Finding structure in reinforcement learning

C OGNITIVE S CIENCE,14, 179-211 (1990).Finding Structure in TimeJ EFFREY L. E LMANUniversity of California, San DiegoTime underlies many interesting human behaviors. Thus, the question ofhow to represent time in connectionist models is very important. Oneapproach is to represent time implicitly by its effects on processing ratherthan explicitly (as in a spatial representation). The current report developsa proposal along these lines first described by Jordan (1986) whichinvolves the use of recurrent links in order to provide networks with adynamic memory. In this approach, hidden unit patterns are fed back tothemselves; the internal representations which develop thus reflect taskdemands in the context of prior internal states. A set of simulations isreported which range from relatively simple problems (temporal versionof XOR) to discovering syntactic/semantic features for words. Thenetworks are able to learn interesting internal representations whichincorporate task demands with memory demands; indeed, in this approachthe notion of memory is inextricably bound up with task processing. Theserepresentations reveal a rich structure, which allows them to be highlycontext-dependent while also expressing generalizations across classes ofitems. These representations suggest a method for representing lexicalcategories and the type/token distinction.___________________________I would like to thank Jay McClelland, Mike Jordan, Mary Hare, Dave Rumelhart, Mike Mozer, Steve Poteet, David Zipser, and Mark Dolson for many stimulating discussions. I thank McClelland, Jordan, and two anonymous reviewers for helpful critical comments on an earlier draft of this paper.This work was supported by contract N00014-85-K-0076 from the Office of Naval Research and contract DAAB-07-87-C-H027 from Army Avionics, Ft. Monmouth. Requests for reprints should be sent to the Center for Research in Language, C-008; University of California, San Diego, CA 92093-0108. The author can be reached via electronic mail as elman@.IntroductionTime is clearly important in cognition. It is inextricably bound up with many behaviors (such as language) which express themselves as temporal sequences. Indeed, it is difficult to know how one might deal with such basic problems as goal-directed behavior, planning, or causation without some way of representing time.The question of how to represent time might seem to arise as a special problem unique to parallel processing models, if only because the parallel nature of computation appears to be at odds with the serial nature of temporal events. However, even within traditional (serial) frameworks, the representation of serial order and the interaction of a serial input or output with higher levels of representation presents challenges. For example, in models of motor activity an important issue is whether the action plan is a literal specification of the output sequence, or whether the plan represents serial order in a more abstract manner (e.g., Lashley, 1951; MacNeilage, 1970; Fowler, 1977, 1980; Kelso, Saltzman, & Tuller, 1986; Saltzman & Kelso, 1987; Jordan & Rosenbaum, 1988). Linguistic theoreticians have perhaps tended to be less concerned with the representation and processing of the temporal aspects to utterances (assuming, for instance, that all the information in an utterance is somehow made available simultaneously in a syntactic tree); but the research in natural language parsing suggests that the problem is not trivially solved (e.g., Frazier & Fodor; 1978; Marcus, 1980). Thus, what is one of the most elementary facts about much of human activity -that it has temporal extent -is sometimes ignored and is often problematic.In parallel distributed processing models, the processing of sequential inputs has been accomplished in several ways. The most common solution is to attempt to “parallels time” by giving it a spatial representation. However, there are problems with this approach, and it is ultimately not a good solution. A better approach would be to represent time implicitly rather than explicitly. That is, we represent time by the effect it has on processing and not as an additional dimension of the input.This paper describes the results of pursuing this approach, with particular emphasis on problems that are relevant to natural language processing. The approach taken is rather simple, but the results are sometimes complex and unexpected. Indeed, it seems that the solution to the problem of time may interact with other problems for connectionist architectures, including the problem of symbolic representation and how connectionist representations encode structure. The current approach supports the notion outlined by Van Gelder (1989; see also Smolensky, 1987, 1988; Elman, 1989), that connectionist representations may have a functional compositionality without being syntactically compositional.The first section briefly describes some of the problems that arise when time is represented externally as a spatial dimension. The second section describes the approach used in this work. The major portion of this report presents the results of applying this new architecture to a diverse set of problems. These problems range in complexity from a temporal version of the Exclusive-OR function to the discovery of syntactic/semantic categories in natural language data.The Problem with TimeOne obvious way of dealing with patterns that have a temporal extent is to represent time explicitly by associating the serial order of the pattern with the dimensionality of the patternvector. The first temporal event is represented by the first element in the pattern vector, the second temporal event is represented by the second position in the pattern vector; and so on. The entire pattern vector is processed in parallel by the model. This approach has been used in a variety of models (e.g., Cottrell, Munro, & Zipser, 1987; Elman & Zipser, 1988; Hanson & Kegl, 1987).There are several drawbacks to this approach, which basically uses a spatial metaphor for time. First, it requires that there be some interface with the world which buffers the input so that it can be presented all at once. It is not clear that biological systems make use of such shift registers. There are also logical problems; how should a system know when a buffer’s contents should be examined?Second, the shift-register imposes a rigid limit on the duration of patterns (since the input layer must provide for the longest possible pattern), and further suggests that all input vectors be the same length. These problems are particularly troublesome in domains such as language, where one would like comparable representations for patterns that are of variable length. This is as true of the basic units of speech (phonetic segments) as it is of sentences.Finally, and most seriously, such an approach does not easily distinguish relative temporal position from absolute temporal position. For example, consider the following two vectors.[ 0 1 1 1 0 0 0 0 0 ][ 0 0 0 1 1 1 0 0 0 ]These two vectors appear to be instances of the same basic pattern, but displaced in space (or time, if we give these a temporal interpretation). However, as the geometric interpretation of these vectors makes clear, the two patterns are in fact quite dissimilar and spatially distant.1 PDP models can of course be trained to treat these two patterns as similar. But the similarity is a consequence of an external teacher and not of the similarity structure of the patterns themselves, and the desired similarity does not generalize to novel patterns. This shortcoming is serious if one is interested in patterns in which the relative temporal structure is preserved in the face of absolute temporal displacements.What one would like is a representation of time which is richer and which does not have these problems. In what follows here, a simple architecture is described which has a number of desirable temporal properties and has yielded interesting results.Networks with MemoryThe spatial representation of time described above treats time as an explicit part of the input. There is another, very different possibility, which is to allow time to be represented by the effect it has on processing. This means giving the processing system dynamic properties which are responsive to temporal sequences. In short, the network must be given memory.1.The reader may more easily be convinced of this by comparing the locations of the vectors [100], [010], and [001] in 3-space. Although these patterns might be considered ’temporally displaced’ versions of the same basic pat-tern, the vectors are very different.activate the output units. The hidden units also feed back to activate the context units. This constitutes the forward activation. Depending on the task, there may or may not be a learning phase in this time cycle. If so, the output is compared with a teacher input and backpropagation of error (Rumelhart, Hinton, & Williams, 1986) is used to incrementally adjust connection strengths. Recurrent connections are fixed at 1.0 and are not subject to adjustment.3 At the next time step t+1 the above sequence is repeated. This time the context units contain values which are exactly the hidden unit values at time t. These context units thus provide the network with memory.Internal Representation of Time. In feedforward networks employing hidden units and a learning algorithm, the hidden units develop internal representations for the input patterns which recode those patterns in a way which enables the network to produce the correct output for a given input. In the present architecture, the context units remember the previous internal state. Thus, the hidden units have the task of mapping both an external input and also the previous internal state to some desired output. Because the patterns on the hidden units are what are saved as context, the hidden units must accomplish this mapping and at the same time develop representations which are useful encodings of the temporal properties of the sequential input. Thus, the internal representations that develop are sensitive to temporal context; the effect of time is implicit in these internal states. Note, however, that these representations of temporal context need not be literal. They represent a memory which is highly task and stimulus-dependent.Consider now the results of applying this architecture to a number of problems which involve processing of inputs which are naturally presented in sequence.Exclusive-ORThe Exclusive-OR (XOR) function has been of interest because it cannot be learned by a simple two-layer network. Instead, it requires at least three-layers. The XOR is usually presented as a problem involving 2-bit input vectors (00, 11, 01, 10) yielding 1-bit output vectors (0, 0, 1, 1; respectively).This problem can be translated into a temporal domain in several ways. One version involves constructing a sequence of 1-bit inputs by presenting the 2-bit inputs one bit at a time (i.e., in two time steps), followed by the 1-bit output; then continuing with another input/output pair chosen at random. A sample input might be:1 0 1 0 0 0 0 1 1 1 1 0 1 0 1. . .Here, the first and second bits are XOR-ed to produce the third; the fourth and fifth are XOR-ed to give the sixth; and so on. The inputs are concatenated and presented as an unbroken sequence.In the current version of the XOR problem, the input consisted of a sequence of 3000 bits constructed in this manner. This input stream was presented to the network shown in Figure 2 (with 1 input unit, 2 hidden units, 1 output unit, and 2 context units), one bit at a time. The task of the network was, at each point in time, to predict the next bit in the sequence. That is, given 3. A little more detail is in order about the connections between the context units and hidden units. In the network used here there were one-for-one connections between each hidden unit and each context unit. This implies there are an equal number of context and hidden units. The upward connections between the context units and the hidden units were fully distributed, such that each context unit activates all the hidden units.averaging of error which was done for Figure 3. If one looks at the output activations it is apparent from the nature of the errors that the network predicts successive inputs to be the XOR of the previous two. This is guaranteed to be successful every third bit, and will sometimes—-fortuitously—also result in correct predictions at other times.It is interesting that the solution to the temporal version of XOR is somewhat different than the static version of the same problem. In a network with 2 hidden units, one unit is highly activated when the input sequence is a series of identical elements (all 1s or 0s), whereas the other unit is highly activated when the input elements alternate. Another way of viewing this is that the network developed units which were sensitive to high and low-frequency inputs. This is a different solution than is found with feedforward networks and simultaneously presented inputs. This suggests that problems may change their nature when cast in a temporal form. It is not clear that the solution will be easier or more difficult in this form; but it is an important lesson to realize that the solution may be different.In this simulation the prediction task has been used in a way which is somewhat analogous to autoassociation. Autoassociation is a useful technique for discovering the intrinsic structure possessed by a set of patterns. This occurs because the network must transform the patterns into more compact representations; it generally does so by exploiting redundancies in the patterns. Finding these redundancies can be of interest for what they tell us about the similarity structure of the data set (cf. Cottrell, Munro, & Zipser, 1987; Elman & Zipser, 1988).In this simulation the goal was to find the temporal structure of the XOR sequence. Simple autoassociation would not work, since the task of simply reproducing the input at all points in time is trivially solvable and does not require sensitivity to sequential patterns. The prediction task is useful because its solution requires that the network be sensitive to temporal structure.Structure in Letter SequencesOne question which might be asked is whether the memory capacity of the network architecture employed here is sufficient to detect more complex sequential patterns than the XOR. The XOR pattern is simple in several respects. It involves single-bit inputs, requires a memory which extends only one bit back in time, and there are only four different input patterns. More challenging inputs would require multi-bit inputs of greater temporal extent, and a larger inventory of possible sequences. Variability in the duration of a pattern might also complicate the problem.An input sequence was devised which was intended to provide just these sorts of complications. The sequence was composed of six different 6-bit binary vectors. Although the vectors were not derived from real speech, one might think of them as representing speech sounds, with the six dimensions of the vector corresponding to articulatory features. Table 1 shows the vector for each of the six letters.The sequence was formed in two steps. First, the 3 consonants (b, d, g) were combined in random order to obtain a 1000-letter sequence. Then each consonant was replaced using the rulesb ¥ bad ¥ diig ¥ guuconsists of a 6-bit rather than a 1-bit vector. One might have reasonably thought that the more extended sequential dependencies of these patterns would exceed the temporal processing capacity of the network. But almost the opposite is true. The fact that there are subregularities (at the level of individual bit patterns) enables the network to make partial predictions even in cases where the complete prediction is not possible. All of this is dependent on the fact that the input is structured, of course. The lesson seems to be that more extended sequential dependencies may not necessarily be more difficult to learn. If the dependencies are structured, that structure may make learning easier and not harder.Discovering the notion “word”It is taken for granted that learning a language involves (among many other things) learning the sounds of that language, as well as the morphemes and words. Many theories of acquisition depend crucially on such primitive types as word, or morpheme, or more abstract categories as noun, verb, or phrase (e.g., Berwick & Weinberg, 1984; Pinker, 1984). It is rarely asked how it is that a language learner knows to begin with that these entities exist. These notions are often assumed to be innate.Yet in fact, there is considerable debate among linguists and psycholinguists about what are the representations used in language. Although it is commonplace to speak of basic units such as “phoneme,” “morpheme,” and “word”, these constructs have no clear and uncontroversial definition. Moreover, the commitment to such distinct levels of representation leaves a troubling residue of entities that appear to lie between the levels. For instance, in many languages, there are sound/meaning correspondences which lie between the phoneme and the morpheme (i.e., sound symbolism). Even the concept “word” is not as straightforward as one might think (cf. Greenberg, 1963; Lehman, 1962). Within English, for instance, there is no consistently definable distinction between words (e.g., “apple”), compounds (“apple pie”) and phrases (“Library of Congress” or “man in the street”). Furthermore, languages differ dramatically in what they treat as words. In polysynthetic languages (e.g., Eskimo) what would be called words more nearly resemble what the English speaker would call phrases or entire sentences.Thus, the most fundamental concepts of linguistic analysis have a fluidity which at the very least suggests an important role for learning; and the exact form of the those concepts remains an open and important question.In PDP networks, representational form and representational content often can be learned simultaneously. Moreover, the representations which result have many of the flexible and graded characteristics which were noted above. Thus, one can ask whether the notion “word” (or something which maps on to this concept) could emerge as a consequence of learning the sequential structure of letter sequences which form words and sentences (but in which word boundaries are not marked).Let us thus imagine another version of the previous task, in which the letter sequences form real words, and the words form sentences. The input will consist of the individual letters (we will imagine these as analogous to speech sounds, while recognizing that the orthographic input is vastly simpler than acoustic input would be). The letters will be presented in sequence, one at a time, with no breaks between the letters in a word, and no breaks between the words of different sentences.Such a sequence was created using a sentence generating program and a lexicon of 15 words.4 The program generated 200 sentences of varying length, from 4 to 9 words. Thesentences were concatenated, forming a stream of 1,270 words. Next, the words were broken into their letter parts, yielding 4,963 letters. Finally, each letter in each word was converted into a 5-bit random vector.The result was a stream of 4,963 separate 5-bit vectors, one for each letter. These vectors were the input and were presented one at a time. The task at each point in time was to predict the next letter. A fragment of the input and desired output is shown in Table 2.Table 6:Input Output0110m0000a0000a0111n0111n1100y1100y1100y1100y0010e0010e0000a0000a1001r1001r1001s1001s0000a0000a0011g0011g0111o0111o0000a0000a0001b0001b0111o0111o1100y1100y0000a0000a0111n0111n0010d0010d0011g0011g0100i0100i1001r1001r0110l0110l1100y1100y4.The program used was a simplified version of the program described in greater detail in the next simulation.as a cue as to the boundaries of linguistic units which must be learned, and it demonstrates the ability of simple recurrent networks to extract this information.Discovering lexical classes from word orderConsider now another problem which arises in the context of word sequences. The order of words in sentences reflects a number of constraints. In languages such as English (so-called “fixed word-order” languages), the order is tightly constrained. In many other languages (the “free word-order” languages), there is greater optionality as to word order (but even here the order is not free in the sense of random). Syntactic structure, selectional restrictions, subcategorization, and discourse considerations are among the many factors which join together to fix the order in which words occur. Thus, the sequential order of words in sentences is neither simple nor is it determined by a single cause. In addition, it has been argued that generalizations about word order cannot be accounted for solely in terms of linear order (Chomsky, 1957; Chomsky, 1965). Rather, there is an abstract structure which underlies the surface strings and it is this structure which provides a more insightful basis for understanding the constraints on word order.While it is undoubtedly true that the surface order of words does not provide the most insightful basis for generalizations about word order, it is also true that from the point of view of the listener, the surface order is the only thing visible (or audible). Whatever the abstract underlying structure be, it is cued by the surface forms and therefore that structure is implicit in them.In the previous simulation, we saw that a network was able to learn the temporal structure of letter sequences. The order of letters in that simulation, however, can be given with a small set of relatively simple rules.5 The rules for determining word order in English, on the other hand, will be complex and numerous. Traditional accounts of word order generally invoke symbolic processing systems to express abstract structural relationships. One might therefore easily believe that there is a qualitative difference in the nature of the computation required for the last simulation, and that required to predict the word order of English sentences. Knowledge of word order might require symbolic representations which are beyond the capacity of (apparently) non-symbolic PDP systems. Furthermore, while it is true, as pointed out above, that the surface strings may be cues to abstract structure, considerable innate knowledge may be required in order to reconstruct the abstract structure from the surface strings. It is therefore an interesting question to ask whether a network can learn any aspects of that underlying abstract structure.Simple sentences. As a first step, a somewhat modest experiment was undertaken. A sentence generator program was used to construct a set of short (two and three-word) utterances. 13 classes of nouns and verbs were chosen; these are listed in Table 3. Examples of each category are given; it will be noticed that instances of some categories (e.g, VERB-DESTROY) may be included in others (e.g., VERB-TRAN). There were 29 different lexical items.5.In the worst case, each word constitutes a rule. More hopefully, networks will learn that recurring orthographic regularities provide additional and more general constraints (cf. Sejnowski & Rosenberg, 1987).Table 3: Categories of lexical items used in sentence simulationCategory ExamplesNOUN-HUM man, womanNOUN-ANIM cat, mouseNOUN-INANIM book, rockNOUN-AGRESS dragon, monsterNOUN-FRAG glass, plateNOUN-FOOD cookie, sandwichVERB-INTRAN think, sleepVERB-TRAN see, chaseVERB-AGPA move, breakVERB-PERCEPT smell, seeVERB-DESTROY break, smashVERB-EA eatThe generator program used these categories and the 15 sentence templates given in Table 4Table 4: Templates for sentence generatorWORD 1WORDS WORD 3NOUN-HUM VERB-EA T NOUN-FOODNOUN-HUM VERB-PERCEPT NOUN-INANIMNOUN-HUM VERB-DESTROY NOUN-FRAGNOUN-HUM VERB-INTRANNOUN-HUM VERB-TRAN NOUN-HUMNOUN-HUM VERB-AG PAT NOUN-INANIMNOUN-HUM VERB-AG PATNOUN-ANIM VERB-EA T NOUN-FOODNOUN-ANIM VERB-TRAN NOUN-ANIMNOUN-ANIM VERB-AG PAT NOUN-INANIMNOUN-ANIM VERB-AG PATNOUN-INANIM VERB-AG PATNOUN-AGRESS VERB-DESTORY NOUN-FRAGNOUN-AGRESS VERB-EA T NOUN-HUMNOUN-AGRESS VERB-EA T NOUN-ANIMNOUN-AGRESS VERB-EA T NOUN-FOODto create 10,000 random two- and three-word sentence frames. Each sentence frame was thenfilled in by randomly selecting one of the possible words appropriate to each category. Each word was replaced by a randomly assigned 31-bit vector in which each word was represented by a different bit. Whenever the word was present, that bit was flipped on. Two extra bits were reserved for later simulations. This encoding scheme guaranteed that each vector was orthogonal to every other vector and reflected nothing about the form class or meaning of the words. Finally, the 27,354 word vectors in the 10,000 sentences were concatenated, so that an input stream of 27,354 31-bit vectors was created. Each word vector was distinct, but there were no breaks between successive sentences. A fragment of the input stream is shown in column 1 of Table 5, with the English gloss for each vector in parentheses. The desired output is given in column 2.Table 5: Fragment of training sequences for sentence simulationINPUT OUTPUT 0000000000000000000000000000010(woman)0000000000000000000000000010000(smash) 0000000000000000000000000010000(smash)0000000000000000000001000000000(plate) 0000000000000000000001000000000(plate)0000010000000000000000000000000(cat) 0000010000000000000000000000000(cat)0000000000000000000100000000000(move) 0000000000000000000100000000000(move)0000000000000000100000000000000(man) 0000000000000000100000000000000(man)0001000000000000000000000000000(break) 0001000000000000000000000000000(break)0000100000000000000000000000000(car) 0000100000000000000000000000000(car)0100000000000000000000000000000(boy) 0100000000000000000000000000000(boy)0000000000000000000100000000000(move) 0000000000000000000100000000000(move)0000000000001000000000000000000(girl) 0000000000001000000000000000000(girl)0000000000100000000000000000000(eat) 0000000000100000000000000000000(eat)0010000000000000000000000000000(bread) 0010000000000000000000000000000(bread)0000000010000000000000000000000(dog) 0000000010000000000000000000000(dog)0000000000000000000100000000000(move) 0000000000000000000100000000000(move)0000000000000000001000000000000(mouse) 0000000000000000001000000000000(mouse)0000000000000000001000000000000(mouse) 0000000000000000001000000000000(mouse)0000000000000000000100000000000(move) 0000000000000000000100000000000(move)1000000000000000000000000000000(book) 1000000000000000000000000000000(book)0000000000000001000000000000000(lionFor this simulation a network was used which was similar to that in the first simulation, except that the input layer and output layers contained 31 nodes each, and the hidden and context layers contained 150 nodes each.The task given to the network was to learn to predict the order of successive words. The training strategy was as follows. The sequence of 27,354 31-bit vectors formed an input sequence. Each word in the sequence was input, one at a time, in order. The task on each input cycle was to predict the 31-bit vector corresponding to the next word in the sequence. At the end of the 27,354 word sequence, the process began again without a break starting with the first。

