Resonances and O-curves in Hamiltonian systems
复标度方法对Pb同位素单粒子共振态的研究
复标度方法对Pb同位素单粒子共振态的研究陈琼;郭建友【摘要】In the framework of the relativistic mean field(RMF) theory, the single-particle resonant states of Pb isotopes were studied with the complex scaling method. The eigenvalue of the scaling Hamiltonian Hθ varying with the rotation angle θ was demonstrated. It was shown that the continuous spectrum of Hθ rotated clockwise with θ, when the angle θ increased to a certain value, the resonant states began to appear, but the position of resonant states remained almost unchanged with the variation of θ. The energies and widths for all the possible resonant states of 201-212Pb were obtained by the θ trajectory of resonance parameters. Furthermore, the energies and widths of resonance states of Pb isotopes with the changes of the mass number of the nucleus were investigated.%在相对论平均场(RMF)理论框架下,采用复标度方法研究Pb同位素的单粒子共振态.研究复标度哈密顿量Hθ的本征值随复标度参数θ变化的情况,研究结果表明:连续谱随θ转动,当θ增加到一定值时,共振态开始出现;共振态出现后,共振态位置不随θ变化.同时利用θ轨迹方法得到201-212Pb的共振态能量和宽度,并进一步研究Pb同位素链的共振态能量和宽度随原子核质量数变化的情况.【期刊名称】《安徽大学学报(自然科学版)》【年(卷),期】2013(037)001【总页数】7页(P37-43)【关键词】相对论平均场理论;复标度方法;单粒子共振态;共振态能量;共振态宽度【作者】陈琼;郭建友【作者单位】安徽大学物理与材料科学学院,安徽合肥230039;安徽大学物理与材料科学学院,安徽合肥230039【正文语种】中文【中图分类】O571.21+1连续谱和共振态是量子物理研究领域的重要课题之一.近年来,随着放射性核束实验技术的发展,大批远离β稳定线的新核素相继诞生.这些弱束缚的奇特核,其中子(或质子)费米面接近连续谱,价核子的束缚能低,可以形成“皮”、“晕”[1]等奇特结构.连续谱对这些奇特核的性质有重要的影响[2-3],而连续谱中起主要作用的是阈值附近的共振态.对于奇特核中的非束缚过程,如刚好大于粒子发射阈的单粒子激发,阈值附近的共振态性质尤为重要[4].另外,科研人员利用无规相位近似研究巨共振现象时发现,连续谱对巨共振的贡献主要来自那些分立的连续谱,即单粒子共振态或Gamow态[5-6].所以,对于奇特核的共振态研究有助于理解这些奇特核的基态和激发态性质.研究共振态的方法有很多种,如 R -矩阵方法[7]、散射相移方法(S)[8]、实稳定化方法(RSM)[9]、耦合常数的解析延拓方法(ACCC)[10]及复标度方法(CSM)[11].近年来,相对论平均场(RMF)理论成功应用于稳定核及远离β稳定线核的性质研究.在RMF理论框架下,发展了研究共振态的一些方法,如RMF -S方法[12]、RMF - RSM 方法[13]、RMF - ACCC 方法[14]和 RMF- CSM[15]方法.比较而言,RMF -CSM方法采用束缚态的技巧研究共振态时计算简单,能够统一描述束缚态、共振态和连续谱,且研究共振态时不受边界条件的限制.作者利用RMF-CSM研究Pb同位素链的单粒子共振态.1 理论框架RMF理论[16-18]的出发点是一个包含核子和介子相互作用的有效拉格朗日密度其中利用经典变分原理可得到核子运动的Dirac方程其中:V(r)和S(r)分别表示矢量势和标量势.对于球形核,Dirac旋量可表示为其中:(θ,φ)是球谐函数;f(r)和g(r)分别是波函数径向部分的上分量和下分量.将(6)式代入(5)式,可得径向Dirac方程(7)式能很好地描述相对论粒子的束缚态问题,为了描述共振态,定义一个复标度算符其中将算符U(θ)作用到(7)式,得复标度Dirac方程其中:ψθ(r)=U(θ)ψ(r)为复标度 Dirac旋量;Hθ =U(θ)H(r)U-1(θ)为变化后的哈密顿量,其式为由文献[19]可知,Dirac哈密顿量H的束缚态解也是Hθ的本征态,H格林算符的共振极点ε=εriΓ/2也是Hθ的本征值,H的连续谱随着θ转动,对于共振态,复能量ε=εr-iΓ/2,其中,εr是共振位置,Γ是共振宽度.2 数值计算和结果用RMF-CSM方法研究Pb同位素链单粒子共振态,参数组选取NL3.在具体计算中,采用基展开方法求解(11)式,谐振子基包含100个谐振子壳,谐振子频率hω0=41A-1/3 MeV,A是核子数.图1为Pb同位素链中子态的本征值随复标度参数θ变化的情况.共振态能量用Er表示,复能量虚部用 Ei表示,Ei与共振宽度Γ 的关系为 Ei= -Γ/2,θ的取值从2.0°到12.0°,间隔Δθ=2.0°.从图1可以发现,当θ比较小时,共振态没有出现,Hθ的本征值是随θ转动的连续谱.当θ增加到一定值时,共振态开始出现,共振态出现后,共振态位置不随θ转动;继续增加θ会出现更多的共振态,但不改变已出现共振态在复能量面的位置.图1 Pb同位素链中子h11/2态的本征值随复标度参数θ变化的情况Fig.1 The eigenvalue of the neutron states of Pb isotope chain varying with the rotation angle θ理论上,用CSM方法确定的共振态与复标度参数θ无关,即只要θ足够大,就能使所有的共振态出现.在实际计算中,数值近似法及采用有限的基数量导致了共振态的能量和宽度与复标度参数θ相关.采用θ轨迹方法[20-21]确定共振态参数步骤如下:首先,在复能量面上画出复能量随θ变化的轨迹;然后,在轨迹图中寻找复能量随θ变化的最小位置(d Eθ/dθ≈0),从而确定共振态的位置.图2为201~212 Pb单中子h11/2态的复能量随θ变化的轨迹,共振态位置用箭头标记,其中,θ的取值从5.2°到12.0°,Δθ=0.4°.图2 201~212 Pb的h11/2单中子共振态的θ轨迹Fig.2 The θ trajectories of single neutron resonance of 201-212 Pb for the state由图2可以得到Pb同位素链的中子h11/2态的共振态能量及宽度.采用θ轨迹方法,同样可以确定Pb同位素链其他单中子共振态的共振能量及宽度(见表1).表1 201~212 Pb单中子共振态的能量和宽度Tab.1 The energies and widthsof single neutron resonant states of 201-212 Pbh h j单中子共振态9/211/2 13/2/MeV 201 Pb 6.072 17 2.259 11 4.901 40 1.148 19 7.146 18 0.14共振态能量/MeV共振态宽度/MeV共振态能量/MeV共振态宽度/MeV共振态能量/MeV共振态宽度4 47 8 42 202 Pb 6.029 87 2.208 34 4.804 75 1.094 84 7.057 16 0.141 95 203Pb 5.989 38 2.205 98 4.727 67 1.058 51 6.968 790.134 31 204 Pb 5.929 02 2.099 50 4.656 66 0.995 70 6.881 20 0.127 55 205 Pb 5.877 72 2.049 41 4.585 34 0.948 83 6.794 36 0.120 77 206Pb 5.826 24 2.036 04 4.516 01 0.902 31 6.707 49 0.114 75 207 Pb 5.763 46 2.020 484.437 67 0.849 53 6.615 15 0.108 43 208 Pb5.697 81 1.985 79 4.362 230.799 50 6.524 68 0.102 43 209Pb 5.638 58 1.930 13 4.310 84 0.779 046.409 59 0.094 17 210 Pb 5.577 08 1.863 38 4.274 42 0.739 52 6.295 320.086 45 211 Pb 5.521 85 1.827 56 4.225 81 0.713 34 6.181 84 0.079 37212Pb 5.462 57 1.780 14 4.183 74 0.687 45 6.069 06 0.07进一步系统地探究了Pb同位素链单粒子共振态性质.图3为Pb同位素链共振态能量和宽度随质量数变化的情况.从图3可以看出,所有共振态能量及共振态宽度都随原子核质量数增加而减小,但其随原子核质量数变化的敏感度不同.从图3a可以发现,对于和这两条能级,共振态能量随原子核质量数的增加而下降得较快,相比较这条能级下降得比较缓慢.从图3b可以发现和这两个态有较宽的共振态宽度,其共振态宽度随原子核质量数增加而变化明显态能级较高,共振宽度较窄、共振态稳定,其共振态宽度随原子核质量数增加变化缓慢.图3 Pb同位素链共振态能量(a)和共振态宽度(b)随质量数变化的情况Fig.3 The energies(a)and widths(b)of resonance states of Pb isotopes varying withthe mass number of the nucleus3 结束语作者用RMF-CSM方法研究了Pb同位素链的单粒子共振态性质.通过计算找出了Pb同位素链的3个单粒子共振态,并采用θ轨迹方法确定了这3个共振态的能量和宽度.对于Pb同位素链,其共振态能量随原子核质量数增加而减小,和这两条能级下降得较快,相对来说能级下降得较慢;其共振态宽度也随原子核质量数增加而减小态有较窄的宽度,且随原子核质量数变化很小,而态及态有较宽的宽度,且随原子核质量数变化较大.参考文献:[1]Tanihata I,Hashimoto O,Shida Y.Measurements of interaction cross sections and nuclear radii in the light p-shell region[J].Phys Rev Lett,1985,55:2676 -2679.[2]Meng J,Ring P.Relativistic Hartree - Bogoliubov description of the neutron halo in 11Li[J].Phys Rev Lett,1996,77:3963-3966.[3]Meng J,Ring P.Giant halo at the neutron drip line[J].Phys Rev Lett,1998,80:460 -463.[4]Sandulescu N,Nugyen V G,Liotta R J.Resonant continuum in the Hartree- Fock+BCSapproximation[J].Phys Rev C,2000,61:061301. [5]Curutchet P,Vertse T,Liotta R J.Resonant random phaseapproximation[J].Phys Rev C,1989,39:1020 -1030.[6]Cao L G,Ma Z Y.Exploration of resonant continuum and giant resonance in the relativistic approach[J].Phys Rev C,2002,66:024311. [7]Hale G M,Brown R E,Jarmie N.Pole structure of the Jπ=+resonance in 5He[J].Phys Rev Lett,1987,59:763-766.[8]Taylor J R,Scattering theory:the quantum theory on nonrelativistic collis-ions[M].New York:John Wiley &Sons,Inc,1972:240 -256. [9]Hazi A U,Taylor H S.Stabilization method of calculating resonance energies:model problem[J].Phys Rev A,1970,1:1109 -1120.[10]Yang S C,Meng J,Zhou S G.Exploration of unbound states by analytical continuation in the coupling constant method within relativistic mean field theory[J].Chin Lett,2001,18:196 -198.[11]Kiyoshi K.Resonances and continuum states in the complex scaling method[J].JPhys Conf Ser,2006,49:73 -78.[12]Sandulescu N,Geng L S,Toki H,et al.Pairing correlations and resonant states in the relativistic mean field theory[J].Phys Rev C,2003,68:054323.[13]Zhang L,Zhou SG,Meng J,et al.Real stabilization method for nuclear single - particle resonances[J].Phys Rev C,2008,77:014312. [14]Zhang SS,Meng J,Zhou SG,et al.Analytic continuation of single-particle resonance energy and wave function in relativistic mean field theory[J].Phys Rev C,2004,70:034308.[15]Guo J Y,Fang X Z,Jiao P,et al.Application of the complex scaling method in relativistic mean - field theory[J].Phys Rev C,2010,82:034318.[16]Möller P,Nix J R,Myers W D,et al.Nuclear ground - state masses and deformations[J].At Data Nucl Data Tables,1995,59:185 -381.[17]Möller P,Bengtsson R,Carlsson BG,et al.Axial and reflection asymmetry of the nuclear ground state[J].At Data Nucl Data Tables,2008,94:758 -763.[18]Hilberath T,Becker S,Bollen G,et al.Ground - state properties of neutron - deficient platinum isotopes[J].Z Phys A,1992,342:1 -15. 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XANES_intro
basics Selection rules (LS coupling)
Text
source /Pubs/AtSpec/node17.html
radial wavefunctions
• electric dipole selection rules require p final
The literature abounds with empirical correlations between edge shifts and formal charge state, and the relationships between pre-edge transitions and symmetry The purpose of this talk to try to dig below the surface of these correlations without getting buried in mathematics. I recommend Simon Bare’s talk on XANES (2005 APS XAFS school) for a good overview of the applications and very interesting figures, and Bruce Ravel’s APS XAFS school) talk on applying the FEFFx program to modeling data.
(consequence of Pauli exclusion principle)
• filling of orbital suppresses white line
Core Hole Lifetime
The core hole (vacancy in the initial state following x-ray absorption) is unstable. It decays in a short time ~ 1 femtosecond, typically by emitting a fluorescence photon or auger electron. By the uncertainty principle, the energy width of a state is inversely proportional to its lifetime. Higher atomic number elements have shorter lifetimes -> greater broadening. This gets to be a limitation for high Z edges although it can be deconvoluted out in favorable cases. Low atomic number (~Z=8) spectra “NEXAFS” can be interpreted in great detail because of sharp spectral lines X-ray Inelastic Scattering can be used to suppress core hole lifetime broadening and explore XANES in detail
Microscopic Description of the Breathing Mode and
For a spherical case the HF equations can be reduced to,
82hm2*(r)R"(r)l(lr21)R(r)ddr82hm2*(r)R' (r)
2(rA,A,A)
... ...
A A((rr21,, 1 2,,12))
...A(rA,A,A)
In the spherical case, the single-particle wave function i(r,,) is given in terms of the radial R (r), the spherical spin harmonic Yjlm(r,) , and the isospin m ( ) functions:
We consider the isoscalar breathing mode in which the neutrons and protons move in phase (∆T=0, ∆S=0).
o
(r,t)o( r,t)
In the scaling model, we have the matter density oscillates as
We use the scattering operator F
A
F f (ri ) i1
where f (r) 1 r2 for monopole excitation, to obtain the strength function 2
S (E ) 0 F n2(E E n ) 1 Im T (fr [ G f)] n
2Stochastic averaging of quasi-generalized Hamiltonian systems
ABSTRACT
A stochastic averaging method for generalized Hamiltonian systems (GHS) subject to light dampings and weak stochastic excitations is proposed. First, the GHS are briefly reviewed and classified into five classes, i.e., non-integrable GHS, completely integrable and non-resonant GHS, completely integrable and resonant GHS, partially integrable and non-resonant GHS and partially integrable and resonant GHS. Then, the averaged Itoˆ and FPK equations and the drift and diffusion coefficients for the five classes of quasi-GHS are derived. Finally, the stochastic averaging for a nine-dimensional quasi-partially integrable GHS is given to illustrate the application of the proposed procedure, and the results are confirmed by using those from Monte Carlo simulation.
