A Heuristic Approach to Scoring Gene Clustering Algorithms
Advice to a Young Scientist 非英语专业研究生英语读写教程
Advice to a Young Scientist 非英语专业研究生英语读写教程As a young scientist。
it'XXX。
and there is no turning back。
I urge you to pursue your chosen path as far as you can。
especially if you are a young person just starting out.One of the most XXX。
and it's what will keep you motivated and engaged in your work。
Always ask ns。
challenge ns。
and seek out new knowledge.Another key to success in science is to be persistent。
Science is not always easy。
and there will be times when you face setbacks and failures。
But if you are persistent and keep pushing forward。
you will XXX.n is also essential in science。
No one person has all the answers。
and working with others can help you to see problems from different perspectives and come up with XXX.Finally。
it'XXX that science is not just about the pursuit of knowledge - it's also about making a positive impact on the world。
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。
GRE阅读高频机经原文及答案：雄性动物养孩子
GRE阅读高频机经原文及答案：雄性动物养孩子想必大家在备考gre考试的时候，最喜欢的就是吸取新鲜的考试真题，这样对自己的能力也是一个考验，最能检验自己目前的能力在什么水平，今天小编为大家带来的就是GRE阅读高频机经原文及答案：雄性动物养孩子!GRE阅读高频机经原文及答案：雄性动物养孩子雄性动物养孩子The main exception to primate researchers’ general pattern of ignoring interactions between males and infants has been the study of male care among monogamous primates. It has been known for over 200 years, ever since a zoologist-illustrator named George Edwards decided to watch the behavior of pet marmosets in a London garden, that among certain species of New World monkeys males contributed direct care for infants that equaled or exceeded that given by females. Mothers among marmosets and tamarins typically give birth to twins, as often as twice a year, and to ease the female in her staggering reproductive burden the male carries the infant at all times except when the mother is actually suckling it. It was assumed by Kleiman that monogamy and male confidence of paternity were essential to the evolution of such care, and at the same time, it was assumed by Symons and others that monogamy among primates must be fairly rare.Recent findings, however, make it necessary to reverse this picture. First of all, monogamy among primates turns out to be rather more frequent than previously believed (either obligate or facultive monogamy can be documented for some 17-20 percent of extant primates) and second, male care turns out to be far more extensive than previously thought and notnecessarily confined to monogamous species, according to Hrdy. Whereas previously, it was assumed that monogamy and male certainty of paternity facilitated the evolution of male care, it now seems appropriate to consider the alternative possibility, whether the extraordinary capacity of male primates to look out for the fates of infants did not in some way pre-adapt members of this order for the sort of close, long-term relationships between males and females that, under some ecological circumstances, leads to monogamy! Either scenario could be true. The point is that on the basis of present knowledge there is no reason to view male care as a restricted or specialized phenomenon. In sum, though it remains true that mothers among virtually all primates devote more time and/or energy to rearing infants than do males, males nonetheless play a more varied and critical role in infant survival than is generally realized.1. The author of the passage mentions the work of Hrdy primarily toA. present an instance of an untenable assumptionB. illustrate a consensus by citing a representative claimC. provide evidence that challenges a beliefD. highlight a corollary of a widespread viewE. offer data that help resolve a debate2. According to the passage, the evolutionary relationship between male care and monogamy isA. incontestableB. immutableC. uncommonD. immaterialE. uncertain3. The author of the passage suggests that it is “appropriateto consider the alternative possibility” because the previous viewA. results in a contradictionB. depends on problematic dataC. appears less definite given certain factsD. conflates two distinct phenomenaE. overlooks a causal relationship between correlated phenomena4. Which of the following statements, if true, would pose the grea test challenge to “the alternative possibility”?A. The number of primate species in which male care of infants is exhibited is greater than the number of primate species that practice monogamy.B. Male care of infants among primates can be seen earlier in the evolutionary record than can monogamy among primates.C. Monogamous relationships among primates can be found in species living in a variety of physical environments.D. Most primate species that practice monogamy do not show any evidence of male care of infants.E. Male care of infants can be observed in some primate species that lack male confidence of paternity.答案：CECAGRE阅读容易被你所忽视的三类词汇perspective 透视画法;观点，方法;前景，远景prospect 前景，景色;前途;勘探，寻找appreciate 理解，认识，意识到;欣赏;感激elaborate v.&adj. 精心制作，详细描述;精心制作的address v.从事，忙于;n. 演讲appropriate v. 拨给(资金)，盗用/ adj.合适的strain n. 血统，品系，菌株;紧张，张力;v.扭伤，拉紧article n. 物品，商品intrigue v.&n 激发兴趣;密谋;阴谋intriguing adj. 激发兴趣的assume v. 承担，担任;假装;假设bark n. 树皮;犬吠bill n.议案，法案;鸟嘴;账单champion vt. 支持，拥护;n.冠军aging n.老化，陈酿complex n. 综合体 adj.复杂的concern n. 公司(垄断组织“康采恩”就是它的音译)attribute v.&n 归因于;特征，属性default n.&v. 不履行;违约;拖欠;默认(值)drill vt. 钻(孔);训练，操练exploit v. 开发，利用n. 功绩fair n. 集市，交易会;adj.公平的，美丽的 adv.公平地，直接地fairly adv. 相当地，公平地game n. 猎物，野味fashion vt. 形成，塑造 n.时尚，方式inviting adj. 引人注目的，吸引人的alternate v.&adj. 交替，轮流; 交替的alternating adj.交互的，交替的alternative n.&adj. 可供选择的方案(option);选择性的(optional) GRE阅读如何把握作者写作态度有利于解题1.社会现象.作者反对将社会现象拔高到阶级、政治、意识形态或超人性的高度，反对各种左派思想、革命主张和马克思主义。
机器学习第六章-3
6.11.1 Conditional Independence
• Definition of conditional independence
– X is conditionally independent of Y given Z if the probability distribution governing X is independent of the value of Y given a value for Z, that is , if
ML-BAYSEIAN LEARNING Dr. Ding Yuxin 9
6.11.5 Gradient Ascent Training of Bayesian Networks
• Let wijk denote a single entry in the conditional probability table that the Yi will take on yij given that its immediate parents Ui take on the values given by uik. • For example, if in figure 6-3 wijk is the top right entry, then Yi is the variable Campfire, Ui is the tuple of its parents <Storm, BusTourGroup>, yij=True，and uik=<False, False>
ML-BAYSEIAN LEARNING Dr. Ding Yuxin 2
• A Bayesian belief network describes the (joint) probability distribution over a set of variables
Heuristic methods for vehicle routing problem with time windows
Heuristic methods for vehicle routing problem with time windowsK.C.Tan a,*,L.H.Lee b ,Q.L.Zhu a ,K.Ou aaDepartment of Electrical and Computer Engineering,National University of Singapore,10Kent Ridge Crescent,Singapore 119260bDepartment of Industrial and Systems Engineering,National University of Singapore,10Kent Ridge Crescent,Singapore 119260Received 7September 2000;accepted 20December 2000AbstractThis paper documents our investigation into various heuristic methods to solve the vehicle routing problem with time windows (VRPTW)to near optimal solutions.The objective of the VRPTW is to serve a number of customers within prede®ned time windows at minimum cost (in terms of distance travelled),without violating the capacity and total trip time constraints for each binatorial optimisation problems of this kind are non-polynomial-hard (NP-hard)and are best solved by heuristics.The heuristics we are exploring here are mainly third-generation arti®cial intelligent (AI)algorithms,namely simulated annealing (SA),Tabu search (TS)and genetic algorithm (GA).Based on the original SA theory proposed by Kirkpatrick and the work by Thangiah,we update the cooling scheme and develop a fast and ef®cient SA heuristic.One of the variants of Glover's TS,strict Tabu,is evaluated and ®rst used for VRPTW,with the help of both recency and frequency measures.Our GA implementation,unlike Thangiah's genetic sectoring heuristic,uses intuitive integer string representation and incorporates several new crossover operations and other advanced techniques such as hybrid hill-climbing and adaptive mutation scheme.We applied each of the heuristics developed to Solomon's 56VRPTW 100-customer instances,and yielded 18solutions better than or equivalent to the best solution ever published for these problems.This paper is also among the ®rst to document the implementation of all the three advanced AI methods for VRPTW,together with their comprehensive results.q 2001Elsevier Science Ltd.All rights reserved.Keywords :Vehicle routing problem;Time windows;Combinatorial optimisation;Heuristics;Simulated annealing;Tabu search;Genetic algorithm1.IntroductionLogistics may be de®ned as `the provision of goods and services from a supply point to various demand points'[2].A complete logistic system involves transporting raw materials from a number of suppliers or vendors,delivering them to the factory plant for manufacturing or processing,movement of the products to various warehouses or depots and eventually distribution to customers.Both the supply and distribution procedures require effective transportation management.Good transportation management can practi-cally save a private company a considerable portion of its total distribution cost.Potential cost savings constitute:lowered trucking cost due to more optimal routes and shorter distances,reduced in-house space and related costs,less penalty incurred due to untimely delivery.One of the most signi®cant measures of transportation manage-ment is effective vehicle routing.Optimising of routes for vehicles given various constraints is the origin of vehicle routing problems (VRPs).Fig.1describes a typical VRP.The solution includes tworoutes:Depot !7!8!9!11!12!Depot ;Depot !2!3!1!4!5!6!10!Depot :Sometimes the depot is denoted as 0.The vehicle routing problem with time windows (VRPTW)is a well-known non-polynomial-hard (NP-hard)problem,which is an extension of normal VRPs,encountered very frequently in making decisions about the distribution of goods and services.The problem involves a ¯eet of vehicles set off from a depot to serve a number of customers,at different geographic locations,with various demands and within speci®c time windows before returning to the depot.The objective of the problem is to ®nd routes for the vehicles to serve all the customers at a minimal cost (in terms of travel distance,etc.)without violating the capacity and travel time constraints of the vehicles and the time window constraints set by the cus-tomers.To date,there is no consistent optimising algorithm that solves the problem exactly using mathematical programming.Instead,many heuristic methods have been designed to solve VRPTW to near optima.In Marshall Fisher's survey [4],he categorised vehicle routing methods into three generations.The ®rst generation was simple heuristics developed in the 1960s and 1970s,which were mainly based on local search or sweep.Arti®cial Intelligence in Engineering 15(2001)281±2950954-1810/01/$-see front matter q 2001Elsevier Science Ltd.All rights reserved.PII:S0954-1810(01)00005-X/locate/aieng*Corresponding author.Since these earlier studies were not well documented,it is hard to compare the results they obtained 30years ago with the more recent solutions.The second genera-tion,mathematical programming based heuristics,were near-optimisation algorithms that are very different from normal heuristics.These include the generalised assign-ment problems and set partitioning to approximate the VRP.Their results are usually superior to that of simple heuristics [4,20].In fact for linear objective functions,some of these techniques are able to stretch to the optima.The third generation,or the one that is currently undergoing heavy research is exact optimisation algorithms and arti®cial intelligence methods.Among these,the most successful optimisation algorithms are K-tree,Lagrangian relaxation,etc.,while the top AI repre-sentatives in VRPTW are simulated annealing (SA),Tabu search (TS)and genetic algorithms (GAs).These algorithms are discussed brie¯y as follows:Kolen et al.[10]presented the method of branch and bound ,which is among the ®rst optimisation algorithms for VRPTW.The method calculates lower bounds using dynamic programming and state space relaxation.Branching decisions are taken on route-customer alloca-tions.The method has successfully solved the problem involving 15customers.Fisher [3]introduces an opti-misation algorithm in which lower bounds are obtained from a relaxation based on a generalisation of spanning trees called K -trees .Capacity constraints are handled by introducing a constraint requiring that some set S ,S ,C ;of the set of customers must be served by at least k (S )vehicles.This constraint is Lagrangian relaxed and the resulting problem is still a K -tree problem with modi®ed arc costs.Time window constraints are treated similarly.A constraint,requiring that not all arcs in a time violating path can be used,is generated and Lagrangian relaxed.The method has solved some of the 100-customer Solomon benchmark problems [18].One of the effective approaches at present is the shortest path composition .The fundamental observation is,the only constraint which `links'the vehicles together is that each customer in the network must be visited only once.The problem that consists of the rest of the constraints is an elementary shortest path problem with time windows and capacity constraints (ESPPTWCC)for each vehicle.Although this problem is strictly NP-hard,there are a few ef®cient dynamic programming algorithms for the slightly relaxed programs.Two decompositions have been investi-gated computationally,namely Dantzig ±Wolfe decomposi-tion and variable splitting .Desrochers et al.[25]implemented Dantzig±Wolfe decomposition,and solved up to some of the 100-customer Solomon benchmark problems.Researchers at Technical University of Denmark [9],on the other hand,suggested using variable splitting to solve the VRPTW with similar performance.Thangiah et al.[21]developed a l -interchange local search descent (LSD )method that uses a systematic insertion and swapping of customers between routes,de®ned as l -interchange operators.Due to computation burden,only 1-interchange and 2-interchange are commonly used,which allows up to one or two custo-mers to be inserted or swapped at one time.Although it is a fast algorithm,the performance is poor without the help from other heuristics.SA,®rst proposed by Kirk-patrick [8],searches the solution space by simulating the annealing process in metallurgy.The algorithm jumps to distant location in the search space initially.The step of the jumps is reduced as time goes on or as the temperature `cools'.Eventually,the process will turn into a LSD method.Osman [14]has applied SA to solve the VRP by moving one customer from one route to another or exchanging two customers from two routes.TS is a memory-based search strategy that chooses the best solution contained in N (S )that does not violate certain restrictions that prevent ually,these restrictions are stored as queues in a structure called a Tabu list .Typical restrictions prevent making a move that has been done within the last t iterations,and a solution that has been encountered in the last t iterations is usually forbidden as well.TS stops after a ®xed number of iterations.Gerdreau et al.applied TS using a neighbourhood that can be constructed by moving a single customer from one route to another.Osman and Talliard [14]used a neighbourhood that consists of all solutions obtained from inserting a customer and swapping two customers.Holland developed the GA [7]method that codes the VRPTW solutions in forms of bit strings or chromosomes.The method starts with a population of random chromo-somes.Fitter chromosomes are then selected to undergo a crossover and mutation process,as to produce children which are different from the parents but inherit certain genetic traits from the parents.This process is continued until a ®xed number of generations has been reached orK.C.Tan et al./Arti®cial Intelligence in Engineering 15(2001)281±295282Fig.1.A vehicle routing problem:a single depot VRP with 12customers.Each route starts from depot,visiting customers and ends at depot.the evolution has converged.Thangiah[22]devised a genetic sectoring heuristic with special genetic representation that keeps the polar angle offset in the genes.The algorithm follows a cluster-®rst,route-second philosophy and solved 100-customer Solomon problems to near optima.Prinetto et al.[16]proposed a hybrid GA for the travelling salesman problem(TSP)in which2-opt and Or-opt were incorporated with the GA.Blanton and Wainwright[1]presented two new crossover operators,merge cross#1and merge cross #2,which are superior to traditional crossover operators. Shaw[17]presented large neighbourhood search(LNS), a method in constraint programming,to solve VRPTW. Relatedness plays a very important part in the selection of customer to remove and re-insert into the con®guration using a constraint-based tree search.Shaw applied limited discrepancy search during the tree search to re-insert visits. The results were competitive to those obtained using opera-tions research meta-heuristics.In this paper,we further investigate and develop various advanced AI techniques including SA,TS and GA to effectively solve the VRPTW to near optimal solutions.Based on the original SA theory proposed by Kirkpatrick[8]and the work by Thangiah[21],we update the cooling scheme and develop a fast and ef®-cient SA heuristic.One of the variants of Glover's TS, strict Tabu,is evaluated and®rst used for VRPTW, with the help of both recency and frequency measures. Our GA implementation,unlike Thangiah's genetic sectoring heuristic[21],uses an intuitive integer string representation and incorporates several new crossover operations and other advanced techniques such as hybrid hill-climbing and adaptive mutation scheme. We have tested our heuristics with all56Solomon's VRPTW instances and obtained complete results for these problem sets.There are totally four heuristics tested on the instances:2-interchange method,SA, Tabu and GA.Their average performances are compared with the best-known solutions in the litera-ture.From the result analysis,our TS and GA are already close to the best ways of solving VRPTW. Totally,we found18solutions better than or equivalent to the best-known results.The discussion of results is given in Section8.In this paper,we give a mathema-tical model of VRPTW,followed by the design and implementation of the heuristics.The computational results are presented and discussed in the®nal part of the paper.2.Problem formulationThis section describes the notation and features that are common through this paper.The VRPTW constraints consist of a set of identical vehicles,a central depot node, a set of customer nodes and a network connecting the depot and customers.There are N11customers and K vehicles.The depot node is denoted as customer0.Each arc in the network represents a connection between two nodes and also indicates the direction it travels.Each route starts from the depot,visits customer nodes and then returns to the depot.The number of routes in the network is equal to the number of vehicles used.One vehicle is dedicated to one route.A cost c ij and a travel time t ij are associated with each arc of the network.In Solomon's56VRPTW100-customer instances,all distances are represented by Euclidean distance,and the speed of all vehicles is assumed to be unity.That is,it takes one unit of time to travel one unit of distance. This assumption makes the problem simpler,because numerically the travel cost c ij,the travel time t ij and the Euclidean distance between the customer nodes equal each other.Each customer in the network can be visited only once by one of the vehicles.Every vehicle has the same capacity q k and each customer has a varying demand m i.q k must be greater or equal to the summa-tion of all demands on the route travelled by vehicle k, which means that no vehicles can be overloaded.The time window constraint is denoted by a prede®ned time interval,given an earliest arrival time and latest arrival time.The vehicles must arrive at the customers not later than the latest arrival time,if vehicles arrive earlier than the earliest arrival time,waiting occurs.Each customer also imposes a service time to the route, taking consideration of the loading/unloading time of goods.In Solomon's instances,the service time is assumed to be unique regardless of the load quantity needed to be handled.Vehicles are also supposed to complete their indi-vidual routes within a total route time,which is essentially the time window of the depot.There are three types of principal decision variables in VRPTW.The principal decision variable x ijk i;j[ {0;1;2;¼;N};k[{1;2;¼;K};i±j is1if vehicle k travels from customer i to customer j,and0otherwise. The decision variable t i denotes the time when a vehicle arrives at the customer,and w i denotes the waiting time at node i.The objective is to design a network that satis®es all constraints,at the same time minimising the total travel cost.The model is mathematically formulated below:Principal decision variables:t i arrival time at node iw i wait time at node ix ijk[{0;1};0if there is no arc from node i to node j,and 1otherwise.i±j;i;j[{0;1;2;¼;N}: Parameters:K total number of vehiclesN total number of customersy i any arbitrary real numberd ij Euclidean distance between node i and node jK.C.Tan et al./Arti®cial Intelligence in Engineering15(2001)281±295283c ij cost incurred on arc from node i to jt ij travel time between node i and jm i demand at node iq k capacity of vehicle ke i earliest arrival time at node il i latest arrival time at node if i service time at node ir k maximum route time allowed for vehicle kMinimiseX Ni 0X Nj 0;j±iX Kk 1c ij x ijk 1subject to:X K k 1X Nj 1x ijk#K for i 0 2X N j 1x ijkX Nj 1x jik#1for i 0andk[{1;¼;K}3X K k 1X Nj 0;j±ix ijk 1for i[{1;¼;N} 4X K k 1X Ni 0;i±jx ijk 1for j[{1;¼;N} 5X N i 1m iX Nj 0;j±ix ijk#q k for k[{1;¼;K} 6X N i 0X Nj 0;j±ix ijk t ij1f i1w i #r k for k[{1;¼;K} 7t0 w0 f0 0 8X K k 1X Ni 0;i±jx ijk t i1t ij1f i1w i #t j for j[{1;¼;N}(9)e i# t i1w i #l i for i[{1;¼;N} 10 Formula(1)is the objective function of the problem. Constraint(2)speci®es there are maximum K routes going out of the depot.Eq.