Self-dual Chern-Simons Vortices on Riemann Surfaces
Synthesis and characterization of novel systems fo
专利内容由知识产权出版社提供
专利名称:Synthesis and characterization of novel systems for guidance and vectorization of molecules of therapeutic interest towards target cells
发明人:Pascal Dumy,Marie-Christine Favrot,Didier Boturyn,Jean-Luc Coll
申请号:US10528320 申请日:20030919 公开号:US07531622B2 公开日:20090512
摘要:A method for preparing a grafted homodetic cyclopeptide forming a framework that defines a grafted upper face and grafted lower face, including synthesizing a linear peptide from modified or unmodified amino acids, some of which carry orthogonal protective groups; intramolecular cyclizing the resulting protetuting some or all of orthogonal protective groups with a protected precursor; and grafting at least one molecule of interest onto one and/or the other face of the framework via an oxime bond.
用密度函数理论和杜比宁方程研究活性炭纤维多段充填机理
密度函数理论和杜比宁方程可以用来研究活性炭纤维在多段充填过程中的吸附行为。
密度函数理论是一种分子统计力学理论,它建立在分子统计学和热力学的基础上,用来研究一种系统中分子的分布。
杜比宁方程是一种描述分子吸附行为的方程,它可以用来计算吸附层的厚度、吸附速率和吸附能量等参数。
在研究活性炭纤维多段充填过程中,可以使用密度函数理论和杜比宁方程来研究纤维表面的分子结构和吸附行为。
通过分析密度函数和杜比宁方程的解,可以得出纤维表面的分子结构以及纤维吸附的分子的种类、数量和能量。
这些信息有助于更好地理解活性炭纤维的多段充填机理。
在研究活性炭纤维的多段充填机理时,还可以使用其他理论和方法来帮助我们更好地了解这一过程。
例如,可以使用扫描电子显微镜(SEM)和透射电子显微镜(TEM)等技术来观察纤维表面的形貌和结构。
可以使用X射线衍射(XRD)和傅里叶变换红外光谱(FTIR)等技术来确定纤维表面的化学成分和结构。
还可以使用氮气吸附(BET)和旋转氧吸附(BJH)等技术来测量纤维表面的比表面积和孔结构。
通过综合运用密度函数理论、杜比宁方程和其他理论和方法,可以更全面地了解活性炭纤维的多段充填机理,从而更好地控制和优化多段充填的过程。
在研究活性炭纤维多段充填机理时,还可以使用温度敏感性测试方法来研究充填过程中纤维表面的动力学性质。
例如,可以使用动态氧吸附(DAC)或旋转杆氧吸附(ROTA)等技术来测量温度对纤维表面吸附性能的影响。
通过对比不同温度下纤维表面的吸附性能,可以更好地了解充填过程中纤维表面的动力学性质。
此外,还可以使用分子动力学模拟方法来研究纤维表面的吸附行为。
例如,可以使用拉曼光谱或红外光谱等技术来测量纤维表面的分子吸附构型。
然后,使用分子动力学模拟方法来模拟不同分子吸附构型下的纤维表面的动力学性质,帮助我们更好地了解活性炭纤维的多段充填机理。
一类陈–西蒙斯–薛定谔方程径向对称解的非存在性结论
设 u ( x) ≡ / 0 则称其为非平凡的,否则称为平凡的。 本文考虑 V ( x ) 是正的径向对称的位势且非线性项 g ( u ) = V0 u sin u 这种情形。很明显,此时 g(u)在无 穷远处不是渐近线性的、不是超线性的,也不是次线性的,但有 0 ≤ 的启发,本文的主要结论如下。
g (u ) u ≤ V0 , ∀u ≠ 0 。受[7]和[8]中方法
基金项目
江汉大学 2014 年度大学生创新训练项目 2014yb189。
参考文献 (References)
[1] [2] [3] [4] [5] [6] [7] [8] Dunne, G. (1995) Self-dual Chern-Simons theories. Springer. Jackiw, R. and Pi, S.-Y. (1990) Classical and quantal non-relativistic Chern-Simons theory. Physical Review D, 42, 3500-3513. Jackiw, R. and Pi, S.-Y. (1992) Self-dual Chern-Simons solitons. Progress of Theoretical Physics Supplement, 107, 1-40. Liu, B., Smith, P., Tataru, D. (preprint) Local wellposedness of Chern-Simons-Schrödinger. Byeon, J., Huh, H. and Seok, J. (2012) Standing waves of nonlinear Schrödinger equations with the gauge field. Journal of Functional Analysis, 263, 1575-1608. Huh, H. (2012) Standing waves of the Schrödinger equation coupled with the Chern-Sinons gauge field. Journal of Mathematical Physics, 53, Article ID: 063702. Wan, Y. and Tan, J. (2014) Standing waves for the Chern-Simons-Schrödinger systems without (AR) condition. Journal of Mathematical Analysis and Applications, 415, 422-434. Wang, Z. and Zhou, H. (2007) Positive solution for a nonlinear stationary Schrödinger-Poisson system in R 3 . Discrete and Continuous Dynamical Systems, 18, 809-816.
T.W. ANDERSON (1971). The Statistical Analysis of Time Series. Series in Probability and Ma
425 BibliographyH.A KAIKE(1974).Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes.Annals Institute Statistical Mathematics,vol.26,pp.363-387. B.D.O.A NDERSON and J.B.M OORE(1979).Optimal rmation and System Sciences Series, Prentice Hall,Englewood Cliffs,NJ.T.W.A NDERSON(1971).The Statistical Analysis of Time Series.Series in Probability and Mathematical Statistics,Wiley,New York.R.A NDRE-O BRECHT(1988).A new statistical approach for the automatic segmentation of continuous speech signals.IEEE Trans.Acoustics,Speech,Signal Processing,vol.ASSP-36,no1,pp.29-40.R.A NDRE-O BRECHT(1990).Reconnaissance automatique de parole`a partir de segments acoustiques et de mod`e les de Markov cach´e s.Proc.Journ´e es Etude de la Parole,Montr´e al,May1990(in French).R.A NDRE-O BRECHT and H.Y.S U(1988).Three acoustic labellings for phoneme based continuous speech recognition.Proc.Speech’88,Edinburgh,UK,pp.943-950.U.A PPEL and A.VON B RANDT(1983).Adaptive sequential segmentation of piecewise stationary time rmation Sciences,vol.29,no1,pp.27-56.L.A.A ROIAN and H.L EVENE(1950).The effectiveness of quality control procedures.Jal American Statis-tical Association,vol.45,pp.520-529.K.J.A STR¨OM and B.W ITTENMARK(1984).Computer Controlled Systems:Theory and rma-tion and System Sciences Series,Prentice Hall,Englewood Cliffs,NJ.M.B AGSHAW and R.A.J OHNSON(1975a).The effect of serial correlation on the performance of CUSUM tests-Part II.Technometrics,vol.17,no1,pp.73-80.M.B AGSHAW and R.A.J OHNSON(1975b).The influence of reference values and estimated variance on the ARL of CUSUM tests.Jal Royal Statistical Society,vol.37(B),no3,pp.413-420.M.B AGSHAW and R.A.J OHNSON(1977).Sequential procedures for detecting parameter changes in a time-series model.Jal American Statistical Association,vol.72,no359,pp.593-597.R.K.B ANSAL and P.P APANTONI-K AZAKOS(1986).An algorithm for detecting a change in a stochastic process.IEEE rmation Theory,vol.IT-32,no2,pp.227-235.G.A.B ARNARD(1959).Control charts and stochastic processes.Jal Royal Statistical Society,vol.B.21, pp.239-271.A.E.B ASHARINOV andB.S.F LEISHMAN(1962).Methods of the statistical sequential analysis and their radiotechnical applications.Sovetskoe Radio,Moscow(in Russian).M.B ASSEVILLE(1978).D´e viations par rapport au maximum:formules d’arrˆe t et martingales associ´e es. Compte-rendus du S´e minaire de Probabilit´e s,Universit´e de Rennes I.M.B ASSEVILLE(1981).Edge detection using sequential methods for change in level-Part II:Sequential detection of change in mean.IEEE Trans.Acoustics,Speech,Signal Processing,vol.ASSP-29,no1,pp.32-50.426B IBLIOGRAPHY M.B ASSEVILLE(1982).A survey of statistical failure detection techniques.In Contribution`a la D´e tectionS´e quentielle de Ruptures de Mod`e les Statistiques,Th`e se d’Etat,Universit´e de Rennes I,France(in English). M.B ASSEVILLE(1986).The two-models approach for the on-line detection of changes in AR processes. In Detection of Abrupt Changes in Signals and Dynamical Systems(M.Basseville,A.Benveniste,eds.). Lecture Notes in Control and Information Sciences,LNCIS77,Springer,New York,pp.169-215.M.B ASSEVILLE(1988).Detecting changes in signals and systems-A survey.Automatica,vol.24,pp.309-326.M.B ASSEVILLE(1989).Distance measures for signal processing and pattern recognition.Signal Process-ing,vol.18,pp.349-369.M.B ASSEVILLE and A.B ENVENISTE(1983a).Design and comparative study of some sequential jump detection algorithms for digital signals.IEEE Trans.Acoustics,Speech,Signal Processing,vol.ASSP-31, no3,pp.521-535.M.B ASSEVILLE and A.B ENVENISTE(1983b).Sequential detection of abrupt changes in spectral charac-teristics of digital signals.IEEE rmation Theory,vol.IT-29,no5,pp.709-724.M.B ASSEVILLE and A.B ENVENISTE,eds.(1986).Detection of Abrupt Changes in Signals and Dynamical Systems.Lecture Notes in Control and Information Sciences,LNCIS77,Springer,New York.M.B ASSEVILLE and I.N IKIFOROV(1991).A unified framework for statistical change detection.Proc.30th IEEE Conference on Decision and Control,Brighton,UK.M.B ASSEVILLE,B.E SPIAU and J.G ASNIER(1981).Edge detection using sequential methods for change in level-Part I:A sequential edge detection algorithm.IEEE Trans.Acoustics,Speech,Signal Processing, vol.ASSP-29,no1,pp.24-31.M.B ASSEVILLE, A.B ENVENISTE and G.M OUSTAKIDES(1986).Detection and diagnosis of abrupt changes in modal characteristics of nonstationary digital signals.IEEE rmation Theory,vol.IT-32,no3,pp.412-417.M.B ASSEVILLE,A.B ENVENISTE,G.M OUSTAKIDES and A.R OUG´E E(1987a).Detection and diagnosis of changes in the eigenstructure of nonstationary multivariable systems.Automatica,vol.23,no3,pp.479-489. M.B ASSEVILLE,A.B ENVENISTE,G.M OUSTAKIDES and A.R OUG´E E(1987b).Optimal sensor location for detecting changes in dynamical behavior.IEEE Trans.Automatic Control,vol.AC-32,no12,pp.1067-1075.M.B ASSEVILLE,A.B ENVENISTE,B.G ACH-D EVAUCHELLE,M.G OURSAT,D.B ONNECASE,P.D OREY, M.P REVOSTO and M.O LAGNON(1993).Damage monitoring in vibration mechanics:issues in diagnos-tics and predictive maintenance.Mechanical Systems and Signal Processing,vol.7,no5,pp.401-423.R.V.B EARD(1971).Failure Accommodation in Linear Systems through Self-reorganization.Ph.D.Thesis, Dept.Aeronautics and Astronautics,MIT,Cambridge,MA.A.B ENVENISTE and J.J.F UCHS(1985).Single sample modal identification of a nonstationary stochastic process.IEEE Trans.Automatic Control,vol.AC-30,no1,pp.66-74.A.B ENVENISTE,M.B ASSEVILLE and G.M OUSTAKIDES(1987).The asymptotic local approach to change detection and model validation.IEEE Trans.Automatic Control,vol.AC-32,no7,pp.583-592.A.B ENVENISTE,M.M ETIVIER and P.P RIOURET(1990).Adaptive Algorithms and Stochastic Approxima-tions.Series on Applications of Mathematics,(A.V.Balakrishnan,I.Karatzas,M.Yor,eds.).Springer,New York.A.B ENVENISTE,M.B ASSEVILLE,L.E L G HAOUI,R.N IKOUKHAH and A.S.W ILLSKY(1992).An optimum robust approach to statistical failure detection and identification.IFAC World Conference,Sydney, July1993.B IBLIOGRAPHY427 R.H.B ERK(1973).Some asymptotic aspects of sequential analysis.Annals Statistics,vol.1,no6,pp.1126-1138.R.H.B ERK(1975).Locally most powerful sequential test.Annals Statistics,vol.3,no2,pp.373-381.P.B ILLINGSLEY(1968).Convergence of Probability Measures.Wiley,New York.A.F.B ISSELL(1969).Cusum techniques for quality control.Applied Statistics,vol.18,pp.1-30.M.E.B IVAIKOV(1991).Control of the sample size for recursive estimation of parameters subject to abrupt changes.Automation and Remote Control,no9,pp.96-103.R.E.B LAHUT(1987).Principles and Practice of Information Theory.Addison-Wesley,Reading,MA.I.F.B LAKE and W.C.L INDSEY(1973).Level-crossing problems for random processes.IEEE r-mation Theory,vol.IT-19,no3,pp.295-315.G.B ODENSTEIN and H.M.P RAETORIUS(1977).Feature extraction from the encephalogram by adaptive segmentation.Proc.IEEE,vol.65,pp.642-652.T.B OHLIN(1977).Analysis of EEG signals with changing spectra using a short word Kalman estimator. Mathematical Biosciences,vol.35,pp.221-259.W.B¨OHM and P.H ACKL(1990).Improved bounds for the average run length of control charts based on finite weighted sums.Annals Statistics,vol.18,no4,pp.1895-1899.T.B OJDECKI and J.H OSZA(1984).On a generalized disorder problem.Stochastic Processes and their Applications,vol.18,pp.349-359.L.I.B ORODKIN and V.V.M OTTL’(1976).Algorithm forfinding the jump times of random process equation parameters.Automation and Remote Control,vol.37,no6,Part1,pp.23-32.A.A.B OROVKOV(1984).Theory of Mathematical Statistics-Estimation and Hypotheses Testing,Naouka, Moscow(in Russian).Translated in French under the title Statistique Math´e matique-Estimation et Tests d’Hypoth`e ses,Mir,Paris,1987.G.E.P.B OX and G.M.J ENKINS(1970).Time Series Analysis,Forecasting and Control.Series in Time Series Analysis,Holden-Day,San Francisco.A.VON B RANDT(1983).Detecting and estimating parameters jumps using ladder algorithms and likelihood ratio test.Proc.ICASSP,Boston,MA,pp.1017-1020.A.VON B RANDT(1984).Modellierung von Signalen mit Sprunghaft Ver¨a nderlichem Leistungsspektrum durch Adaptive Segmentierung.Doctor-Engineer Dissertation,M¨u nchen,RFA(in German).S.B RAUN,ed.