基于小波变换的图像去噪方法的研究开题报告
基于小波变换的图像去噪方法的研究开题报告硕士研究生学位论文选题报告基于小波变换的图像去噪方法的研究一、拟选题目在图像处理中,图像通常都存在着各种不易消除的噪声。
寻求一种既能有效地减小噪声、又能很好地保留图像边缘信息的方法,一直是人们努力追求的目标。
传统的去噪方法很难同时兼顾这两个方面。
而小波分析由于在时域频域同时具有良好的局部化性质和多分辨率分析等优点,所以本文拟用小波变换的方法对图像去噪进行分析研究。
二、课题的目的和意义图像降噪是图像预处理的主要任务之一,其作用是为了提高图像的信噪比,突出图像的期望特征。
不同性质的噪声应采用不同的方法进行消噪。
最简单的也[1]比较通用的消噪算法,是用傅立叶变换直接进行低通滤波或带通滤波。
这种方法虽然简单、易于实现,但它对滤去有用信号频带中的噪声无能为力,并且带宽的选择和高分辨率是有矛盾的。
带宽选的过宽,达不到去噪的目的;选的过窄,噪声虽然滤去的多,但同时信号的高频部分也损失了,不但带宽内的信噪比得不到改善,某些突变点的信息也可能被模糊掉了。
[2]将小波变换应用于信号处理中,是因为它的主要优点是在时间域和频率域中同时具有良好的局部化特性,从而非常适合时变信号的分析和处理。
特别在图像去噪领域中,小波理论受到了许多学者的重视,他们应用小波进行去噪,并获得了非常好的效果。
具体来说,小波去噪方法的成功主要得益于小波变换具有以下特点:(1)低熵性由于小波系数的稀疏分布,使得图像变换后的熵降低了;(2)多分辨率由于小波采用了多分辨率的方法,所以可以非常好地刻画信号的非平稳特征,如边缘、尖峰、断点等;(3)去相关性因为小波变换可以对信号进行去相关,且噪声在变换后有白1硕士研究生学位论文选题报告化趋势,所以小波域比时域更利于去噪;(4)选基灵活性由于小波变换可以灵活选择变换基,所以对不同应用场合,对不同的研究对象,可以选用不同的小波母函数,以获得最佳的去噪效果。
因此,就信号消噪问题而言,它比传统的傅立叶频率域滤波和匹配滤波器更具有灵活性。
代数英语
(0,2) 插值||(0,2) interpolation0#||zero-sharp; 读作零井或零开。
0+||zero-dagger; 读作零正。
1-因子||1-factor3-流形||3-manifold; 又称“三维流形”。
AIC准则||AIC criterion, Akaike information criterionAp 权||Ap-weightA稳定性||A-stability, absolute stabilityA最优设计||A-optimal designBCH 码||BCH code, Bose-Chaudhuri-Hocquenghem codeBIC准则||BIC criterion, Bayesian modification of the AICBMOA函数||analytic function of bounded mean oscillation; 全称“有界平均振动解析函数”。
BMO鞅||BMO martingaleBSD猜想||Birch and Swinnerton-Dyer conjecture; 全称“伯奇与斯温纳顿-戴尔猜想”。
B样条||B-splineC*代数||C*-algebra; 读作“C星代数”。
C0 类函数||function of class C0; 又称“连续函数类”。
CA T准则||CAT criterion, criterion for autoregressiveCM域||CM fieldCN 群||CN-groupCW 复形的同调||homology of CW complexCW复形||CW complexCW复形的同伦群||homotopy group of CW complexesCW剖分||CW decompositionCn 类函数||function of class Cn; 又称“n次连续可微函数类”。
Cp统计量||Cp-statisticC。
LIMIT CYCLE BIFURCATIONS IN NEAR HAMILTONIAN SYSTEMS BY PERTURBING A NILPOTENT CENTER
1. Introduction
The second part of Hilbert 16th problem [Hilbert, 1902], still unsolved even for n = 2, requests a uniform upper bound H (n) on the number of limit cycles appearing in the phase portraits of twodimensional systems of ordinary differential equations whose right-hand sides are polynomials of degree n. In [Li, 2003], this problem may be split into three parts. The first part which is about
MAOAN HAN† Department of Mathematics, Shanghai Normal University, Guilin Rd. 100, Shanghai 200234, P. R. China mahan@ JIAO JIANG Department of Mathematics, Shanghai Maritime University, Shanghai 200135, P. R. China HUAIPING ZHU Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada Received June 26, 2007; Revised March 18, 2008
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Universities in Evolutionary Systems(系统变革中的大学)
Universities in Evolutionary Systems of InnovationMarianne van der Steen and Jurgen EndersThis paper criticizes the current narrow view on the role of universities in knowledge-based economies.We propose to extend the current policy framework of universities in national innovation systems(NIS)to a more dynamic one,based on evolutionary economic principles. The main reason is that this dynamic viewfits better with the practice of innovation processes. We contribute on ontological and methodological levels to the literature and policy discussions on the effectiveness of university-industry knowledge transfer and the third mission of uni-versities.We conclude with a discussion of the policy implications for the main stakeholders.1.IntroductionU niversities have always played a major role in the economic and cultural devel-opment of countries.However,their role and expected contribution has changed sub-stantially over the years.Whereas,since1945, universities in Europe were expected to con-tribute to‘basic’research,which could be freely used by society,in recent decades they are expected to contribute more substantially and directly to the competitiveness offirms and societies(Jaffe,2008).Examples are the Bayh–Dole Act(1982)in the United States and in Europe the Lisbon Agenda(2000–2010) which marked an era of a changing and more substantial role for universities.However,it seems that this‘new’role of universities is a sort of universal given one(ex post),instead of an ex ante changing one in a dynamic institutional environment.Many uni-versities are expected nowadays to stimulate a limited number of knowledge transfer activi-ties such as university spin-offs and university patenting and licensing to demonstrate that they are actively engaged in knowledge trans-fer.It is questioned in the literature if this one-size-fits-all approach improves the usefulness and the applicability of university knowledge in industry and society as a whole(e.g.,Litan et al.,2007).Moreover,the various national or regional economic systems have idiosyncratic charac-teristics that in principle pose different(chang-ing)demands towards universities.Instead of assuming that there is only one‘optimal’gov-ernance mode for universities,there may bemultiple ways of organizing the role of univer-sities in innovation processes.In addition,we assume that this can change over time.Recently,more attention in the literature hasfocused on diversity across technologies(e.g.,King,2004;Malerba,2005;Dosi et al.,2006;V an der Steen et al.,2008)and diversity offormal and informal knowledge interactionsbetween universities and industry(e.g.,Cohenet al.,1998).So far,there has been less atten-tion paid to the dynamics of the changing roleof universities in economic systems:how dothe roles of universities vary over time andwhy?Therefore,this article focuses on the onto-logical premises of the functioning of univer-sities in innovation systems from a dynamic,evolutionary perspective.In order to do so,we analyse the role of universities from theperspective of an evolutionary system ofinnovation to understand the embeddednessof universities in a dynamic(national)systemof science and innovation.The article is structured as follows.InSection2we describe the changing role ofuniversities from the static perspective of anational innovation system(NIS),whereasSection3analyses the dynamic perspective ofuniversities based on evolutionary principles.Based on this evolutionary perspective,Section4introduces the characteristics of a LearningUniversity in a dynamic innovation system,summarizing an alternative perception to thestatic view of universities in dynamic economicsystems in Section5.Finally,the concludingVolume17Number42008doi:10.1111/j.1467-8691.2008.00496.x©2008The AuthorsJournal compilation©2008Blackwell Publishingsection discusses policy recommendations for more effective policy instruments from our dynamic perspective.2.Static View of Universities in NIS 2.1The Emergence of the Role of Universities in NISFirst we start with a discussion of the literature and policy reports on national innovation system(NIS).The literature on national inno-vation systems(NIS)is a relatively new and rapidly growingfield of research and widely used by policy-makers worldwide(Fagerberg, 2003;Balzat&Hanusch,2004;Sharif,2006). The NIS approach was initiated in the late 1980s by Freeman(1987),Dosi et al.(1988)and Lundvall(1992)and followed by Nelson (1993),Edquist(1997),and many others.Balzat and Hanusch(2004,p.196)describe a NIS as‘a historically grown subsystem of the national economy in which various organizations and institutions interact with and influence one another in the carrying out of innovative activity’.It is about a systemic approach to innovation,in which the interaction between technology,institutions and organizations is central.With the introduction of the notion of a national innovation system,universities were formally on the agenda of many innovation policymakers worldwide.Clearly,the NIS demonstrated that universities and their interactions with industry matter for innova-tion processes in economic systems.Indeed, since a decade most governments acknowl-edge that interactions between university and industry add to better utilization of scienti-fic knowledge and herewith increase the innovation performance of nations.One of the central notions of the innovation system approach is that universities play an impor-tant role in the development of commercial useful knowledge(Edquist,1997;Sharif, 2006).This contrasts with the linear model innovation that dominated the thinking of science and industry policy makers during the last century.The linear innovation model perceives innovation as an industry activity that‘only’utilizes fundamental scientific knowledge of universities as an input factor for their innovative activities.The emergence of the non-linear approach led to a renewed vision on the role–and expectations–of universities in society. Some authors have referred to a new social contract between science and society(e.g., Neave,2000).The Triple Helix(e.g.,Etzkowitz &Leydesdorff,1997)and the innovation system approach(e.g.,Lundvall,1988)and more recently,the model of Open Innovation (Chesbrough,2003)demonstrated that innova-tion in a knowledge-based economy is an inter-active process involving many different innovation actors that interact in a system of overlapping organizationalfields(science, technology,government)with many interfaces.2.2Static Policy View of Universities in NIS Since the late1990s,the new role of universi-ties in NIS thinking emerged in a growing number of policy studies(e.g.,OECD,1999, 2002;European Commission,2000).The con-tributions of the NIS literature had a large impact on policy makers’perception of the role of universities in the national innovation performance(e.g.,European Commission, 2006).The NIS approach gradually replaced linear thinking about innovation by a more holistic system perspective on innovations, focusing on the interdependencies among the various agents,organizations and institutions. NIS thinking led to a structurally different view of how governments can stimulate the innovation performance of a country.The OECD report of the national innovation system (OECD,1999)clearly incorporated these new economic principles of innovation system theory.This report emphasized this new role and interfaces of universities in knowledge-based economies.This created a new policy rationale and new awareness for technology transfer policy in many countries.The NIS report(1999)was followed by more attention for the diversity of technology transfer mecha-nisms employed in university-industry rela-tions(OECD,2002)and the(need for new) emerging governance structures for the‘third mission’of universities in society,i.e.,patent-ing,licensing and spin-offs,of public research organizations(OECD,2003).The various policy studies have in common that they try to describe and compare the most important institutions,organizations, activities and interactions of public and private actors that take part in or influence the innovation performance of a country.Figure1 provides an illustration.Thefigure demon-strates the major building blocks of a NIS in a practical policy setting.It includesfirms,uni-versities and other public research organiza-tions(PROs)involved in(higher)education and training,science and technology.These organizations embody the science and tech-nology capabilities and knowledge fund of a country.The interaction is represented by the arrows which refer to interactive learn-ing and diffusion of knowledge(Lundvall,Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing1992).1The building block ‘Demand’refers to the level and quality of demand that can be a pull factor for firms to innovate.Finally,insti-tutions are represented in the building blocks ‘Framework conditions’and ‘Infrastructure’,including various laws,policies and regula-tions related to science,technology and entre-preneurship.It includes a very broad array of policy issues from intellectual property rights laws to fiscal instruments that stimulate labour mobility between universities and firms.The figure demonstrates that,in order to improve the innovation performance of a country,the NIS as a whole should be conducive for innovative activities in acountry.Since the late 1990s,the conceptual framework as represented in Figure 1serves as a dominant design for many comparative studies of national innovation systems (Polt et al.,2001;OECD,2002).The typical policy benchmark exercise is to compare a number of innovation indicators related to the role of university-industry interactions.Effective performance of universities in the NIS is judged on a number of standardized indica-tors such as the number of spin-offs,patents and licensing.Policy has especially focused on ‘getting the incentives right’to create a generic,good innovative enhancing context for firms.Moreover,policy has also influ-enced the use of specific ‘formal’transfer mechanisms,such as university patents and university spin-offs,to facilitate this collabo-ration.In this way best practice policies are identified and policy recommendations are derived:the so-called one-size-fits-all-approach.The focus is on determining the ingredients of an efficient benchmark NIS,downplaying institutional diversity and1These organizations that interact with each other sometimes co-operate and sometimes compete with each other.For instance,firms sometimes co-operate in certain pre-competitive research projects but can be competitors as well.This is often the case as well withuniversities.Figure 1.The Benchmark NIS Model Source :Bemer et al.(2001).Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingvariety in the roles of universities in enhanc-ing innovation performance.The theoretical contributions to the NIS lit-erature have outlined the importance of insti-tutions and institutional change.However,a further theoretical development of the ele-ments of NIS is necessary in order to be useful for policy makers;they need better systemic NIS benchmarks,taking systematically into account the variety of‘national idiosyncrasies’. Edquist(1997)argues that most NIS contribu-tions are more focused onfirms and technol-ogy,sometimes reducing the analysis of the (national)institutions to a left-over category (Geels,2005).Following Hodgson(2000), Nelson(2002),Malerba(2005)and Groenewe-gen and V an der Steen(2006),more attention should be paid to the institutional idiosyncra-sies of the various systems and their evolution over time.This creates variety and evolving demands towards universities over time where the functioning of universities and their interactions with the other part of the NIS do evolve as well.We suggest to conceptualize the dynamics of innovation systems from an evolutionary perspective in order to develop a more subtle and dynamic vision on the role of universities in innovation systems.We emphasize our focus on‘evolutionary systems’instead of national innovation systems because for many universities,in particular some science-based disciplinaryfields such as biotechnology and nanotechnology,the national institutional environment is less relevant than the institu-tional and technical characteristics of the technological regimes,which is in fact a‘sub-system’of the national innovation system.3.Evolutionary Systems of Innovation as an Alternative Concept3.1Evolutionary Theory on Economic Change and InnovationCharles Darwin’s The Origin of Species(1859)is the foundation of modern thinking about change and evolution(Luria et al.,1981,pp. 584–7;Gould,1987).Darwin’s theory of natural selection has had the most important consequences for our perception of change. His view of evolution refers to a continuous and gradual adaptation of species to changes in the environment.The idea of‘survival of thefittest’means that the most adaptive organisms in a population will survive.This occurs through a process of‘natural selection’in which the most adaptive‘species’(organ-isms)will survive.This is a gradual process taking place in a relatively stable environment, working slowly over long periods of time necessary for the distinctive characteristics of species to show their superiority in the‘sur-vival contest’.Based on Darwin,evolutionary biology identifies three levels of aggregation.These three levels are the unit of variation,unit of selection and unit of evolution.The unit of varia-tion concerns the entity which contains the genetic information and which mutates fol-lowing specific rules,namely the genes.Genes contain the hereditary information which is preserved in the DNA.This does not alter sig-nificantly throughout the reproductive life-time of an organism.Genes are passed on from an organism to its successors.The gene pool,i.e.,the total stock of genetic structures of a species,only changes in the reproduction process as individuals die and are born.Par-ticular genes contribute to distinctive charac-teristics and behaviour of species which are more or less conducive to survival.The gene pool constitutes the mechanism to transmit the characteristics of surviving organisms from one generation to the next.The unit of selection is the expression of those genes in the entities which live and die as individual specimens,namely(individual) organisms.These organisms,in their turn,are subjected to a process of natural selection in the environment.‘Fit’organisms endowed with a relatively‘successful’gene pool,are more likely to pass them on to their progeny.As genes contain information to form and program the organisms,it can be expected that in a stable environment genes aiding survival will tend to become more prominent in succeeding genera-tions.