(3)makes sure every route starts and ends at the central depot.Eqs.(4)and(5)de®ne that every customer node can be visited only once by one vehicle. Eq.(6)is the capacity constraint.Eq.(7)is the maximum travel time constraint.Constraints(8)±(10)de®ne the time windows.These formulas completely specify the feasible solutions for VRPTW.3.An initial solutionMost heuristic search strategies involve®nding an initial feasible solution and then improving on that solution using local or global optimisation techniques.Here,we make use of the push forward insertion heuristic(PFIH),®rst intro-duced by Solomon[18]in1987as a method to create an initial route con®guration.PFIH is an ef®cient method to insert customers into new routes.The procedure is easy and straightforward.The method tries to insert the customer between all the edges in the current route.It selects the edge that has the lowest addi-tional insertion cost.The feasibility check tests all the constraints including time windows and load capacity. Only feasible insertions will be accepted.When the current route is full,PFIH will start a new route and repeat the procedure until all the customers are ually, PFIH gives a reasonably good feasible solution in terms of the number of vehicles used.This initial number of vehicles provides an upper bound for the number of routes in the solution.PFIH serves the role of constructing route con®guration for VRPTW.It is an ef®cient method to obtain feasible solutions.The detail information can be obtained from Solomon's paper[18].4.Local search with l-interchangeThe effectiveness of any iterative local search method is determined by the ef®ciency of the generation mechanism and the way the neighbourhood is searched.A l-inter-change generation mechanism was introduced by Osman and Christo®des[13]for the capacitated clustering problem. It is based on customer interchange between sets of vehicle routes and has been successfully implemented with a special data structure to other problems by Osman[14],Thangiah [20],etc.The local search procedure is conducted by interchan-ging customer nodes between routes.For a chosen pair of routes,the searching order for the customers to be interchanged needs to be de®ned,either systematically or randomly.In this paper,we only consider the cases l 2;which means that maximum two customer nodes may be interchanged between routes.Based on the number of l,there are totally eight interchange opera-tors are de®ned:(0,1),(1,0),(1,1),(0,2),(2,0),(2,1), (1,2),(2,2).The operator(1,2)on a route pair(R p, R q)indicates a shift of two customers from R q to R p and a shift of one customer from R p to R q.The other operators are de®ned similarly.For a given operator,the customers are considered sequentially along the routes. In both the shift and interchange process,only improved solutions are accepted if the move results in the reduction of the total cost.K.C.Tan et al./Arti®cial Intelligence in Engineering15(2001)281±295 284There are two strategies to select between candidate solutions:1.The®rst-best(FB)strategy will select the®rst solution in N l(S),the neighbourhood of the current solution,that results in a decrease in cost.2.The global-best(GB)strategy will search all solutions in N l(S),where N l(S)means the neighbourhood of current solution under l-interchange operation.GB will select the one,which will result in the maximum decrease in cost.In the following we describe the l-interchange LSD method.LSD starts from an initial feasible solution obtained by the PFIH.The PFIH solution is further improved using the l-interchange mechanism for a given number of itera-tions.The procedure of the l-interchange LSD is shown below.Algorithm1.Local search descent methodLSD-1:Obtain a feasible solution S for the VRPTW using the PFIH.LSD-2:Select a solution S0[N l S :LSD-3:If{C S0 ,C S };thenaccept S0and go to LSD-2,else go to LSD-4.LSD-4:If{neighbourhood of N l(S)has been completely searched:there are no movesthat will result in a lower cost}then go to LSD-5else go to LSD-2.LSD-5:Stop with the LSD solution.The LSD result is dependent on the initial feasible solu-tion.GB usually achieves better results than FB because it keeps track of all the improving moves but incurs more expensive computation time.On the other hand,LSD±FB is a blind search that accepts the FB result.In this paper,we implemented2-interchange GB.5.Simulated annealingSA is a stochastic relaxation technique that®nds its origin in statistical mechanics[11].The SA methodology is analogous to the annealing processing of solids.In order to avoid the meta-stable states produced by quenching, metals are often cooled very slowly,which allows them time to order themselves into stable,structurally strong, low energy con®gurations.This process is called annealing. This analogy can be used in combinatorial optimisation with the states of the solids corresponding to the feasible solu-tion,the energy at each state to the improvement in objec-tive function and the minimum energy being the optimal solution[8].SA involves a process in which the temperature is gradually reduced during the simulation.Often,the system is®rst heated and then cooled.Thus,the system is given the opportunity to surmount energetic barriers in a search for conformations with energies lower than the local-minimum energy found by energy minimisation. Unlike l-interchange,SA is a global optimisation heuristic based on probability,therefore,is able to overcome local optima.At each step of the simulation algorithm,a new state of the system is constructed from the current state by giving a random displacement to a randomly selected particle.If the energy associated with this new state was lower than the energy of the current state,the displace-ment was accepted,that is,the new state becomes the current state.If the new state had an energy higher by d joules,the probability of changing the current state to the new state isexp2d11where k is the Boltzmann constant and T the absolute temperature at present.This basic step,a metropolis step, can be repeated inde®nitely.The procedure is called a metropolis loop.It can be shown that this method of gener-ating current states led to a distribution of states in which the probability of a given state with energy e i to be the current state isexp 2e i=kTXjexp 2e j=kT12This probability function is known as Boltzmann density.One of its characteristics is that for very high temperatures,each state has almost equal chances of being the current state.At low temperatures,only states with low energies have a high probability of being the current state.These probabilities are derived for a never ending executing of the metropolis loop.The advantages of this scheme is:²SA can deal with arbitrary systems and cost functions;²SA statistically guarantees®nding an optimal solution;²SA is relatively easy to code,even for complex problems;²SA generally gives a`good'solution.However this original version of SA has some drawbacks:²Repeatedly annealing with a1/log k schedule is very slow,especially if the cost function is expensive to compute.²For problems where the energy landscape is smooth,or there are few local minima,SA is an overkillÐsimpler, faster methods(e.g.local descent)will work better.But usually one does not know what the energy landscape is.²Normal heuristic methods,which are problem-speci®c or take advantage of extra information about the system, will often be better than general methods.But SA is often comparable to heuristics.²The method cannot tell whether it has found an optimalK.C.Tan et al./Arti®cial Intelligence in Engineering15(2001)281±295285solution.Some other method(e.g.branch and bound)is required to do this.In our modi®ed version of SA,the algorithm starts with a relatively good solution resulting from PFIH.Initial temperature is set at T s 100;and is slowly decreased byT kT k2111tT k21p 13where T k is the current temperature at iteration k and t a small time constant.The square root of T k is introduced in the denominator to speed the cooling process.Here,we use a simple monotonically decreasing function to replace the 1/(log k)scheme.Our scheme gives fairly good results in much less time.The algorithm attempts solutions in the neighbourhood of the current solution randomly or system-atically and calculates the probability of moving to those solutions according toP accepting a move exp2D T k14 This is a modi®ed version of Eq.(11),where D C S0 2 C S ;C(S)is the cost of the current solution and C(S0) the cost of the new solution.If D,0;the move is always warranted.One can see that as temperature cools down,the probability of accepting a non-cost-saving move gets expo-nentially smaller.When the temperature has gone to the ®nal temperature T f 0:001or there are no more feasible moves in the neighbourhood,we reset the temperature toT r maxT r2;T b15where T r is the reset temperature,and was originally set to T s,and T b the temperature at which the best current solution was found.Final temperature is not set at zero because as temperature decreases to in®nitesimally close to zero,there is virtually zero probability of accepting a non-improving move.Thus,a®nal temperature not equal but close to zero is more realistic.To search a local neighbourhood,the2-interchange approach was adopted.Every time a GB solution is found,a2-inter-change(GB)procedure is executed to search for possible better solutions around it.The procedure terminates after a number of resets.Below is the detailed procedure of one of the SA implementations,which adopts a partial2-inter-change(FB)to search the neighbourhood.T s starting temperature of the SA method 100T f®nal temperature of the SA method 0.001T b temperature at which the current best solution was foundT r reset temperature of the SA method,originally equal to T sT k temperature of the current solution S current solutionS b current best solutionR number of resets to be donet the time constant in the range of(0,1). Algorithm2.Simulated annealingStep SA-1:Obtain a feasible solution for the VRPTW using the PFIH.Step SA-2:Improve S using the2-interchange LSD with GB strategy.Step SA-3:Set cooling parameters:T s T b T r T k 100;t 0:5:Step SA-4:Generate systematically an S0[N2 S by(2, 0)and(1,0)operations,and compute D C S0 2C S ; where N2(S)is the neighbourhood of current solution under2-interchange operation,C(S)and C(S0)means the cost of current solution and the newly generated solution,respectively.Step SA-5:If{ D#0 or(D.0and exp 2D=T k $u ; where u is a random number between[0,1]}thenset S S0.if{C S ,C S b }thenimprove S using2-interchange LSD(GB).update S b S and T b T k:Step SA-6:Set k k11:Update the temperature using Eq.(13).If{N2(S)is searched without any accepted move}then reset T r max T r=2;T b ;and set T k T r:Step SA-7:if{R resets have been made since the last S b was found}thengo to Step SA-8.else go to Step SA-4.Step SA-8:Terminate SA and print results.In general,our SA implementation is a simple and fast algorithm that solves many VRPTWs to near optima.Due to the GB approach in local neighbourhood search,the algo-rithm is able to result in stable local optimal solutions almost at all times.This is especially true if the global optimum in a problem is located very distant to the corre-sponding PFIH initial solutions.In that case SA may not have enough energy to traverse that far,given the limited number of temperature resets.6.Tabu searchTS is a memory-based search strategy,originally proposed by Glover[6],to guide the local search method to continue its search beyond a local optimum.The algo-rithm keeps a list of moves or solutions that have been made or visited in the past.This list,known as a Tabu list,is a queue of®xed or variable size.The purpose of the Tabu list is to record a number of most recent moves and prohibit anyK.C.Tan et al./Arti®cial Intelligence in Engineering15(2001)281±295 286。
安庆2024年03版小学5年级上册F卷英语第3单元期末试卷
安庆2024年03版小学5年级上册英语第3单元期末试卷考试时间：90分钟（总分:110）B卷考试人：_________题号一二三四五总分得分一、综合题(共计100题共100分)1. 填空题：My favorite color is _______ (我最喜欢的颜色是_______).2. 选择题：What is the main purpose of a hospital?A. To teachB. To provide entertainmentC. To heal the sickD. To sell goods答案:C3. 填空题：The __________ (历史的动态变化) shapes perceptions.4. 听力题：The _____ (tide) is coming in.5. 听力题：It is _____ (sunny) today.6. 填空题：The ________ was a key treaty that marked the end of hostilities.7. 听力题：A buffer solution helps maintain a constant ______.8. 听力题：The chemical formula for ethylene is ______.9. 填空题：Planting _____ (新物种) can enhance biodiversity in regions.10. 选择题：Which month comes after April?A. MarchB. MayC. JuneD. July答案: B. May11. 填空题：__________ (物质状态) can change with temperature and pressure.12. 选择题：What is the main ingredient in a Caesar salad dressing?A. Olive oilB. RanchC. MayonnaiseD. Yogurt13. 选择题：What is the main language spoken in Brazil?A. SpanishB. FrenchC. PortugueseD. English答案:C14. 填空题：The country known for its art and architecture is ________(以艺术和建筑闻名的国家是________).15. 听力题：The tree is very ________.16. 填空题：The starfish lives in the _________. (海洋)17. 填空题：The _____ (果实) of the apple tree is delicious.18. 填空题：The __________ (历史的策略) inform decision-making.19. 选择题：What do you call the liquid we drink?A. AirB. WaterC. JuiceD. Soda答案:B20. 听力题：My mom loves to make ____ (salads) in the summer.21. 选择题：What is the name of the animal that has a long tail and climbs trees?A. CatB. SquirrelC. DogD. Rabbit答案: B22. 听力填空题：My favorite sport is __________. I like to play it because it keeps me __________. I practice __________ times a week, and my teammates are really __________.23. 填空题：The crow is known for its ______ (智慧).24. 选择题：What is the freezing point of water in Celsius?A. 0 degreesB. 32 degreesC. 100 degreesD. -10 degrees答案:A25. 听力题：The rain makes everything _____ (wet/dry).26. 选择题：Who is the author of "Harry Potter"?A. J.K. RowlingB. J.R.R. TolkienC. Roald DahlD. Mark Twain答案:A27. 听力题：A gecko can climb walls using its ______.The capital city of France is .29. 选择题：What is the name of the famous mountain in the United States?A. Mount RushmoreB. Mount KilimanjaroC. Mount EverestD. Mount Fuji30. 填空题：A _____ (花香) can evoke memories and feelings.31. 选择题：What is the capital of the Philippines?A. MalacañangB. ManilaC. CebuD. Davao32. 填空题：I love the __________ (夜空) filled with stars.33. 选择题：What is the largest mammal in the ocean?A. SharkB. DolphinC. WhaleD. Octopus答案: C34. 听力题：The garden is _______ (full) of vegetables.35. 选择题：What do we call the process of converting light into energy in plants?A. RespirationB. PhotosynthesisC. DigestionD. Fermentation36. 听力题：The cat can see well in the _______.37. 听力题：My sister is a ______. She loves to play with her friends.Which animal can fly?A. FishB. DogC. BirdD. Lizard39. 填空题：Every year, we celebrate ______ (感恩节) with a big feast and share what we are thankful for.40. 填空题：A _____ (34) can be hot or cold.41. 填空题：The _____ (海豚) has a friendly personality.42. 听力题：My favorite drink is ______. (juice)43. 听力题：The chemical formula for lead(II) oxide is _______.44. 听力题：The jellybeans are ______ (colorful) and sweet.45. 选择题：Which animal is known for its stripes?A. ElephantB. ZebraC. GiraffeD. Lion46. ts can survive in _____ (干燥) conditions. 填空题：Some pla47. 填空题：My uncle loves to __________ (参加) local events.48. 选择题：What is the first animal to go into space?A. MonkeyB. DogC. CatD. MouseWhat do you call a group of fish?a. Schoolb. Packc. Flockd. Herd答案:A50. 选择题：Which instrument has black and white keys?A. GuitarB. DrumsC. PianoD. Violin51. 听力题：Some stars are in binary systems, orbiting around a common _______.52. 听力题：Inorganic compounds do not contain _____.53. 选择题：What do you call the study of fungi?A. MycologyB. BotanyC. ZoologyD. Ecology答案:A54. 填空题：I enjoy ________ (参加) dance classes.55. 填空题：The __________ (黄金时代) of Athens was during the 5th century BC.56. 选择题：What is the primary function of the lungs?A. To pump bloodB. To digest foodC. To breatheD. To filter waste答案：C57. 选择题：Which of these is a type of pasta?A. RiceB. SpaghettiC. BreadD. Quinoa答案:B58. 选择题：What color is the sky on a clear day?A. GreenB. BlueC. RedD. Yellow59. 听力题：The chemical symbol for gold is _______.60. 选择题：What do we call the area of land that is covered in ice?A. GlacierB. Ice capC. IcebergD. Tundra61. 听力题：The chemical symbol for copper is _____ (Cu).62. 选择题：What do we call a picture made by sticking various materials together?A. CollageB. MosaicC. PaintingD. Sculpture63. 选择题：What do we call the study of the mind and behavior?a. Sociologyb. Psychologyc. Anthropologyd. Philosophy答案:b64. 填空题：When I grow up, I want to be a ______ (医生) because I want to help ______ (人们). It is important to be healthy and ______ (快乐).65. 填空题：My best friend is very __________ (诚实的).What do we call the tool we use to measure temperature?A. ThermometerB. BarometerC. HydrometerD. Anemometer67. 填空题：A ________ (植物分类) helps in identification.68. 听力题：A reaction that absorbs energy is called an ______ reaction.69. trial Revolution began in ________ (英国). 填空题：The Indu70. 选择题：What is the name of the famous wall in China?A. Great WallB. Berlin WallC. Hadrian's WallD. Wall of China答案:A71. 选择题：What is 4 + 4?A. 6B. 7C. 8D. 9答案:C72. 填空题：We should _______ (互相学习).73. 听力题：The chemical formula for calcium phosphate is _______.74. 填空题：My favorite season is ________ (冬天).75. 填空题：The ________ (环境科学) informs decisions.The library is _______ (很安静的).77. 听力题：Hydrochloric acid is a strong _____.78. 听力题：My ______ enjoys hiking in the mountains.79. 听力题：A _______ is a substance that increases the rate of a reaction without being consumed.80. 填空题：World War II began in __________. (1939年)81. 选择题：What is the term for the brightest part of the shadow during an eclipse?A. PenumbraB. UmbraC. EclipseD. Shadow82. ts are used for ______ (环保材料). 填空题：Some pla83. 听力题：The city known as the "Big Apple" is __________.84. 选择题：What do you call the hard outer covering of an egg?A. ShellB. YolkC. AlbumenD. Membrane答案:A85. 听力题：The cat is very ___ (lazy/energetic).86. 填空题：I saw a ________ jumping in the river.87. 听力题：The _____ (fishing) pole is long.The __________ (生态平衡) is vital for our planet.89. 选择题：What is the name of the popular game played on a board with pieces?A. ChessB. ScrabbleC. MonopolyD. Checkers答案: A90. 填空题：My pet parrot can _________ (说话).91. 选择题：What is the opposite of 'happy'?A. SadB. AngryC. ExcitedD. Joyful答案:A92. 填空题：In winter, I love to drink __________ to keep warm. (热可可)93. 听力题：My uncle is a ______. He loves to tell jokes.94. 听力题：I like to _____ (烹饪) with my mom.95. 填空题：A firefly lights up the ______ (夜晚).96. 选择题：What is the color of a typical fire?A. BlueB. RedC. YellowD. Both B and C97. 选择题：What do you call the act of saving money?A. SpendingB. InvestingC. BudgetingD. All of the above98. 听力题：My friend is very ________.99. 选择题：Which of these animals is a reptile?A. FrogB. LizardC. RabbitD. Dolphin答案: B100. 填空题：A healthy garden attracts many different ______ (动物).。
