Eulerian subgraphs containing given edges

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泊松融合原理和python代码

泊松融合原理和python代码

泊松融合原理和python代码【原创版】目录1.泊松融合原理概述2.Python 代码实现泊松融合原理3.泊松融合原理的应用正文1.泊松融合原理概述泊松融合原理是一种概率论中的经典理论,由法国数学家泊松提出。

泊松融合原理描述了一个事件在特定时间间隔内发生的概率与另一个事件在相同时间间隔内发生的次数之间的关系。

具体而言,泊松融合原理表明,一个事件在时间间隔Δt 内发生的次数服从泊松分布,即P(X=k)=e^(-λΔt) * (λΔt)^k / k!,其中λ是事件的平均发生率,X 是事件在时间间隔Δt 内发生的次数,k 是事件发生的次数。

2.Python 代码实现泊松融合原理为了验证泊松融合原理,我们可以使用 Python 编写代码模拟事件的发生过程。

以下是一个简单的 Python 代码示例:```pythonimport randomimport mathdef poisson_fusion(lambda_value, dt, num_trials):"""泊松融合原理模拟:param lambda_value: 事件的平均发生率:param dt: 时间间隔:param num_trials: 模拟次数:return: 事件在时间间隔内发生的次数"""count = 0for _ in range(num_trials):# 随机生成一个 0 到 dt 之间的时间间隔t = random.uniform(0, dt)# 计算在时间间隔内事件发生的次数k = int(lambda_value * t)# 计算泊松分布的概率poisson_prob = math.exp(-lambda_value * t) * (lambda_value * t) ** k / k# 根据泊松分布概率随机生成事件发生的次数count += random.choice([0, 1, 2, 3, 4],p=poisson_prob)return count# 示例lambda_value = 1 # 事件的平均发生率为 1dt = 1 # 时间间隔为 1um_trials = 1000 # 模拟次数为 1000# 模拟事件在时间间隔内发生的次数counts = [poisson_fusion(lambda_value, dt, num_trials) for _in range(num_trials)]# 计算平均值和方差mean = sum(counts) / num_trialsvariance = sum((count - mean) ** 2 for count in counts) / num_trialsprint("平均值:", mean)print("方差:", variance)```3.泊松融合原理的应用泊松融合原理在实际应用中有很多场景,例如统计学、保险、生物学等领域。

tpo32三篇托福阅读TOEFL原文译文题目答案译文背景知识

tpo32三篇托福阅读TOEFL原文译文题目答案译文背景知识

tpo32三篇托福阅读TOEFL原文译文题目答案译文背景知识阅读-1 (2)原文 (2)译文 (5)题目 (7)答案 (16)背景知识 (16)阅读-2 (25)原文 (25)译文 (28)题目 (31)答案 (40)背景知识 (41)阅读-3 (49)原文 (49)译文 (53)题目 (55)答案 (63)背景知识 (64)阅读-1原文Plant Colonization①Colonization is one way in which plants can change the ecology of a site.Colonization is a process with two components:invasion and survival.The rate at which a site is colonized by plants depends on both the rate at which individual organisms(seeds,spores,immature or mature individuals)arrive at the site and their success at becoming established and surviving.Success in colonization depends to a great extent on there being a site available for colonization–a safe site where disturbance by fire or by cutting down of trees has either removed competing species or reduced levels of competition and other negative interactions to a level at which the invading species can become established.For a given rate of invasion,colonization of a moist,fertile site is likely to be much more rapid than that of a dry, infertile site because of poor survival on the latter.A fertile,plowed field is rapidly invaded by a large variety of weeds,whereas a neighboring construction site from which the soil has been compacted or removed to expose a coarse,infertile parent material may remain virtually free of vegetation for many months or even years despite receiving the same input of seeds as the plowed field.②Both the rate of invasion and the rate of extinction vary greatly among different plant species.Pioneer species-those that occur only in the earliest stages of colonization-tend to have high rates of invasion because they produce very large numbers of reproductive propagules(seeds,spores,and so on)and because they have an efficient means of dispersal(normally,wind).③If colonizers produce short-lived reproductive propagules,they must produce very large numbers unless they have an efficient means of dispersal to suitable new habitats.Many plants depend on wind for dispersal and produce abundant quantities of small,relatively short-lived seeds to compensate for the fact that wind is not always a reliable means If reaching the appropriate type of habitat.Alternative strategies have evolved in some plants,such as those that produce fewer but larger seeds that are dispersed to suitable sites by birds or small mammals or those that produce long-lived seeds.Many forest plants seem to exhibit the latter adaptation,and viable seeds of pioneer species can be found in large numbers on some forest floors. For example,as many as1,125viable seeds per square meter were found in a100-year-old Douglas fir/western hemlock forest in coastal British Columbia.Nearly all the seeds that had germinated from this seed bank were from pioneer species.The rapid colonization of such sites after disturbance is undoubtedly in part a reflection of the largeseed band on the forest floor.④An adaptation that is well developed in colonizing species is a high degree of variation in germination(the beginning of a seed’s growth). Seeds of a given species exhibit a wide range of germination dates, increasing the probability that at least some of the seeds will germinate during a period of favorable environmental conditions.This is particularly important for species that colonize an environment where there is no existing vegetation to ameliorate climatic extremes and in which there may be great climatic diversity.⑤Species succession in plant communities,i.e.,the temporal sequence of appearance and disappearance of species is dependent on events occurring at different stages in the life history of a species. Variation in rates of invasion and growth plays an important role in determining patterns of succession,especially secondary succession. The species that are first to colonize a site are those that produce abundant seed that is distributed successfully to new sites.Such species generally grow rapidly and quickly dominate new sites, excluding other species with lower invasion and growth rates.The first community that occupies a disturbed area therefore may be composed of specie with the highest rate of invasion,whereas the community of the subsequent stage may consist of plants with similar survival ratesbut lower invasion rates.译文植物定居①定居是植物改变一个地点生态环境的一种方式。