‘Natural selection’,thus,is a gradual process selecting the‘fittest’organisms. Finally,there is the unit of evolution,or that which changes over time as the gene pool changes,namely populations.Natural selec-tion produces changes at the level of the population by‘trimming’the set of genetic structures in a population.We would like to point out two central principles of Darwinian evolution.First,its profound indeterminacy since the process of development,for instance the development of DNA,is dominated by time at which highly improbable events happen (Boulding,1991,p.12).Secondly,the process of natural selection eliminates poorly adapted variants in a compulsory manner,since indi-viduals who are‘unfit’are supposed to have no way of escaping the consequences of selection.22We acknowledge that within evolutionary think-ing,the theory of Jean Baptiste Lamarck,which acknowledges in essence that acquired characteris-tics can be transmitted(instead of hereditaryVolume17Number42008©2008The AuthorsJournal compilation©2008Blackwell PublishingThese three levels of aggregation express the differences between ‘what is changing’(genes),‘what is being selected’(organisms),and ‘what changes over time’(populations)in an evolutionary process (Luria et al.,1981,p.625).According to Nelson (see for instance Nelson,1995):‘Technical change is clearly an evolutionary process;the innovation generator keeps on producing entities superior to those earlier in existence,and adjustment forces work slowly’.Technological change and innovation processes are thus ‘evolutionary’because of its characteristics of non-optimality and of an open-ended and path-dependent process.Nelson and Winter (1982)introduced the idea of technical change as an evolutionary process in capitalist economies.Routines in firms function as the relatively durable ‘genes’.Economic competition leads to the selection of certain ‘successful’routines and these can be transferred to other firms by imitation,through buy-outs,training,labour mobility,and so on.Innovation processes involving interactions between universities and industry are central in the NIS approach.Therefore,it seems logical that evolutionary theory would be useful to grasp the role of universities in innovation pro-cesses within the NIS framework.3.2Evolutionary Underpinnings of Innovation SystemsBased on the central evolutionary notions as discussed above,we discuss in this section how the existing NIS approaches have already incor-porated notions in their NIS frameworks.Moreover,we investigate to what extent these notions can be better incorporated in an evolu-tionary innovation system to improve our understanding of universities in dynamic inno-vation processes.We focus on non-optimality,novelty,the anti-reductionist methodology,gradualism and the evolutionary metaphor.Non-optimality (and Bounded Rationality)Based on institutional diversity,the notion of optimality is absent in most NIS approaches.We cannot define an optimal system of innovation because evolutionary learning pro-cesses are important in such systems and thus are subject to continuous change.The system never achieves an equilibrium since the evolu-tionary processes are open-ended and path dependent.In Nelson’s work (e.g.,1993,1995)he has emphasized the presence of contingent out-comes of innovation processes and thus of NIS:‘At any time,there are feasible entities not present in the prevailing system that have a chance of being introduced’.This continuing existence of feasible alternative developments means that the system never reaches a state of equilibrium or finality.The process always remains dynamic and never reaches an optimum.Nelson argues further that diversity exists because technical change is an open-ended multi-path process where no best solu-tion to a technical problem can be identified ex post .As a consequence technical change can be seen as a very wasteful process in capitalist economies with many duplications and dead-ends.Institutional variety is closely linked to non-optimality.In other words,we cannot define the optimal innovation system because the evolutionary learning processes that take place in a particular system make it subject to continuous change.Therefore,comparisons between an existing system and an ideal system are not possible.Hence,in the absence of any notion of optimality,a method of comparing existing systems is necessary.According to Edquist (1997),comparisons between systems were more explicit and systematic than they had been using the NIS approaches.Novelty:Innovations CentralNovelty is already a central notion in the current NIS approaches.Learning is inter-preted in a broad way.Technological innova-tions are defined as combining existing knowledge in new ways or producing new knowledge (generation),and transforming this into economically significant products and processes (absorption).Learning is the most important process behind technological inno-vations.Learning can be formal in the form of education and searching through research and development.However,in many cases,innovations are the consequence of several kinds of learning processes involving many different kinds of economic agents.According to Lundvall (1992,p.9):‘those activities involve learning-by-doing,increasing the efficiency of production operations,learning-characteristics as in the theory of Darwin),is acknowledged to fit better with socio-economic processes of technical change and innovation (e.g.,Nelson &Winter,1982;Hodgson,2000).Therefore,our theory is based on Lamarckian evolutionary theory.However,for the purpose of this article,we will not discuss the differences between these theo-ries at greater length and limit our analysis to the fundamental evolutionary building blocks that are present in both theories.Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingby-using,increasing the efficiency of the use of complex systems,and learning-by-interacting, involving users and producers in an interac-tion resulting in product innovations’.In this sense,learning is part of daily routines and activities in an economy.In his Learning Economy concept,Lundvall makes learning more explicit,emphasizing further that ‘knowledge is assumed as the most funda-mental resource and learning the most impor-tant process’(1992,p.10).Anti-reductionist Approach:Systems and Subsystems of InnovationSo far,NIS approaches are not yet clear and systematic in their analysis of the dynamics and change in innovation systems.Lundvall’s (1992)distinction between subsystem and system level based on the work of Boulding implicitly incorporates both the actor(who can undertake innovative activities)as well as the structure(institutional selection environment) in innovation processes of a nation.Moreover, most NIS approaches acknowledge that within the national system,there are different institu-tional subsystems(e.g.,sectors,regions)that all influence each other again in processes of change.However,an explicit analysis of the structured environment is still missing (Edquist,1997).In accordance with the basic principles of evolutionary theory as discussed in Section 3.1,institutional evolutionary theory has developed a very explicit systemic methodol-ogy to investigate the continuous interaction of actors and institutional structures in the evolution of economic systems.The so-called ‘methodological interactionism’can be per-ceived as a methodology that combines a structural perspective and an actor approach to understand processes of economic evolu-tion.Whereas the structural perspective emphasizes the existence of independent institutional layers and processes which deter-mine individual actions,the actor approach emphasizes the free will of individuals.The latter has been referred to as methodological individualism,as we have seen in neo-classical approaches.Methodological indi-vidualism will explain phenomena in terms of the rational individual(showingfixed prefer-ences and having one rational response to any fully specified decision problem(Hodgson, 2000)).The interactionist approach recognizes a level of analysis above the individual orfirm level.NIS approaches recognize that national differences exist in terms of national institu-tions,socio-economic factors,industries and networks,and so on.So,an explicit methodological interactionist approach,explicitly recognizing various insti-tutional layers in the system and subsystem in interaction with the learning agents,can improve our understanding of the evolution of innovation.Gradualism:Learning Processes andPath-DependencyPath-dependency in biology can be translated in an economic context in the form of(some-times very large)time lags between a technical invention,its transformation into an economic innovation,and the widespread diffusion. Clearly,in many of the empirical case studies of NIS,the historical dimension has been stressed.For instance,in the study of Denmark and Sweden,it has been shown that the natural resource base(for Denmark fertile land,and for Sweden minerals)and economic history,from the period of the Industrial Revolution onwards,has strongly influenced present specialization patterns(Edquist& Lundvall,1993,pp.269–82).Hence,history matters in processes of inno-vation as the innovation processes are influ-enced by many institutions and economic agents.In addition,they are often path-dependent as small events are reinforced and become crucially important through processes of positive feedback,in line with evolutionary processes as discussed in Section3.1.Evolutionary MetaphorFinally,most NIS approaches do not explicitly use the biological metaphor.Nevertheless, many of the approaches are based on innova-tion theories in which they do use an explicit evolutionary metaphor(e.g.,the work of Nelson).To summarize,the current(policy)NIS approaches have already implicitly incorpo-rated some evolutionary notions such as non-optimality,novelty and gradualism.However, what is missing is a more explicit analysis of the different institutional levels of the economic system and innovation subsystems (their inertia and evolution)and how they change over time in interaction with the various learning activities of economic agents. These economic agents reside at established firms,start-upfirms,universities,govern-ments,undertaking learning and innovation activities or strategic actions.The explicit use of the biological metaphor and an explicit use of the methodological interactionst approach may increase our understanding of the evolu-tion of innovation systems.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing4.Towards a Dynamic View of Universities4.1The Logic of an Endogenous‘Learning’UniversityIf we translate the methodological interaction-ist approach to the changing role of universities in an evolutionary innovation system,it follows that universities not only respond to changes of the institutional environment(government policies,business demands or changes in scientific paradigms)but universities also influence the institutions of the selection envi-ronment by their strategic,scientific and entre-preneurial actions.Moreover,these actions influence–and are influenced by–the actions of other economic agents as well.So,instead of a one-way rational response by universities to changes(as in reductionist approach),they are intertwined in those processes of change.So, universities actually function as an endogenous source of change in the evolution of the inno-vation system.This is(on an ontological level) a fundamental different view on the role of universities in innovation systems from the existing policy NIS frameworks.In earlier empirical research,we observed that universities already effectively function endogenously in evolutionary innovation system frameworks;universities as actors (already)develop new knowledge,innovate and have their own internal capacity to change,adapt and influence the institutional development of the economic system(e.g., V an der Steen et al.,2009).Moreover,univer-sities consist of a network of various actors, i.e.,the scientists,administrators at technology transfer offices(TTO)as well as the university boards,interacting in various ways with indus-try and governments and embedded in various ways in the regional,national or inter-national environment.So,universities behave in an at least partly endogenous manner because they depend in complex and often unpredictable ways on the decision making of a substantial number of non-collusive agents.Agents at universities react in continuous interaction with the learn-ing activities offirms and governments and other universities.Furthermore,the endogenous processes of technical and institutional learning of univer-sities are entangled in the co-evolution of institutional and technical change of the evo-lutionary innovation system at large.We propose to treat the learning of universities as an inseparable endogenous variable in the inno-vation processes of the economic system.In order to structure the endogenization in the system of innovation analysis,the concept of the Learning University is introduced.In thenext subsection we discuss the main character-istics of the Learning University and Section5discusses the learning university in a dynamic,evolutionary innovation system.An evolution-ary metaphor may be helpful to make theuniversity factor more transparent in theco-evolution of technical and institutionalchange,as we try to understand how variouseconomic agents interact in learning processes.4.2Characteristics of the LearningUniversityThe evolution of the involvement of universi-ties in innovation processes is a learningprocess,because(we assume that)universitypublic agents have their‘own agenda’.V ariousincentives in the environment of universitiessuch as government regulations and technol-ogy transfer policies as well as the innovativebehaviour of economic agents,compel policymakers at universities to constantly respondby adapting and improving their strategiesand policies,whereas the university scientistsare partly steered by these strategies and partlyinfluenced by their own scientific peers andpartly by their historically grown interactionswith industry.During this process,universityboards try to be forward-looking and tobehave strategically in the knowledge thattheir actions‘influence the world’(alsoreferred to earlier as‘intentional variety’;see,for instance,Dosi et al.,1988).‘Intentional variety’presupposes that tech-nical and institutional development of univer-sities is a learning process.University agentsundertake purposeful action for change,theylearn from experience and anticipate futurestates of the selective environment.Further-more,university agents take initiatives to im-prove and develop learning paths.An exampleof these learning agents is provided in Box1.We consider technological and institutionaldevelopment of universities as a process thatinvolves many knowledge-seeking activitieswhere public and private agents’perceptionsand actions are translated into practice.3Theinstitutional changes are the result of inter-actions among economic agents defined byLundvall(1992)as interactive learning.Theseinteractions result in an evolutionary pattern3Using a theory developed in one scientific disci-pline as a metaphor in a different discipline mayresult,in a worst-case scenario,in misleading analo-gies.In the best case,however,it can be a source ofcreativity.As Hodgson(2000)pointed out,the evo-lutionary metaphor is useful for understandingprocesses of technical and institutional change,thatcan help to identify new events,characteristics andphenomena.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing。
Vitali’s theorem and WWKL
VITALI’S THEOREM AND WWKLDOUGLAS K.BROWNMARIAGNESE GIUSTOSTEPHEN G.SIMPSONAbstract.Continuing the investigations of X.Yu and others,westudy the role of set existence axioms in classical Lebesgue mea-sure theory.We show that pairwise disjoint countable additivityfor open sets of reals is provable in RCA0.We show that sev-eral well-known measure-theoretic propositions including the VitaliCovering Theorem are equivalent to WWKL over RCA0.1.IntroductionThe purpose of Reverse Mathematics is to study the role of set ex-istence axioms,with an eye to determining which axioms are needed in order to prove specific mathematical theorems.In many cases,it is shown that a specific mathematical theorem is equivalent to the set existence axiom which is needed to prove it.Such equivalences are often proved in the weak base theory RCA0.RCA0may be viewed as a kind of formalized constructive or recursive mathematics,with full clas-sical logic but severely restricted comprehension and induction.The program of Reverse Mathematics has been developed in many publica-tions;see for instance[5,10,11,12,20].In this paper we carry out a Reverse Mathematics study of some aspects of classical Lebesgue measure theory.Historically,the subject of measure theory developed hand in hand with the nonconstructive, set-theoretic approach to mathematics.Errett Bishop has remarked that the foundations of measure theory present a special challenge to the constructive mathematician.Although our program of Reverse Mathematics is quite different from Bishop-style constructivism,we feel that Bishop’s remark implicitly raises an interesting question:Which nonconstructive set existence axioms are needed for measure theory?VITALI’S THEOREM AND WWKL 2This paper,together with earlier papers of Yu and others [21,22,23,24,25,26],constitute an answer to that question.The results of this paper build upon and clarify some early results of Yu and Simpson.The reader of this paper will find that familiarity with Yu–Simpson [26]is desirable but not essential.We begin in section 2by exploring the extent to which measure theory can be developed in RCA 0.We show that pairwise disjoint countable additivity for open sets of reals is provable in RCA 0.This is in contrast to a result of Yu–Simpson [26]:countable additivity for open sets of reals is equivalent over RCA 0to a nonconstructive set existence axiom known as Weak Weak K¨o nig’s Lemma (WWKL).We show in sections 3and 4that several other basic propositions of measure theory are also equivalent to WWKL over RCA 0.Finally in section 5we show that the Vitali Covering Theorem is likewise equivalent to WWKL over RCA 0.2.Measure Theory in RCA 0Recall that RCA 0is the subsystem of second order arithmetic with∆01comprehension and Σ01induction.The purpose of this section is toshow that some measure-theoretic results can be proved in RCA 0.Within RCA 0,let X be a compact separable metric space.We define C (X )= A,the completion of A ,where A is the vector space of rational “polynomials”over X under the sup-norm, f =sup x ∈X |f (x )|.For the precise definitions within RCA 0,see [26]and section III.E of Brown’s thesis [4].The construction of C (X )within RCA 0is inspired by the constructive Stone–Weierstrass theorem in section 4.5of Bishop and Bridges [2].It is provable in RCA 0that there is a natural one-to-one correspondence between points of C (X )and continuous functions f :X →R which are equipped with a modulus of uniform continuity ,that is to say,a function h :N →N such that for all n ∈N and x ,y ∈Xd (x,y )<12n .Within RCA 0we define a measure (more accurately,a nonnegative Borel probability measure)on X to be a nonnegative bounded linear functional µ:C (X )→R such that µ(1)=1.(Here µ(1)denotes µ(f ),f ∈C (X ),f (x )=1for all x ∈X .)For example,if X =[0,1],the unit interval,then there is an obvious measure µL :C ([0,1])→R given by µL (f )= 10f (x )dx ,the Riemann integral of f from 0to 1.We refer to µL as Lebesgue measure on [0,1].There is also the obvious generalization to Lebesgue measure µL on X =[0,1]n ,the n -cube.VITALI’S THEOREM AND WWKL 3Definition 2.1(measure of an open set).This definition is made in RCA 0.Let X be any compact separable metric space,and let µbe any measure on X .If U is an open set in X ,we defineµ(U )=sup {µ(f )|f ∈C (X ),0≤f ≤1,f =0on X \U }.Within RCA 0this supremum need not exist as a real number.(Indeed,the existence of µ(U )for all open sets U is equivalent to ACA 0over RCA 0.)Therefore,when working within RCA 0,we interpret assertions about µ(U )in a “virtual”or comparative sense.For example,µ(U )≤µ(V )is taken to mean that for all >0and all f ∈C (X )with 0≤f ≤1and f =0on X \U ,there exists g ∈C (X )with 0≤g ≤1and g =0on X \V such that µ(f )≤µ(g )+ .See also [26].Some basic properties of Lebesgue measure are easily proved in RCA 0.For instance,it is straightforward to show that the Lebesgue measure of the union of a finite set of pairwise disjoint open intervals is equal to the sum of the lengths of the intervals.We define L 1(X,µ)to be the completion of C (X )under the L 1-norm given by f 1=µ(|f |).