Mutually independent hamiltonian cycles for the pancake graphs and the star graphs

A hamiltonian cycle C of a graph G is an ordered set u₁,u₂,…,u_n(G),u₁ of vertices such that u_i≠u_j for i≠j and u_i is adjacent to u_i+1 for every i{1,2,…,n(G)−1} and u_n(G) is adjacent to u₁, where n(G) is the order of G. The vertex u₁ is the starting vertex and u_i is the ith vertex of C. Two hamiltonian cycles C₁=u₁,u₂,…,u_n(G),u₁ and C₂=v₁,v₂,…,v_n(G),v₁ of G are independent if u₁=v₁ and u_i≠v_i for every i{2,3,…,n(G)}. A set of hamiltonian cycles {C₁,C₂,…,C_k} of G is mutually independent if its elements are pairwise independent. The mutually independent hamiltonicity IHC(G) of a graph G is the maximum integer k such that for any vertex u of G there exist k mutually independent hamiltonian cycles of G starting at u.In this paper, the mutually independent hamiltonicity is considered for two families of Cayley graphs, the n-dimensional pancake graphs P_n and the n-dimensional star graphs S_n. It is proven that IHC(P₃)=1, IHC(P_n)=n−1 if n≥4, IHC(S_n)=n−2 if n{3,4} and IHC(S_n)=n−1 if n≥5.     

Generalization of transitive fraternal augmentations for directed graphs and its applications

Let be a directed graph. A transitive fraternal augmentation of is a directed graph with the same vertex set, including all the arcs of and such that for any vertices x,y,z,

1.

if and then or (fraternity);

2.

if and then (transitivity).

In this paper, we explore some generalization of the transitive fraternal augmentations for directed graphs and its applications. In particular, we show that the 2-coloring number col₂(G)≤O(∇₁(G)∇₀(G)²), where ∇_k(G) (k≥0) denotes the greatest reduced average density with depth k of a graph G; we give a constructive proof that ∇_k(G) bounds the distance (k+1)-coloring number col_k+1(G) with a function f(∇_k(G)). On the other hand, ∇_k(G)≤(col_2k+1(G))^2k+1. We also show that an inductive generalization of transitive fraternal augmentations can be used to study nonrepetitive colorings of graphs.     

Enumeration of labelled chain graphs and labelled essential directed acyclic graphs

A chain graph is a digraph whose strong components are undirected graphs and a directed acyclic graph (ADG or DAG) G is essential if the Markov equivalence class of G consists of only one element. We provide recurrence relations for counting labelled chain graphs by the number of chain components and vertices; labelled essential DAGs by the number of vertices. The second one is a lower bound for the number of labelled essential graphs. The formula for labelled chain graphs can be extended in such a way, that allows us to count digraphs with two additional properties, which essential graphs have.     

基于支持向量回归和核岭回归对血糖值预测的对比分析

为了对比支持向量回归(SVR)和核岭回归(KRR)预测血糖值的效果,本文选择人工智能辅助糖尿病遗传风险的相关数据进行实证分析.首先对数据进行预处理,将处理后的数据导入Python.其次,为了使SVR和KRR的对比结果具有客观性,使用了三种有代表性的核方法(线性核函数,径向基核函数和sigmod核函数).然后,在训练集上采用网格搜索自动调参分别建立SVR和KRR的最优模型,对血糖值进行预测.最后,在测试集上对比分析SVR和KRR预测的均方误差(MSE)和拟合时间等指标.结果表明:均方误差(MSE)都小于0.006,且KRR的MSE比SVR的小0.0002,KRR的预测精度比SVR更高;而SVR的预测时间比KRR的少0.803秒,SVR的预测效率比KRR好.     

回归型支持向量机调节熵函数法的区间扩张研究

为减小由于二进制编码的舍入误差对该问题计算结果的影响,对求解回归支持向量机的一种调节熵方法进行了区间扩张,讨论了区间函数的相关定理与收敛性.对设计的区间算法做了收敛性证明,并给出了数值实验,验证了方法与算法的可行性和有效性.     

