Gray codes extending quadratic matchings

Is it true that every matching in the n-dimensional hypercube can be extended to a Gray code? More than two decades have passed since Ruskey and Savage asked this question and the problem still remains open. A solution is known only in some special cases, including perfect matchings or matchings of linear size. This article shows that the answer to the Ruskey–Savage problem is affirmative for every matching of size at most . The proof is based on an inductive construction that extends balanced matchings in the completion of the hypercube by edges of into a Hamilton cycle of . On the other hand, we show that for every there is a balanced matching in of size that cannot be extended in this way.     

The minimum forcing number of perfect matchings in the hypercube

Let $M$ be a perfect matching in a graph. A subset $S$ of $M$ is said to be a forcing set of $M$, if $M$ is the only perfect matching in the graph that contains $S$. The minimum size of a forcing set of $M$ is called the forcing number of $M$. Pachter and Kim (1998) conjectured that the forcing number of every perfect matching in the $n$-dimensional hypercube is $2^{n?2}$, for all $n\ge 2$. This was revised by Riddle (2002), who conjectured that it is at least $2^{n?2}$, and proved it for all even $n$. We show that the revised conjecture holds for all $n\ge 2$. The proof is based on simple linear algebra.     

Perfect matchings extend to two or more hamiltonian cycles in hypercubes

Kreweras conjectured that every perfect matching of a hypercube $Q_n$ for $n\ge 2$ can be extended to a hamiltonian cycle of $Q_n$. Fink confirmed the conjecture to be true. It is more general to ask whether every perfect matching of $Q_n$ for $n\ge 2$ can be extended to two or more hamiltonian cycles of $Q_n$. In this paper, we prove that every perfect matching of $Q_n$ for $n\ge 4$ can be extended to at least $2^{2^{n?4}}$ different hamiltonian cycles of $Q_n$.     

Edge proximity and matching extension in projective planar graphs

A graph $G$ with at least $2m+2$ vertices is said to be distance $d$ $m$-extendable if, for any matching $M$ of $G$ with $m$ edges in which the edges lie at distance at least $d$ pairwise, there exists a perfect matching of $G$ containing $M$. In this paper we prove that every 5-connected triangulation on the projective plane of even order is distance 3 7-extendable and distance 4 $m$-extendable for any $m$.     

基于图像特征检测和匹配的无人船航行姿态角提取方法

计算机视觉的飞速发展, 使得采用视觉技术辅助无人船航行成为可能. 在无人船巡航过程中, 获取船体航向是航行控制的必备基础. 特征匹配是无人船相关视觉技术中的重要部分, 是目标识别和定位等功能的关键步骤. 获取无人船运动姿态的基本步骤是对图像前后帧进行有效的特征提取和匹配. 针对水域环境中的图像静态特征提取速度慢、精度低的问题, 本文提出一种图像匹配方法以求取无人船的航行姿态角. 首先对图像预处理, 并对有效区域进行特征提取. 其次, 设计一种基于描述子相似度的初始特征匹配策略. 再其次, 筛选特征匹配对, 优化模型参数. 最后, 通过前后帧旋转矩阵计算航行姿态角. 实验表明, 该方法能有效提取无人船的航行姿态角.     

差异化需求下考虑患者预约行为的医疗服务供需匹配方法

如何根据患者的差异化需求,撮合医生与患者双方形成合理有效的医疗服务供需匹配,是医疗服务运作管理中重要的研究问题。本文针对医疗服务中医生与患者的实际需求,提出了一种考虑患者预约行为的匹配决策方法。在该方法中,首先依据患者的预约行为及特征分类;然后,通过计算不同情形下医患双方的差异度,获得了医患双方的满意度矩阵;在此基础上,提出了匹配预约患者与医生的E-HR算法,并进一步构建了匹配剩余患者和医生的多目标优化模型,通过模型求解得到最优匹配结果;最后,通过算例说明了本文提出方法的可行性和实用性。     

Comparison of Multivariate Matching Methods: Structures,Distances, and Algorithms

Abstract

A comparison and evaluation is made of recent proposals for multivariate matched sampling in observational studies, where the following three questions are answered: (1) Algorithms: In current statistical practice, matched samples are formed using “nearest available” matching, a greedy algorithm. Greedy matching does not minimize the total distance within matched pairs, though good algorithms exist for optimal matching that do minimize the total distance. How much better is optimal matching than greedy matching? We find that optimal matching is sometimes noticeably better than greedy matching in the sense of producing closely matched pairs, sometimes only marginally better, but it is no better than greedy matching in the sense of producing balanced matched samples. (2) Structures: In common practice, treated units are matched to one control, called pair matching or 1–1 matching, or treated units are matched to two controls, called 1–2 matching, and so on. It is known, however, that the optimal structure is a full matching in which a treated unit may have one or more controls or a control may have one or more treated units. Optimal 1 — k matching is compared to optimal full matching, finding that optimal full matching is often much better. (3) Distances: Matching involves defining a distance between covariate vectors, and several such distances exist. Three recent proposals are compared. Practical advice is summarized in a final section.     

Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion

Stable local feature detection is a critical prerequisite in the problem of infrared (IR) face recognition. Recently, Scale Invariant Feature Transform (SIFT) is introduced for feature detection in an infrared face frame, which is achieved by applying a simple and effective averaging window with SIFT termed as Y-styled Window Filter (YWF). However, the thermal IR face frame has an intrinsic characteristic such as lack of feature points (keypoints); therefore, the performance of the YWF-SIFT method will be inevitably influenced when it was used for IR face recognition. In this paper, we propose a novel method combining multi-scale fusion with YWF-SIFT to explore more good feature matches. The multi-scale fusion is performed on a thermal IR frame and a corresponding auxiliary visual frame generated from an off-the-shelf low-cost visual camera. The fused image is more informative, and typically contains much more stable features. Besides, the use of YWF-SIFT method enables us to establish feature correspondences more accurately. Quantitative experimental results demonstrate that our algorithm is able to significantly improve the quantity of feature points by approximately 38%. As a result, the performance of YWF-SIFT with multi-scale fusion is enhanced about 12% in infrared human face recognition.