简介：Inthispaper,weconsidertheNeumannboundaryvalueproblemofSchrodingeroperatorwithmeasurepotentia1μ.First,amartingaleformulationoftheNeumannproblemandananalyticcharacterizationofthemartingaleformulationaregiven.Then,byusingtheDirichletformsandStochasticanalysisweobtainanexplicitformulafortheuniqueweaksolotionofthisproblemintermsofreflectingBrownianmotionandit’sboundarylocaltime.

简介：Anewmethodoftreatingmaximumwaveheightasarandomvariableinreliabilityanalysisofbreakwatercaissonsisproposed.Themaximumwaveheightisexpressedasthesignificantwaveheightmultipliedbytheso-calledwaveheightratio.Theproposedwaveheightratioisatypeoftransferfunctionfromthesignificantwaveheighttothemaximumwaveheight.Undertheconditionofabreakingwave,theratioisintrinsicallynonlinear.Therefore,theprobabilitydensityfunctionforthe

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简介：Inthispaperweobtainedgeneralrepresentationformulaeforstronglycontinuouscosineoperatorfunctionsviaprobabilisticapproach,whichincludeWebb’s[1]andShaw’s[2]formulaeandsomenewoneasspecialcases.Wealsogivethequantitativeestimationsforthegeneralformulae.

简介：ＡＳＵＣＣＥＳＳＩＶＥＡＰＰＲＯＸＩＭＡＴＩＯＮＭＥＴＨＯＤＦＯＲＳＯＬＶＩＮＧＰＲＯＢＡＢＩＬＩＳＴＩＣＣＯＮＳＴＲＡＩＮＥＤＰＲＯＧＲＡＭＳＷＡＮＧＪＩＮＤＥ（王金德）（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，ＮａｎｊｉｎｇＵｎｉｖｅｒｓ...

简介：Intheframeworkofgameswithcoalitionstructure,weintroduceprobabilisticOwenvaluewhichisanextensionoftheOwenvalueandprobabilisticShapleyvaluebyconsideringthesituationthatnotallprioriunionsareabletocooperatewithothers.Thenweusefiveaxiomsofprobabilisticefficiency,symmetricwithincoalitions,symmetricacrosscoalitionsapplyingtounanimitygames,strongmonotonepropertyandlinearitytoaxiomatizethevalue.

简介：Statisticalshapepriormodelisemployedtoconstructthedynamicsinprobabilisticcontourestimation.Byapplyingprincipalcomponentanalysis,plausibleshapesamplesareefficientlygeneratedtopredictcontoursamples.Basedontheshape-dependentdynamicsandprobabilisticimagemodel,aparticlefilterisusedtoestimatethecontourwithaspecificshape.Comparedwiththedeterministicapproachwithshapeinformation,theproposedmethodissimpleyetmoreeffectiveinextractingcontoursfromimageswithshapevariationsandocclusion.

简介：Bridgeseismicisolationstrategyisbasedonthereductionofshearforcestransmittedfromthesuperstructuretothepiersbytwomeans:shiftingnaturalperiodandearthquakeinputenergyreductionbydissipationconcentratedinprotectiondevices.Inthispaper,astochasticanalysisofasimpleisolatedbridgemodelfordifferentbridgeanddeviceparametersisconductedtoassesstheefficiencyofthisseismicprotectionstrategy.Toachievethisaim,asimplenonlinearsofteningconstitutivelawisadoptedtomodelawiderangeofisolationdevices,characterizedbyonlythreeessentialmechanicalparameters.Asaconsequenceoftherandomnatureofseismicmotion,aprobabilisticanalysisiscarriedoutandthetimemodulatedKanai-Tajimistochasticprocessisadoptedtorepresenttheseismicaction.TheresponsecovarianceinthestatespaceisobtainedbysolvingtheLyapunovequationforastochasticlinearizedsystem.Afterasensitivityanalysis,thefailureprobabilityreferredtoextremedisplacementandthemeanvalueofdissipatedenergyareassessedbyusingtheintroducedstochasticindicesofseismicbridgeprotectionefficiency.Aparametricanalysisforprotectivedeviceswithdifferentmechanicalparametersisdevelopedforaproperselectionofparametersofisolationdevicesunderdifferentsituations.