(1986).Mechanical Signature Analysis-Theory and Applications.Academic Press,London. L.B REIMAN(1968).Probability.Series in Statistics,Addison-Wesley,Reading,MA.G.S.B RITOV and L.A.M IRONOVSKI(1972).Diagnostics of linear systems of automatic regulation.Tekh. Kibernetics,vol.1,pp.76-83.B.E.B RODSKIY and B.S.D ARKHOVSKIY(1992).Nonparametric Methods in Change-point Problems. Kluwer Academic,Boston.L.D.B ROEMELING(1982).Jal Econometrics,vol.19,Special issue on structural change in Econometrics. L.D.B ROEMELING and H.T SURUMI(1987).Econometrics and Structural Change.Dekker,New York. D.B ROOK and D.A.E VANS(1972).An approach to the probability distribution of Cusum run length. Biometrika,vol.59,pp.539-550.J.B RUNET,D.J AUME,M.L ABARR`E RE,A.R AULT and M.V ERG´E(1990).D´e tection et Diagnostic de Pannes.Trait´e des Nouvelles Technologies,S´e rie Diagnostic et Maintenance,Herm`e s,Paris(in French).428B IBLIOGRAPHY S.P.B RUZZONE and M.K AVEH(1984).Information tradeoffs in using the sample autocorrelation function in ARMA parameter estimation.IEEE Trans.Acoustics,Speech,Signal Processing,vol.ASSP-32,no4, pp.701-715.A.K.C AGLAYAN(1980).Necessary and sufficient conditions for detectability of jumps in linear systems. IEEE Trans.Automatic Control,vol.AC-25,no4,pp.833-834.A.K.C AGLAYAN and R.E.L ANCRAFT(1983).Reinitialization issues in fault tolerant systems.Proc.Amer-ican Control Conf.,pp.952-955.A.K.C AGLAYAN,S.M.A LLEN and K.W EHMULLER(1988).Evaluation of a second generation reconfigu-ration strategy for aircraftflight control systems subjected to actuator failure/surface damage.Proc.National Aerospace and Electronic Conference,Dayton,OH.P.E.C AINES(1988).Linear Stochastic Systems.Series in Probability and Mathematical Statistics,Wiley, New York.M.J.C HEN and J.P.N ORTON(1987).Estimation techniques for tracking rapid parameter changes.Intern. Jal Control,vol.45,no4,pp.1387-1398.W.K.C HIU(1974).The economic design of cusum charts for controlling normal mean.Applied Statistics, vol.23,no3,pp.420-433.E.Y.C HOW(1980).A Failure Detection System Design Methodology.Ph.D.Thesis,M.I.T.,L.I.D.S.,Cam-bridge,MA.E.Y.C HOW and A.S.W ILLSKY(1984).Analytical redundancy and the design of robust failure detection systems.IEEE Trans.Automatic Control,vol.AC-29,no3,pp.689-691.Y.S.C HOW,H.R OBBINS and D.S IEGMUND(1971).Great Expectations:The Theory of Optimal Stop-ping.Houghton-Mifflin,Boston.R.N.C LARK,D.C.F OSTH and V.M.W ALTON(1975).Detection of instrument malfunctions in control systems.IEEE Trans.Aerospace Electronic Systems,vol.AES-11,pp.465-473.A.C OHEN(1987).Biomedical Signal Processing-vol.1:Time and Frequency Domain Analysis;vol.2: Compression and Automatic Recognition.CRC Press,Boca Raton,FL.J.C ORGE and F.P UECH(1986).Analyse du rythme cardiaque foetal par des m´e thodes de d´e tection de ruptures.Proc.7th INRIA Int.Conf.Analysis and optimization of Systems.Antibes,FR(in French).D.R.C OX and D.V.H INKLEY(1986).Theoretical Statistics.Chapman and Hall,New York.D.R.C OX and H.D.M ILLER(1965).The Theory of Stochastic Processes.Wiley,New York.S.V.C ROWDER(1987).A simple method for studying run-length distributions of exponentially weighted moving average charts.Technometrics,vol.29,no4,pp.401-407.H.C S¨ORG¨O and L.H ORV´ATH(1988).Nonparametric methods for change point problems.In Handbook of Statistics(P.R.Krishnaiah,C.R.Rao,eds.),vol.7,Elsevier,New York,pp.403-425.R.B.D AVIES(1973).Asymptotic inference in stationary gaussian time series.Advances Applied Probability, vol.5,no3,pp.469-497.J.C.D ECKERT,M.N.D ESAI,J.J.D EYST and A.S.W ILLSKY(1977).F-8DFBW sensor failure identification using analytical redundancy.IEEE Trans.Automatic Control,vol.AC-22,no5,pp.795-803.M.H.D E G ROOT(1970).Optimal Statistical Decisions.Series in Probability and Statistics,McGraw-Hill, New York.J.D ESHAYES and D.P ICARD(1979).Tests de ruptures dans un mod`e pte-Rendus de l’Acad´e mie des Sciences,vol.288,Ser.A,pp.563-566(in French).B IBLIOGRAPHY429 J.D ESHAYES and D.P ICARD(1983).Ruptures de Mod`e les en Statistique.Th`e ses d’Etat,Universit´e deParis-Sud,Orsay,France(in French).J.D ESHAYES and D.P ICARD(1986).Off-line statistical analysis of change-point models using non para-metric and likelihood methods.In Detection of Abrupt Changes in Signals and Dynamical Systems(M. Basseville,A.Benveniste,eds.).Lecture Notes in Control and Information Sciences,LNCIS77,Springer, New York,pp.103-168.B.D EVAUCHELLE-G ACH(1991).Diagnostic M´e canique des Fatigues sur les Structures Soumises`a des Vibrations en Ambiance de Travail.Th`e se de l’Universit´e Paris IX Dauphine(in French).B.D EVAUCHELLE-G ACH,M.B ASSEVILLE and A.B ENVENISTE(1991).Diagnosing mechanical changes in vibrating systems.Proc.SAFEPROCESS’91,Baden-Baden,FRG,pp.85-89.R.D I F RANCESCO(1990).Real-time speech segmentation using pitch and convexity jump models:applica-tion to variable rate speech coding.IEEE Trans.Acoustics,Speech,Signal Processing,vol.ASSP-38,no5, pp.741-748.X.D ING and P.M.F RANK(1990).Fault detection via factorization approach.Systems and Control Letters, vol.14,pp.431-436.J.L.D OOB(1953).Stochastic Processes.Wiley,New York.V.D RAGALIN(1988).Asymptotic solutions in detecting a change in distribution under an unknown param-eter.Statistical Problems of Control,Issue83,Vilnius,pp.45-52.B.D UBUISSON(1990).Diagnostic et Reconnaissance des Formes.Trait´e des Nouvelles Technologies,S´e rie Diagnostic et Maintenance,Herm`e s,Paris(in French).A.J.D UNCAN(1986).Quality Control and Industrial Statistics,5th edition.Richard D.Irwin,Inc.,Home-wood,IL.J.D URBIN(1971).Boundary-crossing probabilities for the Brownian motion and Poisson processes and techniques for computing the power of the Kolmogorov-Smirnov test.Jal Applied Probability,vol.8,pp.431-453.J.D URBIN(1985).Thefirst passage density of the crossing of a continuous Gaussian process to a general boundary.Jal Applied Probability,vol.22,no1,pp.99-122.A.E MAMI-N AEINI,M.M.A KHTER and S.M.R OCK(1988).Effect of model uncertainty on failure detec-tion:the threshold selector.IEEE Trans.Automatic Control,vol.AC-33,no12,pp.1106-1115.J.D.E SARY,F.P ROSCHAN and D.W.W ALKUP(1967).Association of random variables with applications. Annals Mathematical Statistics,vol.38,pp.1466-1474.W.D.E WAN and K.W.K EMP(1960).Sampling inspection of continuous processes with no autocorrelation between successive results.Biometrika,vol.47,pp.263-280.G.F AVIER and A.S MOLDERS(1984).Adaptive smoother-predictors for tracking maneuvering targets.Proc. 23rd Conf.Decision and Control,Las Vegas,NV,pp.831-836.W.F ELLER(1966).An Introduction to Probability Theory and Its Applications,vol.2.Series in Probability and Mathematical Statistics,Wiley,New York.R.A.F ISHER(1925).Theory of statistical estimation.Proc.Cambridge Philosophical Society,vol.22, pp.700-725.M.F ISHMAN(1988).Optimization of the algorithm for the detection of a disorder,based on the statistic of exponential smoothing.In Statistical Problems of Control,Issue83,Vilnius,pp.146-151.R.F LETCHER(1980).Practical Methods of Optimization,2volumes.Wiley,New York.P.M.F RANK(1990).Fault diagnosis in dynamic systems using analytical and knowledge based redundancy -A survey and new results.Automatica,vol.26,pp.459-474.430B IBLIOGRAPHY P.M.F RANK(1991).Enhancement of robustness in observer-based fault detection.Proc.SAFEPRO-CESS’91,Baden-Baden,FRG,pp.275-287.P.M.F RANK and J.W¨UNNENBERG(1989).Robust fault diagnosis using unknown input observer schemes. In Fault Diagnosis in Dynamic Systems-Theory and Application(R.Patton,P.Frank,R.Clark,eds.). International Series in Systems and Control Engineering,Prentice Hall International,London,UK,pp.47-98.K.F UKUNAGA(1990).Introduction to Statistical Pattern Recognition,2d ed.Academic Press,New York. S.I.G ASS(1958).Linear Programming:Methods and Applications.McGraw Hill,New York.W.G E and C.Z.F ANG(1989).Extended robust observation approach for failure isolation.Int.Jal Control, vol.49,no5,pp.1537-1553.W.G ERSCH(1986).Two applications of parametric time series modeling methods.In Mechanical Signature Analysis-Theory and Applications(S.Braun,ed.),chap.10.Academic Press,London.J.J.G ERTLER(1988).Survey of model-based failure detection and isolation in complex plants.IEEE Control Systems Magazine,vol.8,no6,pp.3-11.J.J.G ERTLER(1991).Analytical redundancy methods in fault detection and isolation.Proc.SAFEPRO-CESS’91,Baden-Baden,FRG,pp.9-22.B.K.G HOSH(1970).Sequential Tests of Statistical Hypotheses.Addison-Wesley,Cambridge,MA.I.N.G IBRA(1975).Recent developments in control charts techniques.Jal Quality Technology,vol.7, pp.183-192.J.P.G ILMORE and R.A.M C K ERN(1972).A redundant strapdown inertial reference unit(SIRU).Jal Space-craft,vol.9,pp.39-47.M.A.G IRSHICK and H.R UBIN(1952).A Bayes approach to a quality control model.Annals Mathematical Statistics,vol.23,pp.114-125.A.L.G OEL and S.M.W U(1971).Determination of the ARL and a contour nomogram for CUSUM charts to control normal mean.Technometrics,vol.13,no2,pp.221-230.P.L.G OLDSMITH and H.W HITFIELD(1961).Average run lengths in cumulative chart quality control schemes.Technometrics,vol.3,pp.11-20.G.C.G OODWIN and K.S.S IN(1984).Adaptive Filtering,Prediction and rmation and System Sciences Series,Prentice Hall,Englewood Cliffs,NJ.R.M.G RAY and L.D.D AVISSON(1986).Random Processes:a Mathematical Approach for Engineers. Information and System Sciences Series,Prentice Hall,Englewood Cliffs,NJ.C.G