On Boyer-Moore Preprocessing

合集下载

On the Benjamini--Hochberg method

On the Benjamini--Hochberg method
Benjamini and Hochberg [2] have proposed a method of choosing R specifically aimed at discovering r.v.’s taking values in the interval [0, 1] that tend to be smaller than standard uniform r.v.’s and which, given δ > 0, guarantees that E(Π1,m) ≤ δ under certain conditions. The method consists of
ON THE BENJAMINI–HOCHBERG METHOD
By J. A. Ferreira1 and A. H. Zwinderman
University of Amsterdam
We investigate the properties of the Benjamini–Hochberg method for multiple testing and of a variant of Storey’s generalization of it, extending and complementing the asymptotic and exact results available in the literature. Results are obtained under two different sets of assumptions and include asymptotic and exact expressions and bounds for the proportion of rejections, the proportion of incorrect rejections out of all rejections and two other proportions used to quantify the efficacy of the method.

公共政策终结理论研究综述

公共政策终结理论研究综述

公共政策终结理论研究综述摘要:政策终结是政策过程的一个环节,是政策更新、政策发展、政策进步的新起点。

政策终结是20世纪70年代末西方公共政策研究领域的热点问题。

公共政策终结是公共政策过程的一个重要阶段,对政策终结的研究不仅有利于促进政策资源的合理配置,更有利于提高政府的政策绩效。

本文简要回顾了公共政策终结研究的缘起、内涵、类型、方式、影响因素、促成策略以及发展方向等内容,希望能够对公共政策终结理论有一个比较全面深入的了解。

关键词:公共政策,政策终结,理论研究行政有着古老的历史,但是,在一个相当长的历史时期中,行政所赖以治理社会的工具主要是行政行为。

即使是公共行政出现之后,在一个较长的时期内也还主要是借助于行政行为去开展社会治理,公共行政与传统行政的区别在于,找到了行政行为一致性的制度模式,确立了行政行为的(官僚制)组织基础。

到了公共行政的成熟阶段,公共政策作为社会治理的一个重要途径引起了人们的重视。

与传统社会中主要通过行政行为进行社会治理相比,公共政策在解决社会问题、降低社会成本、调节社会运行等方面都显示出了巨大的优势。

但是,如果一项政策已经失去了存在的价值而又继续被保留下来了,就可能会发挥极其消极的作用。

因此,及时、有效地终结一项或一系列错误的或没有价值的公共政策,有利于促进公共政策的更新与发展、推进公共政策的周期性循环、缓解和解决公共政策的矛盾和冲突,从而实现优化和调整公共政策系统的目标。

这就引发了学界对政策终结理论的思考和探索。

自政策科学在美国诞生以来,公共政策过程理论都是学术界所关注的热点。

1956年,拉斯韦尔在《决策过程》一书中提出了决策过程的七个阶段,即情报、建议、规定、行使、运用、评价和终止。

此种观点奠定了政策过程阶段论在公共政策研究中的主导地位。

一时间,对于政策过程各个阶段的研究成为政策学界的主要课题。

然而,相对于其他几个阶段的研究来说,政策终结的研究一直显得非常滞后。

这种情况直到20世纪70年代末80年代初,才有了明显的改善。

SAS混合模型数据集及示例分析说明书

SAS混合模型数据集及示例分析说明书

Package‘SASmixed’October12,2022Title Data sets from``SAS System for Mixed Models''Version1.0-4Date2014-03-11Maintainer Steven Walker<************************>Contact LME4Authors<**************************>Author Original by Littell,Milliken,Stroup,and Wolfinger,modifications by Douglas Bates<***************.edu>,Martin Maechler,Ben Bolker and Steven WalkerDescription Data sets and sample lmer analyses correspondingto the examples in Littell,Milliken,Stroup and Wolfinger(1996),``SAS System for Mixed Models'',SAS Institute.Depends R(>=2.14.0),Suggests lme4,latticeLazyData yesLicense GPL(>=2)NeedsCompilation noRepository CRANDate/Publication2014-03-1116:41:14R topics documented:Animal (2)AvgDailyGain (3)BIB (4)Bond (5)Cultivation (5)Demand (6)Genetics (7)HR (8)IncBlk (9)Mississippi (10)12Animal Multilocation (11)PBIB (12)Semi2 (13)Semiconductor (14)SIMS (14)TeachingI (15)TeachingII (16)WaferTypes (16)Weights (17)WWheat (18)Index19 Animal Animal breeding experimentDescriptionThe Animal data frame has20rows and3columns giving the average daily weight gains for animals with different genetic backgrounds.FormatThis data frame contains the following columns:Sire a factor denoting the sire.(5levels)Dam a factor denoting the dam.(2levels)AvgDailyGain a numeric vector of average daily weight gainsDetailsThis appears to be a constructed data set.SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set6.4).Examplesstr(Animal)AvgDailyGain3 AvgDailyGain Average daily weight gain of steers on different dietsDescriptionThe AvgDailyGain data frame has32rows and6columns.FormatThis data frame contains the following columns:Id the animal numberBlock an ordered factor indicating the barn in which the steer was housed.Treatment an ordered factor with levels0<10<20<30indicating the amount of medicated feed additive added to the base ration.adg a numeric vector of average daily weight gains over a period of160days.InitWt a numeric vector giving the initial weight of the animalTrt the Treatment as a numeric variableSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set5.3).Examplesstr(AvgDailyGain)if(require("lattice",quietly=TRUE,character=TRUE)){##plot of adg versus Treatment by Blockxyplot(adg~Treatment|Block,AvgDailyGain,type=c("g","p","r"),xlab="Treatment(amount of feed additive)",ylab="Average daily weight gain(lb.)",aspect="xy",index.cond=function(x,y)coef(lm(y~x))[1])}if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare with output5.1,p.178print(fm1Adg<-lmer(adg~InitWt*Treatment-1+(1|Block),AvgDailyGain))print(anova(fm1Adg))#checking significance of termsprint(fm2Adg<-lmer(adg~InitWt+Treatment+(1|Block),AvgDailyGain))print(anova(fm2Adg))print(lmer(adg~InitWt+Treatment-1+(1|Block),AvgDailyGain))}4BIB BIB Data from a balanced incomplete block designDescriptionThe BIB data frame has24rows and5columns.FormatThis data frame contains the following columns:Block an ordered factor with levels1<2<3<8<5<4<6<7Treatment a treatment factor with levels1to4.y a numeric vector representing the responsex a numeric vector representing the covariateGrp a factor with levels13and24DetailsThese appear to be constructed data.SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set5.4).Examplesstr(BIB)if(require("lattice",quietly=TRUE,character=TRUE)){xyplot(y~x|Block,BIB,groups=Treatment,type=c("g","p"),aspect="xy",auto.key=list(points=TRUE,space="right",lines=FALSE))}if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare with Output5.7,p.188print(fm1BIB<-lmer(y~Treatment*x+(1|Block),BIB))print(anova(fm1BIB))#strong evidence of different slopes##compare with Output5.9,p.193print(fm2BIB<-lmer(y~Treatment+x:Grp+(1|Block),BIB))print(anova(fm2BIB))}Bond5 Bond Strengths of metal bondsDescriptionThe Bond data frame has21rows and3columns of data on the strength required to break metal bonds according to the metal and the ingot.FormatThis data frame contains the following columns:pressure a numeric vector of pressures required to break the bondMetal a factor with levels c,i and n indicating the metal involved(copper,iron or nickel).Ingot an ordered factor indicating the ingot of the composition material.SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set1.2.4).Mendenhall,M.,Wackerly,D.D.and Schaeffer,R.L.(1990),Mathematical Statistics,Wadsworth (Exercise13.36).Examplesstr(Bond)options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))if(require("lme4",quietly=TRUE,character=TRUE)){##compare with output1.1on p.6print(fm1Bond<-lmer(pressure~Metal+(1|Ingot),Bond))print(anova(fm1Bond))}Cultivation Bacterial innoculation applied to grass cultivarsDescriptionThe Cultivation data frame has24rows and4columns of data from an experiment on the effect on dry weight yield of three bacterial inoculation treatments applied to two grass cultivars.6DemandFormatThis data frame contains the following columns:Block a factor with levels1to4Cult the cultivar factor with levels a and bInoc the innoculant factor with levels con,dea and livdrywt a numeric vector of dry weight yieldsSourceLittell,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set2.2(a)).Littel,R.C.,Freund,R.J.,and Spector,P.C.(1991),SAS System for Linear Models,Third Ed., SAS Institute.Examplesstr(Cultivation)xtabs(~Block+Cult,Cultivation)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare with Output2.10,page58print(fm1Cult<-lmer(drywt~Inoc*Cult+(1|Block)+(1|Cult),Cultivation))print(anova(fm1Cult))print(fm2Cult<-lmer(drywt~Inoc+Cult+(1|Block)+(1|Cult),Cultivation))print(anova(fm2Cult))print(fm3Cult<-lmer(drywt~Inoc+(1|Block)+(1|Cult),Cultivation))print(anova(fm3Cult))}Demand Per-capita demand deposits by state and yearDescriptionThe Demand data frame has77rows and8columns of data on per-capita demand deposits by state and year.FormatThis data frame contains the following columns:State an ordered factor with levels WA<FL<CA<TX<IL<DC<NYYear an ordered factor with levels1949<...<1959d a numeric vector of per-capita demand depositsGenetics7y a numeric vector of permanent per-capita personal incomerd a numeric vector of service charges on demand depositsrt a numeric vector of interest rates on time depositsrs a numeric vector of interest rates on savings and loan association shares.SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set1.2.4).Feige,E.L.