Hawking (2000), Desitter Entropy, Quantum Entanglement
- 格式:pdf
- 大小:144.48 KB
- 文档页数:14
Algorithms in Everyday MathematicsAlgorithms in School Mathematics (1)Algorithms in Everyday Mathematics (3)Invented Procedures (3)Alternative Algorithms (5)Focus Algorithms (6)Algorithmic Thinking (7)References (8)An algorithm is a step-by-step procedure designed to achieve a certain objective in a finite time, often with several steps that repeat or “loop” as many times as necessary. The most familiar algorithms are the elementary school procedures for adding, subtracting, multiplying, and dividing, but there are many other algorithms in mathematics.Algorithms in School MathematicsThe place of algorithms in school mathematics is changing. One reason is the widespread availability of calculators and computers outside of school. Before such machines were invented, the preparation of workers who could carry out complicated computations by hand was an important goal of school mathematics. Today, being able to mimic a $5 calculator is not enough: Employers want workers who can think mathematically. How the school mathematics curriculum should adapt to this new reality is an open question, but it is clear that proficiency at complicated paper-and-pencil computations is far less important outside of school today than in the past. It is also clear that the time saved by reducing attention to such computations in school can be put to better use on such topics as problem solving, estimation, mental arithmetic, geometry, and data analysis (NCTM, 1989).Another reason the role of algorithms is changing is that researchers have identified a number of serious problems with the traditional approach to teaching computation. One problem is that the traditional approach fails with a large number of students. Despite heavy emphasis on paper-and-pencil computation, many students never become proficient in carrying out algorithms for the basic operations. In one study, only 60 percent of U.S. ten-year-olds achieved mastery of subtraction using the standard “borrowing” algorithm. A Japanese study found that only 56 percent of third graders and 74 percent of fifth graders achieved mastery of this algorithm. A principal cause for such failures is an overemphasis on procedural proficiency with insufficient attention to the conceptual basis for the procedures. This unbalanced approach produces students who are plagued by “bugs,” such as always taking the smaller digit from the larger in subtraction, because they are trying to carry out imperfectly understood procedures.An even more serious problem with the traditional approach to teaching computation is that it engenders beliefs about mathematics that impede further learning. Research indicates that thesebeliefs begin to be formed during the elementary school years when the focus is on mastery of standard algorithms (Hiebert, 1984; Cobb, 1985; Baroody & Ginsburg, 1986). The traditional, rote approach to teaching algorithms fosters beliefs such as the following:• mathematics consists mostly of symbols on paper;• following the rules for manipulating those symbols is of prime importance;• mathematics is mostly memorization;• mathematics problems can be solved in no more than 10 minutes — or else they cannot be solved at all;• speed and accuracy are more important in mathematics than understanding;• there is one right way to solve any problem;• different (correct) methods of solution sometimes yield contradictory results; and• mathematics symbols and rules have little to do with common sense, intuition, or thereal world.These inaccurate beliefs lead to negative attitudes. The prevalence of math phobia, the social acceptability of mathematical incompetence, and the avoidance of mathematics in high school and beyond indicate that many people feel that mathematics is difficult and unpleasant. Researchers suggest that these attitudes begin to be formed when students are taught the standard algorithms in the primary grades. Hiebert (1984) writes, “Most children enter school with reasonably good problem-solving strategies. A significant feature of these strategies is that they reflect a careful analysis of the problems to which they are applied. However, after several years many children abandon their analytic approach and solve problems by selecting a memorized algorithm based on a relatively superficial reading of the problem.” By third or fourth grade, according to Hiebert, “many students see little connection between the procedures they use and the understandings that support them. This is true even for students who demonstrate in concrete contexts that they do possess important understandings.” Baroody and Ginsburg (1986) make a similar claim: “For most children, school mathematics involves the mechanical learning and the mechanical use of facts — adaptations to a system that are unencumbered by the demands of consistency or even common sense.”A third major reason for changes in the treatment of algorithms in school mathematics is that a better approach exists. Instead of suppressing children’s natural problem-solving strategies, this new approach builds on them (Hiebert, 1984; Cobb, 1985; Baroody & Ginsburg, 1986; Resnick, Lesgold, & Bill, 1990). For example, young children often use counting strategies to solve problems. By encouraging the use of such strategies and by teaching even more sophisticated counting techniques, the new approach helps children become proficient at computation while also preserving their belief that mathematics makes sense. This new approach to computation is described in more detail below.Reducing the emphasis on complicated paper-and-pencil computations does not mean that paper-and-pencil arithmetic should be eliminated from the school curriculum. Paper-and-pencil skills are practical in certain situations, are not necessarily hard to acquire, and are widely expected as an outcome of elementary education. If taught properly, with understanding but without demands for “mastery” by all students by some fixed time, paper-and-pencil algorithms can reinforce students’ understanding of our number system and of the operations themselves. Exploring algorithms can also build estimation and mental arithmetic skills and help students see mathematics as a meaningful and creative subject.Algorithms in Everyday MathematicsEveryday Mathematics includes a comprehensive treatment of computation. Students learn to compute mentally, with paper and pencil, and by machine; they learn to find both exact and approximate results; and, most