The primal-dual method for approximation algorithms and its application to network design p

The primal-dual method for approximation algorithms and its application to network design p

1
Introduction
Many problems of interest in combinatorial optimization are considered unlikely to have efficient algorithms; most of these problems are N P -hard, and unless P = N P they do not have polynomialtime algorithms to find an optimal solution. Researchers in combinatorial optimization have considered several approaches to deal with N P -hard problems. These approaches fall into one of two classes. The first class contains algorithms that find the optimal solution but do not run in polynomial time. Integer programming is an example of such an approach. Integer programmers attempt to develop branch-and-bound (or branch-and-cut, etc.) algorithms for dealing with particular problems such that the algorithm runs quickly enough in practice for instances of interest, although the algorithm is not guaranteed to be efficient for all instances. The second class contains algorithms that run in polynomial time but do not find the optimal solution for all instances. Heuristics and metaheuristics (such as simulated annealing or genetic algorithms) are one approach in this class. Typically researchers develop a heuristic for a problem and empirically demonstrate its effectiveness on instances of interest. In this survey, we will consider another approach in this second class called approximation algorithms. Approximation algorithms are polynomial-time heuristics for N P -hard problems whose solution values are provably close to optimum for all instances of the problem. More formally, an α-approximation algorithm for an optimization problem is an algorithm that runs in polynomial time and produces a solution whose value is within a factor of α of the value of an optimal solution. The parameter α is called the performance guarantee or the approximation ratio of the algorithm. We assume that the value of any feasible solution is nonnegative for the problems we consider; extensions of the notion of performance guarantee have been developed in other cases, but we will not discuss them here. This survey will follow the convention that α ≥ 1 for minimization problems and α ≤ 1 for maximization problems, so that a 2-approximation algorithm for a minimization problem produces a solution of value no more than twice the optimal value, 1 and a 2 -approximation algorithm for a maximization problem produces a solution of value at least

外文翻译---基于离散混沌映射的图像加密并行算法

外文翻译---基于离散混沌映射的图像加密并行算法
这种并行图像加密框架下,我们提出了一种新的算法,这是基于四个基本的转换。因此,我们将描述我们的算法之前,先介绍这些转换。
3.转换
3.1.A-转换
在A转换中,A代表加,能被形式化的定义如下:
a+b=c(1)
加法被定义为按位与操作
转换A有三个基本性质:
(2.1)a+a=0
(2.2)a+b=b+a(2)
(2.3)(a+b)+c=a+(b+c)
在并行模式计算时,许多的PE可以同时读取或写入相同的内存区域(即临界区),
这往往会导致意想不到的执行程序。因此,有必要在关键区域使用一些并行技术管理。
2.2.并行图像的加密框架
为了满足上述要求,我们提出了一个并行图像加密的框架,这是一个四个步骤的过程:
步骤1:整个图像被划分成若干块。
步骤2:每个PE负责确定数量块。一个区域内的像素可以充分使用有效的混乱和扩散进行操作加密。
附件C:译文
基于离散混沌映射的图像加密并行算法
摘要:
最近,针对图像加密提出了多种基于混沌的算法。然而,它们都无法在并行计算环境中有效工作。在本文中,我们提出了一个并行图像加密的框架。基于此框架内,一个使用离散柯尔莫哥洛夫流映射的新算法被提出。它符合所有并行图像加密算法的要求。此外,它是安全、快速的。这些特性使得它是一个很好的基于并行计算平台上的图像加密选择。
这个框架可以非常有效的实现整个图像的扩散。但是,它是不适合在并行计算环境中运行。这是因为当前像素的处理无法启动直到前一个像素已加密。即使有多个处理元素(PE),这种计算仍然是在一个串行模式下工作。此限制了其应用平台,因为许多基于FPGA / CPLD或者数字电路的设备可以支持并行处理。随着并行计算技术的应用,加密速度可以大大加快。

