Invisibility cloaking,inverse problems,and invisible sensors
Insight Problem Solving A Critical Examination of the Possibility
The Journal of Problem Solving • volume 5, no. 1 (Fall 2012)56Insight Problem Solving: A Critical Examination of the Possibilityof Formal TheoryWilliam H. Batchelder 1 and Gregory E. Alexander 1AbstractThis paper provides a critical examination of the current state and future possibility of formal cognitive theory for insight problem solving and its associated “aha!” experience. Insight problems are contrasted with move problems, which have been formally defined and studied extensively by cognitive psychologists since the pioneering work of Alan Newell and Herbert Simon. To facilitate our discussion, a number of classical brainteasers are presented along with their solutions and some conclusions derived from observing the behavior of many students trying to solve them. Some of these problems are interesting in their own right, and many of them have not been discussed before in the psychologi-cal literature. The main purpose of presenting the brainteasers is to assist in discussing the status of formal cognitive theory for insight problem solving, which is argued to be considerably weaker than that found in other areas of higher cognition such as human memory, decision-making, categorization, and perception. We discuss theoretical barri-ers that have plagued the development of successful formal theory for insight problem solving. A few suggestions are made that might serve to advance the field.Keywords Insight problems, move problems, modularity, problem representation1 Department of Cognitive Sciences, University of California Irvine/10.7771/1932-6246.1143Insight Problem Solving: The Possibility of Formal Theory 57• volume 5, no. 1 (Fall 2012)1. IntroductionThis paper discusses the current state and a possible future of formal cognitive theory for insight problem solving and its associated “aha!” experience. Insight problems are con-trasted with so-called move problems defined and studied extensively by Alan Newell and Herbert Simon (1972). These authors provided a formal, computational theory for such problems called the General Problem Solver (GPS), and this theory was one of the first formal information processing theories to be developed in cognitive psychology. A move problem is posed to solvers in terms of a clearly defined representation consisting of a starting state, a description of the goal state(s), and operators that allow transitions from one problem state to another, as in Newell and Simon (1972) and Mayer (1992). A solu-tion to a move problem involves applying operators successively to generate a sequence of transitions (moves) from the starting state through intermediate problem states and finally to a goal state. Move problems will be discussed more extensively in Section 4.6.In solving move problems, insight may be required for selecting productive moves at various states in the problem space; however, for our purposes we are interested in the sorts of problems that are described often as insight problems. Unlike Newell and Simon’s formal definition of move problems, there has not been a generally agreed upon defini-tion of an insight problem (Ash, Jee, and Wiley, 2012; Chronicle, MacGregor, and Ormerod, 2004; Chu and MacGregor, 2011). It is our view that it is not productive to attempt a pre-cise logical definition of an insight problem, and instead we offer a set of shared defining characteristics in the spirit of Wittgenstein’s (1958) definition of ‘game’ in terms of family resemblances. Problems that we will treat as insight problems share many of the follow-ing defining characteristics: (1) They are posed in such a way as to admit several possible problem representations, each with an associated solution search space. (2) Likely initial representations are inadequate in that they fail to allow the possibility of discovering a problem solution. (3) In order to overcome such a failure, it is necessary to find an alternative productive representation of the problem. (4) Finding a productive problem representation may be facilitated by a period of non-solving activity called incubation, and also it may be potentiated by well-chosen hints. (5) Once obtained, a productive representation leads quite directly and quickly to a solution. (6) The solution involves the use of knowledge that is well known to the solver. (7) Once the solution is obtained, it is accompanied by a so-called “aha!” experience. (8) When a solution is revealed to a non-solver, it is grasped quickly, often with a feeling of surprise at its simplicity, akin to an “aha!” experience.It is our position that very little is known empirically or theoretically about the cogni-tive processes involved in solving insight problems. Furthermore, this lack of knowledge stands in stark contrast with other areas of cognition such as human memory, decision-making, categorization, and perception. These areas of cognition have a large number of replicable empirical facts, and many formal theories and computational models exist that attempt to explain these facts in terms of underlying cognitive processes. The main goal58W. H. Batchelder and G. E. Alexander of this paper is to explain the reasons why it has been so difficult to achieve a scientific understanding of the cognitive processes involved in insight problem solving.There have been many scientific books and papers on insight problem solving, start-ing with the seminal work of the Gestalt psychologists Köhler (1925), Duncker (1945), and Wertheimer (1954), as well as the English social psychologist, Wallas (1926). Since the contributions of the early Gestalt psychologists, there have been many journal articles, a few scientific books, such as those by Sternberg and Davidson (1996) and Chu (2009), and a large number of books on the subject by laypersons. Most recently, two excellent critical reviews of insight problem solving have appeared: Ash, Cushen, and Wiley (2009) and Chu and MacGregor (2011).The approach in this paper is to discuss, at a general level, the nature of several fun-damental barriers to the scientific study of insight problem solving. Rather than criticizing particular experimental studies or specific theories in detail, we try to step back and take a look at the area itself. In this effort, we attempt to identify principled reasons why the area of insight problem solving is so resistant to scientific progress. To assist in this approach we discuss and informally analyze eighteen classical brainteasers in the main sections of the paper. These problems are among many that have been posed to hundreds of upper divisional undergraduate students in a course titled “Human Problem Solving” taught for many years by the senior author. Only the first two of these problems can be regarded strictly as move problems in the sense of Newell and Simon, and most of the rest share many of the characteristics of insight problems as described earlier.The paper is divided into five main sections. After the Introduction, Section 2 describes the nature of the problem solving class. Section 3 poses the eighteen brainteasers that will be discussed in later sections of the paper. The reader is invited to try to solve these problems before checking out the solutions in the Appendix. Section 4 lays out six major barriers to developing a deep scientific theory of insight problem solving that we believe are endemic to the field. We argue that these barriers are not present in other, more theo-retically advanced areas of higher cognition such as human memory, decision-making, categorization, and perception. These barriers include the lack of many experimental paradigms (4.1), the lack of a large, well-classified set of stimulus material (4.2), and the lack of many informative behavioral measures (4.3). In addition, it is argued that insight problem solving is difficult to study because it is non-modular, both in the sense of Fodor (1983) but more importantly in several weaker senses of modularity that admit other areas of higher cognition (4.4), the lack of theoretical generalizations about insight problem solv-ing from experiments with particular insight problems (4.5), and the lack of computational theories of human insight (4.6). Finally, in Section 5, we suggest several avenues that may help overcome some of the barriers described in Section 4. These include suggestions for useful classes of insight problems (5.1), suggestions for experimental work with expert problem solvers (5.2), and some possibilities for a computational theory of insight.The Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 592. Batchelder’s Human Problem Solving ClassThe senior author, William Batchelder, has taught an Upper Divisional Undergraduate course called ‘Human Problem Solving” for over twenty-five years to classes ranging in size from 75 to 100 students. By way of background, his active research is in other areas of the cognitive sciences; however, he maintains a long-term hobby of studying classical brainteasers. In the area of complex games, he achieved the title of Senior Master from the United States Chess Federation, he was an active duplicate bridge player throughout undergraduate and graduate school, and he also achieved a reasonable level of skill in the game of Go.The content of the problem-solving course is split into two main topics. The first topic involves encouraging students to try their hand at solving a number of famous brainteasers drawn from the sizeable folklore of insight problems, especially the work of Martin Gardner (1978, 1982), Sam Loyd (1914), and Raymond Smullyan (1978). In addition, games like chess, bridge, and Go are discussed. The second topic involves presenting the psychological theory of thinking and problem solving, and in most cases the material is organized around developments in topics that are covered in the first eight chapters of Mayer (1992). These topics include work of the Gestalt psychologists on problem solving, discussion of experiments and theories concerning induction and deduction, present-ing the work on move problems, including the General Problem Solver (Newell & Simon, 1972), showing how response time studies can reveal mental architectures, and describing theories of memory representation and question answering.Despite efforts, the structure of the course does not reflect a close overlap between its two main topics. The principal reason for this is that in our view the level of theoreti-cal and empirical work on insight problem solving is at a substantially lower level than is the work in almost any other area of cognition dealing with higher processes. The main goal of this paper is to explain our reasons for this pessimistic view. To assist in this goal, it is helpful to get some classical brainteasers on the table. While most of these problems have not been used in experimental studies, the senior author has experienced the solu-tion efforts and post solution discussions of over 2,000 students who have grappled with these problems in class.3. Some Classic BrainteasersIn this section we present eighteen classical brainteasers from the folklore of problem solving that will be discussed in the remainder of the paper. These problems have de-lighted brainteaser connoisseurs for years, and most are capable of giving the solver a large dose of the “aha!” experience. There are numerous collections of these problems in books, and many collections of them are accessible through the Internet. We have selected these problems because they, and others like them, pose a real challenge to any effort to • volume 5, no. 1 (Fall 2012)60W. H. Batchelder and G. E. Alexander develop a deep and general formal theory of human or machine insight problem solving. With the exception of Problems 3.1 and 3.2, and arguably 3.6, the problems are different in important respects from so-called move problems of Newell and Simon (1972) described earlier and in Section 4.6.Most of the problems posed in this section share many of the defining characteristics of insight problems described in Section 1. In particular, they do not involve multiple steps, they require at most a very minimal amount of technical knowledge, and most of them can be solved by one or two fairly simple insights, albeit insights that are rarely achieved in real time by problem solvers. What makes these problems interesting