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Mesoscale wind stress-SST coupling induced feedback to the ocean in the western coast of South America

Journal of Oceanology and Limnology 2021年第3期39卷 785-799页

作者： chaoran cui Rong-Hua ZHANG Yanzhou WEI Hongna WANG Key Laboratory of Ocean Circulation and Waves Institute of Oceanologyand Center for Ocean Mega-ScienceChinese Academy of SciencesQingdao 266071China University of Chinese Academy of Sciences Beijing 100049China Qingdao National Laboratory for Marine Science and Technology Qingdao 266000China Center for Excellence in Quaternary Science and Global Change Chinese Academy of SciencesXi’an 710061China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou 310012China

The feedback induced by mesoscale wind stress-SST coupling to the ocean in the western coast of South America was studied using the Regional Ocean Modeling System(ROMS).To represent the feedback,an empirical mesoscale wind stress perturbation model was constructed from satellite observations,and was incorporated into the ocean *** two experiments with and without the mesoscale wind stress-SST coupling,it was found that SST in the mesoscale coupling experiment was reduced in the western coast of South America,with the maximum values of 0.5℃in the Peru Sea and 0.7℃in the Chile Sea.A mixed layer heat budget analysis indicates that horizontal advection is the main term that explains the reduction in ***,the feedback induced by mesoscale wind stress-SST coupling to the ocean can enhance vertical velocity in the nearshore area through the Ekman pumping,which brings subsurface cold water to the sea *** results indicate that the feedback due to the mesoscale wind stress-SST coupling to the ocean has the potential for reducing the warm SST bias often seen in the large-scale climate model simulations in this region.

关键词： mesoscale air-sea coupling western coast of South America ocean model simulations cooling effect warm bias

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Representing surface wind stress response to mesoscale SST perturbations in western coast of South America using Tikhonov regularization method

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Journal of Oceanology and Limnology 2020年第3期38卷 679-694页

作者： cui chaoran ZHANG Rong-Hua WANG Hongna WEI Yanzhou Key Laboratory of Ocean Circulation and Waves Institute of OceanologyChinese Academy of SciencesQingdao 266071China Center for Ocean Mega-Science Chinese Academy of SciencesQingdao 266071China University of Chinese Academy of Sciences Beijing 100049China Qingdao National Laboratory for Marine Science and Technology Qingdao 266000China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou 310012China

Interaction between mesoscale perturbations of sea surface temperature(SSTmeso)and wind stress(WSmeso)has great influences on the ocean upwelling system and turbulent mixing in the atmospheric boundary *** daily Quik-SCAT wind speed data and AMSR-E SST data,SSTmeso and WSmeso fields in the western coast of South America are extracted by using a locally weighted regression method(LOESS).The spatial patterns of SSTmeso and WSmeso indicate strong mesoscale SST-wind stress coupling in the *** coupling coefficient between SSTmeso and WSmeso is about 0.0095 N/(m^2·℃)in winter and 0.0082 N/(m^2·℃)in *** on mesoscale coupling relationships,the mesoscale perturbations of wind stress divergence(Div(WSmeso))and curl(Curl(WSmeso))can be obtained from the SST gradient perturbations,which can be further used to derive wind stress vector perturbations using the Tikhonov regularization *** computational examples are presented in the western coast of South America and the patterns of the reconstructed WS meso are highly consistent with SSTmeso,but the amplitude can be underestimated *** matching the spatially averaged maximum standard deviations of reconstructed WSmeso magnitude and observations,a reasonable magnitude of WSmeso can be obtained when a rescaling factor of 2.2 is *** current ocean models forced by prescribed wind cannot adequately capture the mesoscale wind stress response,the empirical wind stress perturbation model developed in this study can be used to take into account the feedback effects of the mesoscale wind stress-SST coupling in ocean *** applications are discussed for taking into account the feedback effects of the mesoscale coupling in largescale climate models and the uncoupled ocean models.