fig工技朮注浆抗浮锚杆结介其他措施对抗浮力的加固Grouting anti・floaling anchor rod combined with other nleasures to combat buoyancy reinforcement曹斌(上海建工四建集团有限公司第二工程公司，上海200000)摘要：利用围护桩，灌注桩的抗浮能力作为抗浮储备，结合局部注浆浮锚杆对地下室抗浮进行加固，多种措施综合使用，有效解决地下室抗浮问题关键词：地铁隧道；施工技术；抗浮措施Abstract:Using the retaining piles,the anti-floating capacity of the cast-in-situ piles is used as anti-floating reserve,combined with the local grouti n g fl o at i ng an c hors to reinforce the anti-floating of the basement,and various measures are comprehensively used to effectively solve the anti-floating problem of the basement.Key words:subway tunnel;construction technology:anti-floating measures中图分类号：TU7文献标识码：B文章标号：1003-8965(2019)02-0134-021基本情况某工程地上4栋22-26层；地下1层车库基础底板为400厚筏板+桩承台形式，基坑挖深-5.9m。

项目东面预计于2019年初施工至边上的地铁隧道；围护体系为¢600/0）800灌注桩+双轴搅拌桩止水帷幕，内有一道钢筋混凝土支撑，有108根0800立柱桩，桩长19m,内插钢格构柱。