The Algorithmic Aspects of Uncrowded Hypergraphs

The Algorithmic Aspects of Uncrowded Hypergraphs
SODA'97. y This research was supported by the Deutsche Forschungsgemeinschaft as part of the Collaborative Research Center Computational Intelligence" SFB 531.
Abstract
ห้องสมุดไป่ตู้
1 Introduction
A fundamental problem in Computer Science and Mathematics is to nd a large independent set in an arbitrary graph 6 , 14 , 20 . Recall, that for a graph G = V; E with vertex set V and edgeset E V 2 a subset I V of the vertex set is called independent, if the subgraph induced on I contains no edges e 2 E , i.e., E I 2 = ;. The maximum cardinality of an independent set I is called the independence number G of G . It is well-known, that nding in a graph G an independent set of size G is an NP-hard problem, even for graphs with bounded maximum degree. This suggests to look for approximation algorithms with guaranteed performance ratio which is the quotient of the sizes of the optimal and the found solution in the worst case. The results of Arora, Lund, Motwani, Sudan and Szegedy 10 on interactive proof systems show that, with respect to polynomial time algorithms, there is no constant performance ratio for the independent set problem for graphs on n vertices, indeed no ratio of n1=4 unless P = NP , cf. the work of Bellare, Goldreich and Sudan 13 . Recently, Hstad 26 showed that there is a no performance ratio of n1=2, unless NP = P , and no such ratio of n1, unless NP = coR. With respect to polynomial time algorithms, for triangle-free graphs with maximum degree , a performance ratio of O= ln was given in 23 , 27 , and moreover, if they contain no

超几何分布的英语

超几何分布的英语

超几何分布的英语Here is an essay on the topic of the hypergeometric distribution, written in English with more than 1000 words. The title and any additional instructions have been omitted as requested.The hypergeometric distribution is a discrete probability distribution that describes the number of successes in a sequence of n draws from a finite population without replacement. In other words, it models the probability of obtaining a certain number of items with a desired characteristic from a finite population, given that the population is not replenished after each draw. This distribution is particularly useful in situations where the population size is relatively small, and the sampling is done without replacement, such as in quality control, survey sampling, and experimental design.The hypergeometric distribution is characterized by three parameters: the population size (N), the number of items with the desired characteristic in the population (K), and the number of items drawn from the population (n). The probability mass function (PMF) of the hypergeometric distribution is given by the formula:P(X = x) = (C(K, x) * C(N-K, n-x)) / C(N, n)where:- X is the random variable representing the number of items with the desired characteristic in the n draws- x is the observed value of X- C(a, b) is the binomial coefficient, which represents the number of ways to choose b items from a itemsThe hypergeometric distribution is related to the binomial distribution, but the key difference is that in the binomial distribution, the trials are independent and the probability of success remains constant, whereas in the hypergeometric distribution, the trials are not independent and the probability of success changes with each draw.One of the main applications of the hypergeometric distribution is in quality control. Suppose a manufacturer has produced a batch of N items, and K of them are defective. The manufacturer wants to inspect a sample of n items to determine the quality of the batch. The hypergeometric distribution can be used to calculate the probability of finding x defective items in the sample, which can help the manufacturer make decisions about the batch.Another application of the hypergeometric distribution is in survey sampling. Suppose a researcher wants to estimate the proportion ofa certain characteristic in a population, but the population size is relatively small. The researcher can draw a sample of n individuals from the population and use the hypergeometric distribution to calculate the probability of observing a certain number of individuals with the desired characteristic.The hypergeometric distribution also has applications in experimental design. For example, in a clinical trial, researchers may want to compare the effectiveness of a new drug to a placebo. The researchers can assign participants to the treatment or control group using a hypergeometric distribution, which ensures that the number of participants in each group is balanced.One of the key properties of the hypergeometric distribution is that it is a discrete distribution, meaning that the random variable X can only take on integer values. This property makes the distribution particularly useful in situations where the population size is finite and the sampling is done without replacement.Another important property of the hypergeometric distribution is that it is unimodal, meaning that the probability mass function has a single peak. The location of the peak depends on the values of the three parameters (N, K, and n), and the distribution can be left-skewed, right-skewed, or symmetric depending on the values of these parameters.The hypergeometric distribution also has several special cases. For example, when the population size N is large compared to the sample size n, the hypergeometric distribution approaches the binomial distribution. Similarly, when the number of items with the desired characteristic K is small compared to the population size N, the hypergeometric distribution approaches the Poisson distribution.In addition to its applications in quality control, survey sampling, and experimental design, the hypergeometric distribution has also been used in other areas, such as genetics, ecology, and finance. For example, in genetics, the hypergeometric distribution can be used to model the probability of observing a certain number of mutations in a gene sequence, while in ecology, it can be used to model the probability of observing a certain number of species in a sample of a habitat.Overall, the hypergeometric distribution is a powerful and versatile probability distribution that has numerous applications in a wide range of fields. Its ability to model the probability of success in a finite population without replacement makes it a valuable tool for researchers and practitioners in many different domains.。

