燕麦麸β-葡聚糖降血糖及抗氧化作用研究

目的研究燕麦麸β-葡聚糖的降血糖和抗氧化功效。方法将小鼠分为正常组和模型组,模型组通过注射四氧嘧啶建立糖尿病模型。比较分析各组小鼠体重、摄食量、饮水量、血糖值、耐糖量、肝脏超氧化物歧化酶(SOD)、谷胱甘肽过氧化物酶(GSH-PX)和丙二醛(MDA)含量。结果高剂量组对糖尿病小鼠摄食饮水量的减少和体重增加有显著影响且高剂量组小鼠血糖值降低25.39%。燕麦麸β-葡聚糖对正常小鼠的体重、饮水量、摄食量、血糖值、耐糖量没有明显的作用。与对照组比较,模型组小鼠血糖曲线下面积、SOD、GSH-PX的活性显著提高,体内MDA的含量显著降低。结论燕麦麸β-葡聚糖能降低糖尿病小鼠的血糖值和提高糖尿病小鼠的抗氧化能力,对正常小鼠没有影响。     

Sobolev 广义度量下的各向异性扩散模型

给出了Sobolev空间的一种广义度量, 在该度量下提出了一个新的各向异性增强、扩散方程. 广义度量中的变系数, 较好地控制了方程的扩散行为, 使得新模型不仅能有效增强图像的细节特征, 而且能在噪声去除和边缘保护之间取得较好的平衡, 同时给出了相应的隐式离散算法. 仿真实验结果表明, 新模型和算法是行之有效的.     

Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation

Different from a general density estimation, the crime density estimation usually has one important factor: the geographical constraint. In this paper, a new crime density estimation model is formulated, in which the regions where crime is impossible to happen, such as mountains and lakes, are excluded. To further optimize the estimation method, a learning-based algorithm, named Plug-and-Play, is implanted into the augmented Lagrangian scheme, which involves an off-the-shelf filtering operator. Different selections of the filtering operator make the algorithm correspond to several classical estimation models. Therefore, the proposed Plug-and-Play optimization based estimation algorithm can be regarded as the extended version and general form of several classical methods. In the experiment part, synthetic examples with different invalid regions and samples of various distributions are first tested. Then under complex geographic constraints, we apply the proposed method with a real crime dataset to recover the density estimation. The state-of-the-art results show the feasibility of the proposed model.

     

利用标签相关性先验的弱监督多标签学习方法

多标签学习是一种非常重要的机器学习范式.传统的多标签学习方法是在监督或半监督的情况下设计的.通常情况下,它们需要对所有或部分数据进行准确的属于多个类别的标注.在许多实际应用中,拥有大量标注的标签信息往往难以获取,限制了多标签学习的推广和应用.与之相比,标签相关性作为一种常见的弱监督信息,它对标注信息的要求较低.如何利用标签相关性进行多标签学习,是一个重要但未研究的问题.提出了一种利用标签相关性作为先验的弱监督多标签学习方法(WSMLLC).该模型利用标签相关性对样本相似性进行了重述,能够有效地获取标签指示矩阵;同时,利用先验信息对数据的投影矩阵进行约束,并引入回归项对指示矩阵进行修正.与现有方法相比,WSMLLC模型的突出优势在于:仅提供标签相关性先验,就可以实现多标签样本的标签指派任务.在多个公开数据集上进行实验验证,实验结果表明:在标签矩阵完全缺失的情况下,WSMLLC与当前先进的多标签学习方法相比具有明显优势.