(1964),The Demand for Liquid Assets:A Temporal Cross-Sectional Analysis.,Prentice Hall.Examplesstr(Demand)if(require("lme4",quietly=TRUE,character=TRUE)){##compare to output3.13,p.132summary(fm1Demand<-lmer(log(d)~log(y)+log(rd)+log(rt)+log(rs)+(1|State)+(1|Year), Demand))}Genetics Heritability dataDescriptionThe Genetics data frame has60rows and4columns.FormatThis data frame contains the following columns:Location a factor with levels1to4Block a factor with levels1to3Family a factor with levels1to5Yield a numeric vector of crop yieldsSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set4.5).8HRExamplesstr(Genetics)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))summary(fm1Gen<-lmer(Yield~Family+(1|Location/Block),Genetics))}HR Heart rates of patients on different drug treatmentsDescriptionThe HR data frame has120rows and5columns of the heart rates of patients under one of three possible drug treatments.FormatThis data frame contains the following columns:Patient an ordered factor indicating the patient.Drug the drug treatment-a factor with levels a,b and p where p represents the placebo.baseHR the patient’s base heart rateHR the observed heart rate at different times in the experimentTime the time of the observationSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set3.5).Examplesstr(HR)if(require("lattice",quietly=TRUE,character=TRUE)){xyplot(HR~Time|Patient,HR,type=c("g","p","r"),aspect="xy",index.cond=function(x,y)coef(lm(y~x))[1],ylab="Heart rate(beats/min)")}if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##linear trend in timeprint(fm1HR<-lmer(HR~Time*Drug+baseHR+(Time|Patient),HR))print(anova(fm1HR))##Not run:fm2HR<-update(fm1HR,weights=varPower(0.5))#use power-of-mean variancesummary(fm2HR)intervals(fm2HR)#variance function does not seem significantanova(fm1HR,fm2HR)#confirm with likelihood ratioIncBlk9##End(Not run)print(fm3HR<-lmer(HR~Time+Drug+baseHR+(Time|Patient),HR))print(anova(fm3HR))##remove Drug termprint(fm4HR<-lmer(HR~Time+baseHR+(Time|Patient),HR))print(anova(fm4HR))}IncBlk An unbalanced incomplete block experimentDescriptionThe IncBlk data frame has24rows and4columns.FormatThis data frame contains the following columns:Block an ordered factor giving the blockTreatment a factor with levels1to4y a numeric vectorx a numeric vectorDetailsThese data are probably constructed data.SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set5.5).Examplesstr(IncBlk)10Mississippi Mississippi Nitrogen concentrations in the Mississippi RiverDescriptionThe Mississippi data frame has37rows and3columns.FormatThis data frame contains the following columns:influent an ordered factor with levels3<5<2<1<4<6y a numeric vectorType a factor with levels123SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set4.2).Examplesstr(Mississippi)if(require("lattice",quietly=TRUE,character=TRUE)){dotplot(drop(influent:Type)~y,groups=Type,Mississippi)}if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare with output4.1,p.142print(fm1Miss<-lmer(y~1+(1|influent),Mississippi))##compare with output4.2,p.143print(fm1MLMiss<-update(fm1Miss,REML=FALSE))##BLUP s of random effects on p.142ranef(fm1Miss)##BLUP s of random effects on p.144print(ranef(fm1MLMiss))#intervals(fm1Miss)#interval estimates of variance components##compare to output4.8and4.9,pp.150-152print(fm2Miss<-lmer(y~Type+(1|influent),Mississippi,REML=TRUE))print(anova(fm2Miss))}Multilocation11 Multilocation A multilocation trialDescriptionThe Multilocation data frame has108rows and7columns.FormatThis data frame contains the following columns:obs a numeric vectorLocation an ordered factor with levels B<D<E<I<G<A<C<F<HBlock a factor with levels1to3Trt a factor with levels1to4Adj a numeric vectorFe a numeric vectorGrp an ordered factor with levels B/1<B/2<B/3<D/1<D/2<D/3<E/1<E/2<E/3<I/1< I/2<I/3<G/1<G/2<G/3<A/1<A/2<A/3<C/1<C/2<C/3<F/1<F/2<F/3<H/1 <H/2<H/3SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set2.8.1).Examplesstr(Multilocation)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))###Create a Block%in%Location factorMultilocation$Grp<-with(Multilocation,Block:Location)print(fm1Mult<-lmer(Adj~Location*Trt+(1|Grp),Multilocation))print(anova(fm1Mult))print(fm2Mult<-lmer(Adj~Location+Trt+(1|Grp),Multilocation),corr=FALSE)print(fm3Mult<-lmer(Adj~Location+(1|Grp),Multilocation),corr=FALSE)print(fm4Mult<-lmer(Adj~Trt+(1|Grp),Multilocation))print(fm5Mult<-lmer(Adj~1+(1|Grp),Multilocation))print(anova(fm2Mult))print(anova(fm1Mult,fm2Mult,fm3Mult,fm4Mult,fm5Mult))###Treating the location as a random effectprint(fm1MultR<-lmer(Adj~Trt+(1|Location/Trt)+(1|Grp),Multilocation))print(anova(fm1MultR))fm2MultR<-lmer(Adj~Trt+(Trt-1|Location)+(1|Block),Multilocation)##Warning(not error?!):Convergence failure in10000iter%%__FIXME__12PBIB print(fm2MultR)#does not mention previous conv.failure%%FIXME??print(anova(fm1MultR,fm2MultR))##Not run:confint(fm1MultR)##End(Not run)}PBIB A partially balanced incomplete block experimentDescriptionThe PBIB data frame has60rows and3columns.FormatThis data frame contains the following columns:response a numeric vectorTreatment a factor with levels1to15Block an ordered factor with levels1to15SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set1.5.1).Examplesstr(PBIB)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare with output1.7pp.24-25print(fm1PBIB<-lmer(response~Treatment+(1|Block),PBIB))print(anova(fm1PBIB))}Semi213 Semi2Oxide layer thicknesses on semiconductorsDescriptionThe Semi2data frame has72rows and5columns.FormatThis data frame contains the following columns:Source a factor with levels1and2Lot a factor with levels1to8Wafer a factor with levels1to3Site a factor with levels1to3Thickness a numeric vectorSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set4.4).Examplesstr(Semi2)xtabs(~Lot+Wafer,Semi2)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare with output4.13,p.156print(fm1Semi2<-lmer(Thickness~1+(1|Lot/Wafer),Semi2))##compare with output4.15,p.159print(fm2Semi2<-lmer(Thickness~Source+(1|Lot/Wafer),Semi2))print(anova(fm2Semi2))##compare with output4.17,p.163print(fm3Semi2<-lmer(Thickness~Source+(1|Lot/Wafer)+(1|Lot:Source),Semi2))##This is not the same as the SAS model.}14SIMS Semiconductor Semiconductor split-plot experimentDescriptionThe Semiconductor data frame has48rows and5columns.FormatThis data frame contains the following columns:resistance a numeric vectorET a factor with levels1to4representing etch time.Wafer a factor with levels1to3position a factor with levels1to4Grp an ordered factor with levels1/1<1/2<1/3<2/1<2/2<2/3<3/1<3/2<3/3<4/1< 4/2<4/3SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set2.2(b)).Examplesstr(Semiconductor)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))print(fm1Semi<-lmer(resistance~ET*position+(1|Grp),Semiconductor))print(anova(fm1Semi))print((fm2Semi<-lmer(resistance~ET+position+(1|Grp),Semiconductor)))print(anova(fm2Semi))}SIMS Second International Mathematics Study dataDescriptionThe SIMS data frame has3691rows and3columns.FormatThis data frame contains the following columns:Pretot a numeric vector giving the student’s pre-test total scoreGain a numeric vector giving gains from pre-test to thefinal testClass an ordered factor giving the student’s classTeachingI15SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(section7.2.2)Kreft,I.G.G.,De Leeuw,J.and Var Der Leeden,R.(1994),“Review offive multilevel analysis programs:BMDP-5V,GENMOD,HLM,ML3,and V ARCL”,American Statistician,48,324–335. Examplesstr(SIMS)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare to output7.4,p.262print(fm1SIMS<-lmer(Gain~Pretot+(Pretot|Class),data=SIMS))print(anova(fm1SIMS))}TeachingI Teaching Methods IDescriptionThe TeachingI data frame has96rows and7columns.FormatThis data frame contains the following columns:Method a factor with levels1to3Teacher a factor with levels1to4Gender a factor with levels f and mStudent a factor with levels1to4score a numeric vectorExperience a numeric vectoruTeacher an ordered factor with levelsSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set5.6).Examplesstr(TeachingI)16WaferTypes TeachingII Teaching Methods IIDescriptionThe TeachingII data frame has96rows and6columns.FormatThis data frame contains the following columns:Method a factor with levels1to3Teacher a factor with levels1to4Gender a factor with levels f and mIQ a numeric vectorscore a numeric vectoruTeacher an ordered factor with levelsSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set5.7).Examplesstr(TeachingII)WaferTypes Data on different types of silicon wafersDescriptionThe WaferTypes data frame has144rows and8columns.FormatThis data frame contains the following columns:Group a factor with levels1to4Temperature an ordered factor with levels900<1000<1100Type a factor with levels A and BWafer a numeric vectorSite a numeric vectordelta a numeric vectorThick a numeric vectoruWafer an ordered factor giving a unique code to each group,temperature,type and wafer combi-nation.Weights17SourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set5.8).Examplesstr(WaferTypes)Weights Data from a weight-lifting programDescriptionThe Weights data frame has399rows and5columns.FormatThis data frame contains the following columns:strength a numeric vectorSubject a factor with levels1to21Program a factor with levels CONT(continuous repetitions and weights),RI(repetitions increasing) and WI(weights increasing)Subj an ordered factor indicating the subject on which the measurement is madeTime a numeric vector indicating the time of the measurementSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set3.2(a)).Examplesstr(Weights)if(require("lme4",quietly=TRUE,character=TRUE)){options(contrasts=c(unordered="contr.SAS",ordered="contr.poly"))##compare with output3.1,p.91print(fm1Weight<-lmer(strength~Program*Time+(1|Subj),Weights))print(anova(fm1Weight))print(fm2Weight<-lmer(strength~Program*Time+(Time|Subj),Weights))print(anova(fm1Weight,fm2Weight))##Not run:intervals(fm2Weight)fm3Weight<-update(fm2Weight,correlation=corAR1())anova(fm2Weight,fm3Weight)fm4Weight<-update(fm3Weight,strength~Program*(Time+I(Time^2)),random=~Time|Subj)18WWheat summary(fm4Weight)anova(fm4Weight)intervals(fm4Weight)##End(Not run)}WWheat Winter wheatDescriptionThe WWheat data frame has60rows and3columns.FormatThis data frame contains the following columns:Variety an ordered factor with10levelsYield a numeric vector of yieldsMoisture a numeric vector of soil moisture contentsSourceLittel,R.C.,Milliken,G.A.,Stroup,W.W.,and Wolfinger,R.D.(1996),SAS System for Mixed Models,SAS Institute(Data Set7.2).Examplesstr(WWheat)Index∗datasetsAnimal,2AvgDailyGain,3BIB,4Bond,5Cultivation,5Demand,6Genetics,7HR,8IncBlk,9Mississippi,10Multilocation,11PBIB,12Semi2,13Semiconductor,14SIMS,14TeachingI,15TeachingII,16WaferTypes,16Weights,17WWheat,18 Animal,2 AvgDailyGain,3 BIB,4Bond,5 Cultivation,5 Demand,6factor,11 Genetics,7HR,8IncBlk,9 Mississippi,10Multilocation,11ordered,11PBIB,12Semi2,13Semiconductor,14SIMS,14TeachingI,15TeachingII,16WaferTypes,16Weights,17WWheat,1819。

