Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial impor...Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial importance to farmers, researchers, and decision makers while utilizing and managing red soil resources. SIS created by using ARC/INPO was used to provide data acquisition, systematic model parameter assignment, and visual display of analytic results. Topography, temperature, soil component (e.g., organic matter and pH) and condition of agricultural production were selected as parameters of ISODATA model. Taking Longyou County, Zhejiang Province as the case study area, the effect of the integration and recommendations are discussed for future research.展开更多
In this paper, I described the methods that I used for the creation of Xlets, which are Java applets that are developed for the IDTV environment;and the methods for online data retrieval and processing that I utilized...In this paper, I described the methods that I used for the creation of Xlets, which are Java applets that are developed for the IDTV environment;and the methods for online data retrieval and processing that I utilized in these Xlets. The themes that I chose for the Xlets of the IDTV applications are Earthquake and Tsunami Early Warning;Recent Seismic Activity Report;and Emergency Services. The online data regarding the Recent Seismic Activity Report application are provided by the Kandilli Observatory and Earthquake Research Institute (KOERI) of Bogazici University in Istanbul;while the online data for the Earthquake and Tsunami Early Warning and the Emergency Services applications are provided by the Godoro website which I used for storing (and retrieving by the Xlets) the earthquake and tsunami early warning simulation data, and the DVB network subscriber data (such as name and address information) for utilizing in the Emergency Services (Police, Ambulance and Fire Department) application. I have focused on the methodologies to use digital television as an efficient medium to convey timely and useful information regarding seismic warning data to the public, which forms the main research topic of this paper.展开更多
Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statisticall...Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statistically challenging to discover the GE interaction effects because many published data were just entry means under each environment rather than repeated field plot data. In this study, we propose a new methodology, which can be used to impute replicated trial data sets to reveal GE interactions from the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new imputation method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data. The results from the imputed data were highly consistent with those from the original data set, indicating that imputed data from the method we proposed in this study can be used to reveal information including GE interaction effects harbored in the original data. Therefore, this study could pave a way to detect the GE interactions and other related information from historical crop trial reports when replications were not available.展开更多
Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which ar...Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.展开更多
In this paper,an interactive method is proposed to describe computer animation data and accelerate the process of animation generation.First,a semantic model and a resource description framework(RDF)are utilized to an...In this paper,an interactive method is proposed to describe computer animation data and accelerate the process of animation generation.First,a semantic model and a resource description framework(RDF)are utilized to analyze and describe the relationships between animation data.Second,a novel context model which is able to keep the context-awareness was proposed to facilitate data organization and storage.In our context model,all the main animation elements in a scene are operated as a whole.Then sketch is utilized as the main interactive method to describe the relationships between animation data,edit the context model and make some other user operations.Finally,a context-aware computer animation data description system based on sketch is generated and it also works well in animation generation process.展开更多
In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction ...In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction mechanism. Based on panel data of China's inter-provincial steel output from 2001 to 2015, using spatial econometric model, this paper also explored whether China's provincial steel production shows material orientation, market orientation and traffic orientation, and isolated spatial interactions of interprovincial steel output. The results showed that the inter-provincial steel production in China did show both material orientation, market orientation and traffic orientation and that there was a significant negative spatial interaction, indicating that there might be strong competition and a crowing-out effect between neighboring provinces, and that the smaller the spatial scope, the more significant the spatial interactions of steel production.展开更多
在分布式环境中,数据异质性表现为数据特征差异。专家模型协同存在知识孤立与任务分配不合理的问题,导致专家训练效果参差不齐,难以充分发挥各模型优势,使得整体性能受限。针对这些问题,提出了一种基于多专家协同和信息交互的社会化学...在分布式环境中,数据异质性表现为数据特征差异。专家模型协同存在知识孤立与任务分配不合理的问题,导致专家训练效果参差不齐,难以充分发挥各模型优势,使得整体性能受限。针对这些问题,提出了一种基于多专家协同和信息交互的社会化学习框架(Social Learning Based on Multi-expert Collaboration and Information Interaction,MECII)。该框架结合混合专家模型和社会化学习思想,通过多专家协同、门控网络、自适应信息交互和门控选择约束这四大模块,优化了专家间的知识共享与互补机制,有效解决了分布式学习中的数据异质性和专家协同问题。MECII通过精准的专家选择与任务分配,促进了专家之间的信息流动,使每个专家在处理特定数据时的准确率得到提升,增强了整体模型性能。实验结果表明,MECII在CIFAR-10和CIFAR-100数据集上相比传统的联邦学习基准方法有显著的性能提升,特别是在数据异质性场景下,与先进的FedL2P方法相比,MECII将分类准确率分别提高了6.69个百分点和5.13个百分点,且有效优化了每个专家的准确率。实验结果验证了MECII在促进专家协作和提升个体精度方面具有显著优势。展开更多
文摘Integration of soil information system (SIS) and interactive self-organizing data (ISODATA) was studied to establish proper agricultural developing zones in red soil region of southern China which are of crucial importance to farmers, researchers, and decision makers while utilizing and managing red soil resources. SIS created by using ARC/INPO was used to provide data acquisition, systematic model parameter assignment, and visual display of analytic results. Topography, temperature, soil component (e.g., organic matter and pH) and condition of agricultural production were selected as parameters of ISODATA model. Taking Longyou County, Zhejiang Province as the case study area, the effect of the integration and recommendations are discussed for future research.
