简介：Thispaperthoroughlyinvestigatestheproblemofrobotself-locationbylinecorrespondences.Theoriginalcontributionsarethree-fold:(1)Obtainthenecessaryandsufficientconditiontodeterminelinearlytherobot'sposebytwolinecorrespondences.(2)Showthatifthespacelinesareverticalones,itisimpossibletodeterminelinearlytherobot'sposenomatterhowmanylinecorrespondenceswehave,andtheminimumnumberoflinecorrespondencesis3todetermineuniquely(butnon-linearly)therobot'spose.(3)Showthatifthespacelinesarehorizontalones,theminimumnumberoflinecorrespondencesis3forlineardeterminationand2fornon-lineardeterminationoftherobot'spose.

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.