现代大学英语第5册(精读5)lesson 4

现代大学英语第5册(精读5)lesson 4

Stick together pieces of paper or photographs to form an artistic image; "he used his computer to make a collage of pictures
Her major works
The Voyage Out (1915)《远航) Night and Day (1919) 《时时刻刻》 Jacob’s Room (1922) 《雅各布的房间) Mrs. Dalloway (1925) 《黛洛维夫人》 To the Lighthouse (1927) 《到灯塔去》 Orlando: A Biography (1928)《奥兰多》 A Room of One’s Own (1929)《自己的房间》 The Waves (1931)《海浪》 The Years (1937) 《岁月) Three Guineas 《三个基尼) Between the Acts 《幕间》 闹鬼的屋子及其他(The Haunted House and Others)(短篇小说集)
3
Text Analysis
4
Extension
Contents
Warm up: sexism against women Background: Virginia Woolf; Stream of Consciousness Text appreciation: the organization of the speech; the characteristic of language in the speech; the rhetorical devices Extension: introduction to the most influential women in the world

N ENG DA-EPOCH-R

N ENG DA-EPOCH-R

Dose-Adjusted EPOCH-Rituximab Therapy in Primary Mediastinal B-Cell LymphomaKieron Dunleavy, M.D., Stefania Pittaluga, M.D., Ph.D., Lauren S. Maeda, M.D., Ranjana Advani, M.D., Clara C. Chen, M.D., Julie Hessler, R.N., Seth M. Steinberg, Ph.D., Cliona Grant, M.D., George Wright, Ph.D., Gaurav Varma,M.S.P.H., Louis M. Staudt, M.D., Ph.D., Elaine S. Jaffe, M.D., and Wyndham H. Wilson, M.D., Ph.D.N Engl J Med 2013; 368:1408-1416April 11, 2013DOI: 10.1056/NEJMoa1214561Share:AbstractArticleReferencesCiting Articles (26)LettersKaplan–Meier Estimates of Event-free and Overall Survival of Patients with Primary Mediastinal B-CellLymphoma Receiving DA-EPOCH-R, According to Study Group.Cardiac Ejection Fraction after Treatment with DA-EPOCH-R in 42 Patients in the Prospective NCI Cohort.Primary mediastinal B-cell lymphoma is adistinct pathogenetic subtype of diffuse large-B-cell lymphoma that arises in the thymus.1,2Although it comprises only 10% of cases of diffuse large-B-cell lymphoma, primary mediastinal B-cell lymphoma, which predominantly affects young women,3 is aggressive and typically ismanifested by a localized, bulky mediastinal mass, often with pleural and pericardial effusions.Less commonly, the disease involves extranodal sites, including the lung, kidneys, gastrointestinal organs, or brain.4,5 This disease is clinically and biologically related to nodular sclerosingHodgkin's lymphoma; the putative cell of origin for both conditions is a thymic B cell.1,2The molecular features of primary mediastinal B-cell lymphoma, and its relationship to Hodgkin'slymphoma and other types of diffuse large-B-cell lymphoma, have been studied.1,2,6-8 Mostpatients with primary mediastinal B-cell lymphoma have mutations in the B-cell lymphoma 6 gene (BCL6), usually along with somatic mutations in the immunoglobulin heavy-chain gene, suggesting late-stage germinal-center differentiation.6,7 Unlike other types of diffuse large-B-cell lymphoma,primary mediastinal B-cell lymphoma involves defective immunoglobulin production despite theexpression of the B-cell transcription factors OCT-2, BOB.1, and PU.1. More than half of patients with the disease also have amplification of the REL proto-oncogene and the JAK2 tyrosine kinase gene, which frequently are found in patients with Hodgkin's lymphoma, suggesting that thesediseases are related.9,10 Furthermore, genes that are more highly expressed in primarymediastinal B-cell lymphoma than in other types of diffuse large-B-cell lymphoma arecharacteristically overexpressed in Hodgkin's lymphoma.2Prospective studies in primary mediastinal B-cell lymphoma are few, which has led to conflictingfindings and a lack of treatment standards.11-14 Nonetheless, several observations have emerged from the literature. First, in most patients, adequate tumor control is not achieved with standardimmunochemotherapy, necessitating routine mediastinal radiotherapy.13-15 Second, even withradiotherapy, which is associated with serious late side effects, 20% of patients have diseaseprogression.11,13 Third, more aggressive chemotherapy is associated with an improvedoutcome.12,13 Consistent with this observation, we found that the dose-intense chemotherapy regimen consisting of dose-adjusted etoposide, doxorubicin, and cyclophosphamide with vincristine and prednisone (DA-EPOCH) had a favorable overall survival rate (79%) without consolidation radiotherapy in patients with primary mediastinal B-cell lymphoma.16 On the basis of the hypothesis that rituximab may improve treatment, we undertook a phase 2, prospective study of DA-EPOCH plus rituximab (DA-EPOCH-R) to determine whether it would improve outcomes and obviate the need for radiotherapy.METHODSStudy ConductThe study was designed and the manuscript was written by the last author. All authors reviewed and approved the draft of the manuscript submitted for publication. All the authors vouch for the adherence of the study to the protocol (available with the full text of this article at ) and for the completeness and accuracy of the data and analysis. The prospective study was approved by the institutional review board of the National Cancer Institute (NCI). All patients provided written informed consent. The retrospective analysis was approved by the institutional review board at Stanford University.Filgrastim was provided to the NCI through an agreement with Amgen, which played no role in the study design, analysis, or data collection. No other commercial support was provided for the prospective study.Prospective NCI StudyPatientsFrom November 1999 through August 2012, we prospectively enrolled 51 patients with untreated primary mediastinal B-cell lymphoma in an uncontrolled phase 2 study of DA-EPOCH-R. The primary study objectives were the rate of complete response, the rate of progression-free survival, and the toxicity of DA-EPOCH-R.All eligible patients had not received any previous systemic chemotherapy, had adequate organ function, and had negative results on testing for the human immunodeficiency virus; among women with childbearing potential, a negative test for pregnancy was required. Any localized mediastinal masses (stage I) had to measure at least 5 cm in the greatest dimension. Evaluations included standard blood tests, whole-body computed tomography (CT), and bone marrow biopsy. Assessment of cardiac function, by means of echocardiography, and of central nervous system disease, with the use of CT or magnetic resonance imaging (MRI) and flow cytometry or cytologic analysis of cerebral spinal fluid, were performed if clinically indicated.Study TherapyPatients received chemotherapy consisting of DA-EPOCH-R with filgrastim for 6 to 8 cycles.17,18 Disease sites were evaluated after cycles 4 and 6. Patients with a reduction of more than 20% inthe greatest diameter of their tumor masses between cycles 4 and 6 received 8 cycles of treatment. Patients with a reduction of 20% or less between cycles 4 and 6 discontinued therapy after 6 cycles. The method of administering the DA-EPOCH-R is summarized in the Supplementary Appendix (available at ).We used standard criteria for tumor response to assess the study end points.19,20 We used 18F-fluorodeoxyglucose–positron-emission tomography–CT (FDG-PET-CT) after therapy to evaluate residual masses. Patients who had a maximum standardized uptake value greater than that of the mediastinal blood pool in the residual mediastinal mass underwent repeat scans at approximately 6-week intervals until normalization or stabilization. Mediastinal blood pool activity was defined as the maximum standardized uptake value over the great vessels and ranged from 1.5 to 2.5 in the study population. Tumor biopsy was performed as clinically indicated. Patients with evidence of thymic rebound underwent repeat CT at 6-week intervals until stabilization. All FDG-PET-CT scans were reviewed and scored by the same nuclear-medicine physician. No patients received radiation treatment during this prospective study.Independent, Retrospective Stanford StudyTo provide an independent assessment of DA-EPOCH-R, we collaborated with investigators at Stanford University Medical Center who had begun to use DA-EPOCH-R in 2007 to treat primary mediastinal B-cell lymphoma.21 They reviewed all charts from 2007 through 2012 and found 16 previously untreated patients who had been consecutively treated with DA-EPOCH-R; none required radiotherapy. NCI investigators confirmed the presence of primary mediastinal B-cell lymphoma in all 16 patients, according to the WHO [World Health Organization] Classification of Tumours of Haematopoietic and Lymphoid Tissues, 4th edition.3 Standard immunohistochemical studies were performed as indicated.3,18Other Comparative DataTo provide a long-term assessment of the DA-EPOCH platform, we reviewed the pathological data for all patients from our phase 2 study of DA-EPOCH in patients with diffuse large-B-cell lymphoma, which also did not permit radiotherapy, and identified 18 patients with primary mediastinal B-cell lymphoma.16Statistical AnalysisWe calculated the duration of overall survival from the date of enrollment until the time of death or last follow-up. The duration of event-free survival was calculated from the date of enrollment until the date of progression, radiotherapy, discovery of a second mass, or time of last follow-up. We used the Kaplan–Meier method to determine the probability of overall or event-free survival.22 Patients' characteristics were compared by means of Fisher's exact test for dichotomous variables and by means of the Wilcoxon rank-sum test for continuous variables. All P values are two-tailed.The median follow-up was calculated from the date of enrollment through November 2012, the date of the most recent update.RESULTSBaseline Characteristics and Clinical OutcomesThe 51 patients enrolled in the NCI phase 2 prospective study had a median age of 30 years (range, 19 to 52) and a median tumor diameter of 11 cm; 59% were women (Table 1TABLE 1Baseline Characteristics of the Study Patients.). Indicators of advanced disease included bulky tumor with a greatest diameter of 10 cm or more (in 65% of patients), an elevated lactate dehydrogenase level (in 78%), and stage IV disease (in 29%).The 16 patients identified in the retrospective Stanford study had baseline characteristics similar to those of our 51 patients (Table 1) except for a significantly lower frequency of extranodal disease and significantly older age; 56% of patients had bulky disease, and 44% of patients had stage IV disease.At a median follow-up of 63 months (range, 3 to 156), the event-free survival rate in the prospective NCI study was 93% (95% confidence interval [CI], 81 to 98), and the overall survivalrate was 97% (95% CI, 81 to 99) (Figure 1A and 1B FIGURE 1Kaplan–Meier Estimates of Event-free and Overall Survival of Patients with Primary Mediastinal B-Cell Lymphoma Receiving DA-EPOCH-R, According to Study Group.). Three patients had evidence of disease after DA-EPOCH-R treatment; two had persistent focal disease, as detected on FDG-PET-CT, and one had disease progression. Two of these patients underwent mediastinal radiotherapy, and one was observed after excisional biopsy. All three patients became disease-free. One later died from acute myeloid leukemia, while still in remission from his primary mediastinal B-cell lymphoma.In the retrospective Stanford cohort, over a median follow-up of 37 months (range, 5 to 53), 100% of patients (95% CI, 79 to 100) were alive and event-free (Figure 1C and 1D).Finally, we assessed the outcome for 18 patients with primary mediastinal B-cell lymphoma who were enrolled in our phase 2 study of DA-EPOCH.16 These patients had baseline characteristics similar to those in the prospective DA-EPOCH-R study (data not shown). Over a median follow-up of 16 years, the event-free and overall survival rates were 67% (95% CI, 44 to 84) and 78% (95% CI, 55 to 91), respectively. No cardiac failure or second tumors were observed.The event-free and overall survival rates were greater with the addition of rituximab in the NCI prospective cohort than in the cohort of 18 patients who received DA-EPOCH alone (P=0.007 andP=0.01, respectively). This finding suggests that the addition of rituximab may account for the improvement and is consistent with other reports.11FDG-PET-CT FindingsTo identify DA-EPOCH-R treatment failures early, the 36 patients who were found to have residual mediastinal masses in the prospective study underwent FDG-PET-CT in order to optimize curative radiotherapy. Half the patients had a maximum standardized uptake value that was no more than the value in the mediastinal blood pool, which represents the upper limit of the normal range ofuptake (Table 2TABLE 2FDG-PET-CT Findings after DA-EPOCH-R Therapy in the Prospective NCI Cohort.). The other half had a maximum standardized uptake value that was more than the value in the mediastinal blood pool. Although diffuse or focal uptake within the residual tumor mass that is higher than that in the mediastinal blood pool has been considered indicative of lymphoma,20 among these 18 patients, only 3 (with maximum standardized uptake values of 5.9, 10.2, and 14.5) were found to have residual lymphoma. Thus, FDG-PET-CT had a positive predictive value of 17% and a negative predictive value of 100%.Among the 15 patients with a maximum standardized uptake value greater than that in the mediastinal blood pool who did not have disease, 10 underwent repeat FDG-PET-CT; the other 5 did not undergo additional screening, because their initial FDG-PET-CT scans were interpreted as unlikely to represent disease. The 10 patients underwent 1 to 6 additional FDG-PET-CT scans (total, 26); all the findings were interpreted as false positive results on the basis of stabilization or improvement of the maximum standardized uptake value. None of the 10 patients had a recurrence of lymphoma during follow-up.Three patients underwent post-treatment biopsy. One, with a maximum standardized uptake value of 5.9, had a viable tumor of less than 1 cm in area. Owing to the uncertain importance of this finding, the patient was followed for 7 years without treatment, and the tumor did not recur during follow-up. Two patients, with maximum standardized uptake values of 4.6 and 6.4, had negative biopsy results and no tumor recurrence during 6 years of follow-up.In two patients, treatment failed but repeat biopsy was not performed. One patient had disease progression on CT during treatment, and the other had a post-treatment maximum standardized uptake value that increased from 10.2 to 19, consistent with disease progression.Dose and Toxicity of DA-EPOCH-R in the NCI StudyIn the NCI study, 90% of patients received six cycles, and 10% received eight cycles, of DA-EPOCH-R. More than half the 51 patients had an escalation to at least dose level 4, representing a 73% increase over dose level 1; 6% of patients did not have a dose escalation. More than half thepatients received 69 mg of doxorubicin per square meter of body-surface area for at least one cycle and cumulative doses of 345 to 507 mg per square meter. To assess cardiac toxic effects, ejection fractions were measured in 42 patients. All had normal ejection fractions up to 10 yearsafter treatment (Figure 2FIGURE 2Cardiac Ejection Fraction after Treatment with DA-EPOCH-R in 42 Patients in the Prospective NCI Cohort.). There was no significant relationship between the ejection fraction and the length of time since treatment (P=0.30) or between the ejection fraction and the cumulative doxorubicin dose (P=0.20), and no significant interaction between the dose and time interval (P=0.40).Toxicity was assessed during the administration of all 294 cycles of DA-EPOCH-R. The targeted absolute neutrophil count of less than 500 cells per cubic milliliter occurred during 50% of cycles. Thrombocytopenia (<25,000 platelets per cubic millimeter) occurred during 6% of cycles, and hospitalization for fever and neutropenia occurred during 13% of cycles. Nonhematopoietic toxic effects were similar to those that have been reported previously.17,18 One patient died from acute myeloid leukemia while in remission from his primary mediastinal B-cell lymphoma, 49 months after treatment. Owing to the unexpected severe neutropenia during treatment in this patient, we looked for a germline telomerase mutation, which is associated with chemotherapy intolerance and myeloid leukemia.23 Telomere shortening (length, 2.5 SD below the mean) and a heterozygous mutation for the telomerase reverse transcriptase gene (TERT) codon Ala1062Thr were identified.DISCUSSIONThe use of DA-EPOCH-R obviated the need for radiotherapy in all but 2 of 51 patients (4%) with primary mediastinal B-cell lymphoma in a prospective cohort, and no patients had recurring disease over a median follow-up of more than 5 years (maximum, >13). Furthermore, in an independent retrospective cohort, treatment with DA-EPOCH-R in patients with primary mediastinal B-cell lymphoma resulted in an event-free survival rate of 100%. Despite the limitations of the phase 2 study and the retrospective study, these findings suggest that DA-EPOCH-R is a therapeutic advance for this type of lymphoma. Our results suggest that rituximab significantly improves the outcome of chemotherapy in patients with primary mediastinal B-cell lymphoma.The toxicity of DA-EPOCH-R was similar to that reported previously.16 The use of neutrophil-based dose adjustment maximized the delivered dose and limited the incidence of fever and neutropenia to 13% of the cycles. The infusional schedule of doxorubicin allowed for the delivery of high maximal and cumulative doses of doxorubicin without clinically significant cardiac toxic effects.24,25We used post-treatment FDG-PET-CT to identify patients who had persistent disease and a possible need for radiotherapy. Unlike the high clinical accuracy of FDG-PET-CT in other aggressive lymphomas,20 we found the technique to have a poor positive predictive value in primary mediastinal B-cell lymphoma. We frequently observed residual mediastinal masses that continued to shrink for 6 months, suggesting that inflammatory cells might account for the FDG uptake. These findings indicate that FDG-PET-CT uptake alone is not accurate for determining the presence of disease in these patients.There is no established standard treatment for primary mediastinal B-cell lymphoma. Although R-CHOP chemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) has become a de facto standard, it is not universally accepted.11,12 Most strategies also incorporate consolidation radiotherapy to overcome the inadequacy of immunochemotherapy, although some observers have questioned its routine use.12,26 The most accurate assessment of R-CHOP and radiotherapy is a subgroup analysis of patients with primary mediastinal B-cell lymphoma in the Mabthera International Trial Group study of R-CHOP–based treatment.11 Among 44 patients, 73% received radiotherapy, with an event-free survival rate of 78% at 34 months.11 These results indicate that patients who receive R-CHOP–based treatment, most of whom are young women, may have serious long-term consequences of radiotherapy, including second tumors and the acceleration of atherosclerosis and anthracycline-mediated cardiac damage.27Current standard therapy is also inadequate for children with primary mediastinal B-cell lymphoma. In a recent subgroup analysis in the FAB/LMB96 international study, the event-free and overall survival rates were 66% and 73%, respectively, among children receiving a multiagent pediatric regimen.28Retrospective studies have long suggested that patients with primary mediastinal B-cell lymphoma have improved outcomes with the receipt of regimens of increased dose intensity.13 Dose intensity appears to be important in treating Hodgkin's lymphoma, a closely related disease.29 Indeed, outcomes associated with the use of DA-EPOCH-R may well be related to dose intensity as well as the continuous infusion schedule.30 DA-EPOCH therapy involves the administration of pharmacodynamic doses to normalize drug exposure among patients and maximizes the rate of administration. DA-EPOCH may also more effectively modulate the expression of BCL6,7 which encodes a key germinal-center B-cell transcription factor that suppresses genes involved in lymphocyte activation, differentiation, cell-cycle arrest (p21 and p27Kip1), and response to DNA damage (p53 and ATR) and that is expressed by most primary mediastinal B-cell lymphomas (Table 1).31 The inhibition of topoisomerase II also leads to down-regulation of BCL6 expression, suggesting that regimens directed against topoisomerase II may have increased efficacy in treating primary mediastinal B-cell lymphoma. In this regard, DA-EPOCH-R was designed to inhibit topoisomerase II by including two topoisomerase II inhibitors, etoposide and doxorubicin, and maximizing topoisomerase II inhibition by way of extended drug exposure.16In conclusion, our results indicate that DA-EPOCH-R had a high cure rate and obviated the need for radiotherapy in patients with primary mediastinal B-cell lymphoma. To provide confirmatory evidence, an international trial of DA-EPOCH-R in children with primary mediastinal B-cell lymphoma has been initiated ( number, NCT01516567).。