is that they are posed in such a way as to induce solvers to represent the problem information in an unproductive way. Then the main barrier to finding a solution to one of these problems is to overcome a poor initial problem representation. This may involve such things as a re-representation of the problem, the dropping of an implicit constraint on the solution space, or seeing a parallel to some other similar problem. If the solver finds a productive way of viewing the problem, the solution generally follows rapidly and comes with burst of insight, namely the “aha!” experience. In addition, when non-solvers are given the solu-tion they too may experience a burst of insight.What follows next are statements of the eighteen brainteasers. The solutions are presented in the Appendix, and we recommend that after whatever problem solving activity a reader wishes to engage in, that the Appendix is studied before reading the remaining two sections of the paper. As we discuss each problem in the paper, we provide authorship information where authorship is known. In addition, we rephrased some of the problems from their original sources.Problem 3.1. Imagine you have an 8-inch by 8-inch array of 1-inch by 1-inch little squares. You also have a large box of 2-inch by 1-inch rectangular shaped dominoes. Of course it is easy to tile the 64 little squares with dominoes in the sense that every square is covered exactly once by a domino and no domino is hanging off the array. Now sup-pose the upper right and lower left corner squares are cut off the array. Is it possible to tile the new configuration of 62 little squares with dominoes allowing no overlaps and no overhangs?Problem 3.2. A 3-inch by 3-inch by 3-inch cheese cube is made of 27 little 1-inch cheese cubes of different flavors so that it is configured like a Rubik’s cube. A cheese-eating worm devours one of the top corner cubes. After eating any little cube, the worm can go on to eat any adjacent little cube (one that shares a wall). The middlemost little cube is by far the tastiest, so our worm wants to eat through all the little cubes finishing last with the middlemost cube. Is it possible for the worm to accomplish this goal? Could he start with eating any other little cube and finish last with the middlemost cube as the 27th?The Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 61 Figure 1. The cheese eating worm problem.Problem 3.3. You have ten volumes of an encyclopedia numbered 1, . . . ,10 and shelved in a bookcase in sequence in the ordinary way. Each volume has 100 pages, and to simplify suppose the front cover of each volume is page 1 and numbering is consecutive through page 100, which is the back cover. You go to sleep and in the middle of the night a bookworm crawls onto the bookcase. It eats through the first page of the first volume and eats continuously onwards, stopping after eating the last page of the tenth volume. How many pieces of paper did the bookworm eat through?Figure 2.Bookcase setup for the Bookworm Problem.Problem 3.4. Suppose the earth is a perfect sphere, and an angel fits a tight gold belt around the equator so there is no room to slip anything under the belt. The angel has second thoughts and adds an inch to the belt, and fits it evenly around the equator. Could you slip a dime under the belt?• volume 5, no. 1 (Fall 2012)62W. H. Batchelder and G. E. Alexander Problem 3.5. Consider the cube in Figure 1 and suppose the top and bottom surfaces are painted red and the other four sides are painted blue. How many little cubes have at least one red and at least one blue side?Problem 3.6. Look at the nine dots in Figure 3. Your job is to take a pencil and con-nect them using only three straight lines. Retracing a line is not allowed and removing your pencil from the paper as you draw is not allowed. Note the usual nine-dot problem requires you to do it with four lines; you may want to try that stipulation as well. Figure 3.The setup for the Nine-Dot Problem.Problem 3.7. You are standing outside a light-tight, well-insulated closet with one door, which is closed. The closet contains three light sockets each containing a working light bulb. Outside the closet, there are three on/off light switches, each of which controls a different one of the sockets in the closet. All switches are off. Your task is to identify which switch operates which light bulb. You can turn the switches off and on and leave them in any position, but once you open the closet door you cannot change the setting of any switch. Your task is to figure out which switch controls which light bulb while you are only allowed to open the door once.Figure 4.The setup of the Light Bulb Problem.The Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 63• volume 5, no . 1 (Fall 2012)Problem 3.8. We know that any finite string of symbols can be extended in infinitely many ways depending on the inductive (recursive) rule; however, many of these ways are not ‘reasonable’ from a human perspective. With this in mind, find a reasonable rule to continue the following series:Problem 3.9. You have two quart-size beakers labeled A and B. Beaker A has a pint of coffee in it and beaker B has a pint of cream in it. First you take a tablespoon of coffee from A and pour it in B. After mixing the contents of B thoroughly you take a tablespoon of the mixture in B and pour it back into A, again mixing thoroughly. After the two transfers, which beaker, if either, has a less diluted (more pure) content of its original substance - coffee in A or cream in B? (Forget any issues of chemistry such as miscibility).Figure 5. The setup of the Coffee and Cream Problem.Problem 3.10. There are two large jars, A and B. Jar A is filled with a large number of blue beads, and Jar B is filled with the same number of red beads. Five beads from Jar A are scooped out and transferred to Jar B. Someone then puts a hand in Jar B and randomly grabs five beads from it and places them in Jar A. Under what conditions after the second transfer would there be the same number of red beads in Jar A as there are blue beads in Jar B.Problem 3.11. Two trains A and B leave their train stations at exactly the same time, and, unaware of each other, head toward each other on a straight 100-mile track between the two stations. Each is going exactly 50 mph, and they are destined to crash. At the time the trains leave their stations, a SUPERFLY takes off from the engine of train A and flies directly toward train B at 100 mph. When he reaches train B, he turns around instantly, A BCD EF G HI JKLM.............64W. H. Batchelder and G. E. Alexander continuing at 100 mph toward train A. The SUPERFLY continues in this way until the trains crash head-on, and on the very last moment he slips out to live another day. How many miles does the SUPERFLY travel on his zigzag route by the time the trains collide?Problem 3.12. George lives at the foot of a mountain, and there is a single narrow trail from his house to a campsite on the top of the mountain. At exactly 6 a.m. on Satur-day he starts up the trail, and without stopping or backtracking arrives at the top before6 p.m. He pitches his tent, stays the night, and the next morning, on Sunday, at exactly 6a.m., he starts down the trail, hiking continuously without backtracking, and reaches his house before 6 p.m. Must there be a time of day on Sunday where he was exactly at the same place on the trail as he was at that time on Saturday? Could there be more than one such place?Problem 3.13. You are driving up and down a mountain that is 20 miles up and 20 miles down. You average 30 mph going up; how fast would you have to go coming down the mountain to average 60 mph for the entire trip?Problem 3.14. During a recent census, a man told the census taker that he had three children. The census taker said that he needed to know their ages, and the man replied that the product of their ages was 36. The census taker, slightly miffed, said he needed to know each of their ages. The man said, “Well the sum of their ages is the same as my house number.” The census taker looked at the house number and complained, “I still can’t tell their ages.” The man said, “Oh, that’s right, the oldest one taught the younger ones to play chess.” The census taker promptly wrote down the ages of the three children. How did he know, and what were the ages?Problem 3.15. A closet has two red hats and three white hats. Three participants and a Gamesmaster know that these are the only hats in play. Man A has two good eyes, man B only one good eye, and man C is blind. The three men sit on chairs facing each other, and the Gamesmaster places a hat on each man’s head, in such a way that no man can see the color of his own hat. The Gamesmaster offers a deal, namely if any man correctly states the color of his hat, he will get $50,000; however, if he is in error, then he has to serve the rest of his life as an indentured servant to the Gamesmaster. Man A looks around and says, “I am not going to guess.” Then Man B looks around and says, “I am not going to guess.” Finally Man C says, “ From what my friends with eyes have said, I can clearly see that my hat is _____”. He wins the $50,000, and your task is to fill in the blank and explain how the blind man knew the color of his hat.Problem 3.16. A king dies and leaves an estate, including 17 horses, to his three daughters. According to his will, everything is to be divided among his daughters as fol-lows: 1/2 to the oldest daughter, 1/3 to the middle daughter, and 1/9 to the youngest daughter. The three heirs are puzzled as to how to divide the horses among themselves, when a probate lawyer rides up on his horse and offers to assist. He adds his horse to the kings’ horses, so there will be 18 horses. Then he proceeds to divide the horses amongThe Journal of Problem Solving •Insight Problem Solving: The Possibility of Formal Theory 65 the daughters. The oldest gets ½ of the horses, which is 9; the middle daughter gets 6 horses which is 1/3rd of the horses, and the youngest gets 2 horses, 1/9th of the lot. That’s 17 horses, so the lawyer gets on his own horse and rides off with a nice commission. How was it possible for the lawyer to solve the heirs’ problem and still retain his own horse?Problem 3.17. A logical wizard offers you the opportunity to make one statement: if it is false, he will give you exactly ten dollars, and if it is true, he will give you an amount of money other than ten dollars. Give an example of a statement that would be sure to make you rich.Problem 3.18. Discover an interesting sense of the claim that it is in principle impos-sible to draw a perfect map of England while standing in a London flat; however, it is not in principle impossible to do so while living in a New York City Pad.4. Barriers to a Theory of Insight Problem SolvingAs mentioned earlier, our view is that there are a number of theoretical barriers that make it difficult to develop a satisfactory formal theory of the cognitive processes in play when humans solve classical brainteasers of the sort posed in Section 3. Further these barriers seem almost unique to insight problem solving in comparison with the more fully developed higher process areas of the cognitive sciences such as human memory, decision-making, categorization, and perception. Indeed it seems uncontroversial to us that neither human nor machine insight problem solving is well understood, and com-pared to other higher process areas in psychology, it is the least developed area both empirically and theoretically.There are two recent comprehensive critical reviews concerning insight problem solving by Ash, Cushen, and Wiley (2009) and Chu and MacGregor (2011). These articles describe the current state of empirical and theoretical work on insight problem solving, with a focus on experimental studies and theories of problem restructuring. In our view, both reviews are consistent with our belief that there has been very little sustainable progress in achieving a general scientific understanding of insight. Particularly striking is that are no established general, formal theories or models of insight problem solving. By a general formal model of insight problem solving we mean a set of clearly formulated assumptions that lead formally or logically to precise behavioral predictions over a wide range of insight problems. Such a formal model could be posed in terms of a number