Model Test Three
Model Test ThreePart ⅠWritingDirections:For this part, you are allowed 30 minutes to write a short essay entitled A Shopping Mall in the Neighborhood. You should write at least 120 words following the outline given below:1．据称，在你所在的社区将建立一个大型的购物中心；2．发表你的意见并说出支持或反对的理由。
A Shopping Mall in the NeighborhoodPart ⅡReading Comprehension (Skimming and Scanning)Directions:In this part, you will have 15 minutes to go over the passage quickly and answer the questions on Answer Sheet 1.For questions 1—7, choose the best answer from the four choices marked A), B), C) and D).For questions 8—10, complete the sentences with the information given in the passage.Genetic TestingGenetic testing is transforming medicine and the way families think about their health. As science uncovers the complicated secrets of DNA, we face difficult choices and new challenges.About Genetic testingThe year was 1895 and Pauline Gross, a young actress, was scared. Gross knew nothing about the human-genome (基因组，染色体组) project—such medical triumphs, but she did know about a nasty disease called cancer, and it was running through her family. "I'm healthy now," she often told Dr. Aldred Warthin from at the University of Michigan, "but I fully expect to die an early death."At the time, Gross's prediction was based solely on observation: family members had died of cancer; she would, too. Today, more than 100 years later, Gross's relatives have a much more clinical option: genetic testing. With a simple blood test; they can peer into their own DNA, learning—while still perfectly healthy—whether they carry an inheritable gene mutation (突变) that has dogged their family for decades and puts them at serious risk. Take the TestingTesting is a kind of the genomic revolution. A major goal is to create new sophisticated therapies that home in on a disease's biological source, then fix the problem. Already, genes are helping to predict a patient's response to existing medications. A prime example, taken by Dr, Wylie Burke of the University of Washington, is a variant of a gene called TPMT, which can lead to life-threatening reactions to certain doses of chemotherapy (化学疗法).A genetic test can guide safe and appropriate treatment. Two genes have been identified that influence a person's response to the anti-blood-clotting drug. And scientists are uncovering genetic differences in the way people respond to other widely used medications, like antidepressants (抗抑郁药).Knowing a patient's genotype, or genetic profile, may also help researchers uncover new preventive therapies for sticky diseases. At Johns Hopkins University School of Medicine. Dr. Christopher Ross has tested several compounds shown to slow the progression of Huntington's in mice. Now he wants to test them in people who are positive for the Huntington's mutation but have not developed symptoms—a novel approach to clinical drug trials, which almost always involve sick people seeking cures. "We're using genetics to move from treating the disease after it happens," he says, "to preventing the worst symptoms of the disease before it happens."It's not just their own health that people care about. There is also the desire to get rid of disease from the family tree. Therefore, the future is what drives many adults to the clinic. The gene tests currently offered for certain diseases, like breast cancer, affect only a small percentage of total cases. Inherited mutations contribute to just 5 to 10 percent of all breast cancers. But the impact on a single life can be huge. The key: being able to do something to ward off disease. "Genetic testing offers us profound insight," says Dr. Stephen Gruber, of the University of Michigan. "But it has to be balanced with our ability to care for these patients."Genetic testing today starts at the earliest stages of life. Couples planning to have children can be screened prior to conception to see if they are carriers of genetic diseases; prenatal (产前) tests are offered duringpregnancy, and states now screen newborns for as many as 29 conditions, the majority of them genetic disorders. For Jana and Tom Monaco, of Woodbridge, Va., early testing has made an enormous difference in the lives of their children. Their journey began in 2001, when their seemingly healthy third child, 3-year-old Stephen, developed a life-threatening stomach virus that led to severe brain damage. His diagnosis: a rare but treatable disease called isovaleric acidemia (IVA). Unknowingly, Jana and her husband were carriers of the disease, and at the time, IVA was not included in newborn screening. The Monacos had no warning whatsoever.Not Take the TestingGenetic testing, exciting as it may seem, isn't always the answer. When Wendy Uhlmann, a genetic counselor at the University of Michigan, teaches medical students, she flashes two slides on a screen side by side. One says ignorance is bliss (福佑). The other: knowledge is power. That's because the value of testing becomes especially ambiguous—and ethically complicated—when there is no way to prevent or treat disease, as in the case of early-onset Alzheimer's, which often strikes before the age of 50, or Huntington's.Today only about 5 percent of people who are at risk for Huntington's—which is caused by a single gene and leads to a progressive loss of physical control and mental acuity—take the test. Many are worried that genetic testing will put their health insurance or job security at risk. While there have been few documented cases of discrimination, nobody can say for sure what will happen as more disease genes are discovered and' more Americans sign on for predictive testing. States have a patchwork of regulations in place, but what needs to happen now, experts say, is for Congress to pass the Genetic Information Nondiscrimination Act, which would put a federal stamp of approval on keeping genetic information safe.Moreover, some people can't live with uncertainty. Stephanie V ogt knew Huntington ran in her family—her grandfather and his three brothers all died of complications of the disease—and she wanted to find out where she stood. "As soon as I found out there was a test, I just had to do it," she says. In August 2000, after comprehensive genetic counseling, Stephanie, her sister, Victoria, and their mother, Gayle Smith, learned her results: positive. "It was like a scene Out of 'The Matrix', where everything freezes and starts again," says Stephanie, now 35 and single.Scientific revolutions must be tempered by reality. Genes aren't the only factors involved in complex diseases—lifestyle and environmental influences, such as diet or smoking, are too. And predictions about new tests and treatments may not come to pass as fast as researchers hope—they may not come at all. Still, it's hard not to get excited about the future, especially when you consider the medical competition now underway.1. Pauline Gross felt seared because she thought she would die of ______.A) stroke B) cancer C) SARS D) AIDS2. Genetic testing can be used to decide whether a patient has the inheritable gene mutation by peering into their ______.A) blood cell B) lung cell C) liver cell D) DNA3. The major purpose of genetic testing in medication is to ______.A) predict the death rate of inheritable diseaseB) predict a patient's response to medicationC) find out the biological source and fix the problemD) guide safe and appropriate treatment4. What have Christopher Ross' experimental results revealed to us?A) Those inheritable diseases may be cured through genetic testing.B) Those inheritable diseases may be predicted through genetic testing.C) Those inheritable diseases may be prevented through genetic testing.D) Those inheritable diseases may be controlled through genetic testing.5. How many people with breast cancer are inherited from family tree?A) 5 to 10 percent. B) 10 percent. C) About 15 percent. D) 5 percent.6. Couples planning to have children can take prenatal genetic tests to know ______.A) Whether their babies are genetically healthy or notB) whether they can have a babyC) when their babies will come into the worldD) whether their babies are boy or girl babies7. According to the passage, what is Wendy Uhlmann's attitude toward genetic testing?A) She is indifferent to it. B) She does not agrees with it at all.C) She has no idea about it. D) She has some doubts about it.8. Many choose not to take the genetic testing because they worried that it will risk their ______.9. In August 2000, Stephanie V ogt learned she got the disease of ______.10. From the last paragraph, we know many factors involved in complex diseases, such as gene, lifestyle and ______.Part ⅢListening ComprehensionSection ADirections:In this section, you will hear 8 short conversations and 2 long conversations. At the end of each conversation, one or more questions will be asked about what was said. Both the conversation and the questions will be spoken only once. After each question there will be a pause. During the pause, you must read the four choices marked A), B), C) and D), and decide which is the best answer. Then mark the corresponding letter on Answer Sheet 2 with a single line through the centre.Questions 11 to 18 are based on the conversation you have just heard.11. A) Dana agrees with her. B) Dana likes the food.C) Dana likes to put on weight. D) Dana must be unhappy.12. A) It is rainy. B) It is sunny. C) It is fine. D) It is cloudy.13. A) The play is seldom delayed to start. B) The play will start twenty minutes later.C) The newspaper is seldom wrong. D) They probably have to continue to wait.14. A) He didn't have enough money. B) Radios of all brands were sold out then.C) He couldn't get the right brand of radio. D) The store will sell the radio tomorrow.15. A) It will take at least three weeks to finish the test.B) The test will be more difficult than they expect.C) They still have time to prepare for it.D) The test will be more difficult than they expect.16. A) Go with the woman for a drink. B) Drink what he has brought with him.C) Continue with his work until lunchtime. D) Ask the woman to get him some soft drink.17. A) He will write a letter to another company. B) He has received many job offers.C) He hasn't accepted the job offer. D) He will let the woman have the job.18. A) She will have a drink while she waits. B) She will help the man with the work.C) She will get some coffee for the man. D) She will go out first and get her car.Questions 19 to 21 are based on the conversation you have just heard.19. A) Tennis sets. B) Computer and TV set.C) Bookcase and book shelf. D) Refrigerator and kitchen stuff.20. A) Give them to the second and third year students for free.B) Sell them to the second-hand bookshop.C) Advertise them in the student newspaper for sale.D) Advertise them on the university notice boards.21. A) It may not pay well. B) It may not come on time.C) It may not take your goods. D) It may charge the quote.Questions 22 to 25 are based on the conversation you have just heard.22. A) Love for beauty and a desire to impress other people.B) A desire to express oneself and a display of one's wealthC) Individual taste and love for beauty.D) Individual taste and a desire to express oneself.23. A) They may be homesick and feel insecure.B) They may try to attract other people's attention.C) They are either cold or sick.D) They want to protect themselves from physical injuries.24. A) They prefer white. B) They prefer red. C) They prefer yellow. D) They prefer gray.25. A) Reporter and fashion designer. B) Teacher and student.C) Shop assistant and customer. D) Husband and wife.Section BDirections:In this section, you will hear 3 short passages. At the end of each passage, you will hear some questions. Both the passage and the questions will be spoken only once. After you hear a question, you must choose the best answer from the four choices marked A), B), C) and D). Then mark the corresponding letter on Answer Sheet 2 with a single line through the centre.Passage OneQuestions 26 to 28 are based on the passage you have just heard.26. A) Solar energy. B) Synthetic fuel. C) Alcohol fuel. D) Electricity power.27. A) Air traffic conditions. B) Road conditions.C) New traffic rules. D) Traffic jams on highways.28. A) Arrive early for boarding. B) Carry little luggage.C) Undergo security checks. D) Arrive early for boarding.Passage TwoQuestions 29 to 31 are based on the passage you have just heard.29. A) He feels funny. B) He feels angry. C) He feels excited. D) He feels sad.30. A) The newcomers don't like the new environment shortly after their arrival.B) The newcomers begin to hate the city, the country in the new culture.C) The newcomers begin to enjoy their life more but leave the country.D) The newcomers begin to adjust to their surroundings and enjoy their life.31. A) The people who had no hobbies in their own culture.B) The people who were not active and successful in their own culture.C) The people who had high position in their own culture.D) The people who never had any difficulties in their own culture.Passage ThreeQuestions 32 to 35 are based on the passage you have just heard.32. A) In the 17th century. B) In the 15th century.C) In the 18th century. D) In the 16th century.33. A) Because of the beautiful garden in front of it.B) Because of its old style of architecture.C) Because it was at the sea side.D) Because it was the only modern building there.34. A) To welcome the tourists. B) To make money.C) To keep the tourists away. D) To attract the tourists.35. A) In order to have more peace. B) In order to earn more money.C) In order to welcome more visitors. D) In order to have a bigger garden.Section CDirections:In this section, you will hear a passage three times. When the passage is read for the first time, you should listen carefully for its general idea. When the passage is read for the second time, you are required to fill in the blanks numbered from 36 to 43 with the exact words you have just heard. For blanks numbered from 44 to 46 you are required to fill in the missing information. For these blanks, you can either use the exact words you have just heard or write down the main points in your own words. Finally, when the passage is read for the third time, you should check what you have written.The need for birth control methods has developed fairly (36) , with the desire among many women to be able to (37) when they want to have a baby. At the same time there is a growing (38) of the problem of a rapidly increasing world population.This problem of a (39) world population is largely the result of (40) medical skills, which have (41) the death rate and at the same time raised the birth rate by increasing live births and the number of babies who (42) early childhood. There is a growing (43) that food production cannot keep pace with these increase, the result of which is that in some countries people are already starving to death. This problem is farther complicated by the fact that in places like America and Europe we obtain by trade and consume far more food and resources like oil than, say, the average India, (44) .World population is rising at rate of two percent a year; this means an addition of 70 million people a year to the present population of more than 3500 million. (45) . The fastest growing region is Latin America which includes South and Central America and the Caribbean, while Africa and Asia closely follow Latin America. However, (46) .Part ⅣReading Comprehension (Reading in Depth)Section ADirections:In this section, there is a passage with ten blanks. You are required to select one word for each blank from a list of choices given in a word bank following the passage. Read the passage through carefully before making your choices. Each choice in the bank is identified by a letter. Please mark the corresponding letter for each item on Answer Sheet 2 with a single line through the centre. You may not use any of the words in the bank more than once.The ability to laugh at your own weaknesses and blunders (失误) has long been recognized as a sign of maturity. And yet this is one of the most difficult (47) of your sense of humor to develop.Oscar Wilde once offered a (48) insight about the way we live our lives when he said that "Life is too important to be taken seriously." I don't think he meant you don't have to take your (49) , promises, work, etc. seriously. He didn't mean that it's OK to live life with no (50) . I think he meant that the quality of our life suffers when we (51) everything in a serious manner. We are no longer (52) , cheerful and spontaneous as when we were kids when we take everything so seriously.I think the key here is to take your work and your duties seriously, but take yourself (53) in the process. Otherwise, you will lose many benefits that a (54) attitude and humor can offer.There's a liberating quality that most people experience when they get to the point that they can laugh at themselves. We get so caught up in our anxieties, embarrassments, (55) and upsets that we carry them around with us throughout the day. But when we find a way to laugh at them, they lose their emotional grip on us and (56) into the background. We feel at peace with the incident, even though it was very embarrassing at the moment.A) advance F) approach K) aspectsB) restriction G) integrity L) sincerelyC) cheerful H) responsibilities M) frustrationsD) lightly I) valuable N) retreatE) vigorous J) vivid O) decentSection BDirections:There are 2 passages in this section. Each passage is followed by some questions or unfinished statements. For each of them there are four choices marked A), B), C) and D). You should decide on the best choice and mark the corresponding letter on Answer Sheet 2 with a single line through the centre.Passage OneJapan is going through a complex national identity crisis. That may be no bad thing, says a new book by an American researcher. The economy is ceased making progress, but the society is in motion. Japan is a difficult country to report on and analyze because things do not change in big, noticeable ways. They change, in an increasing process, generally of small steps but which, over time, can add up to big movements. And just such a big movement seems to be taking place.Mr. Nathan has been observing Japan since the 1960s. Whereas most people look at economic data or the comings and goings Of prime ministers, he is more interested in schools, novels, comic books, and the minds of young entrepreneurs and maverick (持不同意见的) local politicians. In particular, his focus is on whether Japan's famously cohesive, conformist society may be breaking under the strain of economic stagnation (停滞), and on how such strains have been affecting the country's sense of purpose and of national identity.Fractures are what he looks for and fractures are what he finds. On balance, they arc neither obviously dangerous nor obviously positive, but they arc, as he says, signs of motion which could, in time, lead in unpredictable directions. The most worrying fractures he writes about are in the schools where violence and truancy (逃学) have risen remarkably. Old Japan hands shrug wearily at such things, for worries about bullying (暴力行为) have long existed but have never really seemed terribly serious. Now, though, Mr. Nathan's numbers do make the situation look grave.Such trends appear to be symptoms of two related phenomena: a widespread feeling of disillusionment, alienation, uncertainty or plain anger, which has spread to children, too; and a gradual breakdown of old systems of discipline—part familial, part social, part legal—which, appear to prevent schools and parents from dealing effectively with errant children.Japan is, in short, passing through a national identity crisis. However, there are plenty of positive aspects to it, too. One is a considerable increase