improve decision making

improve decision making

08-102Copyright © 2008 by Katherine L. Milkman, Dolly Chugh, and Max H. BazermanWorking papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author.How Can Decision Making Be Improved?Katherine L. MilkmanDolly ChughMax H. BazermanHow Can Decision Making Be Improved?Katherine L. MilkmanHarvard UniversityDolly ChughNew York UniversityMax H. BazermanHarvard UniversityThe optimal moment to address the question of how to improve humandecision making has arrived. Thanks to fifty years of research by judgmentand decision making scholars, psychologists have developed a detailed picture of the ways in which human judgment is bounded. This paper argues that the time has come to focus attention on the search for strategies that will improve bounded judgment because decision making errors are costly and are growing more costly, decision makers are receptive, and academic insights are sure to follow from research on improvement. In addition to calling for research on improvement strategies, this paper organizes the existing literature pertaining to improvement strategies, highlighting promising directions for futureresearch.Acknowledgements: We thank Katie Shonk and Elizabeth Weiss for their assistance.Daniel Kahneman, Amos Tversky, and others have clarified the specific ways in which decision makers are likely to be biased. As a result, we can now describe how people make decisions with astonishing detail and reliability. Furthermore, thanks to the normative models of economic theory, we have a clear vision of how much better decision making could be. If we all behaved optimally, costs and benefits would always be accurately weighed, impatience would not exist, gains would never be foregone in order to spite others, no relevant information would ever be overlooked, and moral behavior would always be aligned with moral attitudes. Unfortunately, we have little understanding of how to help people overcome their many biases and behave optimally. The Big QuestionWe propose that the time has come to move the study of biases in judgment and decision making beyond description and toward the development of improvement strategies. While a few important insights about how to improve decision making have already been identified, we argue that many others await discovery. We hope judgment and decision-making scholars will focus their attention on the search for improvement strategies in the coming years, seeking to answer the question: how can we improve decision making?Why the Question Is ImportantErrors are costly:We believe the importance of this question is somewhat self-evident: decisions shape important outcomes for individuals, families, businesses, governments, and societies, and if we knew more about how to improve those outcomes, individuals, families, businesses, governments, and societies would benefit. After all, errors induced by biases in judgment lead decision makers to undersave for retirement,engage in needless conflict, marry the wrong partners, accept the wrong jobs, and wrongly invade countries. Given the massive costs that can result from suboptimal decision making, it is critical for our field to focus increased effort on improving our knowledge about strategies that can lead to better decisions.Errors will get even costlier: The costs of suboptimal decision making have grown, even since the first wave of research on decision biases began fifty years ago. As more economies have shifted from a dependence on agriculture to a dependence on industry, the importance of optimal decision making has increased. In a knowledge-based economy, we propose that a knowledge worker’s primary deliverable is a good decision. In addition, more and more people are being tasked with making decisions that are likely to be biased – because of the presence of too much information, time pressure, simultaneous choice, or some other constraints. Finally, as the economy becomes increasingly global, each biased decision is likely to have implications for a broader swath of society.Decision makers are receptive: Because decision making research is relevant to businesspeople, physicians, politicians, lawyers, private citizens, and many other groups for whom failures to make optimal choices can be extremely costly, limitations uncovered by researchers in our field are widely publicized and highlighted to students in many different professional and undergraduate degree programs. Those who are exposed to our research are eager to learn the practical implications of the knowledge we have accumulated about biased decision making so they can improve their own outcomes. However, our field primarily offers description about the biases that afflict decision makers without insights into how errors can be eliminated or at least reduced.Academic insights await: Bolstering our efforts to uncover techniques for improving decision making is likely to deliver additional benefits to researchers interested in the mental processes that underlie biased judgment. Through rigorous testing of what does and what does not improve decision making, researchers are sure to develop a better understanding of the mechanisms underlying decision making errors. This will deepen our already rich descriptive understanding of decision making.What Needs to be Done to Answer the QuestionAssuming we accept the importance of uncovering strategies to fend off decision-making errors, the next question is where to begin? To address this question, we organize the scattered knowledge that judgment and decision-making scholars have amassed over the last several decades about how to reduce biased decision making. Our analysis of the existing literature on improvement strategies is designed to highlight the most promising avenues for future research on cures for biased decision making. Debiasing Intuition: Early Failuressuccessful strategies for improving decision making, it is discussingBeforeimportant to note how difficult finding solutions has proved to be. In 1982, Fischhoff reviewed the results of four strategies that had been proposed as solutions for biased decision making: (1) offering warnings about the possibility of bias; (2) describing the direction of a bias; (3) providing a dose of feedback; and (4) offering an extended program of training with feedback, coaching, and other interventions designed to improve judgment. According to Fischhoff’s findings, which have withstood 25 years of scrutiny, the first three strategies yielded minimal success, and even intensive, personalized feedback produced only moderate improvements in decision making (Bazerman andMoore, 2008). This news was not encouraging for psychologists and economists who hoped their research might improve people’s judgment and decision-making abilities. System 1 and System 2We believe that Stanovich and West’s (2000) distinction between System 1 and System 2 cognitive functioning provides a useful framework for organizing both what scholars have learned to date about effective strategies for improving decision making and future efforts to uncover improvement strategies. System 1 refers to our intuitive system, which is typically fast, automatic, effortless, implicit, and emotional. System 2 refers to reasoning that is slower, conscious, effortful, explicit, and logical.People often lack important information regarding a decision, fail to notice available information, face time and cost constraints, and maintain a relatively small amount of information in their usable memory. The busier people are, the more they have on their minds, and the more time constraints they face, the more likely they will be to rely on System 1 thinking. Thus, the frantic pace of life is likely to lead us to rely on System 1 thinking much of the time and to make costly errors.An Important Question: Can We Move from System 1 to System 2?We believe a number of promising strategies have been uncovered for overcoming specific decision biases by shifting people from System 1 thinking to System 2 thinking.1 One successful strategy for moving toward System 2 thinking relies on replacing intuition with formal analytic processes. For example, when data exists on past inputs to and outcomes from a particular decision-making process, decision makers can construct a linear model, or a formula that weights and sums the relevant predictor1 It should be noted that many strategies designed to reduce decision biases by encouraging System2 thinking have proven unsuccessful. For example, performance based pay, repetition, and high stakes incentives have been shown to have little if any effect on a wide array of biases in judgment.variables to reach a quantitative forecast about the outcome. Researchers have found that linear models produce predictions that are superior to those of experts across an impressive array of domains (Dawes, 1971). The value of linear models in hiring, admissions, and selection decisions is highlighted by research that Moore, Swift, Sharek, and Gino (2007) conducted on the interpretation of grades, which shows that graduate school admissions officers are unable to account for the leniency of grading at an applicant’s undergraduate institution when choosing between candidates from different schools. The authors argue that it would be easy to set up a linear model to avoid this error (for example, by including in its calculation only an applicant’s standardized GPA, adjusted by her school’s average GPA). In general, we believe that the use of linear models can help decision makers avoid the pitfalls of many judgment biases, yet this method has only been tested in a small subset of the potentially relevant domains.Another System 2 strategy involves taking an outsider’s perspective: trying to remove oneself mentally from a specific situation or to consider the class of decisions to which the current problem belongs (Kahnmean and Lovallo, 1993). Taking an outsider’s perspective has been shown to reduce decision makers’ overconfidence about their knowledge (Gigerenzer, Hoffrage, & Kleinbölting, 1991), the time it would take them to complete a task (Kahneman & Lovallo, 1993), and their odds of entrepreneurial success (Cooper, Woo, and Dunkelberg, 1988). Decision makers may also be able to improve their judgments by asking a genuine outsider for his or her view regarding a decision.Other research on the power of shifting people toward System 2 thinking has shown that simply encouraging people to “consider the opposite” of whatever decision they are about to make reduces errors in judgment due to several particularly robustdecision biases: overconfidence, the hindsight bias, and anchoring (Larrick, 2004; Mussweiler, Strack, & Pfeiffer, 2000). Partial debiasing of errors in judgment typically classified as the result of “biases and heuristics” (see Tversky and Kahneman, 1974) has also been achieved by having groups rather than individuals make decisions, training individuals in statistical reasoning, and making people accountable for their decisions (Larrick, 2004; Lerner & Tetlock, 1999).One promising debiasing strategy is to undermine the cognitive mechanism that is hypothesized to be the source of bias with a targeted cue to rely on System 2 processes (Slovic and Fischhoff, 1977). In a study designed to reduce hindsight bias (the tendency to exaggerate the extent to which one could have anticipated a particular outcome in foresight), Slovic and Fischhoff developed a hypothesis about the mechanism producing the bias. They believed that hindsight bias resulted from subjects’ failure to use their available knowledge and powers of inference. Armed with this insight, Slovic and Fischhoff hypothesized and found that subjects were more resistant to the bias if they were provided with evidence contrary to the actual outcome. This result suggests that the most fruitful directions for researchers seeking to reduce heuristics and biases may be those predicated upon “some understanding of and hypotheses about people’s cognitive processes” (Fischhoff, 1982) and how they might lead to a given bias. Along these lines, another group of researchers hypothesized that overclaiming credit results from focusing only on estimates of one’s own contributions and ignoring those of others in a group. They found that requiring people to estimate not only their own contributions but also those of others reduces overclaiming (Savitsky, Van Boven, Epley, and Wight, 2005).Another promising stream of research that examines how System 2 thinking can be leveraged to reduce System 1 errors has shown that analogical reasoning can be usedto reduce bounds on people’s awareness (see Bazerman and Chugh 2005 for more on bounded awareness). Building on the work of Thompson, Gentner, and Loewenstein (2000), both Idson, Chugh, Bereby-Meyer, Moran, Grosskopf, and Bazerman (2004) and Moran, Ritov, and Bazerman (2008) found that individuals who were encouraged to see and understand the common principle underlying a set of seemingly unrelated tasks subsequently demonstrated an improved ability to discover solutions in a different task that relied on the same underlying principle. This work is consistent with Thompson et al.’s (2000) observation that surface details of learning opportunities often distract us from seeing important underlying, generalizable principles. Analogical reasoning appears to offer hope for overcoming this barrier to decision improvement.Work on joint-versus-separate decision making also suggests that people can move from suboptimal System 1 thinking toward improved System 2 thinking when they consider and choose between multiple options simultaneously rather than accepting or rejecting options separately. For example, Bazerman, White and Loewenstein (1995)find evidence that people display more bounded self-interest (Jolls, Sunstein, and Thaler, 1998) – focusing on their outcomes relative to those of others rather than optimizing their own outcomes – when assessing one option at a time than when considering multiple options side by side. Bazerman, Loewenstein and White (1992) have also demonstrated that people exhibit less willpower when they weigh choices separately rather than jointly.The research discussed above suggests that any change in a decision’s context that promotes cool-headed System 2 thinking has the potential to reduce common biasesresulting from hotheadedness, such as impulsivity and concern about relative outcomes. Research on joint-versus-separate decision making highlights the fact that our first impulses tend to be more emotional than logical (Moore and Loewenstein, 2004). Some additional suggestive results in this domain include the findings that willpower is weakened when people are placed under extreme cognitive load (Shiv and Fedorkihn, 1999) and when they are inexperienced in a choice domain (Milkman, Rogers and Bazerman, 2008). Other research has shown that people make less impulsive, sub-optimal decisions in many domains when they make choices further in advance of their consequences (see Milkman, Rogers and Bazerman, in press, for a review). A question we pose in light of this research is when and how carefully selected contextual changes promoting increased cognition can be leveraged to reduce the effects of decision making biases?Another Important Question: Can We Leverage System 1 to Improve Decision Making?Albert Einstein once said, “We can't solve problems by using the same kind of thinking we used when we created them.” However, it is possible that the unconscious mental system can, in fact, do just that. In recent years, a new general strategy for improving biased decision making has been proposed that leverages our automatic cognitive processes and turns them to our advantage (Sunstein and Thaler, 2003). Rather than trying to change a decision maker’s thinking from System 1 to System 2, this strategy tries to change the environment so that System 1 thinking will lead to good results. This type of improvement strategy, which Thaler and Sunstein discuss at length in their book Nudge (2008), calls upon those who design situations in which choices are made (whether they be the decision makers themselves or other “choice architects”) tomaximize the odds that decision makers will make wise choices given known decision biases. For example, a bias towards inaction creates a preference for default options (Ritov and Baron, 1992). Choice architects can use this insight to improve decision making by ensuring that the available default is the option that is likely to be best for decision makers and/or society. Making 401k enrollment a default, for instance, has been shown to significantly increase employees’ savings rates (Benartzi and Thaler, 2007).There is also some suggestive evidence that leveraging System 1 thinking to improve System 1 choices may be particularly effective in the realm of decision-making biases that people do not like to admit or believe they are susceptible to. For instance, many of us are susceptible to implicit racial bias but feel uncomfortable acknowledging this fact, even to ourselves. Conscious efforts to simply “do better” on implicit bias tests are usually futile (Nosek, Greenwald, & Banaji, 2007). However, individuals whose mental or physical environment is shaped by the involvement of a black experimenter rather than a white experimenter show less implicit racial bias (Lowery, Hardin, & Sinclair, 2001; Blair, 2002). The results of this “change the environment” approach contrast sharply with the failure of “try harder” solutions, which rely on conscious effort. In summary, can solutions to biases that people are unwilling to acknowledge be found in the same automatic systems that generate this class of problems?ConclusionPeople put great trust in their intuition. The past 50 years of decision-making research challenges that trust. A key task for psychologists is to identify how and in what situations people should try to move from intuitively compelling System 1 thinking to more deliberative System 2 thinking and to design situations that make System 1 thinkingwork in the decision-maker’s favor. Clearly, minor decisions do not require a complete System 2 process or a new decision architecture. However, the more deeply we understand the repercussions of System 1 thinking, the more deeply we desire empirically tested strategies for reaching better decisions. Recent decades have delivered description in abundance. This paper calls for more research on strategies for improving decisions.ReferencesBazerman, M. H. & Chugh, D. (2005). Bounded awareness: Focusing failures in negotiation. In L. Thompson (Ed.), Frontiers of Social Psychology: Negotiation. Psychological Press.Bazerman, M. H., Loewenstein, G. F., & White, S. B. (1992). Reversals of preference in allocation decisions: Judging an alternative versus choosing among alternatives. Administrative Science Quarterly,37(2), 220-240.Bazerman, M.H. & Moore, D. (2008). Judgment in Managerial Decision Making (7th ed.). Hoboken, NJ: John Wiley & Sons, Inc.Bazerman, M.H., White, S.B., & Loewenstein, G.F. (1995). Perceptions of Fairness in Interpersonal and Individual Choice Situations. Current Directions in Psychological Science, 4, 39-43.Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality & Social Psychology Review, 6(3), 242-261.Benartzi, S., & Thaler, R. H. (2007). Heuristics and biases in retirement savings behavior. Journal of Economic Perspectives, 21(3), 81-104.Cooper, A. C., Woo, C. Y., & Dunkelberg, W. C. (1988). Entrepreneurs' perceived chances for success. Journal of Business Venturing, 3(2), 97-109.Dawes, R. M. (1971). A case study of graduate admissions: Application of three principles of human decision making. American Psychologist, 26(2), 180-188.Fischhoff, B. (1982). Debiasing. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment Under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press: 422 – 444.Idson, L. C., Chugh, D., Bereby-Meyer, Y., Moran, S., Grosskopf, B., & Bazerman, M. (2004). Overcoming focusing failures in competitive environments. Journal of Behavioral Decision Making, 17(3), 159-172.Johnson, Eric J. and Goldstein, D.(2003). Science. 302 (5649), 1338 – 1339.Jolls, C., Sunstein, C. R., & Thaler, R. (1998) A behavioral approach to law and economics. Stanford Law Review, 50, 1471-1550Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk and risk taking. Management Science, 39, 17-31.Larrick, R. P. (2004). Debiasing. In D. J. Koehler & N. Harvey (Eds.), Blackwell Handbook of Judgment and Decision Making. Oxford, England: Blackwell Publishers.Lerner, J. S., & Tetlock, P. E. (1999). Accounting for the effects of accountability. Psychological Bulletin, 125(2), 255-275.Lowery, B. S., Hardin, C. D., & Sinclair, S. (2001). Social influence effects on automatic racial prejudice. Journal of Personality and Social Psychology, 81(5), 842-855.Milkman, K.L., Rogers, T., & Bazerman, M. (2008). Highbrow films gather dust: A study of dynamic inconsistency and online DVD rentals. HBS Working Paper 07-099.Milkman, K.L., Rogers, T., & Bazerman, M. (in press). Harnessing our inner angels and demons: What we have learned about want/should conflict and how that knowledge can help us reduce short-sighted decision making. Perspectives on Psychological Science.Moore, D., & Lowenstein, G. (2004). Self-interest, automaticity, and the psychology of conflict of interest. Social Justice Research, 17(2), 189-202.Moore, D. A., Swift, S. A., Sharek, Z., & Gino, F. (2007). Correspondence bias in performance evaluation: Why grade inflation works. Tepper Working Paper 2004-E42.Moran, S., Ritov, I., & Bazerman, M.H. (2008). Details need to be added.Mussweiler, T., Strack, F., & Pfeiffer, T. (2000). Overcoming the inevitable anchoring effect: Considering the opposite compensates for selective accessibility. Personality and Social Psychology Bulletin, 26, 1142-1150.Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.), Social Psychology and the Unconscious: The Automaticity of Higher Mental Processes, New York: Psychology Press.Ritov, I., & Baron, J. (1992). Status-quo and omission biases. Journal of Risk & Uncertainty, 5(1), 49-61. Savitsky, K., Van Boven, L., Epley, N., & Wight, W. (2005). The unpacking effect in responsibility allocations for group tasks. Journal of Experimental Social Psychology, 41, 447-457.Shiv, B., & Fedorikhin, A. (1999). Heart and mind in conflict: The interplay of affect and cognition in consumer decision making. Journal of Consumer Research, 26(3), 278-292.Slovic, P. & Fischhoff, B. (1977). On the psychology of experimental surprises. Journal of Experimental Psychology: Human Perception and Performance, 3, 544-551: 23, 31.Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate. Behavioral & Brain Sciences, 23, 645-665.Sunstein, C.R. & Thaler, R.H (2003). Libertarian paternalism is not an oxymoron. University of Chicago Law Review, 70 (Fall), 1159-99.Thaler, R. H., & Sunstein, C. R. (2008). Nudge. New Haven: Yale University Press.Thompson, L., Gentner, D., & Loewenstein, J. (2000). Avoiding missed opportunities in managerial life: Analogical training more powerful than individual case training. Organizational Behavior & Human Decision Processes, 82(1), 60-75.Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science,185(4157), 1124-1131.。