UEGUEN and L.L.S CHARF(1980).Exact maximum likelihood identification for ARMA models:a signal processing perspective.Proc.1st EUSIPCO,Lausanne.D.E.G USTAFSON, A.S.W ILLSKY,J.Y.W ANG,M.C.L ANCASTER and J.H.T RIEBWASSER(1978). ECG/VCG rhythm diagnosis using statistical signal analysis.Part I:Identification of persistent rhythms. Part II:Identification of transient rhythms.IEEE Trans.Biomedical Engineering,vol.BME-25,pp.344-353 and353-361.F.G USTAFSSON(1991).Optimal segmentation of linear regression parameters.Proc.IFAC/IFORS Symp. Identification and System Parameter Estimation,Budapest,pp.225-229.T.H¨AGGLUND(1983).New Estimation Techniques for Adaptive Control.Ph.D.Thesis,Lund Institute of Technology,Lund,Sweden.T.H¨AGGLUND(1984).Adaptive control of systems subject to large parameter changes.Proc.IFAC9th World Congress,Budapest.B IBLIOGRAPHY431 P.H ALL and C.C.H EYDE(1980).Martingale Limit Theory and its Application.Probability and Mathemat-ical Statistics,a Series of Monographs and Textbooks,Academic Press,New York.W.J.H ALL,R.A.W IJSMAN and J.K.G HOSH(1965).The relationship between sufficiency and invariance with applications in sequential analysis.Ann.Math.Statist.,vol.36,pp.576-614.E.J.H ANNAN and M.D EISTLER(1988).The Statistical Theory of Linear Systems.Series in Probability and Mathematical Statistics,Wiley,New York.J.D.H EALY(1987).A note on multivariate CuSum procedures.Technometrics,vol.29,pp.402-412.D.M.H IMMELBLAU(1970).Process Analysis by Statistical Methods.Wiley,New York.D.M.H IMMELBLAU(1978).Fault Detection and Diagnosis in Chemical and Petrochemical Processes. Chemical Engineering Monographs,vol.8,Elsevier,Amsterdam.W.G.S.H INES(1976a).A simple monitor of a system with sudden parameter changes.IEEE r-mation Theory,vol.IT-22,no2,pp.210-216.W.G.S.H INES(1976b).Improving a simple monitor of a system with sudden parameter changes.IEEE rmation Theory,vol.IT-22,no4,pp.496-499.D.V.H INKLEY(1969).Inference about the intersection in two-phase regression.Biometrika,vol.56,no3, pp.495-504.D.V.H INKLEY(1970).Inference about the change point in a sequence of random variables.Biometrika, vol.57,no1,pp.1-17.D.V.H INKLEY(1971).Inference about the change point from cumulative sum-tests.Biometrika,vol.58, no3,pp.509-523.D.V.H INKLEY(1971).Inference in two-phase regression.Jal American Statistical Association,vol.66, no336,pp.736-743.J.R.H UDDLE(1983).Inertial navigation system error-model considerations in Kalmanfiltering applica-tions.In Control and Dynamic Systems(C.T.Leondes,ed.),Academic Press,New York,pp.293-339.J.S.H UNTER(1986).The exponentially weighted moving average.Jal Quality Technology,vol.18,pp.203-210.I.A.I BRAGIMOV and R.Z.K HASMINSKII(1981).Statistical Estimation-Asymptotic Theory.Applications of Mathematics Series,vol.16.Springer,New York.R.I SERMANN(1984).Process fault detection based on modeling and estimation methods-A survey.Auto-matica,vol.20,pp.387-404.N.I SHII,A.I WATA and N.S UZUMURA(1979).Segmentation of nonstationary time series.Int.Jal Systems Sciences,vol.10,pp.883-894.J.E.J ACKSON and R.A.B RADLEY(1961).Sequential and tests.Annals Mathematical Statistics, vol.32,pp.1063-1077.B.J AMES,K.L.J AMES and D.S IEGMUND(1988).Conditional boundary crossing probabilities with appli-cations to change-point problems.Annals Probability,vol.16,pp.825-839.M.K.J EERAGE(1990).Reliability analysis of fault-tolerant IMU architectures with redundant inertial sen-sors.IEEE Trans.Aerospace and Electronic Systems,vol.AES-5,no.7,pp.23-27.N.L.J OHNSON(1961).A simple theoretical approach to cumulative sum control charts.Jal American Sta-tistical Association,vol.56,pp.835-840.N.L.J OHNSON and F.C.L EONE(1962).Cumulative sum control charts:mathematical principles applied to their construction and use.Parts I,II,III.Industrial Quality Control,vol.18,pp.15-21;vol.19,pp.29-36; vol.20,pp.22-28.432B IBLIOGRAPHY R.A.J OHNSON and M.B AGSHAW(1974).The effect of serial correlation on the performance of CUSUM tests-Part I.Technometrics,vol.16,no.1,pp.103-112.H.L.J ONES(1973).Failure Detection in Linear Systems.Ph.D.Thesis,Dept.Aeronautics and Astronautics, MIT,Cambridge,MA.R.H.J ONES,D.H.C ROWELL and L.E.K APUNIAI(1970).Change detection model for serially correlated multivariate data.Biometrics,vol.26,no2,pp.269-280.M.J URGUTIS(1984).Comparison of the statistical properties of the estimates of the change times in an autoregressive process.In Statistical Problems of Control,Issue65,Vilnius,pp.234-243(in Russian).T.K AILATH(1980).Linear rmation and System Sciences Series,Prentice Hall,Englewood Cliffs,NJ.L.V.K ANTOROVICH and V.I.K RILOV(1958).Approximate Methods of Higher Analysis.Interscience,New York.S.K ARLIN and H.M.T AYLOR(1975).A First Course in Stochastic Processes,2d ed.Academic Press,New York.S.K ARLIN and H.M.T AYLOR(1981).A Second Course in Stochastic Processes.Academic Press,New York.D.K AZAKOS and P.P APANTONI-K AZAKOS(1980).Spectral distance measures between gaussian pro-cesses.IEEE Trans.Automatic Control,vol.AC-25,no5,pp.950-959.K.W.K EMP(1958).Formula for calculating the operating characteristic and average sample number of some sequential tests.Jal Royal Statistical Society,vol.B-20,no2,pp.379-386.K.W.K EMP(1961).The average run length of the cumulative sum chart when a V-mask is used.Jal Royal Statistical Society,vol.B-23,pp.149-153.K.W.K EMP(1967a).Formal expressions which can be used for the determination of operating character-istics and average sample number of a simple sequential test.Jal Royal Statistical Society,vol.B-29,no2, pp.248-262.K.W.K EMP(1967b).A simple procedure for determining upper and lower limits for the average sample run length of a cumulative sum scheme.Jal Royal Statistical Society,vol.B-29,no2,pp.263-265.D.P.K ENNEDY(1976).Some martingales related to cumulative sum tests and single server queues.Stochas-tic Processes and Appl.,vol.4,pp.261-269.T.H.K ERR(1980).Statistical analysis of two-ellipsoid overlap test for real time failure detection.IEEE Trans.Automatic Control,vol.AC-25,no4,pp.762-772.T.H.K ERR(1982).False alarm and correct detection probabilities over a time interval for restricted classes of failure detection algorithms.IEEE rmation Theory,vol.IT-24,pp.619-631.T.H.K ERR(1987).Decentralizedfiltering and redundancy management for multisensor navigation.IEEE Trans.Aerospace and Electronic systems,vol.AES-23,pp.83-119.Minor corrections on p.412and p.599 (May and July issues,respectively).R.A.K HAN(1978).Wald’s approximations to the average run length in cusum procedures.Jal Statistical Planning and Inference,vol.2,no1,pp.63-77.R.A.K HAN(1979).Somefirst passage problems related to cusum procedures.Stochastic Processes and Applications,vol.9,no2,pp.207-215.R.A.K HAN(1981).A note on Page’s two-sided cumulative sum procedures.Biometrika,vol.68,no3, pp.717-719.B IBLIOGRAPHY433 V.K IREICHIKOV,V.M ANGUSHEV and I.N IKIFOROV(1990).Investigation and application of CUSUM algorithms to monitoring of sensors.In Statistical Problems of Control,Issue89,Vilnius,pp.124-130(in Russian).G.K ITAGAWA and W.G ERSCH(1985).A smoothness prior time-varying AR coefficient modeling of non-stationary covariance time series.IEEE Trans.Automatic Control,vol.AC-30,no1,pp.48-56.N.K LIGIENE(1980).Probabilities of deviations of the change point estimate in statistical models.In Sta-tistical Problems of Control,Issue83,Vilnius,pp.80-86(in Russian).N.K LIGIENE and L.T ELKSNYS(1983).Methods of detecting instants of change of random process prop-erties.Automation and Remote Control,vol.44,no10,Part II,pp.1241-1283.J.K ORN,S.W.G ULLY and A.S.W ILLSKY(1982).Application of the generalized likelihood ratio algorithm to maneuver detection and estimation.Proc.American Control Conf.,Arlington,V A,pp.792-798.P.R.K RISHNAIAH and B.Q.M IAO(1988).Review about estimation of change points.In Handbook of Statistics(P.R.Krishnaiah,C.R.Rao,eds.),vol.7,Elsevier,New York,pp.375-402.P.K UDVA,N.V ISWANADHAM and A.R AMAKRISHNAN(1980).Observers for linear systems with unknown inputs.IEEE Trans.Automatic Control,vol.AC-25,no1,pp.113-115.S.K ULLBACK(1959).Information Theory and Statistics.Wiley,New York(also Dover,New York,1968). K.K UMAMARU,S.S AGARA and T.S¨ODERSTR¨OM(1989).Some statistical methods for fault diagnosis for dynamical systems.In Fault Diagnosis in Dynamic Systems-Theory and Application(R.Patton,P.Frank,R. Clark,eds.).International Series in Systems and Control Engineering,Prentice Hall International,London, UK,pp.439-476.A.K USHNIR,I.N IKIFOROV and I.S AVIN(1983).Statistical adaptive algorithms for automatic detection of seismic signals-Part I:One-dimensional case.In Earthquake Prediction and the Study of the Earth Structure,Naouka,Moscow(Computational Seismology,vol.15),pp.154-159(in Russian).L.L ADELLI(1990).Diffusion approximation for a pseudo-likelihood test process with application to de-tection of change in stochastic system.Stochastics and Stochastics Reports,vol.32,pp.1-25.T.L.L A¨I(1974).Control charts based on weighted sums.Annals Statistics,vol.2,no1,pp.134-147.T.L.L A¨I(1981).Asymptotic optimality of invariant sequential probability ratio tests.Annals Statistics, vol.9,no2,pp.318-333.D.G.L AINIOTIS(1971).Joint detection,estimation,and system identifirmation and Control, vol.19,pp.75-92.M.R.L EADBETTER,G.L INDGREN and H.R OOTZEN(1983).Extremes and Related Properties of Random Sequences and Processes.Series in Statistics,Springer,New York.L.L E C AM(1960).Locally asymptotically normal families of distributions.Univ.California Publications in Statistics,vol.3,pp.37-98.L.L E C AM(1986).Asymptotic Methods in Statistical Decision Theory.Series in Statistics,Springer,New York.E.L.L EHMANN(1986).Testing Statistical Hypotheses,2d ed.Wiley,New York.J.P.L EHOCZKY(1977).Formulas for stopped diffusion processes with stopping times based on the maxi-mum.Annals Probability,vol.5,no4,pp.601-607.H.R.L ERCHE(1980).Boundary Crossing of Brownian Motion.Lecture Notes in Statistics,vol.40,Springer, New York.L.L JUNG(1987).System Identification-Theory for the rmation and System Sciences Series, Prentice Hall,Englewood Cliffs,NJ.。
分子动力学中的非平衡态研究
分子动力学中的非平衡态研究分子动力学是一种运用计算机模拟系统的方法,研究分子尺度上的物理运动规律的学科。
通过分子动力学模拟,可以更好地解释和预测分子的行为,有助于发展新型材料和探索新的生物医学领域。
然而,通常情况下分子在非平衡态下的运动规律并不容易研究。
非平衡态通常是指分子系统处于一个不稳定或动态变化的状态,例如外部施加强制、化学反应、热力学不平衡等等,这些不同的场景也会在不同的尺度上展示出不同的行为。
为了更好地研究分子在非平衡态下的运动规律,有学者针对不同场景提出了不同的分子动力学模拟方法。
以下将介绍几种常见的方法。
