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Science China Life Sciences 2025年

作者： Yanlong Li Yinuo Fu Yaoyao Li Rui Zhang Jing Yang Huanhuan Ma ling min Xianlong Zhang National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University

High temperature (HT) stress causes male sterility,leading to reduced upland cotton ***,we identified a key gene,Casein Kinase I (GhCKI),that negatively regulates male fertility in upland cotton under ***,conventional genetic manipulations of GhCKI would result in male sterility,hindering its utilization in breeding ***,we engineered quantitative variation for anther thermotolerance-related traits in upland cotton by creating weak promoter alleles of GhCKI genes,using CRISPR/Cas9 and CRISPR/Cpf1genome ***,we screened and identified two new upland cotton plant lines exhibiting a HT-tolerant phenotype with edited GhCKI promoters,and characterized their corresponding heat-tolerant allelic *** research revealed that the primary reason for the HT tolerance of the GhCKI promoter editing mutants is that the trans-acting factors GhMYB73 and GhMYB4,which positively regulate GhCKI expression under HT,failed to bind and activate the expression of ***,our study not only provides a rapid strategy to generate new beneficial alleles but also offers novel germplasm resources and molecular insights for crop HT tolerance breeding.

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Multi-dimensional classification via stacked dependency exploitation

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Science China(Information Sciences) 2020年第12期63卷 104-117页

作者： Bin-Bin JIA min-ling ZHANG School of Computer Science and Engineering Southeast University College of Electrical and Information Engineering Lanzhou University of Technology Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of EducationChina Collaborative Innovation Center of Wireless Communications Technology

Multi-dimensional classification(MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, where most approaches work by assuming directed acyclic graph(DAG) structure or random chaining structure over class spaces. Different from existing probabilistic strategies, a deterministic strategy named Seem for dependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pairwise dependencies are considered which can be modeled more reliably than modeling full dependencies among all class spaces by DAG or chaining structure. In the second-level, the class label of unseen instance *** class space is determined by adaptively stacking predictive outputs from first-level pairwise *** results show that stacked dependency exploitation leads to superior performance against stateof-the-art MDC approaches.

关键词： machine learning multi-dimensional classification class dependencies deterministic strategy stacked dependency exploitation

Compositional metric learning for multi-label classification

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Frontiers of Computer Science 2021年第5期15卷 1-12页

作者： Yan-Ping SUN min-ling ZHANG School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationChina Collaborative Innovation Center for Wireless Communications Technology Nanjing 211100China

Multi-label classification aims to assign a set of proper labels for each instance,where distance metric learning can help improve the generalization ability of instance-based multi-label classification *** multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature *** this paper,a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label *** one hand,compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on com-ponent *** the other hand,compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label *** to the compositional nature of employed distance metric,the resulting problem admits quadratic programming formulation with linear optimization complexity *** number of training *** also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional *** experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.

关键词： machine learning multi-label learning metric learning compositional metric positive semidefinite matrix decomposition

Multi-dimensional Classification via Selective Feature Augmentation

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Machine Intelligence Research 2022年第1期19卷 38-51页

作者： Bin-Bin Jia min-ling Zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina

In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented *** this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented ***, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

关键词： Machine learning multi-dimensional classification feature augmentation feature selection class dependencies

Viscosity Analysis on the Boltzmann Equation

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Acta Mathematica Sinica,English Series 2012年第10期28卷 2139-2152页

作者： min ling ZHENG Xiao Ping YANG School of Science Huzhou Teachers College School of Science Nanjing University of Science&Technology

This paper is devoted to investigating the asymptotic properties of the renormalized so- lution to the viscosity equation δtfε ＋ v · △↓xfε = Q（fε, fε）＋ ε△vfε as ε →0＋. We deduce that the renormalized solution of the viscosity equation approaches to the one of the Boltzmann equation in L^1（（0, T） × RN × R^N）. The proof is based on compactness analysis and velocity averaging theory.

关键词： Viscosity Boltzmann equation renormalized solution viscosity coemcient defect measure

Multi-instance partial-label learning: towards exploiting dual inexact supervision

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Science China(Information Sciences) 2024年第3期67卷 48-61页

作者： Wei TANG Weijia ZHANG min-ling ZHANG School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Physical Sciences The University of Newcastle

Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one groundtruth label and some false positive labels. Specifically, at least one instance pertains to the ground-truth label while no instance belongs to the false positive labels. In this paper, we formalize such problems as multi-instance partial-label learning(MIPL). Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems since the former fails to disambiguate a candidate label set, and the latter cannot handle a multi-instance bag. To address these issues, a tailored algorithm named MIPLGP, i.e., multi-instance partial-label learning with Gaussian processes, is *** first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes. Experimental results on various datasets validate that MIPLGP is superior to well-established multi-instance learning and partial-label learning algorithms for solving MIPL problems.

