou 215123 P.R. China College of Chemistry Chemical Engineering and Materials Science Jiangsu Key Laboratory of Advanced Functional Polymer Materials & Suzhou key Laboratory of Macromolecular Design and Precision Synthesis Soochow University Suzhou 215123 P.R. China

Incorporating chiral molecules in the covalent organic frameworks (COFs) with uniformly ordered pores results in chiral COFs, which have been highly promising candidates for enantioseparation. Herein, a homochiral COF nanochannel membrane is reported by introducing chiral centers (L-phenylalanine methyl ester) into one of the organic ligands for the enantioseparation of chiral amino acids. The separation results show that the D-isomer is preferentially transported through the porous membrane channel. This composite membrane exhibits excellent selectivity for racemic phenylalanine with the highest enantiomeric excess value of up to 99.4 %. The adsorption and molecular modeling studies substantiate the experiment results by showing higher bonding affinity towards D-isomer over L-isomer. The excellent enantioselective gating performance unveils the porous COF skeleton with chiral selectors and the size-matching synergy for stereoselective interactions with chiral isomers.

关键词： chiral separation composite membrane covalent organic framework homochiral membrane membrane chromatography

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The PIWI-specific insertion module helps load longer piRNAs for translational activation essential for male fertility

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Science China(Life Sciences) 2023年第7期66卷 1459-1481页

作者： Xin Wang Di-Hang Lin Yue Yan An-Hui Wang Jiaoyang Liao Qian Meng Wen-Qing Yang Heng Zuo Min-Min Hua Fengjuan Zhang Hongwen Zhu Hu Zhou Tian-Yu Huang Rui He Guangyong Li Yue-Qiu Tan Hui-Juan Shi Lan-Tao Gou Dangsheng Li Ligang Wu Yonggang Zheng Xiang-Dong Fu Jinsong Li Rujuan Liu Guo-Hui Li Mo-Fang Liu Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou310024China State Key Laboratory of Molecular Biology State Key Laboratory of Cell BiologyShanghai Key Laboratory of Molecular AndrologyShanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghai200031China Laboratory of Molecular Modeling and Design State Key Laboratory of Molecular Reaction DynamicsDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian116023China Department of Analytical Chemistry and CAS Key Laboratory of Receptor Research Shanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201203China School of Life Science and Technology ShanghaiTech UniversityShanghai201210China NHC Key Lab of Reproduction Regulation(Shanghai Institute for Biomedical and Pharmaceutical Technologies) Fudan UniversityShanghai200032China Key Laboratory of Fertility Preservation and Maintenance of Ministry of Education School of Basic Medical SciencesNingxia Medical UniversityYinchuan750004China Department of Urology General Hospital of Ningxia Medical UniversityNingxia Medical UniversityYinchuan750004China Institute of Reproductive and Stem Cell Engineering College of Basic of MedicineCentral South UniversityChangsha410000China Department of Physiology Howard Hughes Medical InstituteUniversity of Texas Southwestern Medical CenterDallasTX75390USA Westlake Laboratory of Life Sciences and Biomedicine School of Life SciencesWestlake UniversityHangzhou310024China

PIWI-clade proteins harness pi RNAs of 24–33 nt in *** great puzzles are how PIWI-clade proteins incorporate pi RNAs of different sizes and whether the size matters to PIWI/pi RNA *** we report that a PIWI-Ins module unique in PIWIclade proteins helps define the length of pi *** of PIWI-Ins in Miwi shifts MIWI to load with shorter pi RNAs and causes spermiogenic failure in mice,demonstrating the functional importance of this regulatory ***,we show that longer pi RNAs provide additional complementarity to target m RNAs,thereby enhancing the assembly of the MIWI/e IF3f/Hu R super-complex for translational ***,we identify a c.1108C>T(p.R370W)mutation of HIWI(human PIWIL1)in infertile men and demonstrate in Miwi knock-in mice that this genetic mutation impairs male fertility by altering the property of PIWI-Ins in selecting longer pi *** findings reveal a critical role of PIWI-Ins-ensured longer pi RNAs in fine-tuning MIWI/pi RNA targeting capacity,proven essential for spermatid development and male fertility.

关键词： PIWI piRNAs PIWI-Ins translational activation spermatid development male fertility

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Kinematic calibration of a 3RRPS metamorphic parallel mechanism

Kinematic calibration of a 3RRPS metamorphic parallel mechan...

