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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong wang Zhongkui li Jianan wang qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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The Application of Artificial Intelligence in Alzheimer’s Research

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Tsinghua Science and Technology 2024年第1期29卷 13-33页

作者： qing Zhao Hanrui Xu Jianqiang li Faheem Akhtar Rajput liyan Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Medicine Tsinghua UniversityBeijing 100084China Department of Computer Science Sukkur IBA UniversitySukkur 65200Pakistan Department of Neurology Tsinghua University Yuquan HospitalBeijing 100040China

Alzheimer's disease(AD)is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function,but the etiology of AD is still *** the explosive growth of electronic health data,the application of artificial inteligence(Al)in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches,and improving the disease's diagnostic and prognostic *** paper first briefly introduces Al technologies and applications in medicine,and then presents a comprehensive review of Al in *** simple,it includes etiology discovery based on genetic data,computer-aided diagnosis(CAD),computer-aided prognosis(CAP)of AD using multi-modality data(genetic,neuroimaging and linguistic data),and pharmacological or non-pharmacological approaches for treating ***,some popular publicly available AD datasets are introduced,which are important for advancing Al technologies in AD ***,core research challenges and future research directions are discussed.

关键词： Alzheimer's disease artificial intelligence etiology discovery computer-aided diagnosis computer-aided prognosis treatment

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How mutual funds respond to asymmetric feedback tradingin China’s stock market

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Journal of Management Science and Engineering 2024年第2期9卷 143-160页

作者： Die wan li Yang Xiaoguang Yang School of Finance Zhejiang Gongshang UniversityHangzhou310018China Business School University of New South WalesSydneyAustralia Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijingChina

Previous studies show that individual investors play a dominant role in China's stock *** behavior of chasing-rise being stronger than killing-fall leads to asymmetry of feedback *** article investigates how mutual funds react to this market *** China's stock and fund data from 2003 to 2019,we find that mutual funds tend to hold fewer shares when asymmetric feedback trading of the relevant stock gets more *** negative relationship is robust after controlling past returns,turnover rates,and firm risk factors,moreover,it attenuates when the market sentiment is bullish or when stocks are allowed *** results show that mutual funds'selling towards asymmetric feedback trading does not make excess return but leads to significant risk *** findings may be related to uncertainty associated with asymmetric feedback trading,and thus support the limit market participation theory from the second largest stock market.

关键词： Asymmetric feedback trading Mutual fund shareholdings Uncertainty Market sentiment Short selling

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Neural and Behavioral Measures of Stress-induced Impairment in Error Awareness and Post-error Adjustment

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Neuroscience Bulletin 2024年第7期40卷 937-951页

作者： Na Hu Quanshan Long Xiaoxi wang Quan li qing li Antao Chen School of Preschool and Special Education Kunming UniversityKunming 650214China Faculty of Education Yunnan Normal UniversityKunming 650214China College of Teacher Education Qujing Normal UniversityQujing 655099China Key Laboratory of Cognition and Personality of Ministry of Education Faculty of PsychologySouthwest UniversityChongqing 400715China School of Psychology Shanghai University of SportShanghai 200438China

Exposure to stress negatively affects error processing,but the impact of stress on error awareness remains to be *** the present study,we examined the temporal dynamics of error awareness and post-error adjustment following acute ***-nine healthy men were randomly assigned to the control(n=26)or stress group(n=23).After stress induction,participants completed the error awareness task,and their brain activity was assessed by *** to the control group,the stress group demonstrated lower error awareness accuracy and smaller Pe(error positivity)andΔPe amplitudes following aware error responses,which indicated impairment of error awareness following ***,the stress group had lower accuracy in post-aware error responses than in post-unaware error responses and the control group,which indicated poor post-error adjustment following *** results showed a stress effect on sequential stages of error *** induces impaired error identification,which further generates maladaptive post-error performance.

关键词： Acute stress Error awareness Post-error adjustment Error positivity

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Dt1-SWEET10a partner:Photoperiodic control of seed weight in soybean

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Molecular Plant 2024年第6期17卷 839-841页

作者： li-qing Chen Lalit Dev Tiwari Department of Plant Biology University of Illinois at Urbana-ChampaignUrbanaIL 61801USA DOE Center for Advanced Bioenergy and Bioproducts Innovation University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Institute for Sustainability Energyand EnvironmentUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Seed weight and size are critical agronomic traits essential for determining yield and composition,receiving significant attention throughout domestication(Meyer et al.,2012).Seed weight serves as a reliable indicator of average seed size due to their positive *** of seed weight involves both developmental and environmental cues,affecting cell proliferation or expansion(li et al.,2019).