of formal languages including information processing assumptions, neural networks, computer simulation, stochastic assumptions, or Bayesian assumptions.Since the groundbreaking work by the Gestalt psychologists on insight problem solving, there have been theoretical ideas that have been helpful in explaining the cog-nitive processes at play in solving certain selected insight problems. Among the earlier ideas are Luchins’ concept of einstellung (blind spot) and Duncker’s functional fixedness, • volume 5, no. 1 (Fall 2012)as in Maher (1992). More recently, there have been two developed theoretical ideas: (1) Criterion for Satisfactory Progress theory (Chu, Dewald, & Chronicle, 2007; MacGregor, Ormerod, & Chronicle, 2001), and (2) Representational Change Theory (Knoblich, Ohls-son, Haider, & Rhenius, 1999). We will discuss these theories in more detail in Section 4. While it is arguable that these theoretical ideas have done good work in understanding in detail a few selected insight problems, we argue that it is not at all clear how these ideas can be generalized to constitute a formal theory of insight problem solving at anywhere near the level of generality that has been achieved by formal theories in other areas of higher process cognition.The dearth of formal theories of insight problem solving is in stark contrast with other areas of problem solving discussed in Section 4.6, for example move problems discussed earlier and the more recent work on combinatorial optimization problems such as the two dimensional traveling salesman problem (MacGregor and Chu, 2011). In addition, most other higher process areas of cognition are replete with a variety of formal theories and models. For example, in the area of human memory there are currently a very large number of formal, information processing models, many of which have evolved from earlier mathematical models, as in Norman (1970). In the area of categorization, there are currently several major formal theories along with many variations that stem from earlier theories discussed in Ashby (1992) and Estes (1996). In areas ranging from psycholinguistics to perception, there are a number of formal models based on brain-style computation stemming from Rumelhart, McClelland, and PDP Research Group’s (1987) classic two-volume book on parallel distributed processing. Since Daniel Kahneman’s 2002 Nobel Memorial Prize in the Economic Sciences for work jointly with Amos Tversky developing prospect theory, as in Kahneman and Tversky (1979), psychologically based formal models of human decision-making is a major theoretical area in cognitive psychology today. In our view, there is nothing in the area of insight problem solving that approaches the depth and breadth of formal models seen in the areas mentioned above.In the following subsections, we will discuss some of the barriers that have prevented the development of a satisfactory theory of insight problem solving. Some of the bar-riers will be illustrated with references to the problems in Section 3. Then, in Section 5 we will assuage our pessimism a bit by suggesting how some of these barriers might be removed in future work to facilitate the development of an adequate theory of insight problem solving.4.1 Lack of Many Experimental ParadigmsThere are not many distinct experimental paradigms to study insight problem solving. The standard paradigm is to pick a particular problem, such as one of the ones in Section 3, and present it to several groups of subjects, perhaps in different ways. For example, groups may differ in the way a hint is presented, a diagram is provided, or an instruction。
人工智能词汇
常用英语词汇 -andrew Ng课程average firing rate均匀激活率intensity强度average sum-of-squares error均方差Regression回归backpropagation后向流传Loss function损失函数basis 基non-convex非凸函数basis feature vectors特点基向量neural network神经网络batch gradient ascent批量梯度上涨法supervised learning监察学习Bayesian regularization method贝叶斯规则化方法regression problem回归问题办理的是连续的问题Bernoulli random variable伯努利随机变量classification problem分类问题bias term偏置项discreet value失散值binary classfication二元分类support vector machines支持向量机class labels种类标记learning theory学习理论concatenation级联learning algorithms学习算法conjugate gradient共轭梯度unsupervised learning无监察学习contiguous groups联通地区gradient descent梯度降落convex optimization software凸优化软件linear regression线性回归convolution卷积Neural Network神经网络cost function代价函数gradient descent梯度降落covariance matrix协方差矩阵normal equations DC component直流重量linear algebra线性代数decorrelation去有关superscript上标degeneracy退化exponentiation指数demensionality reduction降维training set训练会合derivative导函数training example训练样本diagonal对角线hypothesis假定，用来表示学习算法的输出diffusion of gradients梯度的弥散LMS algorithm “least mean squares最小二乘法算eigenvalue特点值法eigenvector特点向量batch gradient descent批量梯度降落error term残差constantly gradient descent随机梯度降落feature matrix特点矩阵iterative algorithm迭代算法feature standardization特点标准化partial derivative偏导数feedforward architectures前馈构造算法contour等高线feedforward neural network前馈神经网络quadratic function二元函数feedforward pass前馈传导locally weighted regression局部加权回归fine-tuned微调underfitting欠拟合first-order feature一阶特点overfitting过拟合forward pass前向传导non-parametric learning algorithms无参数学习算forward propagation前向流传法Gaussian prior高斯先验概率parametric learning algorithm参数学习算法generative model生成模型activation激活值gradient descent梯度降落activation function激活函数Greedy layer-wise training逐层贪心训练方法additive noise加性噪声grouping matrix分组矩阵autoencoder自编码器Hadamard product阿达马乘积Autoencoders自编码算法Hessian matrix Hessian矩阵hidden layer隐含层hidden units隐蔽神经元Hierarchical grouping层次型分组higher-order features更高阶特点highly non-convex optimization problem高度非凸的优化问题histogram直方图hyperbolic tangent双曲正切函数hypothesis估值，假定identity activation function恒等激励函数IID 独立同散布illumination照明inactive克制independent component analysis独立成份剖析input domains输入域input layer输入层intensity亮度/灰度intercept term截距KL divergence相对熵KL divergence KL分别度k-Means K-均值learning rate学习速率least squares最小二乘法linear correspondence线性响应linear superposition线性叠加line-search algorithm线搜寻算法local mean subtraction局部均值消减local optima局部最优解logistic regression逻辑回归loss function损失函数low-pass filtering低通滤波magnitude幅值MAP 极大后验预计maximum likelihood estimation极大似然预计mean 均匀值MFCC Mel 倒频系数multi-class classification多元分类neural networks神经网络neuron 神经元Newton’s method牛顿法non-convex function非凸函数non-linear feature非线性特点norm 范式norm bounded有界范数norm constrained范数拘束normalization归一化numerical roundoff errors数值舍入偏差numerically checking数值查验numerically reliable数值计算上稳固object detection物体检测objective function目标函数off-by-one error缺位错误orthogonalization正交化output layer输出层overall cost function整体代价函数over-complete basis超齐备基over-fitting过拟合parts of objects目标的零件part-whole decompostion部分-整体分解PCA 主元剖析penalty term处罚因子per-example mean subtraction逐样本均值消减pooling池化pretrain预训练principal components analysis主成份剖析quadratic constraints二次拘束RBMs 受限 Boltzman 机reconstruction based models鉴于重构的模型reconstruction cost重修代价reconstruction term重构项redundant冗余reflection matrix反射矩阵regularization正则化regularization term正则化项rescaling缩放robust 鲁棒性run 行程second-order feature二阶特点sigmoid activation function S型激励函数significant digits有效数字singular value奇怪值singular vector奇怪向量smoothed L1 penalty光滑的L1 范数处罚Smoothed topographic L1 sparsity penalty光滑地形L1 稀少处罚函数smoothing光滑Softmax Regresson Softmax回归sorted in decreasing order降序摆列source features源特点Adversarial Networks抗衡网络sparse autoencoder消减归一化Affine Layer仿射层Sparsity稀少性Affinity matrix亲和矩阵sparsity parameter稀少性参数Agent 代理 /智能体sparsity penalty稀少处罚Algorithm 算法square function平方函数Alpha- beta pruningα - β剪枝squared-error方差Anomaly detection异样检测stationary安稳性（不变性）Approximation近似stationary stochastic process安稳随机过程Area Under ROC Curve／ AUC Roc 曲线下边积step-size步长值Artificial General Intelligence/AGI通用人工智supervised learning监察学习能symmetric positive semi-definite matrix Artificial Intelligence/AI人工智能对称半正定矩阵Association analysis关系剖析symmetry breaking对称无效Attention mechanism注意力体制tanh function双曲正切函数Attribute conditional independence assumptionthe average activation均匀活跃度属性条件独立性假定the derivative checking method梯度考证方法Attribute space属性空间the empirical distribution经验散布函数Attribute value属性值the energy function能量函数Autoencoder自编码器the Lagrange dual拉格朗日对偶函数Automatic speech recognition自动语音辨别the log likelihood对数似然函数Automatic summarization自动纲要the pixel intensity value像素灰度值Average gradient均匀梯度the rate of convergence收敛速度Average-Pooling均匀池化topographic cost term拓扑代价项Backpropagation Through Time经过时间的反向流传topographic ordered拓扑次序Backpropagation/BP反向流传transformation变换Base learner基学习器translation invariant平移不变性Base learning algorithm基学习算法trivial answer平庸解Batch Normalization/BN批量归一化under-complete basis不齐备基Bayes decision rule贝叶斯判断准则unrolling组合扩展Bayes Model Averaging／ BMA 贝叶斯模型均匀unsupervised learning无监察学习Bayes optimal classifier贝叶斯最优分类器variance 方差Bayesian decision theory贝叶斯决议论vecotrized implementation向量化实现Bayesian network贝叶斯网络vectorization矢量化Between-class scatter matrix类间散度矩阵visual cortex视觉皮层Bias 偏置 /偏差weight decay权重衰减Bias-variance decomposition偏差 - 方差分解weighted average加权均匀值Bias-Variance Dilemma偏差–方差窘境whitening白化Bi-directional Long-Short Term Memory/Bi-LSTMzero-mean均值为零双向长短期记忆Accumulated error backpropagation积累偏差逆传Binary classification二分类播Binomial test二项查验Activation Function激活函数Bi-partition二分法Adaptive Resonance Theory/ART自适应谐振理论Boltzmann machine玻尔兹曼机Addictive model加性学习Bootstrap sampling自助采样法／可重复采样Bootstrapping自助法Break-Event Point／ BEP 均衡点Calibration校准Cascade-Correlation级联有关Categorical attribute失散属性Class-conditional probability类条件概率Classification and regression tree/CART分类与回归树Classifier分类器Class-imbalance类型不均衡Closed -form闭式Cluster簇/ 类/ 集群Cluster analysis聚类剖析Clustering聚类Clustering ensemble聚类集成Co-adapting共适应Coding matrix编码矩阵COLT 国际学习理论会议Committee-based learning鉴于委员会的学习Competitive learning竞争型学习Component learner组件学习器Comprehensibility可解说性Computation Cost计算成本Computational Linguistics计算语言学Computer vision计算机视觉Concept drift观点漂移Concept Learning System /CLS观点学习系统Conditional entropy条件熵Conditional mutual information条件互信息Conditional Probability Table／ CPT 条件概率表Conditional random field/CRF条件随机场Conditional risk条件风险Confidence置信度Confusion matrix混杂矩阵Connection weight连结权Connectionism 连结主义Consistency一致性／相合性Contingency table列联表Continuous attribute连续属性Convergence收敛Conversational agent会话智能体Convex quadratic programming凸二次规划Convexity凸性Convolutional neural network/CNN卷积神经网络Co-occurrence同现Correlation coefficient有关系数Cosine similarity余弦相像度Cost curve成本曲线Cost Function成本函数Cost matrix成本矩阵Cost-sensitive成本敏感Cross entropy交错熵Cross validation交错考证Crowdsourcing众包Curse of dimensionality维数灾害Cut point截断点Cutting plane algorithm割平面法Data mining数据发掘Data set数据集Decision Boundary决议界限Decision stump决议树桩Decision tree决议树／判断树Deduction演绎Deep Belief Network深度信念网络Deep Convolutional Generative Adversarial NetworkDCGAN深度卷积生成抗衡网络Deep learning深度学习Deep neural network/DNN深度神经网络Deep Q-Learning深度Q 学习Deep Q-Network深度Q 网络Density estimation密度预计Density-based clustering密度聚类Differentiable neural computer可微分神经计算机Dimensionality reduction algorithm降维算法Directed edge有向边Disagreement measure不合胸怀Discriminative model鉴别模型Discriminator鉴别器Distance measure距离胸怀Distance metric learning距离胸怀学习Distribution散布Divergence散度Diversity measure多样性胸怀／差别性胸怀Domain adaption领域自适应Downsampling下采样D-separation（ Directed separation）有向分别Dual problem对偶问题Dummy node 哑结点General Problem Solving通用问题求解Dynamic Fusion 动向交融Generalization泛化Dynamic programming动向规划Generalization error泛化偏差Eigenvalue decomposition特点值分解Generalization error bound泛化偏差上界Embedding 嵌入Generalized Lagrange function广义拉格朗日函数Emotional analysis情绪剖析Generalized linear model广义线性模型Empirical conditional entropy经验条件熵Generalized Rayleigh quotient广义瑞利商Empirical entropy经验熵Generative Adversarial Networks/GAN生成抗衡网Empirical error经验偏差络Empirical risk经验风险Generative Model生成模型End-to-End 端到端Generator生成器Energy-based model鉴于能量的模型Genetic Algorithm/GA遗传算法Ensemble learning集成学习Gibbs sampling吉布斯采样Ensemble pruning集成修剪Gini index基尼指数Error Correcting Output Codes／ ECOC纠错输出码Global minimum全局最小Error rate错误率Global Optimization全局优化Error-ambiguity decomposition偏差 - 分歧分解Gradient boosting梯度提高Euclidean distance欧氏距离Gradient Descent梯度降落Evolutionary computation演化计算Graph theory图论Expectation-Maximization希望最大化Ground-truth实情／真切Expected loss希望损失Hard margin硬间隔Exploding Gradient Problem梯度爆炸问题Hard voting硬投票Exponential loss function指数损失函数Harmonic mean 调解均匀Extreme Learning Machine/ELM超限学习机Hesse matrix海塞矩阵Factorization因子分解Hidden dynamic model隐动向模型False negative假负类Hidden layer隐蔽层False positive假正类Hidden Markov Model/HMM 隐马尔可夫模型False Positive Rate/FPR假正例率Hierarchical clustering层次聚类Feature engineering特点工程Hilbert space希尔伯特空间Feature selection特点选择Hinge loss function合页损失函数Feature vector特点向量Hold-out 