(For the precise definitions,see [5]and[26].)In RCA 0we see that L 1(X,µ)is a separable Banach space,but to assert within RCA 0that points of the Banach space L 1(X,µ)represent measurable functions f :X →R is problematic.We shall comment further on this question in section 4below.Lemma 2.2.The following is provable in RCA 0.If U n ,n ∈N ,is a sequence of open sets,then µ∞ n =0U n ≥lim k →∞µ k n =0U n .Proof.Trivial.Lemma 2.3.The following is provable in RCA 0.If U 0,U 1,...,U k is a finite,pairwise disjoint sequence of open sets,then µ k n =0U n ≥k n =0µ(U n ).Proof.Trivial.An open set is said to be connected if it is not the union of two disjoint nonempty open sets.Let us say that a compact separable metric space X is nice if for all sufficiently small δ>0and all x ∈X ,the open ballB (x,δ)={y ∈X |d (x,y )<δ}VITALI’S THEOREM AND WWKL4 is connected.Such aδis called a modulus of niceness for X.For example,the unit interval[0,1]and the n-cube[0,1]n are nice, but the Cantor space2N is not nice.Theorem2.4(disjoint countable additivity).The following is prov-able in RCA0.Assume that X is nice.If U n,n∈N,is a pairwise disjoint sequence of open sets in X,thenµ∞n=0U n=∞n=0µ(U n).Proof.Put U= ∞n=0U n.Note that U is an open set.By Lemmas2.2and2.3,we have in RCA0thatµ(U)≥ ∞n=0µ(U n).It remainsto prove in RCA0thatµ(U)≤ ∞n=0µ(U n).Let f∈C(X)be suchthat0≤f≤1and f=0on X\U.It suffices to prove thatµ(f)≤∞n=0µ(U n).Claim1:There is a sequence of continuous functions f n:X→R, n∈N,defined by f n(x)=f(x)for all x∈U n,f n(x)=0for all x∈X\U n.To prove this in RCA0,recall from[6]or[20]that a code for a continuous function g from X to Y is a collection G of quadruples (a,r,b,s)with certain properties,the idea being that d(a,x)<r im-plies d(b,g(x))≤s.Also,a code for an open set U is a collection of pairs(a,r)with certain properties,the idea being that d(a,x)<r im-plies x∈U.In this case we write(a,r)<U to mean that d(a,b)+r<s for some(b,s)belonging to the code of U.Now let F be a code for f:X→R.Define a sequence of codes F n,n∈N,by putting(a,r,b,s) into F n if and only if1.(a,r,b,s)belongs to F and(a,r)<U n,or2.(a,r,b,s)belongs to F and b−s≤0≤b+s,or3.b−s≤0≤b+s and(a,r)<U m for some m=n.It is straightforward to verify that F n is a code for f n as required by claim1.Claim2:The sequence f n,n∈N,is a sequence of elements of C(X). To prove this in RCA0,we must show that the sequence of f n’s has a sequence of moduli of uniform continuity.Let h:N→N be a modulus of uniform continuity for f,and let k be so large that1/2k is a modulus of niceness for X.We shall show that h :N→N defined by h (m)=max(h(m),k)is a modulus of uniform continuity for all of the f n’s.Let x,y∈X and m∈N be such that d(x,y)<1/2h (m). To show that|f n(x)−f n(y)|<1/2m,we consider three cases.Case1:VITALI’S THEOREM AND WWKL5 x,y∈U n.In this case we have|f n(x)−f n(y)|=|f(x)−f(y)|<1VITALI’S THEOREM AND WWKL 6From (1)we see that for each >0there exists k such that µ(f )− ≤ kn =0µ(f n ).Thus we haveµ(f )− ≤kn =0µ(f n )≤k n =0µ(U n )≤∞ n =0µ(U n ).Since this holds for all >0,it follows that µ(f )≤ ∞n =0µ(U n ).Thus µ(U )≤ ∞n =0µ(U n )and the proof of Theorem 2.4is complete.Corollary 2.5.The following is provable in RCA 0.If (a n ,b n ),n ∈N is a sequence of pairwise disjoint open intervals,then µL ∞ n =0(a n ,b n ) =∞ n =0|a n −b n |.Proof.This is a special case of Theorem 2.4.Remark 2.6.Theorem 2.4fails if we drop the assumption that X is nice.Indeed,let µC be the familiar “fair coin”measure on the Cantor space X =2N ,given by µC ({x |x (n )=i })=1/2for all n ∈N and i ∈{0,1}.It can be shown that disjoint finite additivity for µC is equivalent to WWKL over RCA 0.(WWKL is defined and discussed in the next section.)In particular,disjoint finite additivity for µC is not provable in RCA 0.3.Measure Theory in WWKL 0Yu and Simpson [26]introduced a subsystem of second order arith-metic known as WWKL 0,consisting of RCA 0plus the following axiom:if T is a subtree of 2<N with no infinite path,thenlim n →∞|{σ∈T |length(σ)=n }|VITALI’S THEOREM AND WWKL 7see also Sieg [18].In this sense,every mathematical theorem provable in WKL 0or WWKL 0is finitistically reducible in the sense of Hilbert’s Program;see [19,6,20].Remark 3.2.The study of ω-models of WWKL 0is closely related to the theory of 1-random sequences,as initiated by Martin-L¨o f [16]and continued by Kuˇc era [7,13,14,15].At the time of writing of [26],Yu and Simpson were unaware of this work of Martin-L¨o f and Kuˇc era.The purpose of this section and the next is to review and extend the results of [26]and [21]concerning measure theory in WWKL 0.A measure µ:C (X )→R on a compact separable metric space X is said to be countably additive if µ∞ n =0U n =lim k →∞µ k n =0U n for any sequence of open sets U n ,n ∈N ,in X .The following theorem is implicit in [26]and [21].Theorem 3.3.The following assertions are pairwise equivalent over RCA 0.1.WWKL.2.(countable additivity)For any compact separable metric space Xand any measure µon X ,µis countably additive.3.For any covering of the closed unit interval [0,1]by a sequence of open intervals (a n ,b n ),n ∈N ,we have ∞n =0|a n −b n |≥1.Proof.That WWKL implies statement 2is proved in Theorem 1of [26].The implication 2→3is trivial.It remains to prove that statement 3implies WWKL.Reasoning in RCA 0,let T be a subtree of 2<N with no infinite path.PutT ={σ i |σ∈T,σ i /∈T,i <2}.For σ∈2<N put lh(σ)=length of σanda σ=lh(σ)−1n =0σ(n )2lh(σ).Note that |a σ−b σ|=1/2lh(σ).Note also that σ,τ∈2<N are incompa-rable if and only if (a σ,b σ)∩(a τ,b τ)=∅.In particular,the intervals (a τ,b τ),τ∈ T,are pairwise disjoint and cover [0,1)except for some of the points a σ,σ∈2<N .Fix >0and put c σ=a σ− /4lh(σ),d σ=a σ+ /4lh(σ).Then the open intervals (a τ,b τ),τ∈ T,(c σ,d σ),VITALI’S THEOREM AND WWKL 8σ∈2<N and (1− ,1+ )form a covering of [0,1].Applying statement 3,we see that the sum of the lengths of these intervals is ≥1,i.e. τ∈ T12lh(τ)=1.From this,equation (2)follows easily.Thus we have proved that state-ment 3implies WWKL.This completes the proof of the theorem.It is possible to take a somewhat different approach to measure the-ory in RCA 0.Note that the definition of µ(U )that we have given (Definition 2.1)is extensional in RCA 0.This means that if U and V contain the same points then µ(U )=µ(V ),provably in RCA 0.An alternative approach is the intensional one,embodied in Definition 3.4below.Recall that an open set U is given in RCA 0as a sequence of basic open sets.In the case of the real line,basic open sets are just intervals with rational endpoints.Definition 3.4(intensional Lebesgue measure).We make this defini-tion in RCA 0.Let U = (a n ,b n ) n ∈N be an open set in the real line.The intensional Lebesgue measure of U is defined by µI (U )=lim k →∞µL k n =0(a n ,b n ) .Theorem 3.5.It is provable in RCA 0that intensional Lebesgue mea-sure µI is countably additive on open sets.In other words,if U n ,n ∈N ,is a sequence of open sets,then µI∞ n =0U n =lim k →∞µI k n =0U n .Proof.This is immediate from the definitions,since ∞n =0U n is defined as the union of the sequences of basic open intervals in U n ,n ∈N .Returning now to WWKL 0,we can prove that intensional Lebesgue measure concides with extensional Lebesgue measure.In fact,we have the following easy result.Theorem 3.6.The following assertions are pairwise equivalent over RCA 0.VITALI’S THEOREM AND WWKL91.WWKL.2.µI(U)=µL(U)for all open sets U⊆[0,1].3.µI is extensional on open sets.In other words,for all open setsU,V⊆[0,1],if∀x(x∈U↔x∈V)thenµI(U)=µI(V).4.For all open sets U⊇[0,1],we haveµI(U)≥1.Proof.This is immediate from Theorems3.3and3.5.4.More Measure Theory in WWKL0In this section we show that a good theory of measurable functions and measurable sets can be developed within WWKL0.Wefirst consider pointwise values of measurable functions.Our ap-proach is due to Yu[21,24].Let X be a compact separable metric space and letµ:C(X)→R be a positive Borel probability measure on X.Recall that L1(X,µ)is defined within RCA0as the completion of C(X)under the L1-norm.In what sense or to what extent can we prove that a point of the Banach space L1(X,µ)gives rise to a function f:X→R?In order to answer this question,recall that f∈L1(X,µ)is given by a sequence f n∈C(X),n∈N,which converges to f in the L1-norm; more preciselyf n−f n+1 1≤12nfor all n,and|f m(x)−f m (x)|≤12k.VITALI’S THEOREM AND WWKL10 Then for x∈C fnand m ≥m≥n+2k+2we have|f m(x)−f m (x)|≤m −1i=m|f i(x)−f i+1(x)|≤∞i=n+2k+2|f i(x)−f i+1(x)|≤12k.We need a lemma:Lemma4.2.The following is provable in RCA0.For f∈C(X)and >0,we haveµ({x|f(x)> })≤ f 1/ .Proof.Put U={x|f(x)> }.Note that U is an open set.If g∈C(X),0≤g≤1,g=0on X\U,then we have g≤|f|, hence µ(g)=µ( g)≤µ(|f|)= f 1,henceµ(g)≤ f 1/ .Thus µ(U)≤ f 1/ and the lemma is proved.Using this lemma we haveµ(X\C fnk )=µx∞i=n+2k+2|f i(x)−f i+1(x)|>12i=1VITALI’S THEOREM AND WWKL 11hence by countable additivityµ(X \C f n )≤∞ k =0µ(X \C f nk )≤∞k =012n .This completes the proof of Proposition 4.1.Remark 4.3(Yu [21]).In accordance with Proposition 4.1,forf = f n n ∈N ∈L 1(X,µ)and x ∈ ∞n =0C f n ,we define f (x )=lim n →∞f n (x ).Thus we see thatf (x )is defined on an F σset of measure 1.Moreover,if f =g in L 1(X,µ),i.e.if f −g 1=0,then f (x )=g (x )for all x in an F σset of measure 1.These facts are provable in WWKL 0.We now turn to a discussion of measurable sets within WWKL 0.We sketch two approaches to this topic.Our first approach is to identify measurable sets with their characteristic functions in L 1(X,µ),accord-ing to the following definition.Definition 4.4.This definition is made within WWKL 0.We say that f ∈L 1(X,µ)is a measurable characteristic function if there exists a sequence of closed setsC 0⊆C 1⊆···⊆C n ⊆...,n ∈N ,such that µ(X \C n )≤1/2n for all n ,and f (x )∈{0,1}for all x ∈ ∞n =0C n .Here f (x )is as defined in Remark 4.3.Our second approach is more direct,but in its present form it applies only to certain specific situations.For concreteness we consider only Lebesgue measure µL on the unit interval [0,1].Our discussion can easily be extended to Lebesgue measure on the n -cube [0,1]n ,the “fair coin”measure on the Cantor space 2N ,etc .Definition 4.5.The following definition is made within RCA 0.Let S be the Boolean algebra of finite unions of intervals in [0,1]with rational endpoints.For E 1,E 2∈S we define the distanced (E 1,E 2)=µL ((E 1\E 2)∪(E 2\E 1)),the Lebesgue measure of the symmetric difference of E 1and E 2.Thus d is a pseudometric on S ,and we define S to be the compact separable metric space which is the completion of S under d .A point E ∈ S is called a Lebesgue measurable set in [0,1].VITALI’S THEOREM AND WWKL 12We shall show that these two approaches to measurable sets (Defi-nitions 4.4and 4.5)are equivalent in WWKL 0.Begin by defining an isometry χ:S →L 1([0,1],µL )as follows.For 0≤a <b ≤1defineχ([a,b ])= f n n ∈N ∈L 1([0,1],µL )where f n (0)=f n (a )=f n (b )=f n (1)=0and f n a +b −a 2n +1=1and f n ∈C ([0,1])is piecewise linear otherwise.Thus χ([a,b ])is a measurable characteristic function corresponding to the interval [a,b ].For 0≤a 1<b 1<···<a k <b k ≤1defineχ([a 1,b 1]∪···∪[a k ,b k ])=χ([a 1,b 1])+···+χ([a k ,b k ]).It is straightforward to prove in RCA 0that χextends to an isometryχ: S→L 1([0,1],µL ).Proposition 4.6.The following is provable in WWKL 0.If E ∈ Sis a Lebesgue measurable set,then χ(E )is a measurable characteristic function in L 1([0,1],µL ).Conversely,given a measurable characteristic function f ∈L 1([0,1],µL ),we can find E ∈ Ssuch that χ(E )=f in L 1([0,1],µL ).Proof.It is straightforward to prove in RCA 0that for all E ∈ S , χ(E )is a measurable characteristic function.For the converse,let f be a measurable characteristic function.By Definition 4.4we have that f (x )∈{0,1}for all x ∈ ∞n =0C n .ByProposition 4.1we have |f (x )−f 3n +3(x )|<1/2n for all x ∈C f n .Put U n ={x ||f 3n +3(x )−1|<1/2n }and V n ={x ||f 3n +3(x )|<1/2n }.Then for n ≥1,U n and V n are disjoint open sets.Moreover C n ∩C f n ⊆U n ∪V n ,hence µL (U n ∪V n )≥1−1/2n −1.By countable additivity(Theorem 3.3)we can effectively find E n ,F n ∈S such that E n ⊆U n and F n ⊆V n and µL (E n ∪F n )≥1−1/2n −2.Put E = E n +5 n ∈N .It is straightforward to show that E belongs to S and that χ(E )=f in L 1([0,1],µL ).This completes the proof.Remark 4.7.We have presented two notions of Lebesgue measurable set and shown that they are equivalent in WWKL 0.Our first notion (Definition 4.4)has the advantage of generality in that it applies to any measure on a compact separable metric space.Our second no-tion (Definition 4.5)is advantageous in other ways,namely it is more straightforward and works well in RCA 0.It would be desirable to find a single definition which combines all of these advantages.VITALI’S THEOREM AND WWKL 135.Vitali’s TheoremLet S be a collection of sets.A point x is said to be Vitali covered by S if for all >0there exists S ∈S such that x ∈S and the diameter of S is less than .The Vitali Covering Theorem in its simplest form says the following:if I is a sequence of intervals which Vitali covers an interval E in the real line,then I contains a countable,pairwise disjoint set of intervals I n ,n ∈N ,such that ∞n =0I n covers E except for a set of Lebesgue measure 0.The purpose of this section is to show that various forms of the Vitali Covering Theorem are provable in WWKL 0and in fact equivalent to WWKL over RCA 0.Throughout this section,we use µto denote Lebesgue measure.Lemma 5.1(Baby Vitali Lemma).The following is provable in RCA 0.Let I 0,...,I n be a finite sequence of intervals.Then we can find a pair-wise disjoint subsequence I k 0,...,I k m such thatµ(I k 0∪···∪I k m )≥1VITALI’S THEOREM AND WWKL 14I =[2a −b,2b −a ].)Thusµ(I 0∪···∪I n )≤µ(I k 0∪···∪I k m )≤µ(I k 0)+···+µ(I k m )=3µ(I k 0)+···+3µ(I k m )=3µ(I k 0∪···∪I k m )and the lemma is proved.Lemma 5.2.The following is provable in WWKL 0.Let E be an in-terval,and let I n ,n ∈N ,be a sequence of intervals.If E ⊆ ∞n =0I n ,then µ(E )≤lim k →∞µ k n =0I n .Proof.If the intervals I n are open,then the desired conclusion follows immediately from countable additivity (Theorem 3.3).Otherwise,fix >0and let I n be an open interval with the same midpoint as I n andµ(I n )=µ(I n )+µ(E \A ).(3)VITALI’S THEOREM AND WWKL 15To prove the claim,use Lemma 5.2and the Vitali property to find a finite set of intervals J 1,...,J l ∈I such that J 1,...,J l ⊆E \A andµ(E \(A ∪J 1∪···∪J l ))<13µ(J 1∪···∪J l ).We then have µ(E \(A ∪I 1∪···∪I k ))<212µ(E \A )≤212µ(E \A )=34nµ(E ).Then by countable additivity we have µ E \∞ n =1A n =0and the lemma is proved.Remark 5.4.It is straightforward to generalize the previous lemma to the case of a Vitali covering of the n -cube [0,1]n by closed balls or n -dimensional cubes.In the case of closed balls,the constant 3in the Baby Vitali Lemma 5.1is replaced by 3n .Theorem 5.5.The Vitali theorem for the interval [0,1](as stated in Lemma 5.3)is equivalent to WWKL over RCA 0.Proof.Lemma 5.3shows that,in RCA 0,WWKL implies the Vitali theorem for intervals.It remains to prove within RCA 0that the Vitali theorem for [0,1]implies WWKL.Instead of proving WWKL,we shall prove the equivalent statement 3.3.3.Reasoning in RCA 0,suppose thatVITALI’S THEOREM AND WWKL 16(a n ,b n ),n ∈N ,is a sequence of open intervals which covers [0,1].Let I be the countable set of intervals (a nki ,b nki )= a n +i k(b n −a n ) where i,k,n ∈N and 0≤i <k .Then I is a Vitali covering of [0,1].By the Vitali theorem for intervals,I contains a sequence of pairwise disjoint intervals I m ,m ∈N ,such that µ ∞ m =0I m ≥1.By disjoint countable additivity (Corollary 2.5),we have∞m =0µ(I m )≥1.From this it follows easily that∞n =0|a n −b n |≥1.Thus we have 3.3.3and our theorem is proved.We now turn to Vitali’s theorem for measurable sets.Recall our discussion of measurable sets in section 4.A sequence of intervals I is said to almost Vitali cover a Lebesgue measurable set E ⊆[0,1]if for all >0we have µL (E \O )=0,where O = {I |I ∈I ,diam(I )< }.Theorem 5.6.The following is provable in WWKL 0.Let E ⊆[0,1]be a Lebesgue measurable set with µ(E )>0.Let I be a sequence of intervals which almost Vitali covers E .Then I contains a pairwise disjoint sequence of intervals I n ,n ∈N ,such that µ E \∞ n =0I n =0.Proof.The proof of this theorem is similar to that of Lemma 5.3.The only modification needed is in the proof of the claim.Recall from Definition 4.5that E =lim n →∞E n where each E n is a finite union of intervals in [0,1].Fix m so large thatµ((E \E m )∪(E m \E ))<1VITALI’S THEOREM AND WWKL 17andµ(E m \(A ∪J 1∪···∪J l ))<136µ(E \A )<236µ(E \A )≤236µ(E \A )<236µ(E \A )=3,The Baire category theorem in weak subsystems of second order arith-metic ,Journal of Symbolic Logic 58(1993),557–578.7.O.Demuth and A.Kuˇc era,Remarks on constructive mathematical analysis ,[3],1979,pp.81–129.8.H.-D.Ebbinghaus,G.H.M¨u ller,and G.E.Sacks (eds.),Recursion Theory Week ,Lecture Notes in Mathematics,no.1141,Springer-Verlag,1985,IX +418pages.VITALI’S THEOREM AND WWKL189.Harvey Friedman,unpublished communication to Leo Harrington,1977.10.Harvey Friedman,Stephen G.Simpson,and Rick L.Smith,Countable algebraand set existence axioms,Annals of Pure and Applied Logic25(1983),141–181.11.,Randomness and generalizations offixed point free functions,[1],1990, pp.245–254.15.,Subsystems of Second Order Arithmetic,Perspectives in Mathematical Logic,Springer-Verlag,1998,XIV+445pages.21.Xiaokang Yu,Measure Theory in Weak Subsystems of Second Order Arithmetic,Ph.D.thesis,Pennsylvania State University,1987,vii+73pages.22.,Riesz representation theorem,Borel measures,and subsystems of sec-ond order arithmetic,Annals of Pure and Applied Logic59(1993),65–78. 24.,A study of singular points and supports of measures in reverse mathe-matics,Annals of Pure and Applied Logic79(1996),211–219.26.Xiaokang Yu and Stephen G.Simpson,Measure theory and weak K¨o nig’slemma,Archive for Mathematical Logic30(1990),171–180.E-mail address:dkb5@,giusto@dm.unito.it,simpson@ The Pennsylvania State University。
原子核集体激发的费米液体模型研究
Giant dipole resonances of finite nuclei
The isoscalar giant dipole resonance in Pb208 with a centroid energy E=22.5MeV should be a compression mode, which corresponds to a creation of the L=1 collective excitation of protons or neutrons and an annihilation of the L=1 collective excitation of neutrons or protons simultaneously.
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Relation between isoscalar and isovector giant resonances
The nuclear isovector giant resonances correspond to the nuclear collective excitation that the collective excitation of protons is creating with the energy ES(l), while the collective excitation of neutrons is annihilating with the energy ES(l), and vice versa.
