A Detailed Analysis of a Modified Fritzsch Scheme of Quark Mass Matrices

When it comes to writing an essay about an interest experiment in English,its important to follow a clear structure and provide detailed descriptions of the experiment and its outcomes.Heres an example of how you might structure such an essay:Title:The Fascination of Chemical Reactions:An Experiment with Baking Soda and VinegarIntroduction:Hook the reader with a captivating statement about the wonders of science.Briefly introduce the topic of the experiment:a chemical reaction between baking soda and vinegar.Body Paragraph1:The Purpose of the ExperimentExplain the scientific curiosity that led to the experiment.Describe the hypothesis or expected outcome of the experiment.Body Paragraph2:Materials and MethodsList the materials used for the experiment,such as baking soda,vinegar,a measuring spoon,a balloon,and a funnel.Describe the stepbystep process of conducting the experiment,ensuring clarity and precision.Body Paragraph3:Observations and ResultsDetail the observations made during the experiment,such as the initial mixing of ingredients,the reaction,and any changes in the materials.Discuss the results of the experiment,including whether the hypothesis was confirmed or refuted.Body Paragraph4:Analysis and ConclusionAnalyze the results in the context of scientific principles,such as the release of carbon dioxide gas in the reaction.Reflect on the implications of the experiment,such as its relevance to everyday life or further scientific understanding.Conclusion:Summarize the key findings of the experiment.Reiterate the fascination with chemical reactions and the importance of scientific exploration.Heres a sample excerpt from the essay:The experiment began with a simple hypothesis:that the combination of baking soda and vinegar would produce a chemical reaction,resulting in the inflation of a balloon.Armed with a measuring spoon,a balloon,and a funnel,I carefully measured equal parts of baking soda and vinegar.As I poured the vinegar into the balloon,followed by the baking soda,I observed a rapid fizzing and bubbling.The balloon began to inflate,confirming my hypothesis and demonstrating the power of a chemical reaction.Remember to use clear and concise language,and to explain any scientific concepts in a way that is accessible to your readers.The goal is to convey your enthusiasm for the experiment and to share the knowledge you gained through the process.。

高三英语艺术批评方法科学严谨运用单选题30题1. The artist's work is often described as _____, showing a unique blend of styles.A. revolutionaryB. conventionalC. mundaneD. derivative答案：A。

本题中，“revolutionary”意为“革命性的”，符合描述独特风格融合的艺术作品；“conventional”表示“传统的”，与独特风格不符；“mundane”意思是“平凡的，世俗的”，不能体现作品的独特；“derivative”指“模仿的，派生的”，不符合独特融合的特点。

2. The painting is criticized for being too _____, lacking depth and complexity.A. superficialB. profoundC. intricateD. elaborate答案：A。

“superficial”表示“肤浅的”，符合缺乏深度和复杂性的批评；“profound”意为“深刻的”，与批评内容相反；“intricate”指“复杂精细的”，不符合题意；“elaborate”意思是“精心制作的”，也不符合缺乏深度的描述。

3. The sculpture is praised for its _____ form, which catches theviewer's eye immediately.A. amorphousB. symmetricalC. asymmetricalD. chaotic答案：B。

“symmetrical”表示“对称的”，能让人眼前一亮；“amorphous”意为“无定形的”，通常不具备吸引眼球的特点；“asymmetrical”指“不对称的”，可能不够直接吸引；“chaotic”意思是“混乱的”，不符合吸引人的描述。