importantly, they learn what computations to make and how to interpret their answers. The following sections describe in general terms how Everyday Mathematics approaches exact paper-and-pencil methods for basic operations with whole numbers. For details about particular algorithms and for information about how the program teaches mental arithmetic, estimation, and computation with decimals and fractions, see the Everyday Mathematics Teacher’s Reference Manual.In Everyday Mathematics, computational proficiency develops gradually. In the beginning, before they have learned formal procedures, students use what they know to solve problems. They use their common sense and their informal knowledge of mathematics to devise their own procedures for adding, subtracting, and so on. As students describe, compare, and refine their approaches, several alternative methods are identified. Some of these alternatives are based on students’ own ideas; others are introduced by the teacher or in the materials. For each basic operation, students are expected to become proficient at one or more of these alternative methods.The materials also identify one of the alternative algorithms for each operation as a focus algorithm. The purpose of the focus algorithms is two-fold: (i) to provide back-up methods for those students who do not achieve proficiency using other algorithms, and (ii) to provide a common basis for further work. All students are expected to learn the focus algorithms at some point, though, as always in Everyday Mathematics, students are encouraged to use whatever method they prefer when they solve problems.The following sections describe this process in more detail. Note, however, that although the basic approach is similar across all four operations, the emphasis varies from operation to operation because of differences among the operations and differences in students’ background knowledge. For example, it is easier to invent efficient procedures for addition than for division. There is, accordingly, less expectation that students will devise efficient procedures for solving multidigit long division problems than that they will succeed in finding their own good ways to solve multidigit addition problems.Invented ProceduresWhen they are first learning an operation, Everyday Mathematics students are asked to solve problems involving the operation before they have developed or learned systematic procedures for solving such problems. In second grade, for example, students are asked to solve multidigit subtraction problems. They might solve such problems by counting up from the smaller to the larger number, or by using tools such as number grids or base-10 blocks, or they may use some other strategy that makes sense to them. This stage of algorithm development may be called the invented procedures phase.To succeed in devising effective procedures, students must have a good background in the following areas:• Our system for writing numbers. In particular, students need to understand place value.• Basic facts. To be successful at carrying out multistep computational procedures,students need proficiency with the basic arithmetic facts.• The meanings of the operations and the relationships among operations. To solve 37 –25, for example, a student might reason, “What number must I add to 25 to get 37?”Research indicates that students can succeed in inventing their own methods for solving basic computational problems (Madell, 1985; Kamii & Joseph, 1988; Cobb & Merkel, 1989;Resnick, Lesgold, & Bill, 1990; Carpenter, Fennema, & Franke, 1992). Inventing procedures flourishes when:• the classroom environment is accepting and supportive;• adequate time for experimentation is allotted;• computational tasks are embedded in real-life contexts; and• students discuss their solution strategies with the teacher and with one another.The discussion of students’ methods is especially important. Through classroom discussion, teachers gain valuable insight into students’ thinking and progress, while students become more skilled at communicating mathematics and at understanding and critiquing others’ ideas and methods. Talking about why a method works, whether a method will work in every case, which method is most efficient, and so on, helps students understand that mathematics is based on common sense and objective reason, not the teacher’s whim. Such discussions lay the foundations for later formal work with proof.The invented-procedures approach to algorithm development has many advantages:•Students who invent their own methods learn that their intuitive methods are valid and that mathematics makes sense.•Inventing procedures promotes conceptual understanding of the operations and of base-10 place-value numeration. When students build their own procedures on their priormathematical knowledge and common sense, new knowledge is integrated into a meaningful network so that it is understood better and retained more easily.•Inventing procedures promotes proficiency with mental arithmetic. Many techniques that students invent are much more effective for mental arithmetic than standard paper-and-pencil algorithms. Students develop a broad repertoire of computational methods and the flexibility to choose whichever procedure is most appropriate in any particular situation.•Inventing procedures involves solving problems that the students do not already know how to solve, so they gain valuable experience with non-routine problems. They must learn tomanage their resources: How long will this take? Am I wasting my time with this approach?Is there a better way? Such resource management is especially important in complexproblem solving. As students devise their own methods, they also develop persistence and confidence in dealing with difficult problems.•Students are more motivated when they don’t have to learn standard paper-and-pencil algorithms by rote. People are more interested in what they can understand, and students generally understand their own methods.