Quantized Reductions and Irreducible Representations of W-Algebras

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Necessary conditions for the optimality of variable rate residual vector quantizers

Necessary conditions for the optimality of variable rate residual vector quantizers

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The
Fixed
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90-A geometric setting for the quantum deformation of GLn

90-A geometric setting for the quantum deformation of GLn
ol. 61, No. 2
DUKE MATHEMATICAL JOURNAL (C)
October 1990
A GEOMETRIC SETTING FOR THE QUANTUM DEFORMATION OF GL
A. A. BEILINSON, G. LUSZTIG*,
AND
R. MACPHERSON*
1. Flags and the algebra K. 1.1. We fix an integer n>l. Let i9 be the set of all n n matrices with integer entries such that the entries off diagonal are > 0. Let (R) be the set of all n x n matrices with integer, > 0 entries. Thus, (R) c 19. Let r: 19 ---, N be the map defined by taking the sum of all entries of a matrix. Let (R)a a-l(d); we have (R) Ila>o (R)a, and each (R)a is a finite set. Let V be a vector space of finite dimension d over a field F. Let be the set of all n-step filtrations V1 c V2 c"" V. E The group GL(V) acts naturally on -; its orbits are the fibres of the map --, N" given by

机器学习与数据挖掘笔试面试题

机器学习与数据挖掘笔试面试题
What is a decision tree? What are some business reasons you might want to use a decision tree model? How do you build a decision tree model? What impurity measures do you know? Describe some of the different splitting rules used by different decision tree algorithms. Is a big brushy tree always good? How will you compare aegression? Which is more suitable under different circumstances? What is pruning and why is it important? Ensemble models: To answer questions on ensemble models here is a :
Why do we combine multiple trees? What is Random Forest? Why would you prefer it to SVM? Logistic regression: Link to Logistic regression Here's a nice tutorial What is logistic regression? How do we train a logistic regression model? How do we interpret its coefficients? Support Vector Machines A tutorial on SVM can be found and What is the maximal margin classifier? How this margin can be achieved and why is it beneficial? How do we train SVM? What about hard SVM and soft SVM? What is a kernel? Explain the Kernel trick Which kernels do you know? How to choose a kernel? Neural Networks Here's a link to on Coursera What is an Artificial Neural Network? How to train an ANN? What is back propagation? How does a neural network with three layers (one input layer, one inner layer and one output layer) compare to a logistic regression? What is deep learning? What is CNN (Convolution Neural Network) or RNN (Recurrent Neural Network)? Other models: What other models do you know? How can we use Naive Bayes classifier for categorical features? What if some features are numerical? Tradeoffs between different types of classification models. How to choose the best one? Compare logistic regression with decision trees and neural networks. and What is Regularization? Which problem does Regularization try to solve? Ans. used to address the overfitting problem, it penalizes your loss function by adding a multiple of an L1 (LASSO) or an L2 (Ridge) norm of your weights vector w (it is the vector of the learned parameters in your linear regression). What does it mean (practically) for a design matrix to be "ill-conditioned"? When might you want to use ridge regression instead of traditional linear regression? What is the difference between the L1 and L2 regularization? Why (geometrically) does LASSO produce solutions with zero-valued coefficients (as opposed to ridge)? and What is the purpose of dimensionality reduction and why do we need it? Are dimensionality reduction techniques supervised or not? Are all of them are (un)supervised? What ways of reducing dimensionality do you know? Is feature selection a dimensionality reduction technique? What is the difference between feature selection and feature extraction? Is it beneficial to perform dimensionality reduction before fitting an SVM? Why or why not? and Why do you need to use cluster analysis? Give examples of some cluster analysis methods? Differentiate between partitioning method and hierarchical methods. Explain K-Means and its objective? How do you select K for K-Means?

高斯朴素贝叶斯训练集精确度的英语

高斯朴素贝叶斯训练集精确度的英语

高斯朴素贝叶斯训练集精确度的英语Gaussian Naive Bayes (GNB) is a popular machine learning algorithm used for classification tasks. It is particularly well-suited for text classification, spam filtering, and recommendation systems. However, like any other machine learning algorithm, GNB's performance heavily relies on the quality of the training data. In this essay, we will delve into the factors that affect the training set accuracy of Gaussian Naive Bayes and explore potential solutions to improve its performance.One of the key factors that influence the training set accuracy of GNB is the quality and quantity of the training data. In order for the algorithm to make accurate predictions, it needs to be trained on a diverse and representative dataset. If the training set is too small or biased, the model may not generalize well to new, unseen data. This can result in low training set accuracy and poor performance in real-world applications. Therefore, it is crucial to ensure that the training data is comprehensive and well-balanced across different classes.Another factor that can impact the training set accuracy of GNB is the presence of irrelevant or noisy features in the dataset. When the input features contain irrelevant information or noise, it can hinder the algorithm's ability to identify meaningful patterns and make accurate predictions. To address this issue, feature selection and feature engineering techniques can be employed to filter out irrelevant features and enhance the discriminative power of the model. Byselecting the most informative features and transforming them appropriately, we can improve the training set accuracy of GNB.Furthermore, the assumption of feature independence in Gaussian Naive Bayes can also affect its training set accuracy. Although the 'naive' assumption of feature independence simplifies the model and makes it computationally efficient, it may not hold true in real-world datasets where features are often correlated. When features are not independent, it can lead to biased probability estimates and suboptimal performance. To mitigate this issue, techniques such as feature extraction and dimensionality reduction can be employed to decorrelate the input features and improve the training set accuracy of GNB.In addition to the aforementioned factors, the choice of hyperparameters and model tuning can also impact the training set accuracy of GNB. Hyperparameters such as the smoothing parameter (alpha) and the covariance type in the Gaussian distribution can significantly influence the model's performance. Therefore, it is important to carefully tune these hyperparameters through cross-validation andgrid search to optimize the training set accuracy of GNB. By selecting the appropriate hyperparameters, we can ensure that the model is well-calibrated and achieves high accuracy on the training set.Despite the challenges and limitations associated with GNB, there are several strategies that can be employed to improve its training set accuracy. By curating a high-quality training dataset, performing feature selection and engineering, addressing feature independence assumptions, and tuning model hyperparameters, we can enhance the performance of GNB and achieve higher training set accuracy. Furthermore, it is important to continuously evaluate and validate the model on unseen data to ensure that it generalizes well and performs robustly in real-world scenarios. By addressing these factors and adopting best practices in model training and evaluation, we can maximize the training set accuracy of Gaussian Naive Bayes and unleash its full potential in various applications.。

Irreducibility Testing

Irreducibility Testing

Preprint submitted to Elsevier Science
27 October 2004
its monic reciprocal irreducible factors removed. The purpose of this paper is to establish the following result:
j
∈ {0, 1} for each j,

0
=1
j =0

r
xaj : aj ∈ Z, 0 = a0 < a1 < a2 < · · · < ar .

Leabharlann j =0Thus, S is the set of all polynomials f (x) having each coefficient either 0 or 1 and satisfying f (0) = 0 and Sr is the subset of such polynomials having exactly r + 1 terms. Lemma 1 Let r be a positive integer, and let f (x) ∈ Sr . Then the nonreciprocal part of f (x) is reducible if and only if there is a w(x) ∈ Sr satisfying ˜(x), and w(x)w(x) = f (x)f ˜(x). w(x) = f (x), w(x) = f The above lemma summarizes the contents of Lemma 1 and Lemma 3 of [1]. To explain our next lemma, we note that it is possible for a reciprocal polynomial to have non-reciprocal factors or to consist entirely of non-reciprocal irreducible factors. For example, f (x) = (x3 + x + 1)(x3 + x2 + 1) = x6 + x5 + x4 + 3x3 + x2 + x + 1 has this property. Note that the non-reciprocal part of f (x) is reciprocal (and in fact f (x)) for this example. On the other hand, f (x) ∈ / S . For polynomials in S , the situation is different. Lemma 2 Let f (x) be a reciprocal polynomial in S . Then f (x) is not divisible by a non-reciprocal polynomial in Z[x]. ˜(x) = f (x). Note that this implies f (0) = 1. To prove Lemma 2, observe that f Assume f (x) has a non-reciprocal factor. Then there is an irreducible nonreciprocal polynomial g (x) that divides f (x). We have that necessarily g ˜(x) is irreducible, non-reciprocal, and different from ±g (x). Since g (x) is a factor of ˜(x) = f (x). Hence, f (x) is divisible by f (x), we deduce that g ˜(x) is a factor of f g (x)˜ g (x). In particular, the non-reciprocal part of f (x) is reducible. Let w(x) ˜(x) = f (x) be as given in Lemma 1 with r chosen appropriately. Combining f ˜, we obtain with w w = f f f (x) − w(x) f (x) + w(x) = w(x) − w(x) f (x). As f (x) and w(x) are 0, 1-polynomials with w(x) = f (x), 3