关键词： soybean SWEET1 weight

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Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction

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Computers, Materials & Continua 2024年第5期79卷 3299-3314页

作者： Chuyuan Wei Jinzhe li Zhiyuan wang Shanshan wan Maozu Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China

Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering ***,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction ***,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure *** efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional ***,our model extracts text features of different structures from theinput *** Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural *** addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final *** paper combines these featuresand trains a graph convolutional network for relation *** experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.

关键词： Relation extraction graph convolutional neural networks dependency tree dynamic structure attention

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Photo-induced 1,2-aryl migration of 2-chloro-1-arylpropanone under batch and flow conditions:Rapid,scalable and sustainable approach to 2-arylpropionic acids

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Chinese Chemical Letters 2024年第2期35卷 366-369页

作者： Qiao li Minjie liu Meifen Jiang li wan Yingtang Ning Fen-Er Chen College of Chemical Engineering Zhejiang University of TechnologyHangzhou 310014China Engineering Center of Catalysis and Synthesis for Chiral Molecules Fudan UniversityShanghai 200433China Shanghai Engineering Center of Industrial Catalysis for Chiral Drugs Shanghai 200433China Institute of Pharmaceutical Science and Technology Zhejiang University of TechnologyHangzhou 310014China

We report the photo-mediated 1,2-aryl migration of 2‑chloro-1-arylpropanones to 2-arylpropionic acids using HCOONa as an acid *** pragmatically focused study obviates the multiple-step sequence in the industrially employed,ZnO-promoted rearrangement strategy,and offers rapid access to various 2-arylpropionic acids under environmentally friendly ***,the successful transfer of this batch photochemistry to a continuous flow platform led to improved scalability and enabled the gram-scale synthesis of loxoprofen.

关键词： Green chemistry Flow chemistry 2-Arylpropionic acids Photo-Favorskii reaction

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Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

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Advances in Atmospheric Sciences 2024年第8期41卷 1601-1613页

作者： Jiaqi ZHENG qing liNG Jia li Yerong FENG School of Computer Science and Engineering/Guangdong Provincial Key Laboratory of Computational Science Sun Yat-Sen UniversityGuangzhou 510006China School of Mathematics Sun Yat-Sen UniversityGuangzhou 510275China Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction Guangzhou 510641China Shenzhen Institute of Meteorological Innovation Shenzhen 518038China

Due to various technical issues,existing numerical weather prediction(NWP)models often perform poorly at forecasting rainfall in the first several *** correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting,we propose a deep learning-based approach called UNet Mask,which combines NWP forecasts with the output of a convolutional neural network called *** UNet Mask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation *** overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a *** UNet Mask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask,which provides the corrected 6-hour rainfall *** evaluated UNet Mask on a test set and in real-time *** results showed that UNet Mask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI *** tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNet Mask's forecast *** study shows that UNet Mask is a promising approach for improving rainfall forecasting of NWP models.

关键词： deep learning numerical weather prediction(NWP) 6-hour quantitative precipitation forecast

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Effect of Sc on Al_(3)Fe phase and mechanical properties of as-cast AA5052 aluminum alloy

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China Foundry 2024年第3期21卷 257-264页

作者： Yang li qing Yu Feng-feng Chen Jia-wen He Hong-mei Yang Meng-nie li Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China

The AA5052 aluminum alloy is widely used in automobile and aerospace manufacturing,and with the development of light-weight alloys,it is required that these materials exhibit better mechanical *** studies have demonstrated that the addition of Sc to aluminum alloys can improve both the microstructure and properties of the *** this study,the effect of Sc on the Fe-rich phase and properties of the AA5052 aluminum alloy was studied by adding 0%,0.05%,0.2%,and 0.3%*** results show that with the increase of Sc,the coarse needle-like Fe-rich phase gradually transforms into Chinese-script and then nearly spherical particles,reduce the size of Fe-rich phase,and refine the grain with increase of high angle grain boundaries(HAGBs).These microstructure changes enhance the strength of the AA5052 alloy through Sc *** ductility of the alloy is obviously improved because the addition of a lower amount of Sc changes the morphology of Fe-rich phase from needle-like into a Chinese-script,and it is subsequently reduced as a result of significant increase in HAGBs with increasing Sc content.

关键词： AA5052 aluminum alloy Al3Fe phase mechanical properties grain boundary

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang qing-Wei Dong Guang-Xi wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

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