•Students become adept at changing the representations of ideas and problems, translating readily among manipulatives, words, pictures, and symbols. The ability to represent aproblem in more than one way is important in problem solving. Students also develop theability to transform any given problem into an equivalent, easier problem. For example, 32 –17 can be transformed to 35 – 20 by adding 3 to both numbers.Another argument in favor of the invented-procedures approach is that learning a single standard algorithm for each operation, especially at an early stage, may actually inhibit the development of students’ mathematical understanding. Premature teaching of standard paper-and-pencil algorithms can foster persistent errors and buggy algorithms and can lead students to use the algorithms as substitutes for thinking and common sense.Alternative AlgorithmsOver the centuries, people have invented many algorithms for the basic arithmetic operations. Each of these historical algorithms was developed in some context. For example, one does not need to know the multiplication tables to do “Russian Peasant Multiplication” — all that is required is doubling, halving, and adding. Many historical algorithms were “standard” at some time and place, and some are used to this day. The current “European” method of subtraction, for example, is not the same as the method most Americans learned in school.The U.S. standard algorithms—those that have been most widely taught in this country in the past 100 years—are highly efficient for paper-and-pencil computation, but that does not necessarily make them the best choice for school mathematics today. The best algorithm for one purpose may not be the best algorithm for another purpose. The most efficient algorithm for paper-and-pencil computation is not likely to be the best algorithm for helping students understand the operation, nor is it likely to be the best algorithm for mental arithmetic and estimation. Moreover, if efficiency is the goal, in most situations it is unlikely that any paper-and-pencil algorithm will be superior to mental arithmetic or a calculator.If paper-and-pencil computation is to continue to be part of the elementary school mathematics curriculum, as the authors of Everyday Mathematics believe it should, then alternatives to the U.S. standard algorithms should be considered. Such alternatives may have better cost-benefit ratios than the standard algorithms. Historical algorithms are one source of alternatives. Student-invented procedures are another rich source. A third source is mathematicians and mathematics educators who are devising new methods that are well adapted to our needs today. The Everyday Mathematics approach to computation uses alternative algorithms from all these sources.In Everyday Mathematics, as students explain, compare, and contrast their own invented procedures, several common alternative methods are identified. Often these are formalizations of approaches that students have devised. The column-addition method, for example, was shown and explained to the Everyday Mathematics authors by a first grader. Other alternative algorithms, including both historical and new algorithms, are introduced by the teacher or the materials. The partial-quotients method, for example, first appeared in print in Isaac Greenwood’s Arithmeticks in 1729.Many alternative algorithms, whether based on student methods or introduced by the teacher, are highly efficient and easier to understand and learn than the U.S. traditional algorithms. For example, lattice multiplication requires only a knowledge of basic multiplication facts and the ability to add strings of single-digit numbers, and yet it is more efficient than the traditional long multiplication algorithm for all but the simplest multidigit problems. Students are urged toexperiment with various methods for each operation in order to become proficient at using at least one alternative.The alternative-algorithms phase of algorithm development has significant advantages:• A key belief in Everyday Mathematics is that problems can (and should) be solved in more than one way. This belief in multiple solutions is supported by the alternative-algorithms approach to developing computational proficiency.•Providing several alternative algorithms for each operation affords flexibility. A one-size-fits-all approach may work for many students, but the goal in Everyday Mathematics is to reach all students. One algorithm may work well for one student, but another algorithm may be better for another student.•Different algorithms are often based on different concepts, so studying several algorithms for an operation can help students understand the operation.•Presenting several alternative algorithms gives the message that mathematics is a creative field. In today’s rapidly changing world, people who can break away from traditional ways of thinking are especially valuable.Teaching multiple algorithms for important operations is common in mathematics outside the elementary school. In computer science, for example, alternative algorithms for fundamental operations are always included in textbooks. An entire volume of Donald Knuth’s monumental work, The Art of Computer Programming (1998), is devoted exclusively to sorting and searching. Knuth presents many inefficient sorting algorithms because they are instructive.Focus AlgorithmsThe authors of Everyday Mathematics believe that the invented-procedures/alternative-algorithms approach described above is a radical improvement over the traditional approach to developing computational proficiency. The Everyday Mathematics approach is based on decades of research and was refined during extensive fieldtesting. Student achievement studies indicate, moreover, that when the approach is properly implemented, students do achieve high levels of computational proficiency (Carroll, 1996, 1997; Carroll & Porter, 1997, 1998; Fuson, Carroll, & Drueck, 2000; Carroll, Fuson, & Diamond, 2000; Carroll & Isaacs, in press).In the second edition of Everyday Mathematics, the approach described above is extended in one significant way: For each operation, one of the several alternative algorithms is identified as a focus algorithm. All students are expected to learn the focus algorithms eventually, although, as usual in Everyday Mathematics, proficiency is expected only after multiple exposures over several years. Students are also not required to use the focus algorithms in solving problems if they have alternatives they prefer. For addition, the focus algorithm is partial-sums; for subtraction, trade-first; for multiplication, partial-products; and for division, partial-quotients. (See the Everyday Mathematics Teacher’s Reference Manual for details about these and other algorithms.)The focus algorithms are powerful, relatively efficient, and easy to understand and learn, but they are not meant to short-circuit the invented-procedures/alternative-algorithms approach described above. Students still need to grapple with problems on their own and explore alternative algorithms. The