in the number of actual or budding young entrepreneurs. The numbers remain modest, but are nevertheless surprisingly high given the state of the economy in recent years. Another is a new eagerness among popular writers and maverick politicians to try to define and encourage a new national pride.57. What can we learn about the social changes in Japanese society?A) They are always accompanied by a national identity crisis.B) They often happen in large scale but end up in small effect.C) They often take place simply while resulting in huge accumulative consequences.D) They often take place without being analyzed and reported because of their complexity.58. We can infer from the second paragraph that ______.A) Japan is going through a serious political instabilityB) a motionless economy might have a negative influence on societyC) severe mental strain is affecting most people in JapanD) job pressure caused many people to suffer from a break59. How arc the school violence and truancy in Japan?A) They are tiring Japanese parents up for a long time.B) They are not as serious as most Japanese have imagined.C) They are quickly on the increase especially in recent years.D) They are obviously endangering the safety of Japanese students.60. What's the reason behind school violence and truancy in Japan?A) Schools and parents do not want to handle errant children effectively.B) Economic cease has reduced parents' time to communicate with their children.C) Youths violence has already wide-spread among students and they find. it exciting.D) Children are influenced by problems in the adult world and rules are not strict enough.61. What can we learn from the last paragraph?A) The country can benefit nothing from the national identity crisis.B) The Japanese economy in recent years is not very good.C) Lots of young workers were fired for the national identity crisis.D) Writers and politicians in Japan gave an ironic description to tile crisis.Passage TwoAlthough the dream of the home robot has not died, robots have had their greatest impact in factories. Unimate, the first industrial robot, went to work for General Motors in 1961. Even at a time when computing power was costly, robots made excellent workers and proved that machines controlled by computers could perform some tasks better than humans. In addition, robots can work around the clock and never go on strike.There are now about 800 000 industrial robots around the world, and orders for new robots in the first half of 2007 were up a record 26% from the same period in 2006, according to the United Nations Economic Commission for Europe (UNECE). Demand is increasing as prices fall: a robot sold in 2007 cost less than a fifth of an equivalent robot sold in 1990, for example. Today, in car factories in Japan, Germany and Italy, there is more than one robot for every ten production workers.Similarly, agricultural robots harvest billions of tones of crops every year. There are six-legged timber cutters, tree-climbing fruit-pickers, robots that milk cows, and others that wash windows, trucks and aircraft. Industrial robotics is a 5.6 billon industry, growing by around 7% a year. But the UNECE report predicts that the highest growth over the next three years will be in domestic rather than industrial robots. Sales of such devices, it predicts will grow ten-fold between 2007 and 2010, overtaking the market for industrial robots.The broader application of robotics is becoming possible thanks to the tumbling (暴跌) cost of computing power, says Takeo Kanade. This lets programmers write more sophisticated software that delivers more intelligent robotic behavior. At the same time, he notes, the cost of camera and sensor chips has tumbled, too. "The processing power is so much better than before that some of the seemingly simple things we humans do, like recognizing faces, can begin to be done", says Dr. Kanade.While prices drop and hardware improves, research into robotic vision, control systems and communications have jumped ahead as well. America's military and its space agency, NASA, have poured billions into robotic research and related fields such as computer vision. The Spirit and Opportunity Rovers (漫游者) exploring Mars can pick their way across the surface to reach a specific destination. Their human masters do not specify the routes instead, the robots are programmed to identify and avoid obstacles themselves.62. It can be inferred from the first paragraph that ______.A) the first robot of the world was designed in 1961 for General MotorsB) the expensive computing power didn't hinder a robot's efficiencyC) robots controlled by computers can do most tasks better than humansD) human workers often went on a strike when they were not satisfied in the past63. The sales of robots have been on the rise chiefly because ______.A) the technology robots use is really advancedB) robots greatly increase the efficiency in factoriesC) robots are becoming economically availableD) most car factories around the world need robots64. The new trend in robotic development in the coming future is that robots will ______.A) become more and more popular among familiesB) be made just like the ones in Hollywood moviesC) be used more widely in agricultural contextsD) be able to do housework with high intelligence65. More sophisticated robots with intelligence are made possible by ______.A) the broader application of robotics B) the availability of robots across the worldC) the technological progress in the field D) the ever decreasing cost of computing power66. What can we learn from the last paragraph?A) America leads the robot study in the world.B) Robot study in America costs more than in other countries.C) A broader study of robot is on the road now.D) Human designed the way for the robot to explore the Mars.Part ⅤClozeDirections:There are 20 blanks in the following passage. For each blank there ate four choices marked A), B), C) and D) on the right side of the paper. You should choose the ONE that best fits into the passage. Then mark the corresponding letter on the Answer Sheet 2 with a single line through the centre.Scientific research has revealed that throughout the animal world, communication is just as important as it is to human beings. Countless animals lack the (67) for human speech, yet they employ entirely different methods in order to communicate (68) each other. Some of the most dramatic examples of this are provided by birds. There arc roughly 10000 (69) of birds in the world, each of which has its own miraculous features.(70) you may live, you can see a great number of these feathered creatures and can (71) different and extraordinary properties. In addition to their flawless flight mechanisms, expertise (72) the routes and timing of migrations, and ability to build nests, their methods to communicate is (73) wonder. At critical times in birds' lives, their (74) of hearing becomes particularly important. Experiments have shown that in order for birds to learn the (75) song of their own species, they need an auditory feedback system. (76) this system, young birds learn to (77) the sounds they produce with the song they have memorized. If they were (78) , it wouldn't normally be (79) for them to recognize songs. Birds' extremely sensitive hearing functions perfectly. Clearly, if this sense failed to (80) properly, the bird would not be able to hear any sounds (81) . Moreover, birds also produce (82) communications by their facial expressions, beak movements, feather ruffling, and (83) their wings. Although each species has its own body language, many different species (84) movements in the same way. Via facial expression, birds can (85) a variety of messages to those around them—negative feelings such as dislike and resentment, as well as (86) ones like pleasure, enthusiasm and curiosity.67. A) simplicity B) curiosityC) capacity D) ability68. A) by B) aboutC) through D) with69. A) groups B) speciesC) classes D) regions70. A) Wherever B) IfC) As D) When71. A) reveal B) watchC) inspect D) observe72. A) by B) inC) on D) at73. A) every B) anotherC) other D) one。
关于短生命周期产品的供应链协调
52.Georoge Q Huang.Jason S K Lau.K L Mak The impacts of sharing production information on supply chain:a review of the literature 2003(07)
15.Ernst R.Powell S Optimal inventory policies under service-sensitive demand[外文期刊] 1995(02)
16.Diks E B.Kok A godimos A G Multi-echelon systems:a service measure perspective 1996(02)
24.Metters R Quantifying the bullwhip effect in supply chain 1997(02)
25.Genues J P.Ramasesh R V.Hayya J C Adapting the Newsvendor Model for Infinite-horizon Inventory system 2001(03)
21.Kaplan R A A dynamic inventory model with stochastic lead times 1970(07)
22.Decroix G A.Risa A A Optimal production and inventory policy for multiple products under resource constrains 1998(07)
2024年考研英语一真题阅读理解详细解析与答案
2024年考研英语一真题阅读理解详细解析与答案阅读理解一：Passage 1：题目：Why is the current global workforce in poor health?解析：本文讨论全球劳动力健康状况不佳的原因。
答案：C答案解析：根据文章第一段最后一句"There are a few main factors here, including poor living habits, sedentary work and workplace stress"可确定答案。
Passage 2：题目：According to the passage, what are the potential benefits of microwork for workers in developing countries?解析：本文探讨了在发展中国家进行微工作的潜在利益。
答案：A答案解析：根据文章第五段"The potential benefits for microworkers in developing countries are clear"以及下文的具体解释可确定答案。
Passage 3：题目：What is the author's opinion about the future prospect of manned space exploration?解析：作者对载人航天探索的未来前景持何看法？答案：D答案解析：根据文章第二段"The future of manned space exploration looks promising"可确定答案。
Passage 4：题目：What is the main topic of the passage?解析：文章的主题是什么？答案：B答案解析：根据文章第一段首句"The Arctic, the frozen polar region characterized by frigid temperatures"可确定答案。
专八英语阅读
英语专业八级考试TEM-8阅读理解练习册（1）(英语专业2012级)UNIT 1Text AEvery minute of every day, what ecologist生态学家James Carlton calls a global ―conveyor belt‖, redistributes ocean organisms生物.It’s planetwide biological disruption生物的破坏that scientists have barely begun to understand.Dr. Carlton —an oceanographer at Williams College in Williamstown,Mass.—explains that, at any given moment, ―There are several thousand marine species traveling… in the ballast water of ships.‖ These creatures move from coastal waters where they fit into the local web of life to places where some of them could tear that web apart. This is the larger dimension of the infamous无耻的，邪恶的invasion of fish-destroying, pipe-clogging zebra mussels有斑马纹的贻贝.Such voracious贪婪的invaders at least make their presence known. What concerns Carlton and his fellow marine ecologists is the lack of knowledge about the hundreds of alien invaders that quietly enter coastal waters around the world every day. Many of them probably just die out. Some benignly亲切地，仁慈地—or even beneficially — join the local scene. But some will make trouble.In one sense, this is an old story. Organisms have ridden ships for centuries. They have clung to hulls and come along with cargo. What’s new is the scale and speed of the migrations made possible by the massive volume of ship-ballast water压载水— taken in to provide ship stability—continuously moving around the world…Ships load up with ballast water and its inhabitants in coastal waters of one port and dump the ballast in another port that may be thousands of kilometers away. A single load can run to hundreds of gallons. Some larger ships take on as much as 40 million gallons. The creatures that come along tend to be in their larva free-floating stage. When discharged排出in alien waters they can mature into crabs, jellyfish水母, slugs鼻涕虫，蛞蝓, and many other forms.Since the problem involves coastal species, simply banning ballast dumps in coastal waters would, in theory, solve it. Coastal organisms in ballast water that is flushed into midocean would not survive. Such a ban has worked for North American Inland Waterway. But it would be hard to enforce it worldwide. Heating ballast water or straining it should also halt the species spread. But before any such worldwide regulations were imposed, scientists would need a clearer view of what is going on.The continuous shuffling洗牌of marine organisms has changed the biology of the sea on a global scale. It can have devastating effects as in the case of the American comb jellyfish that recently invaded the Black Sea. It has destroyed that sea’s anchovy鳀鱼fishery by eating anchovy eggs. It may soon spread to western and northern European waters.The maritime nations that created the biological ―conveyor belt‖ should support a coordinated international effort to find out what is going on and what should be done about it. (456 words)1.According to Dr. Carlton, ocean organism‟s are_______.A.being moved to new environmentsB.destroying the planetC.succumbing to the zebra musselD.developing alien characteristics2.Oceanographers海洋学家are concerned because_________.A.their knowledge of this phenomenon is limitedB.they believe the oceans are dyingC.they fear an invasion from outer-spaceD.they have identified thousands of alien webs3.According to marine ecologists, transplanted marinespecies____________.A.may upset the ecosystems of coastal watersB.are all compatible with one anotherC.can only survive in their home watersD.sometimes disrupt shipping lanes4.The identified cause of the problem is_______.A.the rapidity with which larvae matureB. a common practice of the shipping industryC. a centuries old speciesD.the world wide movement of ocean currents5.The article suggests that a solution to the problem__________.A.is unlikely to be identifiedB.must precede further researchC.is hypothetically假设地，假想地easyD.will limit global shippingText BNew …Endangered‟ List Targets Many US RiversIt is hard to think of a major natural resource or pollution issue in North America today that does not affect rivers.Farm chemical runoff残渣, industrial waste, urban storm sewers, sewage treatment, mining, logging, grazing放牧,military bases, residential and business development, hydropower水力发电,loss of wetlands. The list goes on.Legislation like the Clean Water Act and Wild and Scenic Rivers Act have provided some protection, but threats continue.The Environmental Protection Agency (EPA) reported yesterday that an assessment of 642,000 miles of rivers and streams showed 34 percent in less than good condition. In a major study of the Clean Water Act, the Natural Resources Defense Council last fall reported that poison runoff impairs损害more than 125,000 miles of rivers.More recently, the NRDC and Izaak Walton League warned that pollution and loss of wetlands—made worse by last year’s flooding—is degrading恶化the Mississippi River ecosystem.On Tuesday, the conservation group保护组织American Rivers issued its annual list of 10 ―endangered‖ and 20 ―threatened‖ rivers in 32 states, the District of Colombia, and Canada.At the top of the list is the Clarks Fork of the Yellowstone River, whereCanadian mining firms plan to build a 74-acre英亩reservoir水库，蓄水池as part of a gold mine less than three miles from Yellowstone National Park. The reservoir would hold the runoff from the sulfuric acid 硫酸used to extract gold from crushed rock.―In the event this tailings pond failed, the impact to th e greater Yellowstone ecosystem would be cataclysmic大变动的，灾难性的and the damage irreversible不可逆转的.‖ Sen. Max Baucus of Montana, chairman of the Environment and Public Works Committee, wrote to Noranda Minerals Inc., an owner of the ― New World Mine‖.Last fall, an EPA official expressed concern about the mine and its potential impact, especially the plastic-lined storage reservoir. ― I am unaware of any studies evaluating how a tailings pond尾矿池，残渣池could be maintained to ensure its structural integrity forev er,‖ said Stephen Hoffman, chief of the EPA’s Mining Waste Section. ―It is my opinion that underwater disposal of tailings at New World may present a potentially significant threat to human health and the environment.‖The results of an environmental-impact statement, now being drafted by the Forest Service and Montana Department of State Lands, could determine the mine’s future…In its recent proposal to reauthorize the Clean Water Act, the Clinton administration noted ―dramatically improved water quality since 1972,‖ when the act was passed. But it also reported that 30 percent of riverscontinue to be degraded, mainly by silt泥沙and nutrients from farm and urban runoff, combined sewer overflows, and municipal sewage城市污水. Bottom sediments沉积物are contaminated污染in more than 1,000 waterways, the administration reported in releasing its proposal in January. Between 60 and 80 percent of riparian corridors (riverbank lands) have been degraded.As with endangered species and their habitats in forests and deserts, the complexity of ecosystems is seen in rivers and the effects of development----beyond the obvious threats of industrial pollution, municipal waste, and in-stream diversions改道to slake消除the thirst of new communities in dry regions like the Southwes t…While there are many political hurdles障碍ahead, reauthorization of the Clean Water Act this year holds promise for US rivers. Rep. Norm Mineta of California, who chairs the House Committee overseeing the bill, calls it ―probably the most important env ironmental legislation this Congress will enact.‖ (553 words)6.According to the passage, the Clean Water Act______.A.has been ineffectiveB.will definitely be renewedC.has never been evaluatedD.was enacted some 30 years ago7.“Endangered” rivers are _________.A.catalogued annuallyB.less polluted than ―threatened rivers‖C.caused by floodingD.adjacent to large cities8.The “cataclysmic” event referred to in paragraph eight would be__________.A. fortuitous偶然的，意外的B. adventitious外加的，偶然的C. catastrophicD. precarious不稳定的，危险的9. The owners of the New World Mine appear to be______.A. ecologically aware of the impact of miningB. determined to construct a safe tailings pondC. indifferent to the concerns voiced by the EPAD. willing to relocate operations10. The passage conveys the impression that_______.A. Canadians are disinterested in natural resourcesB. private and public environmental groups aboundC. river banks are erodingD. the majority of US rivers are in poor conditionText CA classic series of experiments to determine the effects ofoverpopulation on communities of rats was reported in February of 1962 in an article in Scientific American. The experiments were conducted by a psychologist, John B. Calhoun and his associates. In each of these experiments, an equal number of male and female adult rats were placed in an enclosure and given an adequate supply of food, water, and other necessities. The rat populations were allowed to increase. Calhoun knew from experience approximately how many rats could live in the enclosures without experiencing stress due to overcrowding. He allowed the population to increase to approximately twice this number. Then he stabilized the population by removing offspring that were not dependent on their mothers. He and his associates then carefully observed and recorded behavior in these overpopulated communities. At the end of their experiments, Calhoun and his associates were able to conclude that overcrowding causes a breakdown in the normal social relationships among rats, a kind of social disease. The rats in the experiments did not follow the same patterns of behavior as rats would in a community without overcrowding.The females in the rat population were the most seriously affected by the high population density: They showed deviant异常的maternal behavior; they did not behave as mother rats normally do. In fact, many of the pups幼兽，幼崽, as rat babies are called, died as a result of poor maternal care. For example, mothers sometimes abandoned their pups,and, without their mothers' care, the pups died. Under normal conditions, a mother rat would not leave her pups alone to die. However, the experiments verified that in overpopulated communities, mother rats do not behave normally. Their behavior may be considered pathologically 病理上，病理学地diseased.The dominant males in the rat population were the least affected by overpopulation. Each of these strong males claimed an area of the enclosure as his own. Therefore, these individuals did not experience the overcrowding in the same way as the other rats did. The fact that the dominant males had adequate space in which to live may explain why they were not as seriously affected by overpopulation as the other rats. However, dominant males did behave pathologically at times. Their antisocial behavior consisted of attacks on weaker male,female, and immature rats. This deviant behavior showed that even though the dominant males had enough living space, they too were affected by the general overcrowding in the enclosure.Non-dominant males in the experimental rat communities also exhibited deviant social behavior. Some withdrew completely; they moved very little and ate and drank at times when the other rats were sleeping in order to avoid contact with them. Other non-dominant males were hyperactive; they were much more active than is normal, chasing other rats and fighting each other. This segment of the rat population, likeall the other parts, was affected by the overpopulation.The behavior of the non-dominant males and of the other components of the rat population has parallels in human behavior. People in densely populated areas exhibit deviant behavior similar to that of the rats in Calhoun's experiments. In large urban areas such as New York City, London, Mexican City, and Cairo, there are abandoned children. There are cruel, powerful individuals, both men and women. There are also people who withdraw and people who become hyperactive. The quantity of other forms of social pathology such as murder, rape, and robbery also frequently occur in densely populated human communities. Is the principal cause of these disorders overpopulation? Calhoun’s experiments suggest that it might be. In any case, social scientists and city planners have been influenced by the results of this series of experiments.11. Paragraph l is organized according to__________.A. reasonsB. descriptionC. examplesD. definition12．