Finding community structure in very large networksAaron Clauset,1M.E.J.Newman,2and Cristopher Moore1,31Department of Computer Science,University of New Mexico,Albuquerque,New Mexico87131,USA2Department of Physics and Center for the Study of Complex Systems,University of Michigan,Ann Arbor,Michigan48109,USA 3Department of Physics and Astronomy,University of New Mexico,Albuquerque,New Mexico87131,USA(Received30August2004;published6December2004)The discovery and analysis of community structure in networks is a topic of considerable recent interestwithin the physics community,but most methods proposed so far are unsuitable for very large networksbecause of their computational cost.Here we present a hierarchical agglomeration algorithm for detectingcommunity structure which is faster than many competing algorithms:its running time on a network with nvertices and m edges is O͑md log n͒where d is the depth of the dendrogram describing the communitystructure.Many real-world networks are sparse and hierarchical,with mϳn and dϳlog n,in which case ouralgorithm runs in essentially linear time,O͑n log2n͒.As an example of the application of this algorithm we useit to analyze a network of items for sale on the web site of a large on-line retailer,items in the network beinglinked if they are frequently purchased by the same buyer.The network has more than400000vertices and2ϫ106edges.We show that our algorithm can extract meaningful communities from this network,revealinglarge-scale patterns present in the purchasing habits of customers.DOI:10.1103/PhysRevE.70.066111PACS number(s):89.75.Hc,05.10.Ϫa,87.23.Ge,89.20.HhI.INTRODUCTIONMany systems of current interest to the scientific commu-nity can usefully be represented as networks[1–4].Ex-amples include the internet[5]and the World Wide Web [6,7],social networks[8],citation networks[9,10],food webs[11],and biochemical networks[12,13].Each of these networks consists of a set of nodes or vertices representing, for instance,computers or routers on the internet or people in a social network,connected together by links or edges,rep-resenting data connections between computers,friendships between people,and so forth.One network feature that has been emphasized in recent work is community structure,the gathering of vertices into groups such that there is a higher density of edges within groups than between them[14].The problem of detecting such communities within networks has been well studied. Early approaches such as the Kernighan-Lin algorithm[15], spectral partitioning[16,17],or hierarchical clustering[18] work well for specific types of problems(particularly graph bisection or problems with well defined vertex similarity measures),but perform poorly in more general cases[19].To combat this problem a number of new algorithms have been proposed in recent years.Girvan and Newman[20,21] proposed a divisive algorithm that uses edge betweenness as a metric to identify the boundaries of communities.This al-gorithm has been applied successfully to a variety of net-works,including networks of email messages,human and animal social networks,networks of collaborations between scientists and musicians,metabolic networks,and gene net-works[20,22–30].However,as noted in[21],the algorithm makes heavy demands on computational resources,running in O͑m2n͒time on an arbitrary network with m edges and n vertices,or O͑n3͒time on a sparse graph(one in which m ϳn,which covers most real-world networks of interest). This restricts the algorithm’s use to networks of at most a few thousand vertices with current hardware.More recently a number of faster algorithms have been proposed[31–33].In[32],one of us proposed an algorithm based on the greedy optimization of the quantity known as modularity[21].This method appears to work well both in contrived test cases and in real-world situations,and is sub-stantially faster than the algorithm of Girvan and Newman.A naive implementation runs in time O(͑m+n͒n),or O͑n2͒on a sparse graph.Here we propose a different algorithm that performs the same greedy optimization as the algorithm of[32]and there-fore gives identical results for the communities found.How-ever,by exploiting some shortcuts in the optimization prob-lem and using more sophisticated data structures,it runs far more quickly,in time O͑md log n͒where d is the depth of the“dendrogram”describing the network’s community structure.Many real-world networks are sparse,so that m ϳn;and moreover,for networks that have a hierarchical structure with communities at many scales,dϳlog n.For such networks our algorithm has essentially linear running time,O͑n log2n͒.This is not merely a technical advance but has substantial practical implications,bringing within reach the analysis of extremely large works of107vertices or more should be possible in reasonable run times.As an example, we give results from the application of the algorithm to a recommender network of books from the on-line bookseller ,which has more than400000vertices and 2ϫ106edges.II.THE ALGORITHMModularity[21]is a property of a network and a specific proposed division of that network into communities.It mea-sures when the division is a good one,in the sense that there are many edges within communities and only a few betweenPHYSICAL REVIEW E70,066111(2004)them.Let A v w be an element of the adjacency matrix of the network;thusA v w=ͭ1if vertices v and w are connected,0otherwise,͑1͒and suppose the vertices are divided into communities such that vertex v belongs to community c v.Then the fraction of edges that fall within communities,i.e.,that connect vertices that both lie in the same community,is͚v w A v w␦͑c v,c w͚͒v w A v w=12m͚v wA v w␦͑c v,c w͒,͑2͒where the␦function␦͑i,j͒is1if i=j and0otherwise,andm=12͚v w A v w is the number of edges in the graph.This quan-tity will be large for good divisions of the network,in the sense of having many within-community edges,but it is not, on its own,a good measure of community structure since it takes its largest value of1in the trivial case where all verti-ces belong to a single community.However,if we subtract from it the expected value of the same quantity in the case of a randomized network,we do get a useful measure.The degree k v of a vertex v is defined to be the number of edges incident upon it:k v=͚w A v w.͑3͒The probability of an edge existing between vertices v and w if connections are made at random but respecting vertex de-grees is k v k w/2m.We define the modularity Q to beQ=12m͚v wͫA v w−k v k w2mͬ␦͑c v,c w͒.͑4͒If the fraction of within-community edges is no different from what we would expect for the randomized network, then this quantity will be zero.Nonzero values represent de-viations from randomness,and in practice it is found that a value above about0.3is a good indicator of significant com-munity structure in a network.If high values of the modularity correspond to good divi-sions of a network into communities,then one should be able tofind such good divisions by searching through the possible candidates for ones with high modularity.Whilefinding the global maximum modularity over all possible divisions seems hard in general,reasonably good solutions can be found with approximate optimization techniques.The algo-rithm proposed in[32]uses a greedy optimization in which, starting with each vertex being the sole member of a com-munity of one,we repeatedly join together the two commu-nities whose amalgamation produces the largest increase in Q.For a network of n vertices,after n−1such joins we are left with a single community and the algorithm stops.The entire process can be represented as a tree whose leaves are the vertices of the original network and whose internal nodes correspond to the joins.This dendrogram represents a hier-archical decomposition of the network into communities at all levels.The most straightforward implementation of this idea (and the only one considered in[32])involves storing the adjacency matrix of the graph as an array of integers and repeatedly merging pairs of rows and columns as the corre-sponding communities are merged.For the case of the sparse graphs that are of primary interest in thefield,however,this approach wastes a good deal of time and memory space on the storage and merging of matrix elements with value0, which is the vast majority of the adjacency matrix.The al-gorithm proposed in this paper achieves speed(and memory efficiency)by eliminating these needless operations.To simplify the description of our algorithm let us define the following two quantities:e ij=12m͚v wA v w␦͑c v,i͒␦͑c w,j͒,͑5͒which is the fraction of edges that join vertices in community i to vertices in community j,anda i=12m͚vk v␦͑c v,i͒,͑6͒which is the fraction of ends of edges that are attached to vertices in community i.Then,writing␦͑c v,c w͒=͚i␦͑c v,i͒␦͑c w,i͒,we have,from Eq.(4),Q=12m͚v wͫA v w−k v k w2m͚ͬi␦͑c v,i͒␦͑c w,i͒=͚iͫ12m͚v w A v w␦͑c v,i͒␦͑c w,i͒−12m͚vk v␦͑c v,i͒12m͚wk w␦͑c w,i͒ͬ=͚i͑e ii−a i2͒.͑7͒The operation of the algorithm involvesfinding the changes in Q that would result from the amalgamation of each pair of communities,choosing the largest of them,and performing the corresponding amalgamation.One way to en-visage(and implement)this process is to think of the net-work as a multigraph,in which a whole community is rep-resented by a vertex,bundles of edges connect one vertex to another,and edges internal to communities are represented by self-edges.The adjacency matrix of this multigraph has elements A ijЈ=2me ij,and the joining of two communities i and j corresponds to replacing the i th and j th rows and col-umns by their sum.In the algorithm of[32]this operation is done explicitly on the entire matrix,but if the adjacency matrix is sparse(which we expect in the early stages of the process)the operation can be carried out more efficiently using data structures for sparse matrices.Unfortunately,cal-culating⌬Q ij andfinding the pair i,j with the largest⌬Q ij then becomes time consuming.In our algorithm,rather than maintaining the adjacency matrix and calculating⌬Q ij,we instead maintain and update a matrix of value of⌬Q ij.Since joining two communities with no edge between them can never produce an increase inCLAUSET,NEWMAN,AND MOORE PHYSICAL REVIEW E70,066111(2004)Q,we need only store⌬Q ij for those pairs i,j that are joined by one or more edges.Since this matrix has the same support as the adjacency matrix,it will be similarly sparse,so we can again represent it with efficient data structures.In addition, we make use of an efficient data structure to keep track of the largest⌬Q ij.These improvements result in a considerable saving of both memory and time.In total,we maintain three data structures.(1)A sparse matrix containing⌬Q ij for each pair i,j of communities with at least one edge between them.We store each row of the matrix both as a balanced binary tree[so that elements can be found or inserted in O͑log n͒time]and as a max-heap(so that the largest element can be found in con-stant time).(2)A max-heap H containing the largest element of each row of the matrix⌬Q ij along with the labels i,j of the cor-responding pair of communities.(3)An ordinary vector array with elements a i.As described above we start off with each vertex being the sole member of a community of one,in which case e ij =1/2m if i and j are connected and zero otherwise,and a i =k i/2m.Thus we initially set⌬Q ij=ͭ1/2m−k i k j/͑2m͒2if i,j are connected,0otherwise,͑8͒anda i=k i2m͑9͒for each i.(This assumes the graph is unweighted;weighted graphs are a simple generalization[34].)Our algorithm can now be defined as follows.(1)Calculate the initial values of⌬Q ij and a i according to Eq.(8)and(9),and populate the max-heap with the largest element of each row of the matrix⌬Q.(2)Select the largest⌬Q ij from H,join the corresponding communities,update the matrix⌬Q,the heap H,and a i(as described below),and increment Q by⌬Q ij.(3)Repeat step2until only one community remains.Our data structures allow us to carry out the updates in step2quickly.First,note that we need only adjust a few of the elements of⌬Q.If we join communities i and j,labeling the combined community j,say,we need only update the j th row and column,and remove the i th row and column alto-gether.The update rules are as follows.If community k is connected to both i and j,then⌬Q jkЈ=⌬Q ik+⌬Q jk.͑10a͒If k is connected to i but not to j,then⌬Q jkЈ=⌬Q ik−2a j a k.͑10b͒If k is connected to j but not to i,then⌬Q jkЈ=⌬Q jk−2a i a k.͑10c͒Note that these equations imply that Q has a single peak over the course of the algorithm,since after the largest⌬Q be-comes negative all the⌬Q can only decrease.To analyze how long the algorithm takes using our data structures,let us denote the degrees of i and j in the reduced graph—i.e.,the numbers of neighboring communities—as͉i͉and͉j͉,respectively.Thefirst operation in a step of the algo-rithm is to update the j th row.To implement Eq.(10a),weinsert the elements of the i th row into the j th row,summingthem wherever an element exists in both columns.Since westore the rows as balanced binary trees,each of these͉i͉insertions takes O͑log͉j͉͒ഛO͑log n͒time.We then update the other elements of the j th row,of which there are at most ͉i͉+͉j͉,according to Eqs.(10b)and(10c).In the k th row,we update a single element,taking O͑log͉k͉͒ഛO͑log n͒time, and there are at most͉i͉+͉j͉values of k for which we have todo this.All of this thus takes O͉͑͑i͉+͉j͉͒log n͒time.We also have to update the max-heaps for each row andthe overall max-heap H.Reforming the max-heap corre-sponding to the j th row can be done in O͉͑j͉͒time[35]. Updating the max-heap for the k th row by inserting,raising, or lowering⌬Q kj takes O͑log͉k͉͒ഛO͑log n͒time.Since we have changed the maximum element on at most͉i͉+͉j͉rows, we need to do at most͉i͉+͉j͉updates of H,each of which takes O͑log n͒time,for a total of O(͉͑i͉+͉j͉͒log n). Finally,the update a jЈ=a j+a i(and a i=0)is trivial and can be done in constant time.Since each join takes O(͉͑i͉+͉j͉͒log n)time,the total run-ning time is at most O͑log n͒times the sum over all nodes of the dendrogram of the degrees of the corresponding commu-nities.Let us make the worst-case assumption that the degree of a community is the sum of the degrees of all the vertices in the original network comprising it.In that case,each ver-tex of the original network contributes its degree to all of the communities it is a part of,along the path in the dendrogram from it to the root.If the dendrogram has depth d,there are at most d nodes in this path,and since the total degree of all the vertices is2m,we have a running time of O͑md log n͒as stated.We note that,if the dendrogram is unbalanced,some timesavings can be gained by inserting the sparser row into theless sparse one.In addition,we have found that in practicalsituations it is usually unnecessary to maintain the separatemax-heaps for each row.These heaps are used tofind thelargest element in a row quickly,but their maintenance takesa moderate amount of effort and this effort is wasted if thelargest element in a row does not change when two rows areamalgamated,which turns out often to be the case.Thus wefind that the following simpler implementation works quitewell in realistic situations:if the largest element of the k throw was⌬Q ki or⌬Q kj and is now reduced by Eq.(10b)or (10c),we simply scan the k th row tofind the new largest element.Although the worst-case running time of this ap-proach has an additional factor of n,the average-case run-ning time is often better than that of the more sophisticated algorithm.It should be noted that the dendrograms generated by these two versions of our algorithm will differ slightly as a result of the differences in how ties are broken for the maximum element in a row.However,wefind that in prac-tice these differences do not cause significant deviations in the modularity,the community size distribution,or the com-position of the largest communities.FINDING COMMUNITY STRUCTURE IN VERY LARGE…PHYSICAL REVIEW E70,066111(2004) PURCHASING NETWORKThe output of the algorithm described above is precisely the same as that of the slower hierarchical algorithm of [32].The much improved speed of our algorithm,however,makes possible studies of very large networks for which previous methods were too slow to produce useful results.Here we give one example,the analysis of a copurchasing or “recom-mender”network from the online vendor .Amazon sells a variety of products,particularly books and music,and as part of their web sales operation they list for each item A the ten other items most frequently purchased by buyers of A .This information can be represented as a di-rected network in which vertices represent items and there is an edge from item A to another item B if B was frequently purchased by buyers of A .In our study we have ignored the directed nature of the network (as is common in community structure calculations ),assuming any link between two items,regardless of direction,to be an indication of their similarity.The network we study consists of items listed onthe Amazon web site in August 2003.We concentrate on the largest component of the network,which has 409687items and 2464630edges.The dendrogram for this calculation is of course too big to draw,but Fig.1illustrates the modularity over the course of the algorithm as vertices are joined into larger and larger groups.The maximum value is Q =0.745,which is high as calculations of this type go [21,32]and indicates strong com-munity structure in the network.The maximum occurs when there are 1684communities with a mean size of 243items each.Figure 2gives a visualization of the community struc-ture,including the major communities,smaller “satellite”communities connected to them,and “bridge”communities that connect two major communities with each other.Looking at the largest communities in the network,we find that they tend to consist of items (books,music )in simi-TABLE I.The ten largest communities in the network,which account for 87%of the vertices in the network.Rank Size Description1114538General interest:politics;art/literature;general fiction;human nature;technical books;how things,people,computers,societies work,etc.292276The arts:videos,books,DVDs about the creative and performing arts378661Hobbies and interests I:self-help;self-education;popular science fiction,popular fantasy;leisure;etc.454582Hobbies and interests II:adventure books;video games/comics;some sports;some humor;some classic fiction;some western religious material;etc.59872Classical music and related items61904Children’s videos,movies,music,and books71493Church/religious music;African-descent cultural books;homoerotic imagery 81101Pop horror;mystery/adventure fiction 91083Jazz;orchestral music;easy listening 10947Engineering;practical fashionFIG.1.The modularity Q over the course of the algorithm (the x axis shows the number of joins ).Its maximum value is Q =0.745,where the partition consists of 1684communities.FIG.2.A visualization of the community structure at maximum modularity.Note that some major communities have a large number of “satellite”communities connected only to them (top,lower left,lower right ).Also,some pairs of major communities have sets of smaller communities that act as “bridges”between them (e.g.,be-tween the lower left and lower right,near the center ).CLAUSET,NEWMAN,AND MOORE PHYSICAL REVIEW E 70,066111(2004)lar genres or on similar topics.In Table I,we give informal descriptions of the ten largest communities,which account for about87%of the entire network.The remainder is gen-erally divided into small,densely connected communities that represent highly specific copurchasing habits,e.g.,major works of sciencefiction(162items),music by John Cougar Mellencamp(17items),and books about(mostly female) spies in the American Civil War(13items).It is worth noting that because few real-world networks have community meta-data associated with them to which we may compare the inferred communities,this type of manual check of the ve-racity and coherence of the algorithm’s output is often nec-essary.One interesting property recently noted in some networks [30,32]is that when partitioned at the point of maximum modularity,the distribution of community sizes s appears to have a power-law form P͑s͒ϳs−␣for some constant␣,at least over some significant range.The Amazon copurchasing network also seems to exhibit this property,as we show in Fig.3,with an exponent␣Ӎ2.It is unclear why such a distribution should arise,but we speculate that it could be a result either of the sociology of the network(a power-law distribution in the number of people interested in various topics)or of the dynamics of the community structure algo-rithm.We propose this as a direction for further research.IV.CONCLUSIONSHere,we have described an algorithm for inferring com-munity structure from network topology which works by greedily optimizing the modularity.Our algorithm runs in time O͑md log n͒for a network with n vertices and m edges where d is the depth of the dendrogram.For networks that are hierarchical,in the sense that there are communities at many scales and the dendrogram is roughly balanced,we have dϳlog n.If the network is also sparse,mϳn,then the running time is essentially linear,O͑n log2n͒.This is consid-erably faster than most previous general algorithms,and al-lows us to extend community structure analysis to networks that had been considered too large to be tractable.We have demonstrated our algorithm with an application to a large network of copurchasing data from the on-line retailer .Our algorithm discovers clear communities within this network that correspond to specific topics or genres of books or music,indicating that the copurchasing tendencies of Amazon customers are strongly correlated with subject matter.Our algorithm should allow researchers to analyze even larger networks with millions of vertices and tens of millions of edges using current computing resources,and we look forward to seeing such applications.ACKNOWLEDGEMENTSThe authors are grateful to and Eric Prom-islow for providing the purchasing network data.This work was funded in part by the National Science Foundation under Grant No.PHY-0200909(A.C.,C.M.)and by a grant from the James S.McDonell Foundation(M.E.J.N.).[1]S.H.Strogatz,Nature(London)410,268(2001).[2]R.Albert and A.-L.Barabási,Rev.Mod.Phys.74,47(2002).[3]S.N.Dorogovtsev and J.F.F.Mendes,Adv.Phys.51,1079(2002).[4]M.E.J.Newman,SIAM Rev.45,167(2003).[5]M.Faloutsos,P.Faloutsos,and C.Faloutsos,-mun.Rev.29,251(1999).[6]R.Albert,H.Jeong,and A.-L.Barabási,Nature(London)401,130(1999).[7]J.M.Kleinberg,S.R.Kumar,P.Raghavan,S.Rajagopalan,and A.Tomkins,in Proceedings of the International Confer-ence on Combinatorics and Computing,Lecture Notes in Computer Science V 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When tasked with writing an essay in English on the topic of thinking about problems,its important to approach the subject with a clear structure and a thoughtful perspective.Heres a detailed guide on how to write such an essay,including key points to consider and examples to illustrate your ideas.Title:The Art of ProblemSolving:A Reflection on Critical ThinkingIntroduction:Begin your essay by introducing the importance of problemsolving in our daily lives. You might mention how problemsolving is a fundamental skill that everyone needs, whether in personal relationships,academic settings,or professional environments.Example:In a world that is constantly evolving,the ability to think critically and solve problems is more crucial than ever.It is the cornerstone of innovation,the driving force behind progress,and the key to navigating the complexities of life.Body Paragraph1:Understanding the ProblemDiscuss the first step in problemsolving:understanding the problem at hand.Explain how clarity is essential before attempting to find a solution.Key Points:The need for a clear definition of the problem.The importance of gathering all relevant information.The role of empathy in understanding problems,especially in interpersonal situations.Example:Before one can begin to tackle a problem,it is imperative to have a comprehensive understanding of what the problem actually is.This involves not only identifying the issue but also understanding its root causes and the context in which it arises.Body Paragraph2:Analyzing the ProblemTalk about the process of analyzing the problem.Highlight the importance of breaking down the problem into smaller,manageable parts.Key Points:The benefits of breaking down complex issues.The use of logical reasoning and critical analysis.The role of creativity in finding unconventional solutions.Example:Once the problem is clearly defined,the next step is to analyze it.This involves dissecting the issue into its constituent parts,which can often reveal underlying patterns or connections that might not be immediately apparent.Body Paragraph3:Generating SolutionsDiscuss the brainstorming process and the generation of potential solutions.Emphasize the importance of considering multiple perspectives and ideas.Key Points:The value of brainstorming and collaboration.The need to consider a range of possible solutions.The importance of evaluating the feasibility and effectiveness of each solution.Example:With a thorough understanding and analysis of the problem,the next phase is to generate potential solutions.This is where creativity and innovation come into play,as one considers a variety of approaches to address the issue at hand.Body Paragraph4:Evaluating and Implementing SolutionsTalk about the process of evaluating the proposed solutions and selecting the most effective one.Discuss the implementation process and the potential challenges that may arise.Key Points:The criteria for evaluating solutions.The importance of testing solutions in a controlled environment.The challenges of implementation and the need for adaptability.Example:After generating a range of potential solutions,the task of evaluation begins.This involves assessing each solution based on its feasibility,effectiveness,and alignment with the original problem statement.Conclusion:Summarize the importance of critical thinking in problemsolving.Reflect on how this skill can be developed and improved over time.Example:In conclusion,the process of problemsolving is a testament to the power of criticalthinking.It is a skill that can be honed and refined,allowing individuals to navigate the challenges of life with greater confidence and effectiveness.Final Thoughts:Encourage readers to reflect on their own problemsolving experiences and consider how they can apply the principles discussed in the essay to their own lives.Example:As we continue to face new challenges in our personal and professional lives,let us remember the importance of thinking critically about the problems we encounter.By doing so,we not only find solutions but also grow as individuals in the process.。