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affect in language learning
Approaches and Methods in Language Teaching by Jack C. Richards and Theodore S. Rodgers
Appropriate Methodology and Social Context by Adrian Holliday Beyond Training by Jack C. Richards Collaborative Language Learning and Teaching edited by David Nunan Communicative Language Teaching by William Littlewood Communicative Methodology in Language Teaching by Christopher Brum®t Course Design by Fraida Dubin and Elite Olshtain Culture Bound edited by Joyce Merrill Valdes Designing tasks for the Communicative Classroom by David Nunan Developing Reading Skills by FrancËoise Grellet Developments in ESP by Tony Dudley-Evans and Maggie Jo St John Discourse Analysis for Language Teachers by Michael McCarthy Discourse and Language Education by Evelyn Hatch English for Academic Purposes by R.R. Jordan English for Speci®c Purposes by Tom Hutchinson and Alan Waters Focus on the Language Classroom by Dick Allwright and Kathleen M. Bailey Foreign and Second Language Learning by William Littlewood Language Learning in Intercultural Perspective edited by Michael Byram and
skewnesskurtosis峰度,偏度介绍
Skewness, Kurtosis, and the Normal Curve ©SkewnessIn everyday language, the terms “skewed” and “askew” are used to refer to something that is out of line or distorted on one side. When referring to the shape offrequency or probability distributions, “skewness” refers to asymmetry of the distribution. A distribution with an asymmetric tail extending out to the right is referred to as“positively skewed” or “skewed to the right,” while a distribution with an asymmetric tail extending out to the left is referred to as “negatively skewed” or “skewed to the left.” Skewness can range from minus infinity to positive infinity.Karl Pearson (1895) first suggested measuring skewness by standardizing thedifference between the mean and the mode, that is, σμmode-=sk . Population modesare not well estimated from sample modes, but one can estimate the differencebetween the mean and the mode as being three times the difference between the mean and the median (Stuart & Ord, 1994), leading to the following estimate of skewness:sM sk est median)(3-=. Many statisticians use this measure but with the ‘3’ eliminated, that is, sM sk median)(-=. This statistic ranges from -1 to +1. Absolute values above0.2 indicate great skewness (Hildebrand, 1986).Skewness has also been defined with respect to the third moment about themean: 331)(σμγn X -∑=, which is simply the expected value of the distribution of cubed zscores. Skewness measured in this way is sometimes referred to as “Fisher’sskew ness.” When the deviations from the mean are greater in one direction than in the other direction, this statistic will deviate from zero in the direction of the larger deviations. From sample data, Fisher’s skewness is most often estimated by:)2)(1(31--∑=n n z n g . For large sample sizes (n > 150), g 1 may be distributedapproximately normally, with a standard error of approximately n /6. While one could use this sampling distribution to construct confidence intervals for or tests of hypotheses about γ1, there is rarely any value in doing so.The most commonly used measures of skewness (those discussed here) may produce surprising results, such as a negative value when the shape of the distributionappears skewed to the right. There may be superior alternative measures not commonly used (Groeneveld & Meeden, 1984).It is important for behavioral researchers to notice skewness when it appears in their data. Great skewness may motivate the researcher to investigate outliers. When making decisions about which measure of location to report (means being drawn in the direction of the skew) and which inferential statistic to employ (one which assumes normality or one which does not), one should take into consideration the estimated skewness of the population. Normal distributions have zero skewness. Of course, a distribution can be perfectly symmetric but far from normal. Transformations commonly employed to reduce (positive) skewness include square root, log, and reciprocal transformations.Also see Skewness and the Relative Positions of Mean, Median, and Mode KurtosisKarl Pearson (1905) defined a distribution’s degree of kurtosis as 32-=βη,where 442)(σμβn X -∑=, the expected value of the distribution of Z scores which havebeen raised to the 4th power. β2 is often referred to as “Pearson’s kurtosis,” and β2 - 3 (often symbolized with γ2 ) as “kurtosis excess” or “Fisher’s kurtosis,” even though it was Pearson who defined kurtosis as β2 - 3. An unbiased estimator for γ2 is)3)(2()1(3)3)(2)(1()1(242-------∑+=n n n n n n Z n n g . For large sample sizes (n > 1000), g 2 may bedistributed approximately normally, with a standard error of approximately n /24 (Snedecor, & Cochran, 1967). While one could use this sampling distribution to construct confidence intervals for or tests of hypotheses about γ2, there is rarely any value in doing so.Pearson (1905) introduced kurtosis as a measure of how flat the top of asymmetric distribution is when compared to a normal distribution of the same variance. He referred to more flat-topped distributions (γ2 < 0) as “platykurtic,” less flat -topped distributions (γ2 > 0) as “leptokurtic,” and equally flat -topped distributions as“mesokurtic” (γ2 ≈ 0). Kurtosis is actually more influenced by scores in the tails of the distribution than scores in the center of a distribution (DeCarlo, 1967). Accordingly, it is often appropriate to describe a leptokurtic distribution as “fat in the tails” and a platykurtic distribution as “thin in the tails.”Student (1927, Biometrika , 19, 160) published a cute description of kurtosis, which I quote here: “Platykurtic curves have shorter ‘tails’ than the normal curve of error and leptokurtic longer ‘tails.’ I myself bear in mind the meaning of the words by the above memoria technica , where the first figure represents platypus and the secondkangaroos, noted for lepping.” Please point your browser tomembers.aol./jeff570/k.html , scroll down to “kurtosis,” and look at Student’s drawings. Moors (1986) demonstrated that 1)(22+=Z Var β. Accordingly, it may be best to treat kurtosis as the extent to which scores are dispersed away from the shoulders of a distribution, where the shoulders are the points where Z 2 = 1, that is, Z = ±1. Balanda and MacGillivray (1988) wrote “it is best to define kurtosis vaguely as the location - and scale-free movement of probability mass from the shoulders of a distribution into its centre and tails.” If one starts with a normal distribution and mo ves scores from theshoulders into the center and the tails, keeping variance constant, kurtosis is increased. The distribution will likely appear more peaked in the center and fatter in the tails, like aLaplace distribution (32=γ) or Student’s t with few degrees of freedom (462-=df γ).Starting again with a normal distribution, moving scores from the tails and the center to the shoulders will decrease kurtosis. A uniform distribution certainly has a flat top, with 2.12-=γ, but γ2 can reach a minimum value of -2 when two score values are equally probably and all other score values have probability zero (a rectangular Udistribution , that is, a binomial distribution with n =1, p = .5). One might object that the rectangular U distribution has all of its scores in the tails, but closer inspection will reveal that it has no tails, and that all of its scores are in its shoulders, exactly one standard deviation from its mean. Values of g 2 less than that expected for an uniform distribution (-1.2) may suggest that the distribution is bimodal (Darlington, 1970), but bimodal distributions can have high kurtosis if the modes are distant from the shoulders. One leptokurtic distribution we shall deal with is Student’s t distribution. The kurtosis of t is infinite when df < 5, 6 when df = 5, 3 when df = 6. Kurtosis decreases further (towards zero) as df increase and t approaches the normal distribution. Kurtosis is usually of interest only when dealing with approximately symmetric distributions. Skewed distributions are always leptokurtic (Hopkins & Weeks, 1990). Among the several alternative measures of kurtosis that have been proposed (none of which has often been employed), is one which adjusts the measurement of kurtosis to remove the effect of skewness (Blest, 2003).There is much confusion about how kurtosis is related to the shape ofdistributions. Many authors of textbooks have asserted that kurtosis is a measure of the peakedness of distributions, which is not strictly true.It is easy to confuse low kurtosis with high variance, but distributions with identical kurtosis can differ in variance, and distributions with identical variances can differ in kurtosis. Here are some simple distributions that may help you appreciate that kurtosis is, in part, a measure of tail heaviness relative to the total variance in the distribution (remember the “σ4” in the denominator).Table 1.Kurtosis for 7 Simple Distributions Also Differing in VariancePlatykurtic Leptokurtic When I presented these distributions to my colleagues and graduate students and asked them to identify which had the least kurtosis and which the most, all said A has the most kurtosis, G the least (excepting those who refused to answer). But in fact A has the least kurtosis (-2 is the smallest possible value of kurtosis) and G the most. The trick is to do a mental frequency plot where the abscissa is in standard deviation units. In the maximally platykurtic distribution A, which initially appears to have all its scores in its tails, no score is more than one σ away from the mean - that is, it has no tails! In the leptokurtic distribution G, which seems only to have a few scores in its tails, one must remember that those scores (5 & 15) are much farther away from the mean (3.3 σ) than are the 5’s & 15’s in distribution A. In fact, in G nine percent of the scores are more than three σ from the mean, much more than you would expect in a mesokurtic distribution (like a normal distribution), thus G does indeed have fat tails.If you were you to ask SAS to compute kurtosis on the A scores in Table 1, you would get a value less than -2.0, less than the lowest possible population kurtosis. Why? SAS assumes your data are a sample and computes the g2 estimate of population kurtosis, which can fall below -2.0.Sune Karlsson, of the Stockholm School of Economics, has provided me with the following modified example which holds the variance approximately constant, making it quite clear that a higher kurtosis implies that there are more extreme observations (or that the extreme observations are more extreme). It is also evident that a higher kurtosis also implies that the distribution is more ‘single-peaked’ (this would be even more evident if the sum of the frequencies was constant). I have highlighted the rows representing the shoulders of the distribution so that you can see that the increase in kurtosis is associated with a movement of scores away from the shoulders.Table 2.Kurtosis for Seven Simple Distributions Not Differing in VarianceWhile is unlikely that a behavioral researcher will be interested in questions that focus on the kurtosis of a distribution, estimates of kurtosis, in combination with other information about the shape of a distribution, can be useful. DeCarlo (1997) described several uses for the g2 statistic. When considering the shape of a distribution of scores, it is useful to have at hand measures of skewness and kurtosis, as well as graphical displays. These statistics can help one decide which estimators or tests should perform best with data distributed like those on hand. High kurtosis should alert the researcher to investigate outliers in one or both tails of the distribution.Tests of SignificanceSome statistical packages (including SPSS) provide both estimates of skewness and kurtosis and standard errors for those estimates. One can divide the estimate by it’s standard error to obtain a z test of the null hypothesis that the parameter is zero (as would be expected in a normal population), but I generally find such tests of little use. One may do an “eyeball test” on measures of skewness and kurtosis when deciding whether or not a sample is “normal enough” to use an inferential procedure that assumes normality of the population(s). If you wish to test the null hypothesis that the sample came from a normal population, you can use a chi-square goodness of fit test, comparing observed frequencies in ten or so intervals (from lowest to highest score) with the frequencies that would be expected in those intervals were the population normal. This test has very low power, especially with small sample sizes, where the normality assumption may be most critical. Thus you may think your data close enough to normal (not significantly different from normal) to use a test statistic which assumes normality when in fact the data are too distinctly non-normal to employ such a test, the nonsignificance of the deviation from normality resulting only from low power, small sample sizes. SAS’ PROC UNIVARIATE will test such a null hypothesis for you using the more powerful Kolmogorov-Smirnov statistic (for larger samples) or the Shapiro-Wilks statistic (for smaller samples). These have very high power, especially with large sample sizes, in which case the normality assumption may be less critical for the test statistic whose normality assumption is being questioned. These tests may tell you that your sample differs significantly from normal even when the deviation from normality is not large enough to cause problems with the test statistic which assumes normality. SAS ExercisesGo to my StatData page and download the file EDA.dat. Go to my SAS-Programs page and download the program file g1g2.sas. Edit the program so that the INFILE statement points correctly to the folder where you located EDA.dat and then run the program, which illustrates the computation of g1 and g2. Look at the program. The raw data are read from EDA.dat and PROC MEANS is then used to compute g1 and g2. The next portion of the program uses PROC STANDARD to convert the data to z scores. PROC MEANS is then used to compute g1 and g2 on the z scores. Note that standardization of the scores has not changed the values of g1 and g2. The next portion of the program creates a data set with the z scores raised to the 3rd and the 4th powers. The final step of the program uses these powers of z to compute g1 and g2 using the formulas presented earlier in this handout. Note that the values of g1 and g2 are the same as obtained earlier from PROC MEANS.Go to my SAS-Programs page and download and run the file Kurtosis-Uniform.sas. Look at the program. A DO loop and the UNIFORM function are used to create a sample of 500,000 scores drawn from a uniform population which ranges from 0 to 1. PROC MEANS then computes mean, standard deviation, skewness, and kurtosis. Look at the output. Compare the obtained statistics to the expected valuesfor the following parameters of a uniform distribution that ranges from a to b:Parameter Expected ValueParameter Expected ValueMean2ba +SkewnessStandard Deviation 12)(2a b - Kurtosis -1.2Go to my SAS-Programs page and download and run the file “Kurtosis-T.sas ,” which demonstrates the effect of sample size (degrees of freedom) on the kurtosis of the t distribution. Look at the program. Within each section of the program a DO loop is used to create 500,000 samples of N scores (where N is 10, 11, 17, or 29), each drawn from a normal population with mean 0 and standard deviation 1. PROC MEANS is then used to compute Student’s t for each sample, outputting these t scores into a new data set. We shall treat this new data set as the sampling distribution of t . PROC MEANS is then used to compute the mean, standard deviation, and kurtosis of the sampling distributions of t . For each value of degrees of freedom, compare the obtained statistics with their expected values.Mean Standard DeviationKurtosis2-df df46-dfDownload and run my program Kurtosis_Beta2.sas . Look at the program. Each section of the program creates one of the distributions from Table 1 above and then converts the data to z scores, raises the z scores to the fourth power, andcomputes β2 as the mean of z 4. Subtract 3 from each value of β2 and then compare the resulting γ2 to the value given in Table 1.Download and run my program Kurtosis-Normal.sas . Look at the program. DO loops and the NORMAL function are used to create 10,000 samples, each with 1,000 scores drawn from a normal population with mean 0 and standard deviation 1. PROC MEANS creates a new data set with the g 1 and the g 2 statistics for each sample. PROC MEANS then computes the mean and standard deviation (standard error) for skewness and kurtosis. Compare the values obtained with those expected, 0 for the means, and n /6 and n /24 for the standard errors.ReferencesBalanda & MacGillivray. (1988). Kurtosis: A critical review. American Statistician, 42: 111-119. Blest, D.C. (2003). A new measure of kurtosis adjusted for skewness. Australian &New Zealand Journal of Statistics, 45, 175-179.Darlington, R.B. (1970). Is kurtosis really “peakedness?” The American Statistician, 24(2), 19-22.DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological Methods, 2, 292-307. Groeneveld, R.A. & Meeden, G. (1984). Measuring skewness and kurtosis. The Statistician, 33, 391-399.Hildebrand, D. K. (1986). Statistical thinking for behavioral scientists. Boston: Duxbury. Hopkins, K.D. & Weeks, D.L. (1990). Tests for normality and measures of skewness and kurtosis: Their place in research reporting. Educational and Psychological Measurement, 50, 717-729.Loether, H. L., & McTavish, D. G. (1988). Descriptive and inferential statistics: An introduction , 3rd ed. Boston: Allyn & Bacon.Moors, J.J.A. (1986). The meaning of kurtosis: Darlington reexamined. The American Statistician, 40, 283-284.Pearson, K. (1895) Contributions to the mathematical theory of evolution, II: Skew variation in homogeneous material. Philosophical Transactions of the Royal Society of London, 186, 343-414.Pearson, K. (1905). Das Fehlergesetz und seine Verallgemeinerungen durch Fechner und Pearson. A Rejoinder. Biometrika, 4, 169-212.Snedecor, G.W. & Cochran, W.G. (1967). Statistical methods (6th ed.), Iowa State University Press, Ames, Iowa.Stuart, A. & Ord, J.K. (1994). Kendall’s advanced theory of statistics. Volume 1. Distribution Theory. Sixth Edition. Edward Arnold, London.Wuensch, K. L. (2005). Kurtosis. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (pp. 1028 - 1029). Chichester, UK: Wiley.Wuensch, K. L. (2005). Skewness. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (pp. 1855 - 1856). Chichester, UK: Wiley.Links•/psyc/wuenschk/StatHelp/KURTOSIS.txt -- a log of email discussions on the topic of kurtosis, most of them from the EDSTAT list.•/psyc/WuenschK/docs30/Platykurtosis.jpg -- distribution of final grades in PSYC 2101 (undergrad stats), Spring, 2007.Copyright 2007, Karl L. Wuensch - All rights reserved.Return to My Statistics Lessons Page。