Swarm Intelligence in OptimizationChristian Blum1and Xiaodong Li21ALBCOM Research GroupUniversitat Polit`e cnica de Catalunya,Barcelona,Spaincblum@2School of Computer Science and Information TechnologyRMIT University,Melbourne,Australiaxiaodong@.auSummary.Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade.They are characterized by a decentral-ized way of working that mimics the behavior of swarms of social insects,flocks of birds,or schools offish.The advantage of these approaches over traditional tech-niques is their robustness andflexibility.These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems.In this chapter we focus on two of the most successful examples of op-timization techniques inspired by swarm intelligence:ant colony optimization and particle swarm optimization.Ant colony optimization was introduced as a technique for combinatorial optimization in the early1990s.The inspiring source of ant colony optimization is the foraging behavior of real ant colonies.In addition,particle swarm optimization was introduced for continuous optimization in the mid-1990s,inspired by birdflocking.1IntroductionSwarm intelligence(SI),which is an artificial intelligence(AI)discipline,is concerned with the design of intelligent multi-agent systems by taking inspi-ration from the collective behavior of social insects such as ants,termites, bees,and wasps,as well as from other animal societies such asflocks of birds or schools offish.Colonies of social insects have fascinated researchers for many years,and the mechanisms that govern their behavior remained un-known for a long time.Even though the single members of these colonies are non-sophisticated individuals,they are able to achieve complex tasks in coop-eration.Coordinated colony behavior emerges from relatively simple actions or interactions between the colonies’individual members.Many aspects of the collective activities of social insects are self-organized and work without a central control.For example,leafcutter ants cut pieces from leaves,bring them back to their nest,and grow fungi used as food for their larvae.Weaver44 C.Blum and X.LiFig.1.Ants cooperate for retrieving a heavy prey.(Photographer:Christian Blum) ant workers build chains with their bodies in order to cross gaps between two leaves.The edges of the two leaves are then pulled together,and successively connected by silk that is emitted by a mature larva held by a worker.Another example concerns the recruitment of other colony members for prey retrieval (see,for example,Fig.1).Other examples include the capabilities of termites and wasps to build sophisticated nests,or the ability of bees and ants to orient themselves in their environment.For more examples and a more detailed description see Chap.1of this book,as well as[21,92].The term swarm intelligence wasfirst used by Beni in the context of cellular robotic systems where simple agents organize themselves through nearest-neighbor interaction[4].Meanwhile,the term swarm intelligence is used for a much broader researchfield[21].Swarm intelligence methods have been very successful in the area of optimization, which is of great importance for industry and science.This chapter aims at giving an introduction to swarm intelligence methods in optimization.Optimization problems are of high importance both for the industrial world as well as for the scientific world.Examples of practical optimization problems include train scheduling,timetabling,shape optimization,telecom-munication network design,and problems from computational biology.The research community has simplified many of these problems in order to ob-tain scientific test cases such as the well-known traveling salesman problem (TSP)[99].The TSP models the situation of a traveling salesman who is required to pass through a number of cities.The goal of the traveling sales-man is to traverse these cities(visiting each city exactly once)so that the total traveling distance is minimal.Another example is the problem of protein fold-ing,which is one of the most challenging problems in computational biology, molecular biology,biochemistry,and physics.It consists offinding the func-tional shape or conformation of a protein in two-or three-dimensional space, for example,under simplified lattice models such as the hydrophobic-polar model[169].The TSP and the protein folding problem under lattice modelsSwarm Intelligence in Optimization45 belong to an important class of optimization problems known as combinato-rial optimization(CO).In general,any optimization problem P can be described as a triple(S,Ω,f), where1.S is the search space defined over afinite set of decision variables X i,i=1,...,n.In the case where these variables have discrete domains we deal with discrete optimization(or combinatorial optimization),and in the case of continuous domains P is called a continuous optimization problem.Mixed variable problems also exist.Ωis a set of constraints among the variables;2.f:S→I R+is the objective function that assigns a positive cost value toeach element(or solution)of S.The goal is tofind a solution s∈S such that f(s)≤f(s ),∀s ∈S(in case we want to minimize the objective function),or f(s)≥f(s ),∀s ∈S(in case the objective function must be maximized).In real-life problems the goal is often to optimize several objective functions at the same time.This form of optimization is labelled multiobjective optimization.Due to the practical importance of optimization problems,many algo-rithms to tackle them have been developed.In the context of combinatorial optimization(CO),these algorithms can be classified as either complete or approximate plete algorithms are guaranteed tofind for ev-eryfinite size instance of a CO problem an optimal solution in bounded time (see[133,128]).Yet,for CO problems that are NP-hard[65],no polynomial time algorithm exists,assuming that P=N P.Therefore,complete meth-ods might need exponential computation time in the worstcase.This often leads to computation times too high for practical purposes.In approximate methods such as SI-based algorithms we sacrifice the guarantee offinding op-timal solutions for the sake of getting good solutions in a significantly reduced amount of time.Thus,the use of approximate methods has received more and more attention in the last30years.This was also the case in continuous op-timization,due to other reasons:Approximate methods are usually easier to implement than classical gradient-based techniques.Moreover,generally they do not require gradient information.This is convenient for optimization prob-lems where the objective function is only implicitly given(e.g.,when objective function values are obtained by simulation),or where the objective function is not differentiable.Two of the most notable swarm intelligence techniques for obtaining ap-proximate solutions to optimization problems in a reasonable amount of com-putation time are ant colony optimization(ACO)and particle swarm opti-mization(PSO).These optimization methods will be explained in Sects.246 C.Blum and X.Liand3respectively.In Sect.4we will give some further examples of algorithms for which swarm intelligence was the inspiring source.2Ant Colony OptimizationAnt