哈工大博士学位论文模板

哈工大博士学位论文模板

1.3
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第 2 章 基本使用 ..................................................................................... 3
3.1
学 ................................................................................ 7
3.2 学
..................................................................... 7
第 1 章 绪论 ............................................................................................ 1
1.1
....................................................................................... 1
LATEX TEMPLATE FOR MASTER/DOCTOR THESIS/DISSERTATION OF HARBIN INSTITUTE OF TECHNOLOGY
Candidate: Supervisor: Academic Degree Applied for: Specialty: Affiliation: Date of Defense: Degree-Conferring-Institution:

应用布尔遗传算子求解N皇后问题

应用布尔遗传算子求解N皇后问题
关键词 : Ⅳ皇后 问题 ; 尔遗传 算子; 布 适应度 函数; 遗传 算法
N Q e n r be i v r e y c mp r g wi a k a k g a d c r n e ei a o tm. - u e sp o lm s e f d b o ai t b c t c i ur tg n t l rh i i n h r n n e c gi
SH U AI X unbo. A Shuna So v ng M n. l i N - que ns e al r t bas d go ihm e on boo e ge tc l an ne ope a orC om put r i rt . e Eng ne r ng nd i e i a
具有 良好 全局搜 索性 能的矩阵遗传 算子 组合 应用 , Ⅳ皇后 问题求 解。采用 自然数 和二进制 互换 的编 码方 式, 用Ⅳ皇后 的约 对 应
束条件构 造适应度 函数 , 保证 了算法 的全 局收敛 性 。通 过与 回溯 法和相 关遗传算 法比较 , 实验证 实 了该 方法应用 于求解 Ⅳ皇后 问题 , 具有 良好的搜 索效 率和 求解质量 。
Bo la e ei o eao s p o o e t i r v lc ls ac i g a it f g n t lo i m . h b oe e ei p rtr oe n g n tc p rtr i rp sd o mp o e o a e hn bl o e e c ag rt By te o la g n t o eao r i y i h n c
c mb n d wi ma i g n t o eao wh c h s e r g o a sa c ig b l , i pi z t n o iain e e c lo o ie t h t x e ei p rtr r c ih a b  ̄e lb l erh n a i t a o t i y 1 miai c mbn t g n t ag ・ o o i
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2. Preliminaries In [3], Catlin deÿned the collapsible graphs. Let R ⊆ V (G ). A subgraph of G is called an R-subgraph if both G − E ( ) is connected and v ∈ R if and only if v has odd degree in . A graph G is collapsible if for any even subset R of V (G ), G has an R-subgraph. Catlin showed [3] that every vertex of G is lying in a unique maximal collapsible subgraph of G . The collection of all collapsible graphs is denoted by CL. Clearly CL ⊂ SL. The contraction G=H is obtained from G by contracting each edge of H and deleting the resulting loops. The reduction of G is obtained from G by contracting all maximal
H.-J. Lai / Discrete Mathematics 230 (2001) 63–69
65
collapsible subgraphs, and is denoted by G . A graph G is reduced if G is the reduction of some graph. Theorem 2.1 (Catlin [3]). Let G be a graph. Each of the following holds. (i) [3, Theorem 5] G reduced i G has no nontrivial collapsible subgraph. (ii) [3, Theorem 8] If G is reduced; then G is simple; and contains no K3 ; and (G )63; and G can be covered by at most two edge-disjoint forests. (iii) [3, Theorem 8] If G is reduced; then for any H ⊆ G; either H ∈ {K1 ; K2 } or |E (H )|62|V (H )| − 4. (iv) [3, Theorem 3] If H is a collapsible subgraph of G; then G ∈ CL if and only if G=H ∈ CL. (v) [3, Theorem 3] If H is a collapsible subgraph of G; then G ∈ SL if and only if G=H ∈ SL. The following result proves a conjecture of Catlin in [5], and generalizes Theorems 1.2 and 1.3. Theorem 2.2 (Catlin