计量经济学中英文词汇对照

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

金融博士书目

金融博士书目

金融博士书目经济学、金融学博士书目(A:数学分析微分方程矩阵代数)微观金融学包括金融市场及金融机构研究、投资学金融工程学金融经济学、公司金融财务管理等方面,宏观金融学包括货币经济学货币银行学、国际金融学等方面,实证和数量方法包括数理金融学、金融计量经济学等方面,以下书目侧重数学基础、经济理论和数理金融学部分。

◎函数与分析《什么是数学》,牛津丛书●集合论Paul R. Halmos,Naive Set Theory 朴素集合论(美)哈莫斯(好书,深入浅出但过简洁)集合论(英文版)Thomas Jech(有深度)Moschovakis,Notes on Set Theory集合论基础(英文版)——图灵原版数学·统计学系列(美)恩德滕●数学分析○微积分Tom M. Apostol, Calculus vol Ⅰ&Ⅱ(数学家写的经典高等微积分教材/参考书,写法严谨,40年未再版,致力于更深刻的理解,去除微积分和数学分析间隔,衔接分析学、微分方程、线性代数、微分几何和概率论等的学习,学实分析的前奏,线性代数应用最好的多元微积分书,练习很棒,对初学者会难读难懂,但具有其他教材无法具备的优点。