1. 基于广义热力学的非平衡分子动力学 (NAMD)非平衡分子动力学 (NAMD),是一种基于广义热力学的非平衡分子动力学方法,由 John Eastwood 和 Peter Winn 于 2013 年首次提出并发表,旨在模拟非平衡状态下的分子运动。
该方法在传统分子动力学基础上加入了一些广义热力学理论,可以更准确地模拟能量交换,从而更好地研究分子在非平衡态下的行为。
2. 最大熵方法最大熵方法是另一种常见的非平衡态研究方法,起源于热力学中的最大熵原理。
该方法旨在从分子系统的部分坐标或其他限制条件中推导出整个分子系统的热力学性质,从而更好地描述非平衡态下的分子运动。
最大熵方法可用于模拟混合物、高粘度溶液和生物体系等复杂环境的非平衡态动力学行为。
在遇到高耗散能力或复杂协同机制的情况时,最大熵方法往往比传统方法更加准确。
3. 非平衡态界面动力学 (NIDS)非平衡态界面动力学 (NIDS) 是用于模拟非平衡态界面的分子动力学模拟方法。
在NIDS方法中,模拟系统通常包括两个或更多不同的相,例如气/液界面、液/液界面等等。
该方法可以模拟各种不同类型的非平衡态现象,如张力、相互作用能等,为化学、环境和物理领域中的大量系统提供了一种基本的分子动力学模拟方法。
总之,非平衡态分子动力学是一个快速发展的领域,其应用范围十分广泛。
自演化分子动力学蒙特卡罗方法
自演化分子动力学蒙特卡罗方法自演化分子动力学蒙特卡罗方法(Self-Evolving Molecular Dynamics Monte Carlo,简称SEMDMC)是一种用于模拟复杂多体系统的计算方法。
该方法结合了分子动力学(MD)和蒙特卡罗(MC)方法的优势,能够在较低的计算成本下获得更准确的模拟结果。
一、SEMDMC方法的基本原理SEMDMC方法的基本原理是将模拟系统分为两部分:演化部分和非演化部分。
演化部分由一组有限数量的粒子组成,这些粒子相互作用并遵循牛顿运动定律。
非演化部分由系统的其余部分组成,被视为静态背景。
在模拟过程中,演化部分的粒子会根据牛顿运动定律进行运动。
同时,会使用MC方法对非演化部分进行采样。
通过不断迭代演化部分和非演化部分,可以获得系统的完整配置空间信息。
二、SEMDMC方法的优势SEMDMC方法具有以下优势:1.能够模拟复杂多体系统:SEMDMC方法可以模拟包含大量粒子的复杂系统,例如生物大分子、材料等。
2.计算效率高:SEMDMC方法结合了MD和MC方法的优势,在较低的计算成本下获得更准确的模拟结果。
3.具有良好的可扩展性:SEMDMC方法可以并行化,从而提高计算效率。
三、SEMDMC方法的应用SEMDMC方法已被广泛应用于材料科学、生物物理、化学等领域。
例如,SEMDMC方法已被用于模拟蛋白质折叠、纳米材料的结构和性能等。
四、以下是一些SEMDMC方法的应用实例:1.模拟蛋白质折叠:SEMDMC方法已被用于模拟蛋白质折叠过程。
通过模拟,可以获得蛋白质折叠的自由能景观,从而了解蛋白质折叠的机制。
2.模拟纳米材料的结构和性能:SEMDMC方法已被用于模拟纳米材料的结构和性能。
通过模拟,可以获得纳米材料的原子结构、电子结构、力学性能等信息。
五、总结SEMDMC方法是一种用于模拟复杂多体系统的计算方法。
该方法具有计算效率高、可扩展性好等优势,已被广泛应用于材料科学、生物物理、化学等领域。
Chern-Simons理论
$\textbf{外微分形式}$
实流形$M$上$U\subset M$为一稠密开集,令其足够小使得存在有m维局域坐标满足
\[
M\supset U\ni x\leftrightarrow\widetilde{x}\triangleq (x^1,...,x^m)\in\mathbb{R}^m
$\rho_{i_1...i_r}$为交错张量;
坐标变换后得
\[
y=y(x):df=(\partial\widetilde{f}/\partial x^i)dx^i=(\partial\widetilde{f}/\partial y^i ) (\partial y^i/\partial x^i)dx^i=(\partial\widetilde{f}/\partial y^i ) dy^i
其中曲率张量定义为:$F=dA+A\wedge A$
通常的Chern–Simons $\omega_{2k-1}$形式 由以下方式给出:$d\omega_{2k-1}=\text{Tr}(F^k)$
其中由楔积定义,等式右边正比于联络的第陈类。
一般地,由定义可知Chern–Simons p-形式中的是任意奇数$2k-1$。(可参考规范理论的定义)若$M$是平庸$2k-1$维流形(i.e.三维可定向流形),那么存在映射 $s: M\rightarrow P(M)$;并且从$s^{*}\omega_{2k-1}$在p维流形上的积分是整体几何不变量,且是模增加一整数的规范不变量。
由它(3-形式)可定义Chern–Simons理论的作用量。陈省身与James Harris Simons于1974年合作发表了一篇历史性文章,文中提出了 Chern–Simons理论。$M$为Riemann流形,其联络$A\in \Omega^1(P(M),\mathfrak{g}l(n))$是标架丛$P(M)$上的1-形式Lie代数。给定一流形与1-形式Lie代数,$A$为上面的向量场。可由此定义一族p-形式。
distributed representations of words and phrases and their compositionality
Tomas MikolovGoogle Inc.Mountain View mikolov@Ilya SutskeverGoogle Inc.Mountain Viewilyasu@Kai ChenGoogle Inc.Mountain Viewkai@Greg CorradoGoogle Inc.Mountain View gcorrado@Jeffrey DeanGoogle Inc.Mountain View jeff@AbstractThe recently introduced continuous Skip-gram model is an efficient method forlearning high-quality distributed vector representations that capture a large num-ber of precise syntactic and semantic word relationships.In this paper we presentseveral extensions that improve both the quality of the vectors and the trainingspeed.By subsampling of the frequent words we obtain significant speedup andalso learn more regular word representations.We also describe a simple alterna-tive to the hierarchical softmax called negative sampling.An inherent limitation of word representations is their indifference to word orderand their inability to represent idiomatic phrases.For example,the meanings of“Canada”and“Air”cannot be easily combined to obtain“Air Canada”.Motivatedby this example,we present a simple method forfinding phrases in text,and showthat learning good vector representations for millions of phrases is possible.1IntroductionDistributed representations of words in a vector space help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words.One of the earliest use of word representations dates back to1986due to Rumelhart,Hinton,and Williams[13].This idea has since been applied to statistical language modeling with considerable success[1].The follow up work includes applications to automatic speech recognition and machine translation[14,7],and a wide range of NLP tasks[2,20,15,3,18,19,9].Recently,Mikolov et al.[8]introduced the Skip-gram model,an efficient method for learning high-quality vector representations of words from large amounts of unstructured text data.Unlike most of the previously used neural network architectures for learning word vectors,training of the Skip-gram model(see Figure1)does not involve dense matrix multiplications.This makes the training extremely efficient:an optimized single-machine implementation can train on more than100billion words in one day.The word representations computed using neural networks are very interesting because the learned vectors explicitly encode many linguistic regularities and patterns.Somewhat surprisingly,many of these patterns can be represented as linear translations.For example,the result of a vector calcula-tion vec(“Madrid”)-vec(“Spain”)+vec(“France”)is closer to vec(“Paris”)than to any other word vector[9,8].Figure1:The Skip-gram vector representations that are good at predictingIn this paper we We show that sub-sampling of frequent(around2x-10x),and improves accuracy of we present a simpli-fied variant of Noise model that results in faster training and better vector representations for frequent words,compared to more complex hierarchical softmax that was used in the prior work[8].Word representations are limited by their inability to represent idiomatic phrases that are not com-positions of the individual words.For example,“Boston Globe”is a newspaper,and so it is not a natural combination of the meanings of“Boston”and“Globe”.Therefore,using vectors to repre-sent the whole phrases makes the Skip-gram model considerably more expressive.Other techniques that aim to represent meaning of sentences by composing the word vectors,such as the recursive autoencoders[15],would also benefit from using phrase vectors instead of the word vectors.The extension from word based to phrase based models is relatively simple.First we identify a large number of phrases using a data-driven approach,and then we treat the phrases as individual tokens during the training.To evaluate the quality of the phrase vectors,we developed a test set of analogi-cal reasoning tasks that contains both words and phrases.A typical analogy pair from our test set is “Montreal”:“Montreal Canadiens”::“Toronto”:“Toronto Maple Leafs”.It is considered to have been answered correctly if the nearest representation to vec(“Montreal Canadiens”)-vec(“Montreal”)+ vec(“Toronto”)is vec(“Toronto Maple Leafs”).Finally,we describe another interesting property of the Skip-gram model.We found that simple vector addition can often produce meaningful results.For example,vec(“Russia”)+vec(“river”)is close to vec(“V olga River”),and vec(“Germany”)+vec(“capital”)is close to vec(“Berlin”).This compositionality suggests that a non-obvious degree of language understanding can be obtained by using basic mathematical operations on the word vector representations.2The Skip-gram ModelThe training objective of the Skip-gram model is tofind word representations that are useful for predicting the surrounding words in a sentence or a document.More formally,given a sequence of training words w1,w2,w3,...,w T,the objective of the Skip-gram model is to maximize the average log probability1training time.The basic Skip-gram formulation defines p(w t+j|w t)using the softmax function:exp v′w O⊤v w Ip(w O|w I)=-2-1.5-1-0.5 0 0.511.5 2-2-1.5-1-0.5 0 0.5 1 1.5 2Country and Capital Vectors Projected by PCAChinaJapanFranceRussiaGermanyItalySpainGreece TurkeyBeijingParis Tokyo PolandMoscow Portugal Berlin Rome Athens MadridAnkara Warsaw LisbonFigure 2:Two-dimensional PCA projection of the 1000-dimensional Skip-gram vectors of countries and their capital cities.The figure illustrates ability of the model to automatically organize concepts and learn implicitly the relationships between them,as during the training we did not provide any supervised information about what a capital city means.which is used to replace every log P (w O |w I )term in the Skip-gram objective.Thus the task is to distinguish the target word w O from draws from the noise distribution P n (w )using logistic regres-sion,where there are k negative samples for each data sample.Our experiments indicate that values of k in the range 5–20are useful for small training datasets,while for large datasets the k can be as small as 2–5.The main difference between the Negative sampling and NCE is that NCE needs both samples and the numerical probabilities of the noise distribution,while Negative sampling uses only samples.And while NCE approximately maximizes the log probability of the softmax,this property is not important for our application.Both NCE and NEG have the noise distribution P n (w )as a free parameter.We investigated a number of choices for P n (w )and found that the unigram distribution U (w )raised to the 3/4rd power (i.e.,U (w )3/4/Z )outperformed significantly the unigram and the uniform distributions,for both NCE and NEG on every task we tried including language modeling (not reported here).2.3Subsampling of Frequent WordsIn very large corpora,the most frequent words can easily occur hundreds of millions of times (e.g.,“in”,“the”,and “a”).Such words usually provide less information value than the rare words.For example,while the Skip-gram model benefits from observing the co-occurrences of “France”and “Paris”,it benefits much less from observing the frequent co-occurrences of “France”and “the”,as nearly every word co-occurs frequently within a sentence with “the”.This idea can also be applied in the opposite direction;the vector representations of frequent words do not change significantly after training on several million examples.To counter the imbalance between the rare and frequent words,we used a simple subsampling ap-proach:each word w i in the training set is discarded with probability computed by the formulaP (w i )=1− f (w i )(5)Method Syntactic[%]Semantic[%]NEG-563549761 HS-Huffman53403853NEG-561583661 HS-Huffman5259/p/word2vec/source/browse/trunk/questions-words.txtNewspapersNHL TeamsNBA TeamsAirlinesCompany executives.