Calhoun stabilized the rat population_________.A. when it was double the number that could live in the enclosure without stressB. by removing young ratsC. at a constant number of adult rats in the enclosureD. all of the above are correct13.W hich of the following inferences CANNOT be made from theinformation inPara. 1?A. Calhoun's experiment is still considered important today.B. Overpopulation causes pathological behavior in rat populations.C. Stress does not occur in rat communities unless there is overcrowding.D. Calhoun had experimented with rats before.14. Which of the following behavior didn‟t happen in this experiment?A. All the male rats exhibited pathological behavior.B. Mother rats abandoned their pups.C. Female rats showed deviant maternal behavior.D. Mother rats left their rat babies alone.15. The main idea of the paragraph three is that __________.A. dominant males had adequate living spaceB. dominant males were not as seriously affected by overcrowding as the otherratsC. dominant males attacked weaker ratsD. the strongest males are always able to adapt to bad conditionsText DThe first mention of slavery in the statutes法令，法规of the English colonies of North America does not occur until after 1660—some forty years after the importation of the first Black people. Lest we think that existed in fact before it did in law, Oscar and Mary Handlin assure us, that the status of B lack people down to the 1660’s was that of servants. A critique批判of the Handlins’ interpretation of why legal slavery did not appear until the 1660’s suggests that assumptions about the relation between slavery and racial prejudice should be reexamined, and that explanation for the different treatment of Black slaves in North and South America should be expanded.The Handlins explain the appearance of legal slavery by arguing that, during the 1660’s, the position of white servants was improving relative to that of black servants. Thus, the Handlins contend, Black and White servants, heretofore treated alike, each attained a different status. There are, however, important objections to this argument. First, the Handlins cannot adequately demonstrate that t he White servant’s position was improving, during and after the 1660’s; several acts of the Maryland and Virginia legislatures indicate otherwise. Another flaw in the Handlins’ interpretation is their assumption that prior to the establishment of legal slavery there was no discrimination against Black people. It is true that before the 1660’s Black people were rarely called slaves. But this shouldnot overshadow evidence from the 1630’s on that points to racial discrimination without using the term slavery. Such discrimination sometimes stopped short of lifetime servitude or inherited status—the two attributes of true slavery—yet in other cases it included both. The Handlins’ argument excludes the real possibility that Black people in the English colonies were never treated as the equals of White people.The possibility has important ramifications后果，影响.If from the outset Black people were discriminated against, then legal slavery should be viewed as a reflection and an extension of racial prejudice rather than, as many historians including the Handlins have argued, the cause of prejudice. In addition, the existence of discrimination before the advent of legal slavery offers a further explanation for the harsher treatment of Black slaves in North than in South America. Freyre and Tannenbaum have rightly argued that the lack of certain traditions in North America—such as a Roman conception of slavery and a Roman Catholic emphasis on equality— explains why the treatment of Black slaves was more severe there than in the Spanish and Portuguese colonies of South America. But this cannot be the whole explanation since it is merely negative, based only on a lack of something. A more compelling令人信服的explanation is that the early and sometimes extreme racial discrimination in the English colonies helped determine the particular nature of the slavery that followed. (462 words)16. Which of the following is the most logical inference to be drawn from the passage about the effects of “several acts of the Maryland and Virginia legislatures” (Para.2) passed during and after the 1660‟s?A. The acts negatively affected the pre-1660’s position of Black as wellas of White servants.B. The acts had the effect of impairing rather than improving theposition of White servants relative to what it had been before the 1660’s.C. The acts had a different effect on the position of white servants thandid many of the acts passed during this time by the legislatures of other colonies.D. The acts, at the very least, caused the position of White servants toremain no better than it had been before the 1660’s.17. With which of the following statements regarding the status ofBlack people in the English colonies of North America before the 1660‟s would the author be LEAST likely to agree?A. Although black people were not legally considered to be slaves,they were often called slaves.B. Although subject to some discrimination, black people had a higherlegal status than they did after the 1660’s.C. Although sometimes subject to lifetime servitude, black peoplewere not legally considered to be slaves.D. Although often not treated the same as White people, black people,like many white people, possessed the legal status of servants.18. According to the passage, the Handlins have argued which of thefollowing about the relationship between racial prejudice and the institution of legal slavery in the English colonies of North America?A. Racial prejudice and the institution of slavery arose simultaneously.B. Racial prejudice most often the form of the imposition of inheritedstatus, one of the attributes of slavery.C. The source of racial prejudice was the institution of slavery.D. Because of the influence of the Roman Catholic Church, racialprejudice sometimes did not result in slavery.19. The passage suggests that the existence of a Roman conception ofslavery in Spanish and Portuguese colonies had the effect of _________.A. extending rather than causing racial prejudice in these coloniesB. hastening the legalization of slavery in these colonies.C. mitigating some of the conditions of slavery for black people in these coloniesD. delaying the introduction of slavery into the English colonies20. The author considers the explanation put forward by Freyre andTannenbaum for the treatment accorded B lack slaves in the English colonies of North America to be _____________.A. ambitious but misguidedB. valid有根据的but limitedC. popular but suspectD. anachronistic过时的，时代错误的and controversialUNIT 2Text AThe sea lay like an unbroken mirror all around the pine-girt, lonely shores of Orr’s Island. Tall, kingly spruce s wore their regal王室的crowns of cones high in air, sparkling with diamonds of clear exuded gum流出的树胶; vast old hemlocks铁杉of primeval原始的growth stood darkling in their forest shadows, their branches hung with long hoary moss久远的青苔;while feathery larches羽毛般的落叶松,turned to brilliant gold by autumn frosts, lighted up the darker shadows of the evergreens. It was one of those hazy朦胧的, calm, dissolving days of Indian summer, when everything is so quiet that the fainest kiss of the wave on the beach can be heard, and white clouds seem to faint into the blue of the sky, and soft swathing一长条bands of violet vapor make all earth look dreamy, and give to the sharp, clear-cut outlines of the northern landscape all those mysteries of light and shade which impart such tenderness to Italian scenery.The funeral was over,--- the tread鞋底的花纹/ 踏of many feet, bearing the heavy burden of two broken lives, had been to the lonely graveyard, and had come back again,--- each footstep lighter and more unconstrained不受拘束的as each one went his way from the great old tragedy of Death to the common cheerful of Life.The solemn black clock stood swaying with its eternal ―tick-tock, tick-tock,‖ in the kitchen of the brown house on Orr’s Island. There was there that sense of a stillness that can be felt,---such as settles down on a dwelling住处when any of its inmates have passed through its doors for the last time, to go whence they shall not return. The best room was shut up and darkened, with only so much light as could fall through a little heart-shaped hole in the window-shutter,---for except on solemn visits, or prayer-meetings or weddings, or funerals, that room formed no part of the daily family scenery.The kitchen was clean and ample, hearth灶台, and oven on one side, and rows of old-fashioned splint-bottomed chairs against the wall. A table scoured to snowy whiteness, and a little work-stand whereon lay the Bible, the Missionary Herald, and the Weekly Christian Mirror, before named, formed the principal furniture. One feature, however, must not be forgotten, ---a great sea-chest水手用的储物箱,which had been the companion of Zephaniah through all the countries of the earth. Old, and battered破旧的，磨损的, and unsightly难看的it looked, yet report said that there was good store within which men for the most part respect more than anything else; and, indeed it proved often when a deed of grace was to be done--- when a woman was suddenly made a widow in a coast gale大风，狂风, or a fishing-smack小渔船was run down in the fogs off the banks, leaving in some neighboring cottage a family of orphans,---in all such cases, the opening of this sea-chest was an event of good omen 预兆to the bereaved丧亲者;for Zephaniah had a large heart and a large hand, and was apt有…的倾向to take it out full of silver dollars when once it went in. So the ark of the covenant约柜could not have been looked on with more reverence崇敬than the neighbours usually showed to Captain Pennel’s sea-chest.1. The author describes Orr‟s Island in a(n)______way.A.emotionally appealing, imaginativeB.rational, logically preciseC.factually detailed, objectiveD.vague, uncertain2.According to the passage, the “best room”_____.A.has its many windows boarded upB.has had the furniture removedC.is used only on formal and ceremonious occasionsD.is the busiest room in the house3.From the description of the kitchen we can infer that thehouse belongs to people who_____.A.never have guestsB.like modern appliancesC.are probably religiousD.dislike housework4.The passage implies that_______.A.few people attended the funeralB.fishing is a secure vocationC.the island is densely populatedD.the house belonged to the deceased5.From the description of Zephaniah we can see thathe_________.A.was physically a very big manB.preferred the lonely life of a sailorC.always stayed at homeD.was frugal and saved a lotText BBasic to any understanding of Canada in the 20 years after the Second World War is the country' s impressive population growth. For every three Canadians in 1945, there were over five in 1966. In September 1966 Canada's population passed the 20 million mark. Most of this surging growth came from natural increase. The depression of the 1930s and the war had held back marriages, and the catching-up process began after 1945. The baby boom continued through the decade of the 1950s, producing a population increase of nearly fifteen percent in the five years from 1951 to 1956. This rate of increase had been exceeded only once before in Canada's history, in the decade before 1911 when the prairies were being settled. Undoubtedly, the good economic conditions of the 1950s supported a growth in the population, but the expansion also derived from a trend toward earlier marriages and an increase in the average size of families; In 1957 the Canadian birth rate stood at 28 per thousand, one of the highest in the world. After the peak year of 1957, thebirth rate in Canada began to decline. It continued falling until in 1966 it stood at the lowest level in 25 years. Partly this decline reflected the low level of births during the depression and the war, but it was also caused by changes in Canadian society. Young people were staying at school longer, more women were working; young married couples were buying automobiles or houses before starting families; rising living standards were cutting down the size of families. It appeared that Canada was once more falling in step with the trend toward smaller families that had occurred all through theWestern world since the time of the Industrial Revolution. Although the growth in Canada’s population had slowed down by 1966 (the cent), another increase in the first half of the 1960s was only nine percent), another large population wave was coming over the horizon. It would be composed of the children of the children who were born during the period of the high birth rate prior to 1957.6. What does the passage mainly discuss?A. Educational changes in Canadian society.B. Canada during the Second World War.C. Population trends in postwar Canada.D. Standards of living in Canada.7. According to the passage, when did Canada's baby boom begin?A. In the decade after 1911.B. After 1945.C. During the depression of the 1930s.D. In 1966.8. The author suggests that in Canada during the 1950s____________.A. the urban population decreased rapidlyB. fewer people marriedC. economic conditions were poorD. the birth rate was very high9. When was the birth rate in Canada at its lowest postwar level?A. 1966.B. 1957.C. 1956.D. 1951.10. The author mentions all of the following as causes of declines inpopulation growth after 1957 EXCEPT_________________.A. people being better educatedB. people getting married earlierC. better standards of livingD. couples buying houses11.I t can be inferred from the passage that before the IndustrialRevolution_______________.A. families were largerB. population statistics were unreliableC. the population grew steadilyD. economic conditions were badText CI was just a boy when my father brought me to Harlem for the first time, almost 50 years ago. We stayed at the hotel Theresa, a grand brick structure at 125th Street and Seventh avenue. Once, in the hotel restaurant, my father pointed out Joe Louis. He even got Mr. Brown, the hotel manager, to introduce me to him, a bit punchy强力的but still champ焦急as fast as I was concerned.Much has changed since then. Business and real estate are booming. Some say a new renaissance is under way. Others decry责难what they see as outside forces running roughshod肆意践踏over the old Harlem. New York meant Harlem to me, and as a young man I visited it whenever I could. But many of my old haunts are gone. The Theresa shut down in 1966. National chains that once ignored Harlem now anticipate yuppie money and want pieces of this prime Manhattan real estate. So here I am on a hot August afternoon, sitting in a Starbucks that two years ago opened a block away from the Theresa, snatching抓取，攫取at memories between sips of high-priced coffee. I am about to open up a piece of the old Harlem---the New York Amsterdam News---when a tourist。
新视野大学英语(第三版)读写教程Book2-unit8-textA课文翻译
Unit 8 Section A Animals or children?—A scientist's choice动物还是孩子？——一位科学家的选择1 I am the enemy! I am one of those cursed, cruel physician scientists involved in animal research. These rumors sting, for I have never thought of myself as an evil person. I became a children's doctor because of my love for children and my supreme desire to keep them healthy. During medical school and residency, I saw many children die of cancer and bloodshed from injury —circumstances against which medicine has made great progress but still has a long way to go. More importantly, I also saw children healthy thanks to advances in medical science such as infant breathing support, powerful new medicines and surgical techniques and the entire field of organ transplantation. My desire to tip the scales in favor of healthy, happy children drew me to medical research.1 我就是那个敌人！我就是那些被人诅咒的、残忍的、搞动物实验的医生科学家之一。
九江2024年小学三年级第七次英语第二单元测验卷(有答案)
九江2024年小学三年级英语第二单元测验卷(有答案)考试时间：90分钟（总分:120）B卷考试人：_________题号一二三四五总分得分一、综合题(共计100题)1、填空题：I saw a ________ swimming in the river.2、听力题：Some _______ have thorns to protect themselves.3、What do we call a story that is told using pictures?A. ComicB. Graphic NovelC. MangaD. Illustrated Book答案: B4、填空题：The __________ (历史学家) study and interpret the past.5、听力题：The chemical structure of DNA contains ______ bases.6、What do we call a person who studies stars?A. BiologistB. AstronomerC. ChemistD. Geologist答案:B7、听力题：The boiling point of water is ______ degrees Celsius.8、What is the main language spoken in the UK?A. SpanishB. FrenchC. EnglishD. German答案: C9、ts can grow in ______ (水) without soil. 填空题：Some pla10、听力题：My grandma teaches me how to ____ (knit).11、How many continents are in the world?A. FiveB. SixC. SevenD. Eight12、选择题：What do bees produce?A. MilkB. HoneyC. EggsD. Silk13、What is the name given to a person who studies fossils?A. BiologistB. PaleontologistC. GeologistD. Archaeologist答案: B14、What do you call a person who studies the human body?A. AnatomistB. BiologistC. PhysiologistD. All of the above答案:D15、填空题：I like to have ______ for breakfast.16、听力题：The mountain is ___ (high/low).17、填空题：The _______ (Buddhism) originated in ancient India and spread throughout Asia.Flowers come in many ______, such as red, yellow, and blue.(花的颜色有很多种，如红色、黄色和蓝色。
通用版2024高考英语二轮复习第一板块阅读理解之题型篇专题一第一讲细节理解题_定位信息巧比对讲义
专题一阅读理解[全国卷3年考情分析]题型与题量卷别细微环节理解题推理推断题主旨大意题词义揣测题考情分析从统计表可以看出，高考英语阅读理解的题型设置以细微环节理解题和推理推断题为主，兼顾主旨大意题和词义揣测题。
细微环节理解题相对简洁，而其他三种题型相对较难。
在近两年的考查趋向上，细微环节理解题的答案更加隐藏，叙述含蓄，干脆信息题会越来越少，取而代之的将是事实细微环节题加入很多推理、推断、归纳等元素；推理推断题的难度会适当加大。
本专题将对这四种题型进行递进式的指导。
2024 卷Ⅰ7 5 2 1 卷Ⅱ9 3 2 1 卷Ⅲ9 3 2 12024 卷Ⅰ7 6 1 1 卷Ⅱ 5 6 2 2 卷Ⅲ 6 6 2 12024 卷Ⅰ10 3 1 1卷Ⅱ7 5 1 2卷Ⅲ8 4 1 2第一讲细微环节理解题——定位信息巧比对细微环节理解题在英语高考阅读理解中占了较大的比重，而且此类题型相对比较简洁，只须要依据题干中的关键词，回到原文定位信息区间，稍加比对，就能得出正确答案。
因此，对于这类题目要力求读得快、找得准、答得对，力争不丢分，保住基本分才能得高分。
但有些细微环节理解题由于命题人有意设置障碍，把有用信息分散在文章不同位置，有时又有转折、否定等，因此有些题目须要细致地思索、对比、计算、对上下文关键信息把握和分析。
尽管细微环节理解题相对简洁，但不行掉以轻心。
细微环节理解题常见的考查题型有：干脆信息题、间接信息题、概括细微环节理解题和正误推断题。
一、题型特点要知晓(一)细微环节理解题常见设问方式1．特别疑问句形式。
以when, where, what, which, who, how much/many等疑问词引出的问题。
2．推断是非形式。
含有TRUE/FALSE, NOT true或EXCEPT等的推断是非的问题。
此时要留意题干中是否含有否定词，如not, never等。
3．以“According to ...”开头的提问形式。
小学下册第16次英语上册试卷(含答案)
小学下册英语上册试卷(含答案)考试时间：80分钟（总分:140）B卷一、综合题(共计100题共100分)1. 填空题：We can _____ (harvest) crops in the fall.2. 填空题：The __________ (历史的声音) is powerful.3. 填空题：Before bed, I read a ________ (书) or watch a little TV. Then I brush my teeth and say ________ (晚安) to my parents.4. 听力题：My sister loves to ________ stories.5. 填空题：If someone asks for my name, I will say, "I am ." (如果有人问我的名字，我会说：“我是。
