A branch-and-cut algorithm for the minimum labeling Hamiltonian cycle problem and two variants

This paper proposes a mathematical model, valid inequalities and polyhedral results for the minimum labeling Hamiltonian cycle problem. This problem is defined on an unweighted graph in which each edge has a label. The aim is to determine a Hamiltonian cycle with the least number of labels. We also define two variants of this problem by assigning weights to the edges and by considering the tour length either as an objective or as a constraint. A branch-and-cut algorithm for the three problems is developed, and computational results are reported on randomly generated instances and on modified instances from TSPLIB.     

Qualitative Scene Interpretation Using Planar Surfaces

Our aim is to provide an autonomous vehicle moving into an indoor environment with a visual system to perform a qualitative 3D structure reconstruction of the surrounding environment by recovering the different planar surfaces present in the observed scene.The method is based on qualitative detection of planar surfaces by using projective invariant constraints without the use of depth estimates. The goal is achieved by analyzing two images acquired by observing the scene from two different points of view. The method can be applied to both stereo images and motion images.Our method recovers planar surfaces by clustering high variance interest points whose cross ratio measurements are preserved in two different perspective projections. Once interest points are extracted from each image, the clustering process requires to grouping corresponding points by preserving the cross ratio measurements.We solve the twofold problem of finding corresponding points and grouping the coplanar ones through a global optimization approach based on matching of high relational graphs and clustering on the corresponding association graph through a relaxation labeling algorithm.Through our experimental tests, we found the method to be very fast to converge to a solution, showing how higher order interactions, instead to giving rise to a more complex problem, help to speed-up the optimization process and to reach at same time good results.     

一类链图的优美性

对于由k个完全二部图K2,m1,K2,m2,…,K2,mk（其中k,n,m1,m2,…,mk为大于1的正整数）经过不同的粘接方法而得到的链图T1、链图T2、链图T5的优美性进行了研究。在此基础上对由链图T1和长为n的路Pn的一个端点粘接得到的链图T3和链图T2与长为n的路Pn的一个端点粘接得到的链图T4的优美性进行了研究。用构造的方法给出了这几类图的优美标号,得出这些图都是优美图。这样将m1,m2,…,mk的值均为2的范围扩大到大于1的正整数,从而拓宽了优美图及其应用的道路。最后提出了将链图T1、T2、T3、T4、T5分别首尾粘接而得到的一些图是优美图的猜想。     

A fast connected components labeling algorithm for binary images

A fast label-equivalence-based connected components labeling algorithm is proposed in this paper.It is a combination of two existing efficient methods,which are pivotal operations in two-pass connected components labeling algorithms.One is a fast pixel scan method,and the other is an array-based Union-Find data structure.The scan procedure assigns each foreground pixel a provisional label according to the location of the pixel.That is to say,it labels the foreground pixels following background pixels and foreground pixels in different ways,which greatly reduces the number of neighbor pixel checks.The array-based Union-Find data structure resolves the label equivalences between provisional labels by using only a single array with path compression,and it improves the efficiency of the resolving procedure which is very time-consuming in general label-equivalence-based algorithms.The experiments on various types of images with different sizes show that the proposed algorithm is superior to other labeling approaches for huge images containing many big connected components.     

基于贝叶斯核主成分分析的远距离地形标记方法

由于非结构化室外场景外观特征分布存在动态不确定性以及映射偏移特性,因此在室外移动机器人自主导航的过程中采用预确定外观特征并不能非常有效地进行地形标记.为了解决此问题,提出了基于贝叶斯核主成分分析(BKPCA)的远距离地形标记方法.该方法融合了基于贝叶斯公式的聚类中心后验概率,且采用自定义的核函数,实现了原始特征数据结构在低维空间上的保持,能够提取出适合当前场景地形标记的外观特征.实验结果表明,BKPCA模型有效地提高了远距离地形标记的精度.     

DTPA-c-myc反义核酸的合成及其113Inm标记

合成了DTPA-c-myc反义核酸,并对其进行了^113In^m标记;考察了标记物^113In^m-DTPA-c-myc反义核酸在生理盐水和牛血清中的稳定性和对平滑肌细胞增殖的影响.标记结果显示,在最佳标记条件下标记率为35%～40%,标记物纯化后放化纯度＞90%.^113In^m-DTPA-c-myc反义核酸在生理盐水中48 h放化纯度＞94.0%,在血清中48 h放化纯度仅为76.1%.细胞增殖结果显示：^113In^m-DTPA-c-myc反义核酸浓度达到10 μmol/L（92.5 GBq/L）时抑制率达到最大,^113In^m-DTPA-c-myc反义核酸（放射性反义治疗组）和反义核酸（反义治疗组）、^^113In^mCl3溶液（放射性治疗组）、不加核酸或^113In^mCl3溶液 （对照组）和c-myc正义核酸（正义治疗组）对平滑肌细胞增殖的抑制率分别为84.5%、69.3%、20.6%、15.2%、0,放射性反义治疗组与反义治疗组、放射性治疗组、对照组相比有显著性差异（P＜0.05、P＜0.01、P＜0.01）,反义治疗组和正义治疗组相比也有显著性差异（P＜0.01）.这说明合成的 ^113In^m-DTPA-c-myc反义核酸在体外可有效抑制猪血管平滑肌细胞增殖.     

4个圈不交并图优美性的一些结果

讨论了4个圈不交并图3C4k∪Cn的优美性，给出了其为优美图的必要条件，并用构造性的方法给出了3C4k∪C4k 3,3C4k∪C4k 4,3C8k∪C8k-1的优美标号，证明了它们是优美的。     

153Sm-葡庚糖酸钠的制备

研究了制备条件对153Sm标记GH的影响及标记物的体外稳定性,建立了有效的153Sm标记葡萄糖酸钠(GH)方法。结果表明:用通氮敞开体系、沸水浴加热的标记方法,反应时间不低于30min,GH浓度不低于2.4×10-2mol/L时,可获得标记率大于98%的153Sm-GH;用高比活度153Sm(～4.5TBq/g(Sm))标记,其比活度可达14GBq/g(GH)。该标记物在室温下具有较好的稳定性,放置40h内,其放化纯度>96%。     

Object-based image labeling through learning by example and multi-level segmentation

We propose a method for automatic extraction and labeling of semantically meaningful image objects using “learning by example” and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate detail. Experiments have shown that the proposed multi-level image segmentation results in significant reduction in computation complexity for object extraction and labeling (compared to a single fine-level segmentation) by avoiding unnecessary tests of combinations in finer levels. The multi-level segmentation-based approach also achieves better accuracy in detection and labeling of small objects.     

Application of N-succinimidyl 4-[(18)~F](fluoromethyl) benzoate to protein labeling

N-succinimidyl 4-[18F](fluoromethyl) benzoate for protein labeling was prepared (57%, EOB) in about 30min. Reaction conditions of S18FMB with IgG including pH of solutions, protein concentration, reaction temperature and time were studied. The optimal labeling conditions were: 0.2mg/mL IgG, pH = 7.8-8.5, 25℃, and reaction time 5min.Under these conditions the yield was about 80%. The 18F-labeled protein was purified by size exclusion chromatography.