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简介：最近在翻译BillWagner先生的《EffectiveC#》一书，由于自己早先也有写作Effective.NET的打算，所以对书中很多条款，也有很多自己的思考。如果作为译注来添加，担心把最后的译本添得四不像，不添又甚感遗憾。遂考虑把翻译过程中自己的所思所想直接记录下来，并在自己的blog(http：//blog．dreambrook．com／jzli／)上开辟专门的EffectiveC#区，供大家讨论打磨，弥补作／译者认识不足的地方，相信也许可以收到正常出版渠道不能取得之效果。

简介：Inthispaper,aneweffectivemethodisproposedtofindclassassociationrules(CAR),togetusefulclassassociationrules(UCAR)byremovingthespuriousclassassociationrules(SCAR),andtogenerateexceptionclassassociationrules(ECAR)foreachUCAR.CARmining,whichintegratesthetechniquesofclassificationandassociation,isofgreatinterestrecently.However,ithastwodrawbacks:oneisthatalargepartofCARsarespuriousandmaybemisleadingtousers;theotheristhatsomeimportantECARsaredifficulttofindusingtraditionaldataminingtechniques.Themethodintroducedinthispaperaimstogetovertheseflaws.Accordingtoourapproach,ausercanretrievecorrectinformationfromUCARsandknowtheinfluencefromdifferentconditionsbycheckingcorrespondingECARs.Experimentalresultsdemonstratetheeffectivenessofourproposedapproach.

简介：最小化内存垃圾Item16：MinimizeGarbage

简介：Softwaremodularizationisatechniqueusedtodivideasoftwaresystemintoindependentmodules（packages）thatareexpectedtobecohesiveandlooselycoupled.However,assoftwaresystemsevolveovertimetomeetnewrequire-ments,theirmodularizationsbecomecomplexandgraduallyloosetheirquality.Thus,itischallengingtoautomaticallyoptimizetheclasses＇distributioninpackages,alsoknownasremodularization.Toalleviatethisissue,weintroduceanewapproachtooptimizesoftwaremodularizationbymovingclassestomoresuitablepackages.Inadditiontoimprovingdesignqualityandpreservingsemanticcoherence,ourapproachtakesintoconsiderationtherefactoringeffortasanobjectiveinitselfwhileoptimizingsoftwaremodularization.WeadapttheElitistNon-dominatedSortingGeneticAlgorithm（NSGA-Ⅱ）ofDebetal.tofindthebestsequenceofrefactoringsthat1）maximizestructuralquality,2）maximizesemanticcohesivenessofpackages（evaluatedbyasemanticmeasurebasedonWordNet）,and3）minimizetherefactoringeffort.Wereporttheresultsofanevaluationofourapproachusingopen-sourceprojects,andweshowthatourproposalisabletoproduceacoherentandusefulsequenceofrecommendedrefactoringsbothintermsofqualitymetricsandfromthedeveloper＇spointsofview.

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简介：在这份报纸，我们在基于社区的问答处理答案检索问题。为了充分捕获在问题答案之间的相互作用，配对，我们建议原来的张肌为在他们之间的关联建模的神经网络。问题和候选人答案独立被嵌进不同潜伏的语义空格，并且3方法张肌然后被利用为在潜伏的语义之间的相互作用建模。适当地初始化网络层，我们建议一个新奇算法打电话降噪张肌autoencoder(DTAE)，然后实现用降噪嵌入张肌层上的层和DTAE的词上的autoencoders(DAE)的layerwisepretraining策略。试验性的结果显示出那我们神经网络超过的张肌有另外的竞争神经网络方法的各种各样的基线，和我们的pretrainingDTAE策略改进系统性能和坚韧性。

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简介：Weintroduceanalmost-automatictechniqueforgenerating3Dcarstylingsurfacemodelsbasedonasingleside-viewimage.Ourapproachcombinesthepriorknowledgeofcarstylinganddeformablecurvenetworkmodeltoobtainanautomaticmodelingprocess.Firstly,wedefinetheconsistentparameterizedcurvetemplatefor2Dand3Dcaserespectivelybyanalyzingthecharacteristiclinesforcarstyling.Then,asemi-automaticextractionfromaside-viewcarimageisadopted.Thirdly,statisticmorphablemodelof3Dcurvenetworkisusedtogettheinitialsolutionwithsparsepointconstraints.Withonlyafewpost-processingoperations,theoptimizedcurvenetworkmodelsforcreatingsurfacesareobtained.Finally,thestylingsurfacesareautomaticallygeneratedusingtemplate-basedparametricsurfacemodelingmethod.Morethan503Dcurvenetworkmodelsareconstructedasthemorphabledatabase.Weshowthatthisintelligentmodelingtoolsimplifiestheexhaustedmodelingtask,andalsodemonstratemeaningfulresultsofourapproach.

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