(6)count(w i)×count(w j)Theδis used as a discounting coefficient and prevents too many phrases consisting of very infre-quent words to be formed.The bigrams with score above the chosen threshold are then used as phrases.Typically,we run2-4passes over the training data with decreasing threshold value,allow-ing longer phrases that consists of several words to be formed.We evaluate the quality of the phrase representations using a new analogical reasoning task that involves phrases.Table2shows examples of thefive categories of analogies used in this task.This dataset is publicly available on the web2.4.1Phrase Skip-Gram ResultsStarting with the same news data as in the previous experiments,wefirst constructed the phrase based training corpus and then we trained several Skip-gram models using different hyper-parameters.As before,we used vector dimensionality300and context size5.This setting already achieves good performance on the phrase dataset,and allowed us to quickly compare the Negative Sampling and the Hierarchical Softmax,both with and without subsampling of the frequent tokens. The results are summarized in Table3.The results show that while Negative Sampling achieves a respectable accuracy even with k=5, using k=15achieves considerably better performance.Surprisingly,while we found the Hierar-chical Softmax to achieve lower performance when trained without subsampling,it became the best performing method when we downsampled the frequent words.This shows that the subsampling can result in faster training and can also improve accuracy,at least in some cases.Dimensionality10−5subsampling[%]30027NEG-152730047Table3:Accuracies of the Skip-gram models on the phrase analogy dataset.The models were trained on approximately one billion words from the news dataset.HS with10−5subsamplingLingsugurGreat Rift ValleyRebbeca NaomiRuegenchess grandmasterVietnam+capital Russian+riverkoruna airline Lufthansa Juliette Binoche Check crown carrier Lufthansa Vanessa Paradis Polish zoltyflag carrier Lufthansa Charlotte Gainsbourg CTK Lufthansa Cecile De Table5:Vector compositionality using element-wise addition.Four closest tokens to the sum of two vectors are shown,using the best Skip-gram model.To maximize the accuracy on the phrase analogy task,we increased the amount of the training data by using a dataset with about33billion words.We used the hierarchical softmax,dimensionality of1000,and the entire sentence for the context.This resulted in a model that reached an accuracy of72%.We achieved lower accuracy66%when we reduced the size of the training dataset to6B words,which suggests that the large amount of the training data is crucial.To gain further insight into how different the representations learned by different models are,we did inspect manually the nearest neighbours of infrequent phrases using various models.In Table4,we show a sample of such comparison.Consistently with the previous results,it seems that the best representations of phrases are learned by a model with the hierarchical softmax and subsampling. 5Additive CompositionalityWe demonstrated that the word and phrase representations learned by the Skip-gram model exhibit a linear structure that makes it possible to perform precise analogical reasoning using simple vector arithmetics.Interestingly,we found that the Skip-gram representations exhibit another kind of linear structure that makes it possible to meaningfully combine words by an element-wise addition of their vector representations.This phenomenon is illustrated in Table5.The additive property of the vectors can be explained by inspecting the training objective.The word vectors are in a linear relationship with the inputs to the softmax nonlinearity.As the word vectors are trained to predict the surrounding words in the sentence,the vectors can be seen as representing the distribution of the context in which a word appears.These values are related logarithmically to the probabilities computed by the output layer,so the sum of two word vectors is related to the product of the two context distributions.The product works here as the AND function:words that are assigned high probabilities by both word vectors will have high probability,and the other words will have low probability.Thus,if“V olga River”appears frequently in the same sentence together with the words“Russian”and“river”,the sum of these two word vectors will result in such a feature vector that is close to the vector of“V olga River”.6Comparison to Published Word RepresentationsMany authors who previously worked on the neural network based representations of words have published their resulting models for further use and comparison:amongst the most well known au-thors are Collobert and Weston[2],Turian et al.[17],and Mnih and Hinton[10].We downloaded their word vectors from the web3.Mikolov et al.[8]have already evaluated these word representa-tions on the word analogy task,where the Skip-gram models achieved the best performance with a huge margin.Model Redmond ninjutsu capitulate (training time)Collobert(50d)conyers reiki abdicate (2months)lubbock kohona accedekeene karate rearmJewell gunfireArzu emotionOvitz impunityMnih(100d)Podhurst-Mavericks (7days)Harlang-planning Agarwal-hesitatedVaclav Havel spray paintpresident Vaclav Havel grafittiVelvet Revolution taggers/p/word2vecReferences[1]Yoshua Bengio,R´e jean Ducharme,Pascal Vincent,and Christian Janvin.A neural probabilistic languagemodel.The Journal of Machine Learning Research,3:1137–1155,2003.[2]Ronan Collobert and Jason Weston.A unified architecture for natural language processing:deep neu-ral networks with multitask learning.In Proceedings of the25th international conference on Machine learning,pages160–167.ACM,2008.[3]Xavier Glorot,Antoine Bordes,and Yoshua Bengio.Domain adaptation for large-scale sentiment classi-fication:A deep learning approach.In ICML,513–520,2011.[4]Michael U Gutmann and Aapo Hyv¨a rinen.Noise-contrastive estimation of unnormalized statistical mod-els,with applications to natural image statistics.The Journal of Machine Learning Research,13:307–361, 2012.[5]Tomas Mikolov,Stefan Kombrink,Lukas Burget,Jan Cernocky,and Sanjeev Khudanpur.Extensions ofrecurrent neural network language model.In Acoustics,Speech and Signal Processing(ICASSP),2011 IEEE International Conference on,pages5528–5531.IEEE,2011.[6]Tomas Mikolov,Anoop Deoras,Daniel Povey,Lukas Burget and Jan Cernocky.Strategies for TrainingLarge Scale Neural Network Language Models.In Proc.Automatic Speech Recognition and Understand-ing,2011.[7]Tomas Mikolov.Statistical Language Models Based on Neural Networks.PhD thesis,PhD Thesis,BrnoUniversity of Technology,2012.[8]Tomas Mikolov,Kai Chen,Greg Corrado,and Jeffrey Dean.Efficient estimation of word representationsin vector space.ICLR Workshop,2013.[9]Tomas Mikolov,Wen-tau Yih and Geoffrey Zweig.Linguistic Regularities in Continuous Space WordRepresentations.In Proceedings of NAACL HLT,2013.[10]Andriy Mnih and Geoffrey E Hinton.A scalable hierarchical distributed language model.Advances inneural information processing systems,21:1081–1088,2009.[11]Andriy Mnih and Yee Whye Teh.A fast and simple algorithm for training neural probabilistic languagemodels.arXiv preprint arXiv:1206.6426,2012.[12]Frederic Morin and Yoshua Bengio.Hierarchical probabilistic neural network language model.In Pro-ceedings of the international workshop on artificial intelligence and statistics,pages246–252,2005. [13]David E Rumelhart,Geoffrey E Hintont,and Ronald J Williams.Learning representations by back-propagating errors.Nature,323(6088):533–536,1986.[14]Holger Schwenk.Continuous space language puter Speech and Language,vol.21,2007.[15]Richard Socher,Cliff C.Lin,Andrew Y.Ng,and Christopher D.Manning.Parsing natural scenes andnatural language with recursive neural networks.In Proceedings of the26th International Conference on Machine Learning(ICML),volume2,2011.[16]Richard Socher,Brody Huval,Christopher D.Manning,and Andrew Y.Ng.Semantic CompositionalityThrough Recursive Matrix-Vector Spaces.In Proceedings of the2012Conference on Empirical Methods in Natural Language Processing(EMNLP),2012.[17]Joseph Turian,Lev Ratinov,and Yoshua Bengio.Word representations:a simple and general method forsemi-supervised learning.In Proceedings of the48th Annual Meeting of the Association for Computa-tional Linguistics,pages384–394.Association for Computational Linguistics,2010.[18]Peter D.Turney and Patrick Pantel.From frequency to meaning:Vector space models of semantics.InJournal of Artificial Intelligence Research,37:141-188,2010.[19]Peter D.Turney.Distributional semantics beyond words:Supervised learning of analogy and paraphrase.In Transactions of the Association for Computational Linguistics(TACL),353–366,2013.[20]Jason Weston,Samy Bengio,and Nicolas Usunier.Wsabie:Scaling up to large vocabulary image annota-tion.In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Three,pages2764–2770.AAAI Press,2011.。
Robust Principal Component Analysis
∗ John Wright† , Arvind Ganesh† , Shankar Rao† , and Yi Ma†
Department of Electrical Engineering University of Illinois at Urbana-Champaign Visual Computing Group Microsoft Research Asia
Abstract. Principal component analysis is a fundamental operation in computational data analysis, with myriad applications ranging from web search to bioinformatics to computer vision and image analysis. However, its performance and applicability in real scenarios are limited by a lack of robustness to outlying or corrupted observations. This paper considers the idealized “robust principal component analysis” problem of recovering a low rank matrix A from corrupted observations D = A + E . Here, the error entries E can be arbitrarily large (modeling grossly corrupted observations common in visual and bioinformatic data), but are assumed to be sparse. We prove that most matrices A can be efficiently and exactly recovered from most error sign-and-support patterns, by solving a simple convex program. Our result holds even when the rank of A grows nearly proportionally (up to a logarithmic factor) to the dimensionality of the observation space and the number of errors E grows in proportion to the total number of entries in the matrix. A by-product of our analysis is the first proportional growth results for the related but somewhat easier problem of completing a low-rank matrix from a small fraction of its entries. We propose an algorithm based on iterative thresholding that, for large matrices, is significantly faster and more scalable than general-purpose solvers. We give simulations and real-data examples corroborating the theoretical results.