留出法Featured Learning特点学习Homogeneous 同质Feedforward Neural Networks/FNN前馈神经网络Hybrid computing混杂计算Fine-tuning微调Hyperparameter超参数Flipping output翻转法Hypothesis假定Fluctuation震荡Hypothesis test假定考证Forward stagewise algorithm前向分步算法ICML 国际机器学习会议Frequentist频次主义学派Improved iterative scaling/IIS改良的迭代尺度法Full-rank matrix满秩矩阵Incremental learning增量学习Functional neuron功能神经元Independent and identically distributed/独Gain ratio增益率立同散布Game theory博弈论Independent Component Analysis/ICA独立成分剖析Gaussian kernel function高斯核函数Indicator function指示函数Gaussian Mixture Model高斯混杂模型Individual learner个体学习器Induction归纳Inductive bias归纳偏好Inductive learning归纳学习Inductive Logic Programming／ ILP归纳逻辑程序设计Information entropy信息熵Information gain信息增益Input layer输入层Insensitive loss不敏感损失Inter-cluster similarity簇间相像度International Conference for Machine Learning/ICML国际机器学习大会Intra-cluster similarity簇内相像度Intrinsic value固有值Isometric Mapping/Isomap等胸怀映照Isotonic regression平分回归Iterative Dichotomiser迭代二分器Kernel method核方法Kernel trick核技巧Kernelized Linear Discriminant Analysis／KLDA核线性鉴别剖析K-fold cross validation k折交错考证／k 倍交错考证K-Means Clustering K–均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base 知识库Knowledge Representation知识表征Label space标记空间Lagrange duality拉格朗日对偶性Lagrange multiplier拉格朗日乘子Laplace smoothing拉普拉斯光滑Laplacian correction拉普拉斯修正Latent Dirichlet Allocation隐狄利克雷散布Latent semantic analysis潜伏语义剖析Latent variable隐变量Lazy learning懒散学习Learner学习器Learning by analogy类比学习Learning rate学习率Learning Vector Quantization/LVQ学习向量量化Least squares regression tree最小二乘回归树Leave-One-Out/LOO留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis／ LDA 线性鉴别剖析Linear model线性模型Linear Regression线性回归Link function联系函数Local Markov property局部马尔可夫性Local minimum局部最小Log likelihood对数似然Log odds／ logit对数几率Logistic Regression Logistic回归Log-likelihood对数似然Log-linear regression对数线性回归Long-Short Term Memory/LSTM 长短期记忆Loss function损失函数Machine translation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majority voting绝对多半投票法Manifold assumption流形假定Manifold learning流形学习Margin theory间隔理论Marginal distribution边沿散布Marginal independence边沿独立性Marginalization边沿化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field马尔可夫随机场Maximal clique最大团Maximum Likelihood Estimation/MLE极大似然预计／极大似然法Maximum margin最大间隔Maximum weighted spanning tree最大带权生成树Max-Pooling 最大池化Mean squared error均方偏差Meta-learner元学习器Metric learning胸怀学习Micro-P微查准率Micro-R微查全率Minimal Description Length/MDL最小描绘长度Minimax game极小极大博弈Misclassification cost误分类成本Mixture of experts混杂专家Momentum 动量Moral graph道德图／正直图Multi-class classification多分类Multi-document summarization多文档纲要One shot learning一次性学习Multi-layer feedforward neural networks One-Dependent Estimator／ ODE 独依靠预计多层前馈神经网络On-Policy在策略Multilayer Perceptron/MLP多层感知器Ordinal attribute有序属性Multimodal learning多模态学习Out-of-bag estimate包外预计Multiple Dimensional Scaling多维缩放Output layer输出层Multiple linear regression多元线性回归Output smearing输出调制法Multi-response Linear Regression／ MLR Overfitting过拟合／过配多响应线性回归Oversampling 过采样Mutual information互信息Paired t-test成对 t查验Naive bayes 朴实贝叶斯Pairwise 成对型Naive Bayes Classifier朴实贝叶斯分类器Pairwise Markov property成对马尔可夫性Named entity recognition命名实体辨别Parameter参数Nash equilibrium纳什均衡Parameter estimation参数预计Natural language generation/NLG自然语言生成Parameter tuning调参Natural language processing自然语言办理Parse tree分析树Negative class负类Particle Swarm Optimization/PSO粒子群优化算法Negative correlation负有关法Part-of-speech tagging词性标明Negative Log Likelihood负对数似然Perceptron感知机Neighbourhood Component Analysis/NCA Performance measure性能胸怀近邻成分剖析Plug and Play Generative Network即插即用生成网Neural Machine Translation神经机器翻译络Neural Turing Machine神经图灵机Plurality voting相对多半投票法Newton method牛顿法Polarity detection极性检测NIPS 国际神经信息办理系统会议Polynomial kernel function多项式核函数No Free Lunch Theorem／ NFL 没有免费的午饭定理Pooling池化Noise-contrastive estimation噪音对照预计Positive class正类Nominal attribute列名属性Positive definite matrix正定矩阵Non-convex optimization非凸优化Post-hoc test后续查验Nonlinear model非线性模型Post-pruning后剪枝Non-metric distance非胸怀距离potential function势函数Non-negative matrix factorization非负矩阵分解Precision查准率／正确率Non-ordinal attribute无序属性Prepruning 预剪枝Non-Saturating Game非饱和博弈Principal component analysis/PCA主成分剖析Norm 范数Principle of multiple explanations多释原则Normalization归一化Prior 先验Nuclear norm核范数Probability Graphical Model概率图模型Numerical attribute数值属性Proximal Gradient Descent/PGD近端梯度降落Letter O Pruning剪枝Objective function目标函数Pseudo-label伪标记Oblique decision tree斜决议树Quantized Neural Network量子化神经网络Occam’s razor奥卡姆剃刀Quantum computer 量子计算机Odds 几率Quantum Computing量子计算Off-Policy离策略Quasi Newton method拟牛顿法Radial Basis Function／ RBF 径向基函数Random Forest Algorithm随机丛林算法Random walk随机闲步Recall 查全率／召回率Receiver Operating Characteristic/ROC受试者工作特点Rectified Linear Unit/ReLU线性修正单元Recurrent Neural Network循环神经网络Recursive neural network递归神经网络Reference model 参照模型Regression回归Regularization正则化Reinforcement learning/RL加强学习Representation learning表征学习Representer theorem表示定理reproducing kernel Hilbert space/RKHS重生核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residual Mapping残差映照Residual Network残差网络Restricted Boltzmann Machine/RBM受限玻尔兹曼机Restricted Isometry Property/RIP限制等距性Re-weighting重赋权法Robustness稳重性 / 鲁棒性Root node根结点Rule Engine规则引擎Rule learning规则学习Saddle point鞍点Sample space样本空间Sampling采样Score function评分函数Self-Driving自动驾驶Self-Organizing Map／ SOM自组织映照Semi-naive Bayes classifiers半朴实贝叶斯分类器Semi-Supervised Learning半监察学习semi-Supervised Support Vector Machine半监察支持向量机Sentiment analysis感情剖析Separating hyperplane分别超平面Sigmoid function Sigmoid函数Similarity measure相像度胸怀Simulated annealing模拟退火Simultaneous localization and mapping同步定位与地图建立Singular Value Decomposition奇怪值分解Slack variables废弛变量Smoothing光滑Soft margin软间隔Soft margin maximization软间隔最大化Soft voting软投票Sparse representation稀少表征Sparsity稀少性Specialization特化Spectral Clustering谱聚类Speech Recognition语音辨别Splitting variable切分变量Squashing function挤压函数Stability-plasticity dilemma可塑性 - 稳固性窘境Statistical learning统计学习Status feature function状态特点函Stochastic gradient descent随机梯度降落Stratified sampling分层采样Structural risk构造风险Structural risk minimization/SRM构造风险最小化Subspace子空间Supervised learning监察学习／有导师学习support vector expansion支持向量展式Support Vector Machine/SVM支持向量机Surrogat loss代替损失Surrogate function代替函数Symbolic learning符号学习Symbolism符号主义Synset同义词集T-Distribution Stochastic Neighbour Embeddingt-SNE T–散布随机近邻嵌入Tensor 张量Tensor Processing Units/TPU张量办理单元The least square method最小二乘法Threshold阈值Threshold logic unit阈值逻辑单元Threshold-moving阈值挪动Time Step时间步骤Tokenization标记化Training error训练偏差Training instance训练示例／训练例Transductive learning直推学习Transfer learning迁徙学习Treebank树库algebra线性代数Tria-by-error试错法asymptotically无症状的True negative真负类appropriate适合的True positive真切类bias 偏差True Positive Rate/TPR真切例率brevity简洁，简洁；短暂Turing Machine图灵机[800 ] broader宽泛Twice-learning二次学习briefly简洁的Underfitting欠拟合／欠配batch 批量Undersampling欠采样convergence收敛，集中到一点Understandability可理解性convex凸的Unequal cost非均等代价contours轮廓Unit-step function单位阶跃函数constraint拘束Univariate decision tree单变量决议树constant常理Unsupervised learning无监察学习／无导师学习commercial商务的Unsupervised layer-wise training无监察逐层训练complementarity增补Upsampling上采样coordinate ascent同样级上涨Vanishing Gradient Problem梯度消逝问题clipping剪下物；剪报；修剪Variational inference变分推测component重量；零件VC Theory VC维理论continuous连续的Version space版本空间covariance协方差Viterbi algorithm维特比算法canonical正规的，正则的Von Neumann architecture冯· 诺伊曼架构concave非凸的Wasserstein GAN/WGAN Wasserstein生成抗衡网络corresponds相切合；相当；通讯Weak learner弱学习器corollary推论Weight权重concrete详细的事物，实在的东西Weight sharing权共享cross validation交错考证Weighted voting加权投票法correlation互相关系Within-class scatter matrix类内散度矩阵convention商定Word embedding词嵌入cluster一簇Word sense disambiguation词义消歧centroids质心，形心Zero-data learning零数据学习converge收敛Zero-shot learning零次学习computationally计算(机)的approximations近似值calculus计算arbitrary任意的derive获取，获得affine仿射的dual 二元的arbitrary任意的duality二元性；二象性；对偶性amino acid氨基酸derivation求导；获取；发源amenable 经得起查验的denote预示，表示，是的标记；意味着，[逻]指称axiom 公义，原则divergence散度；发散性abstract提取dimension尺度，规格；维数architecture架构，系统构造；建筑业dot 小圆点absolute绝对的distortion变形arsenal军械库density概率密度函数assignment分派discrete失散的人工智能词汇discriminative有辨别能力的indicator指示物，指示器diagonal对角interative重复的，迭代的dispersion分别，散开integral积分determinant决定要素identical相等的；完整同样的disjoint不订交的indicate表示，指出encounter碰到invariance不变性，恒定性ellipses椭圆impose把强加于equality等式intermediate中间的extra 额外的interpretation解说，翻译empirical经验；察看joint distribution结合概率ennmerate例举，计数lieu 代替exceed超出，越出logarithmic对数的，用对数表示的expectation希望latent潜伏的efficient奏效的Leave-one-out cross validation留一法交错考证endow 给予magnitude巨大explicitly清楚的mapping 画图，制图；映照exponential family指数家族matrix矩阵equivalently等价的mutual互相的，共同的feasible可行的monotonically单一的forary首次试试minor较小的，次要的finite有限的，限制的multinomial多项的forgo 摒弃，放弃multi-class classification二分类问题fliter过滤nasty厌烦的frequentist最常发生的notation标记，说明forward search前向式搜寻na?ve 朴实的formalize使定形obtain获取generalized归纳的oscillate摇动generalization归纳，归纳；广泛化；判断（依据不optimization problem最优化问题足）objective function目标函数guarantee保证；抵押品optimal最理想的generate形成，产生orthogonal(矢量，矩阵等 ) 正交的geometric margins几何界限orientation方向gap 裂口ordinary一般的generative生产的；有生产力的occasionally有时的heuristic启迪式的；启迪法；启迪程序partial derivative偏导数hone 怀恋；磨property性质hyperplane超平面proportional成比率的initial最先的primal原始的，最先的implement履行permit同意intuitive凭直觉获知的pseudocode 伪代码incremental增添的permissible可同意的intercept截距polynomial多项式intuitious直觉preliminary预备instantiation例子precision精度人工智能词汇perturbation不安，搅乱theorem定理poist 假定，假想tangent正弦positive semi-definite半正定的unit-length vector单位向量parentheses圆括号valid 有效的，正确的posterior probability后验概率variance方差plementarity增补variable变量；变元pictorially图像的vocabulary 词汇parameterize确立的参数valued经估价的；可贵的poisson distribution柏松散布wrapper 包装pertinent有关的总计 1038 词汇quadratic二次的quantity量，数目；重量query 疑问的regularization使系统化；调整reoptimize从头优化restrict限制；限制；拘束reminiscent回想旧事的；提示的；令人联想的（ of ）remark 注意random variable随机变量respect考虑respectively各自的；分其他redundant过多的；冗余的susceptible敏感的stochastic可能的；随机的symmetric对称的sophisticated复杂的spurious假的；假造的subtract减去；减法器simultaneously同时发生地；同步地suffice知足scarce罕有的，难得的split分解，分别subset子集statistic统计量successive iteratious连续的迭代scale标度sort of有几分的squares 平方trajectory轨迹temporarily临时的terminology专用名词tolerance容忍；公差thumb翻阅threshold阈，临界。
超材料翻译
Naturematerials LETTERSPublished online : 18 APRIL 2010| DOI:10.1038/NMAT2747A single-layer wide-angle negative-index metamaterial at visible frequencies在可见光频率的一种单层广角负折射率超材料Metamaterials are materials with artificial electromagnetic properties defined by their sub-wavelength structure rather than their chemical composition.基于亚波长结构而非化学结构，超材料也叫人工电磁材料。
Negative-index materials(NIMs) are a special class of metamaterials characterized by an effective negative index that give rise to such unusual wave behavior as backwards phase propagation and negative refraction.负折射率材料（NIMs）是一类具有有效负折射率的特殊超材料，能够产生逆向传播和负折射的不同寻常的波行为。
These extraordinary properties lead to many interesting functions such as sub-diffraction imaging and invisibility cloaking.这些非凡的性能使得有有趣的功能，如子衍射成像和隐蔽伪装So far ,NIMs have been realized through layering of resonant structures,such as spilt-ring resonators ,and have been demonstrated at microwave to infrared frequencies over a narrow range of angles-of-incidence and polarization.到目前，负折射率材料（NIMs）已经通过谐振结构层实现，例如开环谐振器，而且在较窄范围的红外频率内的入射角和偏振也可以证明。
异性材料方解石制备的隐身斗篷macroscopic invisibility cloaking of visible light
© 2011 Macmillan Publishers Limited. All rights reserved.
nature communications | DOI: 10.1038/ncomms1176
T
height H2 and filled with an isotropic material of permittivity ε and µ (µ = 1; blue region in Fig. 1a) is mapped to a quadrilateral region in the physical space with anisotropic electromagnetic properties ε′ and µ′ (brown region in Fig. 1b). Thus, the cloaked region is defined by the small grey triangle of height H1 and half-width d. Mathematically, the transformation is defined by
ARTICLE
Received 15 Sep 2010 | Accepted 4 Jan 2011 | Published 1 Feb 2011
DOI: 10.1038/ncomms1176
Macroscopic invisibility cloaking of visible light
Xianzhong Chen1, Yu Luo2, Jingjing Zhang3, Kyle Jiang4, John B. Pendry2 & Shuang Zhang1
面波反演
vsi
vpi
ρi
hi
_____________________________________________ . . . _____________________________________________
vsn
vpn
ρn
infinite Xia et al., 1999a
Forward calculation
A layered earth model, four parameters
Free surface
_______________________________________
vs1 vs2
vp1 vp2
ρ1 ρ2
h1 h2
_____________________________________________ _____________________________________________ . . . _____________________________________________
Reflection Survey (traveltime)
Traveltime = 2 * depth / Velocity
Fundamental reason for non-uniqueness: The number of data is always limited. The number of unknowns of an earth model is theoretically infinitive.