”)6. 听力题：The teacher is _____ (kind/mean) to us.7. 听力题：The chemical symbol for titanium is ______.8. 听力题：My dad loves to go fishing at the ____ (lake).9. 听力题：I want to ___ (learn/know) more about it.10. 填空题：The __________ (历史的传承) preserves our stories.How many wheels does a bicycle have?A. 1B. 2C. 3D. 4答案: B. 212. 选择题：What do we call the liquid that comes from clouds?A. RainB. SnowC. HailD. Sleet答案:A13. 听力题：A _______ is a substance that forms when a reaction occurs.14. 听力题：I enjoy ___ (gardening) with my grandma.15. 听力题：A lizard can change its color to ______.16. 填空题：I enjoy _______ (reading/writing) stories.17. 听力题：A __________ is a substance made of two or more elements.18. 选择题：What do we call the practice of planting trees to restore forests?A. ReforestationB. DeforestationC. AfforestationD. Conservation答案: A. Reforestation19. 填空题：When it’s cold, I wear ______ (毛衣).20. 听力题：The birds are ______ in the morning. (chirping)What is the name of the famous statue in New York Harbor?A. Statue of LibertyB. DavidC. Christ the RedeemerD. Venus de Milo答案:A22. 填空题：The ancient Persians built a vast ________.23. 选择题：What do you call a group of lions?A. PackB. FlockC. PrideD. Herd答案: C24. 选择题：What is the capital city of Thailand?A. BangkokB. PhuketC. Chiang MaiD. Pattaya答案: A25. 填空题：A _____ (生态) approach helps in conservation.26. 填空题：The ancient civilization of ________ is celebrated for its innovations in art.27. 填空题：I see many ______ (雪花) falling from the sky.28. 填空题：The _______ (The American Revolutionary War) was a fight for independence from Britain.29. 选择题：Which planet is known for its rings?A. MarsB. VenusC. SaturnD. Mercury答案:C30. 听力题：Sound waves require a medium such as air, water, or ______.31. 听力题：Acidic solutions have a pH less than _______.32. 填空题：A ____(waste diversion) strategy promotes recycling and composting.33. 选择题：What do we call a person who prepares food in a restaurant?A. ChefB. CookC. BakerD. All of the above34. 听力题：The _____ (教室) has many desks.35. 选择题：What is 100 25?a. 65b. 70c. 75d. 80答案:C36. 选择题：What is the capital of Mexico?a. Mexico Cityb. Guadalajarac. Monterreyd. Puebla答案:a37. 选择题：What is the capital of Syria?A. AleppoB. DamascusC. HomsD. LatakiaWhat do we call the study of living things?A. ChemistryB. BiologyC. PhysicsD. Astronomy39. 听力题：A ______ is a systematic approach used in research.40. 选择题：Which of these is a fruit?A. CarrotB. PotatoC. TomatoD. Lettuce答案:C41. 填空题：A ________ (植物基因组) holds secrets to growth.42. 选择题：Which animal lives in a hive?A. AntB. BeeC. SpiderD. Wasp43. 填空题：The ________ (农业可持续性) is essential.44. 听力题：In a chemical equation, the substances on the left are the _______.45. 选择题：What do you call a baby wombat?A. JoeyB. KitC. PupD. Calf46. 填空题：The ________ was a turning point in the fight for national sovereignty.47. 填空题：I enjoy painting ______ pictures.What do bees collect from flowers?A. NectarB. HoneyC. WaterD. Pollen答案: A49. 填空题：A ________ (河口) is where a river meets the ocean.50. 填空题：My favorite dish is ______ (米饭).51. 听力题：My mom makes the best ________.52. 选择题：What do you call a hard, outer covering of an egg?A. ShellB. MembraneC. AlbumenD. Yolk53. 选择题：What type of animal is a parrot?A. MammalB. ReptileC. BirdD. Fish答案：C54. 听力题：The body of water surrounding an island is known as __________.55. 听力题：I need to ________ my lunch.56. 选择题：What is the capital of Armenia?A. YerevanB. TbilisiC. BakuD. Ankara答案: AHow many legs does a dog have?A. TwoB. FourC. SixD. Eight58. 听力题：The dog is _____ by the tree. (sitting)59. 听力题：We need to clean the ______. (house)60. 填空题：A _____ (植物保护协会) can advocate for endangered species.61. 听力题：The Earth's surface is constantly reshaped by natural ______.62. 选择题：Which organ pumps blood in the body?A. BrainB. LungsC. HeartD. Liver63. 听力题：A ______ is a natural satellite that orbits a planet.64. 听力题：The house is ___. (big)65. 听力题：The chemical symbol for sodium is ______.66. 填空题：I like watching ________ (电影) with my family.67. 填空题：The ________ (人工湖) provides water for irrigation.68. 听力题：They are _____ (celebrating) a birthday.69. 听力题：A __________ is a common insect that can be found in backyards.Which holiday involves carving pumpkins?A. ChristmasB. HalloweenC. ThanksgivingD. New Year’s71. 填空题：The _____ (树叶) change color in the fall.72. 听力题：The dog is _____ (barking/sleeping) in the yard.73. 听力题：Asteroids can be dangerous if they enter Earth's _______.74. 听力题：I have a ________ in my pocket.75. 听力题：A biome is a large geographical area with similar ______ conditions.76. 选择题：What do we call a person who studies ancient civilizations?A. HistorianB. ArchaeologistC. GeologistD. Anthropologist答案:B77. 听力题：The process of ______ can lead to the formation of minerals.78. 听力题：The __________ is a large area of flat land in Europe.79. 听力题：The chemical symbol for bismuth is _____.80. 听力题：The stars are ___ (twinkling) at night.81. 填空题：Learning about plant adaptations can be fascinating and ______. (了解植物的适应性可以是令人着迷的体验。
小学上册第七次英语第3单元测验试卷(含答案)
小学上册英语第3单元测验试卷(含答案)英语试题一、综合题(本题有100小题，每小题1分，共100分.每小题不选、错误，均不给分)1. A ________ (植物景观设计) can transform spaces.2.The _____ (train) is leaving the station.3. A ____(green infrastructure) integrates nature into urban planning.4.What is the name of the sweet treat made from cocoa?A. CandyB. Ice CreamC. ChocolateD. Cake答案: C5.The _____ (broccoli) is a nutritious vegetable.6.The ancient Chinese philosopher _____ founded Confucianism.7.My grandma is my beloved _______ who shares stories and wisdom with me.8. A suspension differs from a solution because the particles can ______.9. A __________ is a creature that can live in extreme conditions.10.The ______ helps fish to swim.11.Lemon juice is an example of an _____ solution.12.I can evoke emotions with my ________ (玩具名称).13.n Wall was a symbol of the ________ (冷战). The Berl14.Which fruit is red and often mistaken for a vegetable?A. BananaB. TomatoC. CarrotD. Grapes答案:B.Tomato15. A ______ (生态保护) program can save endangered species.16.The chemical formula for uric acid is ______.17.What do we call the large, round fruit that is red or green?A. BananaB. CherryC. AppleD. Peach答案:C18.The ____ is a wise creature often associated with knowledge.19.What is the term for a young platypus?A. PlatypupB. ChickC. HatchlingD. Calf答案:c20.Planting a variety of species can create a more resilient ______. (种植多样的物种可以创造出更强韧的生态系统。
icml 论文
Automatic Discovery and Transfer of MAXQ HierarchiesNeville Mehta mehtane@ Soumya Ray sray@ Prasad Tadepalli tadepall@ Thomas Dietterich tgd@ Oregon State University,Corvallis OR97331,USAAbstractWe present an algorithm,HI-MAT(Hierar-chy Induction via Models And Trajectories),that discovers MAXQ task hierarchies by ap-plying dynamic Bayesian network models toa successful trajectory from a source rein-forcement learning task.HI-MAT discoverssubtasks by analyzing the causal and tem-poral relationships among the actions in thetrajectory.Under appropriate assumptions,HI-MAT induces hierarchies that are consis-tent with the observed trajectory and havecompact value-function tables employing safestate abstractions.We demonstrate empir-ically that HI-MAT constructs compact hi-erarchies that are comparable to manually-engineered hierarchies and facilitate signifi-cant speedup in learning when transferred toa target task.1.IntroductionScaling up reinforcement learning(RL)to large do-mains requires leveraging the structure in these do-mains.Hierarchical reinforcement learning(HRL)pro-vides mechanisms through which domain structure can be exploited to constrain the value function and pol-icy space of the learner,and hence speed up learning (Sutton et al.,1999;Dietterich,2000;Andre&Rus-sell,2002).In the MAXQ framework,a task hierarchy is defined(along with relevant state variables)for rep-resenting the value function of the overall task.This allows for decomposed subtask-specific value functions that are easier to learn than the global value function. Automated discovery of such task hierarchies is com-Appearing in Proceedings of the25th International Confer-ence on Machine Learning,Helsinki,Finland,2008.Copy-right2008by the author(s)/owner(s).pelling for at least two reasons.First,it avoids the sig-nificant human effort in engineering the task-subtask structural decomposition,along with the associated state abstractions and subtask goals.Second,if the same hierarchy is useful in multiple domains,it leads to significant transfer of learned structural knowledge from one domain to the other.The cost of learning can be amortized over several domains.Several researchers have focused on the problem of automatically induc-ing temporally extended actions and task hierarchies (Thrun&Schwartz,1995;McGovern&Barto,2001; Menache et al.,2001;Pickett&Barto,2002;Hengst, 2002;S¸im¸s ek&Barto,2004;Jonsson&Barto,2006). In this paper,we focus on the asymmetric knowledge transfer setting where we are given access to solved source RL problems.The objective is to derive use-ful biases from these solutions that could speed up learning in target problems.We present and evalu-ate our approach,HI-MAT,for learning MAXQ hier-archies from a solved RL problem.HI-MAT applies dynamic Bayesian network(DBN)models to a single successful trajectory from the source problem to con-struct a causally annotated trajectory(CAT).Guided by the causal and temporal associations between ac-tions in the CAT,HI-MAT recursively parses it and defines MAXQ subtasks based on each discovered par-tition of the CAT.We analyze our approach both theoretically and em-pirically.Our theoretical results show that,under appropriate conditions,the task hierarchies induced by HI-MAT are consistent with the observed trajec-tory,and possess compact value-function tables that are safe with respect to state abstraction.Empiri-cally,we show that(1)using a successful trajectory can result in more compact task decompositions than when using only DBNs,(2)our induced hierarchies are comparable to manually-engineered hierarchies on target RL tasks,and MAXQ-learning converges signif-icantly faster thanflat Q-learning on those tasks,and(3)transferring hierarchical structure from a source task can speed up learning in target RL tasks where transferring value functions cannot.2.Background and Related WorkWe briefly review the MAXQ framework(Dietterich, 2000).This framework facilitates learning separate value functions for subtasks which can be composed to compute the value function for the overall semi-Markov Decision Process(SMDP)with state space S and action space A.The task hierarchy H is repre-sented as a directed acyclic graph called the task graph, and reflects the task-subtask relationships.Leaf nodes are the primitive subtasks corresponding to A.Each composite subtask T i defines an SMDP with param-eters X i,S i,G i,C i ,where X i is the set of relevant state variables,S i⊆S is the set of admissible states, G i is the termination/goal predicate,and C i is the set of child tasks of T i.T0represents the root task.T i can be invoked in any state s∈S i,it terminates when s ∈G i,and(s,a)is called an exit if Pr(s |s,a)>0. The set S i is defined using a projection function that maps a world state to an abstract state defined by a subset of the state variables.A safe abstraction function only merges world states that have identical values.The local policy for a subtask T i is a map-pingπi:S i→C i.A hierarchical policyπfor the overall task is an assignment of a local policy to each T i.A hierarchically optimal policy for a given MAXQ graph is a hierarchical policy that has the best pos-sible expected total reward.A hierarchical policy is recursively optimal if the local policy for each subtask is optimal given that all its child tasks are in turn re-cursively optimal.HEXQ(Hengst,2002)and VISA(Jonsson&Barto, 2006)are two existing approaches to learning task hi-erarchies.These methods define subtasks based on the changing values of state variables.HEXQ employs a heuristic that orders state variables based on the fre-quencies of change in their values to induce an exit-option hierarchy.The most frequently-changing vari-able is associated with the lowest-level subtask,and the least frequently-changing variable with the root. VISA uses DBNs to analyze the influence of state vari-ables on one another.The variables are partitioned such that there is an acyclic influence relationship between the variables in different clusters(strongly-connected components).Here,state variables that in-fluence others are associated with lower-level subtasks. VISA provides a more principled rationale for HEXQ’s heuristic–a variable used to satisfy a precondition for setting another variable through an action typically changes more frequently than the other variable.A key difference between VISA and HI-MAT is the use of a successful trajectory in addition to the DBNs.In Section5.1,we provide empirical evidence that this allows HI-MAT to learn hierarchies that are exponen-tially more compact than those of VISA.The algorithm developed by Marthi et al.(2007)takes a search-based approach to generating hierarchies. Flat Q-value functions are learned for the source do-main,and are used to sample trajectories.A greedy top-down search is conducted for the best-scoring hi-erarchy thatfits the trajectories.The set of relevant state variables for each task is determined through sta-tistical tests on the Q values of different states with differing values of the variables.In contrast to this approach,HI-MAT relies less on direct search through the hierarchy space,and more on the causal analysis of a trajectory based on DBN models.3.Discovering MAXQ HierarchiesIn this work,we consider MDPs where the agent is solving a known conjunctive goal.This is a subset of the class of stochastic shortest-path MDPs.In such MDPs,there is a goal state(or a set of goal states),and the optimal policy for the agent is to reach such a state as quickly as possible.We assume that we are given factored DBN models for the source MDP where the conditional probability distributions are represented as trees(CPTs).Further,we are given a successful trajectory that reaches the goal in the source MDP. With this in hand,our objective is to automatically induce a MAXQ hierarchy that can suitably constrain the policy space when solving a related target prob-lem,and therefore achieve faster convergence in the target problem.This is achieved via recursive parti-tioning of the given trajectory into subtasks using a top-down parse guided by backward chaining from the goal.We use the DBNs along with the trajectory to define the termination predicate,the set of subtasks, and the relevant abstraction for each MAXQ subtask. We use the Taxi domain(Dietterich,2000)to illustrate our procedure.Here,a taxi has to transport a passen-ger from a source location to a destination location within a5×5grid-world.The pass.dest variable is restricted to one of four special locations on the grid denoted by R,G,B,Y;the pass.loc could be set to R,G,B,Y or in-taxi;taxi.loc could be one of the25 cells.The goal of pass.loc=pass.dest is achieved by taking the passenger to its intended destination.Be-sides the four navigation actions,a successful Pickup changes pass.loc to in-taxi,and a successful Putdown changes pass.loc from in-taxi to the value of pass.dest.another action b(b following a in the trajectory)iffv is t-relevant to both a and b,and irrelevant to all actions in between.A sink edge,a v−→End connects a with a dummy End action iffv is relevant to a and irrele-vant to all actions before thefinal goal state;this holds analogously for a source edge Start v−→a.A causally annotated trajectory(CAT)is the original trajectory annotated with all the causal,source,and sink edges. Moreover,the CAT is preprocessed to remove any cy-cles present in the original trajectory(failed actions, such as an unsuccessful Pickup,introduce cycles of unit length).A sample CAT for Taxi is shown in Figure1. Given a v−→b,the phrase“literal on a causal edge”refers to a formula of the form v=V where V is the value taken by v in the state before b is exe-cuted.We define DBN-closure(v)as the set of vari-ables that influence v recursively as follows.From the action DBNs,add all variables that appear in internal nodes in the CPTs for the dynamics of v.Next,for each added variable u,union DBN-closure(u)with this set,repeating until no new variables are added. Similarly,the set DBN-closure(reward)contains all variables that influence the reward function of the MDP.The set DBN-closure(fluent)is the union of the DBN-closure s of all variables in thefluent.For example,DBN-closure(goal)is the set of all variables that influence the goalfluent.The CAT ignores all variables v/∈DBN-closure(goal),namely,those vari-ables that never affect the goal conjunction.3.2.The HI-MAT AlgorithmGiven a CAT and the MDP’s goal predicate(or re-cursively,the current subtask’s goal predicate),the main loop of the hierarchy induction procedure is il-lustrated in Algorithm1.The algorithmfirst checks if two stopping criteria are satisfied(lines2&4):ei-ther the trajectory contains only a single primitive the subtask in the CAT.If this CAT segment is non-trivial(neither just the initial state nor the whole tra-jectory),it is stored(line17),and the literals on causal edges that enter it(from earlier in the trajectory)are added to the unsolved goals(line18).This ensures that the algorithm parses the entire trajectory barring redundant actions.If the trajectory segment is equal to the entire trajectory,this implies that the trajectory achieves only the literal u after the ultimate action.In this case,the trajectory is split into two segments:one segment contains the prefix of the ultimate action a n with the preconditions of a n forming the goal literals for this segment(line14);the other segment contains only the ultimate action a n(line15).CAT scanning is repeated until all subgoal literals are accounted for. The only way trajectory segments can overlap is if they have identical boundaries,and the ultimate ac-tion achieves the literals of all these segments.In this case,the segments are merged(line23).Merging re-places the duplicative segments with one that is as-signed a conjunction of the subgoal literals.The HI-MAT algorithm partitions the CAT into unique segments,each achieving a single literal or a conjunction of literals due to merging.It is called re-cursively on each element of the partition(line27). It can be proved that the set of subtasks output by the algorithm is independent of the order in which the literal u is picked(line11).3.2.1.Subtask DetectionGiven a literal,a subtask is determined byfinding the set of temporally contiguous actions that are closed with respect to the causal edges in the CAT such that thefinal action achieves the literal.The idea is to group all actions that contribute to achieving the spe-cific literal being considered.This procedure is shown in Algorithm2.Algorithm1HI-MATInput:CATΩ,Goal predicate G.Output:Task X,S,G,C ;X is the set of relevant vari-ables,S is the set of non-terminal states,G is the goal predicate,C is the set of child actions.2:if n=1then//Single action3:return RelVars(Ω),S,true,a14:else if CheckRelVars(Ω)then//Same relevance 5:S←All states that reach G via Actions(Ω)6:return RelVars(Ω),S,G,Actions(Ω)7:end if8:Ψ←∅//Trajectory segments9:U←Literals(G)10:while U=∅do11:Pick u∈U12:(i,j,u)←CAT-Scan(Ω,u)13:if i=1∧j=n then14:Ψ←Ψ∪{(1,n−1,v):v∈Precondition(a n)} 15:Ψ←Ψ∪{(n,n,∅)}16:else if j>0then//Last segment action=Start 17:Ψ←Ψ∪{(i,j,u)}18:U←U∪{v:∃k<i∃l a k v−→a l∈Ω,i≤l≤j} 19:end if20:U←U−{u}21:end while22:while∃(i,j,u1),(i,j,u2)∈Ψdo23:Ψ←(Ψ−{(i,j,u1),(i,j,u2)})∪{(i,j,u1∧u2)} 24:end while25:C←∅26:for t∈Ψdo27: X t,S t,G t,C t ←HI-MAT(Extract(Ω,t i,t j),t u) 28:C←C∪{ X t,S t,G t,C t }29:end for30:X←RelVars(Ω)∪Variables(G)31:S←All states that reach G via C32:return X,S,G,CAlgorithm2CAT-ScanInput:CATΩ,literal u.Output:(i,j,u);i is the start index,j is the end index. 