文摘In this paper, I described the methods that I used for the creation of Xlets, which are Java applets that are developed for the IDTV environment;and the methods for online data retrieval and processing that I utilized in these Xlets. The themes that I chose for the Xlets of the IDTV applications are Earthquake and Tsunami Early Warning;Recent Seismic Activity Report;and Emergency Services. The online data regarding the Recent Seismic Activity Report application are provided by the Kandilli Observatory and Earthquake Research Institute (KOERI) of Bogazici University in Istanbul;while the online data for the Earthquake and Tsunami Early Warning and the Emergency Services applications are provided by the Godoro website which I used for storing (and retrieving by the Xlets) the earthquake and tsunami early warning simulation data, and the DVB network subscriber data (such as name and address information) for utilizing in the Emergency Services (Police, Ambulance and Fire Department) application. I have focused on the methodologies to use digital television as an efficient medium to convey timely and useful information regarding seismic warning data to the public, which forms the main research topic of this paper.
文摘Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statistically challenging to discover the GE interaction effects because many published data were just entry means under each environment rather than repeated field plot data. In this study, we propose a new methodology, which can be used to impute replicated trial data sets to reveal GE interactions from the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new imputation method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data. The results from the imputed data were highly consistent with those from the original data set, indicating that imputed data from the method we proposed in this study can be used to reveal information including GE interaction effects harbored in the original data. Therefore, this study could pave a way to detect the GE interactions and other related information from historical crop trial reports when replications were not available.
文摘Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.
基金Supported by the National Key Research and Development Plan(2016YFB1001200)the National Natural Science Foundation of China(U1435220,61232013)
文摘In this paper,an interactive method is proposed to describe computer animation data and accelerate the process of animation generation.First,a semantic model and a resource description framework(RDF)are utilized to analyze and describe the relationships between animation data.Second,a novel context model which is able to keep the context-awareness was proposed to facilitate data organization and storage.In our context model,all the main animation elements in a scene are operated as a whole.Then sketch is utilized as the main interactive method to describe the relationships between animation data,edit the context model and make some other user operations.Finally,a context-aware computer animation data description system based on sketch is generated and it also works well in animation generation process.
文摘In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction mechanism. Based on panel data of China's inter-provincial steel output from 2001 to 2015, using spatial econometric model, this paper also explored whether China's provincial steel production shows material orientation, market orientation and traffic orientation, and isolated spatial interactions of interprovincial steel output. The results showed that the inter-provincial steel production in China did show both material orientation, market orientation and traffic orientation and that there was a significant negative spatial interaction, indicating that there might be strong competition and a crowing-out effect between neighboring provinces, and that the smaller the spatial scope, the more significant the spatial interactions of steel production.
文摘在分布式环境中,数据异质性表现为数据特征差异。专家模型协同存在知识孤立与任务分配不合理的问题,导致专家训练效果参差不齐,难以充分发挥各模型优势,使得整体性能受限。针对这些问题,提出了一种基于多专家协同和信息交互的社会化学习框架(Social Learning Based on Multi-expert Collaboration and Information Interaction,MECII)。该框架结合混合专家模型和社会化学习思想,通过多专家协同、门控网络、自适应信息交互和门控选择约束这四大模块,优化了专家间的知识共享与互补机制,有效解决了分布式学习中的数据异质性和专家协同问题。MECII通过精准的专家选择与任务分配,促进了专家之间的信息流动,使每个专家在处理特定数据时的准确率得到提升,增强了整体模型性能。实验结果表明,MECII在CIFAR-10和CIFAR-100数据集上相比传统的联邦学习基准方法有显著的性能提升,特别是在数据异质性场景下,与先进的FedL2P方法相比,MECII将分类准确率分别提高了6.69个百分点和5.13个百分点,且有效优化了每个专家的准确率。实验结果验证了MECII在促进专家协作和提升个体精度方面具有显著优势。