图像融合文献
计算机医学影像与图形第37卷,第2次发行,2013年3月,页码162–173核磁共振影像-3维超声波影像-X射线影像的电磁跟踪图像融合用于注射进心脏内膜的治疗:菌体悬浮液的有效性和可行性作者:查尔斯·瑞特哈特(威斯康辛-麦迪逊大学工程学院生物医学工程系,工程始于1415年,美国麦迪逊威斯康辛州53706),阿密特·凯吉安娜(北美飞利浦研究所,布莱尔克利夫庄园斯卡伯勒路345号,美国纽约10510),维贾伊·帕塔萨拉蒂(北美飞利浦研究所,布莱尔克利夫庄园斯卡伯勒路345号,美国纽约10510),安德鲁·朗(北美飞利浦研究所,布莱尔克利夫庄园斯卡伯勒路345号,美国纽约10510),阿米什·埃拉瓦尔(威斯康辛-麦迪逊大学医学与公共卫生学院心脑血管医学分部,美国麦迪逊威斯康辛州高地大街600号53792)摘要:心肌梗塞是全球导致死亡的主要原因之一。
在小型动物身上的研究已经表明干细胞疗法对心肌梗塞的治疗有显著的作用。
已经提出了一种心内膜心肌导管注射法注入中介传输部位治疗,通过加强细胞的记忆来提高有效性。
当避免危及生命的心肌穿孔时,精确目标定位能到达大块潜在的治疗区域是很关键的。
已经提出的多模块图像融合法是提高这些程序的一种方式,它通过用高分辨率的前端程序成像来增加原有的手术期间成像种类。
原来的方法一直以来缺少组织细胞成像,而且依赖于X射线成像来追踪设备,这些特点使得电离辐射剂量增加。
本文介绍了一种新型的基于导管的靶向治疗图像融合系统,这个系统暂存超声波心动描记术,磁共振,X射线和电磁传感信号来追踪单个有弹性的组织框架。
所有系统的校正与注册都是有效的,而且在最坏情况下目标注册误差小于5 mm。
在运动过程注入会产生心脏注入幻影,这时定位精度的变化范围是0.57~3.81 mm。
临床上的可行性已经在猪体内试验成功,在这个实验中已成功实现定位到心脏区域注射。
关键字:心脏干预;干细胞治疗法;3维超声波;核磁共振成像;X射线;电磁跟踪;图像融合Computerized Medical Imaging and GraphicsVolume 37, Issue 2, March 2013, Pages 162–173MRI—3D ultrasound—X-ray image fusion with electromagnetic tracking for transendocardial therapeutic injections: In-vitro validation and in-vivo feasibility∙Charles R. Hatt, , (University of Wisconsin – Madison, College of Engineering, Department of Biomedical Engineering, 1415 Engineering Drive, Madison, WI 53706, USA)∙Ameet K. Jain, (Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA)∙Vijay Parthasarathy,(Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA)∙Andrew Lang, (Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA)∙Amish N. Raval(University of Wisconsin – Madison, School of Medicine and Public Health, Division of Cardiovascular Medicine, 600 Highland Avenue, Madison, WI 53792, USA)accuracy was validated in a motion enabled cardiac injection phantom, where targeting accuracy ranged from 0.57 to 3.81 mm. Clinical feasibility was demonstrated with in-vivo swine experiments, where injections were successfully made into targeted regions of the heart.Keywords:Cardiac interventions; Stem-cell therapy; 3D ultrasound; MRI; X-ray; Electromagnetic tracking; Image fusion。
数学学院硕士研究生课程内容简介
数学与统计学院硕士研究生课程内容简介学科基础课-------------------- 泛函分析--------------------课程编号:1 课程类别:学科基础课课程名称:泛函分析英文译名:Functional Analysis学时:60学时学分:3学分开课学期:1 开课形式:课堂讲授考核形式:闭卷考试适用学科:基础数学、应用数学、运筹与控制论、课程与教学论授课单位及教师梯队:数学与统计学院,基础数学系教师。
内容简介:本课程介绍紧算子与Fredholm算子、抽象函数简介、Banach代数的基本知识、C*代数、Hilbert 空间上的正常算子、无界正常算子的谱分解、自伴扩张、无界算子序列的收敛性、算子半群、抽象空间常微分方程。
主要教材:张恭庆、郭懋正:《泛函分析讲义》(下册),北京大学出版社,1990年版。
参考书目(文献):1.定光桂:《巴拿赫空间引论》,科学出版社,1984年版。
2.M. Reed, B. Simon, Methods of Modern Mathematical Physics I, Functional Analysis, 1972.3.K. Yosida, Functional Analysis, Sixth Edition, 1980.4.张恭庆、林源渠:《泛函分析讲义》(上册),北京大学出版社,1987。
5.V. Barbu, Nonlinear Semigroups and Differential Equations in Banach Spaces, 1976.6.A. Pazy, Semigroup of Linear Operators and Applications to Partial Differential Equations, 1983.-------------------- 非线性泛函分析--------------------课程编号:2 课程类别:学科基础课课程名称:非线性泛函分析英文译名:Nonlinear Functional Analysis学时:60学时学分:3学分开课学期:2 开课形式:课堂讲授考核形式:闭卷考试适用学科:应用数学、基础数学、运筹学与控制论授课单位及教师梯队:数学与统计学院,应用数学系教师。
J. reine angew. Math. 549 (2002), 47—77 Journal für die reine und angewandte Mathematik (
J.reine angew.Math.549(2002),47—77Journal fu¨r die reine undangewandte Mathematik(Walter de GruyterBerlinÁNew York2002Discrete constant mean curvature surfaces andtheir indexBy Konrad Polthier at Berlin and Wayne Rossman at KobeAbstract.We define triangulated piecewise linear constant mean curvature surfaces using a variational characterization.These surfaces are critical for area amongst continuous piecewise linear variations which preserve the boundary conditions,the simplicial structures, and(in the nonminimal case)the volume to one side of the surfaces.We thenfind explicit formulas for complete examples,such as discrete minimal catenoids and helicoids.We use these discrete surfaces to study the index of unstable minimal surfaces,by nu-merically evaluating the spectra of their Jacobi operators.Our numerical estimates confirm known results on the index of some smooth minimal surfaces,and provide additional in-formation regarding their area-reducing variations.The approach here deviates from other numerical investigations in that we add geometric interpretation to the discrete surfaces.1.IntroductionSmooth submanifolds,and surfaces in particular,with constant mean curvature(cmc) have a long history of study,and modern work in thisfield relies heavily on geometric and analytic machinery which has evolved over hundreds of years.However,nonsmooth sur-faces are also natural mathematical objects,even though there is less machinery available for studying them.For example,consider M.Gromov’s approach of doing geometry using only a set with a measure and a measurable distance function[9].Here we consider piecewise linear triangulated surfaces—we call them‘‘discrete surfaces’’—which have been brought more to the forefront of geometrical research by com-puter graphics.We define cmc for discrete surfaces in R3so that they are critical for volume-preserving variations,just as smooth cmc surfaces are.Discrete cmc surfaces have both in-teresting di¤erences from and similarities with smooth ones.For example,they are di¤erent in that smooth minimal graphs in R3over a bounded domain are stable,whereas discrete minimal graphs can be highly unstable.We will explore properties like this in section2.In section3we will see some ways in which these two types of surfaces are similar. We will see that:a discrete catenoid has an explicit description in terms of the hyperboliccosine function,just as the smooth catenoid has;and a discrete helicoid can be described with the hyperbolic sine function,just as a conformally parametrized smooth helicoid is;and there are discrete Delaunay surfaces which have translational periodicities,just as smooth Delaunay surfaces have.Pinkall and Polthier [17]used Dirichlet energy and a numerical minimization proce-dure to find discrete minimal surfaces.In this work,we rather have the goal to describe dis-crete minimal surfaces as explicitly as possible,and thus we are limited to the more funda-mental examples,for example the discrete minimal catenoid and helicoid.We note that these explicit descriptions will be useful test candidates when implementing a procedure that we describe in the next paragraphs.Discrete surfaces have finite dimensional spaces of admissible variations,therefore the study of linear di¤erential operators on the variation spaces reduces to the linear algebra of matrices.This advantage over smooth surfaces with their infinite dimensional variation spaces makes linear operators easier to handle in the discrete case.This suggests that a useful procedure for studying the spectra of the linear Jacobi operator in the second variation formula of smooth cmc surfaces is to consider the corre-sponding spectra of discrete cmc approximating surfaces.Although similar to the finite ele-ment method in numerical analysis,here the finite element approximations will have geo-metric and variational meaning in their own right.As an example,consider how one finds the index of a smooth minimal surface,that is the number of negative points in the spectrum.The standard approach is to replace the metric of the surface with the metric obtained by pulling back the spherical metric via the Gauss map.This approach can yield the index:for example,the indexes of a complete catenoid and a complete Enneper surface are 1([7]),the index of a complete Jorge-Meeks n -noid is 2n À3([12],[11])and the index of a complete genus k Costa-Ho¤man-Meeks surface is 2k þ3for every k e 37([14],[13]).However,this approach does not yield the eigenvalues and eigenfunctions on compact portions of the original minimal surfaces,as the metric has been changed.It would be interesting to know the eigenfunctions associated to negative eigenvalues since these represent the directions of variations that reduce area.The above procedure of approximating by discrete surfaces can provide this information.In sections 5and 6we establish some tools for studying the spectrum of discrete cmc surfaces.Then we test the above procedure on two standard cases—a (minimal)rectangle,and a portion of a smooth minimal catenoid bounded by two circles.In these two cases we know the spectra of the smooth surfaces (section 4),and we know the discrete minimal sur-faces as well (section 3),so we can check that the above procedure produces good approx-imations for the eigenvalues and smooth eigenfunctions (section 7),which indeed must be the case,by the theory of the finite element method [4],[8].With these successful tests,we go on to consider cases where we do not a priori know what the smooth eigenfunctions should be,such as the Jorge-Meeks 3-noid and the genus 1Costa surface (section 7).The above procedure can also be implemented using discrete approximating surfaces which are found only numerically and not explicitly,such as surfaces found by the method in [17].And in fact,we use the method in [17]to find approximating surfaces for the 3-noid and Enneper surface and Costa surface.Polthier and Rossman,Curvature surfaces48We note also that Ken Brakke’s surface evolver software [3]is an e‰cient tool for numerical index calculations using the same discrete ansatz.Our main emphasis here is to provide explicit formulations for the discrete Jacobi operator and other geometric proper-ties of discrete surfaces.Many of the discrete minimal and cmc surfaces introduced here are available as in-teractive models at EG-Models [19].2.Discrete minimal and cmc surfacesWe start with a variational characterization of discrete minimal and discrete cmc sur-faces.This characterization will allow us to construct explicit examples of unstable discrete cmc surfaces.Note that merely finding minima for area with respect to a volume constraint would not su‰ce for this as that would produce only stable examples.We will later use these discrete cmc surfaces for our numerical spectra computations.The following definitions for discrete surfaces and their variations work equally well in any ambient space R n but for simplicity we restrict to R 3.Definition 2.1.A discrete surface in R 3is a triangular mesh T which has the topology of an abstract 2-dimensional simplicial surface K combined with a geometric C 0realization in R 3that is piecewise linear on each simplex.The geometric realization j K j is determined by a set of vertices V ¼f p 1;...;p m g H R 3.T can be identified with the pair ðK ;V Þ.The simplicial complex K represents the connectivity of the mesh.The 0,1,and 2dimensional simplices of K represent the vertices,edges,and triangles of the discrete surface.Let T ¼ðp ;q ;r Þdenote an oriented triangle of T with vertices p ;q ;r A V .Let pq denote an edge of T with endpoints p ;q A V .For p A V ,let star ðp Þdenote the triangles of T that contain p as a vertex.For an edge pq ,let star ðpq Þdenote the (at most two)triangles of T that contain the edge pq .Definition 2.2.Let V ¼f p 1;...;p m g be the set of vertices of a discrete surface T .A variation T ðt Þof T is defined as a C 2variation of the vertices p iFigure 1.At each vertex p the gradient of discrete area is the sum of the p 2-rotated edge vectors J ðr Àq Þ,as in Equation(1).p i ðt Þ:½0;e Þ!R 3so that p i ð0Þ¼p i E i ¼1;...;m :The straightness of the edges and the flatness of the triangles are preserved as the vertices move.In the smooth situation,the variation at interior points is typically restricted to nor-mal variation,since the tangential part of the variation only performs a reparametrization of the surface.However,on discrete surfaces there is an ambiguity in the choice of normal vectors at the vertices,so we allow arbitrary variations.But we will later see (section 7)that our experimental results can accurately estimate normal variations of a smooth surface when the discrete surface is a close approximation to the smooth surface.In the following we derive the evolution equations for some basic entities under sur-face variations.The area of a discrete surface isarea ðT Þ:¼PT A T area T ;where area T denotes the Euclidean area of the triangle T as a subset of R 3.Let T ðt Þbe a variation of a discrete surface T .At each vertex p of T ,the gradient of area is‘p area T ¼12P T ¼ðp ;q ;r ÞA star pJ ðr Àq Þ;ð1Þwhere J is rotation of angle p 2in the plane of each oriented triangle T .The first derivative of the surface area is then given by the chain ruled dt area T ¼P p A Vh p 0;‘p area T i :ð2ÞThe volume of an oriented surface T is the oriented volume enclosed by the cone of the surface over the origin in R 3vol T :¼16P T ¼ðp ;q ;r ÞA T h p ;q Âr i ¼13P T ¼ðp ;q ;r ÞA Th ~N ;p i Áarea T ;where p is any of the three vertices of the triangle T and~N¼ðq Àp ÞÂðr Àp Þ=jðq Àp ÞÂðr Àp Þj is the oriented normal of T .It follows thatPolthier and Rossman,Curvature surfaces50‘p vol T¼PT¼ðp;q;rÞA star p qÂr=6ð3Þandd dt vol T¼Pp A Vh p0;‘p vol T i:ð4ÞRemark2.1.Note also that‘p vol T¼PT¼ðp;q;rÞA star p À2Áarea TÁ~NþpÂðrÀqÞÁ=6.Furthermore,if p is an interior vertex,then the boundary of star p is closed and PT A star ppÂðrÀqÞ¼0.Hence the qÂr in Equation(3)can be replaced with2Áarea TÁ~N whenever p is an interior vertex.In the smooth case,a minimal surface is critical with respect to area for any variation thatfixes the boundary,and a cmc surface is critical with respect to area for any variation that preserves volume andfixes the boundary.We wish to define discrete cmc surfaces so that they have the same variational properties for the same types of variations.So we will consider variations TðtÞof T thatfix the boundary q T and that additionally preserve volume in the nonminimal case,which we call permissible variations.The condition that makes a discrete surface area-critical for any permissible variation is expressed in the fol-lowing definition.Definition2.3.A discrete surface has constant mean curvature(cmc)if there exists a constant H so that‘p area¼H‘p vol for all interior vertices p.If H¼0then it is minimal.This definition for discrete minimality has been used in[17].In contrast,our definition of discrete cmc surfaces di¤ers from[15],where cmc surfaces are characterized algorithm-ically using discrete minimal surfaces in S3and a conjugation pare also [2]for a definition via discrete integrable systems which lacks variational properties.Remark2.2.If T is a discrete minimal surface that contains a simply-connected dis-crete subsurface T0that lies in a plane,then it follows easily from Equation(1)that the dis-crete minimality of T is independent of the choice of triangulation of the trace of T0.2.0.1.Notation from th e th eoryoffinite elements.Consider a vector-valued functionv pj A R3defined on the n interior vertices V int¼f p1;...;p n g of T.We may extend thisfunction to the boundary vertices of T as well,by assuming v p¼~0A R3for each boundaryvertex p.The vectors v pj are the variation vectorfield of any boundary-fixing variation ofthe formp jðtÞ¼p jþtÁv pj þOðt2Þ;ð5Þthat is,p0jð0Þ¼v pj.We define the vector~v A R3n by~v t¼ðv t p1;...;v tp nÞ:ð6ÞThe variation vectorfield~v can be naturally extended to a piece-wise linear continuous R3-valued function v on T,with v in the following vector space:Polthier and Rossman,Curvature surfaces51Definition2.4.On a discrete surface T we define the space of piecewise linear functionsS h:¼f v:T!R3j v A C0ðTÞ;v is linear on each T A T and v j q T¼0g: This space is named S h,as in the theory offinite elements.Note that any compo-nent function of any function v A S h has bounded Sobolev H1norm.For each triangle T¼ðp;q;rÞin T and each v A S h,v j T ¼v p c pþv q c qþv r c r;ð7Þwhere c p:T!R is the head function on T which is1at p and is0at all other vertices ofT and extends linearly to all of T in the unique way.The functions c pj form a basis(withscalars in R3)for the3n-dimensional space S h.2.0.2.Non-uniqueness of discrete minimal disks.Uniqueness of a bounded mini-mal surface with a given boundary ensures that it is stable.For smooth minimal surfaces, uniqueness can sometimes be decided using the maximum principle of elliptic equations, which ensures that the minimal surface is contained in the convex hull of its boundary, and,if the boundary has a1-1projection to a convex planar curve,then it is unique for that boundary and is a minimal graph.The maximum principle also shows that any mini-mal graph is unique even when the projection of its boundary is not convex.More gener-ally,stability still holds when the surface merely has a Gauss map image contained in a hemisphere,as shown in[1](although their proof employs tools other than the maximum principle).However,such statements do not hold for discrete minimal surfaces.Consider the surface shown in the left-hand side of Figure2,whose height function has a local maxi-mum at an interior vertex.This example does not lie in the convex hull of its boundary and thereby disproves the general existence of a discrete version of the maximum principle.Also, the three surfaces on the right-hand side in Figure3are all minimal graphs over an annular domain with the same boundary contours and the same simplicial structure,and yet they are not the same surfaces,hence graphs with given simplicial structure are not unique.And the left-hand surface in Figure3is a surface whose Gauss map is contained in a hemisphere but which is unstable(this surface is not a graph)—another example of this property is the first annular surface in Figure3,which is also unstable.