colony optimization(ACO)[52]was one of thefirst techniques for ap-proximate optimization inspired by swarm intelligence.More specifically,ACO is inspired by the foraging behavior of ant colonies.At the core of this be-havior is the indirect communication between the ants by means of chemical pheromone trails,which enables them tofind short paths between their nest and food sources.This characteristic of real ant colonies is exploited in ACO algorithms in order to solve,for example,discrete optimization problems.3 Seen from the operations research(OR)perspective,ACO algorithms be-long to the class of metaheuristics[18,68,80].The term metaheuristic,first introduced in[67],derives from the composition of two Greek words.Heuristic derives from the verb heuriskein( υρισκ ιν)which means“tofind”,while the suffix meta means“beyond,in an upper level”.Before this term was widely adopted,metaheuristics were often called modern heuristics[144].In addition to ACO,other algorithms,such as evolutionary computation,iterated local search,simulated annealing,and tabu search,are often regarded as meta-heuristics.For books and surveys on metaheuristics see[144,68,18,80].This section on ACO is organized as follows.First,in Sect.2.1we outline the origins of ACO algorithms.In particular,we present the foraging behavior of real ant colonies and show how this behavior can be transfered into a tech-nical algorithm for discrete optimization.In Sect.2.2we provide a description of ACO in more general terms,outline some of the most successful current ACO variants,and list some representative examples of ACO applications.In Sect.2.3,we shortly describe some recent trends in ACO.2.1The Origins of Ant Colony OptimizationMarco Dorigo and colleagues introduced thefirst ACO algorithms in the early 1990s[46,50,51].The development of these algorithms was inspired by the observation of ant colonies.Ants are social insects.They live in colonies and their behavior is governed by the goal of colony survival rather than being focused on the survival of individuals.The behavior that provided the inspi-ration for ACO is the ants’foraging behavior,and in particular,how ants 3Even though ACO algorithms were originally introduced for the application to discrete optimization problems,the class of ACO algorithms also comprises meth-ods for the application to problems arising in networks,such as routing and load balancing(see,for example,[44]),and continuous optimization problems(see, for example,[159]).In Sect.2.3we will shortly deal with ACO algorithms for continuous optimization.Swarm Intelligence in Optimization47 Nest Food(a)All ants are in the nest.There isno pheromone in the environment ity,50%of the ants take the shortpath(see the circles),and50%takethe long path to the food source(seethe rhombs)short path have arrived earlier at the food source.Therefore,when re-turning,the probability that they again take the short path is higher short path receives,in probabil-ity,a stronger reinforcement,and the probability of taking this path grows.Finally,due to the evapora-tion of the pheromone on the long path,the whole colony will,in prob-ability,use the short pathFig.2.An experimental setting that demonstrates the shortest pathfinding ca-pability of ant colonies.Between the ants’nest and the only food source exist two paths of different lengths.In the four graphics,the pheromone trails are shown as dashed lines whose thickness indicates the trails’strengthcanfind shortest paths between food sources and their nest.When searching for food,ants initially explore the area surrounding their nest in a random manner.While moving,ants leave a chemical pheromone trail on the ground. Ants can smell pheromone.When choosing their way,they tend to choose,in probability,paths marked by strong pheromone concentrations.As soon as an antfinds a food source,it evaluates the quantity and the quality of the food and carries some of it back to the nest.During the return trip,the quantity of pheromone that an ant leaves on the ground may depend on the quantity and quality of the food.The pheromone trails will guide other ants to the food source.It has been shown in[42]that the indirect communication between the ants via pheromone trails—known as stigmergy[70]—enables them tofind shortest paths between their nest and food sources.This is explained in an idealized setting in Fig.2.As afirst step towards an algorithm for discrete optimization we present in the following a discretized and simplified model of the phenomenon explained in Fig.2.After presenting the model we will outline the differences between the model and the behavior of real ants.The considered model consists of a48 C.Blum and X.Ligraph G=(V,E),where V consists of two nodes,namely v s(representing the nest of the ants)and v d(representing the food source).Furthermore,E consists of two links,namely e1and e2,between v s and v d.To e1we assign a length of l1,and to e2a length of l2such that l2>l1.In other words,e1 represents the short path between v s and v d,and e2represents the long path. Real ants deposit pheromone on the paths on which they move.Thus,the chemical pheromone trails are modeled as follows.We introduce an artificial pheromone valueτi for each of the two links e i,i=1,2.Such a value indicates the strength of the pheromone trail on the corresponding path.Finally,we introduce n a artificial ants.Each ant behaves as follows:Starting from v s(i.e., the nest),an ant chooses with probabilityp i=τiτ1+τ2,i=1,2,(1)between path e1and path e2for reaching the food source v d.Obviously,if τ1>τ2,the probability of choosing e1is higher,and vice versa.For returning from v d to v s,an ant uses the same path as it chose to reach v d,4and it changes the artificial pheromone value associated with the used edge.In more detail,having chosen edge e i an ant changes the artificial pheromone valueτias follows:τi←τi+Ql i,(2)where the positive constant Q is a parameter of the model.In other words, the amount of artificial pheromone that is added depends on the length of the chosen path:the shorter the path,the higher the amount of added pheromone.The foraging of an ant colony is in this model iteratively simulated as follows:At each step(or iteration)all the ants are initially placed in node v s.Then,each ant moves from v s to v d as outlined above.As mentioned in the caption of Fig.2(d),in nature the deposited pheromone is subject to an evaporation over time.We simulate this pheromone evaporation in the artificial model as follows:τi←(1−ρ)·τi,i=1,2(3) The parameterρ∈(0,1]is a parameter that regulates the pheromone evap-oration.Finally,all ants conduct their return trip and reinforce their chosen path as outlined above.We implemented this system and conducted simulations with the following settings:l1=1,l2=2,Q=1.The two pheromone values were initialized to0.5each.Note that in our artificial system we cannot start with artificial pheromone values of0.This would lead to a division by0in Eq.1.The results 4Note that this can be enforced because the setting is symmetric,i.e.,the choice of a path for moving from v s to v d is equivalent to the choice of a path for moving from v d to v s.Swarm Intelligence in