focus algorithms have been introduced for two specific reasons. Oneis that they provide reliable alternatives for students who do not develop effective procedures on their own. The focus algorithm for subtraction, for example, is introduced in second grade. Second grade students are not expected to be proficient with the method, though they are expected to be able to solve multidigit subtraction problems in some way, by using counting, number grids, manipulatives, or some other method. A fourth grade student, however, who does not have a reliable method for subtraction despite several years of work with invented procedures and alternative algorithms should focus on the trade-first method so that he or she will have at least one reliable way to subtract with paper and pencil. One aim of the focus-algorithm approach is to promote flexibility while ensuring that all students know at least one reliable method for each operation.Another reason for introducing focus algorithms is to provide a common ground for the further development of mathematical ideas. Most algorithms for operations with whole numbers, for example, can be extended to decimals. This is easier to show in a class at least one whole-number algorithm for each operation is known by every student. Focus algorithms provide a common language that facilitates classroom discussion.Focus algorithms were introduced in response to teachers’ concerns. However, a teacher who has developed an effective strategy for teaching algorithms, and who feels that the focus-algorithm approach is unnecessary or compromises that strategy, is not obliged to adopt the focus-algorithm approach.Algorithmic ThinkingMathematics advances in part through the development of efficient procedures that reduce difficult tasks to routine exercises that can be carried out without effort of thought. Alfred North Whitehead expressed this idea memorably in his book, An Introduction to Mathematics (1911):“It is a profoundly erroneous truism, repeated by all copy books and by eminent people when they are making speeches, that we should cultivate the habit of thinking of what we are doing. The precise opposite is the case. Civilization advances by extending the number of important operations which we can perform without thinking about them” (p. 61).An effective algorithm can be used to efficiently solve an entire class of problems, without having to think through each problem from first principles. Knowing algorithms increases students’ mathematical power, which is a principal goal of school mathematics (NCTM, 1989). The approach described in this paper — invented procedures followed by alternative algorithms, with focus algorithms as a backup and a basis for further work — will produce students who understand their methods and can carry them out proficiently so that they can think about more important things, such as why they are doing what they are doing and what their results mean. The approach improves students’ mental arithmetic skills, helps them understand the operations, and develops sound number sense, including a good understanding of place value. The emphasis on multiple solutions, including both inventing new procedures and making sense of others’inventions, encourages the belief that mathematics is creative and sensible. In Everyday Mathematics, accordingly, an increase in mathematical power through algorithmic proficiency is achieved at the same time that other important objectives are being met.The authors of Everyday Mathematics have also found that the study of paper-and-pencil computational algorithms can be valuable for developing algorithmic thinking in general. Forthis reason, explicit discussions of algorithms occur in lessons devoted to computation. Algorithmic and procedural thinking includes:• understanding specific algorithms or procedures provided by other people,• applying known algorithms to everyday problems,• adapting known algorithms to fit new situations,• developing new algorithms and procedures when necessary, and• recognizing the limitations of algorithms and procedures so they are not usedinappropriately.By studying computational algorithms, students can learn things that will carry over to other areas of their lives. More and more, people need to apply algorithmic and procedural thinking in order to operate technologically advanced devices. Algorithms beyond arithmetic are increasingly important in theoretical mathematics, in applications of mathematics, in computer science, and in many areas outside of mathematics.ReferencesBaroody, A. J., & Ginsburg, H. P. (1986). The relationship between initial meaning and mechanical knowledge of arithmetic. In J. Hiebert (Ed.), Conceptual and proceduralknowledge: The case of mathematics. Hillsdale, NJ: Erlbaum.Carpenter, T. P., Fennema, E., & Franke, M. L. (1992). Cognitively guided instruction: Building the primary mathematics curriculum on children's informal mathematical knowledge. A paper presented at the annual meeting of the American Educational Research Association, April 1992, San Francisco.Carroll, W. M. (1996). Use of invented algorithms by second graders in a reform mathematics curriculum. Journal of Mathematical Behavior, 15: 137-150.Carroll, W. M. (1997). Mental and written computation: Abilities of students in a reform-based curriculum. The Mathematics Educator, 2 (1): 18-32.Carroll, W., & Isaacs, A. (in press). Achievement of students using the University of Chicago School Mathematics Project’s Everyday Mathematics. In S. Senk & D. Thompson, Student outcomes in Standards-oriented school mathematics curriculum projects. Hillsdale, NJ: Erlbaum.Carroll, W., & Porter, D. (1997). Invented algorithms can develop meaningful mathematical procedures. Teaching Children Mathematics 3(7): 370-74.Carroll, W., & Porter, D. (1998). Alternative algorithms for whole-number operations. In L. J.Morrow (Ed.), The teaching and learning of algorithms in school mathematics: 1998yearbook (pp. 106-114). Reston, VA: National Council of Teachers of Mathematics. Carroll, W., Fuson, K., & Diamond, A. (2000). Use of student-constructed number stories in a reform-based curriculum. Journal of Mathematical Behavior, 19: 49-62.Cobb, P. (1985). Two children's anticipations, beliefs, and motivations. Educational Studies in Mathematics, 16: 111-126.Cobb, P., & Merkel, G. (1989). Thinking strategies: Teaching arithmetic through problem solving. In P. Trafton (Ed.), New directions for elementary school mathematics: 1989yearbook. Reston, VA: National Council of Teachers of