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ASME 2020 International design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2020

作者： Chai, Xuheng Nurahmi, Latifah Dai, Jian S. Gan, Dongming MOE Key Laboratory for Mechanism Theory and Equipment Design International Centre for Advanced Mechanisms and Robotics Tianjin University Tianjin China Department of Mechanical Engineering Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia Kinematics and Robotics Lab Centre for Robotics Research King’s College London Strand LondonWC2R 2LS United Kingdom School of Engineering Technology Purdue University West LafayetteIN47907 United States

ISBN: (纸本)9780791883990

In this paper, a kinematic calibration method is developed for a 3rRPS metamorphic parallel mechanism with respect to all unknown parameters. Each limb of the 3rRPS mechanism is composed of (rR), P, and S joints. Two joints are actuated, namely P and r joints, hence the mechanism is able to switch between the 3RPS parallel mechanism and 3US parallel mechanism. The geometric constraint equations of the 3rRPS mechanism are initially established. Then, the optimization problems for the base, platform and actuated prismatic lengths during given trajectory are formulated by using the global search optimization algorithm. A physical model of the 3rRPS metamorphic parallel mechanism is built and an experiment is setup to validate the proposed calibration and optimization models. The external device, i.e., the OptiTrack is used during the experiment for motion capture system. All unknown parameters are identified and optimized by dint of the geometric properties of this mechanism and nonlinear optimization algorithms. The experimental results demonstrate that the proposed calibration method is valid and effective. Copyright © 2020 ASME

关键词： Mechanisms

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Deep High-order Asymmetric Supervised Hashing for Image Retrieval

Deep High-order Asymmetric Supervised Hashing for Image Retr...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yongchao Yang Jianxin Zhang Qian Wang Bin Liu Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Computer Science and Engineering Dalian Minzu University Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Deep hashing has recently been attracting more and more attentions for large-scale image retrieval task owing to its superior performance of search efficiency and less storage space requirements. Among deep hashing models, asymmetric deep hashing performs feature learning on query dataset and directly generates hash code on database images, significantly improving the retrieval performance of deep hashing models. Meanwhile, recently works also establish that high-order statistic of deep features are helpful to obtain more discriminant representations of images. Therefore, to boost the retrieval capability of deep hashing, this work tries to integrate merits of the high-order statistic module and the asymmetric deep hashing architecture, and it further proposes a novel deep high-order asymmetric supervised hashing (DHoASH) for image retrieval. More specifically, we utilize a powerful global covariance pooling module based on matrix power normalization to compute the second-order statistic features of input images, which is fluently embedded into an asymmetric hashing architecture in an end-to-end manner, leading to the generation of more discriminant binary hashing code. Experiment results on two benchmarks illuminates the effectiveness of the proposed DHoASH, which also achieves very competitive retrieval accuracy compared to the state-of-the-art methods.

关键词： Covariance matrices Feature extraction Matrix decomposition Image retrieval Task analysis Convolutional codes

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AsymmetricN-Alkylation of 1H-Indoles via Carbene Insertion Reaction

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Angewandte Chemie 2023年第47期135卷

作者： Quanxin Peng Meirong Huang Guangyang Xu Yan Zhu Dr. Ying Shao Dr. Shengbiao Tang Prof. Dr. Xinhao Zhang Prof. Dr. Jiangtao Sun Jiangsu Key Laboratory of Advanced Catalytic Materials & Technology School of Petrochemical Engineering Changzhou University 1 Gehu Road 213164 Changzhou China Shenzhen Bay Laboratory Lab of Computational Chemistry and Drug Design State Key Laboratory of Chemical Oncogenomics Peking University Shenzhen Graduate School 518055 Shenzhen China

An intermolecular enantioselective N -alkylation reaction of 1 H -indoles has been developed by cooperative rhodium and chiral phosphoric acid catalyzed N−H bond insertion reaction. N -Alkyl indoles with newly formed stereocenter adjacent to the indole nitrogen atom are produced in good yields (up to 95 %) with excellent enantioselectivities (up to >99 % ee). Importantly, both α -aryl and α -alkyl diazoacetates are tolerated, which is extremely rare in asymmetric X−H (X=N, O, S et al.) and C−H insertion reactions. With this method, only 0.1 mol % of rhodium catalyst and 2.5 mol % of chiral phosphoric acid are required to complete the conversion as well as achieve the high enantioselectivity. Computational studies reveal the cooperative relay of rhodium and chiral phosphoric acid, and the origin of the chemo and stereoselectivity.

关键词： Asymmetric Catalysis Carbene Indole N−H Insertion Reaction N-Alkylation

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