Enumeration of 2-regular circulant graphs and directed double networks

We present a formula to enumerate non-isomorphic circulant digraphs of order n with connection sets of cardinality 2. This formula simplifies to C(n,2)=3×2^a−1−4 in the case when n=2^a(a≥3), and when n=p^a(where p is an odd prime and a≥1). The number of non-isomorphic directed double networks are also enumerated.     

图上合作博弈和图的边密度

1977年, Myerson建立了以图作为合作结构的可转移效用博弈模型(也称图博弈), 并提出了一个分配规则, 也即"Myerson 值", 它推广了著名的Shapley值. 该模型假定每个连通集合(通过边直接或间接内部相连的参与者集合)才能形成可行的合作联盟而取得相应的收益, 而不考虑连通集合的具体结构. 引入图的局部边密度来度量每个连通集合中各成员之间联系的紧密程度, 即以该连通集合的导出子图的边密度来作为他们的收益系数, 并由此定义了具有边密度的Myerson值, 证明了具有边密度的Myerson值可以由"边密度分支有效性"和"公平性"来唯一确定.     

Improvements on the density of maximal 1‐planar graphs

A graph is 1‐planar if it can be drawn in the plane such that each edge is crossed at most once. A graph, together with a 1‐planar drawing is called 1‐plane. A graph is maximal 1‐planar (1‐plane), if we cannot add any missing edge so that the resulting graph is still 1‐planar (1‐plane). Brandenburg et al. showed that there are maximal 1‐planar graphs with only edges and maximal 1‐plane graphs with only edges. On the other hand, they showed that a maximal 1‐planar graph has at least edges, and a maximal 1‐plane graph has at least edges. We improve both lower bounds to .     

支持向量机回归方法在地表水水质评价中的应用

将支持向量机方法应用于地表水质评价问题中,建立了多指标水质综合评价的支持向量机回归模型.在地表水质评价标准的基础上采用内插法获得学习样本,经过训练,得到水质评价的分类区间;然后以实测资料对所建模型进行检验,研究结果表明,支持向量机回归模型性能良好、预测精度高、简便易行,是水质评价的一种有效方法,具有广阔的应用前景.     

基于支持向量回归机的水文混沌时间序列预测

支持向量机在系统辨识和分类研究方面比较成熟,目前尚没有提出有效的支持向量回归理论来解决非线性、时变、干扰的复杂问题.支持向量回归机主要用于因果关系点对的回归预测,把支持向量回归机应用于水文混沌时间序列的预测研究是一个有意义的工作.在支持向量机一般理论基础上,提出了水文混沌时间序列支持向量回归机模型,并就模型进行仿真计算,讨论了模型参数对支持向量回归机预测精度的影响,为模型参数寻优提供一般指导原则.直门达水文站径流量混沌时间序列支持向量回归机预测实验表明,水文混沌时间序列支持向量回归机模型是有效的.     

Asymptotics for Wavelet Based Estimates of Piecewise Smooth Regression for Stationary Time Series

Wavelet methods are used to estimate density and (auto-) regression functions that are possibly discontinuous. For stationary time series that satisfy appropriate mixing conditions, we derive mean integrated squared errors (MISEs) of wavelet-based estimators. In contrast to the case for kernel methods, the MISEs of wavelet-based estimators are not affected by the presence of discontinuities in the curves. Applications of this approach to problems of identification of nonlinear time series models are discussed.     

支持向量回归机和多小波变换在数字图像水印算法中的应用

给出一种多小波域上基于支持向量回归机的数字图像水印新算法,算法充分利用了多小波变换和支持向量回归机的优点.通过对新算法实验结果的分析和与其他算法的对比得到:算法对JPEG压缩、模糊处理、锐化等攻击具有很强的鲁棒性.     

基于粒子群-支持向量机定量降水集合预报方法

首先对ECMWF不同物理量场预报因子群进行自然正交展开,选取能充分反映每个预报因子场主要信息的第一主分量作为模型输入.进一步利用粒子群算法对支持向量回归机的相关参数进行优化,以南宁市8个气象站单站逐日降水作为预报对象,建立粒子群-支持向量回归集合预报模型,进行单站逐日降水的数值预报产品释用预报方法研究.利用模型对2015年5-6月南宁市8站进行了逐日降水预报业务试验,结果表明,模型具有较好的预报效果.并提出了利用隶属函数建立可信度函数对不同的预报模型进行评价.     