Maxwell-Chern-Simons模型拓扑解的存在性的开题报告
Maxwell-Chern-Simons模型拓扑解的存在性的开题报告1. 摘要Maxwell-Chern-Simons(MCS)模型是一种描述电磁波和规范场相互作用的场论模型。
该模型有许多有趣的数学性质,包括非平庸拓扑解的存在性。
本文将探讨MCS模型拓扑解的存在性问题,并针对该问题提出一些研究思路和方法。
2. 研究背景和意义MCS模型在凝聚态物理、高能物理和数学物理等领域有广泛的应用。
该模型可以用来描述拓扑绝缘体、拓扑序等物理现象,也可以用来研究拓扑场论的数学性质。
在这些研究中,拓扑解的存在性是一个关键问题。
拓扑解是指场论模型中的一类非平庸解,具有拓扑结构的性质。
例如,这类解可以被表示为曲率形式,也可以建立在拓扑内的不变量上。
拓扑解在物理学和数学中都有广泛的应用,它们可以解释许多复杂的物理现象和几何现象。
在MCS模型中,拓扑解是指具有非零拓扑荷的局域化解。
这些解可以被认为是拓扑缺陷或拓扑激发。
它们具有有趣的性质,并且在拓扑序和凝聚态物理中有广泛的应用。
目前,对于MCS模型拓扑解的存在性问题仍存在许多挑战。
本文将尝试探讨这一问题,并提出一些可能的研究思路和方法。
3. 研究内容和方法在本文中,我们将从以下几个方面研究MCS模型拓扑解的存在性问题:(1)理论分析。
我们将从场论的角度出发,探讨MCS模型的基本性质和数学结构。
通过理论推导,我们可以研究MCS模型中拓扑解的物理和数学性质,并探寻拓扑解的存在性条件。
(2)数值模拟。
我们将借助数值模拟的方法探究MCS模型中的拓扑解。
通过数值模拟,我们可以生成MCS模型的非平庸解,并分析它们的拓扑荷和局域性质。
(3)掺杂材料的制备和实验研究。
我们将尝试通过掺杂材料的制备和实验测量,验证MCS模型的拓扑解。
通过实验数据的分析,我们可以研究MCS模型中拓扑解的影响和性质。
4. 预期结果我们预期将会得到以下结果:(1)理论推导MCS模型拓扑解的存在性条件,并分析拓扑解的拓扑荷和局域性质。
拟牛顿算法范文
拟牛顿算法范文拟牛顿算法(quasi-Newton algorithm),也被称为拟牛顿方法,是一类优化算法,用于求解无约束最优化问题。
它通过使用近似的海森矩阵(Hessian matrix)来迭代地逼近最优解,并可以在一定程度上替代传统的牛顿法。
牛顿法是一种基于二阶导数信息的优化方法,它对目标函数进行二次近似,并以此更新方向和步长。
然而,牛顿法需要计算和存储目标函数的海森矩阵,它的计算复杂度为O(n^2),其中n是目标函数的维度。
当目标函数的维度很高时,计算和存储海森矩阵将变得非常耗时和困难。
为了解决这个问题,拟牛顿算法采用了一种近似的方法来估计海森矩阵。
它基于牛顿法的思想,但使用更简单的Hessian矩阵估计技术。
拟牛顿方法可以通过迭代更新当前点的近似Hessian矩阵,从而逐渐接近最优解。
最著名的拟牛顿算法之一是Broyden-Fletcher-Goldfarb-Shanno (BFGS)算法,它是由四位数学家分别独立提出的。
BFGS算法使用拟牛顿方式更新近似的Hessian矩阵,以此来求解目标函数的最小值。
BFGS 算法在求解大型优化问题和非线性最小二乘问题时表现出色,因为它避免了显式计算和存储原始Hessian矩阵,并使用矩阵乘法来近似它。
另一个常用的拟牛顿方法是L-BFGS(Limited-memory BFGS)算法,它是BFGS算法的一种改进。
L-BFGS算法在迭代过程中,只需要存储有限数量的历史信息,从而降低了内存使用量,并且适用于大型问题。
L-BFGS 算法被广泛应用于机器学习领域的训练模型和优化问题中。
要使用拟牛顿算法求解无约束最优化问题,通常需要考虑以下几个关键步骤:1.选择初始点:需要根据具体问题选择一个合适的初始点作为起点。
2. 选择近似Hessian矩阵:需要选择一种拟牛顿方法,并确定如何估计和更新近似Hessian矩阵。
3. 计算方向和步长:使用近似Hessian矩阵来计算方向,并使用线或其他方法确定步长。
我所知道的计算流体力学大牛们
我所知道的计算流体力学大牛们计算流体力学(CFD)是一门涉及数学、物理学和计算机科学的交叉学科,广泛应用于工程和科学领域。
下面是一些在CFD领域做出重要贡献的大牛们。
1. Jameson, Antony:Jameson教授是CFD领域的先驱之一,他提出了著名的领先-追赶格式方法,用于解决非稳态问题。
他还开发了自适应网格技术,用于提高CFD计算的精度和效率。
2. Anderson, John D. Jr.:Anderson教授是航空航天工程和计算流体力学领域的一位重要学者。
他在CFD中的突出贡献包括开发了NASA 的三维多块结构网格生成软件。
他还合著了《计算流体力学和有限元法》等经典著作。
3. Roache, Patrick J.:Roache教授被誉为CFD方法验证和验证的权威。
他提出了CFD验证的概念,并开发了一系列方法和标准,以确保CFD计算的准确性和可信度。
他的贡献促进了CFD方法的发展和应用。
4. Chorin, Alexandre Joel:Chorin教授提出了著名的分数阶步进方法(Fractional Step Method),用于解决不可压缩流动的Navier-Stokes方程。
这种方法极大地简化了CFD计算的过程,并被广泛应用于工程实践中。
5. Melnik, Roderick V. N.:Melnik教授在CFD领域做出了重要贡献,特别是在多孔介质流动和多孔介质材料的数值模拟方面。
他开发了多孔介质流动的理论模型和数值方法,并应用于地球物理、生物医学和工程等领域。
6. Ferziger, Joel H.:Ferziger教授是CFD领域的重要学者之一,他合著了《计算流体力学》这本广泛使用的教材。
他的研究涵盖了流体力学的各个方面,包括湍流模拟、边界条件处理和数值方法的改进等。
7. Peric, Milovan:Peric教授在CFD的边界条件处理和有限体积方法方面做出了重要贡献。
Duality Equivalence Between Self-Dual And Topologically Massive Non-Abelian Models
a rXiv:h ep-th/014115v28M a y21Duality Equivalence Between Self-Dual And Topologically Massive Non-Abelian Models A.Ilha and C.Wotzasek Instituto de F´ısica,Universidade Federal do Rio de Janeiro,Caixa Postal 68528,21945Rio de Janeiro,RJ,Brazil.(February 7,2008)The non-abelian version of the self-dual model proposed by Townsend,Pilch and van Nieuwenhuizen presents some well known difficulties not found in the abelian case,such as well defined duality operation leading to self-duality and dual equivalence with the Yang-Mills-Chern-Simons theory,for the full range of the coupling constant.These questions are tackled in this work using a distinct gauge lifting technique that is alternative to the master action approach first proposed by Deser and Jackiw.The master action,which has proved useful in exhibiting the dual equivalence between theories in diverse dimensions,runs into trouble when dealing with the non-abelian case apart from the weak coupling regime.This new dualization technique on the other hand,is insensitive of the non-abelian character of the theory and generalize straightforwardly from the abelian case.It also leads,in a simple manner,to the dual equivalence for the case of couplings with dynamical fermionic matter fields.As an application,we discuss the consequences of this dual equivalence in the context of 3D non-abelian bosonization.I.INTRODUCTIONThe bosonization technique that expresses a theory of interacting fermions in terms of free bosons provides a powerful non-perturbative tool for investigations in different areas of theoretical physics with practical applications [1].In two-dimensions these ideas have been extended in an interpolating representation of bosons and fermions which clearly reveals the dual equivalence character of these representations[2].In spite of some difficulties,the bosonization program has been extended to higher dimensions[3,4].In particular the2+1dimensional massive Thirring model(MTM)has been bosonized to a free vectorial theory in the leading order of the inverse mass ing the well known equivalence between the self-dual[5] and the topologically massive models[6]proved by Deser and Jackiw[7]through the master action approach[8],a correspondence has been established between the partition functions for the MTM and the Maxwell-Chern-Simons (MCS)theories[9].The situation for the case of fermions carrying non-abelian charges,however,is less understood due to a lack of equivalence between these vectorial models,which has only been established for the weak coupling regime [10].As critically observed in[11]and[12],the use of master actions in this situation is ineffective for establishing dual equivalences.In this paper we intend tofill up this gap.We propose a new technique to perform duality mappings for vectorial models in any dimensions that is alternative to the master action approach.It is based on the traditional idea of a local lifting of a global symmetry and may be realized by an iterative embedding of Noether counter terms.This technique was originally explored in the context of the soldering formalism[13,14]and is exploited here since it seems to be the most appropriate technique for non-abelian generalization of the dual mapping concept.Using the gauge embedding idea,we clearly show the dual equivalence between the non-abelian self-dual and the Yang-Mills-Chern-Simons models,extending the proof proposed by Deser and Jackiw in the abelian domain.These results have consequences for the bosonization identities from the massive Thirring model into the topologically massive model,which are considered here,and also allows for the extension of the fusion of the self-dual massive modes[14]for the non-abelian case[15].We also discuss the case where charged dynamical fermions are coupled to the vector bosons.For fermions carrying a global U(1)charge we reproduce the result of[16]but the result for the non-abelian generalization is new.The technique of local gauge lifting is developed in section II through specific examples.In section III we show how the gauge embedding idea solves the problem of non-abelian dual equivalence.For completeness wefirst discuss the abelian case showing how the well known results are easily reproduced.The case of dual equivalence between the SD and the MCS when dynamical fermionfields are coupled to the gaugefields is also considered,both in the abelian and in the non-abelian cases.The remaining sections are dedicated to explore this result in the non-abelian bosonization program and to present our conclusions.II.NOETHER GAUGE EMBEDDING METHODRecently there has been a number of papers examining the existence of gauge invariances in systems with second class constraints[17].Basically this involves disclosing,using the language of constraints,hidden gauge symmetries in such systems.This situation may be of usefulness since one can consider the non-invariant model as the gauge fixed version of a gauge theory.By doing so it has sometimes been possible to obtain a deeper and more illuminating interpretation of these systems.Such hidden symmetries may be revealed by a direct construction of a gauge invariant theory out of a non-invariant one[18].The former reverts to the latter under certain gaugefixing conditions.The associate gauge theory is therefore to be considered as the embedded one.The advantage in having a gauge theory lies in the fact that the underlying gauge invariant theory allows us to establish a chain of equivalence among different models by choosing different gaugefixing conditions.In this section we shall review a different technique to achieve this goal:the iterative Noether gauging procedure. For pedagogical reasons,we develop simple illustrations making use of scalarfield theories living in a Minkowski space-time of dimension two.This will allow us to discuss some subtle technical details of this method,regarding the connection between the implementing symmetries and the Noether currents,which are necessary for its application in the2+1dimensional self-dual model.The important point to stress in this review of the iterative Noether procedure is its ability to implement specific symmetries leading to distinct models.To avoid unnecessary complications let us consider the case of a free two dimensional scalarfield theory,S(0)=1and choose to gauge either the axial shift,(i)ϕ→ϕ+ǫ,(2) or the conformal symmetry,(ii)ϕ→ϕ+ǫ∂−ϕ,(3) (∂0±∂1)andǫis a global parameter.In thefirst case it is simple to identify the Noether current as, where∂±=12Jµ=∂µϕ,(4) which comes from the variation of the scalarfield actionδS(0)= d2x Jµ∂µǫ,(5) which is non-vanishing ifǫis lifted to its local version.To compensate for this non-vanishing result we introduce a counter-term together with an ancillary gaugefield Bµ(also called as compensatoryfield)as,S(1)=S(0)− d2x JµBµ,(6) such that its variation reads,δS(1)=− d2x BµδJµ,(7) which is achieved if Bµtransforms as a vectorfield simultaneously with(2).Introducing an extra counter-term as,S(2)=S(1)+1III.DUAL EQUIV ALENCE OF SD AND MCS THEORIESIn this section we discuss the application of the gauge invariant embedding to show the dual equivalence between the SD model with the MCS theory both abelian and non-abelian including the coupling with charged dynamical matter fields.As mentioned in the introduction this has the advantage of possessing a straightforward extension to the non-abelian case for all values of the coupling constant.Let us recall that the essential properties manifested by the three dimensional self-dual theory such as parity breaking and anomalous spin,are basically connected to the presence of the topological and gauge invariant Chern-Simons term.The abelianself-dual model for vector fields was first introduced by Townsend,Pilch and van Nieuwenhuizen[5]through the following action,S χ[f ]= d 3x χ2f µf µ ,(15)where the signature of the topological terms is dictated by χ=±1and the mass parameter m is inserted for dimensional reasons.Here the Lorentz indices are represented by greek letters taking their usual values as µ,ν,λ=0,1,2.The gauge invariant combination of a Chern-Simons term with a Maxwell actionS (MCS )= d 3x 12mǫµνλf µ∂νf λ ,(16)is the topologically massive theory,which is known to be equivalent [7]to the self-dual model (15).f µνis the usual Maxwell field strength,f µν≡∂µf ν−∂νf µ.