A study to determine the opinion of expert practitioners of the most important risks in the developmWord文件下载.docx
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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences
Abstract
ADF95isatooltoautomaticallycalculatenumericalfirstderivativesforanymathematicalexpressionasafunctionofuserdefinedindependentvariables.Accuracyofderivativesisachievedwithinmachineprecision.ADF95maybeappliedtoanyFORTRAN77/90/95conformingcodeandrequiresminimalchangesbytheuser.ItprovidesanewderiveddatatypethatholdsthevalueandderivativesandappliesforwarddifferencingbyoverloadingallFORTRANoperatorsandintrinsicfunctions.Anefficientindexingtechniqueleadstoareducedmemoryusageandasubstantiallyincreasedperformancegainoverotheravailabletoolswithoperatoroverloading.Thisgainisespeciallypronouncedforsparsesystemswithlargenumberofindependentvariables.Awideclassofnumericalsimulations,e.g.,thoseemployingimplicitsolvers,canprofitfromADF95.
Programsummary
Titleofprogram:
ADF95
Catalogueidentifier:
ADVI
ProgramsummaryURL:
http:
//cpc.cs.qub.ac.uk/summaries/ADVI
Programobtainablefrom:
CPCProgramLibrary,Queen'
sUniversityofBelfast,N.Ireland
Computerforwhichtheprogramisdesigned:
allplatformswithaFORTRAN95compiler
Programminglanguageused:
FORTRAN95
No.oflinesindistributedprogram,includingtestdata,etc.:
3103
No.ofbytesindistributedprogram,includingtestdata,etc.:
9862
Distributionformat:
tar.gz
Natureofproblem:
Inmanyareasinthecomputationalsciencesfirstorderpartialderivativesforlargeandcomplexsetsofequationsareneededwithmachineprecisionaccuracy.Forexample,anyimplicitorsemi-implicitsolverrequiresthecomputationoftheJacobianmatrix,whichcontainsthefirstderivativeswithrespecttotheindependentvariables.ADF95isasoftwaremoduletofacilitatetheautomaticcomputationofthefirstpartialderivativesofanyarbitrarilycomplexmathematicalFORTRANexpression.Theprogramexploitsthesparsityinheritedbymanysetofequationstherebyenablingfastercomputationscomparedtoalternatedifferentiationtools
Solutionmethod:
AclassisconstructedwhichappliesthechainruleofdifferentiationtoanyFORTRANexpression,tocomputethefirstderivativesbyforwarddifferencing.Anefficientindexingtechniqueleadstoareducedmemoryusageandasubstantiallyincreasedperformancegainwhensparsitycanbeexploited.Fromauserspointofview,onlyminimalchangestohis/heroriginalcodeareneededinordertocomputethefirstderivativesofanyexpressioninthecode
Restrictions:
Processorandmemoryhardwaremayrestrictboththepossiblenumberofindependentvariablesandthecomputationtime
Unusualfeatures:
ADF95canoperateonusercodethatmakesuseofthearrayfeaturesintroducedinFORTRAN90.AconvenientextractionsubroutinefortheJacobianmatrixisalsoprovided
Runningtime:
Inmanyrealisticcases,theevaluationofthefirstorderderivativesofamathematicalexpressionisonlysixtimesslowercomparedtotheevaluationofanalyticallyderivedandhard-codedexpressions.Theactualfactordependsontheunderlyingsetofequationsforwhichderivativesaretobecalculated,thenumberofindependentvariables,thesparsityandontheFORTRAN95compiler
ArticleOutline
1.Introduction
2.FORTRAN90/95concepts
3.Usage
3.1.Afirstexample
3.2.Asecondexample
3.3.Fulldescription
3.4.Specialcases
3.5.Outputverification
4.Implementation
4.1.Userfunctions
4.2.SupportedFORTRAN90/95intrinsics
4.3.Implementationdetailsoftanh
4.4.Limitations
4.5.Undefinedderivatives
5.Tests
5.1.Verifyingthesolution
5.2.Performanceandcompilercomparison
6.Discussion
Acknowledgements
References
TheACGTMasterOntologyanditsapplications–Towardsanontology-drivencancerresearchandmanagementsystem
JournalofBiomedicalInformatics,InPress,CorrectedProof,Availableonline1May2010
MathiasBrochhausen,AndrewD.Spear,CristianCocos,GabrieleWeiler,LuisMartí
n,AlbertoAnguita,HolgerStenzhorn,EvangeliaDaskalaki,FatimaSchera,UlfSchwarz,SteliosSfakianakis,StephanKiefer,MartinDö
rr,NorbertGraf,ManolisTsiknakis
PurchasePDF(1199K)
Objective:
Thispaperintroducestheobjectives,methodsandresultsofontologydevelopmentintheEUco-fundedprojectAdvancingClinico-genomicTrialsonCancer–OpenGridServicesforImprovingMedicalKnowledgeDiscovery(ACGT).Whiletheavailabledatainthelifescienceshasrecentlygrownbothinamountandquality,thefullexploitationofitisbeinghinderedbytheuseofdifferentunderlyingtechnologies,codingsystems,categoryschemesandreportingmethodsonthepartofdifferentresearchgroups.ThegoaloftheACGTprojectistocontributetotheresolutionoftheseproblemsbydevelopinganontology-driven,semanticgridservicesinfrastructurethatwillenableefficientexecutionofdiscovery-drivenscientificworkflowsinthecontextofmulti-centric,post-genomicclinicaltrials.ThefocusofthepresentpaperistheACGTMasterOntology(MO).Methods:
ACGTprojectresearchersundertookasystematicreviewofexistingdomainandupper-levelontologies,aswellasofexistingontologydesignsoftware,implementationmethods,andend-userinterfaces.Thisincludedthecarefulstudyofbestpractices,designprinciplesandevaluationmethodsforontologydesign,maintenance,implementation,andversioning,aswellasforuseonthepartofdomainexpertsandclinicians.Results:
Todate,theresultsoftheACGTprojectinclude(i)thedevelopmentofamasterontology(theACGT-MO)basedonclearlydefinedprinciplesofontologydevelopmentandevaluation;
(ii)thedevelopmentofatechnicalinfrastructure(theACGTPlatform)thatimplementstheACGT-MOutilizingindependenttools,componentsandresourcesthathavebeendevelopedbasedonopenarchitecturalstandards,andwhichincludesanapplicationupdatingandevolvingtheontologyefficientlyinresponsetoend-userneeds;
and(iii)thedevelopmentofanOntology-basedTrialManagementApplication(ObTiMA)thatintegratestheACGT-MOintothedesignprocessofclinicaltrialsinordertoguaranteeautomaticsemanticintegrationwithouttheneedtoperformaseparatemappingprocess.
2.TheACGTMasterOntology
2.1.Technicaldetails
2.2.Scope
2.3.Aim
2.4.TheACGT-MOandsemanticintegrationintheACGTinfrastructure
3.PrinciplesguidingthedevelopmentoftheACGT-MO
3.1.Theadoptionofaradicallyrestrictivedefinitionoftheterm“ontology”,incompliancewiththeprinciplesofrealism
3.2.Enforcingastrictsubsumptionhierarchy,basedonaformallyspecifiedis_arelation,asopposedtoaloose“subclass”hierarchy
3.3.Avoiding(non-trivial)multipleinheritanceinthehierarchyofuniversals
3.4.AvoidingUnknownXandrelatedclasses
3.5.UsinganUpperOntology,namelyBasicFormalOntology
3.6.UsingOBORelationOntology(RO)asasourceof,andinsightfornewrelations/properties
4.MaintenanceoftheACGT-MO
4.1.TheACGTSubmissionSystem
4.2.TheSubmissionProcess
5.EvaluationoftheACGT-MO
5.1.Criteriaofontologyevaluation
5.1.1.Logicalsoundness
5.1.2.Domaincoverage
5.1.3.Taskorientation
5.1.4.Re-useofexistingontologies
5.2.TheroleoftheOBOFoundryintheevaluationoftheACGT-MO
5.3.TheuseoftheACGT-MOoutsidetheACGTproject
6.ExploitationoftheMOintheACGTproject
6.1.SemanticdataintegrationinACGT
6.1.1.Ontologiesindatabaseintegrationsystems–background
6.1.2.SemanticMediation
6.1.3.Queryprocessing
6.1.4.Themappingprocess
6.2.ObTiMA–anOntology-basedTrialManagementApplicationforACGT
6.2.1.Ontology-basedtrialsetup
6.2.2.Ontology-baseddataintegrationforcross-trialanalysis
6.2.3.OntologyevolutioninObTiMA
6.2.4.Advantagesofontologyintegration
7.Discussion
7.1.SemanticMediationinACGT
7.2.ComparisonoftheACGTstrategywiththecaBIGapproach
7.2.1.OverviewofthecaBIGdataintegrationplatform
7.2.2.caBIGvsACGT–theproblemofmetadata
8.Conclusion
9.Summarytable
9.1.Whatwasalreadyknownonthetopic
9.2.Whatthestudyaddedtoourknowledge
First-principlescomputationofmaterialproperties:
theABINITsoftwareproject
ComputationalMaterialsScience,Volume25,Issue3,November2002,Pages478-492
X.Gonze,J.-M.Beuken,R.Caracas,F.Detraux,M.Fuchs,G.-M.Rignanese,L.Sindic,M.Verstraete,G.Zerah,F.Jollet,M.Torrent,A.Roy,M.Mikami,Ph.Ghosez,J.-Y.Raty,D.C.Allan
Showpreview
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Thedensityfunctionaltheory(DFT)computationofelectronicstructure,totalenergyandotherpropertiesofmaterials,isafieldinconstantprogress.Inordertostayattheforefrontofknowledge,aDFTsoftwareprojectcanbenefitenormouslyfromwidesp
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