语言学概论(英)8一、在下列每小题的四个备选答案中,选出一个正确答案,并将其字母标号填入括号内。

(每小题1 分,共10 分)1.Conversion is a term used in the study of _____ to refer to the derivational process whereby an item comes to belong to a new word class without the addition of anaffix. A.syntax B. semantics C. morphology D. phnology2.Which of the following word is formed by means of blending?( ) A. phone B. telex C. tele D. telephone3.If sentences are syntactically well-formed but semantically ill-formed, they are known as semantically ____ sentences.A. logicalB. anomalousC. acceptableD. natural4.“John married a blond heiress” _____“John is married”. ( )A. presupposesB. entailsC. equalsD. contradicts5.The ________relation between components in a sentence shows us the inner layering of sentences. ( )A. semanticB. syntagmaticC. hierarchicalD. paradigmatic6.The relationships between politeness principle and cooperative principle can be described as that of being ______. ( )A. competingB. complementaryC. contradictoryD. coexistant7.Which of the following statement is true? ( )A. Descriptive linguistics deals with language in generalB. Traditional linguistics is descriptive.C. Theoretical linguistics establishes a theory of language structures and functions and without regard to any practical applications.D. macrolinguistics studies only the structure of language systems.8. Relevance theory is based on a definition of relevance and two general principles: the cognitive principle and the _____ principle. ( )A. communicativeB. qualitativeC. hierarchicalD. Informative9.The audio-lingual method is the application of structuralism and_____ in language teaching. ( )A. functional theoryB. cognitivismC. behaviorismD. corpus approach10. Which of the following is a correct statement about theprinciple of the Cognitive code approach to language learning? ( )A. learning language is not a human characteristic.B. A living language is characterized by rule-free creativity.C. The rules of grammar are psychologically real.D. Language is not a mental phenomenon.二、填空题。

StructureTalks找形的意义Motivation / 动机For our structures we always aim for an optimal solution combining various often contradictory aspects. During the design process structural, functional and aesthetical requirements need to be considered. The priorities may vary for each project, but the objective is always a holistic approach - a `symbiosis´ of all criteria.While finding an optimum for each system two main items should be considered as they have a significant influence on the structure’s performance: force and geometry. At sbp we therefore developed several approaches for a form finding process to achieve the best possible solution for each project.During the form-finding process the top priority is identifying the geometry that enables the optimum force flow within the structure. During the further development of this geometry the focus shifts towards aesthetic and constructional aspects. The boundaries between these two approaches are often blurred.结构设计中我们总是在各种矛盾的因素之间寻求最优方案，结构、功能、美学需求一个都不能少，但是应该优先考虑哪一项呢？当然不同的项目有不同的答案，但目的都是要找到一个综合各项指标的最优的结构方案。

通过做英语作业提高英语水平的英文作文全文共6篇示例，供读者参考篇1Doing My English Homework Helps Me Get Better at EnglishI used to think homework was just something my teachers gave me to torture me after a long day at school. Why did I need to do more work when I was already so tired from all the learning? It didn't seem fair. But then I realized that the homework, especially the English homework, was actually helping me get much better at English.My English homework comes in many different forms. Sometimes I have to write sentences using new vocabulary words we learned in class that day. Other times I have to read a short passage and answer comprehension questions about what I read. My favorite is when we get creative writing assignments and I get to write silly stories using my imagination.At first, the English homework was pretty hard for me. I'm not a native English speaker, so forming sentences that made sense was tricky. My vocabulary was limited, and I didn't always understand the reading passages. But my teacher was patientand would go over anything I struggled with. Little by little, doing the homework helped reinforce what I was learning.The vocabulary practices were really useful for expanding my English word knowledge. Having to use those words in sentences helped cement them in my brain instead of just memorizing them for a test and then forgetting them. Now when I watch TV shows or movies in English, I recognize more words than before.The reading comprehension was tough initially because I would get hung up on words I didn't know and lose the overall meaning. But rereading the passages and referring to the questions got me into the habit of focusing on understanding the main ideas rather than getting stuck on every little word. It prepared me for reading longer books and articles in English.The creative writing is probably what I enjoyed most because I could have fun with the language instead of just following rules. Making up stories allowed me to practice putting English words together in unlimited ways. My teacher would mark up my writing and I'd learn where I made mistakes so I could improve for next time. Getting that feedback was invaluable.Doing my English homework wasn't always exciting, I'll admit. Sometimes I would get frustrated when I got stuck or didn't understand something. But I could tell that the morehomework I did, the more English started making sense to me. It was like exercising a muscle - the more I worked at it, the stronger I became.My English ability is far from perfect, but it's so much better than when I started the homework. Conversations and classes are easier to follow now. Reading is smoother and I don't have to stop as frequently to look up words. Writing paragraphs with decent grammar has become second nature. I'm nowhere near fluent yet, but the homework made a huge difference.If you're learning English, I can't emphasize enough how important it is to do the homework your teacher assigns. It may not be thrilling, but that practice is the key to making real progress. It allows you to apply what you learned in class over and over again until it sticks. It exposes you to vocabulary, reading, writing, listening, and speaking in a low-pressure environment where you can make mistakes and learn from them.So as much as I may have resisted it at first, I'm grateful my teacher made me do English homework. It was hard work, but that hard work paid off immensely. My English keeps improving because of that practice. Having strong English skills opens up so many opportunities for the future too. I may complain about homework sometimes, but deep down I know it's helping me aton. Every English learner should embrace the power of homework - it really does make a huge difference if you stick with it.篇2Doing My English Homework to Become a Better English SpeakerHi there! My name is Emily and I'm in the 5th grade. I really enjoy learning English at school. It's such an interesting and useful language. By doing my English homework, I've been able to improve my English skills a lot. Let me tell you all about it!In class, we learn new vocabulary words, grammar rules, and practice conversation. But the real practice happens when I do my homework assignments. My teacher gives us workbook pages, writing assignments, and reading comprehension activities to complete at home. At first, the homework seemed really hard. I didn't know a lot of the words and the instructions were confusing sometimes. But my parents encouraged me to try my best and use resources like online dictionaries if I was stuck.One of the most helpful homework activities is the vocabulary workbook pages. Each week, we get a list of 10-15new words to learn. The workbook pages have us write the definitions, identify parts of speech, use the words in sentences, and sometimes draw pictures to illustrate the meanings. At first, I'd just try to memorize the words and definitions. But now I make an effort to really understand each new word. When I come across an unfamiliar word in my reading, I'll look it up and add it to my vocabulary list for extra practice. Doing this has helped me learn so many new English words!The writing assignments are also very valuable for improving my English skills. Sometimes we have to write short stories or descriptions of something we did. Other times, we have to write persuasive essays or research reports. No matter what kind of writing it is, I have to think carefully about vocabulary choice, grammar, conventions like capitalization and punctuation, and how to organize my ideas. After I finish a draft, I do multiple rounds of proofreading and editing to fix any mistakes. My parents and sometimes my older brother helps me identify areas to improve too. Revising my work is hard sometimes, but it really does help me write better. My writing has improved so much from all this practice!Another really important part of my English homework is the reading comprehension work. We often have to read passagesfrom books, short stories, news articles, or other texts and then answer questions that check if we understood the main ideas, vocabulary in context, and other details. I've learned that I can't just skim the passages or I'll miss important information. Instead, I have to read slowly, take notes on key points, look up words I don't know, and track details that seem important. Then when I get to the comprehension questions, I can refer back to my notes and the passage itself to find the evidence for my answers. Doing this reinforces my reading skills and also gives me more vocabulary exposure.Sometimes English homework can definitely be a chore. Vocabulary memorization isn't exactly thrilling, and reading dry passages or proofreading essays for the third time is pretty tedious. But I know it's helping me get better and better at understanding, speaking, reading and writing English. It's empowering to be able to communicate well in such a major world language. Also, my parents have explained that building strong English fundamentals now will pay off when I move on to more advanced courses in middle school, high school and college. Doing my English homework is an investment in my future!So while I may grumble about it in the moment, I'm actually pretty grateful that my teacher takes the time to assign quality English homework. My skills have improved tremendously thanks to all the reading, writing, vocabulary, and comprehension work I've done. My advice to anyone learning English is: Don't slack off on those homework assignments! Every worksheet, writing piece, and assigned reading is a chance to get valuable practice. If you put in the hard work now, your English abilities will continue soaring. Before you know it, English homework won't feel so difficult anymore. Thanks for reading, and happy studying!篇3Learning English Through HomeworkHomework can be such a drag sometimes, but I've learned that it's actually really important for improving my English skills. At first, I used to groan whenever my teacher passed out worksheets or assigned writing prompts. Why did we have to do all this extra work at home on top of everything we did in class? It just seemed like a big waste of time. But as I've gotten older, I've realized how valuable English homework truly is.Working on vocabulary words each week has helped me understand so many new terms and phrases. When I first see those long lists of vocab words, they look totally foreign and impossible to memorize. By spending time practicing them through flashcards, writing sentences using the words, or finding examples in books, those strange terms become familiar friends. Pretty soon, I find myself recognizing and using those words in my everyday conversations and writing. My English vocabulary keeps growing bigger and bigger thanks to those vocab lists!The reading comprehension homework has also been a huge help. I used to dread having to read passages and answer question after question about them. It felt like such a chore. But going through that process consistently has trained my brain to focus, comprehend what I'm reading on a deeper level, and understand key ideas. Now when I read books for fun, I find that I can follow the plot and understand themes much better than before. Reading comprehension work has truly improved my English reading abilities.Grammar exercises seemed so boring at first, but now I realize their importance. English has so many little rules for sentence structure, punctuation, verb tenses, and more. All those homework assignments identifying parts of speech, combiningsentences with proper conjunctions, fixing capitalization errors, and unscrambling words into a logical order were reinforcing vital grammar rules. Thanks to that practice, using proper grammar is starting to feel automatic when I speak and write in English. I cringe now when I see grammar mistakes!One final advantage to all this English homework is simply the repetition and reinforcement it provides. The concepts and skills get drilled into my brain through doing the assignments over and over. It's those little steps of consistency that lead to big progress over time. English homework ensures I use and practice those skills regularly rather than letting them get rusty.So as much as I may grumble about having to complete those worksheets, vocabulary lists, reading comprehension questions, grammar exercises, and writing assignments, they've been worth the effort. English is such an important language for communicating in our world. Being skilled in areas like reading, writing, speaking, and understanding English will serve me so well in the future for academics and whatever career I pursue. I'm grateful my teachers have given me a consistent way to actively improve through homework practice. The growth in my English abilities makes all that extra work worthwhile. Homework hasplayed a pivotal role in taking my English skills to a whole new level.篇4Doing My English Homework is the Key to Getting Better at EnglishI used to think doing homework was just a big waste of time. I'd much rather be outside playing with my friends or watching TV shows. Homework seemed so boring and unnecessary. Why did I need to spend hours every night working on stuff I had already learned in class that day?That was before I realized how important doing my English homework actually is for improving my English skills. Now I've changed my mindset completely. I get excited to do my English homework because I know it's helping me get better and better at speaking, reading, and writing in English.The main reason why English homework helps so much is that it gives me extra practice outside of class. In class, we only have a limited amount of time to learn new vocabulary, grammar rules, writing techniques, and other concepts. By doing reinforcing exercises for homework, I get to keep practicing and solidifying what I learned during the school day.For example, when we learn a new list of vocabulary words in class, my teacher has us do activities using those words to help us remember their meanings and usages. But we can't possibly spend the whole class period just drilling those new words. So for homework, we often have exercises where we have to write sentences using the vocabulary or fill-in-the-blank paragraphs with the new words. Going through and actually producing the words and meanings myself, instead of just reading them, really cements the words in my brain.The homework also allows me to work at my own pace. Sometimes I'll fly through the easier homework problems in a matter of minutes. But then I'll get stuck on trickier parts for a while until I finally understand through trying multiple approaches. In class, the teacher has to keep pushing forward, so I don't always master every single concept. But at home, I can take as long as I need until I've got it down.Another great thing about English homework is that it encourages me to look more deeply into areas that confuse me initially. A homework problem might seem baffling at first, but then I'll re-read the explanations and examples we went over in class, look at my notes, or find extra resources online to getclarification. The struggle of having to persevere through challenges is amazing for my learning.English homework also gets me to explore using my English skills in different contexts beyond the classroom. Sometimes I have to write stories from my own imagination. Other times, I read novels or news articles for homework and then discuss them through written responses. This variety keeps me engaged. It's way more interesting than just doing drills from a textbook.My homework also gives my parents a window into what I'm learning and allows them to get involved. When my mom sees me struggling with a homework assignment, she can re-explain certain grammar points using examples I can relate to. Or she'll make sure I don't just copy answers from online but put effort into figuring them out myself. Having my parents' support and oversight keeps me accountable.The more I practice my English through homework, the stronger my skills become. This will allow me to get better grades in school, communicate better with English speakers around the world, understand English movies and books, and potentially get into a good university or career later on. So even when I'm tempted to rush through assignments or copy a friend's work, I don't let myself slack off.After all, isn't the whole reason I'm learning English to become truly proficient in this global language? If so, then doing my homework diligently is one of the best ways to reinforce what I've learned. It's really the key to turning English from just another school subject into a real-life skill I'll have forever.So to all my fellow students out there - don't groan when your English teacher assigns homework! See it as an opportunity to solidify your knowledge and get tons of valuable practice outside of class. If you want to go from just passing English tests to actually mastering the language, homework is your ticket. So grab your pencil, open that workbook, and get ready to improve your English one homework assignment at a time!篇5Doing My English Homework to Become an English MasterHello friends! My name is Emma and I'm 10 years old. I go to Oakwood Elementary School and I'm in 5th grade. I really enjoy learning English and doing my English homework because I want to become an English master! Let me tell you all about how my homework assignments help me get better at English every day.First of all, my vocabulary homework is super helpful. Every week, my teacher Ms. Roberts gives us a list of 20 newvocabulary words to learn. She teaches us the definitions and we have to write sentences using each word. Then we have exercises where we circle the vocabulary words we see in short stories or pick the right definition from multiple choices. Doing this vocabulary homework really expands my word knowledge. The more words I learn, the better I can communicate in English!My favorite kind of English homework is the reading comprehension assignments. For these, we have to read a short story or article and then answer questions about the main idea, the characters, the plot, and other details. I love getting lost in a good story. Reading is amazing practice for improving my English comprehension skills. Plus, I learn so many new words from the context of the stories. The more I read, the easier it is to understand spoken English too.Another way my homework boosts my English is through the writing exercises. Maybe I'll have to write a descriptive paragraph about my favorite place. Or I'll have to write a short narrative story about something that happened to me. Sometimes I even get creative writing assignments where I can use my imagination! Writing is excellent practice for improving my English grammar, vocabulary, and sentence structure. It also helps me get mythoughts across clearly. The more I write, the stronger my English skills become.Don't forget about speaking and listening practice too! Sometimes for homework, we have to record ourselves reading a passage out loud or giving a short speech. Then we get feedback from Ms. Roberts on our pronunciation and fluency. Other times, we have to watch videos in English and answer comprehension questions. Listening to native English speakers is so valuable for training my ear. I'm getting better every day at understanding when people talk at normal speeds.On top of all the homework, we also have tests and quizzes quite often in English class. Getting ready for those assessments really motivates me to study hard and remember all the grammar rules, vocabulary, and reading strategies I've learned. It's so satisfying when I get a good grade because I know my skills are improving!To be honest, doing all this English homework does take a lot of time and effort. But it's 100% worth it. My English abilities have skyrocketed since the beginning of the year. Now I can read longer books, write more advanced stories, understand movies and songs, and have longer conversations. English homework is my secret weapon for success!My dream is to one day become an English master who can speak, read, and write perfectly. I'm already so much better than I used to be thanks to doing my homework diligently. If you want to boost your English to the next level too, just follow my example. Study hard, practice a lot, and never miss a homework assignment! Before you know it, you'll be an English expert. Let's continue working towards our goals together. Thanks for reading my story, and happy studying!篇6Here's an essay about improving English through homework assignments, written from the perspective of an elementary school student, with around 1,000 words:Doing English Homework Helps Me Improve My EnglishHi there! My name is Emma, and I'm a 4th grader at Sunny Hill Elementary School. I love learning new things, and one of my favorite subjects is English. I know that practicing and doing homework is super important for getting better at anything, and English is no exception.When I was younger, I used to think homework was just a chore that teachers gave us to torture us after a long day at school. But as I've gotten older, I've realized that homework,especially English homework, is actually a really helpful tool for improving my skills. Let me tell you why!First of all, English homework gives me extra practice with the things we learn in class. In our English lessons, we learn about grammar rules, new vocabulary words, and how to write different types of texts. But class time flies by so quickly, and it's easy to forget things if we don't keep practicing. That's where homework comes in! When I have to write sentences using the new grammar concepts we learned, or make flashcards for the vocabulary words, it really helps cement that knowledge in my brain.Another great thing about English homework is that it allows me to work at my own pace. In class, we have to move at a certain speed to cover all the material. But with homework, I can take my time and really think through the concepts that are tricky for me. If I'm having trouble with a particular grammar rule or writing assignment, I can re-read the instructions, look back at my notes, or even ask my parents or friends for help. Working through challenges on my own (with a bit of support when I need it) really boosts my confidence and understanding.English homework also gives me a chance to be creative and have fun with the language. Sometimes, our assignments involvewriting stories, poems, or dialogues. I love using my imagination and putting the English skills I've learned into practice in a creative way. It makes the assignments feel more like games than work.I'll be honest, there are definitely times when I don't feel like doing my English homework. After a long day at school, seguida por atividades extracurriculares, sometimes the last thing I want to do is more work. But my parents always remind me that the effort is worth it because it's helping me become a stronger English speaker, reader, and writer. And they're right!Looking back on my English homework from previous years, I can see how much my skills have improved. Assignments that used to be really hard for me are much easier now. My writing has become more sophisticated, and I've expanded my vocabulary tremendously. All of that progress makes me excited to continue doing my English homework because I know it's helping me work toward mastering the language.So to all my fellow students out there – don't think of English homework as a chore or punishment. Think of it as an opportunity! An opportunity to practice, to be creative, to challenge yourself, and to steadily improve your English abilities. The more work you put into it, the more you'll get out of it. Andstrong English skills will benefit you for your whole life, no matter what path you choose. Just keep practicing, keep learning, and don't be afraid to ask for help when you need it. You've got this!。