Stewart 的书范围相同,也较简单。

)Carol and Robert Ash,The Calculus Tutoring Book(不错的微积分辅导教材)R. Courant, F. John, Introduction to Calculus and Analysis vol Ⅰ&Ⅱ(适合工科,物理和应用多)Morris Kline,Calculus, an intuitive approachRon LarsonCalculus (With Analytic Geometry(微积分入门教材,难得的清晰简化,与Stewart同为流行教材)《高等微积分》Lynn H.Loomis / Shlomo StermbergMorris Kline,Calculus: An Intuitive and Physical Approach (解释清晰的辅导教材)Richard Silverman,Modern Calculus with Analytic GeometryMichael,Spivak,Calculus(有趣味,适合数学系,读完它或者Stewart的就可以读Rudin 的Principles of Mathematical Analysis 或者Marsden的Elementary Classical Analysis,然后读Royden的Real Analysis学勒贝格积分和测度论或者Rudin的Functional Analysis 学习巴拿赫和希尔伯特空间上的算子和谱理论)James Stewart,Calculus(流行教材,适合理科及数学系,可以用Larson书补充,但解释比它略好,如果觉得难就用Larson的吧)Earl W. Swokowski,Cengage Advantage Books: Calculus: The Classic Edition(适合工科)Silvanus P. Thompson,Calculus Made Easy(适合微积分初学者,易读易懂)○实分析(数学本科实变分析水平)(比较静态分析)Understanding Analysis,Stephen Abbott,(实分析入门好书,虽然不面面俱到但清晰简明,Rudin, Bartle, Browder等人毕竟不擅于写入门书,多维讲得少)T. M. Apostol, Mathematical AnalysisProblems in Real Analysis 实分析习题集(美)阿里普兰斯,(美)伯金肖《数学分析》方企勤,北大胡适耕,实变函数《分析学》Elliott H. Lieb / Michael LossH. L. Royden, Real AnalysisW. Rudin, Principles of Mathematical AnalysisElias M.Stein,Rami Shakarchi, Real Analysis:MeasureTheory,Integration and Hilbert Spaces,实分析(英文版) 《数学分析八讲》辛钦《数学分析新讲》张筑生,北大社周民强,实变函数论,北大周民强《数学分析》上海科技社○测度论(与实变分析有重叠)概率与测度论(英文版)(美)阿什(Ash.R.B.),(美)多朗-戴德(Doleans-Dade,C.A.)?Halmos,Measure Theory,测度论(英文版)(德)霍尔姆斯○傅里叶分析(实变分析和小波分析各有一半)小波分析导论(美)崔锦泰H. Davis, Fourier Series and Orthogonal FunctionsFolland,Real Analysis:Modern Techniques and Their ApplicationsFolland,Fourier Analysis and its Applications,数学物理方程:傅里叶分析及其应用(英文版)——时代教育.国外高校优秀教材精选(美)傅兰德傅里叶分析(英文版)——时代教育·国外高校优秀教材精选(美)格拉法科斯B. B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the MakingKatanelson,An Introduction to Harmonic AnalysisR. T. Seeley, An Introduction to Fourier Series and IntegralsStein,Shakarchi,Fourier Analysis:An Introduction○复分析(数学本科复变函数水平)L. V. Ahlfors, Complex Analysis ,复分析——华章数学译丛,(美)阿尔福斯(Ahlfors,L.V.)Brown,Churchill,Complex Variables and Applications Convey, Functions of One Complex Variable Ⅰ&Ⅱ《简明复分析》龚升, 北大社Greene,Krantz,Function Theory of One Complex VariableMarsden,Hoffman,Basic Complex AnalysisPalka,An Introduction to Complex Function TheoryW. Rudin, Real and Complex Analysis 《实分析与复分析》鲁丁(公认标准教材,最好有测度论基础)Siegels,Complex VariablesStein,Shakarchi,Complex Analysis 《复变函数》庄坼泰●泛函分析(资产组合的价值)○基础泛函分析(实变函数、算子理论和小波分析)实变函数与泛函分析基础,程其衰,高教社Friedman,Foundations of Modern Analysis《实变与泛函》胡适耕《泛函分析引论及其应用》克里兹格泛函分析习题集(印)克里希南Problems and methods in analysis,Krysicki夏道行,泛函分析第二教程,高教社夏道行,实变函数与泛函分析《数学分析习题集》谢惠民,高教社泛函分析·第6版(英文版) K.Yosida《泛函分析讲义》张恭庆,北大社○高级泛函分析(算子理论)J.B.Conway, A Course in Functional Analysis,泛函分析教程(英文版)Lax,Functional AnalysisRudin,Functional Analysis,泛函分析(英文版)[美]鲁丁(分布和傅立叶变换经典,要有拓扑基础)Zimmer,Essential Results of Functional Analysis○小波分析Daubeches,Ten Lectures on WaveletsFrazier,An Introduction to Wavelets Throughout Linear Algebra Hernandez,《时间序列的小波方法》PercivalPinsky,Introduction to Fourier Analysis and WaveletsWeiss,A First Course on WaveletsWojtaszczyk,An Mathematical Introduction to Wavelets Analysis●微分方程(期权定价、动态分析)○常微分方程和偏微分方程(微分方程稳定性,最优消费组合)V. I. Arnold, Ordinary Differential Equations,常微分方程(英文版)(现代化,较难)W. F. Boyce, R. C. Diprima, Elementary Differential Equations and Boundary Value Problems《数学物理方程》陈恕行,复旦E. A. Coddington, Theory of ordinary differential equationsA. A. Dezin, Partial differential equationsL. C. Evans, Partial Differential Equations丁同仁《常微分方程教程》高教《常微分方程习题集》菲利波夫,上海科技社G. B. Folland, Introduction to Partial Differential EquationsFritz John, Partial Differential Equations《常微分方程》李勇The Laplace Transform: Theory and Applications,Joel L. Schiff(适合自学)G. Simmons, Differntial Equations With Applications and Historecal Notes索托梅约尔《微分方程定义的曲线》《常微分方程》王高雄,中山大学社《微分方程与边界值问题》Zill○偏微分方程的有限差分方法(期权定价)福西斯,偏微分方程的有限差分方法Kwok,Mathematical Models of Financial Derivatives(有限差分方法美式期权定价)?Wilmott,Dewynne,Howison,The Mathematics of Financial Derivatives (有限差分方法美式期权定价)○统计模拟方法、蒙特卡洛方法Monte Carlo method in finance (美式期权定价)D. Dacunha-Castelle, M. Duflo,Probabilités et Statistiques IIFisherman,Monte Carlo Glasserman,Monte Carlo Mathods in Financial Engineering (金融蒙特卡洛方法的经典书,汇集了各类金融产品)Peter Jaeckel,Monte Carlo Methods in Finance(金融数学好,没Glasserman的好)?D. P. Heyman and M. J. Sobel, editors,Stochastic Models, volume 2 of Handbooks in O. R. and M. S., pages 331-434. Elsevier Science Publishers B.V. (North Holland) Jouini,Option Pricing,Interest Rates and Risk ManagementD. Lamberton, B. Lapeyre, Introduction to Stochastic Calculus Applied to Finance (连续时间)N. Newton,Variance reduction methods for diffusion process :H. Niederreiter,Random Number Generation and Quasi-Monte Carlo Methods. CBMS-NSF Regional Conference Series in Appl. Math. SIAMW.H. Press and al.,Numerical recepies.B.D. Ripley. Stochastic SimulationL.C.G. Rogers et D. Talay, editors,Numerical Methods in Finance. Publicationsof the Newton Institute.D.V. Stroock, S.R.S. Varadhan,Multidimensional diffusion processesD. Talay,Simulation and numerical analysis of stochastic differential systems, a review. In P. Krée and W. Wedig, editors,Probabilistic Methods in Applied Physics, volume 451 of Lecture Notes in Physics, chapter 3, pages 54-96.P.Wilmott and al.,Option Pricing (Mathematical models and computation). Benninga,Czaczkes,Financial Modeling ○数值方法、数值实现方法Numerical Linear Algebra and Its Applications,科学社K. E. Atkinson, An Introduction to Numerical AnalysisR. Burden, J. Faires, Numerical Methods《逼近论教程》CheneyP. Ciarlet, Introduction to Numerical Linear Algebra and Optimisation, Cambridge Texts in Applied MathematicsA. Iserles, A First Course in the Numerical Analysis of Differential Equations, Cambridge Texts in Applied Mathematics 《数值逼近》蒋尔雄《数值分析》李庆杨,清华《数值计算方法》林成森J. Stoer, R. Bulirsch, An Introduction to Numerical AnalysisJ. C. Strikwerda, Finite Difference Schemes and Partial Differential Equations L. Trefethen, D. Bau, Numerical Linear Algebra《数值线性代数》徐树芳,北大其他(不必)《数学建模》Giordano《离散数学及其应用》Rosen《组合数学教程》Van Lint◎几何学和拓扑学(凸集、凹集)●拓扑学○点集拓扑学Munkres,Topology:A First Course《拓扑学》James R.MunkresSpivak,Calculus on Manifolds◎代数学(深于数学系高等代数)(静态均衡分析)○线性代数、矩阵论(资产组合的价值)M. Artin,AlgebraAxler, Linear Algebra Done RightCurtis,Linear Algeria:An Introductory ApproachW. Fleming, Functions of Several VariablesFriedberg, Linear Algebra Hoffman & Kunz, Linear AlgebraP.R. Halmos,Finite-Dimensional Vector Spaces(经典教材,数学专业的线性代数,注意它讲抽象代数结构而不是矩阵计算,难读)J. Hubbard, B. Hubbard, Vector Calculus, Linear Algebra, and Differential Forms: A Unified ApproachN. Jacobson,Basic Algebra Ⅰ&ⅡJain《线性代数》Lang,Undergraduate AlgeriaPeter D. Lax,Linear Algebra and Its Applications(适合数学系)G. Strang, Linear Algebra and its Applications(适合理工科,线性代数最清晰教材,应用讲得很多,他的网上讲座很重要)●经济最优化Dixit,Optimization in Economic Theory●一般均衡Debreu,Theory of Value●分离定理Hildenbrand,Kirman,Equilibrium Analysis(均衡问题一般处理)Magill,Quinzii,Theory of Incomplete Markets(非完备市场的均衡)Mas-Dollel,Whinston,Microeconomic Theory(均衡问题一般处理)Stokey,Lucas,Recursive Methods in Economic Dynamics (一般宏观均衡)经济学、金融学博士书目(B:概率论、数理统计、随机)◎概率统计●概率论(金融产品收益估计、不确定条件下的决策、期权定价)○基础概率理论(数学系概率论水平)《概率论》(三册)复旦Davidson,Stochastic Limit TheoryDurrett,The Essential of Probability,概率论第3版(英文版)W. Feller,An Introduction to Probability Theory and its Applications概率论及其应用(第3版)——图灵数学·统计学丛书《概率论基础》李贤平,高教G. R. Grimmett, D. R. Stirzaker, Probability and Random ProcessesRoss,S. A first couse in probability,中国统计影印版;概率论基础教程(第7版)——图灵数学·统计学丛书(例子多)《概率论》汪仁官,北大王寿仁,概率论基础和随机过程,科学社《概率论》杨振明,南开,科学社○基于测度论的概率论测度论与概率论基础,程式宏,北大D. L. Cohn, Measure TheoryDudley,Real Analysis and ProbabilityDurrett,Probability:Theory and ExamplesJacod,Protter,Probability Essentials Resnick,A Probability PathShirayev,Probability严加安,测度论讲义,科学社钟开莱,A Course in Probability Theory○随机过程微积分Introduction of diffusion processes (期权定价)K. L. Chung, Elementary Probability Theory with Stochastic ProcessesCox,Miller,The Theory of StochasticR. Durrett, Stochastic calculus黄志远,随机分析入门黄志远《随机分析学基础》科学社姜礼尚,期权定价的数学模型和方法,高教社《随机过程导论》KaoKarlin,Taylor,A First Course in Stochastic Prosses(适合硕士生)Karlin,Taylor,A Second Course in Stochastic Prosses(适合硕士生)随机过程,劳斯,中国统计J. R. Norris,Markov Chains(需要一定基础)Bernt Oksendal, Stochastic differential equations(绝佳随机微分方程入门书,专注于布朗运动,比Karatsas和Shreve的书简短好读,最好有概率论基础,看完该书能看懂金融学术文献,金融部分没有Shreve的好)Protter,Stochastic Integration and Differential Equations (文笔优美)D. Revuz, M. Yor, Continuous martingales and Brownian motion(连续鞅)Ross,Introduction to probability model(适合入门)Steel,Stochastic Calculus and Financial Application(与Oksendal的水平相当,侧重金融,叙述有趣味而削弱了学术性,随机微分、鞅)《随机过程通论》王梓坤,北师大○概率论、随机微积分应用(连续时间金融)Arnold,Stochastic Differential Equations《概率论及其在投资、保险、工程中的应用》BeanDamien Lamberton,Bernard Lapeyre. Introduction to stochastic calculus applied t o finance.David Freedman.Browian motion and diffusion.Dykin E. B. Markov Processes.Gihman I.I., Skorohod A. V.The theory of Stochastic processes 基赫曼,随机过程论,科学Lipster R. ,Shiryaev A.N. Statistics of random processes.Malliaris,Brock,Stochastic Methods in Economics and FinanceMerton,Continuous-time FinanceSalih N. Neftci,Introduction to the Mathematics of Financial DerivativesSteven E. Shreve ,Stochastic Calculus for Finance I: The Binomial Asset Pric ing Model;II: Continuous-Time Models(最佳的随机微积分金融(定价理论)入门书,易读的金融工程书,没有测度论基础最初几章会难些,离散时间模型,比Naftci的清晰,S hreve的网上教程也很优秀)Sheryayev A. N. Ottimal stopping rules.Wilmott p., J.Dewynne,S. Howison. Option Pricing: Mathematical Models and Compu tations.Stokey,Lucas,Recursive Methods in Economic Dynamics Wentzell A. D. A Course in the Theory of Stochastic Processes.Ziemba,Vickson,Stochastic Optimization Models in Finance○概率论、随机微积分应用(高级)Nielsen,Pricing and Hedging of Derivative SecuritiesRoss,《数理金融初步》An Introduction to Mathematical Finance:Options and othe r TopicsShimko,Finance in Continuous Time:A Primer○概率论、鞅论P. Billingsley,Probability and MeasureK. L. Chung & R. J. Williams,Introduction to Stochastic IntegrationDoob,Stochastic Processes严加安,随机分析选讲,科学○概率论、鞅论Stochastic processes and derivative products (高级)J. Cox et M. Rubinstein : Options MarketIoannis Karatzas and Steven E. Shreve,Brownian Motion and Stochastic Calculu s(难读的重要的高级随机过程教材,若没有相当数学功底,还是先读Oksendal的吧,结合Rogers & Williams的书读会好些,期权定价,鞅)M. Musiela - M. Rutkowski : (1998) Martingales Methods in Financial Modelling ?Rogers & Williams,Diffusions, Markov Processes, and Martingales: Volume 1, F oundations;Volume 2, Ito Calculus (深入浅出,要会实复分析、马尔可夫链、拉普拉斯转换,特别要读第1卷)David Williams,Probability with Martingales(易读,测度论的鞅论方法入门书,概率论高级教材)○鞅论、随机过程应用Duffie,Rahi,Financial Market Innovation and Security Design:An Introduction,Journal of Economic Theory Kallianpur,Karandikar,Introduction to Option Pricing TheoryDothan,Prices in Financial Markets (离散时间模型)Hunt,Kennedy,Financial Derivatives in Theory and Practice何声武,汪家冈,严加安,半鞅与随机分析,科学社Ingersoll,Theory of Financial Decision MakingElliott Kopp,Mathematics of Financial Markets(连续时间)Marek Musiela,Rutkowski,Martingale Methods in Financial Modeling(资产定价的鞅论方法最佳入门书,读完Hull书后的首选,先读Rogers & Williams、Karatzas and Sh reve以及Bjork打好基础)○弱收敛与随机过程收敛Billingsley,Convergence of Probability MeasureDavidson,Stochastic Limit TheoremEthier,Kurtz,Markov Process:Characterization and Convergence Hall,Marting ale Limit TheoremsJocod,Shereve,Limited Theorems for Stochastic Process Van der Vart,Weller,Weak Convergence and Empirical Process◎运筹学●最优化、博弈论、数学规划○随机控制、最优控制(资产组合构建)Borkar,Optimal control of diffusion processesBensoussan,Lions,Controle Impulsionnel et Inequations Variationnelles Chiang,Elements of Dynamic Optimization Dixit,Pindyck,Investment under UncertaintyFleming,Rishel,Deterministic and Stochastic Optimal ControlHarrison,Brownian Motion and Stochastic Flow SystemsKamien,Schwartz,Dynamic OptimizationKrylov,Controlled diffusion processes○控制论(最优化问题)●数理统计(资产组合决策、风险管理)○基础数理统计(非基于测度论)R. L. Berger, Cassell, Statistical InferenceBickel,Dokosum,Mathematical Stasistics:Basic Ideas andSelected TopicsBirrens,Introdution to the Mathematical and Statistical Foundation of Econom etrics数理统计学讲义,陈家鼎,高教Gallant,An Introduction to Econometric TheoryR. Larsen, M. Mars, An Introduction to Mathematical Statistics《概率论及数理统计》李贤平,复旦社Papoulis,Probability,random vaiables,and stochastic processStone,《概率统计》《概率论及数理统计》中山大学统计系,高教社○基于测度论的数理统计(计量理论研究)Berger,Statistical Decision Theory and Bayesian Analysis陈希儒,高等数理统计Shao Jun,Mathematical StatisticsLehmann,Casella,Theory of Piont EstimationLehmann,Romano,Testing Statistical Hypotheses《数理统计与数据分析》Rice○渐近统计Van der Vart,Asymptotic Statistics○现代统计理论、参数估计方法、非参数统计方法参数计量经济学、半参数计量经济学、自助法计量经济学、经验似然经济学、金融学博士书目(C:计量经济学、数理金融)统计学基础部分1、《统计学》《探索性数据分析》 David Freedman等,中国统计(统计思想讲得好)2、Mind on statistics 机械工业(只需高中数学水平)3、Mathematical Statistics and Data Analysis 机械工业(这本书理念很好,讲了很多新东西)4、Business Statistics a decision making approach 中国统计(实用)5、Understanding Statistics in the behavioral science 中国统计回归部分1、《应用线性回归》中国统计(蓝皮书系列,有一定的深度,非常精彩)2、Regression Analysis by example,(吸引人,推导少)3、《Logistics回归模型——方法与应用》王济川郭志刚高教(不多的国内经典统计教材)多元1、《应用多元分析》王学民上海财大(国内很好的多元统计教材)2、Analyzing Multivariate Data,Lattin等机械工业(直观,对数学要求不高)3、Applied Multivariate Statistical Analysis,Johnson & Wichem,中国统计(评价很高)《应用回归分析和其他多元方法》Kleinbaum《多元数据分析》Lattin时间序列1、《商务和经济预测中的时间序列模型》弗朗西斯著(侧重应用,经典)2、Forecasting and Time Series an applied approach,Bowerman & Connell(主讲Box-Jenkins(ARIMA)方法,附上了SAS和Minitab程序)3、《时间序列分析:预测与控制》 Box,Jenkins 中国统计《预测与时间序列》Bowerman抽样1、《抽样技术》科克伦著(该领域权威,经典的书。