Mathematics.Fuson, K., Carroll, W. M., & Drueck, J. V. (2000) Achievement results for second and third graders using the Standards-based curriculum Everyday Mathematics. Journal for Research in Mathematics Education 31 (3): 277-295.Greenwood, I. (1729). Arithmeticks. Boston: T. Hancock.Hiebert, J. (1984). Children's mathematical learning: The struggle to link form and understanding. Elementary School Journal, 84 (5): 497-513.Kamii, C., & Joseph, L. (1988). Teaching place value and double-column addition. Arithmetic Teacher, 35 (6), pp. 48-52.Knuth, D. E. (1998). The art of computer programming, volume 3: Sorting and searching.Reading, Massachusetts: Addison-Wesley.Madell, R. (1985). Children's natural processes. Arithmetic Teacher, 32 (7), pp. 20-22. National Council of Teachers of Mathematics. (1989). Curriculum and evaluation standards for school mathematics. Reston, VA: Author.Resnick, L. B., Lesgold, S., & Bill, V. (1990). From protoquantities to number sense. A paper prepared for the Psychology of Mathematics Education Conference, Mexico City. Whitehead, A. N. (1911). An introduction to mathematics. New York: Holt.(3/8/01)。
高智商名人史蒂芬·霍金简介个人资料(2)时间缝隙至于时光机的关键点,霍金强调就是所谓的“四度空间”,科学家将其命名为“虫洞”。
霍金强调,“虫洞”就在我们四周,只是小到肉眼很难看见,它们存在于空间与时间的裂缝中。
他指出,宇宙万物非平坦或固体状,贴近观察会发现一切物体均会出现小孔或皱纹,这就是基本的物理法则,而且适用于时间。
时间也有细微的裂缝、皱纹及空隙,比分子、原子还细小的空间则被命名为“量子泡沫”,“虫洞”就存在于其中。
史蒂芬·霍金回到过去而科学家们企图穿越空间与时间的极细隧道或快捷方式,则不断在量子天地中形成、消失或改造,它们连结两个不同的空间及时间。
部分科学家认为,有朝一日也许能够抓住“虫洞”,将它无限放大,使人类甚至宇宙飞船可以穿越;另外若动力充足加上完备科技,科学家或许也可以建造一个巨大的“虫洞”。
霍金指出,理论上时光隧道或“虫洞”不只能带着人类前往其他行星,如果虫洞两端位于同一位置,且以时间而非距离间隔,那么宇宙飞船即可飞入,飞出后仍然接近地球,只是进入所谓“遥远的过去”。
因为在4度空间中,10分钟也许是n小时。
不过霍金警告,不要搭时光机回去看历史。
飞去未来史蒂芬·威廉·霍金表示,如果科学家能够建造速度接近光速的宇宙飞船,那么宇宙飞船必然会因为不能违反光速是最大速限的法则,而导致舱内的时间变慢,那么飞行一个星期就等于是地面上的100年,也就相当于飞进未来。
霍金举人造卫星为例,指卫星在轨道运行时,由于受地球重力影响较小,卫星上的时间比地上时间稍快。
由此,霍金就设想出一艘大型极速宇宙船,可在1秒内加速至时速9.7万公里,6年内加速至光速的99.99%,比史上最快的宇宙船阿波罗10号快2000倍。
船上的乘客就是变相飞向未来,作出名副其实的时间旅行。
四度空间即使是在太空中,万物也都有时间的长度,在时间中漫游,意味着穿越该“4度空间”。
霍金举例指出,开车直线行进等于是在“1度空间”中行进,而左转或右转等于加上“2度空间”,至于在曲折蜿蜒的山路上下行进,就等于进入“3度空间”。
哈金斯公式
哈金斯公式指的是信息论中的一种实用公式,也被称为《哈金斯
大统一定律》,是由美国信息理论学家Claude E. Shannon于1948年
提出的。
这个公式表明在任何一个概率环境下,任何一个单独的事物(原子)存在特定的信息量,这种信息量可以用来衡量事物的不确定性
或复杂度的程度。
哈金斯公式:H(X)= -∑ p(xi)logp(xi) (i∈X)
其中:
H(X)是定义为X上不确定性的度量,可以称之为信息熵,即X的熵;
Xi 是X的属性值;
P(Xi)是X的概率分布,表示X的每一个属性值Xi出现的概率。
可以看到,哈金斯公式是根据概率论来测量信息量的,即它依据
当前系统中每个事件发生的概率来衡量这个系统所具有的信息量。
因
为概率分布越均匀,说明每一个属性值出现的机会就越平均,此时熵
也就越大,即此时系统中信息量越大;反之,如果概率分布不均匀,
说明每个属性值出现的机会就不平均,此时熵也就越小,即此时系统
中信息量越小。
哈金斯公式的一个重要应用就是数据压缩,目的是在保证原始数
据的完整性的前提下,通过减少不必要的数据量,来降低数据的体积,以此加快数据传输的速度。
例如,我们可以使用哈金斯公式来编码文
本文件,这样可以减少不必要的字节数从而达到压缩文件体积的目的。
归纳下来,哈金斯公式是根据概率论来测量信息量的,可以用来
衡量系统中的不确定性、复杂度等,主要应用于数据压缩、信息传输,以及信号处理等方面。
霍索恩效应1、概念:霍索恩效应(英语:Hawthorne effect),又称霍桑效应,是心理学上的一种实验者效应,是指当被观察者知道自己成为被观察对象而改变行为倾向的反应。
2、起源:霍桑效应起源于1927年至1932年期间,美国哈佛大学心理学教授埃尔顿·梅奥带领学生和研究人员在西方电器公司位于伊利诺伊州的霍桑工厂进行的一系列心理学实验。
3、实验内容:霍桑研究是在1924年至1932年进行的,因经济大恐慌而终止。
1927年在“继电器装配试验室实验”(Relay Assembly Test Room Experiments),Fritz J. Roethlisberger 与William J. Dickson 给出了大量实验细节,但是很少解释。
哈佛大学商学院的心理学教授乔治·埃尔顿·梅奥作了简短总结,包括表述了不管照明条件、休息时间的长短与次数及工作日数等因素如何,只要给予工人积极关注与自我管理权都可以创造积极的团体氛围而提高产量。
霍桑研究是一系列对工人在改善各种条件下(薪酬、照明条件、工间休息等)其生产效率变化情况的研究,但在一段时间后发现,这些条件的改善并未对生产率上升产生明显效果,有些甚至回到初始的状况。
这个现象在单个工人以及团体测试中都存在。
实验者设计的变量既不是唯一的也不是显著的主导生产率变化的因素。
由梅奥教授等作出的一个解释是:“六个人组成了一支团体,这个团体在实验诚心且自发的进行了合作。
”此即后来提出的“非正式组织”概念。
1955年,兰斯伯格尔(Henry A. Landsberger)重新解释了实验成果并定名为“霍桑效应”。
4、实验结论:(1)改变工作条件和劳动效率之间没有直接的因果关系;(2)提高生产效率的决定因素是员工情绪,而不是工作条件;(3)关心员工的情感和员工的不满情绪,有助于提高劳动生产率。
5、霍索恩效应的优缺点:(1)优点●能够清楚地发现员工关心的事项。
系统辨识是一门研究如何从系统的输入和输出数据中推断系统内部动态特性的学科。
在工程领域中,系统辨识被广泛应用于控制、信号处理、预测等领域。
随着科技的发展,系统辨识的应用范围也在不断扩大。
为了更好地掌握系统辨识的知识和技能,许多经典的英文书籍被撰写出来,以帮助读者深入了解系统辨识的理论和实践。
以下是一些经典的书籍:1. 《System Identification: Theory for the User. 2nd Edition》by P. J. G. Ramadge and W. M. Wetherall。
这本书是系统辨识领域的经典之作,涵盖了系统辨识的基本理论和实践方法,包括最小二乘法、递归辨识算法、稳定性分析等内容。
2. 《System Identification: Parameter and State Estimation》by Freddy Lindell。
这本书主要介绍了参数估计和状态估计的方法,包括最小二乘法、极大似然估计、卡尔曼滤波器等。
书中还通过实例演示了如何应用这些方法进行系统辨识。
3. 《Identification of Parameters in Linear Dynamic Systems》by H. Hjalmarsson。
这本书专注于线性动态系统的参数辨识,介绍了最小二乘法和递归最小二乘法等参数估计方法,以及如何应用这些方法进行系统辨识。
4. 《System Identification with an Introduction to Experimental Data Analysis》by Yuriy V. Gaiarsa and Joseph T. Lizier。
这本书不仅介绍了系统辨识的基本理论和方法,还详细介绍了如何进行实验数据分析和处理,包括数据预处理、噪声抑制、数据平滑等内容。
5. 《System Identification: A Practical Guide for Engineers and Scientists》by Tore Hagglund and Mats Åström。
萨斯塔莫宁模型结果的单位萨斯塔莫宁模型是一种常用于管理学和经济学领域的模型,用于评估和预测项目或决策的效果。
它由四个关键因素组成:情境(Situation)、目标(Target)、策略(Strategy)和结果(Outcome)。
在这个模型中,结果是最关键的,它衡量了目标是否达成以及策略是否成功。
我们首先来讨论萨斯塔莫宁模型结果的单位:时间和效果。
在模型中,时间是一个重要的度量单位,在评估结果时,我们需要考虑到时间的因素。
时间可以以天、周、月或年为单位,根据具体的项目或决策来确定。
通过将时间作为单位,我们可以看到目标的实现情况以及策略的持续效果。
效果是另一个关键的度量单位,它用于衡量目标的实现程度以及策略的成功与否。
效果可以是定性的或定量的,具体取决于项目的性质和目标的定义。
例如,如果目标是增加销售额,效果可以通过比较实际销售额和预期销售额来评估。
如果目标是提高客户满意度,效果可以通过客户调查或反馈来评估。
在评估萨斯塔莫宁模型结果时,我们需要考虑到时间和效果的综合影响。
例如,一个策略可能在短期内带来积极的效果,但在长期内效果可能逐渐减弱。
另一方面,一个策略可能在短期内没有明显的效果,但在长期内会逐渐显现出来。
因此,我们需要综合考虑时间和效果来评估策略的成功与否。
萨斯塔莫宁模型结果的单位还可以根据具体的项目或决策来确定。
例如,对于一个市场营销活动,结果的单位可以是销售额、市场份额或品牌知名度。
对于一个培训项目,结果的单位可以是员工满意度、技能提升或绩效改进。
根据具体的项目或决策,我们可以选择合适的单位来评估结果。
萨斯塔莫宁模型结果的单位是时间和效果。
通过考虑时间和效果的综合影响,我们可以评估和预测项目或决策的效果。
在选择单位时,需要根据具体的项目或决策来确定,以便更准确地评估结果。
同时,我们还需要综合考虑时间和效果的因素,以全面了解策略的成功与否。
2021-2000年上市公司企业范霍恩可持续发展模型、企业可持续发展模型
1. 范霍恩可持续发展模型:
范霍恩可持续发展模型(FHH Model)是德国学者范霍恩于1995年提
出的一种企业可持续发展的理论模型。
该模型以三个主要维度为基础,即经济、社会和环境。
企业必须在这三个方面实现平衡,才能实现可
持续发展。
在这个模型中,企业要考虑到利益相关者的不同需求和期望,制定合适的战略和行动计划,以实现可持续的经济、社会和环境
效益。
2. 企业可持续发展模型:
企业可持续发展模型是一种以推动企业可持续发展为核心的经营理念。
该模型的主要目标是在长期内实现企业的稳定增长和对社会和环境的
持续贡献。
这个模型注重企业在经济、社会和环境方面的平衡发展,
并将可持续性作为企业制定战略的重要因素。
企业可持续发展模型鼓
励企业在满足利益相关者的需求的基础上,进行环保和社会公益活动,推动全社会和环境的可持续发展。
斯霍滕定理证明斯霍滕定理,又被称为海洛因-斯霍滕定理,是数学领域中一个著名的定理,由德国数学家海洛因受到法国数学家斯霍滕的启发于19世纪三十年代发现的。
斯霍滕定理论证明了在一组阶数相同的多项式中,可以用比它更低阶的多项式去近似它,其表达能力也是极大的。
它的证明的难度也非常大,尤其是19世纪时期数学证明的难度更大,所以海洛因是当今数学史上一位伟大的数学家。
斯霍滕定理的证明中,首先海洛因指出,一组多项式中,可以把它们变为一组尽可能近似的多项式,就是一个多项式,比这个多项式更低阶的多项式也可以用来表达它。
因此,他提出,如果可以找到一组多项式,在它们的阶数相同的情况下,可以用比它更低阶的多项式近似它,那么就可以证明这个定理了。
但是由于在19世纪时期,数学家们并不清楚如何找到一组多项式,在它们的阶数相同的情况下,可以用比它更低阶的多项式近似它,因此海洛因也尝试用数学证明这个定理,但是他也没有给出有效的证明。
但是随着数学的发展,后来斯霍滕也给了定理的证明,他将定理的证明分为三个步骤:首先,他将定理的本质抽象为一个数学问题,其核心是利用线性代数的概念来求解定理的本质;其次,他将这个数学概念引入到数学分析中;最后,他将所有这些概念结合起来,解决了定理的证明。
经过斯霍滕的努力,斯霍滕定理被提出,并得到证实。
它的证明给了数学家们一个新的思路,即多项式的近似运算,它的思想也影响了后人进行多项式运算研究,被认为是一个重要的数学定理。
斯霍滕定理的意义与其发现的背景也有关系,它被用于拟合二维、三维曲面等一类曲线,因此有助于数据可视化和数据处理,在数学上它也能够帮助我们解决复杂的定理证明问题,所以对于现代数学研究仍然至关重要。
总而言之,斯霍滕定理是一个重要的数学发现,它的证明难度也非常大,海洛因和斯霍滕的努力让它终于得到证明。
现代,斯霍滕定理的运用也越来越广泛,所以它仍然是一个重要的数学定理,对于数学研究具有重要意义。
Vaidya—Schwarzschild—de Sitter时空中时间尺度变换
的纯规范势
马勇; 赵峥
【期刊名称】《《数学物理学报:A辑》》
【年(卷),期】1997(017)003
【摘要】同稳态时空一样,动态Vaidya-Schwarzschild-deSitter时空中的Hawking效应可看作时间尺度变换下的补偿效应,补偿场纯规范势的一个分量表征黑洞温度,另一个表征黑洞温度变化率.
