干酪素型啤酒标签胶的研究进展

从制备原理及分散体系、交联剂、改性助剂等方面综述了干酪素型啤酒标签胶的研究进展,并对该领域研究进行了展望。     

Construction, properties and specific fluorescent labeling of a bovine prothrombin mutant engineered with a free C-terminal cysteine

To define the role of phosphatidylserine-induced conformationalchanges in prothrombin activation during blood coagulation,a recombinant bovine prothrombin was constructed, characterizedand shown to have a globally native-like conformation. We introduceda cysteine to replace the penultimate residue (Gly581) of apreviously constructed active site mutant, and expressed thedouble mutant in Chinese hamster ovary cells at the level of0.6 µg/ml of cell culture medium. Specific labeling withfluorescein maleimide was accomplished by limited reductionwith dithiothreitol to free the engineered cysteine while maintainingthe native-like functional properties of the molecule. The averagestoichiometry of labeling was 0.84 probe/protein. The locationof the probe at the C-terminus was confirmed by proteolysisby native thrombin, by Taipan venom, and by carboxypeptidaseY. Both the double mutant and labeled prothrombin could be activatedby snake venoms and the prothrombinase but, as expected, thedouble mutant meizothrombin did not autolyze as does nativemeizothrombin. Thus, for the first time, a native-like but specificallylabeled prothrombin has been constructed. This molecule willbe an essential tool for elucidating the structural role ofmembranes during prothrombin activation. In addition, the methodsdescribed might be usefully applied to labeling of an odd, engineeredcysteine in other disulfide bond-containing proteins.     

整数的最佳带符号二进制表示的随机生成算法

整数的带符号数字(BSD)表示广泛应用于计算机算术、密码学、数字信号处理等领域。给定整数有多种最佳带符号二进制表示。对整数的最佳带符号二进制表示的性质进行研究,给出了整数的最佳带符号二进制表示随机生成算法,所得算法能快速产生给定整数的随机最佳带符号二进制表示。     

Bidirectional Label Propagation over Graphs

Graph-Based label propagation algorithms are popular in the state-of-the-art semi-supervised learning research. The key idea underlying this algorithmic family is to enforce labeling consistency between any two examples with a positive similarity. However, negative similarities or dissimilarities are equivalently valuable in practice. To this end, we simultaneously leverage similarities and dissimilarities in our proposed semi-supervised learning algorithm which we term Bidirectional Label Propagation (BLP). Different from previous label propagation mechanisms that proceed along a single direction of graph edges, the BLP algorithm can propagate labels along not only positive but also negative edge directions. By using an initial neighborhood graph and class assignment constraints inherent among the labeled examples, a set of class-specific graphs are learned, which include both positive and negative edges and thus reveal discriminative cues. Over the learned graphs, a convex propagation criterion is carried out to ensure consistent labelings along the positive edges and inconsistent labelings along the negative edges. Experimental evidence discovered in synthetic and real-world datasets validates excellent performance of the proposed BLP algorithm.     

平衡序列图的结构

考察了平衡序列图的结构,给出了平衡标号与序列标号的关系,提出了用二部划分确定平衡标号的方法,得到了平衡标号特征与导出边标号最小值的关系式,给出非平衡的序列树。     

TrueType字体自动打标机软件设计

通过分析TrueType字体文件结构和提取出字符轮廓信息,实现了对字符的无失真任意放大和缩小以及旋转等特效,曲线轮廓可以进行填充。将二次Bezier曲线构成的字符轮廓,通过最小二分法转换成一组能够保持曲线轮廓形状的小折线,将原有的字体轮廓数据从用曲线轮廓表示转变为用多边形顶点控制,用一组点及其先后顺序来描述轮廓。研究了字形交叉的消隐处理方法,解决了笔画重叠的问题。打标机将得到的多边形顶点信息准确打标。     

支持MPLS流量工程的选播路由

对选播服务的网络负载均衡做了研究,提出一种支持多协议标签交换（MPLS）流量工程的选播路由算法（ART）．ART基于最小冲突路由机制,通过MPLS约束路由的标记分发协议（CR LDP）建立1条从选播服务器到客户方向的标记交换路径（LSP）,并预留带宽资源．仿真结果表明,ART能平衡网络的负载,在选播服务中实现网络资源的优化利用,并且在传输服务数据流时,能减少时延及数据包的丢失．     

融合语义差别和流型学习的偏标记学习方法

偏标记学习是一种重要的弱监督学习框架。在偏标记学习中,每个实例与一组候选标记相关联,它的真实标记隐藏在候选标记集合中,且在学习过程中不可获知。为了消除候选标记对学习过程的影响,提出了一种融合实例语义差别最大化和流型学习的偏标记学习方法(partial label learning by semantic difference and manifold learning, PL-SDML)。该方法是一个两阶段的方法：在训练阶段,基于实例的语义差别最大化准则和流型学习方法为训练实例生成标记置信度;在预测阶段,使用基于最近邻投票的方法为未知实例预测标记类别。在四组人工改造的UCI数据集中,在平均70%的情况下优于其他对比算法。在四组真实偏标记数据集中,相比其他对比算法,取得了0.3%～13.8%的性能提升。     

融合视觉风格和标签约束的少数民族服装图像解析

目的 少数民族服装款式结构复杂,视觉风格各异。由于缺少民族服装语义标签、局部特征繁杂以及语义标签之间存在相互干扰等因素导致少数民族服装图像解析准确率和精度较低。因此,本文提出了一种融合视觉风格和标签约束的少数民族服装图像解析方法。方法 首先基于本文构建的包含55个少数民族的服装图像数据集,按照基本款式结构、着装区域、配饰和不同视觉风格自定义少数民族服装的通用语义标签和民族语义标签,同时设置4组标注对,共8个标注点;然后,结合自定义语义标签和带有标注对的训练图像,在深度完全卷积神经网络SegNet中加入视觉风格以融合局部特征和全局特征,并引入属性预测、风格预测和三元组损失函数对输入的待解析图像进行初步解析;最后,通过构建的标签约束网络进一步优化初步解析结果,避免标签相互干扰,得到优化后的最终解析结果。结果 在构建的少数民族服装图像数据集上进行验证,实验结果表明,标注对有效提升了局部特征的检测准确率,构建的视觉风格网络能够有效融合少数民族服装的全局特征和局部特征,标签约束网络解决了标签之间相互干扰的问题,在结合视觉风格网络和标签约束网络后,能够明显提升少数民族服装解析的平均精度,像素准确度达到了90.54%。结论 本文提出的融合视觉风格和标签约束的少数民族服装图像解析方法,能够提高少数民族服装图像解析的准确率和精度,对传承祖国文化、保护非物质文化遗产具有很好的意义。     

基于局部概率抽样的标签噪声过滤方法

分类学习任务中,在获取数据的过程中会不可避免地产生噪声,特别是标签噪声的存在不仅使得学习模型更复杂,而且容易造成过拟合并导致分类器泛化能力的下降。标签噪声过滤算法虽然在一定程度上可以解决上述问题,但是仍然存在噪声识别能力较差、分类效果不够理想以及过滤效率低等问题。针对这些问题,提出一种基于标签置信度分布的局部概率抽样方法来进行标签噪声过滤。首先利用随机森林分类器对样本的标签进行投票,从而获取每个样本的标签置信度;然后根据标签置信度的大小,将样本划分为易识别样本和难识别样本;最后分别采用不同的过滤策略对样本进行过滤。实验结果表明,在标签噪声存在的情况下,所提方法在大多数案例上能够保持较高的噪声识别能力,并且在分类泛化性能上也具有明显优势。