vs = (vs1, vs2, ..., vsn)T: vp = (vp1, vp2, ..., vpn)T: d = (d1, d2, ..., dn)T: h = (h1, h2, ..., hn-1)T:
计量经济学中英文词汇对照
Common variance Common variation Communality variance Comparability Comparison of bathes Comparison value Compartment model Compassion Complement of an event Complete association Complete dissociation Complete statistics Completely randomized design Composite event Composite events Concavity Conditional expectation Conditional likelihood Conditional probability Conditionally linear Confidence interval Confidence limit Confidence lower limit Confidence upper limit Confirmatory Factor Analysis Confirmatory research Confounding factor Conjoint Consistency Consistency check Consistent asymptotically normal estimate Consistent estimate Constrained nonlinear regression Constraint Contaminated distribution Contaminated Gausssian Contaminated normal distribution Contamination Contamination model Contingency table Contour Contribution rate Control
微积分术语中英文对照
微积分术语中英文对照A、B:Absolute convergence ：绝对收敛Absolute extreme values ：绝对极值Absolute maximum and minimum ：绝对极大与极小Absolute value ：绝对值Absolute value function ：绝对值函数Acceleration ：加速度Antiderivative ：原函数，反导数Approximate integration ：近似积分(法) Approximation ：逼近法by differentials ：用微分逼近linear ：线性逼近法by Simpson’s Rule ：Simpson法则逼近法by the Trapezoidal Rule ：梯形法则逼近法Arbitrary constant ：任意常数Arc length ：弧长Area ：面积under a curve ：曲线下方之面积between curves ：曲线间之面积in polar coordinates ：极坐标表示之面积of a sector of a circle ：扇形之面积of a surface of a revolution ：旋转曲面之面积Asymptote ：渐近线horizontal ：水平渐近线slant ：斜渐近线vertical ：垂直渐近线Average speed ：平均速率Average velocity ：平均速度Axes, coordinate ：坐标轴Axes of ellipse ：椭圆之对称轴Binomial series ：二项式级数Binomial theorem：二项式定理C:Calculus ：微积分differential ：微分学integral ：积分学Cartesian coordinates ：笛卡儿坐标一般指直角坐标Cartesian coordinates system ：笛卡儿坐标系Cauch’s Mean Value Theorem ：柯西中值定理Chain Rule ：链式法则Circle ：圆Circular cylinder ：圆柱体，圆筒Closed interval ：闭区间Coefficient ：系数Composition of function ：复合函数Compound interest ：复利Concavity ：凹性Conchoid ：蚌线Conditionally convergent：条件收敛Cone ：圆锥Constant function ：常数函数Constant of integration ：积分常数Continuity ：连续性at a point ：在一点处之连续性of a function ：函数之连续性on an interval ：在区间之连续性from the left ：左连续from the right ：右连续Continuous function ：连续函数Convergence ：收敛interval of ：收敛区间radius of ：收敛半径Convergent sequence ：收敛数列series ：收敛级数Coordinates：坐标Cartesian ：笛卡儿坐标cylindrical ：柱面坐标polar ：极坐标rectangular ：直角坐标spherical ：球面坐标Coordinate axes ：坐标轴Coordinate planes ：坐标平面Cosine function ：余弦函数Critical point ：临界点Cubic function ：三次函数Curve ：曲线Cylinder：圆筒, 圆柱体, 柱面Cylindrical Coordinates ：圆柱坐标D:Decreasing function ：递减函数Decreasing sequence ：递减数列Definite integral ：定积分Degree of a polynomial ：多项式之次数Density ：密度Derivative ：导数of a composite function ：复合函数之导数of a constant function ：常数函数之导数directional ：方向导数domain of ：导数之定义域of exponential function ：指数函数之导数higher ：高阶导数partial ：偏导数of a power function ：幂函数之导数of a power series ：羃级数之导数of a product ：积之导数of a quotient ：商之导数as a rate of change ：导数当作变化率right-hand ：右导数second ：二阶导数as the slope of a tangent ：导数看成切线之斜率Determinant ：行列式Differentiable function ：可导函数Differential ：微分Differential equation ：微分方程partial ：偏微分方程Differentiation ：求导法implicit ：隐求导法partial ：偏微分法term by term ：逐项求导法Directional derivatives ：方向导数Discontinuity ：不连续性Disk method ：圆盘法Distance ：距离Divergence ：发散Domain ：定义域Dot product ：点积Double integral ：二重积分change of variable in ：二重积分之变数变换in polar coordinates ：极坐标二重积分E、F、G:Ellipse ：椭圆Ellipsoid ：椭圆体Epicycloid ：外摆线Equation ：方程式Even function ：偶函数Expected Valued ：期望值Exponential Function ：指数函数Exponents , laws of ：指数率Extreme value ：极值Extreme Value Theorem ：极值定理Factorial ：阶乘First Derivative Test ：一阶导数试验法First octant ：第一卦限Focus ：焦点Fractions ：分式Function ：函数Fundamental Theorem of Calculus ：微积分基本定理Geometric series ：几何级数Gradient ：梯度Graph ：图形Green Formula ：格林公式H:Half-angle formulas ：半角公式Harmonic series ：调和级数Helix ：螺旋线Higher Derivative ：高阶导数Higher mathematics 高等数学Horizontal asymptote ：水平渐近线Horizontal line ：水平线Hyperbola ：双曲线Hyperboloid ：双曲面I:Implicit differentiation ：隐求导法Implicit function ：隐函数Improper integral ：反常积分, 广义积分Increasing，Decreasing Test ：递增或递减试验法Increment ：增量Increasing Function ：增函数Indefinite integral ：不定积分Independent variable ：自变量Indeterminate from ：不定型Inequality ：不等式Infinite point ：无穷极限点Infinite series ：无穷级数Inflection point ：反曲点Instantaneous velocity ：瞬时速度Integer ：整数Integral ：积分Integrand ：被积函数Integration ：积分Integration by part ：分部积分法Intercepts ：截距Intermediate value of Theorem ：中值定理Interval ：区间Inverse function ：反函数Inverse trigonometric function ：反三角函数Iterated integral ：逐次积分L:Laplace transform ：Laplace 变换Law of sines：正弦定理Law of Cosines ：余弦定理Least upper bound ：最小上界Left-hand derivative ：左导数Left-hand limit ：左极限Lemniscate ：双钮线Length ：长度Level curve ：等高线L'Hospital's rule ：洛必达法则Limacon ：蚶线Limit ：极限Linear approximation：线性近似Linear equation ：线性方程式Linear function ：线性函数Linearity ：线性Linearization ：线性化Line in the plane ：平面上之直线Line in space ：空间之直线Local extreme ：局部极值Local maximum and minimum ：局部极大值与极小值Logarithm ：对数Logarithmic function ：对数函数M、N、O:Maximum and minimum values ：极大与极小值Mean Value Theorem ：均值定理Multiple integrals ：重积分Multiplier ：乘子Natural exponential function ：自然指数函数Natural logarithm function ：自然对数函数Natural number ：自然数Normal line ：法线Normal vector ：法向量Number ：数Octant ：卦限Odd function ：奇函数One-sided limit ：单边极限Open interval ：开区间Optimization problems ：最佳化问题Order ：阶Ordinary differential equation ：常微分方程Origin ：原点Orthogonal ：正交的P、Q:Parabola ：拋物线Parabolic cylinder ：抛物柱面Paraboloid ：抛物面Parallelepiped ：平行六面体Parallel lines ：平行线Parameter ：参数Partial derivative ：偏导数Partial differential equation ：偏微分方程Partial fractions ：部分分式Partial integration ：部分积分Partition ：分割Period ：周期Periodic function ：周期函数Perpendicular lines ：垂直线Piecewise defined function ：分段定义函数Plane ：平面Point of inflection ：反曲点Polar axis ：极轴Polar coordinate ：极坐标Polar equation ：极方程式Pole ：极点Polynomial ：多项式Positive angle ：正角Point-slope form ：点斜式Power function ：幂函数Product ：积Quadrant ：象限Quotient Law of limit ：极限的商定律Quotient Rule ：商定律R:Radius of convergence ：收敛半径Range of a function ：函数的值域Rate of change ：变化率Rational function ：有理函数Rationalizing substitution ：有理代换法Rational number ：有理数Real number ：实数Rectangular coordinates ：直角坐标Rectangular coordinate system ：直角坐标系Relative maximum and minimum ：相对极大值与极小值Revenue function ：收入函数Revolution , solid of ：旋转体Revolution , surface of ：旋转曲面Riemann Sum ：黎曼和Right-hand derivative ：右导数Right-hand limit ：右极限Root ：根S:Saddle point ：鞍点Scalar ：纯量Secant line ：割线Second derivative ：二阶导数Second Derivative Test ：二阶导数试验法Second partial derivative ：二阶偏导数Sector ：扇形Sequence ：数列Series ：级数Set ：集合Shell method ：剥壳法Sine function ：正弦函数Singularity ：奇点Slant, Oblique asymptote ：斜渐近线Slope ：斜率Slope-intercept equation of a line ：直线的斜截式Smooth curve ：平滑曲线Smooth surface ：平滑曲面Solid of revolution ：旋转体Space ：空间Speed ：速率Spherical coordinates ：球面坐标Squeeze Theorem ：夹挤定理Step function ：阶梯函数Strictly decreasing ：严格递减Strictly increasing ：严格递增Substitution rule ：替代法则Sum ：和Surface ：曲面Surface integral ：面积分Surface of revolution ：旋转曲面Symmetry ：对称T:Tangent function ：正切函数Tangent line ：切线Tangent plane ：切平面Tangent vector ：切向量Taylor’s formula ：泰勒公式Total differential ：全微分Trigonometric function ：三角函数Trigonometric integrals ：三角积分Trigonometric substitutions ：三角代换法Tripe integrals ：三重积分V、X、Z:Value of function ：函数值Variable ：变量Vector ：向量Velocity ：速度Vertical asymptote ：垂直渐近线V olume ：体积X-axis ：x轴X -coordinate ：x坐标X -intercept ：x截距Zero vector ：函数的零点Zeros of a polynomial ：多项式的零点。
负折射率和光学隐形装置
负折射率材料以及隐形装置的研制成功，不仅给 促进了纳米、光学等向光学科的发展，而且给人们 提出了更多的课题。
参考文献：
1. Steven A. Cummer, Bogdan-Ioan Popa, David Schurig, David R. Smith, John Pendry: Full-wave simulations of electromagnetic cloaking structures[J]
被遮蔽物
隐形材质
真空
隐形材料
Vladimir Shalaev等人进行了一组二维的光学隐形实验。上图显示的是683.2 纳米的红光通过一个二维的光学隐形装置的传播情况。控制隐形材质的相关 变量（主要是介电常数和磁导率）， Vladimir Shalaev等人使光线光滑的绕 过了障碍物，也就是，使被遮蔽物隐形了。
这就是这种负折射率材质的立体模型
Pendry和D.R. Smith在 他们的论文中指出，如 左图所示，电磁波在负 折射率介质（a）中发生 的折射的确与正常介质 （b）中的不同。 Nhomakorabeaa
b
在负折射 率介质透 镜中，电 磁波也发 生了与在 通常介质 中完全相 反的折射 现象。
但是Pendry等人的模型存在着很大的局限， 光波的频率范围是4.5×1014 Hz～7.3×10 14 Hz，而Pendry & Smith模型的有效范围是 GHz频段，也就是10 9 Hz。这种材料还不能 真正使可见光波发生负折射现象。
计量经济学名词
计量经济学名词A校正R2〔Adjusted R-Squared〕：多元回归剖析中拟合优度的量度，在估量误差的方差时对添加的解释变量用一个自在度来调整。
统一假定〔Alternative Hypothesis〕：检验虚拟假定时的相对假定。
AR〔1〕序列相关〔AR(1) Serial Correlation〕：时间序列回归模型中的误差遵照AR〔1〕模型。
渐近置信区间〔Asymptotic Confidence Interval〕：大样本容量下近似成立的置信区间。
渐近正态性〔Asymptotic Normality〕：适当正态化后样本散布收敛到规范正态散布的估量量。
渐近性质〔Asymptotic Properties〕：当样本容量有限增长时适用的估量量和检验统计量性质。
渐近规范误〔Asymptotic Standard Error〕：大样本下失效的规范误。
渐近t 统计量〔Asymptotic t Statistic〕：大样本下近似听从规范正态散布的t 统计量。
渐近方差〔Asymptotic Variance〕：为了取得渐近规范正态散布，我们必需用以除估量量的平方值。
渐近有效〔Asymptotically Efficient〕：关于听从渐近正态散布的分歧性估量量，有最小渐近方差的估量量。
渐近不相关〔Asymptotically Uncorrelated〕：时间序列进程中，随着两个时点上的随机变量的时间距离添加，它们之间的相关趋于零。
衰减偏误〔Attenuation Bias〕：总是朝向零的估量量偏误，因此有衰减偏误的估量量的希冀值小于参数的相对值。
自回归条件异方差性〔Autoregressive Conditional Heteroskedasticity, ARCH〕：静态异方差性模型，即给定过去信息，误差项的方差线性依赖于过去的误差的平方。
一阶自回归进程[AR〔1〕]〔Autoregressive Process of Order One [AR(1)]〕：一个时间序列模型，其以后值线性依赖于最近的值加上一个无法预测的扰动。
负折射率隐身衣英文版
Ran Duan,1 Elena Semouchkina,2,* and Ravi Pandey1
1Leabharlann Abstract: The geometric optics principles are used to develop a unidirectional transmission cloak for hiding objects with dimensions substantially exceeding the incident radiation wavelengths. Invisibility of both the object and the cloak is achieved without metamaterials, so that significant widths of the cloaking bands are provided. For the preservation of wave phases, the λ-multiple delays of waves passing through the cloak are realized. Suppression of reflection losses is achieved by using half-λ multiple thicknesses of optical elements. Due to periodicity of phase delay and reflection suppression conditions, the cloak demonstrates efficient multiband performance confirmed by full-wave simulations.