Optimization49 0 0.20.40.60.810 50 100150% o f a n t s u s i n g t h e s h o r t p a t h iteration (a)Colony size:10ants 0 0.2 0.4 0.6 0.8 1 0 50 100 150% o f a n t s u s i n g t h e s h o r t p a t h iteration (b)Colony size:100antsFig.3.Results of 100independent runs (error bars show the standard deviation for each 5th iteration).The x-axis shows the iterations,and the y-axis the percentage of the ants using the short pathof our simulations are shown in Fig.3.They clearly show that over time the artificial colony of ants converges to the short path,i.e.,after some time all ants use the short path.In the case of 10ants (i.e.,n a =10,Fig.3(a))the random fluctuations are bigger than in the case of 100ants (Fig.3(b)).This indicates that the shortest path finding capability of ant colonies results from a cooperation between the ants.The main differences between the behavior of the real ants and the behav-ior of the artificial ants in our model are as follows:1.While real ants move in their environment in an asynchronous way,the artificial ants are synchronized,i.e.,at each iteration of the simulated system,each of the artificial ants moves from the nest to the food source and follows the same path back.2.While real ants leave pheromone on the ground whenever they move,artificial ants only deposit artificial pheromone on their way back to the nest.3.The foraging behavior of real ants is based on an implicit evaluation of a solution (i.e.,a path from the nest to the food source).By implicit solution evaluation we mean the fact that shorter paths will be completed earlier than longer ones,and therefore they will receive pheromone reinforcement more quickly.In contrast,the artificial ants evaluate a solution with re-spect to some quality measure which is used to determine the strength of the pheromone reinforcement that the ants perform during their return trip to the nest.Ant System for the TSP:The First ACO AlgorithmThe model that we used in the previous section to simulate the foraging behavior of real ants in the setting of Fig.2cannot directly be applied to CO problems.This is because we associated pheromone values directly with50 C.Blum and X.Lisolutions to the problem(i.e.,one parameter for the short path,and one pa-rameter for the long path).This way of modeling implies that the solutions to the considered problem are already known.However,in combinatorial op-timization we intend tofind an unknown optimal solution.Thus,when CO problems are considered,pheromone values are associated with solution com-ponents instead.Solution components are the units from which solutions to the tackled problem are assembled.Generally,the set of solution components is expected to befinite and of moderate size.As an example we present the first ACO algorithm,called Ant System(AS)[46,51],applied to the TSP, which we mentioned in the introduction and which we define in more detail in the following:Definition1.In the TSP is given a completely connected,undirected graph G=(V,E)with edge weights.The nodes V of this graph represent the cities, and the edge weights represent the distances between the cities.The goal is to find a closed path in G that contains each node exactly once(henceforth called a tour)and whose length is minimal.Thus,the search space S consists of all tours in G.The objective function value f(s)of a tour s∈S is defined as the sum of the edge weights of the edges that are in s.Concerning the AS approach,the edges of the given TSP graph can be considered solution components,i.e.,for each e i,j is introduced a pheromone valueτi,j.The task of each ant consists in the construction of a feasible TSP solution,i.e.,a feasible tour.In other words,the notion of task of an ant changes from“choosing a path from the nest to the food source”to“con-structing a feasible solution to the tackled optimization problem”.Note that with this change of task,the notions of nest and food source lose their meaning.Each ant constructs a solution as follows.First,one of the nodes of the TSP graph is randomly chosen as start node.Then,the ant builds a tour in the TSP graph by moving in each construction step from its current node(i.e., the city in which it is located)to another node which it has not visited yet. At each step the traversed edge is added to the solution under construction. When no unvisited nodes are left the ant closes the tour by moving from her current node to the node in which it started the solution construction.This way of constructing a solution implies that an ant has a memory T to store the already-visited nodes.Each solution construction step is performed as follows. Assuming the ant to be in node v i,the subsequent construction step is done with probabilityp(e i,j)=τi,j{k∈{1,...,|V|}|v k/∈T}τi,k,∀j∈{1,...,|V|},v j/∈T.(4)Once all ants of the colony have completed the construction of their solution, pheromone evaporation is performed as follows:Swarm Intelligence in Optimization51Fig.4.The ACO frameworkτi,j←(1−ρ)·τi,j,∀τi,j∈T(5) Then the ants perform their return trip.Hereby,an ant—having constructed a solution s—performs for each e i,j∈s the following pheromone deposit:τi,j←τi,j+Qf(s),(6)where Q is again a positive constant and f(s)is the objective function value of the solution s.As explained in the previous section,the system is iterated—applying n a ants per iteration—until a stopping condition(e.g.,a time limit) is satisfied.Even though the AS algorithm has proved that the ants’foraging behavior can be transferred into an algorithm for discrete optimization,it gas generally been found to be inferior to state-of-the-art algorithms.Therefore,over the years several extensions and improvements of the original AS algorithm were introduced.They are all covered by the definition of the ACO framework, which we will outline in the following.2.2Ant Colony Optimization:A General DescriptionThe ACO framework,as we know it today,wasfirst defined by Dorigo and colleagues in1999[48].The recent book by Dorigo and St¨u tzle gives a more comprehensive description[52].The definition of the ACO framework covers most—if not all—existing ACO variants for discrete optimization problems. In the following,we give a general description of this framework.The basic way of working of an ACO algorithm is graphically shown in Fig.4.Given a CO problem to be solved,onefirst has to derive afinite set C of solution components which are used to assemble solutions to the CO problem. Second,one has to define a set of pheromone values T.This set of values52 C.Blum and X.Liis commonly called the pheromone model,which is—seen from a technicalpoint of view—a parameterized probabilistic model.The pheromone modelis one of the central components of ACO.The pheromone valuesτi∈T are usually associated with solution components.5The pheromone model is usedto probabilistically generate solutions to the problem under consideration byassembling them from the set of solution components.In general,the ACOapproach attempts to solve an optimization problem by iterating the followingtwo steps:•candidate solutions are constructed using a pheromone model,that is,a parameterized probability distribution over the solution space;•the candidate solutions