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| 1 | Additivity of pore structural parameters of granular activated carbons derived from different coals and their blends显示文摘A series of granular activated carbons(GACs) were prepared by briquetting method from Chinese coals of different ranks and their blends,with coal pitch as the binder.Pore structural parameters including BET specific surface area(SBET),total pore volume(VT) and average pore diameter(da) were measured and calculated as well as process parameters such as yield of char(CY) and burn-off(B).The relationship between the pore structural parameters of the GAC from coal blend(BC-GAC) and the ones of the GACs from corresponding single coals(SC-GACs) was analyzed,in which an index,the relative error(d),was presented to define the bias between fitted values and experimental values of these parameters of the BC-GACs.The results show that the BC-GAC keeps qualitatively the pore structural features of the SC-GACs;as concerned as the quantitative relationship,the pore structural parameters of the BC-GAC from coal blend consisting of non-caking coals can be obtained by adding proportionally the pore structural parameters of the SC-GACs with d less than 10%.Meanwhile,for the BC-GAC from coal blend containing weak caking bituminous coal,the d increases up to 25% and the experimental pore size distribution differs greatly from the fitted one. | Yao Xin Xie Qiang Yang Chuan Zhang Bo Wan Chaoran Cui Shanshan | 2016 | International Journal of Mining Science and Technology2016，26，4： | 6 |
| 2 | Pre-course student performance prediction with multi-instance multi-label learning显示文摘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. | Yuling MA Chaoran CUI Xiushan NIE Gongping YANG Kashif SHAHEED Yilong YIN | 2019 | Science China(Information Sciences)2019，62，2： | 5 |
| 3 | Representing surface wind stress response to mesoscale SST perturbations in western coast of South America using Tikhonov regularization method显示文摘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 layer.Using 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 region.The coupling coefficient between SSTmeso and WSmeso is about 0.0095 N/(m^2·℃)in winter and 0.0082 N/(m^2·℃)in summer.Based 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 method.The 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 significantly.By 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 used.As 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 modeling.Further applications are discussed for taking into account the feedback effects of the mesoscale coupling in largescale climate models and the uncoupled ocean models. | CUI Chaoran ZHANG Rong-Hua WANG Hongna WEI Yanzhou | 2020 | Journal of Oceanology and Limnology2020，38，3： | 2 |
| 4 | Multi-task MIML learning for pre-course student performance prediction显示文摘In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning activities.However,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 students.In 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 starting.However,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 end.In 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 commencement.To better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)problem.Besides,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 framework.Extensive experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods. | Yuling Ma Chaoran Cui Jun Yu Jie Guo Gongping Yang Yilong Yin | 2020 | Frontiers of Computer Science2020，14，5： | 1 |
| 5 | Evaluating and improving the interpretability of item embeddings using item-tag relevance information显示文摘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 factors.Recently,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation performance.However,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 embeddings.In this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance matrix.Specifically,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 space.We 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 embeddings.Experiments 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 sparser.By 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. | Tao LIAN Lin DU Mingfu ZHAO Chaoran CUI Zhumin CHEN Jun MA | 2020 | Frontiers of Computer Science2020，14，3： | 0 |
| 6 | Graph CA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing显示文摘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 learning.To 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. | Xinhua Wang Shasha Zhao Lei Guo Lei Zhu Chaoran Cui Liancheng Xu | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，11： | 0 |
| 7 | Mesoscale wind stress-SST coupling induced feedback to the ocean in the western coast of South America显示文摘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 model.Comparing 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 SST.Specifically,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 surface.These 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. | Chaoran CUI Rong-Hua ZHANG Yanzhou WEI Hongna WANG | 2021 | Journal of Oceanology and Limnology2021，39，3： | 0 |
| 8 | Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration显示文摘The multispectral remote sensing image(MS-RSI)is degraded existing multi-spectral camera due to various hardware limitations.In this paper,we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration.First,the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor.Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem.To 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 image.When 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. | Leilei Geng Chaoran Cui Qiang Guo Sijie Niu Guoqing Zhang Peng Fu | 2020 | Computers, Materials & Continua2020，，10： | 0 |
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