【总页数】6页(P314-319)
【作者】马勇; 赵峥
【作者单位】重庆师范学院物理系; 北京师范大学物理系
【正文语种】中文
【中图分类】P145.8
【相关文献】
1.动态Vaidya-de Sitter时空中Dirac粒子的能级 [J], 张冠芬
2.Vaidya-de Sitter时空背景下标量粒子的能量 [J], 孟庆苗
3.Vaidya-de Sitter时空几何中玻色子的能量 [J], 孟庆苗
4.Vaidya-Bonner-de Sitter时空背景下黑洞周围标量粒子的能量分布 [J], 李洪奇
5.Vaidya-Bonner-de Sitter(VBD)时空的熵 [J], 史旺林;刘兴业;赵峥
因版权原因,仅展示原文概要,查看原文内容请购买。
a r X i v :h e p -t h /0002145 v 1 17 F eb 2000hep-th/0002145DeSitter Entropy,Quantum Entanglement and AdS/CFT Stephen Hawking ∗,Juan Maldacena †and Andrew Strominger †∗Department of Applied Mathematics and Theoretical Physics,Centre for Mathematical Sciences Wilberforce Road,Cambridge CB3OW A,UK †Department of Physics Harvard University Cambridge,MA 02138,USA Abstract A deSitter brane-world bounding regions of anti-deSitter space has a macroscopic entropy given by one-quarter the area of the observer horizon.A proposed variant of the AdS/CFT correspondence gives a dual description of this cosmology as conformal field theory coupled to gravity in deSitter space.In the case of two-dimensional deSitterspace this provides a microscopic derivation of the entropy,including the one-quarter,as quantum entanglement of the conformal field theory across the horizon.Contents1.Introduction (1)2.Classical Geometry (2)3.Dual Representations (4)4.DeSitter Entropy (5)4.1.Semiclassical Macroscopic Entropy (6)4.2.Microscopic Entropy (6)5.Four Dimensions (7)6.Black Hole Formation on the Brane (9)1.IntroductionDespite advances in the understanding of black hole entropy,a satisfactory microscopic derivation of the entropy of deSitter space[1]remains to be found.In this paper we address this issue in the context of a deSitter space arising as a brane-world of the type discussed by Randall and Sundrum[2]1bounding two regions of anti-deSitter space.It is natural to suppose that such theories are dual,in the spirit of AdS/CFT[3],to a conformal theory on the brane-world coupled to gravity with a cutoff.The cutoffscales with the deSitter radius in such a way that the usual AdS/CFT correspondence is recovered when the cutoffis taken to infinity.This duality provides an alternate description of the deSitter cosmology which can be used,in the case of two dimensions,for a microscopic derivation of the deSitter entropy.Wefind that the entropy can be ascribed to the quantum entanglement of the CFT vacuum across the deSitter horizon.Quantum entanglement entropy can also be viewed as the entropy of the thermal Rindler particles near the horizon,thereby avoiding reference to the unobservable region behind the horizon.Our derivation is closely related to the observation of reference[4]that in two dimen-sions black hole entropy can be ascribed to quantum entanglement if Newton’s constant is wholly induced by quantumfluctuations of ordinary matterfields(see also[5,6,7,8]). In the context of[4]this seemed to be a rather artificial and unmotivated assumption. However the AdS brane-world scenarios do appear to have this feature.The basic reason is that,in a semiclassical expansion,the Einstein action on the brane arises mainly from bulk degrees of freedom2which correspond,in the dual picture,to ordinary matterfieldson the brane.The semiclassical expansion in the bulk corresponds to a large N expansion in the brane,in which the leading term in Newton’s constant is induced by matterfields.Our derivation of two-dimensional deSitter entropy is similar to the derivation of black hole entropy in[9]in that it uses a branefield theory dual to the spacetime gravity theory to compute the entropy.However it differs in that in[9]the black hole entropy was given by the logarithm of the number of unobserved microstates of the black hole,whereas here the deSitter entropy arises from entanglement with the unobserved states behind the horizon.3 Alternatively,it can be viewed as the number of microstates of the thermal gas of Rindler particles near the horizon.This latter viewpoint is closer to that of[9].This issue is explored in thefinal section by throwing a black hole in the bulk of AdS at the brane. When the bulk black hole reaches the brane,the brane state collapses to a brane black hole.At all stages the entropy is accounted for by a thermal gas on the brane.Formally,the derivation can be generalized to higher-dimensional deSitter spaces which bound higher-dimensional anti-deSitter spaces.It was conjectured by Susskind and Uglum[6]that there is a general a precise relationship between entanglement entropy and the one loop correction to Newton’s constant.Based on this,Jacobson[5]argued that black hole entropy can be ascribed to quantum entanglement if Newton’s constant is wholly induced.However,while we are sympathetic to the conjecture of[6],and itfits well with the discussion herein,its status remains unclear[10,11].The basic problem is that in greater than two dimensions the corrections have power law divergences and hence are regulator dependent.This makes precise statements difficult above two dimensions.A further significantfly in the ointment-even in two dimensions-is that there is no known example of the type of brane-world scenario considered in[2]embedded in a fully consistent manner into string theory4.The observations of the present paper are relevant only if such examples exist.For the time being however they provide intriguing connections along the circle of ideas pursued in[1-9].2.Classical GeometryAdS3Fig.1:Euclidean instanton geometry.The brane is an S2which bounds twopatches of euclidean AdS3=H3.The euclidean action for a onebrane coupled to gravity with a negative cosmological constant(Λ=−116π d3x√L2)+Td2σ√4πL2V3−M p coth r B2(sinh2r B+2r B)+4πT L2sinh2r B.(2.6)The action(2.6)has an extremum atM ptanh r B=3.Dual RepresentationsLet us assume there is a unitary quantum theory whose semiclassical gravitational dynamics is described by(2.1).Such a theory should have two dual descriptions.5The first is,as described above,a three-dimensional bulk theory containing gravity and a brane.The second description is as a two-dimensional effective theory of the lightfields on the brane worldvolume.These lightfields include holographic matter living on the brane.To see this wefirst consider a single copy of AdS3in coordinates(2.2)with afixed boundary at r=r B,for large r B.If we keep the metric on the boundaryfixed,but integrate over the bulk metric,the resulting theory has a holographic description as a1+1conformal field theory on a sphere of radius with central charge c=3LM p(D2−3D+2)local degrees of freedom(after implementing the constraints).2Hence for the present case of D=2the radion-gravity system has no local degrees of freedom.For our purposes we need only the gravity part of the effective action,with the radion field set at the minimum of its potential.The gravity effective action is most easily represented in conformal gaugeds22=e2ρdˆs22,(3.2)where d ˆs 2is the unit metric on S 2obeyingˆR z ¯z =ˆg z ¯z , d2z ˆg z ¯z =4π(3.3)in complex coordinates.One then finds 7S g =−LM p 2 2ˆg z ¯z e 2ρ.