Noncompact Heisenberg spin magnets from high-energy QCD II. Quantization conditions and ene

Noncompact Heisenberg spin magnets from high-energy QCD II. Quantization conditions and ene

A(s, t) ∼ −is
(iα ¯ s )N
N =2
¯ s EN /4 s−α ξa,N (t)ξb,N (t) , (α ¯ s σN ln s)1/2
(1.1)
where α ¯ s = αs Nc /π is the QCD coupling constant and the sum goes over an arbitrary number of reggeized gluons exchanged in the t−channel, N = 2, 3, .... In the GLLA approximation, the reggeized gluons interact with each other elastically and form colour-singlet compound states [7]. These states can be defined as solutions to the Bartels-Kwiecinski-Praszalowicz (BKP) equation [3, 8] HN Ψ(z1 , z2 , ..., zN ) = EN Ψ(z1 , z2 , ..., zN ) . (1.2) The effective QCD Hamiltonian HN acts on the colour SU (Nc ) charges of N reggeized gluons and their two-dimensional transverse coordinates, zk (k = 1, ..., N ), which belong to the hyperplane orthogonal to the momenta of two scattered particles. The contribution to the scattering amplitude (1.1) of the compound states built from N reggeized gluons, Ψ(z1 , z2 , ..., zN ), has the ¯ s EN /4 standard Regge form ∼ s−α . It is dominated at large s by the contribution of the ground state EN for Eq. (1.2). Obviously, it increases (or decreases) with s if the energy of the ground state is negative (or positive). As we will show below, the spectrum of (1.2) is gapless (see Eq. (5.6) below) and, as a consequence, one has to retain in (1.1) the contribution of the excited states next to the ground state. This amounts to appearance of the additional factor (α ¯s σN ln s)−1/2 in the r.h.s. of (1.1). The residue factors ξa(b),N (t) measure the overlap of Ψ(z1 , z2 , ..., zN ) with the wave functions of the scattered particles and they depend, in general, on the momentum 2 transferred, t, and the colour factor, 1/Nc . Calculation of the spectrum of the compound states (1.2) for arbitrary number of reggeized gluons N and eventual resummation of their contribution to the scattering amplitude (1.1) is a longstanding problem in high-energy QCD [9, 10, 11, 12]. At N = 2, the solution to (1.2) has been found a long time ago – the well-known Balitsky-Fadin-Kuraev-Lipatov (BFKL) Pomeron [7]. At N = 3 the solution to (1.2) – the Odderon state in QCD [13], was formulated only a few years ago by Janik and Wosiek [14] by making use of the remarkable integrability properties of the effective QCD Hamiltonian [1, 2]. Their solution has been later verified in Refs. [15, 16]. The methods employed at N = 3 in [14] can not be generalized, however, to higher (N ≥ 4) reggeized gluon compound states and very little is known about the solutions to (1.2) for N ≥ 4. Recently, a significant progress has been made in solving the Schr¨ odinger equation (1.2) for higher N in the multi-colour limit, Nc → ∞ and α ¯ s = fixed [5, 16]. The first results of the calculation of the ground state energy EN for higher reggeized gluon compound states in multi-colour QCD 2

资本资产定价模型实证检验-R

资本资产定价模型实证检验-R

一些早期的实证检验
• 大多数早期检验都使用一个时间序列(首次)回归估计贝塔,然后使 用一个横截面(二次)回归检验CAPM模型中推出的假设。 • 林特纳做出的CAPM的实证研究: 首先对样本中的301只普通股票分别计算贝塔,方法是:用每只股票 的年收益率对样本中所有股票的平均收益率进行回归,使用数据时间 段是1954-1963年,接着进行二次截面回归。 结果显示与CAPM模型相违背。
ft p 存在线性关系。 (3) pt (4)β p 的回归系数 γ 1应等于 Rmf R ft
R R 与β
γ 1 >0,因为市场投资组合也是风险资产,总体上其报酬应大于无风险 资产报酬。
资本资产定价模型的检验
• 在这个模型背后的三个建设: (1)市场模型在每期都成立 (2)CAPM在每期都成立 (3)贝塔随时间变化是稳定的。 用事后数据来检验这一模型,实际上就是同时检验这三个假设。
资本资产定价模型Ⅱ
• 资本资产定价模型(CAPM)是指一种资产预期报酬是 其与市场投资组合报酬间协方差的线性函数。
E Ri R f β E Rm R f

f • 为无风险的报酬, Rm 为市场投资组合的报酬。 • 套利定价模型(Ross,1976),指任何风险性资产的报酬 是影响资产报酬各种共同因素的线性组合,套利定价模 型(APT)较CAPM更一般,因它包括更多的因素。
R
资本资产定价模型的检验
• 对CAPM进行检验时,有学者疑问模型能否很好地拟合数 据。 • 由于 E R 是预期报酬,而预期是不能观测的,因此,需 将预期的形式转换成可以用观测数据检验的形式,这可通 过假设总体上一种资产的报酬等于预期报酬 E R 。 • 这就提出一个问题,如何证明可以用实现值来检验期望模 型? • 有两类辩护理由:简单辩护认为期望值平均来说是正确的 实际值可以作为期望值的替代。 复杂辩护假设证券收益与一个市场组合的收益线性相关。 这个模型称为市场模型。