(We define stability of discrete cmc surfaces in section5.)The influence of the discretization on nonuniqueness,like as in the annular examples of Figure3,can also be observed in a more trivial way for a discrete minimal graph over a simply connected convex domain.The two surfaces on the right-hand side of Figure2have the same trace,i.e.they are identical as geometric surfaces,but they are di¤erent as discrete surfaces.Interior vertices may be freely added and moved inside the middle planar square without a¤ecting minimality(see Remark2.2).In contrast to existence of these counterexamples we believe that some properties of smooth minimal surfaces remain true in the discrete setting.We say that a discrete surface is a disk if it is homeomorphic to a simply connected domain.Conjecture2.1.Let T H R3be a discrete minimal disk whose boundary projects in-jectively to a convex planar polygonal curve,then T is a graph over that plane.The authors were able to prove this conjecture with the extra assumption that all the triangles of the surface are acute,using the fact that the maximum principle(a height function cannot attain a strict interior maximum)actually does hold when all triangles are acute.One can ask if a discrete minimal surface T with given simplicial structure and boundary is unique if it has a1-1perpendicular or central projection to a convex polygonal domain in a plane.The placement of the vertices need not be unique,as we saw in Remark 2.2,however,one can consider if there is uniqueness in the sense that the trace of T in R3is unique:Conjecture2.2.Let G H R3be a polygonal curve that eitherðAÞ:projects injec-tively to a convex planar polygonal curve,orðBÞ:has a1-1central projection from a point p A R3to a convex planar polygonal curve.Let K be a given abstract simplicial disk,and let g:q K!G be a given piecewise linear map.If T is a discrete minimal surface that is a geometric realization of K so that the map q K!q T equals g,then the trace of T in R3is uniquely determined.Furthermore,T is a graph in the caseðAÞ,and T is contained in the cone of G over p in the caseðBÞ.We have the following weaker form of Conjecture2.2,which follows from Corollary5.1of section5in the case that there is only one interior vertex:Conjecture 2.3.If a discrete minimal surface is a graph over a convex polygonal do-main ,then it is stable .3.Explicit discrete surfacesHere we describe explicit discrete catenoids and helicoids,which seem to be the first explicitly known nontrivial complete discrete minimal surfaces (with minimality defined variationally).3.1.Discrete minimal catenoids.To derive an explicit formula for embedded com-plete discrete minimal catenoids,we choose the vertices to lie on congruent planar polygo-nal meridians,with the meridians placed so that the traces of the surfaces will have dihedral symmetry.We will find that the vertices of a discrete meridian lie equally spaced on a smooth hyperbolic cosine curve.Furthermore,these discrete catenoids will converge uniformly in compact regions to the smooth catenoid as the mesh is made finer.We begin with a lemma that prepares the construction of the vertical meridian of the discrete minimal catenoid,by successively adding one horizontal ring after another starting from an initial ring.Since our construction will lead to pairwise coplanar triangles,the star of each individual vertex can be made to consist of four triangles (see Remark 2.2).We now derive an explicit representation of the position of a vertex surrounded by four such triangles in terms of the other four vertex positions.The center vertex is assumed to be coplanar with each of the two pairs of two opposite vertices,with those two planes becoming the plane of the vertical meridian and the horizontal plane containing a dihedrally symmetric polygonal ring (consisting of edges of the surface).See Figure 4.Lemma 3.1.Suppose we have four vertices p ¼ðd ;0;e Þ,q 1¼ðd cos y ;Àd sin y ;e Þ,q 2¼ða ;0;b Þ,and q 3¼ðd cos y ;d sin y ;e Þ,for given real numbers a ,b ,d ,e ,and angle y so that b 3e .Then there exists a choice of real numbers x and y and a fifth vertex q 4¼ðx ;0;y Þso that the discrete surface formed by the four triangles ðp ;q 1;q 2Þ,ðp ;q 2;q 3Þ,ðp ;q 3;q 4Þ,and ðp ;q 4;q 1Þis minimal ,i.e.‘p area ðstar p Þ¼0;if and only if2ad >ðe Àb Þ21þcos y:Figure 4.The construction in Lemma 3.1and a discrete minimal catenoid.Polthier and Rossman,Curvature surfaces54Furthermore,when x and y exist,they are unique and must be of the formx¼2ð1þcos yÞd3þðaþ2dÞðeÀbÞ2 2adð1þcos yÞÀðeÀbÞ2;y¼2eÀb:Proof.First we note that the assumption b3e is necessary.If b¼e,then one may choose y¼b,and then there is a free1-parameter family of choices of x,leading to a trivial planar surface.For simplicity we apply a vertical translation and a homothety about the origin of R3 to normalize d¼1,e¼0,and by doing a reflection if necesary,we may assume b<0.Let c¼cos y and s¼sin y.We derive conditions for the coordinate components of‘p area to vanish.The second component vanishes by symmetry of star ing the definitionsc1:¼ðaÀ1Þs2Àb2ð1ÀcÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2b2ð1ÀcÞþðaÀ1Þ2s2q;c2:¼abþbffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2b2ð1ÀcÞþðaÀ1Þ2s2q;thefirst(resp.third)component of‘p area vanishes ifc1¼y2ð1ÀcÞÀðxÀ1Þs2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2y2ð1ÀcÞþðxÀ1Þ2s2q;resp:c2¼ÀðxÀ1ÞyÀ2yffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2y2ð1ÀcÞþðxÀ1Þ2s2q:ð8ÞDividing one of these equations by the other we obtainxÀ1¼c2yð1ÀcÞþ2c1c2sÀc1yy;ð9Þso x is determined by y.It now remains to determine if one canfind y so that c2s2Àc1y30.If xÀ1is chosen as in equation(9),then thefirst minimality condition of equation(8)holds if and only if the second one holds as well.So we only need to insert this value for xÀ1into thefirst minimality condition and check for solutions y.When c130, wefind that the condition becomes1¼c2s2Àc1yj c2s2Àc1y jyj y jÀð1ÀcÞy2À2s2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2ð1ÀcÞc22s4þ4c21s2þÀ2ð1ÀcÞc21þs2ð1ÀcÞ2c22Áy2 q:SinceÀð1ÀcÞy2À2s2<0,note that this equation can hold only if c2s2Àc1y and y have opposite signs,so the equation becomes1¼ð1ÀcÞy2þ2s2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2ð1ÀcÞc22s4þ4c21s2þÀ2ð1ÀcÞc21þs2ð1ÀcÞ2c22Áy2q;Polthier and Rossman,Curvature surfaces55which simplifies to1¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffið1ÀcÞy2þ2s2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffið1ÀcÞc22s2þ2c21 q:This implies y2is uniquely determined.Inserting the valuey¼G b;onefinds that the above equation holds.When y¼b<0,wefind that c2s2Àc1y<0, which is impossible.When y¼Àb>0,wefind that c2s2Àc1y<0if and only if 2að1þcÞ>b2.And when y¼Àb and2að1þcÞ>b2,we have the minimality condition whenx¼2þ2cþab2þ2b2 2aþ2acÀb:Inverting the transformation we did at the beginning of this proof brings us back to the general case where d and e are not necessarily1and0,and the equations for x and y be-come as stated in the lemma.When c1¼0,we haveðaÀ1Þð1þcÞ¼b2andðxÀ1Þð1þcÞ¼y2,so,in particular, we have a>1and therefore2að1þcÞ>b2.The right-hand side of equation(8)implies y¼Àb and x¼a.Again,inverting the transformation from the beginning of this proof, we have that x and y must be of the form in the lemma for the case c1¼0as well.rThe next lemma provides a necessary and su‰cient condition for when two points lie on a scaled cosh curve,a condition that is identical to that of the previous lemma.That these conditions are the same is crucial to the proof of the upcoming theorem.Lemma3.2.Given two pointsða;bÞandðd;eÞin R2with b3e,and an angle y with j y j<p,there exists an r so that these two points lie on some vertical translate of the modified cosh curvegðtÞ¼0@r cosh teÀbarccosh1þ1rðeÀbÞ21þcos y!"#;t1A;t A R;if and only if2ad>ðeÀbÞ2 1þcos y.Proof.Define^d¼eÀb1þcos y.Without loss of generality,we may assume0<a e dand e>0,and henceÀe e b<e.If the pointsða;bÞandðd;eÞboth lie on the curve gðtÞ, thenarccosh1þ^d2r2!¼arccoshdrÀsignðbÞÁarccoshar;Polthier and Rossman,Curvature surfaces 56where signðbÞ¼1if b f0and signðbÞ¼À1if b<0.Note that if b¼0,then a must equalr(and so arccosh a r¼0).This equation is solvable(for either value of signðbÞ)if and only ifd r þffiffiffiffiffiffiffiffiffiffiffiffiffiffid2r2À1r!arþffiffiffiffiffiffiffiffiffiffiffiffiffia2r2À1r!¼1þ^d2r2þ^drffiffiffiffiffiffiffiffiffiffiffiffiffi2þ^d2r2swhen b e0,ord r þffiffiffiffiffiffiffiffiffiffiffiffiffiffid2rÀ1 sa r þffiffiffiffiffiffiffiffiffiffiffiffiffia2rÀ1s¼1þ^d2rþ^drffiffiffiffiffiffiffiffiffiffiffiffiffi2þ^d2rswhen b f0,for some r Að0;a .The right-hand side of these two equations has the follow-ing properties:(1)It is a nonincreasing function of r Að0;a .(2)It attains somefinite positive value at r¼a.(3)It is greater than the function2^d2=r2.(4)It approaches2^d2=r2asymptotically as r!0.The left-hand sides of these two equations have the following properties:(1)They attain the samefinite positive value at r¼a.(2)Thefirst one is a nonincreasing function of r Að0;a .(3)The second one is a nondecreasing function of r Að0;a .(4)The second one attains the value d=a at r¼0.(5)Thefirst one is less than the function4ad=r2.(6)Thefirst one approaches4ad=r2asymptotically as r!0.It follows from these properties that one of the two equations above has a solution for some r if and only if2ad>^d2.This completes the proof.rWe now derive an explicit formula for discrete minimal catenoids,by specifying the vertices along a planar polygonal meridian.Then the traces of the surfaces will have dihe-dral symmetry of order k f3.The surfaces are tessellated by planar isosceles trapezoids like a Z2grid,and each trapezoid can be triangulated into two triangles by choosing a di-Polthier and Rossman,Curvature surfaces57agonal of the trapeziod as the interior edge.Either diagonal can be chosen,as this does not a¤ect the minimality of the catenoid,by Remark 2.2.The discrete catenoid has two surprising features.First,the vertices of a meridian lie on a scaled smooth cosh curve (just as the profile curve of smooth catenoids lies on the cosh curve),and there is no a priori reason to have expected this.Secondly,the vertical spacing of the vertices along the meridians is constant.Theorem 3.1.There exists a four-parameter family of embedded and complete discrete minimal catenoids C ¼C ðy ;d ;r ;z 0Þwith dihedral rotational symmetry and planar meridians .If we assume that the dihedral symmetry axis is the z-axis and that a meridian lies in the xz-plane ,then ,up to vertical translation ,the catenoid is completely described by the following properties :(1)The dihedral angle is y ¼2p k,k A N ,k f 3.(2)The vertices of the meridian in the xz-plane interpolate the smooth cosh curvex ðz Þ¼r cosh 1raz ;witha ¼r d arccosh 1þ1r 2d 21þcos y!;where the parameter r >0is the waist radius of the interpolated cosh curve ,and d >0is the constant vertical distance between adjacent vertices of the meridian .(3)For any given arbitrary initial value z 0A R ,the profile curve has vertices of the form ðx j ;0;z j Þwithz j ¼z 0þj d ;x j ¼x ðz j Þ;where x ðz Þis the meridian in item 2above .(4)The planar trapezoids of the catenoid may be triangulated independently of each other (by Remark 2.2).Proof.By Lemma 3.1,if we have three consecutive vertices ðx n À1;z n À1Þ,ðx n ;z n Þ,and ðx n þ1;z n þ1Þalong the meridian in the xz -plane,they satisfy the recursion formulax n þ1¼ðx n À1þ2x n Þ^d 2þ2x 3n 2x n x n À1À^d 2;z n þ1¼z n þd ;ð10Þwhere d ¼z n Àz n À1and ^d¼d =ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1þcos y p .As seen in Lemma 3.1,the vertical distance be-Polthier and Rossman,Curvature surfaces58。
疼痛共情的fmri研究综述
疼痛共情的fmri研究综述对于疼痛共情的FMRI研究摘要:共情这种能够共享他⼈感受的能⼒,在我们的社会情感⽣活中⼗分重要。
近年来研究者们对于疼痛的共情做了⽐较深⼊的FMRI研究。
研究发现观察者间接感受疼痛激活的⽹络包括了直接感受疼痛激活的⽹络,尤其是在脑岛和扣带前回⽪质。
这些发现在⼀定程度上表明了共情是建⽴在直接体验和间接体验情感表征的共享⽹络机制上的。
此外,共情还与环境有关,共情可以引起深层次的与社会认知相关⽹络的激活。
脑岛与扣带前回的活动可以映射到负责表现和预测⾃我以及他⼈感受的区域,根据不断变化的环境产⽣维持⾃我平衡的适应性⾏为。
关键词:社会神经科学脑岛扣带前回⽪质fmri 情绪疼痛引⾔:共情是⼈类情绪体验和社会合作中⼀个“残酷”的组成因素。
和我们亲近的⼈乃⾄陌⽣⼈共享情绪状态的能⼒使我们能够理解并预测他⼈的感受,动机和⾏为。
前⼈关于共情的研究主要集中在哲学和⾏为主义⼼理学范畴(Batson 2009,de Vignemont & Singer 2006, Eisenberg 2000,Hoffman 2000),⽽最近,社会神经科学则为我们提供了以⼤脑为基础的重要的新视⾓来研究共情。
在这篇综述⾥我们列出了通过脑成像技术获得的主要成就。
研究者们应⽤fmri 技术得到了⼤量重要的结论。
这些结论普遍认为我们在观察其他⼈的感情状态时激活的⼤脑⽹络包括了对这些情感状态⾃⾝之前的体验所激活的⽹络,这证明了共情在⼀定程度上依赖于共享的⽹络机制的观点。
(de Vignemont & Singer 2006,Keysers & Gazzola 2007, Preston & de Waal,2002).特别的,研究者们发现前脑岛(AI)和背前及前中扣带回⽪质(dACC/aMCC)在我们对于厌恶,愉悦或者不快,以及对于⾝体上和⼼理上的疼痛,尴尬等情绪的间接体验和反馈起到了⾄关重要的作⽤(Figure 1a).根据AI,dACC和aMCC的结构和功能的连结模式以及他们对于感觉,情绪和认知过程的参与,研究者们认为这些区域可能主要负责产⽣对实际和预测中的他⼈及⾃⼰的情绪体验以及适应性反馈。
数学专业词汇及翻译
一、字母顺序表 (1)二、常用的数学英语表述 (7)三、代数英语(高端) (13)一、字母顺序表1、数学专业词汇Aabsolute value 绝对值 accept 接受 acceptable region 接受域additivity 可加性 adjusted 调整的 alternative hypothesis 对立假设analysis 分析 analysis of covariance 协方差分析 analysis of variance 方差分析 arithmetic mean 算术平均值 association 相关性 assumption 假设 assumption checking 假设检验availability 有效度average 均值Bbalanced 平衡的 band 带宽 bar chart 条形图beta-distribution 贝塔分布 between groups 组间的 bias 偏倚 binomial distribution 二项分布 binomial test 二项检验Ccalculate 计算 case 个案 category 类别 center of gravity 重心 central tendency 中心趋势 chi-square distribution 卡方分布 chi-square test 卡方检验 classify 分类cluster analysis 聚类分析 coefficient 系数 coefficient of correlation 相关系数collinearity 共线性 column 列 compare 比较 comparison 对照 components 构成,分量compound 复合的 confidence interval 置信区间 consistency 一致性 constant 常数continuous variable 连续变量 control charts 控制图 correlation 相关 covariance 协方差 covariance matrix 协方差矩阵 critical point 临界点critical value 临界值crosstab 列联表cubic 三次的,立方的 cubic term 三次项 cumulative distribution function 累加分布函数 curve estimation 曲线估计Ddata 数据default 默认的definition 定义deleted residual 剔除残差density function 密度函数dependent variable 因变量description 描述design of experiment 试验设计 deviations 差异 df.