河南省普通高中2024届高三下学期3月高考适应性测试英语试卷学校：___________姓名：___________班级：___________考号：___________一、阅读理解As any gardener knows, nature doesn’t need much space to grow strong and healthy—give her an inch, and she’ll take a mile! Here are four impressive examples of nature reclaiming (开发利用) our world for itself with amazing results.Houtouwan, ChinaIt lies on the island of Shengshan Town on the furthermost edge of a group of islands. The only way to get there is by private boat, or by bus, and then by ship. Its isolation (隔绝) was one of the prime factors leading to the abandonment of the village in the 1990s. Now, its walls and streets become green with overgrowth.Spreepark, GermanySpreepark was closed in 2001 and the local plant life soon got to work. Structures in use since the park originally opened in 1969 were quickly covered by leaves. Now, an initiative aims to bring the site back to life.Vallone dei Mulini, ItalyIts high humidity (湿度) encouraged a microclimate perfect for plant growth. As the abandoned buildings fell apart, the ruins and their surroundings became completely overgrown. Photographs taken of the site in 2006 went widespread online.Beng Mealea, CambodiaThough constructed around 900 years ago, this grand temple is far less frequented than its more famous neighbour, Angkor Wat. In 2020, it was submitted for consideration as a UNESCO World Heritage Site. Natural decline, among other factors, has caused serious damage to the site, allowing the surrounding jungle to overrun and combine with it. 1．What mainly caused Houtouwan’s abandonment?A. Its wet climate.B. The overgrowth of plants.C. Its separate location.D. The aging of walls and streets.2．Which place once became popular online?A. Houtouwan.B. Spreepark.C. Vallone dei Mulini.D. Beng Mealea.3．What feature do the four places share?A. They are World Heritage Sites.B. They are being taken back by nature.C. They are nearly 1,000 years old.D. They are regaining their original state.While climbing the Great Wall is a once-in-a-lifetime dream for many, Jim Spear has taken it a step further, spending the last 18 years as a village r residing beneath this ancient wonder.“Never did I dream I would have the chance to visit the Great Wall, let alone live under it,” said 68-year-old Spear, a self-taught architect from the United States.Spear’s interest in China began during his college days. It deepened when he met Tang, a Chinese girl, in 1980, and they got married two years later. In 1986, he decided to drop out of his doctoral studies in Chinese politics at the University of California and moved to China “to get to the heart of things”. “I realized that if I became a scho lar of China, based overseas, I wouldn’t be able to experience what was happening in China,” Spear said.In 1995, the couple secured a long-term rent of a traditional village farmhouse in Mutianyu and decided to make it their full-time home ten years later. Shortly after possessing full-time village life, he rented an abandoned schoolhouse and transformed it into a restaurant and art glass factory for a sustainable tourism business. He also turned a former factory into a hotel and helped renovate (翻新) over 20 households into restaurants. Besides, he explored other ways to support those residents in rural areas. “I want to do something for them,” Spear said.Spear’s designs reflect his natural talent for fusing (融合) traditional and modern elements, adopting the Great Wall style. However, Spear emphasized his approach involves creating designs and views “that echo (呼应) the Great Wall, not copy it”. In 2014, Spear received the Great Wall Friendship Award from the Beijing government.Talking about the future, Spear sees abundant possibilities in China, driven by significant domestic demand and a growing emphasis on preserving historic structures. 4．What do we know about Spear from the first two paragraphs?A. He likes to climb the Great Wall.B. He came to China when he was 18.C. He once dreamed of becoming a villager.D. He has lived beneath the Great Wall for years.5．What’s Spear’s purpose of moving to China when he was in college?A. To see a real China.B. To marry a Chinese girl.C. To work as an architect.D. To study Chinese politics.6．What is special about Spear’s designs?A. They are inspired by rural residents.B. They copy the style of the Great Wall.C. They have received a world-wide prize.D. They connect the past with the present.7．What will Spear possibly plan to do in the future?A. Continue to engage in cultural exchange.B. Work for another award in structure preserving.C. Find more ways to support the rural residents.D. Conduct further study in Chinese historic structures.Why do we find ugly animals so appealing? And what makes odd-looking creatures so cute?Evolution (进化) plays a role. According to Austrian zoologist Konrad Lorenz, human attraction to childish features, such as big eyes, large heads and soft bodies, is an evolutionary adaptation that helps ensure that adults care for their young, guaranteeing the survival of their species. Odd-looking animals such as blobfish, pugs, and bulldogs all share these childish qualities that initiate an affectionate response among humans. And these childish characteristics increase a person’s “protective behavior, attention and willingness to care” for the individual and reduce the “likelihood of attacks towards a child”, says Marta Borgi, a researcher.Ugly animals often have other value—some, like the blobfish or the naked mole rat, live in extreme environments that they have adapted to in remarkable ways. Scientists are keen to study these animals to understand whether their biology might provide fresh insights that could lead to treatments for human health conditions such as cancer, heart disease and other deadly diseases.Our fascination with ugly-cute animals can also be traced back to culturally-based causes. “The ugly-cute thing is very fashionab le,” says Rowena Packer, a lecture r of animal behavior. “This is partly driven by social media, with many influential people showing off pet pugs and French bulldogs on the Internet,” she says.But there are some serious welfare concerns around this trend. Vets are urging people not to choose a flat-faced dog, because they suffer from serious health problems. Pugs and French bulldogs which have been selectively produced experience breathing difficulties, repeated skin infections and eye diseases.We may want to rethink our love for “ugly-cute” animals because of their silly featureslike protruding (鼓出的) eyes and wrinkly faces.8．Why do people like ugly animals according to Konrad Lorenz?A. People appriciate their efforts to survive.B. People appriciate their super adaptability.C. People are attracted by their childish looks.D. People are fond of their fast response speed.9．What is Paragraph 4 mainly about?A. What media are changing people.B. How public practices influence people.C. Whether social media is worth believing.D. Why celebrities show off their pet animals.10．What’s the author’s attitude towards people’s love for ugly animals?A. Opposed.B. Supportive.C. Indifferent.D. Cautious.11．How does the author mainly answer the questions raised in Paragraph 1?A. By quoting different researchers’ findings.B. By showing some examples of keeping pets.C. By observing people’s behavior towards animals.D. By referring to authoritative evolutionary theory.An innovative creation will help transform treating diseases. Scientists at Tufts University and Harvard University’s Wyss Institute developed tiny biological robots “Anthrobots” from human cells. These Anthrobots possess the astonishing ability to move across surfaces and have exhibited a remarkable healing (治愈) effect by stimulating neuron (神经元) growth in damaged lab dish regions. This discovery serves as a crucial stepping stone toward the researchers’ vision of employing biological robots as innovative too ls for healing, and disease treatment.This breakthrough originates from earlier research conducted by Michael Levin, Professor of Biology at Tufts University School of Arts & Sciences, and Josh Bongard at the University of Vermont. They once created biological robots called Xenobots from frog cells, capable of various functions including self-copying, for a limited number of cycles. However, it was unclear if biological robots could be formed using cells from other species.In their latest study, Levin and Tufts PhD student Gizem Gumuskaya discovered thatthose observed in Xenobots.Anthrobots showed the ability to move across a surface covered in human neurons grown in a lab dish, facilitating new growth to fill gaps caused by cell layer damage.“It is extremely interesting and completely unexpected that normal patients’ cells, without changing their DNA, can move on their own and encourage neuron growth across a re gion of damage. We’re now looking at how the healing mechanism works, and asking what else these constructs can do,” says Levin.One of the main advantages of using human cells lies in constructing biological robots from a patient’s cells to perform heali ng tasks without leading to immune (免疫的) responses. These Anthrobots naturally break down after a few weeks and can be easily absorbed into the body once their function is complete.Anthrobots can only survive under specific laboratory conditions, posing no risk of exposure or unintended spread outside the controlled environment. They do not reproduce, have no genetic changes, and therefore carry no risk of developing beyond safety measures. 12．What do Anthrobots do in healing patients?A. Replace human cells.B. Facilitate neuron growth.C. Create new human cells.D. Move across tissue surfaces.13．What does the underlined word “crafted” in Paragraph 2 mean?A. Made.B. Divided.C. Copied.D. Designed.14．What is one of the main advantages of Anthrobots?A. They can be easily created from patients’ cells.B. They can be used in many controlled environments.C. They can avoid causing immune responses.D. They can have genetic changes when necessary.15．Which magazine is the text most probably taken from?A. Advanced Science.B. Sportsnet Magazine.C. Art in America.D. National Geographic.二、七选五16．Although it’s an age of typing, handwriting still matters. Today the danger of the technology which computers and the typing people are using on writing is becoming extremely enormous. ① .As primary school pupils and college students return for a new school year in the North America, many will place a greater-than-ever reliance on computers to take notes and write papers. Children are not just encouraged but required to bring laptops to class, with whichsome parents are disappointed. ②, and their professors complain of this serious distraction (分心) in classrooms.③, from recalling a random series of words to better grasping the concept of complicated ideas. In a study from 2014, students typing wrote down almost twice as many words and more passages verbatim (一字不差地) from lectures, suggesting they were not understanding so much as rapidly copying the material. Handwriting, which takes longer for nearly all university-level students, forces note-takers to integrate ideas into their own words.④ . Those taking notes by hand also perform better on tests when they are later able to study from their notes.Many studies have co nfirmed handwriting’s benefits, and many countries have taken action. About half American states have mandated (强制执行) more teaching of handwriting since 2010. In Sweden there is a similar campaign. ⑤. England’s national subjects already include teaching basic knowledge of handwriting by age seven. However, several school systems in America have gone so far as to ban most laptops. This is too extreme.A. And a heated debate is going about itB. They would no longer need to complainC. Writing on paper can improve everythingD. But so many are fond of typing in their jobsE. College students message instead of listening to lecturesF. This aids conceptual understanding at the moment of writingG. The government pushes for more handwriting and fewer devices三、完形填空（15空）Last year, cardiologist (心脏病专家) Steve Lome came to truly understand what it means to be at the right place at the right time. During a half-marathon (半程马拉松), heand then he continued the race.wouldn’t have been around to save him, too.in the very same half-marathon this year!17．A. save B. lose C. accept D. believe18．A. match B. mark C. joint D. way19．A. extent B. distance C. area D. point20．A. coach B. guide C. competitor D. volunteer21．A. confused B. relieved C. worried D. disappointed22．A. leaving B. arriving C. collapsing D. understanding23．A. tell B. predict C. decide D. imagine24．A. temporarily B. accidentally C. eventually D. immediately25．A. moved away B. stayed around C. wandered about D. looked over26．A. noticed B. identified C. missed D. reached27．A. dizzy B. thirsty C. uneasy D. pleased28．A. analyzing B. evaluating C. timing D. recording29．A. agreed B. failed C. refused D. paused30．A. meaning B. proving C. clarifying D. indicating31．A. shape B. touch C. peace D. happiness四、短文填空32．Acupuncture (针灸) has been a treatment for countless patients for thousands of years in China. Before modern medicine came to life, stone tools ① (use) to relieve pain. Over time, this natural practice developed into a comprehensive medical system and shaped the root of acupuncture.Acupuncture is a treatment that is aimed ②(promote) the body’s self-regulating functions. Its principles are in line with the philosophical concepts of traditional Chinese medicine, ③emphasizes comprehensive treatment, meridian (经脉) adjustment and balance of bodily functions.④ (practice) vary in forms. Needle insertion (插入) is the most common method, which is carried out ⑤ inserting hair-thin needles into meridians, or specific points on the body that channel vital energy. Practitioners use needles to ⑥(effective) unblock theflow of energy and restore yin and yang balance.Looking beyond China, acupuncture has become a global treatment. Over the years, acupuncture ⑦ (see) many advancements in scientific research and modern medicine. According to a 2019 WHO report, acupuncture is used in 113 of its 120 member countries, ⑧(illustrate) its widespread recognition and application.Acupuncture, as ⑨ ancient Chinese treatment, is a reflect of a rich history and ⑩(significance) Chinese culture.五、书面表达33．假如你是李华。