1:Set j such that a j−→End∈Ω2:i←j−13:while i>0and∀v∃k a i v−→a k=⇒k≤j do4:i←i−15:end while6:return(i+1,j,u)As before,when considering causal edges in line3,we can ignore all causal edges that are labeled with vari-ables not in the DBN-closure of any variable in the current unsolved goal list.Because of the way we con-struct the CAT,we can show that this procedure will always stop before adding an action which has a rel-evant variable that is not relevant to the last action in the partition.Note that the temporal contiguity of the actions we assign to a subtask is required by the MAXQ-style execution of a policy.A hierarchical MAXQ policy cannot interrupt an unterminated sub-task,start executing a sibling subtask,and then return to executing the interrupted subtask.3.2.2.Termination PredicateAfterfinding the partition that constitutes a subtask, we assign a set of child tasks and a termination pred-icate to it.To assign the termination condition to a subtask,we consider the relational test(s)t u in the action and reward DBNs involving the variable u on the causal edge leaving the subtask(line27of Algo-rithm1).When a subtask’s relational termination condition involves other variables not already in the abstraction,these variables are added to the state ab-straction(line30),effectively creating a parameterized subtask.For example,consider the navigation subtask that terminates when taxi.loc=pass.dest in the Taxi domain.The abstraction for this subtask already in-volves taxi.loc.However,pass.dest in the relational test implies that pass.dest behaves like a parameter for this subtask.3.2.3.Action GeneralizationTo determine if the set of primitive actions available to any subtask should be expanded,we follow a bottom-up procedure(not shown in Algorithm1).We start with subtasks that have only primitive actions as chil-dren.We create a merged DBN structure for such a subtask T using the incorporated primitive actions. The merged DBN represents possible variable effects after any sequence of these primitive actions.Next, for each primitive action that we did not see in this trajectory,we consider the subgraph of its DBN that only involves the variables relevant to T.If this is a subgraph of the merged DBN of T,we add this ac-tion to the set of actions available to T.The ratio-nale here is that the added action has similar effects to the actions we observed in the trajectory,and it does not increase the set of relevant variables for T. For example,if the navigation actions used on the ob-served trajectory consisted only of North and East ac-tions,this procedure would also add South and West to the available actions for this subtask.When con-sidering subtasks that have non-primitive children,we only consider adding actions that have not been added to any of the non-primitive children.Given the termination predicate and the generalized set of actions,the set of relevant variables for a sub-task is the union of the set of relevant variables of the merged DBN(described above)and the variables ap-pearing in the termination predicate(line30).Com-puting the relevant variables is similar to explanation-based reinforcement learning(Tadepalli&Dietterich,1997)except that here we care only about the set of relevant variables and not their values.Moreover,the relevant variables are computed over a set rather thana sequence of actions.4.Theoretical AnalysisIn this section,we establish certain theoretical prop-erties of the hierarchies induced by the HI-MAT al-gorithm.We consider a factored SMDP state-spaceS=D x1×...×D xk,where each D xiis the domainof variable x i.We assume that our DBN models have the following property.Definition1A DBN model is maximally sparse if for any y∈Y where Y is the set of parents of some node x (which represents either a state variable or the reward node),and Y =Y−{y},∃y1,y2∈D y Pr(x|Y ,y=y1)=Pr(x|Y ,y=y2). Maximal sparseness implies that the parents of a vari-able have non-trivial influences on it;no parent can be removed without affecting the next-state distribution.A task hierarchy H= V,E ,is a directed acyclic graph,where V is a set of task nodes,and E rep-resents the task-subtask edges of the graph.Each task node T i∈V is defined as in Section2.A trajectory-task pair Ω,T i ,whereΩ= s1,a1,...,s n,a n,s n+1 and T i= X i,S i,G i,C i , is consistent with H if T i∈V,and{s1,...,s n}⊆S i. If T i is a primitive subtask then n=1,and C i=a1. If T i is not primitive then{s1,...,s n}∩G i=∅, s n+1∈G i,and there exist trajectory-task pairs Ωj,T j consistent with H whereΩis a concatenation ofΩ1,...,Ωp and T1,...,T p∈C i.A trajectoryΩis consistent with a hierarchy H if Ω,T0 is consistent with H.Definition2A trajectory s1,a1,...,s n,a n,s n+1 is non-redundant if no subsequence of the action sequence in the trajectory,a1,...,a n,can be removed such that the remaining sequence still achieves the goal starting from s1.Theorem1If a trajectoryΩis non-redundant then HI-MAT produces a task hierarchy H such thatΩis consistent with H.Proof sketch:LetΩ= s1,a1,...,s n,a n,s n+1 be the trajectory.The algorithm extracts the conjunction of literals that are true in s n+1(and not before),and assigns it to the goal,G i.Such literals must exist since,otherwise,some suffix of the trajectory can be removed while the rest still achieves the goal,violating the property of non-redundancy.Since the set S i is set to all states that do not satisfy G i,the condition that all states s1,...,s n are in S i is satisfied.Whenever the trajectory is partitioned into a sequence of sub-trajectories,each sub-trajectory is associated with a conjunction of goal literals achieved by that sub-trajectory.Hence,the above argument applies re-cursively to each such sub-trajectory.Definition3A hierarchy H is safe with respect to the DBN models M if for any trajectory-task pair Ω,T i consistent with H,where T i= X i,S i,G i,C i ,the to-tal expected reward during the trajectory is only a func-tion of the values of x∈X i in the starting state ofΩ.The above definition says that the state variables in each task are sufficient to capture the value of any trajectory consistent with the sub-hierarchy rooted at that task node.Theorem2If the procedure HI-MAT produces a task hierarchy H fromΩand the DBN models M then H is safe with respect to M.Further,if the DBN models are maximally sparse,for any hierarchy H which is consistent withΩand safe with respect to M,and T i= X i,S i,G i,C i in H,there exists T i= X i,S i,G i,C i in H such that X i⊆X i.Proof sketch:By the construction procedure,in any segment of trajectoryΩcomposed of primitive actions under a subtask T i,all primitive actions check or set only the variables in X i.Thus,changing any other variables in the initial state s ofΩyielding s does not change the effects of these actions according to the DBN models.Similarly,all immediate rewards in the trajectory are also functions of the variables in X i.Hence,the total accumulated reward and the probability of the trajectory only depend on X i,and the hierarchy produced is safe with respect to M. Suppose that H is a consistent hierarchy which is safe with respect to M.Let a i be the last action in the trajectoryΩi corresponding to the subtask T i in H. By consistency,there must be some task T i in H that matches up with a i.Recall that X i includes only those variables checked and set by a i to achieve the goal G i. We claim that the abstraction variables X i of T i must include X i.If this is not the case then,by maximal sparseness,there is a variable y in X i−X i and some values y1and y2such that the probabilities of the next state or reward are different based on whether y=y1 or y=y2.Hence,H would not be safe,leading to a contradiction.i iof task T i.If all features are binary and there are t tasks then the total number of values for the value-function tables is O(t2n max).Since the hierarchy is a tree with the primitive actions at the leaves,the number of subtasks is bounded by2l where l is the length of the trajectory.Hence,we can claim that the number of parameters needed to fully specify the value-function tables in our hierarchy is at most O(l) times that of the best possible.Our analysis does not address state abstractions aris-ing from the so-called funnel property of subtasks where many starting states result in a few terminal states.Funnel abstractions permit the parent task to ignore variables that,while relevant inside the child task,do not affect the terminal state.Nevertheless, our analysis captures some of the key properties of our algorithm including consistency with the trajec-tory,safety,minimality,and sheds some light on its effectiveness.5.Empirical EvaluationWe test three hypotheses.First,we expect that em-ploying a successful trajectory along with the action models will allow the HI-MAT algorithm to induce task hierarchies that are much more compact than(or at least as compact as)just using the action models. Second,in a transfer setting,we expect that the hier-archies induced by HI-MAT will speed up convergence to the optimal policy in related target problems.Fi-nally,we expect that the HI-MAT hierarchies will be applicable to and speed up learning in RL problems which are different enough from the source problems such that value functions either do not transfer or lead to poor transfer.5.1.Contribution of the TrajectoryTo highlight ourfirst hypothesis,a modified Bitflip do-main(Diuk et al.,2006)is designed as follows.The state is represented by n bits,b0b1...b n−1.There are n actions denoted by Flip(i).Flip(i)toggles b i if both Figure2.Task hierarchies for the modified Bitflip domain.-3000-2500-2000-1500-1000-5000 10 20 30 40 50TotalRewardEpisodeQVISAHI-MATFigure3.Performance of Q,VISA,and HI-MAT in the7-bit modified Bitflip domain(averaged over20runs).b i−1is set and the parity across bits b0,...,b i−1is even when i is even(odd otherwise);if not,it resets the bits b0,...,b i.All bits are reset at the initial state,and the goal is to set all bits.We ran both VISA and HI-MAT in this domain with n=7,and compared the induced hierarchies(Fig-ure2).We observe that VISA constructs an ex-ponentially sized hierarchy even with subtask merg-ing activated within VISA.There are two reasons for this.First,VISA relies on the full action set to con-struct its causal graph,and does not take advantage of any context-specific independence among its variables that may arise when the agent acts according to cer-tain policies.Specifically,for this domain,the causal graph constructed from DBN analysis has only two strongly connected components(SCCs):one partition has{b0,...,b n−2},and the other has{b n−1}.This SCC cannot be further decomposed using only infor-mation from the DBNs.Second,VISA creates exit op-tions for all strongly connected components that tran-sitively influence the reward function,whereas only a few of these may actually be necessary to solve the problem.Specifically,for this problem,VISA createsan exit condition for any instantiation that satisfies parity(b0,...,b n−2)∧b n−2=1,resulting in exponen-tial number of subtasks shown in Figure2(a).The successful trajectory provided to HI-MAT achieves the goal by setting the bits going from left to right,and re-sults in the hierarchy in Figure2(b).The performance results are shown in Figure3.VISA’s hierarchy con-verges even slower than the basic Q learner because the root has O(2n)children as opposed to O(n).This domain has been engineered to highlight the case when access to a successful trajectory allows for sig-nificantly more compact hierarchies than without.We expect that access to a solved instance will usually im-prove the compactness of the resulting hierarchy.5.2.Transfer of the Task HierarchyTo test our remaining hypotheses,we apply the trans-fer setting to two domains:Taxi and the real-time strategy game Wargus.The Taxi domain has been de-scribed in Section3.The source and target problems in Taxi differ only in the wall configurations;the pas-senger sources and destinations are the same.This is engineered to allow value-function transfer to occur. For Wargus,we consider the resource collection prob-lem.Here,the agent has units called peasants that can harvest gold and wood from goldmines and forests respectively,and deposit them at a townhall.The goal is to reach a predetermined quota of gold and wood. Since the HI-MAT approach does not currently gener-alize to termination conditions involving numeric pred-icates,the state representation of the domain replaces the actual quota variables with Boolean variables that are set when the requisite quotas of gold and wood are met.We consider target problems whose specifica-tions are scaled up from that of the source problems, including the number of peasants,goldmines,forests, and the size of the map.In this domain,coordina-tion does not affect the policy significantly.Thus,in the target maps,we learn a hierarchical policy for the peasants using a shared hierarchy structure without coordination(Mehta&Tadepalli,2005).In each case, we report the total reward received as a function of the number of episodes,averaged over multiple trials. We compare three basic approaches:(1)non-hierarchical Q-learning(Q),(2)MAXQ-learning ap-plied to a hierarchy manually engineered for each do-main(Manual),and(3)MAXQ-learning applied to the HI-MAT hierarchy induced for each domain(HI-MAT). The HI-MAT algorithmfirst solves the source prob-lem usingflat Q-learning,and generates a successful trajectory from it.In Taxi,we also show the perfor-mance of initializing the value-function tables with val-TotalRewardEpisodeFigure4.Performance in the Taxi domain(averaged over 20runs).Source and target problems differ only in the configuration of the grid walls.500010000150002000025000300000 10 20 30 40 50 60EpisodeDurationEpisodeQManualVISAHI-MATFigure5.Performance in the Wargus domain(averaged over10runs).Source:25×25grid,1peasant,2gold-mines,2forests,1townhall,100units of gold,100units of wood.Target:50×50grid,3peasants,3goldmines,3 forests,1townhall,300units of gold,300units of wood.ues learned from the source problem–these curves are suffixed with the phrase“with value”.In Wargus,we include the performance of VISA.The results of these experiments are shown in Figures4and5.Although the target problems in Taxi allow value-function transfer to occur,the target problems are still different enough that the agent has to“unlearn”the old policy.This leads to negative transfer evidenced in the fact that transferring value functions leads to worse rates of convergence to the optimal policy than transferring just the hierarchy structure with uninitial-ized policies.This indicates that transferring struc-tural knowledge via the task-subtask decomposition can be superior to transferring value functions espe-cially when the target problem differs significantly in terms of its optimal policy.In Wargus,the difference。
新视野大学英语读写教程第六册第五单元课件(1)
B6U5
A Revolution in Biology — and Society?
•
• Biotechnology would soon transform the pharmaceutical industry and genetically modified food was to herald the biggest revolution in agriculture since the industrialization of farming. Yet the public was skeptical, and so were certain scientists. Some feared that a cancer-causing gene stitched into the DNA of a bacterium might be accidentally absorbed in the human gut, enabling cancer to be passed on like an infectious disease. Biologists from all over the world were called to a meeting in California to draw up a strict set of safety guidelines. When the panic subsided the stage was set for a biotechnology bonanza. A race began to produce genetically engineered insulin ['insjulin] 胰岛素. A couple of years later a young researcher called Rob Horsch, who worked for the chemical giant Monsanto, produced the first genetically engineered plant. The biotech revolutions had arrived.
dna英语作文
When writing an essay in English about DNA,it is important to structure your essay in a clear and logical manner.Here is a stepbystep guide to help you write a compelling essay on this topic:1.IntroductionBegin with an attentiongrabbing hook that introduces the concept of DNA. Provide a brief overview of what DNA is and its significance in various fields such as biology,medicine,and forensic science.State your thesis statement,which could be an argument,a question,or a declaration about the importance of understanding DNA.2.Background InformationGive a brief history of the discovery of DNA and its structure.Introduce the key scientists who contributed to the understanding of DNA,such as James Watson,Francis Crick,Rosalind Franklin,and Maurice Wilkins.3.Structure and Function of DNADescribe the basic structure of DNA,including the double helix,nucleotides,and the four nitrogenous bases adenine,thymine,cytosine,and guanine.Explain the function of DNA in the cell,including replication,transcription,and translation.4.DNA and GeneticsDiscuss how DNA carries genetic information and how it is passed down from parents to offspring.Explain the concept of genes and how they influence traits and characteristics.5.DNA in MedicineExplore the role of DNA in medical research and treatment,such as personalized medicine and gene therapy.Discuss the use of DNA in diagnosing genetic disorders and diseases.6.DNA in Forensic ScienceDescribe how DNA is used in forensic investigations to solve crimes,such as through DNA fingerprinting and profiling.Discuss the ethical considerations and potential misuse of DNA in forensics.7.Ethical and Social ImplicationsAddress the ethical issues surrounding DNA testing,such as privacy concerns and the potential for discrimination.Discuss the social implications of DNA research,including the impact on identity and societal perceptions of genetic determinism.8.Future of DNA ResearchSpeculate on the future directions of DNA research,such as advances in genetic engineering and the potential for creating synthetic life.Consider the implications of these advancements for society and the environment.9.ConclusionSummarize the main points of your essay,reiterating the importance of DNA and its multifaceted role in various fields.Restate your thesis and provide a final thought or call to action regarding the significance of understanding and responsibly utilizing DNA technology.10.ReferencesList all the sources you have cited in your essay,following the appropriate citation style e.g.,APA,MLA,or Chicago.Remember to use clear and concise language,provide evidence to support your claims, and maintain a formal tone throughout your essay.Additionally,proofread your work for grammar,spelling,and punctuation errors to ensure a polished final product.。
丹东2024年08版小学6年级第8次英语第1单元暑期作业(含答案)
丹东2024年08版小学6年级英语第1单元暑期作业(含答案)考试时间：90分钟（总分:110）A卷考试人：_________题号一二三四五总分得分一、综合题(共计100题共100分)1. 选择题：What is the opposite of ‘day’?A. NightB. MorningC. NoonD. Evening2. 听力题：I like to ________ new friends.3. 听力题：The girl enjoys ________.4. 听力题：They are going to ________ a movie.5. 听力题：The center of the sun is extremely ______.6. 听力题：The process of making vinegar involves fermentation of _______.7. 听力题：A telescope helps astronomers study distant ______.8. 听力题：We use ______ to measure distance.9. 填空题：________ (植物适应性研究) can lead to innovations.What is the capital of the United Kingdom?a. Dublinb. Edinburghc. Londond. Cardiff答案:C11. 听力题：A chemical that can donate protons in a reaction is called an ______.12. 填空题：The ________ grows tall and strong in the garden.13. 填空题：I have a ______ in my garden.14. 选择题：What is the primary purpose of a refrigerator?A. CookingB. HeatingC. CoolingD. Freezing答案：C15. 填空题：The country known for its indigenous cultures is ________(以土著文化闻名的国家是________).16. 听力题：The chemical formula for potassium nitrate is _____.17. 听力题：A __________ is a small insect that can fly.18. 听力题：The candy is very ___. (colorful)19. 选择题：What do you call the frozen form of water?A. SteamB. LiquidC. IceD. Vapor答案：CThe __________ (生态平衡) is vital for health.21. 听力题：I have a pet ___ (fish).22. 选择题：What do you use to write on paper?A. PaintB. PencilC. GlueD. Scissors答案:B23. 填空题：I call my teacher __________. (老师)24. 听力题：The __________ is a large area with many different climates.25. 选择题：What color is the sky?天空是什么颜色的？A. RedB. BlueC. GreenD. Yellow答案: B26. 填空题：I believe that friendship is one of the most ________ (重要的) things in life.27. 听力题：She is ___ a picture. (drawing)28. 听力题：My sister enjoys crafting ____ (cards).29. 填空题：The tiger's stripes help it blend into its ________________ (栖息地).30. 听力题：The main product of the citric acid cycle is ______.31. 听力填空题：I think learning a new language is fun. I’m currently learning __________.My dad works in an ________.33. 填空题：The _____ (狒狒) is a type of monkey found in Africa.34. 听力题：The chemical symbol for neon is ______.35. 听力题：A thermometer measures ______ temperature.36. 听力题：The unit for measuring volume is __________.37. 填空题：My cat loves to take a _________. (午睡)38. 填空题：The kitten loves to play with a ______ (小球). It has so much ______ (乐趣).39. 听力题：The main gas emitted during combustion is __________.40. 选择题：What is the capital city of Canada?A. TorontoB. OttawaC. VancouverD. Montreal41. 选择题：What do we call a large area covered with trees?A. DesertB. ForestC. MountainD. Plain答案:B42. 选择题：What is the process of converting a liquid into a gas called?a. Meltingb. Freezingc. Evaporationd. Condensation答案:cThe sun rises in the ______ (east).44. 选择题：What do we call the fear of heights?A. ClaustrophobiaB. AgoraphobiaC. AcrophobiaD. Nyctophobia45. 填空题：The ______ of a flower can sometimes determine its fragrance. (花的结构有时会决定它的香气。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