are used to modify the pheromone values in a way that is deemed to bias future sampling towards high-quality solutions.The pheromone update aims to concentrate the search in regions of the search space containing high-quality solutions.It implicitly assumes that good solutions consist of good solution components.In the following we give a more detailed description of solution constructionand pheromone update.Solution ConstructionArtificial ants can be regarded as probabilistic constructive heuristics that as-semble solutions as sequences of solution components.Thefinite set of solutioncomponents C={c1,...,c n}is hereby derived from the discrete optimization problem under consideration.For example,in the case of AS applied to the TSP(see previous section)each edge of the TSP graph was considered a so-lution component.Each solution construction starts with an empty sequence s= .Then,the current sequence s is at each construction step extended by adding a feasible solution component from the set N(s)⊆C\s.6The specification of N(s)depends on the solution construction mechanism.In the example of AS applied to the TSP(see previous section)the solution con-struction mechanism restricted the set of traversable edges to the ones that connected the ants’current node to unvisited nodes.The choice of a solution component from N(s)is at each construction step performed probabilistically with respect to the pheromone model.In most ACO algorithms the respective probabilities—also called the transition probabilities—are defined as follows:p(c i|s)=[τi]α·[η(c i)]βc j∈N(s)[τj]α·[η(c j)]β,∀c i∈N(s),(7)5Note that the description of ACO as given for example in[48]allows pheromone values also to be associated with links between solution components.However, for the purpose of this introduction it is sufficient to assume pheromone values associated with components.6Note that for this set operation the sequence s is regarded as an ordered set.whereηis an optional weighting function,that is,a function that,sometimesdepending on the current sequence,assigns at each construction step a heuris-tic valueη(c j)to each feasible solution component c j∈N(s).The values that are given by the weighting function are commonly called the heuristic infor-mation.Furthermore,the exponentsαandβare positive parameters whosevalues determine the relation between pheromone information and heuristicinformation.In the previous section’s TSP example,we chose not to use anyweighting functionη,and we setαto1.Pheromone UpdateDifferent ACO variants mainly differ in the update of the pheromone val-ues they apply.In the following,we outline a general pheromone update rulein order to provide the basic idea.This pheromone update rule consists oftwo parts.First,a pheromone evaporation,which uniformly decreases all thepheromone values,is performed.From a practical point of view,pheromoneevaporation is needed to avoid a too-rapid convergence of the algorithm to-wards a suboptimal region.It implements a useful form of forgetting,favoringthe exploration of new areas in the search space.Second,one or more solu-tions from the current and/or from earlier iterations are used to increase thevalues of pheromone trail parameters on solution components that are part ofthese solutions:w s·F(s),(8)τi←(1−ρ)·τi+ρ·{s∈S upd|c i∈s}for i=1,...,n.S upd denotes the set of solutions that are used for the up-date.Furthermore,ρ∈(0,1]is a parameter called evaporation rate,andF:S→I R+is a so-called quality function such that f(s)<f(s )⇒F(s)≥F(s ),∀s=s ∈S.In other words,if the objective function value of a solu-tion s is better than the objective function value of a solution s ,the quality of solution s will be at least as high as the quality of solution s .Equation (8)also allows an additional weighting of the quality function,i.e.,w s∈I R+ denotes the weight of a solution s.Instantiations of this update rule are obtained by different specifications of S upd and by different weight settings.In most cases,S upd is composed of some of the solutions generated in the respective iteration(henceforth denoted by S iter)and the best solution found since the start of the algorithm(henceforth denoted by s bs).Solution s bs is often called the best-so-far solution.A well-known example is the AS-update rule,that is,the update rule of AS(see also Sect.2.1).The AS-update rule,which is well known due to the fact that AS was thefirst ACO algorithm to be proposed in the literature,is obtained from update rule(8)by setting S upd←S iter and w s=1,∀s∈S upd.An example of a pheromone update rule that is more used in practice is the IB-update rule(where IB stands for iteration-best).The IB-update rule isTable1.A selection of ACO variantsACO variant Authors Main referenceDorigo,Maniezzo,and Colorni[51] Rank-based AS(RAS)Bullnheimer,Hartl,and Strauss[26]MAX–MIN Ant System(MM AS)St¨u tzle and Hoos[164]Ant Colony System(ACS)Dorigo and Gambardella[49]Hyper-Cube Framework(HCF)Blum and Dorigo[16]given by S upd←{s ib=argmax{F(s)|s∈S iter}}with w s ib=1,that is, by choosing only the best solution generated in the respective iteration for updating the pheromone values.This solution,denoted by s ib,is weighted by1.The IB-update rule introduces a much stronger bias towards the good solutions found than the AS-update rule.However,this increases the danger of premature convergence.An even stronger bias is introduced by the BS-update rule,where BS refers to the use of the best-so-far solution s bs.In this case,S upd is set to{s bs}and s bs is weighted by1,that is,w sbs =1.In practice,ACO algorithms that use variations of the IB-update or the BS-update rule and that additionally include mechanisms to avoid premature convergence achieve better results than algorithms that use the AS-update rule.Examples are given in the following section.Well-Performing ACO VariantsEven though the original AS algorithm achieved encouraging results for the TSP problem,it was found to be inferior to state-of-the-art algorithms for the TSP as well as for other CO problems.Therefore,several extensions and improvements of the original AS algorithm were introduced over the years. An overview is provided in Table1.These ACO variants mostly differ in the pheromone update rule that is applied.In addition to these ACO variants,the ACO community has developed additional algorithmic features for improving the search process performed by ACO algorithms.A prominent example is the so-called candidate list strategy, which is a mechanism to restrict the number of available choices at each solution construction ually,this restriction applies to a number of the best choices with respect to their transition probabilities(see Eq.7).For example,in the case of the application of ACS(see Table1)to the TSP, the restriction to the closest cities at each construction step both improved thefinal solution quality and led to a significant speedup of the algorithm (see[61]).The reasons for this are as follows:First,in order to construct high-quality solutions it is often enough to consider only the“promising”choices at each construction step.Second,to consider fewer choices at each construction step speeds up the solution construction process,because the。