A.鞋子部位名称鞋头盖apron 鞋头前片forepart 鞋楦后锥体back con边条foxing 鞋楦后锥back cone top 楦前锥体front cone面宽度plane width 卷跟full breast heel 后邦高度back鞋面补强backing 鞋眼腹墙heel breast 后跟heel底跟密接heel fit on outsole 后贴片backstrap 后跟邦脚线heel feather line 后带backstrap 掌园ball girth 脚掌ball鞋踵垫heel pad 鞋跟斜度heel pitch 鞋台bottom鞋跟端点heel point 迭合中底blended insole 后跟座宽度heel seat width 中底滚边insole binding 满帮close toe & close back 中底insole脚背instep 脚背宽度instep girth 脚领(领口) collar鞋带孔lace hole 圆锥形跟cone heel 鞋楦last鞋后踵counter 鞋底中层middle sole 鞋后套counter pocket 内底midsole 鞋领口cuff 露踵式open back挡泥片mudguard 鞋眼片eyelet 楦缘feather edge鞋头开口式open toe 外底/大底outsole 细腰形跟flared heel装饰片overlay 扁平足flat foot 鞋套shoe over脚的量测foot measurement 鞋面片pieces 鞋头部分toe part鞋头翘度toe spring 鞋腰quarter 鞋舌tongue插腰quarter insertion 鞋头套垫top bumper 圆轴形跟reel heel楦脊ridge 天皮top lift; top-piece 鞋口top line没有包金属眼套的鞋眼raw eyelet 鞋面upper 硬质后踵片rigid backer 鞋面前片vamp 方圆形鞋头round square toe 鞋头长度vamp length鞋头前片两翼部分vamp wing 后帮seat lasting 硬座垫seat sock鞋腰waist 鞋统shaft 腰园waist girth插中板shank board 鞋边墙side wall 半插wedge腰帮side lasting 两侧帮side vamp 沿条welt全帮whole vamp 鞋里垫皮skeleton lining 宽度width斜形鞋头slant toe 翼形前片式wing tip 细长形鞋头slender toe条状外帮式amalfi constructing 中底垫皮sock lining 鞋喉点throat鞋口长度throat opening 鞋跟尖tip 套头toe box鞋头toe cap 前帮toe lasting 固特异沿条Goodyear welt 拉帮结构string lasted cons 条状外邦式凉鞋结构mlfi construction交叉紧带式interlaced straps cons. 外翻帮结构stitchdown consa各种鞋型名称短靴ankle boot 舞台鞋arena boot 绣花鞋embroidery shoe 中统靴half boot 休闲运动鞋athleisure shoe 高跟鞋high heel shoe仓床台布面鞋espadrium 运动鞋athletic shoe 加州鞋California shoe 健身凉鞋exercise sandal 帆布便鞋canvas casual shoe 婴儿鞋baby shoe钓鱼鞋fishing shoe 室内鞋house shoe 无内里平底女鞋bouerina 皮条编织鞋huarches 便鞋casual shoe 平底便鞋flaty shoe芭蕾舞鞋ballet shoe 童鞋children shoe 足球鞋football shoe密鞋close shoe 室内鞋indoor shoe 棒球鞋baseball shoe皮鞋lea. Shoe 射出底injection sole 篮球鞋basketball shoe短筒军靴combat boot 塑料鞋plastic 盛装女鞋court shoe长靴knee boot 海滩鞋beach comber 异沿条鞋good year welt shoe 马靴frye boot 保龄球鞋bowling shoe 礼服鞋dress shoe高尔夫球鞋golf shoe 中童女鞋girl’s shoe 长鞋boot牛仔靴cowboy shoe 功夫鞋kungfu shoe 女鞋ladies shoe麻布鞋leisure shoe 男鞋men’s shoe 休闲鞋leisure shoe包子鞋loafer shoe 登山鞋mountaineering shoe 大童女鞋misses shoe 无后套拖鞋mule 缎面鞋satin shoe 护士鞋nurses shoe露趾尖式凉鞋peeptoe 素面女高跟鞋plain pump 外翻鞋stitch down shoe 雨鞋rain shoe 便鞋(无松紧带式) slip-on 工作鞋work shoe加州式结构拖鞋popsilce 便鞋(有松紧带式)slip-in 散步鞋walking shoe E制鞋用工具及机械设备英文词汇a. 底部空气枪air gun 铝楦aluminum last 毛刷brush拉邦钳lasting pincer 铁槌hammer 两节式鞋楦hinge last拔钉器nail puller 冲孔器piercer 钳子pincer上糊机lateing ma 鞋拔shoe horn 真空吸尘机vacuum cleaner 贴沿条机welt attaching machine 纸盒成型机carton making ma 木楦wood last上胶机cementing and attaching ma 输送带conveyor 集尘设备dust collector 前邦机forepart last ma. 加热箱heated chamber 烤箱oven射出成型机injection molding ma. 后踵整形机heel seat pounding & shaping ma.气压钉跟机pneumatic heel tacking ma. 自调式压底机universal sole attaching ma.真空箱式加硫定型机vacuum tank vulcanizing ma. 后踵定型机backpart molding ma.皮带输送机belt conveyor 鞋面定型机boot vamp moulding ma.整形机moulding ma. 定型机shaping ma. 打钉机nailing ma. 气压拔楦机pnamatic last slipping ma. 送料机raw material delivery ma.急速加热冷却后踵定型机quick heating and cooling counter shaping ma.真空干燥机vacuum dryer 扫刀wiper 腰帮机cement side laster内线缝底机mackay stitcher 后邦机counter lasting ma. 硅热机ceramic heater 砂轮研磨机emely wheel grinding ma 清胶机glue cleaning ma. 拔楦机last extracting蒸温式热定型机humid heat setting unit 中底打钉机insole stapling gun打包机packing ma. 鞋面蒸湿机upper conditioner 铆合机rivet setter墙式压底机walled sole attaching ma. 滚筒输送机roller conveyor 划线机shoe lined ma. 回转式真空加硫定型机rotary vacuum vulcanizing ma. 磨底机sole rubbing ma.立体回转式输送带solid rotating conveyor 前邦机toe lasting ma. 加硫机vulcanizing ma.中邦机waist lasting ma. 磨皮机buffing ma. 贴合机cementing &attaching ma.冷却机cooling ma. 钻孔机drilling ma. 鞋眼打孔机eyelet puncher悬挂式干燥机hanging dryer 打孔机holing ma. 外线机out seam stitcher打包机packing ma.b. 面部针杆blade 尺ruler 剪刀scissor 平车bed type sewing ma.正常型针车ordinary type sewing ma. 样版级放机pattern grading ma.高头车post type sewing ma. 高周波烫印机high frenquency stamping ma.修内里机lining trimming ma. 针车stitching ma. 车针needle蒸气式热风烫孤机steam heating pressing ma. 量皮机surface measuring and calculating ma.切条机strip cutting ma. 高速型针车high speed type sewing ma. 片皮机splitting ma.剪带机strap cutting ma. 滚边机folding ma. 修边机trimming ma.热溶胶套头印置机thermoplastic toe puff applying ma. 人字型针车zigzag type sewing ma.平台式针车flat bed sewing ma. 高头车high post sewing ma.C.质量管理试验用机器耐磨试验机abrasion tester 老化试验机ageing tester 温度计moisture tester拉力试验机tensile strength tester 压缩永久变形试验机permanent compression tester粘度计vislosity meter 万能材料试验机universal materials tester破裂强度试验机bursting strength tester 染色脱色试验机dreing decoloring tester耐黄变试验机non-yellowing ma. 耐硫化试验机action of sulphury apor test ma.永久伸长率试验机permanent tension tester 硬度计hardness tester厚度计thickness guage 曲折弯度试验机flexing tester 耐寒试验机freezing tester耐滑试验机slip resistance testerD.机器设备控制板control panel 斩刀cutting die 切割垫板cutting board量脚器footgauge 斩板hardness plank 空气压缩机air compressor模子mold 起子screw driver 粉碎机crusher斩断板cutting block 泡棉贴合机foam pasting ma. 鞋底溢料消除机flast trimming ma.加热箱heated chamber 中底包边机insole edge covering ma.中底组合成型机insole assembling ma. 中底定型机insole moulding ma.摇臂式裁断机swing bean cutting 裁断机cutting ma.E.其它类英文词汇楦头底板last bottom 鞋头跷度toe spring 鞋带头lace tip鞋眼扣环eyelet clip 中底垫皮sock lining 填腹bottom filler砂轮式磨粗stone roughing 手攀鞋hand lasting 头橡胶片前套rubberized toe box拔空气钉pull out staple 贴底outsole attaching 后跟包皮heel cover鞋面开口throat of vamp 直条滚边straight binding 鞋后密口closed back鞋腰内里补强arch bandage 内外腰鞋面in and out vamp 鞋头补强vamp reinforcer沿条foxing 布料cloth material 铁心shank单底(模子底) unit sole 药水糊neoprene cement 停留时间time dwell中底滚边insole binding 后跟垫片heel pad 港宝前套serlyn toe box 鞋面邦角上糊lasting allowance cementing 大底压着outsole press大底车缝outsole stitching 前邦机指示灯toe lasting machine light 装饰带ornament后包片outside counter pocket 鞋口滚边top line binding 反口领cuff鞋后开口open back 鞋腰开口open shank 边饰side trim脚背带quarter strap 全片式鞋垫full sock 斩刀记号齿size notch帆布canvas EV A中扦E.V.A. midsole 中底midsole鞋后高度back height 鞋头长度vamp length 鞋眼开口位eyestay opening中底钉合insole attaching 热溶胶式鞋头ther moplastic toe box 拔楦pull out last放置铁心putting on shank 后跟打钉heel nailing 鞋领collar鞋统shaft of boot 鞋舌滚边tongue binding 后跟拉环pull strap鞋头密口closed toe 鞋后直条backstay 无接缝鞋面seamless vamp抽条状鞋面vamp stripping 半扦鞋垫wedge sock 鞋面裁刀upper cutting dies鞋子种类(shoes variety) Athletic shoes /Sports shoes运动鞋Casual shoes便鞋Hiking shoes/Travelling shoes旅游鞋Slipper拖鞋Sandals凉鞋Boots马靴Work boots工作鞋Axido沙滩鞋Canvas帆布鞋Sport boots运动鞋Pumps高跟鞋Climbing shoes登山鞋Football shoes足球鞋Jogging shoes慢跑鞋Basketball shoes篮球鞋Canvas帆布鞋Casualshoes便鞋Pumps高跟鞋Leisure休闲鞋Tennis shoes网球鞋Baseball shoes棒球鞋Aerobic shoes舞蹈鞋二.部位名称(Location name) upper vamp鞋面counter后套binding滚边last 楦头top line鞋口sole edge底边seams缝合处bows 饰片stain染色adhered粘贴bumper包头片steel toe钢头eyelet and lace鞋眼带embroidery 电绣quarter鞋腰(身) Tongue鞋舌Insole中底Weit 沿条Ornament饰物Toe box头套Siping切沟Soles底台Zigzag万能Eye stay眼套Collar领口Top 面线Back strap后吊带Outsole大底Mid sole-EVA EVA中插Open toe鞋头开口Open back鞋后开口Lace loop穿环Reinforcements补强Hand sews手缝马克Reinforce tape补强带Inside corners内弯角Insole board中底纸板Textile lining布料衬里Plat forms包边中底台Wovens/weaves编织类Lining内里Heel 脚跟Foxing护条Eye let 鞋眼片Overlay饰片Socks鞋垫Box toe鞋衬里Print印刷Perforation冲孔Toe cap鞋头Toe part鞋头部Bobbin底线Bar加强线Heat sealed熔封鞋的一搬缺点:(Defect) Vamp wrinking鞋面皱纹Stitching not even车不平均Tricot shows up特丽可得露出来Wrapping not even攀帮不均匀Shank too loose铁心太松Heel not straight后套不正Sole come off底脱胶Wrong pattern 纸板错误Wrong material材料错误Wrong cutting裁断错误Toe broken down鞋头凹陷Color variation色差Wrong last 楦头错误Wrong colour颜色错误Damaged upper鞋面破损Not in pair不配双Crooked back stay 后套歪斜Crooked upper鞋面弯曲Crooked屈曲,不平顺Vamp length鞋头大小Lasting off center攀歪Sole laying not proper(centered)贴底不正确Back strap too high(low,long,short)后拉带太高Abrasion磨损Stitching not on the mark不照记号齿车Angle on insole中底发角Vamp open up鞋面掀开Wrapping not tight攀帮不紧Shank not straight铁心不正Cementing comes off脱胶Outsole not smooth大底不平Gore too weak松紧带弹性不佳Cement on heel鞋眼沾胶Cementing no good接着力不佳Cleanness not enough清洁度不佳Vamp split off 鞋面爆开Color not matching色差Loose thread脱线Broken thread断线Trun yellow变黄Turn dark变黑Pinch in 缩进去Flatten变平Rough粗糙x-ray透痕smeary污染hairy 起毛gaping 缝隙sole adhesion大底欠胶chip碎屑dirty 脏的get mildew发霉back height 后跟高度over cemented溢胶bottom dirty大底不洁半PU（仿PU）mipu.semi编织woven 牛皮calf拨丝钉pull last 大底车线outsole拨楦pull out last 定型加硫箱neat setter布扣ornament 鞋扣buckie布料cloth material 灯心绒corduroy裁刀cutting dies 裁断垫皮cutting pad裁断组大底outsole 鞋垫sock tining层皮跟leather stack heel 印刷跟printed.heel长毛里boa 毛暄felt 布里backing 加温式贴合flame成型组后踵heel curre 跟踵crown of last弹性stretch 皱布micro fab法兰绒flannel 不织布non woven 橡胶发泡Rubber pange帆布canvas 反毛皮suede PU poly urethane跟heel 中底insole 大底outsole构型construction 材料material后跟包皮heel cover 中底边insole binding后上片mustache 处包片outside counter后踵整型back part 大底压着outsole press夹子lasting 攀鞋用手头钉Lasting tacks金属PU semwtauic pu/met pu金属PVC semwtauic pvc拉菲草raffia 镜面ratent拉链纹PU raffia pu 沙丁布satin冷冻箱cooling chamber 放置铁心putting on shank 贴底outsole 里包片inside counter 鞋领collar螺丝钉screw 前帮机lasting pincers麻布linen 沙绸mesh毛巾布terry cloth 泡棉foam 绸面mesh毛孔蚊PVC regularpvc模子底shell sole 模子底moulded sole内腰inside 处腰outside尼龙nylon 麻布flax 特立可得tricot牛巴革nubuck 植毛绒velvet牛皮leather 仿牛皮imitation leather皮泉硬纸eather board 心纸底板shank board柔软PVC casiling pvc三角跟wedge 包头tip binding砂纸型打粗机sand paper 砂轮式打粗stone roughing蛇纹snake AR-18PU truekid wet pu D-3PU wet pu生胶底plantation crepe 射出底injection crepe双包探纹PU pulgup pu泰国绸Thai silk 绸布mircofabric烫金wash gold 透明PVC clear pvc提花布tmaterial 山东绸gorsgrain天皮top life 