简介：一个概括概率的模型在这研究被开发在早期的运动下面预言沉积乘火车，滚动，并且拾起模式。建议模型的新奇是它在它的明确的表达合并床的概率密度功能砍强调，而不是近床的速度变化，到为沉积乘火车上的两流动骚乱和床表面不规则的效果的报道。建议模型在它的明确的表达合并描述床表面不规则的三个参数的集体效果，也就是，相对粗糙，容量的部分和亲戚在活跃的层以内沉积粒子放。模型的另一特色是它为估计电梯提供一个标准并且基于识别联合拖系数提起并且拖对沉积粒子起作用的力量是相互依赖的并且与粒子伸出和收拾行李的密度变化。模型用好、粗糙的沉积的实验室数据被验证并且与以前出版的模型相比。学习结果证明所有检查模型为好沉积数据足够地表演，在沉积粒子有更多的一致阶段和相对粗糙的地方不是一个因素。建议模型特别地适合粗糙的沉积数据，在增加的床不规则被在模型明确的表达介绍的新参数捕获的地方。作为结果，建议模型在粗糙的沉积数据的情况下与另外的模型相比为电梯系数产出更小的预言错误和身体上可接受的价值。

简介：纸为同时的图象分类和注解建议一个新奇概率的生产模型。模型考虑范畴信息能为图象注解提供珍贵信息的事实。一旦一幅图象的范畴被查明，注解词的范围能被缩小，并且产生无关的注解词的概率能被减少。到这个目的，根据班注解图象的想法在模型被介绍。用变化方法，模型的近似推理和参数评价算法被导出，并且为分类并且注解新图象的有效近似也被给。我们的模型的力量在二真实世界数据集上被表明：1600图象LabelMe数据集和1791图象UIUC运动数据集。实验结果证明分类表演与几个最先进的分类模型同样，当注解表演比几个最先进的注解模型的好时。

简介：Gossipingisapopulartechniqueforprobabilisticreliablemulticast(orbroadcast).However,itisoftendifficulttounderstandthebehaviorofgossipingalgorithmsinananalyticfashion.Indeed,existinganalysesofgossipalgorithmsareeitherbasedonsimulationorbasedonideasborrowedfromepidemicmodelswhileinheritingsomefeaturesthatdonotseemtobeappropriateforthesettingofgossiping.Ononehand,inepidemicspreading,aninfectednodetypicallyintendstospreadtheinfectionanunboundednumberoftimes(orrounds);whereasingossiping,aninfectednode(i.e.,anodehavingreceivedthemessageinquestion)mayprefertogossipthemessageaboundednumberoftimes.Ontheotherhand,theoftenassumedhomogeneityinepidemicspreadingmodels(especiallythateverynodehasequalcontacttoeveryoneelseinthepopulation)hasbeensilentlyinheritedinthegossipingliterature,meaningthatanexpensivemembershipprotocolisoftenneededformaintainingnodes'views.Motivatedbytheseobservations,theauthorspresentacharacterizationofapopularclassoffault-tolerantgossipschemes(knownas'push-basedgossiping')basedonanovelprobabilisticmodel,whiletakingtheafore-mentionedfactorsintoconsideration.

简介：Inthispaperusinganargumentfrom[1],weproveoneoftheprobabilisticversionofHardy'sinequality.

简介：Researchersoftensummarizetheirworkintheformofscientificposters.Postersprovideacoherentandefficientwaytoconveycoreideasexpressedinscientificpapers.Generatingagoodscientificposter,however,isacomplexandtime-consumingcognitivetask,sincesuchpostersneedtobereadable,informative,andvisuallyaesthetic.Inthispaper,forthefirsttime,westudythechallengingproblemoflearningtogeneratepostersfromscientificpapers.Tothisend,adata-drivenframework,whichutilizesgraphicalmodels,isproposed.Specifically,givencontenttodisplay,thekeyelementsofagoodposter,includingattributesofeachpanelandarrangementsofgraphicalelements,arelearnedandinferredfromdata.Duringtheinferencestage,themaximumaposterior(MAP)estimationframeworkisemployedtoincorporatesomedesignprinciples.Inordertobridgethegapbetweenpanelattributesandthecompositionwithineachpanel,wealsoproposearecursivepagesplittingalgorithmtogeneratethepanellayoutforaposter.Tolearnandvalidateourmodel,wecollectandreleaseanewbenchmarkdataset,calledNJU-FudanPaper-Posterdataset,whichconsistsofscientificpapersandcorrespondingposterswithexhaustivelylabelledpanelsandattributes.Qualitativeandquantitativeresultsindicatetheeffectivenessofourapproach.