An inverse boundary value problem for the Oseen equation
u ? (u0 grad)u ? grad p = 0; div u = 0;
where the vector eld u = (u1; u2)T and the function p model the velocity and the pressure of an incompressible viscous uid. It is obtained by linearizing the Navier{Stokes equation with kinematic viscosity > 0 around the constant velocity u0. In this paper, without
1 Introduction
Regularized iterative methods for the solution of inverse obstacle scattering problems for time-harmonic acoustic waves, i.e., for inverse boundary value problems for the Helmholtz equation have been studied and successfully applied by various authors (see the monograph 1] and the literature therein). Although a number of problems concerning the convergence analysis of these methods are still unresolved we found it challenging to investigate whether they also have the potential to work for inverse uid ow problems. Therefore, as a model problem, in this paper we investigate a two-dimensional inverse Dirichlet problem for the stationary Oseen equation
称职英语阅读练习
第39课五星级---阅读文章孙老师特别提示：请同学勿必将下例文章想办法记住，这关系到30分！万不可马虎！！！第四部分: 阅读理解08理工新增15篇(五星级文章)第二篇: Electric Backpack c第六篇: Flying the Hyper1 Skies c第七篇: Sugar Power for Cell Phones c第十三篇: Invisibility Ring c第十四篇: Japanese Car Keeps Watch for Drunk Drivers c第十七篇: A Sunshade for the Planet c第十八篇: Thirst for Oil c第二十篇: Explorer of the Extreme Deep c第二十一篇: Plant Gas c第三十三篇: Smart Window b第三十五篇: Where Have All the Bees Gone? b第三十六篇: Listening Device Provides Landslide Early Warning b第四十八篇: ‗Hidden‘ Species May Be Surprisingly Common a第四十九篇: Why Humans Walk On Two Legs a第五十篇: Black Hokes Trigger Stars to Self—Destruct a第六部分: 完形填空三篇第二篇: Biological Identification Technologies c第十二篇: Paper or Plastic? b第十三篇: Debate over the Use of Renewable Energy a第十三篇: Invisibility 看不见的东RingScientists can‘t yet make an invisibility cloak斗蓬like the one that Harry Potter uses But,for the first time, they‘ve constructed a simple cloaking device装置that makes itself and something placed inside it invisible to microwaves.When a person ―sees‖ an object, his or her eye senses many different waves of visible light as they bounce off the object. The eye and brain then work together to organize these sensations and reconstruct the object‘s original shape. So, to make an object invisible, scientists have to keep waves from bouncing off it. And they have to make sure the object casts no shadow. Otherwise, the absence of reflected light on one side would give the object away.Invisibility isn‘t possible yet with waves of light that the human eye can see. But it is now possible with microwaves. Like visible light, microwaves are form of radiant energy. They are part of the electromagnetic spectrum, which also includes radio waves, infrared light, ultraviolet rays, X rays, and gamma rays. The wavelengths of microwaves are shorter than those of radio waves but longer than those of visible light.The scientists‘ new “invisibility device” is the size of a drink coaster and shaped like a ring. The ring is made of a special material with unusual ability. When microwaves strike the ring, very few bounce off it. Instead, they pass through the ring, which bends the waves all the way around until they reach the opposite. The waves then return to their original paths.To a detector set up to receive microwaves on the other side of the ring, it looks as if the waves never changed their paths – as if there were no object in the way! So, the ring is effectively invisible.When the researchers put a small copper loop inside the ring, it too, is nearly invisible. However, the cloaking device and anything inside it do cast a pale shadow. And the device works only for microwaves, not for visible light or any kind of electromagnetic radiation. So, Harry potter’s invisibility cloak doesn’t have any real competition yet.练习:1.Harry Potter is mentioned in the passage, because scientistsA can now make an invisible cloak of the same kind as he uses.B try to make an invisible cloak of the same kind as he uses.C try to invent a device similar in idea to the invisible cloak he uses.D know that it is possible to make an invisible cloak of the same kind.2. What is true of microwaves?A Their wavelengths are shorter than those of visible light.B Their wavelengths are longer than those of visible light.C They are different from visible light as they are a kind of radiant energy.D They are visible to the human eye.3. What is NOT true of the invisibility device?A It is made of a special material with unusual ability.B Microwaves bounce off it when they strike it.C Microwaves pass through it when they strike it.D It bends microwaves all the way around until they reach the opposite side.4. What does the word “coaster”杯垫mean in the passage?A A disk or plate placed under a drinking glass to protect a table top.B A vessel engaged in coastal trade.C A roller coaster.D A resident of a coastal area.5. Harry Potter‘s invisibility cloak doesn‘t have any real competition yet, becauseA scientists have not found out how his cloak works.B the cloaking device is a total failure.C the cloaking works only for microwaves.D the cloaking device works only for visible light.第十四篇:Japanese Car Keeps Watch for Drunk喝醉了的DriversA concept 概念car developed by Japanese company Nissan has a breathalyzer—like detection system and other instruments that could help keep drunk or over – tired drivers off the road.The car‘s sensors check odors inside the car and monitor a driver‘s sweat for traces of alcohol. An in –car computer system can issue an alert or even lock up the ignition system if the driver seems over – the –limit. The air odor sensors are fixed firmly and deeply in the driver and passenger seats, while a detector in the gear –shift knob measures perspiration from the driver‘s palm.Other carmakers have developed similar detection systems. For example, Sweden‘s Volvo has developed a breathalyzer attached to a car’s seat belt that drivers must blow into before the engine will start.Nissan‘s new concept vehicle also includes a dashboard –mounted camera that tracks a drivers alertness by monitoring their eyes. It will sound an alarm and issue a spoken warning in Japanese or English if it judges that the driver needs to pull over and rest.The car technology is still in development, but general manager Kazuhiro Doi says the combination of different detection systems should improve the overall effectiveness of the technology. ―For example, if the gear –shift sensor was bypassed by a passenger using it instead of the driver, the facial recognition system would still be used,‖ Doi says.Nissan has no specific timetable for marketing the system, but aims to use technology to cut the number of fatalities死亡（事故）involving its vehicles to half 1995 levels by 2015.The car’s seat belt can also tighten if drowsiness is detected,while an external camera checks that the car is keeping to its lane properly. However, Doi admits that some of the technology, such as the alcohol odor sensor, should be improved. ―If you drink one beer, it‘s going to register, so we need to study what‘s the appropriate level for the system to activate,‖ he says.In the UK, some research groups are using similar advanced techniques to understand driver behavior and the effectiveness of different road designs.练习:1. Which of the following statements is NOT true of the Japanese concept car?A It has a sensor system that could issue a warning if the driver is drunk.B It has sensors that detect traces of alcohol inside the car.C It has sensors locked up in the ignition点火system.D It has a breathalyzer – like detection system.2. What has Volvo developed?A The same detection system mentioned in the previous paragraph.B A breathalyzer attached to a car’s seat belt.C A smart car seat belt.D An intelligent engine.3. What is the function of the camera mentioned in Paragraph 4?A It monitors the driver’s eyes to see if he needs a rest.B It judges if the driver wants to pull over.C It judges if the driver wants to take a rest.D It issues an alarm when the driver speaks.4. According to Doi,A the overall effectiveness of the detection technology has improved.B Nissan is making timetable to market the detection system.C it is impossible to improve the overall effectiveness of the detection system.D Nissan aims to improve the detection technology to reduce the fatality rate.5. Which of the following is NOT mentioned on Paragraph 6?A An external camera checks that the car is going properly.B The car will automatically keep to its lane.C The seat belt will tighten when the driver is found drowsy.D The technology of the alcohol odor sensor should be improved.第十七篇: A Sunshade遮阳光之物for the Planet[天]行星(理工) p140 Even with the best will in the world, reducing our carbon碳emissions (光、热等的)散发not going to prevent global 全球的warming. It has become clear that even if we take the most strong measures to control emissions, the uncertainties in our climate 气候models模式still leave open the possibility of extreme warming and rises in sealevel. At the same time, resistance by governments and special interest groups makes it quite possible that the actions suggested by climate scientists might not be implemented soon enough.Fortunately, if the worst comes to the worst, scientists have a few tricks up their sleeves. For most part they have strongly resisted discussing these options for fear of inviting a sense of complacency满足, 安心that might thwart efforts to tackle解决the root根of the problem. Until now, that is. A growing number of researchers are taking a fresh look at large—scale ― geoengineering‖ projects that might be used to counteract global warming. ―I use the analogy of methadone,‖ says Stephen, a climate researcher at Stanford University in California who was among the first to draw attention to global warming. “If you have a heroin addict, the correct treatment is hospitalization,and a long rehab. But if they absolutely refuse, methadone is better than heroin.‖Basically the idea is to apply “sunscreen”to the whole planet. One astronomer has come up with a radical plan to cool Earth: launch trillions of feather—light discs into space, where they would form a vast would block the sun‘s rays. It‘s controversial, but recent studies suggest there are ways to deflect just enough of the sunlight reaching the Earth‘s surface to counteract the warming produced by the greenhouse effect. Global climate models show that blocking just 1.8 per cent of the incident energy in the sun‘s rays would cancel out the warming effects produced by a doubling of greenhouse gases in the atmosphere.That could be crucial, because even the most severe emissions—control measures being proposed would leave us with a doubling of carbon dioxide by the end of this century, and that would last for at least a century more.练习:1. According to the first two paragraphs, the author thinks thatA strong measures have been taken by the government to prevent global warming.B to reduce carbon emissions is an impossible mission.C despite the difficulty, scientists have some options to prevent global warming.D actions suggested by scientists will never be realized2. Scientists resist talking about their options because they don‘t want people toA know what they are doing.B feel their efforts are useless.C think the problem has been solved.D see the real problem.3. What does Stephen Schneider say about a heroin addict and methadone?A Methadone is an effective way to treat a hard heroin addict.B Methadone is not a correct way to treat a heroin addict.C Hospitalization together with methadone can work effectively with a heroin addict.D Methadone and heroin are equally effective in treating a heroin addict4. What is Stephen Schneider‘s idea of preventing global warming?A To ask governments to take stronger measures.B To increase the sunlight reaching the Earth.C To apply sunscreen to the Earth.D To decrease greenhouse gases.5. What is NOT true of the effectiveness of “sunscreen”, according to the last paragraph?A It deflects sunlight reaching the Earth to counteract the warming.B It blocks the incident energy in the sun‘s rays.C It is a controversial method.D It decreases greenhouse gases in the atmosphere.