关键词： machine learning multi-instance partial-label learning multi-instance learning partial-label learning Gaussian processes

Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1248-1261页

作者： Yi-Bo Wang Jun-Yi Hang min-ling Zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China IEEE

Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen *** each class might possess its own characteristics,the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning,where the predictive model is induced based on tailored features specific to each class label instead of the identical instance *** a representative approach,LIFT generates label-specific features by conducting clustering ***,its performance may be degraded due to the inherent instability of the single clustering *** improve this,a novel multi-label learning approach named SENCE(stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble)is proposed,which stabilizes the generation process of label-specific features via clustering ensemble ***,more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization(EM)*** experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.

关键词： Clustering ensemble expectation-maximization al-gorithm label-specific features multi-label learning

Impact of atrazine and nitrogen fertilizers on the sorption of chlorotoluron in soil and model sorbents

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Journal of Environmental Sciences 2007年第3期19卷 327-331页

作者： GAO min-ling DAI Shu-gui MA Yong-min ZHANG Ping College of Environmental Science and Engineering Nankai University Tianjin 300071 China Center of Disease Control and Prevention Tianjin 300011 China

Sorption of chlorotoluron in ammonium sulfate, urea and atrazine multi-solutes system was investigated by batch experiments. The results showed application of nitrogen fertilizers to the soil could affect the behavior of chlorotoluron. At the same concentration of N, sorption of chlorotoluron decreased as the concentration of atrazine increased on the day 0 and 6 in soil, respectively. The sorption of chlorotoluron increased from 0 to 6 d when soils were preincubated with deionized water, ammonium sulfate and urea solution for 6 d. That indicated incubation time was one of the most important factors for the sorption of chlorotoluron in nitrogen fertilizers treatments. The individual sorption isotherms of chlorotoluron in rubbery polymer and silica were strictly linear in single solute system, but there were competition sorption between pesticides or between pesticides and nitrogen fertilizers. That indicated the sorption taken place by concurrent solid-phase dissolution mechanism and sorption on the interface of water-organic matter or water-mineral matter.

关键词： sorption chlorotoluron atrazine nitrogen fertilizer competitive

Efficient Synthesis of 3,5,6,8-Tetrahydro-4H-thiopyrano[4′,3′:4,5]· thieno[2,3-d]pyrimidin-4-ones via an Iminophosphorane

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Chemical Research in Chinese Universities 2010年第1期26卷 60-65页

作者： LI Wen-jing LIU min FENG ling-ling DING ming-wu Key Laboratory of Pesticide ＆ Chemical Biology of ministry of Education Central China Normal University Wuhan 430079 P. R. China

3,5,6,8-Tetrahydro-4H-thiopyrano[4＇,3＇：4,5]thieno[2,3-d]pyrimidin-4-ones（7） were synthesized via the base catalytic reactions of amines or phenols with carbodiimides（5）, which were obtained from the aza-Wittig ... 详细信息

关键词： 3,5,6,8-Tetrahydro-4H-thiopyrano[4',3':4,5]thieno[2,3-d]pyrimidin-4-one Aza-Wittig reaction Carbodii- mide Isocyanate

Transfer synthetic over-sampling for class-imbalance learning with limited minority class data

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Frontiers of Computer Science 2019年第5期13卷 996-1009页

作者： Xu-Ying LIU Sheng-Tao WANG min-ling ZHANG School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China Collaborative Innovation Center for Wireless Communications Technology Nanjing 210096 China

The problem of limited minority class data is encountered in manyclass imbalanced applications, but has received little attention. Synthetic over-sampling, as popular class-imbalance learning methods, could introduce much noise when minority class has limited data since the synthetic samples are not i.i.d. samples of minority class. Mos t sophisticated synthetic sampling methods tackle this problem by denoising or generating samples more consistent with ground-truth data distribution. But their assumptions about true noise or ground-truth data distribution may not hold. To adapt synthetic sampling to the problem of limited minority class data, the proposed Traso framework treats synthetic minority class samples as an additional data source, and exploits transfer learning to transfer knowledge from them to minority class. As an implementation, TrasoBoost method firstly generates synthetic samples to balance class sizes. Then in each boosting iteration, the weights of synthetic samples and original data decrease and increase respectively when being misclassified, and remain unchanged otherwise. The misclassified synthetic samples are potential noise, and thus have smaller influence in the following iterations. Besides, the weights of minority class instances have greater change than those of majority class instances to be more influential. And only original data are used to estimate error rate to be immune from noise. Finally, since the synthetic samples are highly related to minority class, all of the weak learners are aggregated for prediction. Experimental results show TrasoBoost outperforms many popular class-imbalance learning methods.

关键词： machine learning data mining class imbalance over sampling boosting transfer learning