(degree of freedom) 自由度 diagnostic 诊断dimension 维discrete variable 离散变量discriminant function 判别函数discriminatory analysis 判别分析distance 距离distribution 分布D-optimal design D-优化设计Eeaqual 相等 effects of interaction 交互效应 efficiency 有效性eigenvalue 特征值equal size 等含量equation 方程error 误差estimate 估计estimation of parameters 参数估计estimations 估计量evaluate 衡量exact value 精确值expectation 期望expected value 期望值exponential 指数的exponential distributon 指数分布 extreme value 极值F factor 因素,因子 factor analysis 因子分析 factor score 因子得分 factorial designs 析因设计factorial experiment 析因试验fit 拟合fitted line 拟合线fitted value 拟合值 fixed model 固定模型 fixed variable 固定变量 fractional factorial design 部分析因设计 frequency 频数 F-test F检验 full factorial design 完全析因设计function 函数Ggamma distribution 伽玛分布 geometric mean 几何均值 group 组Hharmomic mean 调和均值 heterogeneity 不齐性histogram 直方图 homogeneity 齐性homogeneity of variance 方差齐性 hypothesis 假设 hypothesis test 假设检验Iindependence 独立 independent variable 自变量independent-samples 独立样本 index 指数 index of correlation 相关指数 interaction 交互作用 interclass correlation 组内相关 interval estimate 区间估计 intraclass correlation 组间相关 inverse 倒数的iterate 迭代Kkernal 核 Kolmogorov-Smirnov test柯尔莫哥洛夫-斯米诺夫检验 kurtosis 峰度Llarge sample problem 大样本问题 layer 层least-significant difference 最小显著差数 least-square estimation 最小二乘估计 least-square method 最小二乘法 level 水平 level of significance 显著性水平 leverage value 中心化杠杆值 life 寿命 life test 寿命试验 likelihood function 似然函数 likelihood ratio test 似然比检验linear 线性的 linear estimator 线性估计linear model 线性模型 linear regression 线性回归linear relation 线性关系linear term 线性项logarithmic 对数的logarithms 对数 logistic 逻辑的 lost function 损失函数Mmain effect 主效应 matrix 矩阵 maximum 最大值 maximum likelihood estimation 极大似然估计 mean squared deviation(MSD) 均方差 mean sum of square 均方和 measure 衡量 media 中位数 M-estimator M估计minimum 最小值 missing values 缺失值 mixed model 混合模型 mode 众数model 模型Monte Carle method 蒙特卡罗法 moving average 移动平均值multicollinearity 多元共线性multiple comparison 多重比较 multiple correlation 多重相关multiple correlation coefficient 复相关系数multiple correlation coefficient 多元相关系数 multiple regression analysis 多元回归分析multiple regression equation 多元回归方程 multiple response 多响应 multivariate analysis 多元分析Nnegative relationship 负相关 nonadditively 不可加性 nonlinear 非线性 nonlinear regression 非线性回归 noparametric tests 非参数检验 normal distribution 正态分布null hypothesis 零假设 number of cases 个案数Oone-sample 单样本 one-tailed test 单侧检验 one-way ANOVA 单向方差分析 one-way classification 单向分类 optimal 优化的optimum allocation 最优配制 order 排序order statistics 次序统计量 origin 原点orthogonal 正交的 outliers 异常值Ppaired observations 成对观测数据paired-sample 成对样本parameter 参数parameter estimation 参数估计 partial correlation 偏相关partial correlation coefficient 偏相关系数 partial regression coefficient 偏回归系数 percent 百分数percentiles 百分位数 pie chart 饼图 point estimate 点估计 poisson distribution 泊松分布polynomial curve 多项式曲线polynomial regression 多项式回归polynomials 多项式positive relationship 正相关 power 幂P-P plot P-P概率图predict 预测predicted value 预测值prediction intervals 预测区间principal component analysis 主成分分析 proability 概率 probability density function 概率密度函数 probit analysis 概率分析 proportion 比例Qqadratic 二次的 Q-Q plot Q-Q概率图 quadratic term 二次项 quality control 质量控制 quantitative 数量的,度量的 quartiles 四分位数Rrandom 随机的 random number 随机数 random number 随机数 random sampling 随机取样random seed 随机数种子 random variable 随机变量 randomization 随机化 range 极差rank 秩 rank correlation 秩相关 rank statistic 秩统计量 regression analysis 回归分析regression coefficient 回归系数regression line 回归线reject 拒绝rejection region 拒绝域 relationship 关系 reliability 可*性 repeated 重复的report 报告,报表 residual 残差 residual sum of squares 剩余平方和 response 响应risk function 风险函数 robustness 稳健性 root mean square 标准差 row 行 run 游程run test 游程检验Sample 样本 sample size 样本容量 sample space 样本空间 sampling 取样 sampling inspection 抽样检验 scatter chart 散点图 S-curve S形曲线 separately 单独地 sets 集合sign test 符号检验significance 显著性significance level 显著性水平significance testing 显著性检验 significant 显著的,有效的 significant digits 有效数字 skewed distribution 偏态分布 skewness 偏度 small sample problem 小样本问题 smooth 平滑 sort 排序 soruces of variation 方差来源 space 空间 spread 扩展square 平方 standard deviation 标准离差 standard error of mean 均值的标准误差standardization 标准化 standardize 标准化 statistic 统计量 statistical quality control 统计质量控制 std. residual 标准残差 stepwise regression analysis 逐步回归 stimulus 刺激 strong assumption 强假设 stud. deleted residual 学生化剔除残差stud. residual 学生化残差 subsamples 次级样本 sufficient statistic 充分统计量sum 和 sum of squares 平方和 summary 概括,综述Ttable 表t-distribution t分布test 检验test criterion 检验判据test for linearity 线性检验 test of goodness of fit 拟合优度检验 test of homogeneity 齐性检验 test of independence 独立性检验 test rules 检验法则 test statistics 检验统计量 testing function 检验函数 time series 时间序列 tolerance limits 容许限total 总共,和 transformation 转换 treatment 处理 trimmed mean 截尾均值 true value 真值 t-test t检验 two-tailed test 双侧检验Uunbalanced 不平衡的 unbiased estimation 无偏估计 unbiasedness 无偏性 uniform distribution 均匀分布Vvalue of estimator 估计值 variable 变量 variance 方差 variance components 方差分量 variance ratio 方差比 various 不同的 vector 向量Wweight 加权,权重 weighted average 加权平均值 within groups 组内的ZZ score Z分数2. 最优化方法词汇英汉对照表Aactive constraint 活动约束 active set method 活动集法 analytic gradient 解析梯度approximate 近似 arbitrary 强制性的 argument 变量 attainment factor 达到因子Bbandwidth 带宽 be equivalent to 等价于 best-fit 最佳拟合 bound 边界Ccoefficient 系数 complex-value 复数值 component 分量 constant 常数 constrained 有约束的constraint 约束constraint function 约束函数continuous 连续的converge 收敛 cubic polynomial interpolation method三次多项式插值法 curve-fitting 曲线拟合Ddata-fitting 数据拟合 default 默认的,默认的 define 定义 diagonal 对角的 direct search method 直接搜索法 direction of search 搜索方向 discontinuous 不连续Eeigenvalue 特征值 empty matrix 空矩阵 equality 等式 exceeded 溢出的Ffeasible 可行的 feasible solution 可行解 finite-difference 有限差分 first-order 一阶GGauss-Newton method 高斯-牛顿法 goal attainment problem 目标达到问题 gradient 梯度 gradient method 梯度法Hhandle 句柄 Hessian matrix 海色矩阵Independent variables 独立变量inequality 不等式infeasibility 不可行性infeasible 不可行的initial feasible solution 初始可行解initialize 初始化inverse 逆 invoke 激活 iteration 迭代 iteration 迭代JJacobian 雅可比矩阵LLagrange multiplier 拉格朗日乘子 large-scale 大型的 least square 最小二乘 least squares sense 最小二乘意义上的 Levenberg-Marquardt method 列文伯格-马夸尔特法line search 一维搜索 linear 线性的 linear equality constraints 线性等式约束linear programming problem 线性规划问题 local solution 局部解M medium-scale 中型的 minimize 最小化 mixed quadratic and cubic polynomialinterpolation and extrapolation method 混合二次、三次多项式内插、外插法multiobjective 多目标的Nnonlinear 非线性的 norm 范数Oobjective function 目标函数 observed data 测量数据 optimization routine 优化过程optimize 优化 optimizer 求解器 over-determined system 超定系统Pparameter 参数 partial derivatives 偏导数 polynomial interpolation method 多项式插值法Qquadratic 二次的 quadratic interpolation method 二次内插法 quadratic programming 二次规划Rreal-value 实数值 residuals 残差 robust 稳健的 robustness 稳健性,鲁棒性S scalar 标量 semi-infinitely problem 半无限问题 Sequential Quadratic Programming method 序列二次规划法 simplex search method 单纯形法 solution 解 sparse matrix 稀疏矩阵 sparsity pattern 稀疏模式 sparsity structure 稀疏结构 starting point 初始点 step length 步长 subspace trust region method 子空间置信域法 sum-of-squares 平方和 symmetric matrix 对称矩阵Ttermination message 终止信息 termination tolerance 终止容限 the exit condition 退出条件 the method of steepest descent 最速下降法 transpose 转置Uunconstrained 无约束的 under-determined system 负定系统Vvariable 变量 vector 矢量Wweighting matrix 加权矩阵3 样条词汇英汉对照表Aapproximation 逼近 array 数组 a spline in b-form/b-spline b样条 a spline of polynomial piece /ppform spline 分段多项式样条Bbivariate spline function 二元样条函数 break/breaks 断点Ccoefficient/coefficients 系数cubic interpolation 三次插值/三次内插cubic polynomial 三次多项式 cubic smoothing spline 三次平滑样条 cubic spline 三次样条cubic spline interpolation 三次样条插值/三次样条内插 curve 曲线Ddegree of freedom 自由度 dimension 维数Eend conditions 约束条件 input argument 输入参数 interpolation 插值/内插 interval取值区间Kknot/knots 节点Lleast-squares approximation 最小二乘拟合Mmultiplicity 重次 multivariate function 多元函数Ooptional argument 可选参数 order 阶次 output argument 输出参数P point/points 数据点Rrational spline 有理样条 rounding error 舍入误差(相对误差)Sscalar 标量 sequence 数列(数组) spline 样条 spline approximation 样条逼近/样条拟合spline function 样条函数 spline curve 样条曲线 spline interpolation 样条插值/样条内插 spline surface 样条曲面 smoothing spline 平滑样条Ttolerance 允许精度Uunivariate function 一元函数Vvector 向量Wweight/weights 权重4 偏微分方程数值解词汇英汉对照表Aabsolute error 绝对误差 absolute tolerance 绝对容限 adaptive mesh 适应性网格Bboundary condition 边界条件Ccontour plot 等值线图 converge 收敛 coordinate 坐标系Ddecomposed 分解的 decomposed geometry matrix 分解几何矩阵 diagonal matrix 对角矩阵 Dirichlet boundary conditions Dirichlet边界条件Eeigenvalue 特征值 elliptic 椭圆形的 error estimate 误差估计 exact solution 精确解Ggeneralized Neumann boundary condition 推广的Neumann边界条件 geometry 几何形状geometry description matrix 几何描述矩阵 geometry matrix 几何矩阵 graphical user interface(GUI)图形用户界面Hhyperbolic 双曲线的Iinitial mesh 初始网格Jjiggle 微调LLagrange multipliers 拉格朗日乘子Laplace equation 拉普拉斯方程linear interpolation 线性插值 loop 循环Mmachine precision 机器精度 mixed boundary condition 混合边界条件NNeuman boundary condition Neuman边界条件 node point 节点 nonlinear solver 非线性求解器 normal vector 法向量PParabolic 抛物线型的 partial differential equation 偏微分方程 plane strain 平面应变 plane stress 平面应力 Poisson's equation 泊松方程 polygon 多边形 positive definite 正定Qquality 质量Rrefined triangular mesh 加密的三角形网格 relative tolerance 相对容限 relative tolerance 相对容限 residual 残差 residual norm 残差范数Ssingular 奇异的二、常用的数学英语表述1.Logic∃there exist∀for allp⇒q p implies q / if p, then qp⇔q p if and only if q /p is equivalent to q / p and q are equivalent2.Setsx∈A x belongs to A / x is an element (or a member) of Ax∉A x does not belong to A / x is not an element (or a member) of AA⊂B A is contained in B / A is a subset of BA⊃B A contains B / B is a subset of AA∩B A cap B / A meet B / A intersection BA∪B A cup B / A join B / A union BA\B A minus B / the diference between A and BA×B A cross B / the cartesian product of A and B3. Real numbersx+1 x plus onex-1 x minus onex±1 x plus or minus onexy xy / x multiplied by y(x - y)(x + y) x minus y, x plus yx y x over y= the equals signx = 5 x equals 5 / x is equal to 5x≠5x (is) not equal to 5x≡y x is equivalent to (or identical with) yx ≡ y x is not equivalent to (or identical with) yx > y x is greater than yx≥y x is greater than or equal to yx < y x is less than yx≤y x is less than or equal to y0 < x < 1 zero is less than x is less than 10≤x≤1zero is less than or equal to x is less than or equal to 1| x | mod x / modulus xx 2 x squared / x (raised) to the power 2x 3 x cubedx 4 x to the fourth / x to the power fourx n x to the nth / x to the power nx −n x to the (power) minus nx (square) root x / the square root of xx 3 cube root (of) xx 4 fourth root (of) xx n nth root (of) x( x+y ) 2 x plus y all squared( x y ) 2 x over y all squaredn! n factorialx ^ x hatx ¯ x barx ˜x tildex i xi / x subscript i / x suffix i / x sub i∑ i=1 n a i the sum from i equals one to n a i / the sum as i runs from 1 to n of the a i4. Linear algebra‖ x ‖the norm (or modulus) of xOA →OA / vector OAOA ¯ OA / the length of the segment OAA T A transpose / the transpose of AA −1 A inverse / the inverse of A5. Functionsf( x ) fx / f of x / the function f of xf:S→T a function f from S to Tx→y x maps to y / x is sent (or mapped) to yf'( x ) f prime x / f dash x / the (first) derivative of f with respect to xf''( x ) f double-prime x / f double-dash x / the second derivative of f with r espect to xf'''( x ) triple-prime x / f triple-dash x / the third derivative of f with respect to xf (4) ( x ) f four x / the fourth derivative of f with respect to x∂f ∂ x 1the partial (derivative) of f with respect to x1∂ 2 f ∂ x 1 2the second partial (derivative) of f with respect to x1∫ 0 ∞the integral from zero to infinitylimx→0 the limit as x approaches zerolimx→0 + the limit as x approaches zero from abovelimx→0 −the limit as x approaches zero from belowlog e y log y to the base e / log to the base e of y / natural log (of) ylny log y to the base e / log to the base e of y / natural log (of) y一般词汇数学mathematics, maths(BrE), math(AmE)公理axiom定理theorem计算calculation运算operation证明prove假设hypothesis, hypotheses(pl.)命题proposition算术arithmetic加plus(prep.), add(v.), addition(n.)被加数augend, summand加数addend和sum减minus(prep.), subtract(v.), subtraction(n.)被减数minuend减数subtrahend差remainder乘times(prep.), multiply(v.), multiplication(n.)被乘数multiplicand, faciend乘数multiplicator积product除divided by(prep.), divide(v.), division(n.)被除数dividend除数divisor商quotient等于equals, is equal to, is equivalent to 大于is greater than小于is lesser than大于等于is equal or greater than小于等于is equal or lesser than运算符operator数字digit数number自然数natural number整数integer小数decimal小数点decimal point分数fraction分子numerator分母denominator比ratio正positive负negative零null, zero, nought, nil十进制decimal system二进制binary system十六进制hexadecimal system权weight, significance进位carry截尾truncation四舍五入round下舍入round down上舍入round up有效数字significant digit无效数字insignificant digit代数algebra公式formula, formulae(pl.)单项式monomial多项式polynomial, multinomial系数coefficient未知数unknown, x-factor, y-factor, z-factor 等式,方程式equation一次方程simple equation二次方程quadratic equation三次方程cubic equation四次方程quartic equation不等式inequation阶乘factorial对数logarithm指数,幂exponent乘方power二次方,平方square三次方,立方cube四次方the power of four, the fourth power n次方the power of n, the nth power开方evolution, extraction二次方根,平方根square root三次方根,立方根cube root四次方根the root of four, the fourth root n次方根the root of n, the nth root集合aggregate元素element空集void子集subset交集intersection并集union补集complement映射mapping函数function定义域domain, field of definition值域range常量constant变量variable单调性monotonicity奇偶性parity周期性periodicity图象image数列,级数series微积分calculus微分differential导数derivative极限limit无穷大infinite(a.) infinity(n.)无穷小infinitesimal积分integral定积分definite integral不定积分indefinite integral有理数rational number无理数irrational number实数real number虚数imaginary number复数complex number矩阵matrix行列式determinant几何geometry点point线line面plane体solid线段segment射线radial平行parallel相交intersect角angle角度degree弧度radian锐角acute angle直角right angle钝角obtuse angle平角straight angle周角perigon底base边side高height三角形triangle锐角三角形acute triangle直角三角形right triangle直角边leg斜边hypotenuse勾股定理Pythagorean theorem钝角三角形obtuse triangle不等边三角形scalene triangle等腰三角形isosceles triangle等边三角形equilateral triangle四边形quadrilateral平行四边形parallelogram矩形rectangle长length宽width附:在一个分数里,分子或分母或两者均含有分数。
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2
Preliminaries
We start by considering a Hamiltonian function H ∈ C ∞ (Ω, R), Ω a domain in R2n , containing the critical point x = 0. Precisely, we assume that x = 0 is an elliptic critical point, and therefore we write H (x) = H[2] (x) + H(x), where H[2] (x) =
i=1 n 2 ωi (x2 i + xi+n ),
H(x) = O(|x|3 ).
We consider the case in which the quadratic form H[2] has non trivial zeros. Moreover we assume the following hypotheses. (H1 ) There exists an integer N ≥ 3 such that, for any h = (h1 , . . . , hn ) ∈ n Zn + \ {0}, |h| := i=1 hi ≤ N − 1, ω, h = 0. 2
Resonances and O-curves in Hamiltonian systems
arXiv:0709.0349v1 [math.DS] 4 Sep 2007
Paolo Butt` a∗
Piero Negrini∗
February 1, 2008
Abstract We investigate the problem of the existence of trajectories asymptotic to elliptic equilibria of Hamiltonian systems in the presence of resonances.
1
O-curves, that have as asymptotic direction one of the two rays of the model system. To prove this result we analyze the Hamiltonian system in a neighborhood of each ray. Then, by using a suitable set of variables we build up a new differential system with a non degenerate hyperbolic equilibrium. According to the choice of the ray, the relationship between these two differential systems allows us to conclude that the local stable manifold of this hyperbolic equilibrium corresponds either to a family of O+ -curves or to a family of O− -curves of the Hamiltonian system. Before concluding, let us recall that the existence of O-curves was analyzed by Lyapunov in the case of analytic differential systems. Many results on this subject can be found in the book of Zubov [12]. In particular, in Chapter III, perturbations of homogeneous polynomial differential systems having rays are studied and O-curves are obtained as power series of the variable t−α , α being a positive rational number depending on the degree of the homogeneous system. Moreover, in the general case, the coefficient of each term in the expansion is found to be a polynomial function of the variable log t. The Hamiltonian functions we consider here are just C r functions (the integer r depending on the degree of the resonance) therefore we need to apply general hyperbolic theory, i.e. fixed point theorems in suitable functional spaces. In the case C ∞ the method of expansion in power series could still be used, at least at a formal level. In fact the existence of formal series representing O-curves is effective: a deep result by Kutsnesov [6] allows to conclude that there exist true O-curves having the formal series as asymptotic expansion. In conclusion, we can repeat here the words one can read at the end of the introduction to Chapter III of the book of Zubov : “the basic ideas of almost all the results are in the works of Lyapunov”.1IntFra bibliotekoduction
In the recent Congress held in Saint Petersbourg, dedicated to the 50th anniversary of A.M. Lyapunov, the V.V. Kozlov conference [4] has been devoted to the so-called Lyapunov’s First Method, applied in particular to the problem of the existence of O+ -curves (resp. O− -curves) that are integral curves asymptotic in the future (resp. in the past) to the equilibria of Lagrangian systems. This problem is obviously related to the problem of the inversion of Dirichlet-Lagrange Theorem and the papers [5, 9] contain the first important results for analytic potential functions with degenerate critical point. We refer to [8] for a review on further researches in this subject. In the framework of Hamiltonian systems, a large number of papers have been devoted to the study of the relationship between instability (as well stability) of equilibria and resonances. We quote here just some essential references [2, 3, 7, 11]: in all these papers the instability was proved by constructing suitable Cetaev’s functions. We were therefore stimulated to study, by means of the First Method, the existence of O-curves of Hamiltonian systems in the presence of resonances. The Hamiltonian function we consider is supposed to have a non degenerate elliptic fixed point F (see Section 2). The corresponding Birkhoff normal form turns out to be a perturbation of an integrable Hamiltonian function. Moreover the integrable system admits two distinct straight lines r± which are O± -curves. Following the russian terminology, these orbits are called “rays of the model system”. Their existence is the starting point to build up the Cetaev function for the complete Hamiltonian system (resonances of order 3 or 4 are considered in [3]). In Section 3 we prove that the complete Hamiltonian system admits