关键词： mesoscale air-sea coupling Tikhonov’s regularization method western coast of South America

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Multi-task MIML learning for pre-course student performance prediction

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Frontiers of Computer Science 2020年第5期14卷 113-121页

作者： Yuling Ma chaoran cui Jun Yu Jie Guo Gongping Yang Yilong Yin School of Software Shandong UniversityJinan250100China School of Information Engineering Shandong Yingcai CollegeJinan250104China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning ***,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual *** this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course ***,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the *** this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its *** better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)***,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified *** experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

关键词： educational data mining academic early warning system student performance prediction multi-instance multi-label learning multi-task learning

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Graph CA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2108-2123页

作者： Xinhua Wang Shasha Zhao Lei Guo Lei Zhu chaoran cui Liancheng Xu School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China

With the popularity of online learning in educational settings, knowledge tracing(KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations,we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation *** model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed Graph CA method compared with several state-of-the-art baselines.

关键词： Contrastive learning counterfactual representation graph neural network knowledge tracing

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Evaluating and improving the interpretability of item embeddings using item-tag relevance information

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Frontiers of Computer Science 2020年第3期14卷 143-158页

作者： Tao LIAN Lin DU Mingfu ZHAO chaoran cui Zhumin CHEN Jun MA College of Data Science Taiyuan University of TechnologyJinzhong 030600China Software College Shandong UniversityJinan 250101China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 101408China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent ***,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation ***,the item-item co-occurrence information,constructed from the sparse and long-tail distributed user-item interaction matrix,is over-estimated for rare items,which could lead to bias in learned item *** this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance ***,we design two metrics to quantitatively evaluate the interpretability of item embeddings from different viewpoints:interpretability of individual dimensions of item embeddings and semantic coherence of local neighborhoods in the latent *** also propose a tag-informed item embedding(TIE)model that jointly factorizes the user-item interaction matrix,the item-item co-occurrence matrix and the item-tag relevance matrix with shared item embeddings so that different forms of information can co-operate with each other to learn better item *** on the MovieLens20M dataset demonstrate that compared with other state-of-the-art MF methods,TIE achieves better top-N recommendations,and the relative improvement is larger when the user-item interaction matrix becomes *** leveraging the itemtag relevance information,individual dimensions of item embeddings are more interpretable and local neighborhoods in the latent space are more semantically coherent;the bias in learned item embeddings are also mitigated to some extent.

关键词： recommender system matrix factorization item embedding item-tag relevance interpretability

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Pre-course student performance prediction with multi-instance multi-label learning

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Science China(Information Sciences) 2019年第2期62卷 200-205页

作者： Yuling MA chaoran cui Xiushan NIE Gongping YANG Kashif SHAHEED Yilong YIN Software College Shandong University School of Information Engineering Shandong Yingcai College School of Computer Science and Technology Shandong University of Finance and Economics

Dear editor,Studying courses is one of the most basic and important tasks for college students. For each new course, the initial period of learning is crucial for students, and seriously influences subsequent learning activities. However, given a large number of classes in universities, it has become impossible for teachers to keep track of the individual performance of each student. In these circumstances, it is desirable to predict each student’s performance on a certain course prior to its commencement.

关键词： KNN DT Pre-course student performance prediction with multi-instance multi-label learning SVM Figure BCS

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Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration

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Computers, Materials & Continua 2020年第10期65卷 913-928页

作者： Leilei Geng chaoran cui Qiang Guo Sijie Niu Guoqing Zhang Peng Fu School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China School of Computer Science and Engineering University of JinanJinan250024China School of Computer Science and Engineering Nanyang Technological University639798Singapore School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China

The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware *** this paper,we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI ***,the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the *** the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation *** improve the accuracy of core tensor coding,the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by exploiting the sparse distribution prior in *** applied to MS-RSI restoration,our experimental results have shown that the proposed algorithm can better reconstruct the sharpness of the image textures and can outperform several existing state-of-the-art multispectral image restoration methods in both subjective image quality and visual perception.