The effect on pressure drop across horizontal pipe and control valve for air palm oil two-phase flow

The effect on pressure drop across horizontal pipe and control valve for air palm oil two-phase flow

The effect on pressure drop across horizontal pipe andcontrol valve for air/palm oil two-phase flowR.Rani Hemamalini *,P.Partheeban,J.Sarat Chandrababu,S.Sundaram*Department of Chemical Engineering,Regional Engineering College,Tiruchirappalli 620015,IndiaReceived 5February 2003;received in revised form 28October 2004AbstractStudies on operating characteristics of control valves with two-phase flow have not been given much attention in the literature despite its industrial importance during design and selection as well as during plant operation.However,lit-erature shows considerable work with two-phase flow through pipes and different geometrical shapes of flow ducts.The present work attempts to study experimentally the effect of two-phase flow on pressure drop across the control valve for different volume fractions of the fluids.A typical fluid system of palm oil (liquid phase)and air (gas phase)has been used for the studies.The pressure drop in a horizontal straight pipe upstream of the valve is also considered to test the correlations from the literature on two-phase pressure drop.The same is extended to represent the pressure drop across the valve.The operating characteristics are obtained from the pressure drop relationship and valve opening.It is found that Lockhart–Martini (L–M)parameter and the quality (fraction of liquid)are found to correlate well with the two-phase multiplier defined based on pressure drop with gas phase.The installed characteristics of the valve for varying pressure drop and quality is presented.Ó2005Elsevier Ltd.All rights reserved.1.IntroductionSimultaneous flow of two or more immiscible phases is termed as multiphase flow.The common class of mul-tiphase flow is he two-phase flow such as gas–liquid,gas–solid,liquid–liquid and solid–liquid flows.Gas–liquid flow is complex because of the existence of deform-able interfaces and the fact that one of the phases is compressible.A wide range of interfacial configurations is possible in such two-phase flow.Systems involving multiphase fluid flow occur widely in nature and in industry.Two-phase flows occur in many engineering applications,particularly in equip-ment related to oil,chemical processes and power gener-ation industries.This type of flow is encountered in an increasing number of important situations and a clear understanding of the rates of transfer of momentum,heat and mass will be required for logical and careful de-sign of operation of a very wide variety of engineering equipment and processes.Thus an understanding of multiphase phenomena is essential and hence extensive research is being pursued in this area.However,one important area,which has received little attention,is the study of pressure losses in control valves due to two-phase flow.The studies in two-phase flow through pipes have been conducted for the past 60years.The first detailed0017-9310/$-see front matter Ó2005Elsevier Ltd.All rights reserved.doi:10.1016/j.ijheatmasstransfer.2004.11.012*Corresponding authors.Address:Department of Electron-ics and Instrumentation Engineering,Jaya Engineering College,Thiruninravur,Tamil Nadu 602024,India.Tel.:+9104426390041.E-mail address:ranihema@ (R.R.Hemamalini).International Journal of Heat and Mass Transfer 48(2005)2911–2921/locate/ijhmtstudy on two-phaseflow was carried out by Lockhart and Martinelli in1949.Design of pipeline for the simul-taneousflow of oil and gas was discussed by Baker[3] and Hoogendoorn[12]has studied the gas–liquidflow in horizontal pipes.The two-phase slugflow in horizon-tal and inclined tubes was discussed by Vermeulen and Ryan[20].Beretta et al.[4]has studied pressure drop for horizontal oil–waterflow in small diameter tubes. In another study Awwad et al.[1]analyzedflow patterns and pressure drop with air–water in horizontal helicoi-dal pipes.A comparison of existing theories on two-phaseflow was analyzed by Kordyban[14].Pressure gradient due to friction for the two-phase mixtures in smooth tubes and channels was studied by Chisolm [5].They are the successors in thefield of two-phaseflow and the studies were concentrated on developing the flow pattern model for horizontal,inclined and vertical flow in pipes.Oliemans and Ooms[16]gave a semi-empirical model for the Core-annularflow of oil and water through a pipeline.Core-annularflow of two immisciblefluids through pipeline was studied by Bai et al.[2]and they used an oil with viscosity of600times the water viscosity and found that the reduction of drag force on the order one thousand.Hewitt[11]studied pressure gradients in liquid–liquidflows and displayed significant peaks when plotted as a function of water fraction for a given veloc-ity;the response depends on the mixing processes be-tween the phases.Sotgia et al.[19]experimented in oil–water viscosity ratios from about560to about 1300and reported that the thorough mixing of the two liquids,which served to eliminate entrance effects on the test section,was attained in a calming section (L/D=200)before entering the test section.The struc-ture of two-phaseflow in ducts with sudden contractions and its effects on the pressure drop was studied by Gug-liemini et al.[10].Literature scanned has not reported two-phaseflow through control valves.Hence,this study will be relevant for guiding the selection,design and setting the para-meters from operating characteristics.In the present study,the pressure drop characteristics have been experimentally investigated for air–palm oil through a control valve.The data are compared with earlier research carried out by Dowlati et al.[6],Awwad et al.[1],Salcudean et al.[17],Fairhurs[7]and Simpson et al.[18].2.The experimental setup and procedureA schematic diagram of the experimental set up is shown in Fig.1.The test section is a GI40schedule pipe of1m length and23.5mm inside diameter.The up-stream section of this pipe,of length0.5m,ensures fully developed conditions.The control valve isfitted at the down stream end of the test pipe.Thefluids are dis-charged to a tank where discharge pressure is constant. The liquid is metered through Krone Marshall magnetic flow meter.Purified dry air from an Ingersoll Rand com-pressor with a pressure regulator(0–2.5kg/cm2)pressure regulator was metered through a non-return valve using a Placka rotameter.The pressure drop across the valve and the pipe was measured with a differential pressure transducer.An electro pneumatic converter is used to actuate the pressure valve.The density and kinematic viscosity of palm oil used in the experiments are 888.25kg/m3and 4.44·10À2N s/m2.During experi-NomenclatureC C factor in Eq.(10)D diameter of pipe,mD e equivalent diameter of valve opening,m D o Orifice diameter,mf friction factorl length of pipe,mL valve stem position(or lift)m exponentQ a airflow rate,m3/sQ l liquidflow rate,m3/sNRe Reynolds numberV velocity offlow,m/sX qualityGreek symbolsq density of liquid/gas,kg/m3l dynamic viscosity liquid/gas,N s/m2D P pressure drop,Pav2ttLockhart–Martinelli(L–M)parameter/l two-phase multiplier based on pure liquids Eqs.(7a)and(12)/a two-phase multiplier based on pure gases Eq.(7b)Subscriptsa airl liquid,palm oilp pipe sectionv valve sectionTPf two-phaseflow2912R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–2921mentation the temperature of two-phaseflow varies at ±3°C.The experiments were carried out for four different valve openings and different volume fractions.Air and palm oilflow rates varied from50to150l/h and25to 100l/h respectively(non-uniformflow)with varying valve openings from25%to100%.The system was initially tested with palm oil(single-phaseflow)for different control valve openings.In sub-sequent experiments,the volume fraction of palm oil was varied by dispersingfiltered dry air into palm oil into the calming section.The airflow rates are main-tained at constant pressure and measured using cali-brated rotameter.3.Results and discussion3.1.Single phaseflow(liquidflow)The friction factor for both pipe section and valve section is estimated from the measured pressure drop and correspondingflow velocity,using the relationships (Eqs.(1)–(3)).The subscriptsÔPÕandÔVÕrefer to pipe sec-tion and valve section respectively.NRe¼q l V l Dl lð1Þf P¼D P P2q l V2lDlð2Þf V¼D P V2q l VlD eðÞð3Þwhere,the equivalent diameter,D e for valve is deter-mined based on the geometry of the control valve.The turbulentflow is observed in the air phase and laminar flow is observed in the oil phase,as the viscosity of oil is less.3.1.1.Definition of equivalent diameter for valve openingThe valve assembly was dismantled and the orifice opening and trim configuration was measured for differ-ent lift positions.The maximum valve opening was 0.125m.Fig.2shows the details of stem contour and opening.For the valve section based on valve opening,R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–29212913an equivalent diameter was determined defined by Eq.(4).Equivalent diameter d e=4·hydraulic radius whereD e¼4p40.01252ÀD2o ÀÁp0.0125þD oðÞ!D e¼0.0125ÀD oð4ÞThe value of equivalent diameter for various lifts is given in Table1.The friction factor f–NRe relation for the pipe section is shown in Fig.3for pure liquid.From this graph a gen-eral relation of the form.f¼aNRe mð5Þwas established for pure liquid and the constantsÔaÕand ÔmÕare determined by regression analysis.The estimated values ofÔaÕandÔmÕare0.304andÀ0.077respectively. For the valve section,the data could not befitted with a single set of constants,as it could be possible withflow through the pipe.The constants are different for differ-ent valve openings.Fig.4shows the graph for friction factor vs Reynolds number for palm oil in the valve sec-tion for different valve openings.The values of constants of Eq.(5)ÔaÕandÔmÕfor pure liquid and valve section are given in Table2.3.2.Two-phaseflow(pressure drop vs Q l/Q a)The measured pressure drop across pipe section and across control valve for different liquid to air ratios (Q l/Q a)are plotted and shown in Figs.5and6respec-tively.It is observed that maximum pressure drop for the valve side(23.57kN/m2)is nearly68times greater than that for pipe side(0.346kN/m2).3.3.Pressure drop vs quality XThe term quality defines the fraction of dispersed phase in two-phaseflow and can estimated as given by Eq.