试验设计中多元回归分析方法的研究

在一试验设计的实例分析研究的基础上 ,提出了多元回归分析在试验设计中的应用方法。通过对试验数据建立的回归模型 ,可以预测到更好的因素水平 ,对第二轮试验设计具有重要的指导作用     

基于Logistic回归的水质预测研究

在环境系统评价中,水环境质量等级评价是其中十分重要的工作.鉴于对水环境研究中,水质级别为分类变量不能利用传统回归方法分析的特征,基于logistic回归方法建立了一种水质级别预测模型.利用长江流域的水质监测数据,将logistic回归应用于水质数据分析,进行水质建模,对水质级别做出预测.研究结果表明利用logistic回归进行水质分析,具有良好的拟合和预测效果.     

Nonasymptotic Bounds on the L 2 Error of Neural Network Regression Estimates

The estimation of multivariate regression functions from bounded i.i.d. data is considered. The L ₂ error with integration with respect to the design measure is used as an error criterion. The distribution of the design is assumed to be concentrated on a finite set. Neural network estimates are defined by minimizing the empirical L ₂ risk over various sets of feedforward neural networks. Nonasymptotic bounds on the L ₂ error of these estimates are presented. The results imply that neural networks are able to adapt to additive regression functions and to regression functions which are a sum of ridge functions, and hence are able to circumvent the curse of dimensionality in these cases.     

前馈神经网络和Logit回归的比较研究

近年来 ,前馈神经网络广泛地应用在 Logit回归作为标准统计方法的分析领域 .但却很少作它们之间的直接比较 ,本文是 Logit回归和前馈神经网络“比较研究”的一个尝试 ,说明了一些理论结果和特性 ,讨论了在它们的应用中碰到的一些实际问题 ,还进一步用分析的和模拟的两种方法研究了一些重要的渐近概念、过分拟合以及模型选择等问题 ,最后讨论并给出一些结论     

回归分析与基于MIV的RBF神经网络在PM2.5的相关因素分析中的应用

PM2.5作为大气首要污染物,严重影响着人们的身体健康.为了研究影响PM2.5的相关指标,以武汉市的空气数据为研究对象,通过多元线性回归、偏最小二乘回归、基于MIV的RBF神经网络回归等方法对AQI中6个基本监测指标的PM2.5(含量)与其它5项分指标及其对应污染物(含量)之间的相关性进行分析;通过比较,基于MIV的RBF神经网络回归模型拟合度达到0.9302,效果最好,而且也优于BP人工神经网络回归算法,因此得出了精确可靠的影响PM2.5的指标权重大小,为减排PM2.5提供了可靠的理论依据.     

The effect of gossip on social networks

In this article, we develop a simple model for the effect of gossip spread on social network structure. We define gossip as information passed between two individuals A and B about a third individual C which affects the strengths of all three relationships: it strengthens A‐B and weakens both B‐C and A‐C. We find, in both an analytic derivation and model simulations, that if gossip does not spread beyond simple triads, it destroys them but if gossip propagates through large dense clusters, it strengthens them. Additionally, our simulations show that the effect of gossip on network metrics (clustering coefficient, average‐path‐length, and sum‐of‐strengths) varies with network structure and average‐node‐degree. © 2010 Wiley Periodicals, Inc. Complexity 16: 39‐47, 2011     

Feasibility of motion planning on acyclic and strongly connected directed graphs

Motion planning is a fundamental problem of robotics with applications in many areas of computer science and beyond. Its restriction to graphs has been investigated in the literature, for it allows one to concentrate on the combinatorial problem abstracting from geometric considerations. In this paper, we consider motion planning over directed graphs, which are of interest for asymmetric communication networks. Directed graphs generalize undirected graphs, while introducing a new source of complexity to the motion planning problem: moves are not reversible. We first consider the class of acyclic directed graphs and show that the feasibility can be solved in time linear in the product of the number of vertices and the number of arcs. We then turn to strongly connected directed graphs. We first prove a structural theorem for decomposing strongly connected directed graphs into strongly biconnected components. Based on the structural decomposition, we show that the feasibility of motion planning on strongly connected directed graphs can be decided in linear time.