关键词： Multispectral remote sensing image restoration modified Gaussian mixture sparse core tensor tensor dictionary learning

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北太平洋北部的风能输入门户

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海洋学报 2021年第3期43卷 40-47页

作者：张宇崔超然杨倩管玉平中国科学院南海海洋研究所热带海洋环境国家重点实验室广东广州510301 中国科学院海洋研究所山东青岛266071 南方海洋科学与工程广东省实验室(广州) 广东广州511458 中国科学院大学海洋学院北京100049

风不仅驱动了上层海洋的环流,也是深层海洋运动的主要能量来源。本文主要研究了北太平洋北部的风能输入的季节性分布特征和年际变化趋势,包括风向表面波、表层地转流和表层非地转流的能量输入。基于SODA3数据的结果表明,风能输入门户随... 详细信息

风不仅驱动了上层海洋的环流,也是深层海洋运动的主要能量来源。本文主要研究了北太平洋北部的风能输入的季节性分布特征和年际变化趋势,包括风向表面波、表层地转流和表层非地转流的能量输入。基于SODA3数据的结果表明,风能输入门户随季节变化显著,其中黑潮延伸区是冬季门户,副极地流涡是春、秋季门户,大洋东边界则是夏季门户,能量输入强度逐次递减。21世纪以来,秋冬风能输入明显减弱,春季增加,夏季无显著变化。就变化趋势的空间分布而言,向表面波的能量输入由风场主导,而向表层地转流和非地转流的能量输入则由流场主导。这些机械能输入结果对进一步认识该海域的动力机制有重要意义。

关键词：风能输入门户区域表面波表层地转流表层非地转流

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基于深度强化学习的国内金融市场投资比较研究

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南京大学学报（自然科学版） 2023年第2期59卷 333-342页

作者：操东林崔超然杨潇山东省区块链金融重点实验室山东财经大学济南250014 山东财经大学计算机科学与技术学院济南250014 山东财经大学管理科学与工程学院济南250014

近年来,随着全球经济的迅速发展,参与金融投资的投资者增多,如何在复杂的金融市场中自动选择交易策略使收益最大化成为研究热点.强化学习可以通过与实际环境的交互来寻找最优的交易策略,使投资收益最大化.现有的方法大都是将一到两个强... 详细信息

近年来,随着全球经济的迅速发展,参与金融投资的投资者增多,如何在复杂的金融市场中自动选择交易策略使收益最大化成为研究热点.强化学习可以通过与实际环境的交互来寻找最优的交易策略,使投资收益最大化.现有的方法大都是将一到两个强化学习算法应用于金融市场并比较算法在单一交易任务上的表现,此外,这些研究大都针对国外的股票、证券市场或加密货币市场,对国内金融市场的研究甚少.针对上述问题,面向国内金融投资市场,系统性地验证了不同类型的多种深度强化学习代表性算法在单只股票交易、多只股票交易和投资组合分配三个投资任务上的有效性.通过观察在累计收益率、夏普比率、最大回撤等评价指标上的回测结果对算法进行比较,结果显示在不同的投资任务中选取合适的强化学习算法可以有效地提升收益.

关键词：强化学习值函数策略梯度投资组合股票交易

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基于自注意力移动平均线的时间序列预测

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南京大学学报（自然科学版） 2022年第4期58卷 649-657页

作者：苏雅茜崔超然曲浩山东财经大学计算机科学与技术学院济南250014 山东大学软件学院济南250101

时间序列预测有广阔的应用前景,吸引了越来越多的研究人员对其进行深入的研究.移动平均线是最常用的技术指标之一,它可以对过去一段时间内时间序列的整体变化规律进行概括,常常用来进行时间序列趋势预测.然而,传统的移动平均线指标是通... 详细信息

时间序列预测有广阔的应用前景,吸引了越来越多的研究人员对其进行深入的研究.移动平均线是最常用的技术指标之一,它可以对过去一段时间内时间序列的整体变化规律进行概括,常常用来进行时间序列趋势预测.然而,传统的移动平均线指标是通过赋予时间序列数据相等或预定义的权重计算得到的,忽略了不同时间点重要性的细微差别;另外,对于不同的时间序列数据采用相同的权重,忽视了不同时间序列内在特性的差异.为了解决上述问题,提出一个自适应的自注意力移动平均线(Self-Attentive Moving Average,SAMA)指标.利用循环神经网络对时间序列的输入信号进行编码后,引入自注意力机制来自适应地确定不同时间点上数据的权重以计算移动平均值.此外,还使用多个自注意头对不同尺度的SAMA指标进行建模,最后将它们组合起来进行时间序列预测.在两个真实数据集上的大量实验证明了该方法的有效性,数据集和代码已在https://github. com/YY-Susan/SAMA上发布.

关键词：时间序列预测自注意力机制移动平均线多尺度指标融合

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