第二十一篇：Plant植物Gas (C级)Scientists have been studying natural sources of methane沼气for decades十年but hadn’t regarded plants as a producer, notes Frank Keppler, a geochemist地球化学at the Max Planck Institute for Nuclear Physics in Heldelberg, Germany1. Now Keppler and his colleagues find that plants, from grasses to trees, may also be sources of the greenhouse gas. This is really surprising, because most scientists assumed that methane production requires an oxygen-free environment.Previously, researchers had thought that it was impossible for plants to make significant amounts of the gas. They had assumed that microbes2 need to be in environments without oxygen to produce methane. Methane is a greenhouse gas, like carbon dioxide. Gases such as meth ane and carbon dioxide trap heat in Earth‘s atmosphere and contribute to global warming.In its experiments,Keppler’s team used sealed chambers that contained the same concentration of oxygen that Earth’s atmosphere has. They measured the amounts of methane that were released by both living plants and dried plant material, such as fallen leaves.With the dried plants, the researchers took measurement at temperatures ranging from 30 degrees Celsius摄氏to 70 degrees C. At 30 degrees C, they found, a gram克of dried plant material released up to 3 nanograms of methane per hour. (One nanogram is a billionth of a gram.) With every 10-degree rise in temperature, the amount of methane released each hour roughly doubled.Living plants growing at their normal temperatures released as much as 370 nanograms of methane per gram of plant tissue per hour. Methane emissions tripled when living and dead plant was exposed to sunlight.Because there was plenty of oxygen available, it‘s unlikely that the types of bacteria that normally make methane were involved. Experiments on plants that were grown in water rather than soil also resulted in methane emissions. That‘s another strong sign that the gas came from the plants and not soil microbes.The new finding is an ―interesting observation,‖ says Jennifer Y. King, a biogeochemist at the University of Minnesota in St. Paul3. Because some types of soil microbes consume methane, they may prevent plant-produced methane from reaching the atmosphere.Field tests will be needed to assess the plant‘s influence, she notes. (367 words)练习：1. What was scientists‘ understanding of methane?A) It was produced from plants.B) It was not a greenhouse gas.C) It was produced in oxygen-free environments.D) It traps more heat than any other greenhouse gas.2. To test whether plants are a source of methane, the scientists createdA) a oxygen-free environment.B) an environment with the same concentration of oxygen as the Earth has.C) a carbon dioxide-free environment.D) an environment filled with the greenhouse gas3Which statement is true of the methane emissions散发of plants in the experiment?A) The lower the temperature, the higher the amount of methane emissions.B) Living plants release less methane than dried plants at the same temperature.C) When exposed to sunlight, plants stop releasing methane.D) The higher the temperature, the greater the amount of methane emissions.4. Which of the following about methane is Not mentioned in the passage ?A) Plants growing in soil release methane.B) Plants growing in water release methane.C) Soil microbes consume methane.D) Microbes微生物in plants produce methane.5. What is the beneficial point of some microbes consuming plant-produced methane?A) Methane becomes less poisonous.B) methane is turned into a fertilizer.C) Les s methane reaches the atmosphere.D) Air becomes cleaner..D) did not know what species was being studied.A级共三篇，以前已讲过一篇，由于文章过于深奧。
Science Fact or Fiction The Plausibility of 10 Sci-Fi Concepts
If science fiction ruled the world, time travel and teleportation would be commonplace, and humanlike intelligentmachines and cyborgs would be walking amongst us. But just how likely are these and other far-out ideas? Here,LiveScience examines the plausibility of 10 popular sci-fi concepts.From the Klingons in "Star Trek" to the skinny, oval-eyed creatures in classic alien abduction tales, many pop-culturedepictions of extraterrestrials have been decidedly humanlike. But what is the likelihood intelligent alien life wouldresemble humankind?Scientists have proposed solid arguments for and against E.T. developing a body plan similar to ours. At face value, itseems unlikely organisms on another world that underwent eons of unique evolutionary history should fit comfortably intoour clothes.But perhaps evolutionary circumstances similar to those that led us to develop limbs and fingers to manipulate toolsarose on alien planets. Maybe being bipeds with bilateral symmetry is a prerequisite for building socially andtechnologically advanced societies. In this respect, some researchers say we possess a "pretty optimal design for anintelligent being," said Seth Shostak, senior astronomer at the SETI (Search for Extraterrestrial Intelligence)Institute. It could be that there is no other choice but for intelligent beings to look like humans.Nothing, so far as we know, can travel faster than light, according to one of the pillars of modern physics, Einstein'sgeneral theory of relativity. Whereas, general relativity says objects cannot travel faster than the speed of light asmeasured in local surrounding space it doesn't place limits on the speeds at which space itself expands or contracts.It's this "loophole" some physicists are hanging their faster-than-light hat on. A "warp bubble" around a ship, forinstance, could make space-time itself contract in front of the ship and expand behind it. "The warp bubble is a volumeof space that might be able to move at speeds faster than light as measured by space surrounding the bubble," saidGerald Cleaver, a professor of physics at Baylor University. "Objects inside the warp bubble would be at rest withregard to the warp bubble but would also be moving faster than the speed of light with regard to the surrounding spaceoutside the bubble."In science fiction, planet-busting superweapons are all the rage. Yet even more terrifying is the wherewithal to takeout an entire star.The dastardly deed is theoretically possible, however, and even on time scales not stretching into millions of years."There's one scheme to me that seems not quite plausible, but it's close," said Mike Zarnstorff, an experimental plasmaphysicist and deputy director for research at the Princeton Plasma Physics Laboratory.A black hole launched into the sun would "feed and grow exponentially," Zarnstorff told Life's Little Mysteries, andtherefore would "self-propel" a star towards its doom. "A black hole could suck in all the mass of the sun," Zarnstorffsaid.Any chance you will "beaming" up or down anytime soon? Scientifically speaking, teleportation faces some extremeobstacles, ones that even the redoubtable Montgomery Scott would find trouble working around."With the teleportation of a large object, you run up against a conceptual 'no,'" said Sidney Perkowitz, a physicist atEmory University in Atlanta.To date, scientists have transported quantum information, in one case between photons nearly 10 miles (16 kilometers)from one another. Even so, such quantum teleportation is a far cry from teleporting actual material or even a person,with ideas for doing so — such as wormholes — remain entirely speculative and chockfull of challenges. Even so, theseachievements could lead to less-fanciful, though still impressive, technologies, such as quantum computers.In the "Star Trek" universe, cloaking devices on Romulan and Klingon spaceships create all sorts of tactical nightmaresfor their human foes. Hiding in plain sight is certainly a handy trick for a person, too, as fans of "The Invisible Man"and the "Harry Potter" series know well.Science has given us glimpses, as it were, of how these anti-detection technologies might be possible. But full-fledgedinvisibility cloaks like those of science fiction and fantasy remain quite a ways off."I won't call it impossible, but it's implausible what you see in 'Harry Potter,'" said David Smith, professor ofelectrical and computer engineering at Duke University. "That's perfect movie invisibility — too perfect."Nevertheless, research into rendering objects invisible has made leaps and bounds just in the last several years.Partial cloaks that work like sophisticated camouflage — much like the shimmering distortion of the Predator alien inthe 1987 movie of the same name — might be more realistically achievable, Smith said.To an extent, Earth is a living planet, as biological beings do indeed swim, crawl and fly through our world's uppermostlayers of ocean, land and sky. But all that is still a far cry from the literally living, conscious planets that makeappearances in many sci-fi and fantasy stories. Take the living planet Mogo in "Green Lantern," which can change itsclimate and grow foliage in desired patterns on its surface at will.Or consider the moon Pandora from the 2009 film "Avatar," where flora and fauna have evolved tentaclelike organs thatenable them to neurally interlink with each other. A globe-spanning consciousness exists, with Pandora's trillioninterconnected trees acting like cells in a colossal brain, dwarfing our mind's 100 billion neurons.In reality, the development of a planet-scale "being" looks to be an extreme long shot. Based on the chemistries andbehaviors of life and nonlife, don't bet on Mogo or Pandora, scientists say. "The way evolution works, I can't see ithappening," said Peter Ward, a professor of paleontology at the University of Washington.In many futuristic tales, our heroic protagonists are often helped — and sometimes harmed — by intelligent machinesfar more clever than an iPhone.Artificial intelligence research has quite a ways to go, however, before Star Trek-esque visions are realized. Robotsand computers have already proved far more reliable and proficient than humans at specific tasks, such as assembly-linework or crunching numbers. Yet machines cannot handle a range of activities that strike us as basic, such as tying ashoe while holding a conversation."What we have learned so far from 50 to 60 years of AI research is that surpassing human intelligence in a very narrowarea or maybe even in a task-oriented way — like playing a particular game — as sophisticated as it may be, is a loteasier than creating machines that have what we call the 'common sense' of a 3-year-old child," said Shlomo Zilberstein,a professor of computer science at the University of Massachusetts.Given the pace of progress, however, many scientists believe highly intelligent machines will be available in the comingdecades. But it is less clear when (or if) computers will achieve human-like "sentience," in terms of self-interest andfree will — a premise very much at the heart of many sci-fi stories.This jack-of-all-trades tool ranks as a science-fiction staple right alongside lasers and faster-than-light travel. Aninvisible tractor beam on the Death Star hauled in the Millennium Falcon in the original "Star Wars" flick, while ashimmering ray — which doubled as a repulsing beam — saved the crew's bacon multiple times on "Star Trek."In sci-fi, tractor beams often consist of exotic-sounding particles and energies. In our day and age, using regular ol'light to hold and manipulate objects tractor beam-style is already a reality, albeit on very tiny scales.NASA engineers think tractor beam-like technologies could graduate to bigger tasks, like collecting large dust particleson Mars or from the tail of a comet.In theory, continued improvement could someday lead to tractor beams not all that dissimilar to that deployed on theStarship Enterprise.If sci-fi flicks, the likes of "Terminator" and "Matrix," have it right, a war pitting humanity against machines willsomeday destroy civilization. Given the current pace of technological development, does the "robopocalypse" scenarioseem more far-fetched or prophetic? The fate of the world could tip in either direction, depending on who you ask.While researchers in the computer science field disagree on the road ahead for machines, they say humans' relationshipwith machines probably will be harmonious, not murderous. Yet there are a number of scenarios that could lead to non-biological beings aiming to exterminate humanity."The technology already exists to build a system that will destroy the whole world, intentionally or unintentionally, ifit just detects the right conditions," said Shlomo Zilberstein, a professor of computer science at the University ofMassachusetts.Speaking of the "Matrix," could knowledge such as how to practice kung fu be uploaded into the brain in mere seconds viaa futuristic computer jacked into the skull, as happens to Keanu Reeves' character?Some emerging research suggests the pace of learning a skill can be technologically boosted. For instance, with so-called decoded neurofeedback, scientists have used functional magnetic resonance imaging to trigger brain activitypatterns in the visual cortex that match those from a previously known mental state, thereby improving performance onsuch visual tasks.Perhaps someday, with major advances in several fields, the acquisition of knowledge and skill could happen atbroadband-like speeds across surgically implanted and external hardware. "The concept is not totally implausible," saidBruce McNaughton, a neuroscientist at the University of Lethbridge in Canada. "I suggest that you check back in a coupleof hundred years."更多英语学习：企业英语/。
Invariants of Legendrian knots in circle bundles
arXiv:math/0208214v1 [math.SG] 27 Aug 2002
JOSHUA M. SABLOFF Abstraver a Riemann surface that supports a contact structure transverse to the fibers. This paper presents a combinatorial definition of a differential graded algebra (DGA) that is an invariant of Legendrian knots in M . The invariant generalizes Chekanov’s combinatorial DGA invariant of Legendrian knots in the standard contact 3-space using ideas from Eliashberg, Givental, and Hofer’s contact homology. The main difficulty lies in dealing with what are ostensibly 1-parameter families of generators for the DGA; these are solved using “Morse-Bott” techniques. As an application, the invariant is used to distinguish two Legendrian knots that are smoothly isotopic, realize a non-trivial homology class, but are not Legendrian isotopic.