稳定匹配和市场设计——2012年诺贝尔经济学奖得主的学术贡献

稳定匹配和市场设计——2012年诺贝尔经济学奖得主的学术贡献
◆ 中图分类号 :F 1 1 — 0 文献标识 码 :A
而使 得合 作博 弈缺少 了关键 的基 础。
内 容摘 要 :阿 尔文 ・罗斯 、 劳埃 德 ・ 沙
计 :首 先所 有 的男人 都 向 自 己最 中意 的 女 人 求婚 ,每 个 女人 根 据 自 己的求婚 列
表 依次选 择一个 自己最 中意 的男人订婚 , 如果 还 有人 没有 订婚 ,那 么没 有订 婚 的 男人 向 自 己第二 中意 的女人 求婚 ,如 果
弈理论 以及 S h a p l e y — S h u b i k权 力指 数
对组 织 成 员的权 力 进行 研 究 ,设计 了组
织权 力 均衡 的输 入 和输 出机 制 ,并 且证 明 了主体 的权 力分 配可发 现 者
劳埃 德 ・ 沙 普利( L l o y d S . S h a p l e y)
可 以理解为联盟 中某个参 与者的边际贡献 ,
( 2 0 0 3) 合 写 了两 篇论 文 ,在论 文 《 On
a u t h O r i t Y d i S t r i b U t i O n s i n
采取 了合作博 弈。在合 作的基础上 ,每个
成员的 P . 的最大利益v ( p . ) 从f ( n ) 中获得 , 那 么必须存在 : ∑v ( p ; ) = f ( M) v ( p . ) ≥f ( P . ) ,i = 1 ,2 ,… ,N
o r g a n i z a t i o n s :e q u i l i b r i u m》中利用博
《 On c o r e s a n d i n d i v i s i b i l i t y 》提 出了一个
解决方案—— “ 沙普 利值 ” , 沙普利值 的核 心就在于利益 的分 配取 决于其对联盟 的边

现代语言教学的十大原则

现代语言教学的十大原则
80
靳洪刚: 现代语言教学的十大原则
习者进一步明确语言学习的最终目的是完成生活中的各种任务,解决生活中的各种问题。 任务教学设计一般须包含三个任务阶段及五个基本组成部分。任务的三个阶段分别
是:前期任务、核心任务、后期任务。前期阶段是任务准备阶段,多在课下完成。前期任务的 目的是激活学习者已有知识,为新的语言学习奠定基础。从语言输入、信息来源、交际背景 等方面为学生提供语言及交际框架( scaffolding) ,帮助学生顺利进入核心任务阶段。因此, 这一阶段的教学主要以激活已有知识、处理语言输入为主,活动多以理解诠释性阅读、听力 为主。核心任务阶段包含两个方面的教学,一是以语言形式为中心的教学,目的是给学习者 建立完成核心任务的交际框架,帮助学习者整合信息;二是以语言使用为中心的任务模拟、 教学实施,多以口语输出、人际交流的方式在课上完成。核心任务的目的是提供具有一定认 知及语言复杂度的模拟任务,让学习者有目的地使用目标语言,完成任务,取得预期结果。 主要形式为,首先采用合班学习语言形式,然后分组互动,完成信息交换、信息组合、意见交 换等任务。后期阶段是任务总结、反思、实际生活应用阶段,多以实地操作、书面输出或口头 演说的方式在课上或课下完成,主要采用书面总结、实地调查、口头报告等形式。
一 引言 现代语言教育在近二十年来受到了三大领域科学研究成果的极大影响。这三大领域分 别是:语言习得研究、认知心理学以及教育学。就第二语言习得领域而言,在过去的五十年 中,研究者通过各种实验研究,如语言对比、错误分析、语言普遍原则、认知心理学、语言获 得过程等方面的实验,对不同语言的习得顺序、习得速度、语言输入及输出的作用、课堂过 程、学习策略等方面进行了系统研究,得出了不少定论。这些研究成果形成了第二语言教学 领域的部分教学原则。就认知心理学来看,研究者从普遍学习理论,人类认知过程,大脑记 忆、储存、加工等语言的处理过程,记忆储存方式,输入频率,视觉、听觉凸显性,反例对比等 方面,提出具体的语言学习理论及第二语言教学策略,极大地影响了第二语言课堂过程及学 习过程的教学原则。就教育学来看,研究者强调教学要以学习者为中心,要让学习者参与学 习过程,进行各种合作及个人化的教学,强调与实际经验结合起来。从这一理论出发形成了 多种第二语言教学方法,它们强调以学生为中心,以沟通为目的,通过任务教学的方式达到 第二语言教学的目的。 这些领域的科学研究及学科发展成果引入第二语言教学领域后,语言教学领域发生了 巨大变革和根本性转变。这一根本性转变表现在语言教学的六个方面:第一,就语言教学原 则( methodological principles) 而言,现代语言教学在教学经验的基础上,更重视借鉴科学的 实证研究来指导教学( empirically motivated methodological principles) ;第二,就教学内容( instructional content) 而言,现代语言教学不再是单一的语言知识的学习,而是跨越三种交际模

杨焕明研究员,1988年在哥本哈根大学获得遗传学博士学位,

杨焕明研究员,1988年在哥本哈根大学获得遗传学博士学位,

杨焕明研究员,1988年在哥本哈根大学获得遗传学博士学位,后在法国马塞免疫中心、美国哈佛大学医学院和洛杉矶加州大学从事遗传学及基因组学研究。

1994年回国,在中国医学科学院任教授。

1997年受聘为丹麦Aarhus大学客座教授。

1998年组建中国科学院遗传研究所人类基因组中心,并出任主任。

1999年任中国科学院北京基因组信息学中心暨北京华大基因研究中心(以下简称华大中心)主任,2003年,被任命为新成立的中国科学院北京基因组研究所所长。

目前,杨焕明研究员受聘为James D.Watson基因组科学研究所教授及中国农大兼职教授。

1994年杨焕明研究员获国家自然科学基金委首批“全国杰出人才基金”,目前担任欧洲全球生命科学促进会(EAGLES)副主席及国际“人类基因组单体型图(HapMap)计划”协作组中国协调人。

杨焕明研究员自1980年以来,一直从事人类遗传学、人类基因定位与基因组科学的研究。

带领团队先后完成了嗜热杆菌基因组学研究、“1%人类基因组测序和分析”、“水稻基因组测序和分析”、SARS病毒基因组测序及诊断试剂研究、家蚕基因组测序和分析、家鸡基因组测序和分析,与合作者一起在SCIENCE、NATURE等杂志上发表了一系列相关论文。

获奖情况:
1.2003年度中国科学院杰出科技成就奖集体奖。

2.2003年4月获日本经济新闻社亚洲科技创新奖。

3.2002年12月获科学美国人杂志评选2002年度科研领袖。

4.2002年获香港求是科技基金会:“杰出科技成就集体奖”。

5.2002年度国家自然科学奖二等奖。

自由民主与选举民主

自由民主与选举民主

自由民主与选举民主作者:邰浴日一个自诩为民主政体的国家究竟应当具备哪些核心特征?是不是只要具备了一个多党竞争的选举制度就可以被称为民主制度?如果不是,那么除了选举之外,民主制度还需要具备哪些特征?这是本文试图予以回答的问题。

这篇论文将首先分析民主制度的内涵,并进而探寻民主制度背后的自由主义原则;在第二部分,本文将以美国的宪政制度设计为例来分析自由民主制度所应具备的一些核心要素;本文的第三部分将以一些在第三波民主化浪潮中的国家为例,指出在缺少了一定形式的制度安排的情况下,民主制度并不能有效平稳地运行,亦达不到保障公民权利的目的。

由此我们将提出对于自由民主与选举民主的区分,并对两者的优劣进行对比分析;最后我们将得出结论,即仅仅建立选举民主的制度是远远不够的,一个民主政体的题中应有之义,应当是努力追求建立一个完备的自由民主制度。

一、民主的含义本文的主题是讨论民主制度,那么首先要问的是,民主的定义是什么?著名经济学家熊彼特在其名著《资本主义、社会主义与民主》一书中提出,一个现代民族国家,如果其最强有力的决策者中多数是通过公平、诚实、定期的选举产生的,而且在这样的选举中候选人可以自由地竞争选票,并且实际上每个成年公民都有投票权,那么,这个国家就有了民主政体。

(Schumpeter, 1942,引自Huntington, 1997: 6)如亨廷顿所指出的那样,熊彼特的这一对于民主的程序性定义受到了后世普遍的关注和讨论,如今已得到了在这一领域从事研究的学者的公认。

(Huntington, 1997: 6-7)如我们所知,民主与自由是紧密相关的,事实上,民主制度所依据的理论基础,便来源于古典的自由主义理论。

为了进一步探寻民主制度的内涵与基础,我们不得不对古典自由主义理论的基本主张予以适当的关注。

美国著名政治学者高于斯教授在他的著作中指出了古典自由主义的四个主要特征:首先,古典自由主义将宽容视作人类社会的首要美德,赋予其极高的正面价值;其次,古典自由主义将某种特定的人类自由赋予了特殊的重要性;第三,(古典)自由主义者们都普遍信奉个人主义;最后,古典自由主义以一种对于无限制的集中、专断的权力的担忧与警惕为特征,因此,对于这种权力的限制便一直成为自由主义政治的一个主要目标(Geuss, 2005:14)。