et al. [6]). Let G be a connected graph. If F (G )62; then either G ∈ CL; or the reduction of G is in {K2 ; K2; t ; (t ¿1)}. Let (G ) denote the maximum number of edge-disjoint spanning trees of G . Catlin recently proved a relationship between (G ) and Ä (G ), the edge-connectivity. Theorem 2.3 (Catlin [5]). Let G be a graph and let p¿1 be an integer. The following are equivalent: (i) Ä (G )¿2p. (ii) For any X ⊆ E (G ) with |X |6p; (G − X )¿p. Let G be a graph and let X ⊆ E (G ). The graph GX is obtained from G by replacing each edge e ∈ X with ends ue and ve by a (ue ; ve )-path Pe of length 2, where the internal vertex w(e) of the path Pe is newly added. Lemma 2.4. Let p¿2 be an integer; let G be a graph and let X ⊆ E (G ). Each of the following holds: (i) G has a spanning eulerian subgraph H such that X ⊆ E (H ) if and only if GX ∈ SL = SE(0). (ii) G ∈ SE(l) if and only if for any X ⊆ E (G ) with |X |6l; GX ∈ SL. (iii) (G − X )¿2 if and only if (GX )¿2. (iv) If F (G ) = 0 (that is; (G )¿2) and if X = {e1 ; e2 } ⊆ E (G ); then F (GX )62.
Abstract For an integer l¿0, deÿne SE(l) to be the family of graphs such that G ∈ SE(l) if and only if for any edge subset X ⊆ E (G ) with |X |6l, G has a spanning eulerian subgraph H with X ⊆ E (H ). The graphs in SE(0) are known as supereulerian graphs. Let f(l) be the minimum value of k such that every k -edge-connected graph is in SE(l). Jaeger and Catlin independently proved f(0) = 4. We shall determine f(l) for all values of l¿0. Another problem concerning the existence of eulerian subgraphs containing given edges is also discussed, and former results in [J. Graph Theory 1 (1977) 79 –84] and [J. Graph Theory 3 (1979) 91–93] are extended. c 2001 Elsevier Science B.V. All rights reserved. Keywords: Eulerian subgraph; Supereulerian graph; Collapsible graph
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H.-J. Lai / Discrete Mathematics 230 (2001) 63–69
Theorem 1.1 (Boesch et al. [1] and Jaeger [7]). Let H H . The following are equivalent:
Байду номын сангаас
be a subgraph of a graph
E-mail address: hjlai@ (H.-J. Lai) 0012-365X/01/$ - see front matter c 2001 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 2 - 3 6 5 X ( 0 0 ) 0 0 0 7 0 - 4
1. Introduction Graphs in this note are ÿnite and loopless. Undeÿned terms and notation are from [2]. We use H ⊆ G to denote the fact that H is a subgraph of G . For a graph G , O(G ) denotes the set of all vertices of odd degree in G . A graph G with O(G ) = ∅ is an even graph, and a connected even graph is an eulerian graph. A graph is supereulerian if it has a spanning eulerian subgraph. The collection of all supereulerian graphs will be denoted by SL. For the literature on the subject of supereulerian graphs, see Catlin’s excellent survey [4]. As indicated by the authors in [1], characterizing supereulerian graphs appears very di cult. Pulleyblank in [8] pointed out that the problem of determining if a graph G is supereulerian is NP-complete. A bond is a minimal edge-cut. A bond X of G is an odd bond if |X | is odd. In [1] Boesch et al. proved Theorem 1.1 below, and in [7], Jaeger presented an elegant simple proof.
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