数据分析英语试题及答案一、选择题（每题2分，共10分）1. Which of the following is not a common data type in data analysis?A. NumericalB. CategoricalC. TextualD. Binary2. What is the process of transforming raw data into an understandable format called?A. Data cleaningB. Data transformationC. Data miningD. Data visualization3. In data analysis, what does the term "variance" refer to?A. The average of the data pointsB. The spread of the data points around the meanC. The sum of the data pointsD. The highest value in the data set4. Which statistical measure is used to determine the central tendency of a data set?A. ModeB. MedianC. MeanD. All of the above5. What is the purpose of using a correlation coefficient in data analysis?A. To measure the strength and direction of a linear relationship between two variablesB. To calculate the mean of the data pointsC. To identify outliers in the data setD. To predict future data points二、填空题（每题2分，共10分）6. The process of identifying and correcting (or removing) errors and inconsistencies in data is known as ________.7. A type of data that can be ordered or ranked is called________ data.8. The ________ is a statistical measure that shows the average of a data set.9. A ________ is a graphical representation of data that uses bars to show comparisons among categories.10. When two variables move in opposite directions, the correlation between them is ________.三、简答题（每题5分，共20分）11. Explain the difference between descriptive andinferential statistics.12. What is the significance of a p-value in hypothesis testing?13. Describe the concept of data normalization and its importance in data analysis.14. How can data visualization help in understanding complex data sets?四、计算题（每题10分，共20分）15. Given a data set with the following values: 10, 12, 15, 18, 20, calculate the mean and standard deviation.16. If a data analyst wants to compare the performance of two different marketing campaigns, what type of statistical test might they use and why?五、案例分析题（每题15分，共30分）17. A company wants to analyze the sales data of its products over the last year. What steps should the data analyst take to prepare the data for analysis?18. Discuss the ethical considerations a data analyst should keep in mind when handling sensitive customer data.答案：一、选择题1. D2. B3. B4. D5. A二、填空题6. Data cleaning7. Ordinal8. Mean9. Bar chart10. Negative三、简答题11. Descriptive statistics summarize and describe thefeatures of a data set, while inferential statistics make predictions or inferences about a population based on a sample.12. A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A small p-value suggests that the observed data is unlikely under the null hypothesis, leading to its rejection.13. Data normalization is the process of scaling data to a common scale. It is important because it allows formeaningful comparisons between variables and can improve the performance of certain algorithms.14. Data visualization can help in understanding complex data sets by providing a visual representation of the data, making it easier to identify patterns, trends, and outliers.四、计算题15. Mean = (10 + 12 + 15 + 18 + 20) / 5 = 14, Standard Deviation = √[(Σ(xi - mean)^2) / N] = √[(10 + 4 + 1 + 16 + 36) / 5] = √52 / 5 ≈ 3.816. A t-test or ANOVA might be used to compare the means ofthe two campaigns, as these tests can determine if there is a statistically significant difference between the groups.五、案例分析题17. The data analyst should first clean the data by removing any errors or inconsistencies. Then, they should transformthe data into a suitable format for analysis, such ascreating a time series for monthly sales. They might also normalize the data if necessary and perform exploratory data analysis to identify any patterns or trends.18. A data analyst should ensure the confidentiality andprivacy of customer data, comply with relevant data protection laws, and obtain consent where required. They should also be transparent about how the data will be used and take steps to prevent any potential misuse of the data.。