Beyond_Moore's_Law

Beyond_Moore's_Law

Beyond Moore’s LawXxxxxx XxCollege of Electronic and Information EngineeringXXXXXX XX, ChinaAbstract—In this paper, the historical effects and benefits of Moore’s law for semiconductor technologies are reviewed, and argue d that the Moore’s law is reaching practical and fundamental limits with continued scaling. It is offered that there are some different trends of the developments of Moore’s law.Keywords—Moore’s Law; limits; scaling; trends; developmentI.I NTRODUCTIONOne of the remarkable technological achievements of the last 50 years is the integrated circuit. Built upon previous decades of research in materials and solid state electronics, and beginning with the pioneering work of Jack Kilby and Robert Noyce, the capabilities of integrated circuits have grown at an exponential rate[1].Codified as Moore’s law, which decrees that the number of transistors on a chip will double every 18 months, integrated circuit technology has had and continues to have a transformative impact on society.This paper endeavors to describe Moore’s law for complementary metal–oxide–semiconductor (CMOS) technology, examine its limits, conider some of the alternative future pathways for CMOS technologies.II.L IMITATION O F M OORE’S L AWMoore’s law describes the exponentia l growth in the complexity of integrated circuits year on year, driven by the search for profit. This rapid progress has delivered astonishing levels of functionality and cost-effectiveness, as epitomized by the boom in sophisticated digital consumer electronics products[2].However, this exponential growth in complexity cannot continue forever, and there is increasing evidence that, as transistor geometries shrink towards atomic scales, the limits to Moore's law may be reached over the next decade or two. As the physical limits are approached, other factors, such as design costs, manufacturing economics and device reliability, all conspire to make progress through device scaling alone ever more challenging, and alternative ways forward must be sought[3].III.M ORE M OORE A ND M ORE-T HAN-M OORE The combined need for digital and non-digital functionalities in an integrated system is translated as a dual trend in the International Technology Roadmap for Semiconductors: miniaturization of the digital functions (“More Moore”) and functional diversification (“More-than-Moore”).Fig. 1.More Moore and More-than-MooreA.More MooreMiniaturization and its associated benefits in terms of performances are the traditional parameters in Moore’s Law. Some innovations are now rising, such as changing device structure, finding new channel material, changing connecting wire, using high k technology, changing architecture system and improve fabrication process. We know that Moore’s Law will be invalid one day, but through these technologies, this trend for improving performance will continue.More Moore means to continue shrinking of physical feature sizes of the digital functionalities (logic and memory storage) in order to improve density (cost per function reduction) and performance (speed and power).But More Moore will face some challenges too, like high power density, approaching physical limitation, unreliable process and devices, expensive research and fabrication cost, and most importantly, when the scale is enough small, then continuing scaling does not bring substantial performance improvements.B.More-than-MooreWhile packing more transistors on a chip to add power and performance is still a key focus, developing novel More-than-Moore technologies on top of the Moore's Law technologies toprovide further values to semiconductor chips has also becomea more important issue.More-than-Moore means that Incorporation into devices offunctionalities that do not necessarily scale according to “M oore’s L aw”, but provide addition al value in different ways.More-than-Moore approach allows for the non-digitalfunctionalities to migrate from the system board-level into thepackage (sip) or onto the chip (soc).The second trend is characterized by functionaldiversification of semiconductor-based devices. These non-digital functionalities do contribute to the miniaturization ofelectronic systems, although they do not necessarily scale at the same rate as the one that describes the development of digital functionality. Consequently, in view of added functionality, this trend may be designated “More-than-Moore” (MtM).But we will face some problems by using the technologies of More-than-Moore. Such as integration of More Moore with More-than-Moore and Creation of intelligent compact systems.More-than-Moore technologies cover a wide range of fields. For example, MEMS applications include sensors, actuators and ink jet printers. RF CMOS applications include Bluetooth, GPS and Wi-Fi. CMOS image sensors are found in most digital cameras. High voltage drivers are used to power LED lights. These applications add value to computing and memory devices that are made from the traditional Moore's Law technology.Fig. 2.2007 ITRS “Moore’s Law and More” Definition Graphic Proposal Fig.2 is a definition of “Moore’s Law and More”. The red part is More Moore and the blue part is More-than-Moore. The red part contains the computing and data storage logic, while the blue part contains RF, HH Power, Passives, Sensors, Actuators, Bio-chips, Fluidics and other functionalities.parisonComparing More Moore and More-than-Moore from Fig.3 and Fig.4, we can draw some conclusions:•More Moore has smallest footprint but limited functionality.•More-than-Moore has full functionality but complex supple chain.They all have advantages and disadvantages. We can use them according to specific application. Fig. 3.Following Moore’s Law is one approach:Monolithic CMOS logic System-on-ChipFig. 4.Adding More-than-Moore is another: System-on-Chip and System-in-PackageIn modern society, the concept of Internet of Things is verypopular. In the past, people may pay attention to computingand storage more, so the IC industry develops rapidly following Moore’s Law. But now, people pay more attention tointernet of things, biomedical and so on. That is to say, peopleneed more demands of the IC besides the computing andstorage functionality.Fig. 5.An ideal application of More-than-MooreFig.5 is an example of More-than-Moore, of course, it is anideal application. But it shows some benefits of this trend.IV.B EYOND C MOSA.What is Beyond CMOSWhat we have talked about before is all based on Si-based CMOS technology. But we should realize that Si-based CMOS technology cannot do everything, especially when the transistors continue shrinking of physical feature sizes towards atomic scale.Fig. 6.More Moore, More-than-Moore and Beyond CMOSWhat More Moore do is to continue to go forward along the road of “Moore’s Law”. And More-than-Moore do is to d evelop and extend “Moore’s Law”. When the scaling bellows about 10nm, traditional Si-based CMOS technology may be invalid. So what Beyond CMOS want to do is to invent new devices or technologies when Si-based CMOS comes across its limitation.Fig. 7.Some new devices and technologyThe main idea of Beyond CMOS is to invent one or several new type switches which can replace the Si-based CMOS to process information. So these ideal devices need to have higher function density, higher performance, less power consumption, acceptable manufacturing cost, stable enough and suitable for large-scale manufacturing and so on.B.Several new devices1)Tunneling FET(TFET)TFET mainly according to the principle of tunneling of quantum mechanics, directly goes through channel from the source to drain rather than by diffusion. Fig. 8.Tunneling FET(TFET)2)Quantum Cellular Automata (QCA)Representing the binary information by changing the structure of the Cellular.Transmitting information based on neighbor interaction.Fig. 9.Quantum Cellular Automata (QCA)Fig. 10.Quantum Cellular Automata (QCA)3)Atomic Switch(QCA)Atomic switch based on the formation and the annihilation of the metal atoms bridge between the two electrons, forming a gate-control switch mode.Fig. 11.Atomic Switch4)SpinFETSpinFET use the spinning direction of electron to carry information.Fig. 12.SpinFETFig. 13.SpinFETThey all have advantages or dis advantages. Maybe they are not the best devices, but they represent the potential development trend of the devices in the future[4].V.S UMMARYMicroelectronics therefore seeks to develop in new ways, not only to continue to deliver better performance in traditional markets, but also to grow into new markets based on devising new, non-electronic, functions on integrated circuits.Microelectronics relies on complementary metal oxide semiconductor (CMOS) technology, the backbone of the electronics industry. Beyond Moore's law, it is foreseen that microelectronics will be a platform to support optical, chemical and biotechnology to deliver a step change beyond electronics-only integration.R EFERENCES[1]Cavin R K, Lugli III P, Zhirnov V V. Science and engineering beyondMoore's law[J]. Proceedings of the IEEE, 2012, 100(Special Centennial Issue): 1720-1749.[2]Cumming D R S, Furber S B, Paul D J. Beyond Moore's law[J].Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2014, 372(2012).[3]Saraswat K C. How far can we push Si CMOS and what are thealternatives for future ULSI[C]//Device Research Conference (DRC), 2014 72nd Annual. IEEE, 2014: 3-4.[4]Kazior T E. Beyond CMOS: heterogeneous integration of III–V devices,RF MEMS and other dissimilar materials/devices with Si CMOS to create intelligent microsystems[J]. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 2014, 372(2012): 20130105.。

dompmartin等,2000 -回复

dompmartin等,2000 -回复

dompmartin等,2000 -回复在《[dompmartin等,2000]》一文中,作者们提出了一个关于[某个主题]的问题,并通过一系列实证研究和数据分析展示了他们的研究结果。

在本文中,我将一步一步回答这个问题,并对作者们的研究进行分析和评价。

首先,我会简要介绍文章的背景和研究问题。

在[dompmartin等,2000]中,作者们对[某个主题]进行了研究,并提出了一个关键的问题:[问题的具体表述]。

这个问题具有重要意义,因为它对[相关领域的研究或实践]有着重要的启示和影响。

接下来,我会总结作者们的研究方法和实证研究。

根据[dompmartin 等,2000]中的描述,作者们采用了[某种研究方法]来回答他们的研究问题,并在实验中收集了大量的数据。

通过对这些数据的详细分析和解释,作者们得出了一系列有力的结论,这些结论对于理解和解决[相关主题]方面的问题具有重要的意义。

然后,我会详细阐述作者们的研究结果。

根据[dompmartin等,2000]中的结果,作者们发现了许多有意义的现象和规律。

例如,他们发现[某个现象或关系]的存在,并指出这个现象或关系对于[相关领域的研究或实践]有着重要的启示。

此外,作者们还提供了一些具体的案例和数据来支持他们的观点,并详细讨论了这些案例和数据的意义和贡献。

在对作者们的研究结果进行详细解释之后,我会对他们的研究进行评价和分析。

首先,我会强调作者们的研究方法的优势和创新之处。

根据[dompmartin等,2000]中的描述,作者们的方法有许多优点,包括[具体的方法优势]。

这些优势使得他们的研究结果更具有说服力和可靠性。

然而,我也会指出该研究存在的一些限制和不足之处,并提出一些改进的建议。

最后,我会总结本文的重点和对该主题的重要性。

根据[dompmartin 等,2000]中的内容,作者们的研究结果对于[相关领域的研究或实践]有着重要的启示和应用价值。

通过对这些研究结果的全面解释和分析,可以更深入地了解[相关主题]的本质和规律,并为未来的研究和实践提供指导和建议。

摩尔定律 自然杂志

摩尔定律    自然杂志

摩尔定律自然杂志Moore‟s law is not just for computers摩尔定律之他用Predicting the future of technology often seems a fool‟s game. In 1946 for example, Thomas J. Watson, founder of International Business Machines — now known simply as IBM — is said to have made the prediction that the world would need just five computers. But US researchers now say that technological progress really is predictable — and back up the claim with evidence regarding 62 different technologies.预测技术未来的发展趋势常被视作愚蠢。

例如,国际商用机器公司(如今简称为IBM)的创始人托马斯·J·沃森(Thomas J. Watson)在1946年曾预言世界仅需要五台计算机。

但是,美国研究人员近日说科技发展的确是可预测的。

为了证实这种看法,他们列出了有关62种不同技术的证据。

The claim is nothing new. But what a group of researchers at the Santa Fe Institute in New Mexico and the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts, have done is to put it to the test.In a study published in PLoS ONE1, they compared several mathematical laws that purport to describe how the costs of technologies evolve, and found that the most accurate was one proposed as early as 1936.这种看法已是老生常谈。

奥苏贝尔学习动机理论对小学数学教学的启示

奥苏贝尔学习动机理论对小学数学教学的启示

The Science Education Article CollectsTotal.437 October2018(B)总第437期2018年10月(中)摘要学习动机对于学生虽然是非智力因素,但它却是影响学生学习的重要因素之一,直接关联着课堂教学效果,因此学习动机一直都是教育心理学和教育工作者所关心的重点问题。

美国心理学家奥苏贝尔认为学习动机主要由认知内驱力、自我提高内驱力和附属内驱力三部分组成。

本文旨在通过对奥苏贝尔学习动机理论的分析和理解,进而结合小学数学教学中存在的问题提出关于小学数学教学的若干建议。

关键词学习动机小学数学教学启示The Enlightenment of Ausubel's Learning Motivation Theory on Primary Mathematics Teaching//Yang Ping Abstract Learning motivation is seen as a non-intellectual fac-tor,but it is one of the important factors affecting students'learn-ing.It is directly related to classroom teaching effects.Therefore, learning motivation has always been a key issue of educational psychology and educators.American psychologist Ausubel be-lieves that the motivation for learning is mainly composed of cog-nitive drive,self-improvement drive and subsidiary drive.This paper aims to put forward some suggestions on mathematics teaching in primary schools through the analysis and understand-ing of Ausubel's learning motivation theory and the problems ex-isting in primary mathematics teaching.Key words learning motivation;primary mathematics teaching; enlightenment奥苏贝尔认为学习动机包括三方面内容,即认知内驱力、自我提高内驱力和附属内驱力。

bf算法与kmp算法执行流程

bf算法与kmp算法执行流程

bf算法与kmp算法执行流程英文回答:The Boyer-Moore (BM) algorithm and the Knuth-Morris-Pratt (KMP) algorithm are two popular string matching algorithms used to find occurrences of a pattern within a larger text. Both algorithms aim to improve the efficiency of the search process by utilizing different techniques.The Boyer-Moore algorithm is based on two main ideas: the bad character rule and the good suffix rule. The bad character rule allows us to skip unnecessary comparisons by considering the rightmost occurrence of the mismatched character in the pattern. This rule helps us determine the number of characters we can shift the pattern by, reducing the number of comparisons needed. The good suffix rule, on the other hand, allows us to shift the pattern by a larger distance when a suffix of the pattern matches a part of the text. By combining these two rules, the Boyer-Moore algorithm can skip a significant number of unnecessarycomparisons, making it efficient for large texts.The KMP algorithm, on the other hand, uses a different approach to improve efficiency. It constructs a partial match table, also known as the failure function, which helps determine the maximum length of the proper suffix of the pattern that is also a prefix. This information allows us to avoid unnecessary comparisons by shifting the pattern by the appropriate distance. The KMP algorithm avoids rechecking characters that have already been matched, making it efficient for patterns with repeated characters.In terms of execution flow, the Boyer-Moore algorithm follows these steps:1. Preprocessing: The algorithm constructs two tables, the bad character table and the good suffix table, based on the pattern.2. Searching: The algorithm starts comparing the pattern with the text from right to left. If a mismatch occurs, it uses the bad character rule and the good suffixrule to determine the shift distance and continues searching.The KMP algorithm follows these steps:1. Preprocessing: The algorithm constructs the partial match table based on the pattern.2. Searching: The algorithm compares the pattern with the text from left to right. If a mismatch occurs, it uses the partial match table to determine the shift distance and continues searching.中文回答:Boyer-Moore(BM)算法和Knuth-Morris-Pratt(KMP)算法是两种常用的字符串匹配算法,用于在较大的文本中查找模式的出现。

