知识追踪根据学生的历史学习轨迹来实现对其知识掌握水平的实时评估与跟踪,从而预测学生未来的学习表现,是在线学习系统实现个性化学习体验的核心技术之一.与传统方法相比,现有的基于深度神经网络的知识追踪模型取得了显著优势,但其通...知识追踪根据学生的历史学习轨迹来实现对其知识掌握水平的实时评估与跟踪,从而预测学生未来的学习表现,是在线学习系统实现个性化学习体验的核心技术之一.与传统方法相比,现有的基于深度神经网络的知识追踪模型取得了显著优势,但其通常依赖大量训练数据.在学生答题早期,交互数据极度稀缺,所以训练一个复杂、有效的深度知识追踪模型十分具有挑战性.针对此问题,提出一种基于元学习增强的早期知识追踪框架(Meta-Learning-Enhanced Knowledge Tracing,MetaKT).给定目标知识追踪任务和其他若干个相关辅助任务,MetaKT首先在辅助任务上训练模型,然后利用目标任务的数据对预训练后的模型进行微调直至模型收敛.在七个公开数据集上以常用的DKT和DKVMN为基准进行实验,结果发现,提出的MetaKT框架使DKT和DKVMN模型分别在27和33(共35)个测试场景中的AUC(Area under Curve)获得了提升.展开更多
利用美国的全球海洋同化资料SODA(simple ocean data assimilation)2.2.4(1871—2008)中的风应力数据,估算了风输入给南海波浪的能量。结果表明,风向南海波浪输入能量的年均值约为0.2TW,其空间分布冬季以南海北部为主,夏季以南部为主且...利用美国的全球海洋同化资料SODA(simple ocean data assimilation)2.2.4(1871—2008)中的风应力数据,估算了风输入给南海波浪的能量。结果表明,风向南海波浪输入能量的年均值约为0.2TW,其空间分布冬季以南海北部为主,夏季以南部为主且强度比冬季要弱得多;风对南海波浪能量的输入一直呈减少趋势,用欧洲中期天气预报中心的再分析资料ERA-40(European Centre for Medium-Range Weather Forecasts re-analysis-40)(1957—2002)和ERA-20C(1900—2010)中的风场和海浪资料得到的趋势也是如此,1950年以来每年减少0.43%。用ERA-interim(1979—2014)中的有效波高数据可以把风给风浪和涌浪的能量输入区分开,两者的空间分布皆以南海北部为主,而给风浪的能量输入在南海南部还有一个高值区。尽管风输入给涌浪的能量略有增加,但给风浪的能量输入在不断减少,两者之和仍是减少。究其原因,控制南海的东亚季风最近几十年一直在减弱。这些结果对认识南海波浪未来的变化及其预报具有意义。展开更多
海面风不仅是驱动上层海洋运动的主要动力,其能量也是维持海洋表层流动的主要机械能来源。为了分析南海表层流风能输入的变化,用SODA(Simple Ocean Data Assimilation)(1901—2010)资料估算了风向南海表层流(表层地转流+表层非地转流)...海面风不仅是驱动上层海洋运动的主要动力,其能量也是维持海洋表层流动的主要机械能来源。为了分析南海表层流风能输入的变化,用SODA(Simple Ocean Data Assimilation)(1901—2010)资料估算了风向南海表层流(表层地转流+表层非地转流)的能量输入。结果表明,风向南海表层流、表层地转流和表层非地转流输入的能量总体均呈减少趋势, 110年间分别减小了约56%、65%和49%。导致风能输入减小的最主要因素是风应力的减弱(减小了35%)。由于南海受季风系统的控制,风向表层流及其各成分输入的能量呈现出显著的季节性变化。冬季风能输入最强,高值区位于南海西部及北部区域,呈一个显著的"回力镖"状结构。这些结果对深入认识南海环流具有理论意义。展开更多
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 dai...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.展开更多
文摘知识追踪根据学生的历史学习轨迹来实现对其知识掌握水平的实时评估与跟踪,从而预测学生未来的学习表现,是在线学习系统实现个性化学习体验的核心技术之一.与传统方法相比,现有的基于深度神经网络的知识追踪模型取得了显著优势,但其通常依赖大量训练数据.在学生答题早期,交互数据极度稀缺,所以训练一个复杂、有效的深度知识追踪模型十分具有挑战性.针对此问题,提出一种基于元学习增强的早期知识追踪框架(Meta-Learning-Enhanced Knowledge Tracing,MetaKT).给定目标知识追踪任务和其他若干个相关辅助任务,MetaKT首先在辅助任务上训练模型,然后利用目标任务的数据对预训练后的模型进行微调直至模型收敛.在七个公开数据集上以常用的DKT和DKVMN为基准进行实验,结果发现,提出的MetaKT框架使DKT和DKVMN模型分别在27和33(共35)个测试场景中的AUC(Area under Curve)获得了提升.
文摘利用美国的全球海洋同化资料SODA(simple ocean data assimilation)2.2.4(1871—2008)中的风应力数据,估算了风输入给南海波浪的能量。结果表明,风向南海波浪输入能量的年均值约为0.2TW,其空间分布冬季以南海北部为主,夏季以南部为主且强度比冬季要弱得多;风对南海波浪能量的输入一直呈减少趋势,用欧洲中期天气预报中心的再分析资料ERA-40(European Centre for Medium-Range Weather Forecasts re-analysis-40)(1957—2002)和ERA-20C(1900—2010)中的风场和海浪资料得到的趋势也是如此,1950年以来每年减少0.43%。用ERA-interim(1979—2014)中的有效波高数据可以把风给风浪和涌浪的能量输入区分开,两者的空间分布皆以南海北部为主,而给风浪的能量输入在南海南部还有一个高值区。尽管风输入给涌浪的能量略有增加,但给风浪的能量输入在不断减少,两者之和仍是减少。究其原因,控制南海的东亚季风最近几十年一直在减弱。这些结果对认识南海波浪未来的变化及其预报具有意义。
文摘海面风不仅是驱动上层海洋运动的主要动力,其能量也是维持海洋表层流动的主要机械能来源。为了分析南海表层流风能输入的变化,用SODA(Simple Ocean Data Assimilation)(1901—2010)资料估算了风向南海表层流(表层地转流+表层非地转流)的能量输入。结果表明,风向南海表层流、表层地转流和表层非地转流输入的能量总体均呈减少趋势, 110年间分别减小了约56%、65%和49%。导致风能输入减小的最主要因素是风应力的减弱(减小了35%)。由于南海受季风系统的控制,风向表层流及其各成分输入的能量呈现出显著的季节性变化。冬季风能输入最强,高值区位于南海西部及北部区域,呈一个显著的"回力镖"状结构。这些结果对深入认识南海环流具有理论意义。
基金Supported by the National Key Research and Development Program of China(No.2017YFC1404102(2017YFC1404100))the National Program on Global Change and Air-sea Interaction(No.GASI-IPOVAI-06)+3 种基金the National Natural Science Foundation of China(Nos.41490644(41490640),41690122(41690120))the Chinese Academy of Sciences Strategic Priority Project(No.XDA19060102)the NSFC Shandong Joint Fund for Marine Science Research Centers(No.U1406402)the Taishan Scholarship and the Recruitment Program of Global Experts。
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.
