Introduction:Risk assessment for high-risk populations is critical for preventing Type 2 Diabetes Mellitus(T2DM).Although China’s public health services have continuously contributed to early grassroots diagnosis of ...Introduction:Risk assessment for high-risk populations is critical for preventing Type 2 Diabetes Mellitus(T2DM).Although China’s public health services have continuously contributed to early grassroots diagnosis of diabetes for years,universally applicable tools for identifying latent high-risk elderly populations urgently need to account for heterogeneity,robustness,and generalizability.Therefore,this study developed and validated the integrated Chinese Adapted Risk Evaluation for Diabetes Mellitus(iCARE-DM)model for elderly Chinese individuals.Methods:The iCARE-DM model was developed based on pooled effect estimates from a meta-analysis of cohort studies that identified T2DM risk factors in East Asian populations and validated in three multicenter Chinese populations.Predictive performance was evaluated using area under the curve(AUC),sensitivity,specificity,accuracy,log-rank tests,and compared with the guideline-recommended model(i.e.,New Chinese Diabetes Risk Score,NCDRS)as well as four machine learning(ML)models.Results:The iCARE-DM model achieved AUC values of 0.741,0.783,and 0.766,outperforming the NCDRS model by at least 12%.Although the bestperforming ML model achieved AUC values comparable to the iCARE-DM model,its performance varied significantly across populations(with a range as high as 9%).Subgroup analyses of the iCARE-DM model confirmed consistent performance across age,gender and rural-urban groups.Conclusion:The iCARE-DM model demonstrated higher accuracy than the NCDRS model and exhibited superior robustness and generalizability compared to the ML models.The iCARE-DM model provides a robust,culturally adapted tool for T2DM risk assessment in elderly Chinese individuals.展开更多
当前的基于词向量的多文档摘要方法没有考虑句子中词语的顺序,存在异句同向量问题以及在小规模训练数据上生成的摘要冗余度高的问题。针对这些问题,提出基于PV-DM(Distributed Memory Model of Paragraph Vectors)模型的多文档摘要方法...当前的基于词向量的多文档摘要方法没有考虑句子中词语的顺序,存在异句同向量问题以及在小规模训练数据上生成的摘要冗余度高的问题。针对这些问题,提出基于PV-DM(Distributed Memory Model of Paragraph Vectors)模型的多文档摘要方法。该方法首先构建单调亚模(Submodular)目标函数;然后,通过训练PV-DM模型得到句子向量计算句子间的语义相似度,进而求解单调亚模目标函数;最后,利用优化算法抽取句子生成摘要。在标准数据集Opinosis上的实验结果表明该方法优于当前主流的多文档摘要方法。展开更多
Quantum teleportation via the entangled channel composed of a two-qubit Heisenberg XYZ model with Dzyaloshinski-Moriya （DM） interaction in the presence of intrinsic decoherence has been investigated. We find that th...Quantum teleportation via the entangled channel composed of a two-qubit Heisenberg XYZ model with Dzyaloshinski-Moriya （DM） interaction in the presence of intrinsic decoherence has been investigated. We find that the initial state of the channel plays an important role in the teleported state and the average fidelity of teleportation. When the initial channel is in the state ｜ψ1 （0）〉 = a｜00〉 ＋ b｜11〉, the average fidelity is equal to 1/3 constantly, which is independent of the DM interaction and the intrinsic decoherence effect. But when the channel is initially in the state ｜ψ2（0）〉 = a｜01〉 ＋ b｜10〉, the average fidelity is always larger than 2/3. Moreover, under a certain condition, the average fidelity can be enhanced by adjusting the DM interaction, and the intrinsic decoherence leads to a suppression of the fluctuation of the average fidelity.展开更多
基金Supported by the National Key R&D Program of China(2022YFC3600600)Sichuan Science and Technology Program(2024ZYD0102,2025YFHZ0069)+1 种基金Chengdu Science and Technology Program(2024-YF05-01784-SN)Sichuan Preventive Medicine Association(SYYXH202403).
