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AI for Supporting the Freedom of Drawing

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Machine Intelligence Research 2024年第1期21卷 63-88页

作者： Xiaohua sun Juexiao Qin College of Design and Innovation Tongji UniversityShanghai 200082China

Artificial intelligence(AI)has recently been developing rapidly in image processing and *** is not only starting to take over repetitive and tedious tasks but is also involved in creative *** is one of the areas with the greatest potential for collaboration between humans and *** is an important approach to express various information,but due to the lack of appropriate knowledge and skills,ordinary people without long-time training are unable to draw *** there have been various attempts at human-AI collaboration in drawing,it is difficult for researchers to consider the wide variety of specific problems and develop univer-sal methods due to the openness,improvisation,and individuality of *** this paper,we first analysed the contents of drawing and the general creation process in ***,we have described a mechanism for using AI to enable people to regain the freedom of drawing ***,we have developed a framework that describes methods for analysing specific problems and quickly finding solutions by building connections between the influencing factors in drawing,the demands of humans,and possible implementa-tion *** framework also reveals a broad scope of possibilities for applying AI to support people in drawing.

关键词： Intention understanding drawing support drawing art human-artificial intelligence(AI)collaboration

Pretreating and normalizing metabolomics data for statistical analysis

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Genes & Diseases 2024年第3期11卷 188-205页

作者： Jun sun Yinglin Xia Division of Gastroenterology and Hepatology Department of MedicineDepartment of Microbiology/ImmunologyUIC Cancer CenterUniversity of Illinois ChicagoJesse Brown VA Medical Center Chicago(537)ChicagoIL 60612USA Division of Gastroenterology and Hepatology Department of MedicineUniversity of Illinois ChicagoChicagoIL 60612USA

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological *** is emerging as a powerful tool generally for pre-cision ***,integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and ***,metabo-lomics data are very ***/pretreating and normalizing procedures on metabolomics data are usually required before statistical *** this review article,we comprehensively review various methods that are used to preprocess and pretreat metabolo-mics data,including MS-based data and NMR-based data preprocessing,dealing with zero and/or missing values and detecting outliers,data normalization,data centering and scaling,data *** discuss the advantages and limitations of each *** choice for a suitable preprocessing method is determined by the biological hypothesis,the characteristics of the data set,and the selected statistical data analysis *** then provide the perspective of their applications in the microbiome and metabolome research.

关键词： Data centering and scaling Data normalization Data transformation Missing values MS-Baseddata preprocessing NMRData preprocessing Outliers Preprocessing/pretreatment

Catalysis by subsurface metal atoms in Al(001) through electron transfer

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National Science Review 2024年第4期11卷 12-13页

作者： Bo sun Ding Ma Beijing National Laboratory for Molecular Science New Cornerstone Science Laboratory College of Chemistry and Molecular Engineering Peking University

Heterogeneous catalysis plays a crucial role in the chemical industry, which has evolved from the investigation of chemisorption to an electronic structure[1]. Diverse strategies, such as alloying,doping, and confining, have been applied to enhance the catalytic properties of active sites through electronic modulation. The electron transfer between active sites and substrates profoundly influences catalytic activity [2].

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Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

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China Communications 2024年第5期21卷 261-279页

作者： sun Yi Wang Zhouyang Beijing University of Posts and Telecommunications School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications International School

The distribution of data has a significant impact on the results of *** the distribution of one class is insignificant compared to the distribution of another class,data imbalance *** will result in rising outlier values and ***,the speed and performance of classification could be greatly *** the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification *** with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone *** we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector *** introduce the cost control to solve the problem of sample ***,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is *** can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

关键词： fuzzy ordered regression(OR) relaxing variables twin support vector machine

In silico optimization of actuation performance in dielectric elastomercomposites via integrated finite element modeling and deep learning

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Theoretical & Applied Mechanics Letters 2024年第1期14卷 48-56页

作者： Jiaxuan Ma Sheng sun Materials Genome Institute Shanghai UniversityShanghai 200444China Zhejiang Laboratory Hangzhou 311100China Shanghai Frontier Science Center of Mechanoinformatics Shanghai UniversityShanghai 200444China

Dielectric elastomers(DEs)require balanced electric actuation performance and mechanical integrity under applied *** high dielectric particles as fillers provides extensive design space to optimize concentration,morphology,and distribution for improved actuation performance and material *** study presents an integrated framework combining finite element modeling(FEM)and deep learning to optimize the microstructure of DE *** first calculates actuation performance and the effective modulus across varied filler combinations,with these data used to train a convolutional neural network(CNN).Integrating the CNN into a multi-objective genetic algorithm generates designs with enhanced actuation performance and material modulus compared to the conventional optimization approach based on FEM approach within the same *** framework harnesses artificial intelligence to navigate vast design possibilities,enabling optimized microstructures for high-performance DE composites.