(17)The non-abelian version of the vector self-dual model (15),which is our main concern in this work,is given byS χ= d 3x tr −14mǫµνλ F µνF λ−24m 2F µνF µν+χ3F µF νF λ ,(20)only in the weak coupling limit g →0so that the Yang-Mills term effectively vanishes 1.To study the dual equivalence of (18)and (20)for all coupling regimes and the consequences over the bosonization program is main contribution of this work.Next we analyze the dualization procedure in the massive spin one self-dual theories using the Noether gauging procedure.To begin with,it is useful to clarify the meaning of the self duality inherent in the action (15).The equation of motion in the absence of sources is given by,f µ=χ1Here we are using the bosonization nomenclature that relates the Thirring model coupling constant g 2with the inverse mass of the vector model;see discussion after Eq.(62)∂µfµ=0,2+m2 fµ=0.(22) From(21)and(22)we see that there is only one massive excitation whose value is m.Afield dual to fµis defined as,⋆fµ=1mǫµνλ∂ν⋆fλ=fµ,(24)obtained by exploiting(22),thereby validating the definition of the dualfibining these results with(21),we conclude that,fµ=χ⋆fµ.(25) Hence,depending on the signature ofχ,the theory will correspond to a self-dual or an anti self-dual model.To prove the exact equivalence between the self-dual model and the Maxwell Chern-Simons theory,we start with the zeroth-iterated action(15)which is non-invariant by gauge transformations of the basic vectorfield fµ.To construct from it an abelian gauge model,we have to consider the gauging of the following symmetry,δfµ=∂µξ,(26) whereξis an infinitesimal local parameter.Under such transformations,the action(15)change as,δSχ= d3x Jµ(f)∂µξ,(27) where the Noether currents are defined by,Jµ(f)≡−fµ+χ2BµBµ ,(32)which is invariant under the combined gauge transformations(26)and(29).The gauging of the U(1)symmetry is complete.To return to a description in terms of the original variables,the auxiliary vectorfield is eliminated from (32)by using the equation of motion,Bµ=−Jµ.(33)Note that taking variations on both sides of this equation and using the gauge invariance of the Chern-Simons form we obtain consistency with the conditionδfµ=δBµ.It is now crucial to note that,by using the explicit structures for the currents,the above action(32)forms a gauge invariant combination expressed by the action(16)which is the Maxwell Chern-Simons theory.Our goal has been achieved.The iterative Noether dualization procedure has precisely incorporated the abelian gauge symmetries in the self-dual model to yield the gauge invariant Maxwell Chern-Simons theory.The free case considered above can also be extended to couplings with external,field-independent sources.To illustrate this point,we consider a coupling between dynamical U(1)charged fermions and self-dual vector bosons [16].In the abelian case,this model is written asS[f,ψ]=Sχ[f]+ d3x −e fµJµ+¯ψ(i∂/−M)ψ ,(34)where Jµ=¯ψγµψand M is the fermionic mass.Sχis the self-dual action(15).The Noether current associated with this action isχKµ=−fµ+BµBµ .(36)2After the elimination of Bµthrough its equations of motion,we get ourfinal theory,S=S(MCS)+ d3x e2JµJµ+eχfµGµ+L D ,(37) where S(MCS)is the Maxwell Chern-Simons action(16)and L D is the free Dirac Lagrangian.Here Gµ=1ǫµνλ(∂µ+Fµ) Fν,(39)mwhere the operator inside the square brackets in the right-hand side acts on the basicfield Fνdefining⋆Fλas the dual of Fν.Repeating this operation,and using the equations of motion obtained by varying(18)with respect to FλχFλ=with the Noether currents being defined as,Jµ=−Fµ+χ2δ(JµJµ)−δ(JµBµ)−JµδBµ ,(45) where we have used the following transformation rule for the gaugingfield,δFµ=−δBµ−δJµ.(46) This prompt us to define the following second iterated action,S(2)=Sχ+ d3x tr 14mǫµνλ FµνFλ−22BµBµ (47) which is gauge invariant after noticing that the transformation rule(46)fixes the Bµfield asBµ=−χ2m ǫµνλFµνJλ+e2Bosonization was developed in the context of the two-dimensional scalarfield theory and has been one of the main tools available to investigate the non-perturbative behavior of some interactivefield theories[1].For some time this concept was thought to be an exclusive property of two-dimensional space-times where spin is absent and one cannot distinguish between bosons and fermions.It was only recently that this powerful technique were extended to higher dimensional space-times[24,25][9,26].The bosonization mapping in D=3,first discussed by Polyakov[27],shows that this is a relevant issue in the context of transmutation of spin and statistics in three dimensions.The equivalence of the three dimensional effective electromagnetic action of the CP1model with a charged massive fermion to lowest order in inverse(fermion)mass has been proposed by Deser and Redlich[28].Using their results bosonization was extended to three dimensions in the1/m expansion[9].These endeavor has led to promising results in diverse areas such as,for instance,the understanding of the universal behavior of the Hall conductance in interactive fermion systems[29].For higher dimensions,due to the absence of an operator mapping a la Mandelstan,the situation is more complicate and even the bosonization identities extracted from these procedures relating the fermionic current with the bosonic topological current is a consequence of a non-trivial current algebra.Moreover,contrary to the two dimensional case, in dimensions higher than two there are no exact results with the exception of the current mapping[22,23].Besides, while the two-dimensional fermionic determinant can be exactly computed,here it is neither exact nor complete, having a non-local structure.However,for the large mass limit in the one-loop of perturbative evaluation,a local expression materializes.This procedure,is in a sense,opposite to what is done in1+1dimensions where bosonization is a set of operator identities valid at length scales short compared with the Compton wavelength of the fermions while in D=3only the long distance regime is considered.In this section we review how the low energy sector of a theory of massive self-interacting,G-charged fermions,the massive Thirring Model in2+1dimensions,can be bosonized into a gauge theory,the Yang-Mills-Chern-Simons gauge theory thanks to the results of the preceeding section.A.The Non-Abelian MappingIn the sequence we investigate the problem of identifying a bosonic equivalent of a three dimensional theory of self-interacting fermions with symmetry group G and show how it is possible to bosonize the low-energy regime of the theory.We follow the same strategy as in reference[9]but follow the notation of[10]that is slightly different than [9].We seek a bosonic theory which reproduces correctly the low-energy regime of the massive fermionic theory.To begin with we define the G-current,j aµ=¯ψi t a ijγµψj,(51) whereψi are N two-component Dirac spinors in the fundamental representation of G,i,j=1,...,N and a= 1,...,dim G.Here t a and f abc are the generators and the structure constants of the symmetry group G,respectively and j aµis a G-current.The(Euclidean)fermionic partition function for the three-dimensional massive Thirring model is,Z T h= D¯ψDψe− ¯ψi(∂/+m)ψi−g22 d3x j aµj aµ= D aµe− d3x tr(12g2 d3x tr(aµaµ).(54)The determinant of the Dirac operator is an unbounded operator and requires regularization.For D=2this deter-minant can be computed exactly,both for abelian and non-abelian symmetries.Based on general grounds only,one may say that this determinant consists of a Chern-Simons action standing as the leading term plus an infinite series of terms depending on the dual of the vectorfield,˜Fµ∼ǫµνλ∂νAλ,including those terms that are non-local andnon-quadratic in˜Fµ.For the D=3the actual computation of this determinant will give parity breaking and parity conserving terms that are computed in powers of the inverse mass,χln det(i∂/+m+a/)=aµaνaλ),(56)3is the non-abelian Chern-Simons action and the parity conserving contributions,infirst-order,is the Yang-Mills action1I P C[a]=−16πS CS[a](59) Using this result we can write Z T h in the formZ T h= D aµexp(−S SD[a]),(60) where S SD is the non-abelian version of the self-dual action introduced in[5],S SD[a]=116πS CS[a](61)Therefore,to leading order in1/m we have established the identification Z T h≈Z SD.Now,recalling that the model with dynamics defined by the non-abelian self-dual action is equivalent to the Yang-Mills-Chern-Simons theory,we use this connection to establish the equivalence of the non-abelian massive Thirring model and the YMCS theory asZ T h≈Z Y MCS.(62) It is interesting to observe that the Thirring coupling constant g2/N in the fermionic model is mapped into the inverse mass spin1massive excitation,m=π/g2.Now comes an important observation.Unlike the master approach,our result is valid for all values of the coupling constant.The proof,based on the use of an“interpolating Action”S I,is seen to run into trouble in the non-abelian case.That the non-abelian extension of this kind of equivalences is more involved was already recognized in[7]and [10],and shown that the non-abelian self-dual action is not equivalent to a Yang-Mills-Chern-Simons theory(the natural extension of the abelian MCS theory)but to a model where the Yang-Mills term vanishes in the limit g2→0 [10].B.Current IdentitiesTo infer the bosonization identities for the currents which derive from the equivalence found in the last section,we add a source for the Thirring current leading to the following functional generator,Z T h[b]= D¯ψDψD aµe− d3x ¯ψ(i∂/+m+a/+b/)ψ+1tr d3x bµbµ· D aµe−S SD[a]+12g2after shifting aµ→aµ−bµ.In order to connect this with the Yang-Mills-Chern-Simons system we repeat the steps of the last section to obtain,Z T h[bµ]≈Z MCS[bµ](64) We have therefore established,to order1/m,the connection between the Thirring and self-dual models in the non-abelian context,now in the presence of sources.This is,in its most general form,the result we were after.It provides a complete low-energy bosonization prescription,valid for any g2,of the matrix elements of the fermionic current.From(64)we see,from simple differentiation w.r.t.the source,that the bosonization rule for the fermion current,to leading order in1/m,readsj aµ→iχπ)ǫµν∂νφwhile in this case it should be considered as the analog of the Wess-Zumino-Witten currents.Notice that as in the abelian case the bosonized expression for the fermion current is topologically conserved.We thus see that the non-abelian bosonization of free,G-charged massive fermions in2+1dimensions leads to the non-abelian Chern-Simons theory,with the fermionic current being mapped to the dual of the gaugefield strength. This result holds only for length scales large compared