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 $m$.     

基于混合表面等离子体弯曲波导的紧凑型宽带偏振分束转换器的设计研究

设计了一种宽带的基于混合表面等离子体弯曲波导的偏振分束转换器, 使用有限元法计算硅波导、混合表面等离子体波导的模场分布和有效折射率, 为器件建模仿真分析提供了依据. 使用时域有限差分法优化器件结构, 以获得最佳的性能参数. 由于混合表面等离子体波导的双折射增强, 设计获得的偏振分束转换器具有超小尺寸和宽带的工作特性. 仿真结果表明 在1550nm的中心波长处消光比大于23dB, 插入损耗小于0.8dB; 在80nm的带宽上, 横磁偏振光转换为横电偏振光的偏振转换效率大于95％.     

Improved and self-tuned occurrence heuristics

In this note we present three efficient variations of the occurrence heuristic, adopted by many exact string matching algorithms and first introduced in the well-known Boyer–Moore algorithm. Our first heuristic, called improved-occurrence heuristic, is a simple improvement of the rule introduced by Sunday in his Quick-Search algorithm. Our second heuristic, called worst-occurrence heuristic, achieves its speed-up by selecting the relative position which yields the largest average advancement. Finally, our third heuristic, called jumping-occurrence heuristic, uses two characters for computing the next shift. Setting the distance between these two characters optimally allows one to maximize the average advancement. The worst-occurrence and jumping-occurrence heuristics tune their parameters according to the distribution of the characters in the text. Experimental results show that the newly proposed heuristics achieve very good results on average, especially in the case of small alphabets.     

多对一双方匹配市场中的最优化

在双方市场中定义的博弈概念，可以使市场同方参与者的收益同时达到最大．这种最优化存在的理论依据是选择匹配的稳定性．用博弈论的分析与证明方法研宄多对一双方匹配市场中的最优化．在替代偏好和LAD（Law of Aggregate Demend）偏好下，证明由企业作选择的选择函数一定是个稳定匹配，由工人做选择的选择函数也是一个稳定匹配．     

Generalized matching theorems for closed coverings of convex sets

In this paper we use fixed point and coincidence theorems due to Park [8] to give matching theorems concerning closed coverings of nonempty convex sets in a real topological vector space. Our new results extend previously given ones due to Ky Fan [2], [3], Shih [10], Shih and Tan [11], and Park [7].     

Erdős matching conjecture for almost perfect matchings

In 1965 Erd?s asked, what is the largest size of a family of k-element subsets of an n-element set that does not contain a matching of size $s+1$? In this note, we improve upon a recent result of Frankl and resolve this problem for $s>101k^3$ and $(s+1)k?n<(s+1)(k+\frac{1}{100k})$.     

Addendum to: “Hamilton-laceable bi-powers of locally finite bipartite graphs” [Discrete Math. 345 (2022) 112777]

In this addendum we give a short and easy negative answer to the two questions raised in the original article.     

运用CAFP教学提升学生化学意志素质的教学案例

鉴于当前学生化学意志素质缺乏的问题，探讨以认知、情绪唤醒、情感、意志为主控目标，在预习、尝试学习、交错搭配合作学习中，开展CAFP教学研究，以促成情意因素对认知的调动、动力、强化等功能。     

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.