简介:象在1975一样早,Shamos和Hoey首先给了O(nlgn)-timedivide-and-conquer算法(嘘算法在短)为发现点的最靠近的对的问题。在联合的一个过程,在一些点需要被计算的3n之间的欧几里得距离,计算距离的全面复杂性当时因此是3nlgn。因为距离的计算与另外的基本操作相比是更昂贵的,怎么嘘改善从计算距离的复杂性的方面的算法被考虑。在1998,周,熊和朱由把这复杂性归结为2nlgn改进了SHalgorithm。在这篇论文,我们做进一步的改进。计算距离的Theoverall复杂性被归结为(3nlgn)/2,它仅仅是一半SHalgorithm的。
简介:<正>夏新LC47V1P具有1920×1080的全高清分辨率,6000:1的高对比度,智能背光技术,画面清晰,色彩还原真实。此外,还具备4组HDMI高清接口,接口兼容性出色,BT院线,功能强大。夏新LC47V1P的参数:屏幕尺寸:47英寸屏幕比例:16:9最佳分辨率:1920×1080亮度:800cd/m~2对比度6000:1(动态)响应时间:4ms垂直可视角度:178°水平可视角度178°输入端子:TV端子×1、AV端子×2、S端子×1、VGA输入×1、HDMI输入×4、USB输入×2其他性能:智能背光、静像功能、无风扇设计、游戏模式、主题变换、频道查找
简介:Curvemodelingisoneofthebasicworkincomputeraidedgeometricdesignandcomputergraphics.Fortheimplicitconicfittingprobleminthispaper,theresearchmethodsthattheobjectivefunctionbasedontheminimalalgebraicdistanceandgeometricdistancearesummarized.Theadvantagesanddisadvantagesofeverymethodareanalyzedsimply,andtheapplicationsoftheconicfittingarelisted.
简介:Thewidespreadoflocation-basedsocialnetworksbringsaboutahugevolumeofusercheck-indata,whichfacilitatestherecommendationofpointsofinterest(POIs).Recentadvancesondistributedrepresentationshedlightonlearninglowdimensionaldensevectorstoalleviatethedatasparsityproblem.CurrentstudiesonrepresentationlearningforPOIrecommendationembedbothusersandPOIsinacommonlatentspace,andusers'preferenceisinferredbasedonthedistance/similaritybetweenauserandaPOI.SuchanapproachisnotinaccordancewiththesemanticsofusersandPOIsastheyareinherentlydifferentobjects.Inthispaper,wepresentanoveltranslation-based,timeandlocationaware(TransTL)representation,whichmodelsthespatialandtemporalinformationasarelationshipconnectingusersandPOIs.Ourmodelgeneralizestherecentadvancesinknowledgegraphembedding.Thebasicideaisthattheembeddingofa〈time,location〉paircorrespondstoatranslationfromembeddingsofuserstoPOIs.SincethePOIembeddingshouldbeclosetotheuserembeddingplustherelationshipvector,therecommendationcanbeperformedbyselectingthetop-kPOIssimilartothetranslatedPOI,whichareallofthesametypeofobjects.Weconductextensiveexperimentsontworeal-worlddata.sets.TheresultsdemonstratethatourTransTLmodelachievesthestate-of-the-artperformance.Itisalsomuchmorerobusttodatasparsitythanthebaselines.
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