(3.4)The equations of motion for constant fields giveρ=ln .(3.5)The action (3.4)evaluated at this solution agrees with (2.9)We note also that the total gravity plus matter central charge vanishes,as required for general covariance.These considerations determine (3.4).4.DeSitter EntropyIn this section we give macroscopic and microscopic derivations of the entropy.4.1.Semiclassical Macroscopic EntropyThe macroscopic entropy can be computed directly in three dimensions from the area entropy-lawS dS =Area 4G 2.(4.3)The area in this formula is just the area of the observer horizon (θ=0,πin (2.10))which consists of two points and is therefore equal to 2.G 2is determined as the (field-dependent)coefficient of the the scalar curvature R =1G 2=2LM p ρ=2LM p ln .(4.4)Inserting (4.4)into (4.3)reproduces (4.2).4.2.Microscopic EntropyLet us now consider the entropy from the point of view of the brane CFT with c= 3M p L.An SO(2,1)invariant vacuum for quantumfield theory in lorentzian deSitter space |0 can be defined as the state annihilated by positive frequency modes in the metricds22= 2−dt2+dx22+π2π.The vacuum|0 is a pure state of this CFT.However a single observer can probe features of this state only within the observer horizons,i.e.in the diamond region covered by the coordinates(2.10).The results of all such measurements are described by an observable density matrixρobs.ρobs is constructed from the pure density matrix|0 0|by tracing over the unobservable sector of the Hilbert space supported behind the horizon. The entropyS ent=−trρobs lnρobs(4.6) is nonzero because of correlations between the quantum states inside and outside of the horizon.S ent is called the entanglement entropy because it measures the extent to which the observable and unobservable Hilbert spaces are entangled.Note that the entanglement entropy,defined this way,agrees with the entropy of the gas of particles at the local Rindler temperature.A general formula for S ent was derived in[21,4]:S ent=c6,(4.7)where∆is the short distance cutoffandρis the conformal factor of the metric in the coordinates(in our case(4.5))used to define the vacuum evaluated at the boundary (consisting of two points)of the unobserved region.From(4.5)we see that the boundary is at t=0,soρ=ln .Putting this all together and using c=3M p L we getS ent=LM p ln ,(4.8) in agreement with(2.8).This result is a generalization to the two-dimensional deSitter case of the observation of [4]that,in a two-dimensional theory in which the entirety of Newton’s constant is induced from matter,the Bekenstein-Hawking black hole entropy can be microscopically derived as entanglement entropy.The missing ingredient in both of these previous discussions was a motivation for the assumption that Newton’s constant is induced.Here we see it is natural -or at least equivalent to other assumptions-in the brane-world context.5.Four DimensionsWe can also consider a four dimensional brane world model.We have a four di-mensional brane bounding two AdS5regions.If we consider perturbations of the four dimensional metric we can analyze the system byfirstfinding afive dimensional solution which has a given four dimensional metric at the brane.The solution will look likeds2=L2 gµν(z,x)dxµdxν+dz216πG5 z≥ d5x√16πG5 2d4x√G4=8N dof8G5(5.3)where N dof is the quantity that appears in all AdS/CFT calculations involving the stress tensor,calculations such as the two point function of the stress tensor or the free energy atfinite temperature,etc.It can be viewed as the effective number of degrees of freedom of the CFT,(N dof=N2/4for N=4SYM).This form for the four dimensional Newton constant is very suggestive.It is of the general form expected for induced gravity in four2.dimensions.If we start in four dimensions with a theory with infinite or very large Newton constant and we integrate out the matterfields we expect to get a four dimensional value for the Newton constant which is rougly as in(5.3)[24,25,26,6,27,10,28,29].The precise value that we would get seems to depend on the cutoffprocedure.Indeed,if we use heat kernel regularization we would get that for N=4Yang Mills this cuadratic divergence cancels. The gravity procedure offixing the boundary at somefinite distance must correspond to a suitable cutofffor thefield theory and it is not obvious that we should get the same results for divergent terms.Indeed,the supergravity regularization procedure would also give a divergent value for the vacuum energy(which is being cancelled by the brane).Again in theories where the four dimensional Newton constant is induced one can interpret black hole entropy as entanglement entropy[5].If we consider a four dimensional metric with a horizon,like a black hole or de-Sitter space we indeedfind that the entropy is given byS=A42π(5.4)where we just used the relation of the4d Newton constant and the four dimensional parameters.The right hand side can be interpreted as entanglement entropy.In other words,we can compute the entanglement entropy in thefield theory as entropy of the gas of particles in thermal Rindler space and we would obtain precisely the right hand side of (5.4).We can do the entropy calculation at weak coupling in weakly coupled N=4SYM and we would obtain agreement up to a numerical factor,which could be be related to the ignorance of the cutoffprocedure,but more fundamentally can also be related to strong coupling effects like the3/4appearing in the relation between the weakly coupled and the strongly coupled expressions for the free energy.6.Black Hole Formation on the BraneThe entropy of a bulk black hole in the interior of AdS can be accounted for by representing it as a thermal state in the brane theory on its boundary.Atfirst this may seem to be at odds with the accounting given here of the entropy of a black hole on the brane in terms of quantum entanglement.In this section we will attempt to reconcile the accounts by throwing a bulk black hole at the brane and watching it turn into a brane black hole.9Consider a bulk black hole at the origin of AdS at temperature T H whose size is large compared to the AdS radius L but small compared to the brane radius .This has a stable ground state in which there is a cloud of thermal radiation surrounding the black hole.In the brane theory,this is