关键词： Dielectric elastomer composites Multi-objective optimization Finite element modeling Convolutional neural network

The Dbar-dressing method for the(2+1)-dimensional Date–Jimbo–Kashiwara–Miwa equation

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Communications in Theoretical Physics 2024年第1期76卷 17-23页

作者： Shifei sun Biao Li School of Mathematics and Statistics Ningbo UniversityNingbo315211China

In this work,the(2+1)-dimensional Date–Jimbo–Kashiwara–Miwa(DJKM)equation is studied by means of the ■-dressing method.A new ■ problem has been constructed by analyzing the characteristic function and the Green’s function of its Lax *** on solving the ■ equation and choosing the proper spectral transformation,the solution of the DJKM equation is ***,the more general solution of the DJKM equation can be also obtained by ensuring the evolution of the time spectral data.

关键词： (2+1)-dimensional DJKM equation inverse spectral problem Green's function characteristic function ■-dressing method

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

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Computers, Materials & Continua 2024年第2期78卷 2721-2739页

作者： Haipeng sun Xueyan Ding Jian sun HuaYu Jianxin Zhang School of Computer Science and Engineering Dalian Minzu UniversityDalian116600China Institute of Machine Intelligence and Bio-Computing Dalian Minzu UniversityDalian116600China 3Yi Language Research RoomChina Ethnic Languages Translation Centre Beijing100080China

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and *** the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character ***,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different ***,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original *** maps are then subjected to a differentiable binarization process,resulting in an approximate binarization *** map helps to identify the boundaries of the text ***,the text detection box is generated after the post-processing *** the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters ***,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text *** experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,*** also,we have compared the detection and re

关键词： Yi characters text detection text recognition attention mechanism deep neural network

Jackknife Model Averaging for Quantile Single-Index Coefficient Model

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Journal of Systems Science & Complexity 2024年第4期37卷 1685-1713页

作者： sun Xianwen ZHANG Lixin School of Mathematical Sciences Zhejiang UniversityHangzhou 310058China School of Statistics and Mathematics Zhejiang Gongshang UniversityHangzhou 310018China

In the past two decades,model averaging,as a way to solve model uncertainty,has attracted more and more *** this paper,the authors propose a jackknife model averaging(JMA) method for the quantile single-index coefficient model,which is widely used in *** model misspecification,the model averaging estimator is proved to be asymptotically optimal in terms of minimizing out-of-sample quantile *** experiments are conducted to compare the JMA method with several model selections and model averaging methods,and the results show that the proposed method has a satisfactory *** method is also applied to a real dataset.

关键词： Final prediction error JMA criterion model averaging quantile loss single-index coefficient model

Genome-wide SNP markers provided insights into the reproductive strategy and genetic diversity of the green tide causative species Ulva prolifera in China

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Journal of Oceanology and Limnology 2024年第2期42卷 533-543页

作者： Song sun Weiwei MA Nan WANG Song FENG Yan sun CAS Key Laboratory of Marine Ecology and Environmental Sciences Institute of OceanologyChinese Academy of SciencesQingdao 266071China Laboratory for Marine Ecology and Environmental Science Laoshan LaboratoryQingdao 266237China Jiaozhou Bay Marine Ecosystem Research Station Chinese Academy of SciencesQingdao 266071China University of Chinese Academy of Sciences Beijing 100049China

Ulva prolifera is the causative species of the annually occurring large-scale green tides in China since *** specific biological features on reproductivity strategies,as well as intra-species genetic diversity,are still largely unknown,especially at the genome level,despite their importance in understanding the formation and outbreak of massive green *** the present study,the restriction site-associated DNA genotyping approach(2b-RAD)was adopted to identify the genome-wide single-nucleotide polymorphisms(SNPs)of 54 individual thalli including samples collected from Subei Shoal in 2019 and Qingdao coast from 2019 to *** genotype results revealed that most of the thalli in 2019 and 2020 were haploid gametophytes,while only half of the thalli were gametophytes in 2021,indicating flexibility in the reproductive strategies for the formation of the green tides among different years and the dominance of asexual and vegetative reproductive mode for the floating ***,population analysis was conducted,and it revealed a very low genetic diversity among samples from Subei Shoal and the Qingdao coast in the same year and a higher divergence among samples in different *** results showed the efficiency of 2b-RAD in the exploration of SNPs in *** and provided the first genome-wide scale evidence for the origin of the large-scale green tides on the Qingdao *** study improved our understanding of the reproductive strategy and genetic diversity of the green tide causative species and will help further reveal the biological causes of the green tide in China.

关键词： green tide Ulva prolifera 2b-RAD single-nucleotide polymorphism(SNP) reproductive strategy genetic diversity