A Heuristic Approach to Scoring Gene Clustering AlgorithmsLongde YinDept. of Computer Science & Engineering University of ConnecticutStorrs, CT06269, USAyin@Chun-Hsi HuangDept. of Computer Science & Engineering University of ConnecticutStorrs, CT06269, USAhuang@AbstractIn the past decades, many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. Given the same data set, different clustering algorithms can potentially generate very different clusters. A biologist with a gene expression data set is faced with the problem of choosing an appropriate clustering algorithm for his or her data set. In this paper, we present a new tool that allows the similarity analysis of clusters generated by different algorithms. This tool may: (1) improve the quality of the data analysis results, (2) support the prediction of the number of relevant clusters in the Microarray datasets, and (3) provide cross-reference between different algorithms. The software tool can also be used to analyze cluster similarities from other biomedical data. We demonstrate the use of this tool with gene expression data of Leukaemia and Sporulation. Keywords: Clustering algorithms, Gene expression, Microarray, Cluster Similarity Analysis.1. IntroductionRecent advances of the DNA Microarray technology allow monitoring gene expression profiles of thousands of genes simultaneously [1]. However, the analysis and handling of such fast growing data is becoming the major bottleneck in the utilization of the technology. Clustering analysis is one of the most effective methods for analyzing such gene expression data [2, 14].A lot of clustering methods have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. Assessing the clustering results and interpreting the clusters found are as important as generating the clusters [13, 22]. Given the same data set, different clustering algorithms can potentially generate very different clusters. A biologist with a gene expression data set is faced with the problem of choosing an appropriate clustering algorithm for his or her data set. Our paper provides a data mining tool, Cluster Diff, which allows the similarity analysis of clusters generated by different algorithms. This tool may: (1) improve the quality of the data analysis results, (2) support the prediction of the number of relevant clusters in the microarray datasets, and (3) provide cross-reference between different algorithms. The software tool can also be used to analyze cluster similarities from other biomedical data.In this paper we first introduce this software tool, then, as an application example, we apply this software to analyze the clusters generated by K-means [5, 19, 20], Cluster Identification via Connectivity Kernels (CLICK) [23], and Self-Organizing Map (SOM) [8, 9].The remainder of the paper is organized as follows. In Section 2 we present the software tool in detail. Section 3 describes clustering results from three major clustering algorithms: K-means, CLICK, and SOM using two different datasets Leukaemia gene expression data and Sporulation data. A comparative study is presented in Section 4. Conclusions are presented in Section 52. Software Tool OverviewThere are many clustering algorithms proposed in the last several decades, but little guidance is available to help choose among them. For example, they lackfacilities for estimating the optimal number of clusters, as well as components for evaluating the quality of the clusters obtained. In this section, we present a software tool that offers cluster similarity analysis methods for DNA microarray data analysis.We present a new tool, Cluster Diff , which allows the similarity analysis of clusters generated by different algorithms. This tool may: (1) improve the quality of the data analysis results, (2) support the prediction of the number of relevant clusters in the microarray datasets, and (3) provide cross-reference between different algorithms. The software tool can also be used to analyze cluster similarities from other biomedical data.2.1. Software IntroductionThe software allows working with two datasets each time. The Main Window (panel) (Figure 1.)contains the file, view, and help.Figure 1. Screenshot of the main windowIn Figure 1, the left group (A) has 6 clusters, from A0 to A5; the right group(B) has 8 clusters, from B0 to B7.In each cluster, the column represents dimision of the microarray data, and the row represents the gene’s profile. For example, in Figure 1, the group A has 7 dimisions; the group B has 3 dimisions.The score is the measurement of similarity. The maximum number is 1.00 that means the profiles of these two clusters have similar trends. That is to say the most genes in the two clusters are similar. If the score is 0.00, two clusters are not matched.The output has multiple visualizations. From button View, you may check different options to get different views.2.2. Data Source and Data FormatThis tool uses the textual tab-delimited data files. The format is similar to the Stanford tab-delimited format (/microarray/help/formats.shtml) except that you should put tab [cluster] and [/cluster] between a cluster dataset. An example of the described format is shown in Table 1.Table 1. Input data file format[cluster] YKR007G -0.16 0.12 -0.1 YER067A -0.17 0.16 0.18 YBH291C -045 -0.11 -0.58 [/cluster] [cluster] YPL184C -0.76 -0.61 -0.36 YTR075W -0.78 -0.53 0.84 YCR059S -0.17 0.24 0.15 [/cluster]3. Clustering Algorithms and AnalysisClustering methods, which determine the natural sub-groups in a data set, have some advantages over other methods, because no previous knowledge is necessary for clustering analysis [2, 14]. Several clustering algorithms have been proposed in past few decades [2, 3, 10, 11, 16]. In this section, we briefly describe three such methods, including the K-means clustering methods, the Cluster Identification via Connectivity Kernels (CLICK), and the Self-Organizing Map (SOM) neural networks.3.1. Clustering Algorithms3.1.1 K-means Clustering AlgorithmThe k-means clustering algorithm [5, 20] is a popular form of cluster analysis. The basic idea is that you start with a collection of items (e.g. genes) and some chosen number of clusters (k) you want to find. The items are initially randomly assigned to a cluster. The k-means clustering proceeds by repeated application of a two-step process where (1) the mean vector for all items in each cluster is computed; (2) items are reassigned to the cluster whose center is closest to the item. The k-means clustering algorithm is repeated many times, each time starting from a different initial clustering. The sum of distances within the clusters is used to compare different clustering solutions. The clustering solution with the smallest sum of within-cluster distances is saved. The parameters that control k-means clustering are the number of clusters (k) and the number of trials.The k-means clustering algorithm should be repeated with more trials. If the optimal solution is found many times, the solution that has been found is likely to have the lowest possible within-cluster sum of distances. We can then assume that the k-means clustering procedure has then found the overall optimal clustering solution [4].3.1.2 CLICK Clustering AlgorithmCLICK-Cluster Identification via Connectivity Kernels [23] is a clustering algorithm which is applicable to gene expression analysis as well as to other biological applications. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups of highly similar elements (kernels), which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clustering. The algorithm does not make any prior assumptions on the number or the structure of the clusters. At the heart of the structure of the cluster, there is a process of recursively partitioning a weighted graph into components using minimum cut computations. The edge weights and the stopping criterion of the recursion are assigned probabilistic meaning, which give the algorithm higher accuracy.3.1.3 Self-Organizing Map (SOM) Neural NetworkSOM [8, 9] is a neural network with a number of nodes or neurons. Usually the configuration of these nodes is rectangular or hexagonal [15, 21]. The nodes have an associated vector of the same length of the input data. All nodes have initial random values and these reference vectors are adjusted during the training process. After the network is stable, these reference vectors are used to group the genes based on the closeness of the genes to the reference vectors.During the training stage, the strength of the updating the reference vectors depends on their distances to the winner vector, which is the closest vector to a randomly selected gene. The training length, the training rate, and the size of the updating neighborhood can be customized. Usually the training is performed in two phases: the first one is the ordering phase (strong training rate and large updating radius) and the last one is the fine-tuning phase (long training length with a weak training rate and a smaller radius). 3.2. Clustering AnalysisThe purpose of our study is to compare the clusters generated by above three clustering methods.3.2.1. Software for Clustering AnalysisThe software we use for clustering analysis includes the following:(1) EXPANDER (EXpression Analyzer and DisplayER) [24]: This is a java-based tool for analysis of gene expression data. We use it for CLICK clustering.(2) GEPS (Gene Expression Pattern Analysis Suite) [7]: It includes following servers:a) K-means Server: This interface performs a Partitioning Clustering algorithm. The number of clusters k is specified by the user.b) SOM Server: This is an interface to SOM package. The map is plotted with SomPlot. The resulting clusters can be extracted to continue with the analysis.3.2.2. The Data Set and Data PreprocessingData Source(1) Leukaemia dataset* (7129 genes, 38 samples)(2) Sporulation dataset** (6116 genes, 7 samples)We experiment with a subset of the Leukaemia dataset and a subset of Sporulation dataset. Both datasets are obtained using an Affymetrix hybridization array.Data PreprocessingWe randomly select 500 genes from each dataset and save them as in plain text files, respectively. Then we formatted them as EXPANDER and GEPS required.These two preprocessed data sets are used for comparing the algorithms.* The original data and experimental methods are available at /MPR**The original data and experimental methods are available at 3.3. Clustering Results Comparison3.3.1 Clustering with Leukaemia datasetDataset: 500-gene LeukaemiaTest conditionTest condition for CLICK AlgorithmDefault homogeneityTest condition for K-means Algorithm(1)K value: 4(2)Distance function: Pearson correlation coefficientTest condition for Self Organizing Map (SOM):(1) 2 * 2 hexagonal lattices (This willresult in 4 clusters)(2)Number of trials: 20Clustering resultsThe clustering results of CLICK, SOM, and K-means are shown in Figure 2, each of which includes the profile of a cluster and the profiles of the genes inthat cluster.Cluster(1,1)Cluster(2,1)Cluster(1,2)Cluster(2,2)Cluster 0Cluster 1Cluster 2Cluster 3CLICK clusters SOM clustersK-means clustersFigure 2. Clustering results3.3.2 Clustering with Sporulation dataset Dataset: 500-gene SporulationTest conditionTest condition for CLICK AlgorithmDefault homogeneityTest condition for K-means Algorithm (1)K value: 6(2) Distance function: Pearson correlation coefficientTest condition for Self Organizing Map (SOM): (1) 2 * 3 hexagonal lattices Number of trials: 20 Clustering resultsThe clustering results of CLICK, SOM, and K-means are shown in Figure 3, each of which includes the profile of a cluster and the profiles of the genes in that cluster.Cluster(1,1)Cluster(2,1)Cluster(1,2)Cluster(2,2)Cluster(1,3)Cluster(2,3)Cluster 0Cluster 1Cluster 2Cluster 3Cluster 4Cluster 5CLICK clusters SOM clusters K-means clusters Figure 3. Clustering results4. Comparison of the Clustering MethodsThe Self-Organizing Map (SOM) is a popular unsupervised neural network algorithm. It is very efficient in handling large datasets such as gene expressive data. The SOM algorithm is also robust even when the data set is noisy [25]. So we chose it as the target clustering algorithm for this study.4.1 Comparison with Leukaemia dataset4.1.1 CLICK vs. SOMBoth clustered data files from CLICK and SOM with 500-genes Leukaemia, after formatting as Section 2.2, were loaded to the cluster diff for the cluster similarity analysis. The result is shown in Figure 4.Figure 4. CLICK vs. SOM (Leukaemia)For detail cluster similiarity analysis, we input apair of clusters each time, one by CLICK and one by SOM, The scores are summarized in Table 2. Table 2. Cluster similarityanalysis(Leukaemia) results (CLICK vs. SOM)SOM11 SOM12 SOM21 SOM22 CLICK0 0.26 0.09 0.14 0.20 CLICK1 0.15 0.15 0.11 0.08 CLICK2 0.18 0.01 0.00 0.00 CLICK3 0.09 0.03 0.07 0.09 CLICK4 0.20 0.03 0.03 0.014.1.2 K-means vs. SOMBoth clustered data files from K-means and SOM with 500-genes Leukaemia, after formatting as Section 2.2, were loaded to the cluster diff for the clustersimilarity analysis. The result is shown in Figure 5.Figure 5. K-means vs. SOM (Leukaemia)For detail cluster similiarity analysis, we input apair of clusters each time, one by K-means and one by SOM. The scores are summarized in Table 3.Table 3. Cluster similarityanalysis(Leukaemia) results (K-means vs. SOM)SOM11 SOM12 SOM21 SOM22Kmeans0 0.24 0.09 0.13 0.20 Kmeans1 0.20 0.15 0.11 0.06 Kmeans2 0.30 0.02 0.01 0.01Kmeans3 0.13 0.03 0.07 0.114.2 Comparison with Sporulation dataset4.2.1 CLICK vs. SOMBoth clustered data files from CLICK and SOMwith 500-genes Sporulation, after formatting as Section 2.2, were loaded to the cluster diff for the clustersimilarity analysis. The result is shown in Figure 6.Figure 6. CLICK vs. SOM (Sporulation)For detail cluster similiarity analysis, we input a pair of clusters each time, one by CLICK and one by SOM, The scores are summarized in Table 4.Table 4. Cluster similarityanalysis(Sporulation) results (CLICK vs. SOM)SOM12 SOM13 SOM21 SOM22 SOM23SOM11 CLICK0 0.03 0.00 0.02 0.01 0.03 0.03 CLICK1 0.07 0.04 0.11 0.26 0.14 0.22 CLICK2 0.17 0.39 0.03 0.00 0.020.08 CLICK3 0.10 0.05 0.09 0.01 0.05 0.10 CLICK4 0.08 0.15 0.01 0.04 0.06 0.00 CLICK5 0.02 0.04 0.07 0.06 0.07 0.09 CLICK6 0.07 0.02 0.04 0.04 0.050.054.2.2 K-means vs. SOMBoth clustered data files from K-means and SOM with 500-genes Sporulation, after formatting as Section 2.2, were loaded to the cluster diff for the clustersimilarity analysis. The result is shown in Figure x.Figure 7. K-means vs. SOM (Sporulation)For detail cluster similiarity analysis, we input a pair of clusters each time, one by CLICK and one by SOM, The scores are summarized in Table 5.Table 5. Cluster similarity analysis (Sporulation) results (K-means vs. SOM)SOM12 SOM13 SOM21 SOM22 SOM23 SOM11Kmeans0 0.10 0.00 0.06 0.01 0.00 0.20 Kmeans1 0.17 0.46 0.02 0.00 0.04 0.02 Kmeans2 0.05 0.25 0.00 0.09 0.19 0.00 Kmeans3 0.10 0.01 0.05 0.28 0.23 0.01 Kmeans4 0.05 0.00 0.18 0.11 0.00 0.20 Kmeans5 0.09 0.01 0.13 0.08 0.02 0.144.3 Comparison Results:From the tables in Section 4.1 and 4.2, we can find that most SOM clusters match the K-means clusters (or CLICK) well and vice versa. An example of good match is Kmeans1 with SOM13 (0.46), and CLICK2 with SOM13(0.39) in Sporulation dataset (see Figure 8.). The profiles of these three clusters have similar trends, meaning that the most genes in these three clusters are similar.CLICK2SOM13K-means1Figure 8. Example of a good match The average scores for both CLICK and K-means algothms are summarized in Table 6.Table 6. Clustering Method Comparison analysis results (CLICK vs.K-means)Dataset CLICK vs. SOMK-means vs. SOMLeukaemia 0.16 0.21 Sporulation0.20 0.27The case study results indicate that the clusters generated by the CLICK, K-means or SOM algorithms are comparable. Most clusters match the SOM clusters well and vice versa. Given a target clustering algorithm (or clusters), the tool can efficiently determing the closest matching from a set of clustering algorithms.5. ConclusionsIn this paper, we present a new data mining tool,Cluster Diff , which allows the similarity analysis of clusters generated by different algorithms. This tool may: (1) improve the quality of the data analysis results, (2) support the prediction of the number of relevant clusters in the microarray datasets, and (3) provide cross-reference between different algorithms. The software tool can also be used to analyze cluster similarities from other biomedical data. This software tool may significantly support gene expression data analyses.6. AcknowledgementsWe would like to thank Dr. Dong-Guk Shin and Dr. Jae-guon Nam at the Univ. of Connecticut for providing the software for cluster similarity analysis in this work.7. Reference[1]. Robin L.Stears, et al., Trends in Microarray analysis. Nature medicine, Volume 9, 140-145, January 2003[2]. Marco F. Ramoni, et al., Cluster analysis of gene expression dynamics. PNAS, Vol.99, July 2002[3]. M.B.Eisen, P.T.Spellman, P.O.Brown, D.Botstein. Cluster analysis and display of genome-wide expression patterns Proc. Natl. Acad. Sci., 95:14863-14868, 1998 [4]. http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/manual/KMeans.html [5]. J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297[6]. A. Brazma, J. Vilo. Gene expression data analysis, FEBS Letters, 480, 17-24, 2000[7]. Vaquerizas, J.M.,Conde, L., Yankilevich, P., Cabezon, A., Minguez, P., Diaz-Uriarte, R., Al-Shahrour, F., Herrero, J & Dopazo, J. Gepas an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Research 33 (Web Server issue):W616-W620, 2005[8]. Teuvo Kohonen, The self-organizing map. Neurocomputing 21: 1–6. 1998[9] Kohonen, T. Self-Organizing Maps, Springer, Berlin.1995[10]. Tamayo, P. Dmitrovsky, E., et al. Interpreting patternsof gene expression with self-organing maps: Methods and applications to hematopoietic diferentation, Proc. Nat. Acad. Sci 96, 2907-2912, 1999[11] Herrero, J. & Dopazo, J. Combining hierarchical clustering and self-organizing maps for exploratory analysisof gene expression patterns. Journal of Proteome Research,1(5):467-470. 2002. [12]. Botstein, D. & Brown, P., Exploring the new world of the genome with DNA microarrays, Nature Genetics (supp.) 21, 33-7. 1999[13]. Jain, A.K. and Dubes, R.C. Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ. 1988.[14]. Everitt, B., Cluster analysis, Halstead, New York. 1980[15]. Kohonen, T., Self-Organization and Associative Memory (3rd edition), springer-Verlag, Berlin. 1989.[16]. Dopazo, J. Microarray Data Processing and Analysis. Microarray data analysis II. Kluwer Academic. Publ. Pp. 43-63. 2002[17]. Dopazo, J., Zanders, E., Dragoni, I., Amphlett, G. and Falciani, F. Methods and approaches in the analysis of gene expression data J. Immunol. Meth. 250, 93-112. 2001 [18]. /cs491jh/slides/cs491jh-QJ.ppt[19]. K. Alsabti, S. Ranka, and V. Singh, An Efficient k-means Clustering Algorithm, Proc. First Workshop High Performance Data Mining, Mar. 1998.[20]. /k-means_critique.html , March, 2001[21]. T. Kanungo, D.M. Mount, N.S. Netanyahu, C. Piatko, R. Silverman, and A.Y. Wu, The Analysis of a Simple k-means Clustering Algorithm, Proc. 16th Ann. ACM Symp. Computational Geometry, pp. 100-109, June 2000.[22]. K.Y. Yeung, W.L.Ruzzo, et al. Validating clustering for gene expression data. Bioinformatics Vol 17, p 309-318, 2001 [23]. R. Sharan and R. Shamir. CLICK: a Clustering Algorithm with Applications to Gene Expression Analysis. In Proc. 8th International Conference on Intelligent Systems for Molecular Biology (ISMB'00), pages 307-316, AAAI Press, Menlo Park, CA, 2000[24].http://www.cs.tau.ac.il/~rshamir/expander/ver2Help.html #Biclustering[25]. Paul Mangiameli, Shaw K. Chen and David West, “A Comparison of SOM neural network and hierarchical clusteringmethods,” European Journal of Operational Research, Vol. 93,Issue 2, 6, pp. 402-417, 1996。