(6).Table1Equivalent diameters for valve openingPercentage of lift Orifice diameter(D o)(m)Equivalent diameter(D e)(m)10000.0125750.00940.0031500.00630.0063250.00310.00942914R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–2921X¼11þq lq aQ lQ að6ÞThe experimentally measured pressure drop variation with quality,X is plotted and shown in Figs.7and8for the pipe section and for the control valve section respec-tively.The pressure drop varies linearly withflow rate linearly for both valve and pipe section as observed from the Figs.7and8.For pipe section,D P is observed to decrease with in-crease in quality.However,as valve opening increases, the pressure drop is found to increase.This is due to the increase inflow with valve opening.For valve section,the pressure drop across the valve is observed to decreases linearly with quality and valve opening.The rate of change is more pronounced at lower valve opening than at valve opening more than 50percent.Relatively,the variation is insignificant at valve openings75%and100%.The maximum pressure drop across pipe is0.363kN/ m2at quality of0.064whereas the pressure drop acrossTable2Constants for Eq.(5)for various valve opening for valve sectionConstants Valve opening(%)255075100a1288.633.7610.0742.99mÀ0.98À0.25À0.59À1.21R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–29212915the valve is23.56kN/m2at the same quality.The varia-tion of pressure drop with quality,X is in agreement with literature[17].3.4.Two-phase multiplier vs quality X[7]Another relationship useful in two-phase studies is the two-phase multiplier for/a¼ðd p=d zÞTPfað7aÞ/l¼ðd p=d zÞTPfðd p=d zÞlð7bÞcorrelating the pressure drop and quality.The two-phase multiplier is defined as the ratio of pressuredrop with two-phaseflow and pressure drop with sin-gle phase either gas or liquid and is given by theEq.(7).The pressure drop with single phase in the denomina-tor of Eq.(7)can be estimated using friction factor,fand velocity,V a or V l as given by Eq.(8).d pl¼2f l q l V2l=Dð8aÞd pd za¼2f a q a V2a=Dð8bÞ2916R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–2921Here/l is two-phase multiplier based on liquid,which can be palm oil(in the present case)or can be any other liquid under study.In this study/l is related to the qual-ity for the two-phase system in pipe section and valve sec-tion.The/l observed experimentally are plotted against quality and shown in Figs.9and10.The two-phase mul-tiplier increases with quality for both pipe section and valve section and in agreement with literature[17].3.5.Relation between two phase multiplier and Lockhart–Martinelli(L–M)parameterA method of predicting pressure drop in two-phase flow from the studies on one of the single phase has been suggested[15]in terms of L–M parameter v tt defined by Eqs.(9)and(10).v2tt¼ð1ÀXÞ!2Àm qalllamð9Þwhere‘‘m’’is the value obtained for single phaseflow from the relation f=aNRe m.The two-phase multiplier /l is related to L–M parameters by the Eq.(10):/2l¼D P TPfD P l¼1þCvttþ1v2ttð10ÞGrant and coworkers[8,9,13]have reported a value of8forÔCÕfor v tt<0.2for their study with good results. For v tt>0.2,the D P TPf predicted from single-phase flow,the results with C=8are not satisfactory.Figs. 11and12presents/l vs v tt graphically for both pipe section and valve section respectively.The two-phase multiplier is observed to decrease with increase in Lockart–Maritinelli parameter(v tt).R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–29212917The relationship given by Eq.(10)isfitted by regres-sion analysis and the value ofÔCÕis estimated and given in Table3for pipe section and valve section.The‘‘C’’values are different for both pipe section and valve sec-tion.The correlation coefficient for the parameterÔCÕis found to be0.89.Thus the two-phase multiplier can be used to predict the pressure drop across control valve section for differ-ent fractions of gas–liquid system.3.6.Valve characteristicsThe installed characteristics of the valve are the plots for fraction of maximumflow rate vs the fraction of valve opening at different pressure drop across the con-trol valve.These plots are useful to determine the suit-ability of the valve under study and generally used in the selection of the type of valve during the process design.Using the correlations,the installed characteristics of the control valve with two-phaseflow are estimated and are shown in Figs.13and14.Fig.13shows the charac-teristics at constant quality of0.5.Thisfigure is helpful in identifying the operability or gain of the valve at given conditions.Similarly,Fig.14shows the characteristics at con-stant pressure drop of600Pa for different quality of the two-phase system.This plot is helpful to identify the operating region of quality range for which the valve is suitable under given conditions.2918R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–2921From both Figs.13and14,it can be concluded that maximum permissible D P for the valve for full range applicability is600Pa and for a quality of0.2.4.ConclusionsTwo-phaseflow through control valve in series with pipe has been studied for different quality of two-phase system.Pressure drop has been related to quality.How-ever,the two-phase multiplier and L–M parameter cor-relation is found to be in good agreement with the results.The installed characteristics of the valve are rep-resented for different quality and pressure drop.The study has been specific to the experimental set-up used in this work.The correlations especially between two-phase multiplier based on liquid phase and L–M para-meter will be useful in design of process piping and con-trol valves for two-phaseflow.The study enables one to predict the required pressure drop information from the data with single-phaseflow through pipe/valve.The valve used being an equal percentage valve,the data can be extended to other types of valves such as linear and quick opening by appropriate definition of equiva-lent diameter for valve opening.The valve characteris-tics in terms of pressure drop and quality clearlyTable3Values of C used in Eq.(10)for pipe section and valve sectionValve opening(%)Pipe section Valve section25120150120175751100751R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–29212919indicates the maximum value,which the specific control valve can accommodate for proper utilization of the valve.These correlations can be tried for other types and size of valves also and their useful range can be established.In this study,a straight pipe was used in the upstream of the control valve.Further work in this area using coiled pipes before and/or after the control valve is being carried out to assess their effect on the valve characteristics.References[1]A.Awwad,R.C.Xin,Z.F.Dong,M.A.Ebadian,H.M.Soliman,Flow patterns and pressure drop in air/water two-phaseflow in horizontal helicoidal pipes,Trans.ASME,J.Fluids Eng.117(1995)720–726.[2]R.Bai,K.Chen, D.Joseph,‘‘Lubricated pipelining:stability of core-annularflow.Part5:Experiments and comparison with theory,J.Fluid Mech.240(1992)97–142.[3]O.Baker,Design of pipe line for the simultaneousflow ofoil and gas,Oil Gas J.53(1954)184–185.[4]A.Beretta,P.Ferrari,L.Galbaatti,P.A.Andreni,Hor-izontal oil–waterflow in small diameter tubes:pressure drop,mun.Heat Mass Transfer24(1997)231–239.[5]D.Chisholm,Pressure gradient due to friction duringtheflow of evaporating two-phase mixtures in smooth tubes and channels,J.Heat Mass Transfer16(1972)347–356.[6]B.Dowlati,A.M.C.Chan,M.Kawaji,Hydrodynamics oftwo-phaseflow across horizontal in-line and staggered rod bundles,Trans.ASME,J.Fluids Eng.114(1992)450–456.[7]C.P.Fairhurst,Component pressure loss during two-phaseflow,in:International Conference on the Physical Model-ling of Multi-phase Flow,England,1983,pp.1–24.[8]I.D.R.Grant,I.Murray,Pressure drop on the shell-side ofa segmentally baffled shell and tube heat exchanger withvertical two-phaseflow,NEL Report No.500,EastKilbride,Glasgow,National Engineering Laboratory, 1972.[9]I.D.R.Grant,I.C.Finlay,D.Harries,Flow and pressuredrop during vertically up-ward two-phaseflow past a tube bundle with and without bypass leakage,I.Chem.E./I.Mech. E.Joint Symp.on Multiphase Flow Systems, University of Strathclyde Glasgow,2–4April1974,2,Paper17.London:The Institution of Chemical Engineers,1974.[10]G.Guglielmini,A.Muzzio,G.Sotgia,The structure oftwo-phaseflow in ducts with sudden contractions and its effects on the pressure drop,Invited Lecture to Experi-mental Heat Transfer,Fluid Mechanics and Thermody-namics1997,ETS ed.,1997.[11]G.F.Hewitt,From gas–liquid to liquid–liquidflow:adifficult journey?in:Proceedings of International Sympo-sium on Liquid–Liquid Two-Phase Flow and Transport Phenomena,Antalya,Turkey,1997,pp.3–19.[12]G.L.Hoogendoorn,Gas–liquidflow in horizontal pipes,Chem.Eng.Sci.9(1959)205–217.[13]K.Ishihara,J.W.Palen,J.Taborek,Critical review ofcorrelations for predicting two-phase pressure drop across tube banks,Heat Transfer Eng.1(3)(1979)1–8.[14]E.Kordyban,Horizontal slugflow:a comparison ofexisting theories,Trans.ASME,J.Fluids Eng.112(1990) 75–83.[15]R.J.Lockhart,R.C.Martinelli,Proposed correlation ofdata for isothermal two-phase,two-componentflow in pipes,Chem.Eng.Prog.45(1)(1949)39–48.[16]R.V.A.Oliemans,G.Ooms,Core-annularflow of oil andwater through a pipeline,Multiphase Sci.Technol.2(1986) 427–477.[17]M.Salcudean,J.H.Chun,D.C.Groeneveld,Effect offlowobstructions on theflow pattern transitions in horizontal two-phaseflow,Int.J.Multiphase Flow9(1)(1983)87–90.[18]H.C.Simpson,D.H.Rooney,E.Grattan,Two phaseflowthrough gate valve and orifice plates,in:Proceedings of International Conference on the Physical Modelling of Multi-Phase Flow,1983,pp.25–40.[19]G.Sotgia,G.Sparta,E.Vensola,P.Tartarini,Experi-mental results on pressure reductions andflow regime transitions in oil–water mixtures,in:Proceedings of the5th2920R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–2921World Conference on Experimental Heat Transfer,Fluid Mechanics and Thermodynamics,Thessaloniki,Greece, 2001,pp.1763–1770.[20]L.R.Vermeulen,J.T.Ryan,Two-phase slugflow inhorizontal and inclined tubes,Canad.J.Chem.Eng.49 (1971)195–201.R.R.Hemamalini et al./International Journal of Heat and Mass Transfer48(2005)2911–29212921。