人类基因组概况ppt课件

人类基因组概况ppt课件
A+T含量 G+C含量 不能确定的碱基 重复序列(不含异染色质) 编码序列(基因)数目 功能未知基因比例 外显子最多的基因 SNP数量 SNP密度
2.91Gbp
54% 38% 9% 35% 26588 42% Titin(234) 约300万个 1/12500 bp
最长的染色体 最短的染色体 基因最多的染色体 基因最少的染色体 基因密度最大的染色体 基因密度最小的染色体 重复序列含量最高的染色体
It is essentially immoral not to get it (the human genome sequence) done as fast as possible.
James Watson
人类基因组计划的完成,使得我们今天有可能来探 讨基因组的概,但我们仍然无法来谈论细节。
重复序列含量最低的染色体
编码外显子序列的比例 基因的平均长度
2(240 Mbp) Y(19 Mbp) 1(2453) Y(104) 19(23/Mb) 13,Y(5/Mb) 19(57%)
2,8,10,13,18(36%)
1.1~1.4% 27 Kb
女 平均 男
染色体上距着丝粒越远,重组率越高
4. Francis S. Collins, Eric D. Green, Alan E. Guttmacher, Mark S. Guyer :A Vision for the Future of Genomics Research. A blueprint for the genomic era. Nature Apr 24 2003: 835.
而 Celera 的测序样本来自5个人:分别属于西班牙裔、 亚洲裔、非洲裔、美洲裔和高加索裔(2男3女),是从21个志 愿者样本中挑选的。

Instructional_design

Instructional_design

Instructional designFrom Wikipedia, the free encyclopediaInstructional Design(also called Instructional Systems Design (ISD)) is the practice of maximizing the effectiveness, efficiency and appeal of instruction and other learning experiences. The process consists broadly of determining the current state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. Ideally the process is informed by pedagogically(process of teaching) and andragogically(adult learning) tested theories of learning and may take place in student-only, teacher-led or community-based settings. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models but many are based on the ADDIE model with the five phases: 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. As a field, instructional design is historically and traditionally rooted in cognitive and behavioral psychology.HistoryMuch of the foundations of the field of instructional design was laid in World War II, when the U.S. military faced the need to rapidly train large numbers of people to perform complex technical tasks, fromfield-stripping a carbine to navigating across the ocean to building a bomber—see "Training Within Industry(TWI)". Drawing on the research and theories of B.F. Skinner on operant conditioning, training programs focused on observable behaviors. Tasks were broken down into subtasks, and each subtask treated as a separate learning goal. Training was designed to reward correct performance and remediate incorrect performance. Mastery was assumed to be possible for every learner, given enough repetition and feedback. After the war, the success of the wartime training model was replicated in business and industrial training, and to a lesser extent in the primary and secondary classroom. The approach is still common in the U.S. military.[1]In 1956, a committee led by Benjamin Bloom published an influential taxonomy of what he termed the three domains of learning: Cognitive(what one knows or thinks), Psychomotor (what one does, physically) and Affective (what one feels, or what attitudes one has). These taxonomies still influence the design of instruction.[2]During the latter half of the 20th century, learning theories began to be influenced by the growth of digital computers.In the 1970s, many instructional design theorists began to adopt an information-processing-based approach to the design of instruction. David Merrill for instance developed Component Display Theory (CDT), which concentrates on the means of presenting instructional materials (presentation techniques).[3]Later in the 1980s and throughout the 1990s cognitive load theory began to find empirical support for a variety of presentation techniques.[4]Cognitive load theory and the design of instructionCognitive load theory developed out of several empirical studies of learners, as they interacted with instructional materials.[5]Sweller and his associates began to measure the effects of working memory load, and found that the format of instructional materials has a direct effect on the performance of the learners using those materials.[6][7][8]While the media debates of the 1990s focused on the influences of media on learning, cognitive load effects were being documented in several journals. Rather than attempting to substantiate the use of media, these cognitive load learning effects provided an empirical basis for the use of instructional strategies. Mayer asked the instructional design community to reassess the media debate, to refocus their attention on what was most important: learning.[9]By the mid- to late-1990s, Sweller and his associates had discovered several learning effects related to cognitive load and the design of instruction (e.g. the split attention effect, redundancy effect, and the worked-example effect). Later, other researchers like Richard Mayer began to attribute learning effects to cognitive load.[9] Mayer and his associates soon developed a Cognitive Theory of MultimediaLearning.[10][11][12]In the past decade, cognitive load theory has begun to be internationally accepted[13]and begun to revolutionize how practitioners of instructional design view instruction. Recently, human performance experts have even taken notice of cognitive load theory, and have begun to promote this theory base as the science of instruction, with instructional designers as the practitioners of this field.[14]Finally Clark, Nguyen and Sweller[15]published a textbook describing how Instructional Designers can promote efficient learning using evidence-based guidelines of cognitive load theory.Instructional Designers use various instructional strategies to reduce cognitive load. For example, they think that the onscreen text should not be more than 150 words or the text should be presented in small meaningful chunks.[citation needed] The designers also use auditory and visual methods to communicate information to the learner.Learning designThe concept of learning design arrived in the literature of technology for education in the late nineties and early 2000s [16] with the idea that "designers and instructors need to choose for themselves the best mixture of behaviourist and constructivist learning experiences for their online courses" [17]. But the concept of learning design is probably as old as the concept of teaching. Learning design might be defined as "the description of the teaching-learning process that takes place in a unit of learning (eg, a course, a lesson or any other designed learning event)" [18].As summarized by Britain[19], learning design may be associated with:∙The concept of learning design∙The implementation of the concept made by learning design specifications like PALO, IMS Learning Design[20], LDL, SLD 2.0, etc... ∙The technical realisations around the implementation of the concept like TELOS, RELOAD LD-Author, etc...Instructional design modelsADDIE processPerhaps the most common model used for creating instructional materials is the ADDIE Process. This acronym stands for the 5 phases contained in the model:∙Analyze– analyze learner characteristics, task to be learned, etc.Identify Instructional Goals, Conduct Instructional Analysis, Analyze Learners and Contexts∙Design– develop learning objectives, choose an instructional approachWrite Performance Objectives, Develop Assessment Instruments, Develop Instructional Strategy∙Develop– create instructional or training materialsDesign and selection of materials appropriate for learning activity, Design and Conduct Formative Evaluation∙Implement– deliver or distribute the instructional materials ∙Evaluate– make sure the materials achieved the desired goals Design and Conduct Summative EvaluationMost of the current instructional design models are variations of the ADDIE process.[21] Dick,W.O,.Carey, L.,&Carey, J.O.(2004)Systematic Design of Instruction. Boston,MA:Allyn&Bacon.Rapid prototypingA sometimes utilized adaptation to the ADDIE model is in a practice known as rapid prototyping.Proponents suggest that through an iterative process the verification of the design documents saves time and money by catching problems while they are still easy to fix. This approach is not novel to the design of instruction, but appears in many design-related domains including software design, architecture, transportation planning, product development, message design, user experience design, etc.[21][22][23]In fact, some proponents of design prototyping assert that a sophisticated understanding of a problem is incomplete without creating and evaluating some type of prototype, regardless of the analysis rigor that may have been applied up front.[24] In other words, up-front analysis is rarely sufficient to allow one to confidently select an instructional model. For this reason many traditional methods of instructional design are beginning to be seen as incomplete, naive, and even counter-productive.[25]However, some consider rapid prototyping to be a somewhat simplistic type of model. As this argument goes, at the heart of Instructional Design is the analysis phase. After you thoroughly conduct the analysis—you can then choose a model based on your findings. That is the area where mostpeople get snagged—they simply do not do a thorough-enough analysis. (Part of Article By Chris Bressi on LinkedIn)Dick and CareyAnother well-known instructional design model is The Dick and Carey Systems Approach Model.[26] The model was originally published in 1978 by Walter Dick and Lou Carey in their book entitled The Systematic Design of Instruction.Dick and Carey made a significant contribution to the instructional design field by championing a systems view of instruction as opposed to viewing instruction as a sum of isolated parts. The model addresses instruction as an entire system, focusing on the interrelationship between context, content, learning and instruction. According to Dick and Carey, "Components such as the instructor, learners, materials, instructional activities, delivery system, and learning and performance environments interact with each other and work together to bring about the desired student learning outcomes".[26] The components of the Systems Approach Model, also known as the Dick and Carey Model, are as follows:∙Identify Instructional Goal(s): goal statement describes a skill, knowledge or attitude(SKA) that a learner will be expected to acquire ∙Conduct Instructional Analysis: Identify what a learner must recall and identify what learner must be able to do to perform particular task ∙Analyze Learners and Contexts: General characteristic of the target audience, Characteristic directly related to the skill to be taught, Analysis of Performance Setting, Analysis of Learning Setting∙Write Performance Objectives: Objectives consists of a description of the behavior, the condition and criteria. The component of anobjective that describes the criteria that will be used to judge the learner's performance.∙Develop Assessment Instruments: Purpose of entry behavior testing, purpose of pretesting, purpose of posttesting, purpose of practive items/practive problems∙Develop Instructional Strategy: Pre-instructional activities, content presentation, Learner participation, assessment∙Develop and Select Instructional Materials∙Design and Conduct Formative Evaluation of Instruction: Designer try to identify areas of the instructional materials that are in need to improvement.∙Revise Instruction: To identify poor test items and to identify poor instruction∙Design and Conduct Summative EvaluationWith this model, components are executed iteratively and in parallel rather than linearly.[26]/akteacher/dick-cary-instructional-design-mo delInstructional Development Learning System (IDLS)Another instructional design model is the Instructional Development Learning System (IDLS).[27] The model was originally published in 1970 by Peter J. Esseff, PhD and Mary Sullivan Esseff, PhD in their book entitled IDLS—Pro Trainer 1: How to Design, Develop, and Validate Instructional Materials.[28]Peter (1968) & Mary (1972) Esseff both received their doctorates in Educational Technology from the Catholic University of America under the mentorship of Dr. Gabriel Ofiesh, a Founding Father of the Military Model mentioned above. Esseff and Esseff contributed synthesized existing theories to develop their approach to systematic design, "Instructional Development Learning System" (IDLS).The components of the IDLS Model are:∙Design a Task Analysis∙Develop Criterion Tests and Performance Measures∙Develop Interactive Instructional Materials∙Validate the Interactive Instructional MaterialsOther modelsSome other useful models of instructional design include: the Smith/Ragan Model, the Morrison/Ross/Kemp Model and the OAR model , as well as, Wiggins theory of backward design .Learning theories also play an important role in the design ofinstructional materials. Theories such as behaviorism , constructivism , social learning and cognitivism help shape and define the outcome of instructional materials.Influential researchers and theoristsThe lists in this article may contain items that are not notable , not encyclopedic , or not helpful . Please help out by removing such elements and incorporating appropriate items into the main body of the article. (December 2010)Alphabetic by last name∙ Bloom, Benjamin – Taxonomies of the cognitive, affective, and psychomotor domains – 1955 ∙Bonk, Curtis – Blended learning – 2000s ∙ Bransford, John D. – How People Learn: Bridging Research and Practice – 1999 ∙ Bruner, Jerome – Constructivism ∙Carr-Chellman, Alison – Instructional Design for Teachers ID4T -2010 ∙Carey, L. – "The Systematic Design of Instruction" ∙Clark, Richard – Clark-Kosma "Media vs Methods debate", "Guidance" debate . ∙Clark, Ruth – Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load / Guided Instruction / Cognitive Load Theory ∙Dick, W. – "The Systematic Design of Instruction" ∙ Gagné, Robert M. – Nine Events of Instruction (Gagné and Merrill Video Seminar) ∙Heinich, Robert – Instructional Media and the new technologies of instruction 3rd ed. – Educational Technology – 1989 ∙Jonassen, David – problem-solving strategies – 1990s ∙Langdon, Danny G - The Instructional Designs Library: 40 Instructional Designs, Educational Tech. Publications ∙Mager, Robert F. – ABCD model for instructional objectives – 1962 ∙Merrill, M. David - Component Display Theory / Knowledge Objects ∙ Papert, Seymour – Constructionism, LOGO – 1970s ∙ Piaget, Jean – Cognitive development – 1960s∙Piskurich, George – Rapid Instructional Design – 2006∙Simonson, Michael –Instructional Systems and Design via Distance Education – 1980s∙Schank, Roger– Constructivist simulations – 1990s∙Sweller, John - Cognitive load, Worked-example effect, Split-attention effect∙Roberts, Clifton Lee - From Analysis to Design, Practical Applications of ADDIE within the Enterprise - 2011∙Reigeluth, Charles –Elaboration Theory, "Green Books" I, II, and III - 1999-2010∙Skinner, B.F.– Radical Behaviorism, Programed Instruction∙Vygotsky, Lev– Learning as a social activity – 1930s∙Wiley, David– Learning Objects, Open Learning – 2000sSee alsoSince instructional design deals with creating useful instruction and instructional materials, there are many other areas that are related to the field of instructional design.∙educational assessment∙confidence-based learning∙educational animation∙educational psychology∙educational technology∙e-learning∙electronic portfolio∙evaluation∙human–computer interaction∙instructional design context∙instructional technology∙instructional theory∙interaction design∙learning object∙learning science∙m-learning∙multimedia learning∙online education∙instructional design coordinator∙storyboarding∙training∙interdisciplinary teaching∙rapid prototyping∙lesson study∙Understanding by DesignReferences1.^MIL-HDBK-29612/2A Instructional Systems Development/SystemsApproach to Training and Education2.^Bloom's Taxonomy3.^TIP: Theories4.^Lawrence Erlbaum Associates, Inc. - Educational Psychologist -38(1):1 - Citation5.^ Sweller, J. (1988). "Cognitive load during problem solving:Effects on learning". Cognitive Science12 (1): 257–285.doi:10.1016/0364-0213(88)90023-7.6.^ Chandler, P. & Sweller, J. (1991). "Cognitive Load Theory andthe Format of Instruction". Cognition and Instruction8 (4): 293–332.doi:10.1207/s1532690xci0804_2.7.^ Sweller, J., & Cooper, G.A. (1985). "The use of worked examplesas a substitute for problem solving in learning algebra". Cognition and Instruction2 (1): 59–89. doi:10.1207/s1532690xci0201_3.8.^Cooper, G., & Sweller, J. (1987). "Effects of schema acquisitionand rule automation on mathematical problem-solving transfer". Journal of Educational Psychology79 (4): 347–362.doi:10.1037/0022-0663.79.4.347.9.^ a b Mayer, R.E. (1997). "Multimedia Learning: Are We Asking theRight Questions?". Educational Psychologist32 (41): 1–19.doi:10.1207/s1*******ep3201_1.10.^ Mayer, R.E. (2001). Multimedia Learning. Cambridge: CambridgeUniversity Press. ISBN0-521-78239-2.11.^Mayer, R.E., Bove, W. Bryman, A. Mars, R. & Tapangco, L. (1996)."When Less Is More: Meaningful Learning From Visual and Verbal Summaries of Science Textbook Lessons". Journal of Educational Psychology88 (1): 64–73. doi:10.1037/0022-0663.88.1.64.12.^ Mayer, R.E., Steinhoff, K., Bower, G. and Mars, R. (1995). "Agenerative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text". Educational TechnologyResearch and Development43 (1): 31–41. doi:10.1007/BF02300480.13.^Paas, F., Renkl, A. & Sweller, J. (2004). "Cognitive Load Theory:Instructional Implications of the Interaction between InformationStructures and Cognitive Architecture". Instructional Science32: 1–8.doi:10.1023/B:TRUC.0000021806.17516.d0.14.^ Clark, R.C., Mayer, R.E. (2002). e-Learning and the Science ofInstruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Pfeiffer. ISBN0-7879-6051-9.15.^ Clark, R.C., Nguyen, F., and Sweller, J. (2006). Efficiency inLearning: Evidence-Based Guidelines to Manage Cognitive Load. SanFrancisco: Pfeiffer. ISBN0-7879-7728-4.16.^Conole G., and Fill K., “A learning design toolkit to createpedagogically effective learning activities”. Journal of Interactive Media in Education, 2005 (08).17.^Carr-Chellman A. and Duchastel P., “The ideal online course,”British Journal of Educational Technology, 31(3), 229-241, July 2000.18.^Koper R., “Current Research in Learning Design,” EducationalTechnology & Society, 9 (1), 13-22, 2006.19.^Britain S., “A Review of Learning Design: Concept,Specifications and Tools” A report for the JISC E-learning Pedagogy Programme, May 2004.20.^IMS Learning Design webpage21.^ a b Piskurich, G.M. (2006). Rapid Instructional Design: LearningID fast and right.22.^ Saettler, P. (1990). The evolution of American educationaltechnology.23.^ Stolovitch, H.D., & Keeps, E. (1999). Handbook of humanperformance technology.24.^ Kelley, T., & Littman, J. (2005). The ten faces of innovation:IDEO's strategies for beating the devil's advocate & driving creativity throughout your organization. New York: Doubleday.25.^ Hokanson, B., & Miller, C. (2009). Role-based design: Acontemporary framework for innovation and creativity in instructional design. Educational Technology, 49(2), 21–28.26.^ a b c Dick, Walter, Lou Carey, and James O. Carey (2005) [1978].The Systematic Design of Instruction(6th ed.). Allyn & Bacon. pp. 1–12.ISBN020*******./?id=sYQCAAAACAAJ&dq=the+systematic+design+of+instruction.27.^ Esseff, Peter J. and Esseff, Mary Sullivan (1998) [1970].Instructional Development Learning System (IDLS) (8th ed.). ESF Press.pp. 1–12. ISBN1582830371. /Materials.html.28.^/Materials.htmlExternal links∙Instructional Design - An overview of Instructional Design∙ISD Handbook∙Edutech wiki: Instructional design model [1]∙Debby Kalk, Real World Instructional Design InterviewRetrieved from "/wiki/Instructional_design" Categories: Educational technology | Educational psychology | Learning | Pedagogy | Communication design | Curricula。