简介：Inmanyapplicationsanddomains,temporalconstraintsbetweenactions,andtheirprobabilitiesplayanimportantrole.Weproposethefirstapproachintheliteraturecopingwithprobabilisticquantitativeconstraints.Toachievesuchachallenginggoal,weextendthewidelyusedsimpletemporalproblem(STP)frameworktoconsiderprobabilities.Specifically,weproposei)aformalrepresentationofprobabilisticquantitativeconstraints,ii)analgorithm,basedontheoperationsofintersectionandcomposition,forthepropagationofsuchtemporalconstraints,andiii)facilitiestosupportqueryansweringonasetofsuchconstraints.Asaresult,weprovideuserswiththefirsthomogeneousmethodsupportingthetreatment(representing,reasoning,andquerying)ofprobabilisticquantitativeconstraints,asrequiredbymanyapplicationsanddomains.

简介：Thispaperaddressesthehighdimensionsampleproblemindiscriminateanalysisundernonparametricandsupervisedassumptions.SincethereisakindofequivalencebetweentheprobabilisticdependencemeasureandtheBayesclassificationerrorprobability,weproposetouseaniterativealgorithmtooptimizethedimensionreductionforclassificationwithaprobabilisticapproachtoachievetheBayesclassifier.TheestimatedprobabilitiesofdifferenterrorsencounteredalongthedifferentphasesofthesystemarerealizedbytheKernelestimatewhichisadjustedinameansofthesmoothingparameter.Experimentresultssuggestthattheproposedapproachperformswell.

简介：ThelevelicethicknessandcompressivestrengthatthefourmeasuringstationsintheLiaodongBayareinferredaccordingtothehydrologicandmeteorologicdatathere,thentheyearlyextremeiceforcesonasolitarypilearecalculatedbytheuseofappropriateformulaoficeforcesanditsprobabilisticdistributionisdetermined.Generally,theyearlyextremeiceforcefollowsWeibulldistributionbestascomparedwithNormal,Lognormal,andExtremeValueIdistribution.Ontheotherhand,theshort-termdistributionoficeforcesonasolitarypileisobtainedfromthemodelexperimentdataanalysis:ItdoesnotrefuseExtremeValueIdistribution.

简介：InthispaperweprovideaprobabilisticapproachtothefollowingDirichletProblem{(∑x4(αijxj)+∑bixi+ξ)u=0,iDu=g,onD,withoutassumingthattheeigenvaluesoftheoperator∑xi(αijxj)+∑bixi+ξwithDirichletboundaryconditionsareallstrictlynegative.TheresultsofthispapergeneralizedthoseofMa.

简介：Inthispaper,aqualitativemodelcheckingalgorithmforverificationofdistributedprobabilisticreal-timesystems(DPRS)ispresented.ThemodelofDPRS,calledreal-timeprobabilisticprocessmodel(RPPM),isovercontinuoustimedomain.ThepropertiesofDPRSaredescribedbyusingdeterministictimedautomata(DTA).Thekeypartinthealgorithmistomapcontinuoustimetofinitetimeintervalswithflagvariables.Comparedwiththeexistingalgorithms,thisalgorithmusesmoregeneraldelaytimeequivalenceclassesinsteadoftheunitdelaytimeequivalenceclassesrestrictedbyeventsequence,andavoidsgeneratingtheequivalenceclassesofstatesonlyduetothepassageoftime.Theresultshowsthatthisalgorithmischeaper.