with the Compton wavelength of the fermion,since our results were obtained for large fermion mass.It is important to notice that the limit g2→0used in earlier approaches corresponding to free fermions(but not to an abelian gauge theory)was not taken here at any stage.This is important since Yang-Mills coupling is proportional to g2,which is why we are left with a Yang-Mills-Chern-Simons action and not the pure Chern-Simons theory of[10].V.CONCLUSIONSThe rationale of different phenomena in planar physics have greatly benefitted from the use of2+1dimensionalfield theories including the parity breaking Chern-Simons term.In this scenario it is important to establish connections among different models so that a unifying picture emerges.In this context we have shown,in earlier work,that the soldering formalism has established a direct link between self-dual models of opposite helicities with the Proca model [14].Other instances includes the recent extension of the functional bosonization program interpolating from fermions to bosons in a coherent picture[9,10].In the context of thefirst it has been argued that the soldering formalism is equivalent to canonical transformation albeit in the Lagrangian side while for the later the mentioned mapping between SD and MCS models has been used to establish a formal equivalence between the partition functions of the abelian version of MTM and a theory of interacting bosons.The non-abelian extension of this analysis,for the full range of the coupling constant,has been the main concern of the present work since only partial results were reported in the literature.Other directions have also been investigated,with new results,that includes the proof of the self-duality property of(18)and the coupling with G-charged dynamical matterfields.Our analysis has shown how the gauge lifting approach sheds light on the question of dual equivalence between SD and topologically massive theories with new results for the non-abelian case.This discussion becomes the central issue when deriving bosonization rules in D=3,for fermions carrying non-abelian charges since,up to date,only prescriptions based on the Master action of Ref.[7]were used,apart from[12]with conclusions consistent with[10]. These derivations of the bosonization mappings suffered from well known difficulties related to the dual equivalence, restricting the results to be limited to weak coupling constant only.Therefore,regarding the non-abelian bosonization in D=3dimensions,we believe that the method developed here,which is simpler and better suited to deal with non-abelian symmetries,completes the program initiated in[10]and confirms the exact identities found in[22,23].This new approach has also been used with dynamical fermionicfields leading naturally to the necessity of a Thirring like term to establish the equivalence of the fermionic sectors in both sides.Such equivalence may be extended to the scalar case[21].The bosonization for D≥4poses no difficulties as long as the fermionic determinant can be evaluated in some approximation and is expected to yield a gauge invariant piece.This is of importance since the description of chargedfermionicfields in terms of gaugefields has brought new perspectives and a deeper insight on the non perturbative dynamics of planar physics[30]that might be extended to higher dimensions.。
两相流多尺度作用模型和能量最小方法
两相流多尺度作用模型和能量最小方法对于多相流动问题,常常需要使用多尺度方法来描述液相和气相在微观尺度下的细节,以及它们在宏观尺度下的行为。
其中一个主要的挑战是如何将微观和宏观尺度的信息进行耦合,以便能够得到可靠的多相流动模型。
传统的多尺度方法通常基于保守或非保守的宏观方程,其中微观细节被嵌入到宏观尺度中。
但是,这样的方法可能会导致较大的计算量和不精确的结果,并且不易扩展到大规模问题。
为了克服这些限制,近年来出现了一些基于能量最小化的多尺度方法。
这些方法基于能量原理,将多尺度系统看作一个能量最小化的问题,并通过寻找能量最小化路径来获得宏观尺度下的解。
在多尺度方法中,重要的参数是多尺度耦合系数,通常是通过能量最小化方法来确定。
这个系数描述了微观和宏观尺度之间的耦合强度,并直接影响到模型的准确性。
可以使用机器学习方法来计算耦合系数,以便更精确地描述多相流动行为。
除了多尺度方法,还有一些其他的方法可以用于多相流动问题的建模。
例如,质量守恒方程和动量守恒方程可以用于描述单一物质的流动,但对于多相流动问题则需要分别考虑不同物质的流动,并引入界面张力等额外的耦合项。
此外,对于强烈非均匀的多相流动,如气液泡动,有时需要将三维宏观模型与二维微观模型相结合,以便更好地描述流动行为。
总之,多相流动问题需要考虑多尺度耦合和耦合项,以便更准确地描述液相和气相的行为。
能量最小化是一种可行的方法,可以通过寻找能量最小化路径来获得宏观尺度下的解。
同时,机器学习方法可以用来计算耦合系数,以提高模型的准确性。
除此之外,还需要使用其他的方法来描述不同物质的流动以及强烈非均匀的流动行为。
三维Chern-Simons理论中Wilson圈的全息研究的开题报告
三维Chern-Simons理论中Wilson圈的全息研究
的开题报告
题目:三维Chern-Simons理论中Wilson圈的全息研究
摘要:
在三维Chern-Simons理论中,Wilson圈是一种重要的物理量,它可以用来描述在这个理论中的拓扑性质。
Wilson圈的全息研究可以为我们提供更深入的了解这个理论的性质,并且在物理学和数学领域都有广泛的应用。
本文主要研究三维Chern-Simons理论中Wilson圈的全息理论,并通过在AdS空间中的Wilson线计算,建立起从Wilson圈到在广义伦格方程的路径积分中出现的不变量之间的联系。
首先,我们将介绍三维Chern-Simons理论的基本概念和Wilson圈的定义。
然后,我们将阐述AdS/CFT对偶理论和Wilson线的计算方法,并在此基础上给出Wilson圈在AdS/CFT对偶理论中的求解方法。
最后,我们将讨论从Wilson圈到广义伦格方程的路径积分中出现的不变量之间的联系,进一步探讨三维Chern-Simons理论的全息性质。
通过本文的研究,我们将能够对三维Chern-Simons理论的全息性质有一个更加深入的了解,为物理学和数学领域的研究提供更多的思路和方法。
培养创新的团队文化
培养创新的团队文化团队文化在现代商业环境中扮演着至关重要的角色。
一个积极、创新的团队文化可以激发团队成员的潜能,促进协作和创新,从而帮助企业在竞争中取得优势。
本文将探讨培养创新的团队文化的重要性,并提供一些实用的建议。
一、创新的意义创新是企业持续发展的动力之一。
创新不仅仅是指引领产品和服务的创新,也包括组织和管理的创新。
在当今日新月异的商业世界,企业必须不断学习和适应变化。
创新的团队文化可以帮助企业在快速变化的市场中保持竞争力。
创新的团队文化还可以激发团队成员的潜能。
通过鼓励员工表达自己的想法和意见,团队可以汇聚各种不同的观点和创意。
这有助于促进跨功能团队之间的合作,以及促使员工积极参与到解决问题和改进流程的过程中。
二、打造创新的团队文化1. 鼓励多样性和包容性一个多样化和包容性的团队文化可以为创新提供良好的土壤。
多样化的团队能够汇集不同背景和经验的人,从而促进不同思维方式的碰撞与交流。
在这样的环境中,团队成员会更容易接受不同的观点和意见,从而进一步激发创新的火花。
2. 鼓励学习和持续发展在培养创新的团队文化中,学习和持续发展是关键。
团队成员应被鼓励不断学习新知识和技能,并提供机会参加培训和进修课程。
此外,团队应该设立反思和总结的机制,以便从过去的经验中吸取教训,不断改进和创新。
3. 建立积极的沟通机制积极的沟通是创新团队文化的基石。
团队成员应该鼓励开放、坦诚和透明的沟通方式,以减少信息的滞后和误解。
此外,团队应该建立一个互相支持和协作的氛围,鼓励团队成员分享他们的观点、想法和建议。
4. 奖励与认可创新为了培养创新的团队文化,企业应该建立激励机制,奖励和认可那些提出创新想法和创造性解决问题的团队成员。
这可以通过奖金制度、晋升机会或其他形式的奖励来实现。
这样的激励机制可以增强员工的动力和积极性,促进团队文化的创新氛围。
5. 培养团队合作创新的团队文化是建立在合作基础上的。
团队成员应该被鼓励进行跨团队合作和知识共享,以便分享和学习最佳实践。
2016化学诺奖:分子机器将开创新时代
2016化学诺奖:分子机器将开创新时代
蒋琳
【期刊名称】《中国经济报告》
【年(卷),期】2016(0)11
【摘要】和生命分子一样,索维奇、斯托达特和费林加的人造分子系统能执行受控任务,化学因此向着全新世界迈出了第一步法国斯特拉斯堡大学的让-彼埃尔·索维奇(Jean-Pierre Sauvage)教授、美国西北大学的詹姆斯·弗雷泽·斯图达特(James Fraser Stoddart)爵士以及荷兰格罗宁根大学的伯纳德·L·费林加(Bernard
L.Feringa)教授共享2016年诺贝尔化学奖,他们因在分子机器的设计和合成上的贡献而获奖。
这让许多人不禁感慨。
【总页数】2页(P118-119)
【关键词】分子机器;维奇;斯图;埃尔;纳米;达特;光化学;诺贝尔奖;诺贝尔化学奖【作者】蒋琳
【作者单位】复旦大学化学系
【正文语种】中文
【中图分类】O6-19
【相关文献】
1.2016年诺贝尔化学奖揭晓告诉你什么是分子机器 [J], 朱平
2.浅谈2016年诺贝尔化学奖:分子机器 [J], 成楚旸
3.2016年诺贝尔化学奖——分子机器的发展简史及研究进展 [J], 姚旭阳
4.分子机器的设计与合成——2016年诺贝尔化学奖解读 [J], 李川
5.分子机器的回顾与展望——2016年诺贝尔化学奖简介 [J], 黄甫;赵伟;车延科;江华
因版权原因,仅展示原文概要,查看原文内容请购买。
- 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。
1 2
Electronic mail: stkim@skku.ac.kr Electronic mail: yoonbai
Chern-Simons gauge theories have provide intriguing questions and answers to various subjects of both physics and mathematics. One of interdisciplinary topics attracted attention is so-called self-dual Chern-Simons solitons [1, 2, 3]. A natural extension is to include gravity which can be background [4, 5] or dynamical [6, 7]. Once the Bogomolnyi-type bound is obtained and detailed mathematical properties of those self-dual vortices are studied in the Chern-Simons Higgs model in the presence of background gravity, it would be helpful to address related physics-wise problems involving condensed matter systems, e.g., quantum Hall effects, supergravity, Lorentz-symmetry breaking due to parity-violating term, existence of time-like closed curve around gravitating spinning strings, cosmological implication of cosmic strings, and even cosmological constant problem. Because it is applicable to diverse fields, mathematical study of self-dual Chern-Simons solitons is going on. The existence of a topological multi-vortex solution of relativistic Chern-Simons Higgs theory in flat R2 is shown by Wang [8]. In the same setting, rotationallysymmetric nontopological solitons and vortices were proven to exist by Spruck and Yang [9]. Yang also proved the existence of a topological self-dual multi-vortex solution when the gauge symmetry is extended to non-Abelian [10]. When the topological vortices or nontopological solitons are generated in condensed matter systems or in the early universe, they are likely to form a lattice structure or a network. In such sense important works have been done on torus [11, 12, 13, 14] or on standard sphere [4, 15]. Condensed matter experiments are usually performed by turning on constant external electric or magnetic field. In relation to this, Chae et al. demonstrated the existence of soliton solutions of self-dual Chern-Simons Higgs model coupled to an external background charge density [16]. Another study to have cosmological implication was done by Choe with nontopological soliton solutions under decaying metric [17]. Now let us take into account curved spacetime geometry of a straight string in the early universe. Then, extremely-small core region of the string is curved by matter fields, and the intermediate region is slightly-curved or locally-flat because of no graviton to the transverse directions. However, the asymptote of the global universe is known to be flat. All of such geometry should be dynamically determined by examining Einstein equations in exact sense, but it is practically too difficult to do with mathematical rigor. A meaningful starting point is to assume a physically-allowable set of background metrics and to study possible string configurations. In this paper, we study Chern-Simons Higgs theory on a uniformly Euclidean metric, which is not necessarily radial. A spatial metric γij = b(x, y )δij is called uniformly Euclidean metric if there exist positive constants a1 and a2 with a1 ≤ b(x, y ) ≤ a2 . We show 2
math-ph/0012045
Self-dual Chern-Simons Vortices on Riemann Surfaces
arXiv:math-ph/0012045v1 27 Dec 2000
Seongtag Kim1 Department of Mathematics and Institute of Basic Science, Sungkyunkwan University, Suwon 440-746, Korea Yoonbai Kim2 BK21 Physics Research Division and Institute of Basic Science, Sungkyunkwan University, Suwon 440-746, Korea
the existence of a self-dual topological multi-vortex solution and the fast decay property of a solution at infinity. The mathematical conditions we bring up are relevant to the physical situation discussed in the above, e.g., the gravity is not far from that of the flat case at the end of universe. A brief outline of the paper is in order. In section 2, under the most general static metric, we shall derive the Bogomolnyi type bound of the Chern-Simons Higgs theory in background gravity. In section 3, we present the existence and asymptotic behavior of a solution of the self-dual Chern-Simons vortices. Conclusions with some discussions about our results are presented in section 4.
Abstract We study self-dual multi-vortex solutions of Chern-Simons Higgs theory in a background curved spacetime. The existence and decaying property of a solution are demonstrated.
where the metric of two-dimensional spatial hypersurface can always be diagonalized by a conformal gauge γij = δij b(xk ). Later we shall show that the Bogomolnyi bound is attained only when the lapse function N (xi ) is constant, i.e., N (xk ) = 1 after a rescaling of time coordinate t. The Chern-Simons Higgs theory is described by the action S= 1 √ κ ǫµνρ d3 x g √ Aµ ∂ν Aρ + g µν Dµ φDν φ − V (|φ|) , 2 g 2 (2.2)