represented as homogeneous thermal state at temperature T H. The statistical entropy of this state agrees with Bekenstein-Hawking entropy of the black hole.10The center of mass of the black hole can be given momentum by the action of an AdS D+1SO(D,2)isometry.These isometries are broken by the presence of the brane, but if black hole is not too near the brane this should not matter.The SO(D,2)action will impart momenta to the black hole and make it oscillate about the origin.The brane version of such a state can be found by applying an SO(D,2)conformal transformation to the thermal brane state.The resulting state will carry conformal charges and have energy densities with bipolar oscillations.The statistical entropy of this oscillating state will of course still agree with Bekenstein-Hawking entropy of the oscillating black hole.In the above discussion we implicitly assumed that thefield theory was defined on the cylinder(S3×R).When we think of thefield theory defined inflat space or de-Sitterspace we are looking only at some coordinate patch of the AdS cylinder.In this case we only see half a period of oscillation which can be interpreted as a gas of particles in the field theory that contracts and expands again.If the oscillation is made large enough,the black hole actually reaches the brane where it will stick,at least temporarily.The brane picture of this process is that the oscillations in the energy density have become so large that the thermal radiation collapses to from a black hole.11Before collapse,the entropy is accounted for on the brane as the entropy of thermal radiation.After collapse,it is accounted for by the thermal gas of Rindler particles near the horizon.(In general the black hole formation is not adiabatic.)This latter entropy is localized within a distance of the order of the cutofffrom the horizon.This could be described by saying that all stages the entropy is stored in thermal radiation,and this radiation hovers outside the horizon when the black hole is formed.In this descriptionthe statistical origin of the entropy of bulk and brane black holes appears to be similar. Eventually the black hole will evaporate and thefinal state will be just outgoing thermal radiation on the brane theory.It is interesting that there is a“correspondence principle”in the sense that when the AdS black hole has a radius of the order of thefive dimensional anti-de-Sitter space and it makes a grazing collision with the brane,then the entropies calculated as a thermal gas and as entanglement entropy are the same up to a numerical constant.Such a black hole would have a Schwarschild radius in the boundary theory equal to thefield theory cutoff .In closing,it remains tofind a fully consistent quantum realization of such a brane-world scenario to which our observations can be applied.Alternately,perhaps it is ap-plicable in a more general setting.One of the important lessons of string duality is that something which is classical from one point of view can be quantum from another.What is needed here is a point of view from which Newton’s constant-usually regarded as a largely classical quantity-is a purely quantum effect.It is notable in this regard that closed string poles arise as a loop effect in open string theory,indicating that there might be a way in which the full closed string dynamics is contained in open stringfield theory. In that case Newton’s constant could be induced.AcknowledgementsThis work was supported in part by DOE grant DE-FG02-91ER40654.J.M.was also supported in part by the Sloan and Packard fellowships.We would like to thank T. Jacobson,A.Tseytlin and H.Verlinde for discussions.References[1]G.W.Gibbons and S.W.Hawking,Action integrals and partition functions in quan-tum gravity,Phys.Rev.D15(1977)2752.[2]L.Randall and R.Sundrum,An alternative to compactification,Phys.Rev.Lett.83(1999)4690,hep-th/9906064.[3]J.Maldacena,The large N limit of superconformalfield theories and supergravity,Adv.Theor.Math.Phys.2(1998)231,hep-th/9711200.[4]T.Fiola,J.Preskill,A.Strominger and S.P.Trivedi,Black hole thermodynamics andinformation loss in two-dimensions,Phys.Rev.D50(1994)3987,hep-th/9403137.[5]T.Jacobson,Black hole entropy and induced gravity,gr-qc/9404039.[6]L.Susskind and J.Uglum,Black hole entropy in canonical quantum gravity and su-perstring theory,Phys.Rev.D50(1994)2700,hep-th/9401070.[7]V.Frolov,D.Fursaev and A.I.Zelnikov,Statistical origin of black hole entropy ininduced gravity,Nucl.Phys.B486(1997)339,hep-th/9607104.[8]V.Frolov and D.Fursaev,Mechanism of generation of black hole entropy in Sakharov’sinduced gravity,Phys.Rev.D56(1997)2212,hep-th/9703178.[9] A.Strominger and C.Vafa,Microscopic origin of the Beckenstein-Hawking entropy,Phys.Lett.B379(1996)99,hep-th/9601029.[10] D.Kabat,Black hole entropy and entropy of entanglement,Nucl.Phys.B453(1995),hep-th/9503016.[11]T.Jacobson,On the nature of black hole entropy,gr-qc/9908031.[12]H.Verlinde,Holography and compactification,hep-th/9906182.[13]S.S.Gubser,AdS/CFT and gravity,hep-th/9912001.[14]J.DeBoer,E.Verlinde.and H.Verlinde,On the holographic renormalization group,hep-th/9912012.[15]J.D.Brown and M.Henneaux,Central charges in the canonical realization of asymp-totic symmetries:An example from three dimensional gravity,Commun.Math.Phys.(1986),104.[16]L.Susskind and E.Witten,The holographic bound in Anti-de-Sitter space,hep-th/9805114.[17] A.Peet and J.Polchinski,UV/IR relations in AdS dynamics,Phys.Rev.D59(1999)065011,hep-th/9809022.[18]M.Henningson and K.Skenderis,JHEP9807(1998)023,hep-th/9806087.[19]V.Balasubramanian and P.Kraus,A stress tensor for anti de Sitter gravity,Commun.Math.Phys.(1999)208,hep-th/9902121.[20]S.Hawking and C.Hunter,Gravitational entropy and global structure,Phys.Rev.D59(1999)044025,hep-th/9808085.[21] C.Holzhey,rsen and F.Wilczek,Geometric and renormalized entropy in con-formalfield theory,Nucl.Phys.B424(1994)443,hep-th/9403108.[22]P.Frampton and C.Vafa,Conformal approach to particle phenomenology,hep-th/9903226.[23] E.Witten,New dimensions infield theory and string theory,/online/susyc9[24]L.Bombelli,R.Koul,J.Lee and R.Sorkin,A quantum source of entropy for blackholes,Phys.Rev.D34(1986)373.[25]M.Srednicki,Phys.Rev.Lett.71(1993)666,Entropy and area,hep-th/9303048.[26]S.Carlip and C.Teitelboim,The offshell black hole gr-qc/9312002.[27] C.Callan and F.Wilczek,On geometric entropy,Phys.Lett.B333(1994)55;hep-th/9401072.[28]J-G.Demers,France and R.Myers,Black hole entropy and renormalization,gr-qc/9507042.[29] rsen and F.Wilczek,Renormalization of black hole entropy and of the gravita-tional coupling constant,Nucl.Phys.B58(1996)249,hep-th/9506066.。