Coherent measures of Risk一致风险测度完整版资料

Coherent measures of Risk一致风险测度完整版资料

What
How can I
hypotheses
carry out the
shouldThI e resultcsowmipllubteation
make ?
(in time) ?
however absolutely general
Part 1: Defining a Risk Measure
The qualitative concept of “risk” and “risk premium”
No practitioner in 2003 would ever give up to these advantages anymore
The deadly sin of VaR
Unfortunately however VaR
1. Violates the subadditivity axiom and so is not coherent
We still wonder what concept of risk Value at Risk has in mind !
A natural question
Is it possible to find coherent measures which are as versatile and flexible as VaR ?
(Monotonicity) if then ()()
(Positive Homogeneity) if (Translational Invariance)
0 then ()()
( a)( )a
(Subadditivity)
( )( )( )
Value atSRtisrka(nVagRe): ahoswititmwoaryksseem, this is the question To compute VmaRo, wsetneferdetoqsupeecinfytly asked to risk managers

Higher-Order Quantization on a Lie Group

Higher-Order Quantization on a Lie Group
2
1
1

1
Introduction
This paper is devoted mainly to the concept of higher-order polarization on a Lie group as a powerful tool in search of irreducibility of the representations of the group, and/or the irreducibility of quantizations in a group-theoretic quantization approach, in those anomalous cases where standard (geometric) methods do not succeed. The co-adjoint orbit method has proven to fail in quantizing, for instance, non-K¨ ahler orbits of certain Lie groups or orbits without any invariant (first-order or standard) polarization. Also, the configuration-space image of quantization of rather elementary physical systems cannot be obtained in a natural way via the Geometric Quantization technique, but it is obtained easily with the aid of higher-order polarizations. Higher-order polarizations are naturally defined on the (left) enveloping algebra of a Lie group as higher-order differential operators acting on complex functions on the group manifold. Since higher-order polarizations do not coincide, in general, with the enveloping algebra of any first-order polarization subalgebra, the space of wave functions is not necessarily associated with any particular classical configuration space, thus leading to a breakdown of the notion of classical limit for those systems which are anomalous (see Sec. 4). It should be remarked that higher-order polarizations excludes nondifferential operators, such as discrete transformations (like parity o charge conjugation), so that a higher-order quantization on a group does not guarantee a full reduction of the representation in the general case; it can lead, for instance, to the direct sum of irreducible representations which are only distinguished by the eigenvalues of a discrete operator commuting with the representation (see the example of Sec. 4). A generalization of the concept of polarization including this sort of non-local operators is beyond the scope of the present paper, but will be studied elsewhere. The first general attempt to look at the problem of quantization in terms of groups of transformations was done by L. van Hove [1], who tried to associate a group of unitary transformations with the group of canonical transformations on the phase space M = R2n . The main point in van Hove’s approach was to consider a Hilbert space of complex valued functions on the phase space M , and to implement on these wave functions the action of canonical transformations as unitary transformations according to 1) φ → Uφ 2 (1)

Primes, Pi, and Irrationality Measure

Primes, Pi, and Irrationality Measure

ζ ( s) := ∑
1 s = ne 1 − p

( ℜ( s) > 1 )
together with Euler's formula ζ (2) = π 2 6 [BB, p. 383] and the irrationality of π 2 [BB, p. 353], [HW, p. 47]. If there were only finitely many primes, then ζ (2) would be rational, a contradiction. A quantified version of Euclid's theorem is Bertrand's postulate [HW, p. 343]: pn +1 < 2 pn for all n ≥ 1, where pn denotes the nth prime. In this note we quantify the folklore proof, using the notion of irrationality measure [BB, Section 11.3]. Definition 1. Let ξ be an irrational number. A positive real number µ is an irrationality measure for ξ if
Primes, π , and Irrationality Measure
Jonathan Sondow
ABSTRACT. A folklore proof of Euclid's theorem on the infinitude of primes uses the Euler product and the irrationality of ζ (2) = π 2 6 . A quantified form of Euclid's Theorem is Bertrand's postulate pn +1 < 2 pn . By quantifying the folklore proof using an irrationality measure for 6 π 2 , we give a proof (communicated to Paulo Ribenboim in 2005) of a much weaker upper bound on pn +1. 1. INTRODUCTION. A folklore proof of Euclid's theorem on the infinitude of primes uses the Euler product for the zeta function [HW, p. 246]

MnMg共掺杂对锂离子电池正极材料的影响

MnMg共掺杂对锂离子电池正极材料的影响

Rechargeable Li-ion cells are key components of the portable, entertainment, computing, and telecommunication equipment.1,2 The safety of lithium-ion batteries is central to their continued success in the market place. LiCoO2-based Li-ion batteries are presently dominant in cellphone and computer applications, where the thermal instability of delithiated LiCoO2 in electrolyte can be managed because of the small cells. Larger cells often incorporate less expensive and less reactive materials such as Li͓ Ni1/3Mn1/3Co1/3͔O2, LiFePO4, or LiMn2O4 positive electrode materials. However, these have equal or lower volumetric energy density than LiCoO2. Materials which have higher volumetric energy density and improved thermal stability compared with these materials are desired. The impact of Al substitutions on the thermal stability of layered lithium transition-metal oxides is well known. The thermal stability of Al-substituted Li͓ Co1−zAlz͔O2,3-5 Li͓ Ni1−zAlz͔O2,6 Li͓ Ni1/3Mn1/3Co1/3−zAlz͔O2,7,8 and Li͓ Ni0.4Mn0.4Co0.2−zAlz͔O2 9 increases as the Al content increases. However, the specific capacity decreases at a rate of about Ϫ280 mAh/g per z = 1 for all these samples,5 so there is an important trade-off between thermal stability, which impacts Li-ion cell safety, and energy density. Oh et al. reported that the charge–discharge capacity retention of zirconium-doped LiNi0.8Co0.2O2 cathode materials was superior to the pristine cathode, especially when they were tested to a high voltage cutoff.10 According to Bang et al., Zr-doped Li͓ Ni0.45Co0.1Mn0.45−xZrx͔O2 ͑ x = 0.02͒ shows better cycling performance and better thermal stability. According to differential scanning calorimetry results, the major exothermic reaction between the charged electrode material and electrolyte was delayed from 252 to 289°C.11 Jeong et al. found that a Zr-doped LiNi0.5Mn0.5O2 electrode shows about 50 mAh/g higher capacity than that of the pristine LiNi0.5Mn0.5O2 electrode and has better thermal stability.12 Luo and Dahn recently studied the effect of Zr-substituted LiNi1/3Mn1/3Co1/3O2. The results showed that Zr substitution, from a safety standpoint, yield minimal improvements.13 Luo et al. carefully studied the crystal chemistry and electrochemical properties of Li͓ Co1−zMgz͔O2 and Li1−zMgz͓ Co1−zMgz͔O2.14 There have been reports that Mg substitution for Ni, Mn, or Co in Li͓ Ni͑1/3−z͒Mn͑1/3−z͒Co͑1/3−z͒Mgz͔O2, Mg substitution for Ni in Li͓ Ni͑0.6−x͒MgxCo0.25Mn0.15͔O2, and substitutions of Mg and Ti for Ni in Li͓ Ni1−zMgz/2Tiz/2͔O2 15-17 all improve the thermal stability of the charged electrode materials. Adding Mg