钉书针staple 钉子nail填腹bottom filler 后跟垫片heel pad透明跟clear heel 珍珠沙pearl橡胶糊Rubber cement 药水糊Neoprene小牛皮calfskin leather 小山羊皮kidskin leather鞋垫sock lining 商标/布标logo label鞋后密口closed back 鞋后密口open shank鞋口topline 停留时间time dwell鞋口滚边topline binding 反口领cuff鞋面upper vanp 内里lining鞋面upper 鞋面前端vamp 鞋腰quarter鞋舌tongue 鞋舌扣环tongue coop鞋头toe 鞋舌tongue 领口collar鞋头长度vamp length 鞋眼开口部位eyestay opening鞋头开口open toe 鞋后开口open back鞋头翘度toe spring 处理剂primer 胶水cement鞋眼eyelet 鞋带lace 防水台platform型体号style#art.co 楦last压花皮embossed leather 皮克龙Picalon沿条welt 配汤色matching （鞋面）车线upper stttch羊皮sheepskin 巴西PU Brazilian pu腰帮机side lasting 后帮机heel seat质腹bottom filler 鞋跟垫片heel pad中底insole 大底outsole 鞋带lace中底insole 镜面皮putent leather中底midsole 中插wedge 后跟heel中底垫皮sock lining 单底unit sole中底钉合insole attacching 前帮机toe lating中底滚边insole binding 鞋后高度back height猪皮pigskin leather 磨面皮smooth leather装饰带ornament 鞋流shaft of boot拉链纹PU raffia pu 沙丁布satin 麻布linen 沙绸mesh 提花布tmaterial 山东绸gorsgrain 泰国绸Thai silk 绸布mircofabric 拉菲草raffia 镜面ratent 烫金wash gold 透明PVC clear pvc 透明跟clear heel 蛇纹snake AR-18PU truekid wet pu D-3PU wet pu 珍珠沙pearl 弹性stretch 皱布micro fab 编织woven 牛皮calf 羊皮sheepskin 巴西PU Brazilian pu 牛巴革nubuck 植毛绒velvet 金属PU semwtauic pu/met pu 金属PVC semwtauic pvc 半PU（仿PU）mipu.semi 柔软PVC casiling pvc 毛孔蚊PVC regularpvc 双包探纹PU pulgup pu 鞋结构构型construction 材料material 型体号style#art.co 楦last 跟heel 中底insole 大底outsole 鞋面upper vanp 内里lining 鞋头toe 鞋舌tongue 领口collar 鞋垫sock lining 商标/布标logo label 布扣ornament 鞋扣buckie 鞋眼eyelet 鞋带lace 防水台platform 三角跟wedge 包头tip binding 内腰inside 处腰outside 层皮跟leather stack heel 印刷跟printed.heel 沿条welt 配汤色matching （鞋面）车线upper stttch裁断组大底outsole 鞋垫sock tining 质腹bottom filler 鞋跟垫片heel pad 皮泉硬纸eather board 心纸底板shank board 牛皮leather 仿牛皮imitation leather 裁刀cutting dies 裁断垫皮cutting pad 布料cloth material 灯心绒corduroy 帆布canvas 反毛皮suede PU poly urethane 尼龙nylon 麻布flax 特立可得tricot 毛巾布terry cloth 泡棉foam 绸面mesh 法兰绒flannel 不织布non woven 橡胶发泡Rubber pange 长毛里boa 毛暄felt 布里backing 加温式贴合flame 中底insole 镜面皮putent leather 小牛皮calfskin leather 小山羊皮kidskin leather 猪皮pigskin leather 磨面皮smooth leather 压花皮embossed leather 皮克龙Picalon 鞋头开口open toe 鞋后开口open back 鞋后密口closed back 鞋后密口open shank 鞋面upper 鞋面前端vamp 鞋腰quarter 鞋舌tongue 鞋舌扣环tongue coop 后上片mustache 处包片outside counter 里包片inside counter 鞋领collar 装饰带ornament 鞋流shaft of boot 后跟包皮heel cover 中底边insole binding 鞋口滚边topline binding 反口领cuff 成型组后踵heel curre 跟踵crown of last 鞋头翘度toe spring 处理剂primer 胶水cement 中底insole 大底outsole 鞋带lace 中底垫皮sock lining 单底unit sole 生胶底plantation crepe 射出底injection crepe 中底midsole 中插wedge 后跟heel 天皮top life 钉书针staple 钉子nail 螺丝钉screw 前帮机lasting pincers 夹子lasting 攀鞋用手头钉Lasting tacks 楦头润滑剂Last slip 根皮Heel cover 橡胶糊Rubber cement 药水糊Neoprene 模子底shell sole 模子底moulded sole 鞋口topline 停留时间time dwell 中底滚边insole binding 鞋后高度back height 鞋头长度vamp length 鞋眼开口部位eyestay opening 填腹bottom filler 后跟垫片heel pad 中底钉合insole attacching 前帮机toe lating 腰帮机side lasting 后帮机heel seat 砂纸型打粗机sand paper 砂轮式打粗stone roughing 后踵整型back part 大底压着outsole press 拨楦pull out last 定型加硫箱neat setter 拨丝钉pull last 大底车线outsole 冷冻箱cooling chamber 放置铁心putting on shank 贴底outsole 剂Last slip 根皮Heel coverAdhesive粘胶剂Athletic shoes 运动鞋Binding/laces/braids/cords 镶边、线带、鞋带Bottoming room machinery 鞋底机Box toes 内包头Buckles and ornament 鞋扣和饰件Casual 休闲鞋Chemical additives 化学添加剂Containers/boxes 包盒Cutting room machinery 裁断机1/17/08Dance / theatrical 跳舞鞋/戏剧鞋Dress shoes 精致鞋/时装鞋Exotic leather 进口皮革Fabrics 织物Fashion boot 时尚靴Fsteners 紧固件Fillers 鞋撑/填料Finishing room machinery 整理机Footwear inserts 鞋垫/插入件Heels/wedges/toplifts 鞋跟/楔形鞋跟/鞋後跟底层1/18/08Insoles 内底Lasting room machinery 绷帮（装楦）机Lasts 鞋楦Lining material 衬里材料Molds 鞋模Nails/tacks 钉子、鞋钉、平头钉Non-wovens 非织布、不织布Orthopedics 矫形鞋Outdoor/hiking boots 室外、徒步旅行靴Outsoles 外底1/21/08Plastic protective shoes 室外、徒步旅行靴Outsoles 外底Plastic protective shoes 塑料保护鞋Rebuilt second hand machinery 二手设备Rubber/plastic footwear 橡塑鞋Safety boots & shoes 安全靴和鞋Safety toes / safety inserts 安全鞋头、安全插件Sandals 凉鞋Shanks 弓形垫Shoe care products 护鞋产品1/22/08Shoe findings 鞋匠全套工具Shoe boards 鞋板Slippers 拖鞋Soling materials 外底材料Stamping systems/foils 烫印，烫印箔Stitching machinery 缝纫机，针车Top lifts 后跟底层Unit soles 成型底，单元底Upper material 鞋帮材料Work/duty/service footwear 工作鞋1/23/08 Cloth shoes 布鞋High-heeled shoes 高跟鞋Shareholding system 股份制Migrant labor 打工、流动劳力Order supervisor 跟单Sole 大底、鞋底Sewing 缝纫、针车Work shoes 工作鞋Synthetic leather 合成革Skateboard 滑板鞋1/24/08 Grass-sliding shoe 滑草鞋Basketball shoes 篮球鞋Tourist shoes, travel shoes 旅游鞋Cotton shoes 棉鞋Racing shoes 跑鞋Split 二层皮Artificial leather 人造皮革Three-in-one factory “三合一”厂房Dress shoes, fashion shoes 时装鞋Last release 脱楦1/26/08Heel part 后帮Skating shoes 滑冰鞋Skiting shoes 滑雪鞋Family-owned firm 家族企业Glue 胶Jogging shoes 慢跑鞋Insoles 内底Leather 皮革Toe part, vamp 前帮Flip-flops 人字拖鞋1/28/08Beach shoes 沙滩鞋Children shoes 童鞋Sole-upper linking 网鞋Serial number 流水标Collar foam 反口泡棉Strap 扣环Quarter overlay 后上片Technical 技术部Books 吊环Slant 斜度1/29/08Shape 形状Gore 松紧带Tongue label 舌标Ankle patch 反口饰片Applying 刷、上胶Gusset tongue 舌翼Label foxing 后套标Quarter patch 腰身接片Toe cap 前套Singie 单针1/30/08Vamp 鞋头Auto-stick 自粘Dual 双针Backpare 后跟Back panel 胶底饰片Reinforcement 补强Lace hole 鞋带孔Rubber 橡胶底Tongue panels 鞋舌两侧Eyerow 鞋眼1/31/2008Side wall 前包片Tongue lining 舌里Plug 鞋盖Base color 底色Backpart insole 后叉Tongue 鞋舌Toe cap 前包片Tongue patch 舌饰片Bending 折边Earnest areant热切2/4/2008Foxing strap 后套吊环Foam 泡棉Vampreinf 橡胶片Forepart insole 前叉Laces 鞋带Cotton thread 棉线Yelets 眼扣Medial outsole 胶底外侧Lace strap 鞋带吊环Vein 纹路2/11/08Non-woven fabric 不织布Strap 扣环Center outsole 胶底中心Eyerow lining 鞋眼里Wrapping 包边Toe outsole 胶底前侧Sketch line 箱子Collar patch 后旁片Eyerow overlay 鞋眼里Sketch line 画线部位2/12/08Case 箱子Collar patch 后旁片Eyerow overlay 鞋眼带饰片Midsole 中底、中插Lace hole 鞋带孔Emboss 烙印Outsole 大底Cardboard foller 纸板Outsole logo 大底商标Style 型体2/13/08Label-sock 鞋垫标Overlay 饰片Supper tuff 长纤不织布Backstay 后眼片Cement 胶水Double canvas 双面中底帆布Footbed 鞋垫Cementing 上胶Plastic heel 塑胶跟Punch patteran 打洞纸板2/14/08Eyelet punching 打后跟Arels 楦曲Put cardboard and tissue paper 上套板纸图Filler 填料Insole staple on last 入中底于楦头Tick therg form 垫熔胶Lay toe box and counter pocket cementing 入前后套并上糊Toe lasting 前帮Top lift 天皮Boat heel 般型跟2/15/08Cuban 古巴跟Forepart lasting 中邦Inside and outside of collar 反口内外Counter lasting 后邦Heel cover 包跟皮Toe skiving 磨头Moulded sole 模仔底Heating 加硫Foxing tape 填充条Waxing 打脑2/16/08Counter 后眼套Pull-out staple 拔钉Toe cap 鞋头盖Sewing thread 缝条Vamp 前帮Poughing 打粗Reinforcing rows 加强钉Laying and cementing on welt and shank 贴糊中插铁心Boxtoe 套头Hammer 捶平2/19/08Quarter 后腰片Massage 按摩Eyerow 鞋眼Inside welting stitching 车内线Heel pad 鞋眼垫片Cementing 上糊Outsole 外底Drying 烘干Quarter lining 后腰内里Gluing 贴合2/20/08Back stay 后贴片Insole covering 包中底Heel lift 鞋眼包皮Assembling 成型Toe 鞋头Priming 药水处理Vamp lining 鞋面垫衬Heating and drying 加热烘干Vamp 前帮（鞋面）Sole laying 贴合2/21/08Sole press压底冷却Mock stitching 假饰缝条Heating 加热Shaft 鞋统Loose thread 去线头Reinforcemed heel 后跟补强Tying 梆标线Quarter 后帮Adjust to spring and feather line and etc 调整Buffress heel nail 鞋跟固定钉2/22/08Bootie 内靴Cover heel 包跟Ink（aint）印刷Stiftener 后撑套Back strap 后套Wrapping 攀鞋Medral 内腰Heel covering 包跟Lateral 外腰Vamp scissoring and cementing 鞋面成型(剪裁)2/25/08Active PU cement 活化胶Vamp folding and cementing 粘贴鞋面Outside welting stitching 车外线C/T packing 大包装Toe puff 前饰片Sole press 夺机Fore section 前段Side trim 边饰片Pull-out last 拔楦Kiln 干燥室2/26/08Bartack 加强缝线Ctriming 清洁线尾Mulling 蒸湿鞋面Rerforation 饰洞Top lift gluing 贴跟Middle section 中段Toe bumper 鞋头档泥板Toop lift cement 鞋眼上糊Rear section 后段Sock lining 中底2/27/08Grind edge of sole 磨鞋边Solid rotatine 立体回转式Heel 跟Assembly 鞋底组合Steam heating pressing 蒸气式热风Insole lining 中底滚边Buffing 磨边Drying 烘干Cone 椎体Clean 清理鞋子2/29/08Side view 侧视Socking 活动中底Heat seating 加热定型Notch 记号齿Toe spring 鞋头翘度Insole making 中底制作Bottom side forming 底部成型Front view 正视Assembly 组合Last slipping 脱楦3/3/08Toe view 由上往下看Shank 加铁心Toe roughing 磨鞋头Heel pitch 鞋眼斜度Chilling 冷冻Assembly 成型，组合Back curve 后跟弧度Unlasting 拔楦头Sole units 成品底Base 鞋跟上面3/4/08Put in sockliner 上鞋垫Mulling machine 蒸湿机Rake 鞋跟背面Inserting 入楦Sole making 鞋底制作Heel breast 鞋跟正面Heating room 烘箱Rubber outsole 橡胶外底Bottom filler 鞋底填充物Roughing 研磨、打粗3/5/08Lady pump making process 高跟女鞋制作流程Runner 外翻鞋Put in sockliner 上鞋垫Non-vulcanizing sport shoes madding process 非加硫运动鞋制作流程Foxing like band类似边条Treeing with 整理清洗去渍油Middle lining 中垫片Rand 没条Put tissue 塞纸团Sock lining 前垫皮3/7/08Rib 内沿条Lacing 绑纸团Back one 鞋楦後锥体Pair-making 小包装Back cone toe plane with 鞋楦後锥面宽度Back height 後帮高度Open toe 鞋头开口式Side lasting 腰邦Bottom 鞋台Over lay 装饰片3/8/08Side wall 鞋边墙Backing 鞋面补强Over shoe 鞋套Skeleton lining 鞋里垫皮Backpart 後跟Pieces 鞋面片Slant toe 斜形鞋头Back scam 鞋头跟接缝Plug 鞋栓Sock lining 中底垫皮3/10/08Backstrap 後带Curve 弧度Slender toe 细长形鞋头Backstay 後贴片Pitch 斜度Tab 垂片Ball 脚掌Wind tip 翼形前片式Ball girth 掌围H/F process 高周波3/11/08Throat opening 鞋口长度Blended insole 叠合中底Side lasting 腰帮Hroat 鞋喉点Edge 楦缘Breast 正面Top bumper鞋头套垫Breast line 跟胸线Mesh version 网布作法V amp 鞋面前片3/12/08Cap 鞋尖鞋头套Leather version 牛皮作法Vamp length 鞋头长度Collar 鞋领Consistently 均匀Vamp wind 鞋面前片两翼部位Cone heel 圆锥形跟Hocks side of magic strap 勾带String lasted cons 拉帮结构Feather counter pocket 鞋後套3/13/08Hoops side of magic strap 毛带Cuff 鞋领口Quarter insertion 插腰Interlaced straps 交叉紧带式鞋面结构Eyelet stay 鞋眼片Toe box 套头Stitch down cons 外翻邦结构Flange heel 凸圆Seat lasting 後邦Flared heel 细腰形跟3/14/08Soat sock 跟座垫Flat foot 扁平足Tip 鞋跟尖Heel counter pocket後套Foot measurement 脚的量测Saddle 鞋鞍片Heel counter pocket logo後套标Front cone 枕头椎体Side vamp 两侧邦Eye stay 鞋眼。