学术英语写作_东南大学中国大学mooc课后章节答案期末考试题库2023年1.Sequence markersin English are a certain group of items that link sentencestogether into a larger unit of _______.参考答案:discourse2.When the author uses “Methodology” as the title of this section, he/she needsto provide the_______for how the experiment was designed and conducted for the current study.参考答案:rationales3.“Shopping malls are wonderful places.” is a weak thesis statemen t in that itrestates conventional wisdom.参考答案:错误4.One way is to examine one thing thoroughly and then examine the other. Thismethod is called _____ comparison or contrast.参考答案:block5. A strong thesis statement makes a claim that offers some point about thesignificance ofour evidence that requires further argumentation.参考答案:正确6.Strictly speaking, the purpose of _______ is to show similarities while contrastis used to show differences.参考答案:comparison7.In the elements of the Method Section, ______ refer to the precautions taken tomake sure that the data are valid.参考答案:Restrictions8.Paraphrasing is to explain the original ideas of a passage, chapter, article orbook in fewer words.参考答案:错误9.To avoid plagiarism and conform to academic ethics, you need to providereference to every citation and check for plagiarism before submitting your paper.参考答案:正确10.Which of the following tenses could be used to discuss previously publishedworks which is generally considered to be established knowledge?参考答案:The present simple11.Which of the following tenses could be used when the year of publication isstated within the main sentence.参考答案:The past tense12.Which of the following reporting verbs could be categorized as strong?参考答案:reject13.Reporting verbs can indicate either参考答案:All of the options.14.What is included in a complex model of literature review but NOT included ina simple one?参考答案:Research question15.You can choose any information or data from the graphwhen you describeagraph.参考答案:错误16.Redundancy, raising a totally new point, understatement, anticlimax are thetypical issues in structuring the Conclusion.参考答案:错误17.Unlike the Abstract and Introduction，the Conclusions section does providebackground details.参考答案:错误18. 1. The register of the following discourse is____.I, James Bond, take you, JudithKroll, to be my wife, to have and to hold from this day forward, for better, for worse, for richer, for poorer, in sickness and in health, to love and to cherish, till death us do part, according to God's holy law, in the presence of God Imake thisvow.参考答案:static19.What should you do when you write a literature review?参考答案:Include a critical analysis of various opinions from credible sources.20.To end the Discussion section which also has a Conclusion, the author mayadmit what she/he has not been able to do and as a consequence cannotprovide conclusions on.参考答案:正确21.If the authors are to announce the results of their study, they can just statethe results without saying “we think that…”参考答案:正确22.You can use “he or she” to avoid gender discrimination every time when youmean “everyone”.参考答案:错误23.When writing an academic paper, you should nominalize as many words aspossible.参考答案:错误24.Beginning the Discussion section an author would possibly refer back topapers he/she cited in the Review of the Literature.参考答案:正确25.“The U.S. constitution” is not a good title for an essay, because it is toogeneral.参考答案:正确26.“What implications are revealed in my results?” is a question to considerafter drafting the Discussion section.参考答案:错误27.The process paragraphs are usually developed step by step in a chronologicalor logical sequence.参考答案:正确28.The Results Section can only be presented both in diagrams or graphs.参考答案:错误29.The Method Section can be called Materials and Methods in naturalsciences.参考答案:正确30.The Method Section is considered the most important section becauseitappears in the middle of a research paper.参考答案:错误31.Nominalization is the process of converting simple nouns within a sentenceto complex nouns.参考答案:错误32.If you can discuss a cause without having to discuss any other causes thenvery likely it is an indirect cause.参考答案:错误33.Oversimplification should be avoided because many problems have complexcauses and complex effects.参考答案:正确34.First personal pronouns can never be used in academic paper.参考答案:错误35. A weak thesis statement either makes no claim or makes a claim that doesnot need proving.参考答案:正确36.One of the key elements of the Conclusion section is a final judgment on theimportance and significance of the findings in terms of their implications and impact, along with possible applications to other areas.参考答案:正确37.Effects are the consequences of an event and they respond to the question“Why did that event happen?”参考答案:错误。