透明人英语作文800字
透明人英语作文800字英文回答：The Invisible Man.The concept of invisibility has intrigued humans for centuries, leading to countless tales of invisible beings in literature, folklore, and film. The idea of being able to move undetected, to witness events without being seen, and to play tricks on unsuspecting individuals holds a certain allure that has captured the human imagination.In the realm of science fiction, the concept of invisibility has taken many forms. From the classic novel "The Invisible Man" by H.G. Wells to the more modern portrayal in the "Invisible Man" film franchise, the idea of a person becoming invisible has been explored from various perspectives.One common approach to invisibility in science fictioninvolves the use of advanced technology. In "The Invisible Man," Dr. Griffin develops a serum that allows him to refract light around his body, rendering him invisible to the naked eye. In the film franchise, Adrian Griffin utilizes a suit that employs cloaking technology to achieve the same effect.Another approach to invisibility in science fiction is through the manipulation of light. In the novel "The Invisible Man," Dr. Griffin discovers a chemical compound that allows him to alter his body's refractive index, making him effectively invisible. Similarly, in the 2014 film "The Invisible Woman," the protagonist gains theability to manipulate light waves to achieve invisibility.Beyond the realm of science fiction, the concept of invisibility has also been explored in philosophical and ethical contexts. The question of whether invisibility would be a blessing or a curse has been debated by thinkers for centuries. Some argue that invisibility would grant unprecedented freedom and power, while others contend that it would lead to isolation and moral dilemmas.The ethical implications of invisibility areparticularly intriguing. If one could move unseen, what would prevent them from engaging in unethical or illegal activities? Conversely, could invisibility be used for the greater good, such as uncovering corruption or preventing crimes?Ultimately, the concept of invisibility remains a fascinating and complex subject that continues to inspire both artistic and scientific endeavors. Whether in therealm of fiction or the realm of possibility, the idea of being able to move unseen holds a profound allure that continues to capture the human imagination.中文回答：隐形人。
The Secret to Invisibility Cloaks
# The Secret to Invisibility CloaksThe concept of invisibility cloaks has long fascinated the human imagination, but what could be the secret behind achieving true invisibility?One potential approach lies in the manipulation of light. Light interacts with objects, bouncing off them and entering our eyes, allowing us to see. To make an object invisible, the light rays would need to pass around it as if the object weren't there at all. This could be achieved through the use of metamaterials, which have properties not found in nature. These materials can be engineered to bend light in specific ways. For instance, imagine a fabric made of metamaterials that bends light around a person wearing it, creating the illusion of invisibility.Another possibility is based on the idea of cloaking an object by creating a perfect mirror image of the background on its surface. This would require highly advanced technology to sense the background in real-time and project the corresponding image onto the object. A practical example could be a small device that scans the surrounding environment and projects the captured scene onto the surface of the invisibility cloak, making the wearer blend seamlessly into the background.The control of electromagnetic waves is also crucial. Electromagnetic waves, of which light is a form, can be manipulated using advanced physics and engineering. By precisely controlling the way these waves interact with an object, it might be possible to make the object effectively "invisible" to the observer. This could involve complex systems that adjust the properties of the object based on the incoming electromagnetic radiation.However, it's important to note that these are all theoretical concepts and face significant technical challenges. Current technology is still far from achieving a practical invisibility cloak that works under all conditions. But the ongoing research in this field holds great promise.In conclusion, the secret to invisibility cloaks remains largely within the realm of scientific exploration and speculation. While we are not yet able to create true invisibility, the pursuit of this goal is driving advancements in materials science, physics, and engineering. The day when invisibility cloaks become a reality may be closer than we think, but for now, it remains a captivating mystery waiting to be unlocked by the ingenuity of scientists and researchers.。
统计学专业英语词汇
A
Absolute deviation,绝对离差
Absolute number,绝对数
Absolute residuals,绝对残差
Acceleration array,加速度立体阵
Acceleration in an arbitrary direction,任意方向上的加速度
Acceleration normal,法向加速度
Counting,计数
Counts,计数/频数
Covariance,协方差
Covariant,共变
Cox Regression, Cox回归
Criteria for fitting,拟合准则
Criteria of least squares,最小二乘准则
Critical ratio,临界比
Critical region,拒绝域
Cluster analysis,聚类分析
Cluster sampling,整群抽样
Code,代码
Coded data,编码数据
Coding,编码
Coefficient of contingency,列联系数
Coefficient of determination,决定系数
Coefficient of multiple correlation,多重相关系数
Average,平均数
Average confidence interval length,平均置信区间长度
Average growth rate,平均增长率
B
Bar chart,条形图
Bar graph,条形图
Base period,基期
Bayes theorem,贝叶斯定理
从负折射超材料到光学隐身衣
从负折射超材料到光学隐身衣黄志洵【摘要】In 1964,Russian physicist V .Vesalago adapts Maxwell ’ s equations to show that it is possible for left-handed materials ( LHM) witha negative refraction index to exist .In 1996 and 1999,J.Pendry begins to design the LHM.In 2000,D.Smith creates the first LHM,it bend microwavesin opposite direc-tion to normal .Negative refraction in LHM has recently attracted much interest .It is also called meta-ma-terial,and that do not exist in nature .It can alter the propagation of electro -magneticwaves,resulting in negative refraction of electro -magnetic waves ,resulting in negative refraction and invisible cloaking .So far,invisibility cloaking experiments at microwaves and optical frequencies have been performedin 2D and 3 D situation .They are a notable breakthrough over the concept of the conventional stealth technology . The other applications of LHM are the left -handed transmission line ,imaging,etc. <br> In this paper ,a brief review on the history and the research advances of the negative refraction study and the invisibility cloak design was given firstly .And then ,we discuss the meaning of theoretical and ex-perimental works in these areaes .We say that the situation with negative refraction in practice is extraordi-narily complicated .And we discuss the relations between the negative refraction index and the negative wave velocity ,the negative refraction index and the negative Goos -Hänchen shift .%1964年俄罗斯科学家V．Vesalago 从Maxwell方程组出发断言具有负折射率的左手材料（LHM）可以存在，1996年和1999年J．Pendry提出了LHM的初始设计。
可靠度翻译
使用SVM法分析边坡可靠度摘要:一阶二次矩可靠性分析方法(FOSM)通常用于边坡稳定性分析。
该种方法需要设计方面的随机变量的值及随机变量的功能函数的偏导数。
当功能函数是隐函数时该种计算方法是非常繁杂的。
然而在地质情况复杂的边坡稳定性分析时，随机变量的功能函数功能通常是隐性的。
目前用来分析边坡稳定的主要方法是极限平衡法(LEM)。
针对这个问题,本文提出支持向量机(SVM)法，该方法是在可靠度分析方法的基础上将SVM法和FOSM法相结合而得到的。
该方法采用支持向量机的方法来近似表达随机变量的功能函数，从而得到该函数基于SVM法的明确的表达函数。
SVM。
法通常对由LEN法分析的到的实际随机变量值的功能函数进行一些处理。
通过SVM模型,我们可以得到大量的随机变量值及其功能函数或其偏导函数,并运用于常规的FOSM法中.文中给出了一些例子用于说明SVM法在边皮可靠性分析中的运用。
结果表明,本文提出的方法适用于那些功能函数是隐函数的边坡的可靠性分析。
1、概述在公路建设、基坑开挖及水坝的设计和施工过程中边坡稳定性分析时十分重要的。
传统评价边坡的方法就是计算其安全系数。
这种方法存在着一个很大的缺点，就是在计算过程中材料的参数、孔隙水压力以及外荷载对结构安全的作用模式具有不确定性。
事实上,该种方法对边坡进行的设计通常是过于保守的。
为了避免这样的结果,我们通常是用可靠性分析对边坡稳定性进行分析。
在最近几十年里,大量的边坡可靠性分析方法被提出。
然而,这些方法却没有像他们的提出者所期望的那样被广泛的运用。
所谓的一阶二次矩法 (FOSM)是目前最有效的可靠性分析边坡的方法(FOSM)。
该方法在每步迭代计算步骤都需要知道随机变量的功能函数或其偏导函数。
因此在该方法计算中需要大量的随便变量功能函数或偏导函数。
但基本随机变量的功能函数是容易求出时，这种计算可以有效的进行。
然而,当表功能函数是隐藏的时候，改方法的计算就会变得很繁杂和耗时。
平行线拐点问题六种模型题型
平行线拐点问题六种模型题型平行线拐点问题六种模型题型所谓6种题型，提示了部分题目的内容，但如果作为选题依据，作用非常有限。
如果是为了更好的选题，搞清楚MCM与ICM的区别，可能更有帮助。
选哪道题不是特别重要，重要的是应该“尽快”选题。
竞赛时间是固定的，选题的时间越长，做题的时间越少。
选题多花1小时，意味着建模和写论文的时间就少了1小时。
能获什么奖主要看实力，其次看运气。
准备越充分，胜算越大。
如果不想碰运气的话，早点动手准备吧。
六种题型怎么理解首先，MCM/ICM（2016年起）每年共有6道题，不是6种题，MCM是ABC三题，ICM是DEF三题。
对6道题目类型的描述，不是严格的划分，角度和依据都不相同。
continuous和discrete是指模型的类型，data insights是指问题数据的特征，operations research/network science和environmental science是指问题涉及到的学科，而environmental science和policy 又是指问题本身的背景。
这不是按照同一标准对题目进行划分，之间有重叠。
最显然的，如果认为continuous和discrete是互补的，那么其他4道题目应该可以分别归入其中某一类。
其次，这些一两个词的描述过于笼统、宽泛，无法体现题目的具体特征，特别是A、B、F 题的描述，提供的信息非常少，说了几乎等于没说。
continuous、discrete 把所有的模型全包括了。
policy范围也太广，人类主宰世界，方方面面都可能涉及政策问题。
而且F题也是2016年新增加的，只有2016年一年的题目（难民问题），暂时还看不出来什么规律。
而C题和D 题的特征相对具体一些。
比如，针对2016年起MCM新增加的C题，COMAP（Consortium for Mathematics and Its Applications）专门发布了一份文档（中文简介）说明其特征。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
WIPA2010Inverse Problems and Applications.Valpara´ıso,January18-22,2010 Invisibility cloaking,inverse problems,and invisible sensors
Matti Lassas
Department of Mathematics and Statistics,University of Helsinki,Finland
Abstract
There has recently been considerable interest in the possibility,both theo-retical and practical,of invisibility of objects to different types of waves.We
review several examples of cloaking enclosures covered with special materials.
The invisibility cloaking has a close connection to counterexamples for the uniqueness of the inverse problem for the conductivity equation[1,2].For
instance,consider Calderon’s inverse problem,that is,the question whether
the voltage and current measurements on the boundary determine uniquely
the conductivity inside a body.To prove uniqueness results for such inverse
problems one usually assumes that the conductivity is bounded both below
and above by strictly positive constants.If this condition is violated,one
can cover any object with a properly chosen anisotropic material so that the
covered object appears in all boundary measurement similar to a domain
with constant conductivity.This kind of counterexample gives theoretical
instructions how to cover an object so that it appears“invisible”in zero
frequency measurements.In this talk we consider similar kind of results for
other frequencies[3,4,5,6].We note that on practical level,the engineered
materials needed for invisibly cloaking are inherently prone to dispersion,so
that realistic cloaking must currently be considered as occurring at a single
wavelength,or very narrow range of wavelengths.
We also consider resent results on invisible sensors[7],based on approx-imate transformation optics cloaks.It is generally believed that transforma-
tion optics based cloaking,besides rendering the cloaked region invisible to
detection by scattering of incident waves,also shields the region from those
same waves.We demonstrate a coupling between the cloaked and uncloaked
regions,exposing a difference between cloaking for rays and waves.Interior
resonances allow this coupling to be amplified,and careful choice of param-
eters leads to effective cloaks with degraded shielding.As one application,
we describe how to use transformation optics to hide sensors in the cloaked
region and yet enable the sensors to efficiently measure waves incident on the
exterior of the cloak,an effect similar to the plasmon based approach using
medium with negative refractive index[8].The presented results have been
done in collaboration with A.Greenleaf,Y.Kurylev and G.Uhlmann. References
[1]A.Greenleaf,ssas,G.Uhlmann:On nonuniqueness for Calderon’s in-
verse problem,Mathematical Research Letters10(2003),685-693.
[2]A.Greenleaf,ssas,G.Uhlmann:Anisotropic conductivities that cannot
detected in Electrical Impedance Tomography.Physiological Measurement,24 (2003),413-420.
[3]U.Leonhardt:Optical Conformal Mapping,Science312(2006),1777-1780.
[4]J.Pendry,D.Schurig,D.Smith:Controlling Electromagnetic Fields,Science
312(2006),1780-1782.
[5]A.Greenleaf,Y.Kurylev,ssas,G.Uhlmann:Full-wave invisibility of
active devices at all frequencies,Communications in Mathematical Physics 275(2007),749-789.
[6]A.Greenleaf,Y.Kurylev,ssas,G.Uhlmann:Electromagnetic worm-
holes and virtual magnetic monopoles from metamaterials.Physical Review Letters99,183901(2007)
[7]A.Greenleaf,Y.Kurylev,ssas,G.Uhlmann:Cloaking vs.shielding in
transformation optics,arXiv:0912.1872v1(2009)
[8]A.Alu,N.Engheta:Cloaking a sensor,Phys.Rev.Lett.102,233901(2009)。