辽宁师范大学硕士学位论文大学英语专业新生的语音错误的补救教学法姓名：***申请学位级别：硕士专业：英语语言文学指导教师：***20040501大学荚浯专鼗毅生熬语音错误憋辜｝救教学法安丽按要；经过了拐孛、瘫孛六年熬荚谖学露，大学荚谖专韭薪生在串岔语语音系统孛裘凌如下特点：掌握所有音索和基本的韵律特征，已经处于语音学习的高级阶段，然而在实际教学中他们的语音水平却不能令人满意，存程备种各样语音锩误。

这些错误究其原因主要来自于这样几个方面：即汉语的干扰，学生对英语音系举和语音学知识的欠缺｛鬟及在交际语壤审攀生捷嗣语誊谨误等等。

戈热强大学英语谮港教学效果我弱爨矮对犬学阶段静语音教学的奉质有清凝地认识，扶弼据出有针对健的解决方案。

对予英语专她的新生来讲，朱来的职业要求他们在语音方砸达到更高的骤求，而不仅仪照可理解和被接受。

而随好的语音对于英语专业的学生也尤为重要，语音能力的好坏也很大稷壤土影响着萁饿语言技能抟掌捱。

大学英语专渡一年级是封好语音基础的燕簧黔段。

瓣容覆疑浯窘教学爱：要｛｜越鄹生静是够重褫，著斑设立攀猿酶语音谦。

熬疆如何对火学英语专业新生进行语音教学才是研究者和教师应该考虑的问题。

大学英语专业新生的语音课要注意中学和大学之间的教学衔接，避免造成资源浪费。

本文旨在道过深入分析大学英谬专业额生所艇应出的在音素、韵律特征及谖用等各个疆誉基嚣上熬镶误，寻求其锩谟豹攫溪嚣接矮，簸瑟鸯铮瓣瞧蘧撬遗｛｝教法瓣耨生进行鞍为系统、科举的语音教学。

补救教学法有助于清晰地认识到大学英语专波新生中介语语音系统中所建构的正确语音和所存在的种种错误，对臌确语音加以肯定和强化，对错误语音进行系统的错误分析。

分析其来源和实质，从倘有的放矢地从增强学熊瓣英汉舞秘瀣蠢瓣差舅意识，翅强英语蠢系学窝语音学期｛更教学及浯趸学练联教学凡穷露提出大学英语专韭薪生谣脊教学鹩解决蠢寨，默丽提蒜靛率，达到曼好豹教学效果。

补救法是一种行之有效的方法，它合理±呶衔接了中学英谬教学和大学英语教学，有效地增强了大学英语语音教学的教学效果，节约了时间和资源。

reinforcement learning英文文献Title: Reinforcement Learning: An Overview of English LiteratureIntroduction:Reinforcement Learning (RL) is a subfield of machine learning that focuses on training agents to make sequential decisions by interacting with an environment. In this article, we will provide an overview of the English literature on reinforcement learning, discussing its key concepts, algorithms, applications, challenges, and future directions.1. Key Concepts of Reinforcement Learning:1.1 Definition and Components of RL:- Definition of RL: RL is a learning paradigm where an agent learns to take actions in an environment to maximize cumulative rewards.- Components of RL: RL consists of an agent, an environment, a policy, rewards, and value functions.1.2 Markov Decision Process (MDP):- Explanation of MDP: MDP is a mathematical framework used to model RL problems, where an agent interacts with an environment in discrete time steps.- Components of MDP: States, actions, transition probabilities, rewards, and discount factor.1.3 Exploration and Exploitation:- Exploration: The agent explores the environment to discover new strategies and actions that may lead to higher rewards.- Exploitation: The agent exploits the learned knowledge to make decisions that maximize the expected rewards.1.4 Reward Functions and Value Functions:- Reward Function: Determines the immediate feedback an agent receives after taking an action.- Value Function: Estimates the expected cumulative future rewards an agent will receive from a particular state or action.2. Reinforcement Learning Algorithms:2.1 Q-Learning:- Explanation of Q-Learning: Q-Learning is a model-free RL algorithm that learns an action-value function to make optimal decisions.- Exploration vs. Exploitation in Q-Learning: Balancing exploration and exploitation using epsilon-greedy or softmax policies.2.2 Policy Gradient Methods:- Explanation of Policy Gradient Methods: These methods directly optimize the policy function to maximize the expected rewards.- REINFORCE Algorithm: Basic policy gradient algorithm using Monte Carlo sampling.2.3 Deep Q-Network (DQN):- Introduction to DQN: Combines Q-Learning with deep neural networks to handle high-dimensional state spaces.- Experience Replay and Target Network: Techniques used to stabilize DQN training.3. Applications of Reinforcement Learning:3.1 Game Playing:- RL in Atari Games: Deep RL algorithms achieving human-level performance in various Atari games.- AlphaGo: Reinforcement learning-based algorithm that defeated world champion Go players.3.2 Robotics:- RL for Robot Control: Training robots to perform complex tasks such as grasping objects or walking.- Sim-to-Real Transfer: Transferring learned policies from simulations to real-world robotic systems.3.3 Autonomous Driving:- RL for Autonomous Vehicles: Teaching self-driving cars to navigate traffic and make safe decisions.- Safe Exploration: Ensuring RL agents learn policies that do not compromise safety.4. Challenges in Reinforcement Learning:4.1 Exploration vs. Exploitation Trade-off:- The challenge of balancing exploration to discover optimal policies while exploiting known strategies.4.2 Sample Efficiency:- RL algorithms often require a large number of interactions with the environment to learn effective policies.4.3 Generalization:- Extending learned policies to new, unseen environments or tasks.5. Future Directions in Reinforcement Learning:5.1 Multi-Agent RL:- Studying RL in settings where multiple agents interact and learn simultaneously.5.2 Hierarchical RL:- Developing RL algorithms that can learn and exploit hierarchies of actions and goals.5.3 Meta-Learning:- Training RL agents to quickly adapt to new tasks or environments.Conclusion:Reinforcement Learning is a dynamic field of research that has witnessed significant advancements in recent years. This article provided an overview of the key concepts, algorithms, applications, challenges, and future directions in reinforcement learning, showcasing its potential to revolutionize various domains, including gaming, robotics, and autonomous driving. As researchers continue to explore new techniques and algorithms, the future of reinforcement learning holds great promise for solving complex decision-making problems.。

reinforcement learning basedReinforcement learning (RL) is a type of machine learning that deals with an agent learning how to make decisions in an environment in order to maximize cumulative rewards. It is based on the concept of trial and error, where the agent learns through interaction with the environment and receiving feedback in the form of rewards or punishments.RL algorithms typically consist of an agent, an environment, and a reward function. The agent takes actionsin the environment, and the environment responds with new states and rewards. The goal of RL is to find an optimal policy that maximizes the expected cumulative reward over time.The key idea behind RL is the use of an exploration-exploitation trade-off. Initially, the agent explores different actions and their outcomes to learn about the environment. As the agent gains knowledge, it starts to exploit the learned information to select actions that are expected to lead to higher rewards.Reinforcement learning has been successfully applied to a wide range of problems, including game playing (e.g., AlphaGo), robotics, autonomous driving, and recommendation systems. It is particularly useful in scenarios where a clear set of rules or optimal solutions is not available, and the agent needs to learn through trial and error.Some popular RL algorithms include Q-learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), andActor-Critic methods. These algorithms often use neuralnetworks as function approximators to estimate the expected rewards based on the current state and action.Overall, reinforcement learning offers a powerful approach for training intelligent agents to interact with complex environments and make optimal decisions. However, it also comes with challenges such as high sample complexity and the need for careful reward shaping and exploration strategies.。

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鞋眼上衬片：EYEROW OLAY PC后套补强：COUNTER REINFORCEMENT 鞋眼内里：INSIDE EYESTAY前宝：TOE BOX 后里：COUNTER POCKET 后口泡棉：COLLAR PADDED 摩术扣：VERCLO后跟垫片heel pad 鞋扣：BUCKLE EV A中底半插：DIE CUT EVA DROP IN天皮：TOP LIFE 腰铁:SHANK 反口领: CUFF 鞋口:TOPLINE鞋口滚边:TOPLINE BINDING 滚边: BINDING鞋子材料：灯心绒corduroy 帆布：CANV AS ，反绒皮：SUEDE 鸡皮绒/布绒：microfiber龟裂皮:CRACK LEATHER 血筋皮:VEIN SKIN LEATHER 打粗皮: COA TED LEATHER富贵绒:IMITATION SUEDE 双面绒:SYNTHETIC SUEDE 龟裂皮:CRACK LEATHER不织布：NON WOVEN 二层皮：ACTION LEATHER 拉菲草raffia 镜面PU：PATENT PU 沙丁面：SATIN 毛布面：TERRY CLOTH 泡棉：FOAM 网布：MESH尼龙：NYLON 特立可得：TRICOT 牛巴革：NUBUCK 提花布tmaterial压纹PU:EMBOSSED PU 顺色:MA TCHING 仿皮:IMITATION LEATHER稀纹PU: LIZARD PU 疯马皮:CRAZY HORSE LEATHER 裂纹皮:SCRAPE LEATHER鞋子机器:冲刀:cutting dies 前帮机:LASTING PINCERS腰帮机: SIDE LASTING鞋子做法及工艺类:放置铁心putting on shank 填腹bottom filler 印刷:PRINTING空压:EMBOSS 反光条:REFLECTIVE PIPING 热切:HEAT SEAL印压:HEAT EMBOSS 电绣:EMBROIDER 高频印刷:HEAT EMBOSS鞋子出现的问题描述:Vamp wrinking鞋面皱纹Stitching not even车不平均Tricot shows up特丽可得露出来Wrapping not even攀帮不均匀Shank too loose铁心太松Heel not straight后套不正Sole come off底脱胶Wrong pattern 纸板错误Wrong material材料错误Wrong cutting裁断错误Toe broken down鞋头凹陷Color variation色差Wrong last 楦头错误Wrong colour颜色错误Damaged upper鞋面破损Not in pair不配双Crooked back stay 后套歪斜Crooked upper鞋面弯曲Crooked屈曲,不平顺Vamp length鞋头大小Lasting off center攀歪Sole laying not proper(centered)贴底不正确Back strap too high(low,long,short)后拉带太高Abrasion磨损Stitching not on the mark不照记号齿车Angle on insole中底发角V amp open up鞋面掀开Wrapping not tight攀帮不紧Shank not straight铁心不正Cementing comes off脱胶Outsole not smooth大底不平Gore too weak松紧带弹性不佳Cement on heel鞋沾胶Cementing no good接着力不佳Cleanness not enough清洁度不佳Vamp split off 鞋面爆开Color not matching色差Loose thread脱线Broken thread断线Trun yellow变黄Turn dark变黑Pinch in 缩进去Flatten变平Rough粗糙x-ray透痕smeary污染hairy 起毛gaping 缝隙sole adhesion大底欠胶chip碎屑dirty 脏的get mildew发霉back height 后跟高度over cemented溢胶bottom dirty大底不洁本文来源于：梦想岛［摘自：更新时间：2008-4-21 ］热★★★1．按物料来分有：大底（RB）、中底（E VA）、TPU、PU、气垫、贴片。