2016年郑州大学357英语翻译基础考研题一、Sentence translation（TransIate the following text into Chinese (25 points)）1. It is not known how rare this resemblance is, or whether it is most often seen in inclusions of silicates such as garnet, whose crystallography is generally somewhat similar to that of diamond; but when present, the resemblance is regarded as compelling evidence that the diamonds and inclusions are truly cogenetic.4generated the tensions that ultimately led to the American Revolution.【答案】现在，人们尚不知这种类似有多么罕见，也不知道这种类似是否最常见于像石榴石一样的硅酸盐内含物中——此类内含物的晶体结构通常和金刚石类似。

但这种类似一旦存在，即可视作金刚石与内含物是同源的强力证据。

【解析】本句的句子既长，专有名词又多，从句层出不穷，故而难懂。

在本句中出现的专有名词中，除了silicates硅酸盐这个单词常常出现于理科文章中，需要记忆之外，其他单词阅读现场简单处理一番既可：inclusions可以猜出是被包含的物质；garnet石榴石；crystallography只要能从词头推出这个单词与晶体有关即可。

【难度系数】4【分值】52. A long-held view of the history of the English colonies that became the United States has been that England’s policy toward these colonies before 1763 was dictated by commercial interests and that a change to more imperial policy, dominated by expansionist militarist objective, generated the tensions that ultimately led to the American Revolution.【答案】关于这块后来独立为美国的英国殖民地，人们长久以来的看法是：英国在1763年以前对于该殖民地的政策为经济利益所支配；后来，出于扩张主义的军事目标，英国的政策变得更倾向于帝国主义—这种改变导致了冲突的产生，并最终引发了美国革命。

frisch's methodFrisch's method refers to a technique used in economics, particularly in the field of macroeconomics, to estimatethe natural rate of unemployment. The natural rate of unemployment is the rate of unemployment that exists when the labor market is in equilibrium, with the economy operating at full potential. Frisch's method is named after the Norwegian economist Ragnar Frisch, who was a Nobel laureate in Economics.The method involves analyzing the relationship between unemployment and other macroeconomic variables, such as inflation, labor force participation, and productivity. It aims to determine the underlying, or natural, rate of unemployment that is not influenced by temporary factors or cyclical fluctuations in the economy.One aspect of Frisch's method involves usingstatistical techniques to filter out short-termfluctuations in unemployment data and identify the long-term trend. This can involve applying time series analysis and econometric modeling to the data.Another aspect of Frisch's method involves considering the impact of structural changes in the economy on the natural rate of unemployment. This includes factors such as technological advancements, changes in labor market institutions, and shifts in the demographic composition of the workforce.Furthermore, Frisch's method takes into account the concept of hysteresis, which suggests that the level of unemployment at any given time can influence the future path of unemployment. In other words, prolonged periods of high unemployment can have lasting effects on the natural rate of unemployment.It's important to note that while Frisch's method provides a framework for estimating the natural rate of unemployment, it is not without its limitations. The method relies on various assumptions about the behavior of economic variables and the structure of the labor market,and the estimates it produces are subject to uncertainty.In conclusion, Frisch's method is a valuable tool in macroeconomic analysis for understanding the natural rateof unemployment. By considering a range of economic factors and employing statistical techniques, it helps economists and policymakers gain insights into the underlying dynamics of the labor market. However, like any economic methodology, it should be used with caution and in conjunction withother indicators and models to form a comprehensive understanding of the economy.。