Inclusions of second quantization algebras

Inclusions of second quantization algebras
The second named author is supported in part by MURST and EU.
c 0000 (copyright holder)
· · · ⊂ R(M−1 ) ⊂ R(M0 ) ⊂ R(M1 ) ⊂ R(M2 ) . . .
1
2
FRANCA FIGLIOLINI AND DANIELE GUIDO
1. Introduction. In this note we study inclusions of second quantization algebras, namely inclusions R(M0 ) ⊂ R(M1 ) of von Neumann algebras on the Fock space eH (H is a separable complex Hilbert space) generated by the Weyl unitaries with test functions in the closed, real linear subspaces M0 , M1 of H. More precisely we concentrate our attention to the case where both R(M0 ) and R(M1 ) are standard w.r.t. the vacuum vector e0 ∈ eH , since in this case the tower and tunnel associated with the inclusion (and the corresponding relative commutants) can themselves be realized as second quantization algebras on the same space: First we show that the class of irreducible inclusions of standard second quantization algebras is non empty, and that they are depth two inclusions, namely R(M0 )′ ∩ R(M3 ) is a factor. Then we prove that, when M0 ⊂ M1 is a (not necessarily irreducible) inclusion of standard spaces with finite codimension n, R(M ) is isomorphic to the cross product of R(N ) with Rn . On the contrary, when the codimension is infinite, we show that the inclusion may be non regular (see subsection 4.1). Second quantization algebras and their inclusions occur when studying algebras of local observables for the free fields. Inclusions of local observable algebras are in general neither irreducible nor come from a finite codimension inclusion of real vector spaces. In [9] however, local algebras for conformal field theories on the real line are studied, and it is shown that the inclusion of the real vector space corresponding to a bounded interval for the n + p-th derivative of the current algebra into the real vector space for the same interval and the n-th derivative theory has codimension p (and is irreducible when p = 1). We show in Theorem 4.1 that the corresponding inclusion of second quantization algebras is given by a cross product for any n ≥ 0, p > 0. Our analysis was also motivated by results concerning depth two inclusions of von Neumann algebras. It is well known that, analyzing the Jones’ tower associated with the inclusion of a von Neumann algebra N into the cross product M of the same algebra with an outer action of locally compact group, the family of
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1
2
TOMOYUKI ARAKAWA
Hale Waihona Puke Let Oκ be the Bernstein-Gelfand-Gelfand category of g of level κ − h∨ . Let ), V ∈ Oκ , be the cohomology associated to the quantized Drinfeld-Sokolov ∞ • ¯+ , V ⊗Cχ+ ), reduction defined by Feigin and Frenkel ([8]). Thus, H+ (V ) = H 2 +• (Ln − 1 ¯+ = n ¯+ ⊗C[t, t ] ⊂ g and Cχ+ is a certain one-dimensional representawhere Ln i ¯+ . Then, the correspondence V tion of Ln H+ (V ) (i ∈ Z) defines a family of functors from Oκ to the category of Wκ (¯ g)-modules ([8]). However, because the description of this functor is rather complicated in general, Frenkel, Kac and i Wakimoto [11] introduced another functor V H− (V ) (i ∈ Z) from Oκ to the ∞ • ¯− , V ⊗Cχ− ). Here, category of Wκ (¯ g)-modules, defined by H− (V ) = H 2 +• (Ln − 1 ¯− = n ¯− ⊗C[t, t ] ⊂ g and Cχ− is again a certain one-dimensional representation Ln ¯− . It turns out that the corresponding functor of Ln
• H+ (V
V
indeed has nice properties: it is exact, it sends Verma modules to Verma modules, and simple modules to simple modules under certain conditions. We now describe our results more precisely. Let h be the Cartan subalgebra of ¯∗ be the restriction. Let M (Λ) be the Verma module of ¯ ∈h g and let h∗ ∋ Λ → Λ ¯ + be the set of g of highest weight Λ ∈ h∗ , L(Λ) its unique simple quotient. Let ∆ positive roots of ¯ g, identified with the subset of the set ∆re of positive real roots of + g in the standard way. Main Theorem 1 (Theorem 6.5.1 ). Let Λ be non-degenerate (that is, Λ, α ¯∨ ∈ Z 0 ¯ for all α ¯ ∈ ∆+ ) and non-critical (that is, κ = Λ + ρ, K = 0). Then, H− (L(Λ)) ∼ = L(γΛ ¯ ). Note that the condition that Λ is non-degenerate does not imply that the integral Weyl group W Λ of Λ is finite (and indeed it is infinite when Λ is admissible, see 0 [16]). We also show that H− (M (λ)) ∼ = M(γλ ¯ ), where M(γλ ¯ ) is the Verma module of Wκ (¯ g) of highest weight γλ ¯ , see Theorem 6.1.2. Thus, combined with our previous result [1], Main Theorem 1 gives the character of L(γλ ¯ ) with any non-degenerate ¯∈h ¯∗ ) highest weight γλ ¯ , see Theorem 6.6.1. Here, an infinitesimal character γλ ¯ (λ ∨ ¯ ¯ is called non-degenerate if λ, α ¯ ∈ Z for all α ¯ ∈ ∆+ . By applying Main Theorem 1 to non-degenerate admissible weights ([16]), the irreduciblity conjecture of Frenkel, Kac and Wakimoto [11] is proved (for the “−”reduction). Therefore, combined with our previous result [1], the existence of modular invariant representations of W (¯ g) is proved. Now the “+”-reduction is not as simple as the “−”-reduction. However, we have the following theorem. Main Theorem 2 (Theorem 6.7.1). Suppose that Λ ∈ h∗ is non-critical and satisfies the following condition: ¯ + , 1 ≤ n ≤ ht α}. Λ, α∨ ∈ Z for all α ∈ {−α ¯ + nδ ; α ¯∈∆
where A(Wκ (¯ g)) is the Zhu algebra of Wκ (¯ g) ([12]) and Z (¯ g) is the center of the universal enveloping algebra U (¯ g), see Theorem 4.2.3. By (1), the irreducible highest weight representations of Wκ (¯ g) are parameterized by central characters of Z (¯ g). Let γλ g) → C be the central character defined as the evaluation at the Verma ¯ : Z (¯ ¯∈h ¯∗ . Let L(γ ¯ ) be the irreducible representation ¯ of highest weight λ module of g λ of Wκ (¯ g) of highest weight γλ g) corre¯ , that is, the irreducible representation Wκ (¯ sponding to γλ ¯ . To determine the characters of all L(γλ ¯ ) is, certainly, one of the most important problems in representation theory of W -algebras.
arXiv:math/0403477v2 [math.QA] 11 May 2004
QUANTIZED REDUCTIONS AND IRREDUCIBLE REPRESENTATIONS OF W -ALGEBRAS
TOMOYUKI ARAKAWA Dedicated to Akihiro Tsuchiya on the occasion of his 60th birthday
1. Introduction Since introduced by Zamalodchikov [23], the symmetry by W -algebras has been significantly important in conformal field theories ([2, 3]). However, not much is known about the representation theory of W -algebras. In this paper we study the representations of the W -algebra W (¯ g) associated to an arbitrary simple Lie g, and determine the characters of its irreducible highest weight representaalgebra ¯ tions under certain conditions. Those characters are expressed by Kazhdan-Lusztig polynomials. As a consequence, we prove the conjecture of Frenkel, Kac and Wakimoto ([11]) on the existence of the minimal series representations (= the modular g). invariant representations) of W (¯ g be a finite-dimensional simple Lie algebra with a triangular decomposition Let ¯ ¯− ⊕ ¯ ¯+ . Let g = ¯ ¯ g=n h⊕n g⊗C[t, t−1 ] ⊕ CK ⊕ CD be the affine Lie algebra associated to ¯ g. Let Wκ (¯ g) be the W -algebra associated to ¯ g at level κ − h∨ , defined by Feigin and Frenkel via the quantized Drinfeld-Sokolov reduction [8, 10]. We have: (1) g), A(Wκ (¯ g)) ∼ = Z (¯
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