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简介：Weimproveestimatesforthedistributionofprimitiveλ-rootsofacompositemodulusqyieldinganasymptoticformulaforthenumberofprimitiveλ-rootsinanyintervalIoflength|I|>>q1/2+c.Similarresultsareobtainedforthedistributionoforderedpairs(x,x-1)withxaprimitiveA-root,andforthenumberofprimitiveλ-rootssatisfyinginequalitiessuchas|x-x-1|

简介：Inthispaper,weinvestigateaclassofHamiltoniansystemsarisinginnonlinearcompositemedia.Bydetailedanalysisandcomputationweobtainadecayingestimatesonthesemigroupandprovetheorbitalinstabilityoftwofamiliesofexplicitsolitarywavesolutions(slowfamilyinanisotropiccaseandsolitarywavesinisotropiccase),whichtheoreticallyverifytherelatedguessandnumericalresults.

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简介：Inthispaper,wepresentanonmonotonealgorithmforsolvingnonsmoothcompositeoptimizationproblems.Theobjectivefunctionoftheseproblemsiscompositedbyanonsmoothconvexfunctionandadifferentiablefunction.Themethodgeneratesthesearchdirectionsbysolvingquadraticprogrammingsuccessively,andmakesuseofthenonmonotonelinesearchinsteadoftheusualArmijo-typelinesearch.Globalconvergenceisprovedunderstandardassumptions.Numericalresultsaregiven.

简介：Recently,variableselectionbasedonpenalizedregressionmethodshasreceivedagreatdealofattention,mostlythroughfrequentist'smodels.ThispaperinvestigatesregularizationregressionfromBayesianperspective.OurnewmethodextendstheBayesianLassoregression(ParkandCasella,2008)throughreplacingtheleastsquarelossandLassopenaltybycompositequantilelossfunctionandadaptiveLassopenalty,whichallowsdifferentpenalizationparametersfordifferentregressioncoefficients.BasedontheBayesianhierarchicalmodelframework,anefficientGibbssamplerisderivedtosimulatetheparametersfromposteriordistributions.Furthermore,westudytheBayesiancompositequantileregressionwithadaptivegroupLassopenalty.Thedistinguishingcharacteristicofthenewlyproposedmethodiscompletelydataadaptivewithoutrequiringpriorknowledgeoftheerrordistribution.Extensivesimulationsandtworealdataexamplesareusedtoexaminethegoodperformanceoftheproposedmethod.Allresultsconfirmthatournovelmethodhasbothrobustnessandhighefficiencyandoftenoutperformsotherapproaches.

简介：作者在一个三维的分层的领域考虑热转移问题的一种特定的类型，与边界上以及在接口上的非线性的Stefan-Boltzmann条件。决定边界的未知部分(或腐蚀)由Cauchy，可达到的部分上的数据是在工程的一个重要反的问题。这个问题的数学模型被介绍，前面的问题的well-posedness和反的问题的唯一被获得。关键词反的热问题-Stefan-Boltzmann条件-唯一2000苏布杰克特先生分类35R30-中国(号码07JC14001)的上海自然科学基金会支持的35K20工程，中国的教育部的博士程序基金会，为数学(号码10826105)的Tianyuan资金，国家基本研究程序(号码2005CB321701)和111工程(没有。B08018)。

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简介：Wedevelopadatadrivenmethod(probabilitymodel)toconstructacompositeshapedescriptorbycombiningapairofscale-basedshapedescriptors.Theselectionofapairofscale-basedshapedescriptorsismodeledasthecomputationoftheunionoftwoevents,i.e.,retrievingsimilarshapesbyusingasinglescale-basedshapedescriptor.Thepairofscale-basedshapedescriptorswiththehighestprobabilityformsthecompositeshapedescriptor.Givenashapedatabase,thecompositeshapedescriptorsfortheshapesconstituteaplanarpointset.AVoR-Treeoftheplanarpointsetisthenusedasanindexingstructureforefficientqueryoperation.Experimentsandcomparisonsshowtheeffectivenessandefficiencyoftheproposedcompositeshapedescriptor.