文摘Introduction:Risk assessment for high-risk populations is critical for preventing Type 2 Diabetes Mellitus(T2DM).Although China’s public health services have continuously contributed to early grassroots diagnosis of diabetes for years,universally applicable tools for identifying latent high-risk elderly populations urgently need to account for heterogeneity,robustness,and generalizability.Therefore,this study developed and validated the integrated Chinese Adapted Risk Evaluation for Diabetes Mellitus(iCARE-DM)model for elderly Chinese individuals.Methods:The iCARE-DM model was developed based on pooled effect estimates from a meta-analysis of cohort studies that identified T2DM risk factors in East Asian populations and validated in three multicenter Chinese populations.Predictive performance was evaluated using area under the curve(AUC),sensitivity,specificity,accuracy,log-rank tests,and compared with the guideline-recommended model(i.e.,New Chinese Diabetes Risk Score,NCDRS)as well as four machine learning(ML)models.Results:The iCARE-DM model achieved AUC values of 0.741,0.783,and 0.766,outperforming the NCDRS model by at least 12%.Although the bestperforming ML model achieved AUC values comparable to the iCARE-DM model,its performance varied significantly across populations(with a range as high as 9%).Subgroup analyses of the iCARE-DM model confirmed consistent performance across age,gender and rural-urban groups.Conclusion:The iCARE-DM model demonstrated higher accuracy than the NCDRS model and exhibited superior robustness and generalizability compared to the ML models.The iCARE-DM model provides a robust,culturally adapted tool for T2DM risk assessment in elderly Chinese individuals.
文摘当前的基于词向量的多文档摘要方法没有考虑句子中词语的顺序,存在异句同向量问题以及在小规模训练数据上生成的摘要冗余度高的问题。针对这些问题,提出基于PV-DM(Distributed Memory Model of Paragraph Vectors)模型的多文档摘要方法。该方法首先构建单调亚模(Submodular)目标函数;然后,通过训练PV-DM模型得到句子向量计算句子间的语义相似度,进而求解单调亚模目标函数;最后,利用优化算法抽取句子生成摘要。在标准数据集Opinosis上的实验结果表明该方法优于当前主流的多文档摘要方法。
基金Project supported by the National Natural Science Foundation of China (Grant Nos 60708003, 60578050 and 10434060)the National Basic Research Program of China (Grant No 2006CB921604)+1 种基金the Shanghai Science and Technology Committee (GrantNo 07JC14017)by the Director Fund of State Key Laboratory of Precision Spectroscopy
文摘Quantum teleportation via the entangled channel composed of a two-qubit Heisenberg XYZ model with Dzyaloshinski-Moriya （DM） interaction in the presence of intrinsic decoherence has been investigated. We find that the initial state of the channel plays an important role in the teleported state and the average fidelity of teleportation. When the initial channel is in the state ｜ψ1 （0）〉 = a｜00〉 ＋ b｜11〉, the average fidelity is equal to 1/3 constantly, which is independent of the DM interaction and the intrinsic decoherence effect. But when the channel is initially in the state ｜ψ2（0）〉 = a｜01〉 ＋ b｜10〉, the average fidelity is always larger than 2/3. Moreover, under a certain condition, the average fidelity can be enhanced by adjusting the DM interaction, and the intrinsic decoherence leads to a suppression of the fluctuation of the average fidelity.
文摘数据挖掘语言标准化的研究是开发新一代数据挖掘系统的关键。DMX(Data Mining Extensions,数据挖掘扩展)是OLE DBFor DM规范支持的数据挖掘查询语言,支持数据挖掘系统直接对关系数据库进行挖掘,是数据挖掘原语标准化发展中的一个突破。该文介绍了OLE DB For DM规范下数据挖掘的主要步骤,给出了Microsoft SQL Server Analysis Services中基于DMX的实现方法。