带自增长的文本输入窗[发明专利]

带自增长的文本输入窗[发明专利]

专利名称:带自增长的文本输入窗
专利类型:发明专利
发明人:E·L·彭宁顿二世,A·J·加塞德,J·W·皮提罗斯,S·J·戴维斯,T·A·基林斯基
申请号:CN200480003248.8
申请日:20040728
公开号:CN1864155A
公开日:
20061115
专利内容由知识产权出版社提供
摘要:一种用户输入屏面动态地扩展以容纳用户输入,诸如手写或键盘输入。

根据要写入或键入的语言,扩展会发生在四个可能的方向的中的一个或两个上。

例如,当写入英语单词时,输入屏面会随着用户写入向右扩展,当输入屏面向右完全扩展时接着向下扩展。

申请人:微软公司
地址:美国华盛顿州
国籍:US
代理机构:上海专利商标事务所有限公司
代理人:钱慰民
更多信息请下载全文后查看。

BM算法总结

BM算法总结

BM算法(全称Boyer-Moore Algorithm)是一种精确字符串匹配算法(只是一个启发式的字符串搜索算法)。

BM算法不同于KMP算法,采用从右向左比较的方法,同时引入了两种启发式Jump规则,即Bad-Character和Good-Suffix,来决定模板向右移动的步长。

BM算法的基本流程:文本串T(待匹配的字符串,长度n),模式串为P(用于去匹配的字符串模板,长度设为m)。

首先将T与P进行左对齐,然后进行从右向左比较(此时不移动模板和文本的相对位置,仅仅比较对齐位置上面是否相同,方向是右向左)。

若是某趟比较不匹配时,BM算法就采用两条启发式规则,即前面提到的Bad-Character和Good-Suffix,来计算模式串P向右移动的步长,直到整个匹配过程的结束。

1)Bad-characterBM 算法在上图中从右向左匹配中第一个字符就出现不一致的情况,此时需要采用两种情况来处理:a)如果T 中不匹配字符E 在模式P 中没有出现,那么我们很容易就能理解为E开始的m 长度的字符串不可能匹配到P(直观,无需解释),我们可以直接把P 跳过E,匹配后面的内容。

b)如果E 在模式P 中未进行匹配的字段中出现了,则以该字符E 进行对齐。

BM算法实现2)Good Suffix若发现某个字符不匹配的同时,已有部分字符匹配成功,则按如下两种情况进行:a)如果在P中位置t处已经匹配部分P’在P中某位置t’也出现,且位置t’的前一个字符与位置t的前一个字符不相同,则将P右移使t’对应t所在的位置。

b)如果P中任何位置已经匹配部分P’没有再出现,则找到与P’的后缀P’’相同的P的最长前缀x,向右移动P,使x对应刚才P’’后缀所在位置下面两个链接解释的很好:/sealyao/archive/2009/09/18/4568167.aspxhttp://www-igm.univ-mlv.fr/~lecroq/string/node14.html#SECTION00140void preBmBc(char *x, int m, int bmBc[]) //坏字符表预处理,x就是上文中的模式串P {int i; //注意:bmBc数组的下标是字符,而不是数字for (i = 0; i < ASIZE; ++i) //初始将所有ASIZE=256个字符都赋初值为模式串的长度m,bmBc[i] = m; //也就是说模式串中没出现的字符,相应的移动距离都为m for (i = 0; i < m - 1; ++i)bmBc[x[i]] = m - i - 1;}Suffixes[]数组的计算方法。

  1. 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. 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. 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
Introduction
The need to search for occurrences of some string within some other string arises in countless applications. Exact string matching is a fundamental task in computer science that has been studied extensively. Given a pattern string P and a typically much longer text string T , the task of exact string matching is to find all locations in T where P occurs. Let |s| denote the length of a string s. Also let the notation xi refer to the ith character of a string x, counting from the left, and let the notation xh..i denote the substring of x that is formed by the characters of x from its hth position to the ith position. Here we require that h ≤ i. If i > |x| or i < 1, we interpret the character xi to be a non-existing 1
On Boyer-Moore Preprocபைடு நூலகம்ssing
Heikki Hyyr¨ o Department of Computer Sciences University of Tampere, Finland Heikki.Hyyro@cs.uta.fi
Abstract Probably the two best-known exact string matching algorithms are the linear-time algorithm of Knuth, Morris and Pratt (KMP), and the fast on average algorithm of Boyer and Moore (BM). The efficiency of these algorithms is based on using a suitable failure function. When a mismatch occurs in the currently inspected text position, the purpose of a failure function is to tell how many positions the pattern can be shifted forwards in the text without skipping over any occurrences. The BM algorithm uses two failure functions: one is based on a bad character rule, and the other on a good suffix rule. The classic linear-time preprocessing algorithm for the good suffix rule has been viewed as somewhat obscure [8]. A formal proof of the correctness of that algorithm was given recently by Stomp [14]. That proof is based on linear time temporal logic, and is fairly technical and a-posteriori in nature. In this paper we present a constructive and somewhat simpler discussion about the correctness of the classic preprocessing algorithm for the good suffix rule. We also highlight the close relationship between this preprocessing algorithm and the exact string matching algorithm of Morris and Pratt (a pre-version of KMP). For these reasons we believe that the present paper gives a better understanding of the ideas behind the preprocessing algorithm than the proof by Stomp. This paper is based on [9], and thus the discussion is originally roughly as old as the proof by Stomp.
character that does not match with any character. A string y is a prefix of x if y = x1..h for some h > 0. In similar fashion, y is a suffix of x if y = xh..|x| for some h ≤ |x|. It is common to denote the length of the pattern string P by m and the length of the text T by n. With this notation P = P1..m and T = T1..n , and the task of exact string matching can be defined more formally as searching for such indices j for which Tj −m+1..j = P . A naive “Brute-Force” approach for exact string matching is to check each possible text location separately for an occurrence of the pattern. This can be done for example by sliding a window of length m over the text. Let us say that the window is in position w when it overlaps the text substring Tw−m+1..w . The position w is checked for a match of P by a sequential comparison between the characters Pi and Tw−m+i in the order i = 1 . . . m. The comparison is stopped as soon as Pi = Tw−m+i or all m character-pairs have matched (in which case Tw−m+1..w = P ). After the window position w has been checked, the window is shifted one step right to the position w + 1, and a new comparison is started. As there are n − m + 1 possible window positions, and checking each location may involve up to m character comparisons, the worst-case run time of the naive method is O(mn). Morris and Pratt have presented a linear O(n) algorithm for exact string matching [11]. Let us call this algorithm MP. It improves the above-described naive approach by using a suitable failure function, which utilizes the information gained from previous character comparisons. The failure function enables to move the window forward in a smart way after the window position w has been checked. Later Knuth, Morris and Pratt presented an O(n) algorithm [10] that uses a slightly improved version of the failure function. Boyer and Moore have presented an algorithm that is fast in practice, but O(mn) in the worst case. Let us call it BM. Subsequently also many variants of BM have been proposed (e.g. [7, 2, 6]). The main innovation in BM is to check the window in reverse order. That is, when the window is at position w, the characters Pi and Tw−m+i are compared from right to left in the order i = m . . . 1. This enables to use a failure function that can often skip over several text characters. BM actually uses two different failure functions, δ1 and δ2 . The former is based on so-called bad character rule, and the latter on so-called good suffix rule. The failure function δ1 of BM is very simple to precompute. But the original preprocessing algorithm given in [4] for the δ2 function has been viewed as somewhat mysterious and incomprehensible [8]. Stomp even states that the algorithm is known to be “notoriously difficult” [14]. An example of this is that the algorithms shown in [4, 10] were slightly erroneous, and a corrected version was given without any detailed explanations by Rytter [12]. A formal proof of the correctness of the preprocessing algorithm was given recently by Stomp [14]. He analysed the particular version shown in [3, 1], and also found and corrected a small error that concerns running out of bounds of an array. Stomp’s proof is based on linear temporal logic and it is a-posteriori in nature: he first shows the algorithm, and then proceeds to prove that that given algorithm computes δ2 correctly. The proof is also fairly technical, and does not shed too much light 2
相关文档
最新文档