/LMAR=0/XLINE=3/FONT#0=NONM/FONT#1=BAXM30/FONT#2=MATH30/FONT#3=FIX25/FONT#4=GRKL30
␈↓ ↓N␈↓α␈↓ W1


␈↓ ↓N␈↓␈↓ α	MOST RECENT CS REPORTS - 1979               ␈↓ π∞␈↓unknowns␈α≤are␈α≤essentially␈α≤the␈α≠unknown
                                            ␈↓ π∞␈↓"accessory␈α↔parameters"␈α⊗z␈↓#vk␈↓#,␈α↔and␈α⊗solving
␈↓ ↓N␈↓No. 3␈↓ ¬E(month)                                ␈↓ π∞␈↓this system with a standard subroutine.

␈↓ ↓N␈↓    Listed␈α∞below␈α∞are␈α
abstracts␈α∞of␈α∞the␈α
most        ␈↓ π∞␈↓    New␈α⊃features␈α⊃of␈α⊂this␈α⊃work␈α⊃include␈α⊂the
␈↓ ↓N␈↓recent␈α∂reports␈α∞published␈α∂by␈α∂the␈α∞Computer         ␈↓ π∞␈↓evaluation␈α≠of␈α~integrals␈α≠within␈α≠the␈α~disk
␈↓ ↓N␈↓Science Department of Stanford University.  ␈↓ π∞␈↓rather␈α↔than␈α↔along␈α↔the␈α↔boundary,␈α⊗making
                                            ␈↓ π∞␈↓possible␈α"the␈α!treatment␈α"of␈α!unbounded
␈↓ ↓N␈↓    TO␈α REQUEST␈α REPORTS:␈α  Check␈α the              ␈↓ π∞␈↓polygons;␈α⊂the␈α⊂use␈α⊂of␈α⊂a␈α⊂compound␈α⊂form␈α∂of
␈↓ ↓N␈↓appropriate␈α⊂places␈α⊂on␈α⊂the␈α⊂enclosed␈α⊂order         ␈↓ π∞␈↓Gauss-Jacobi␈α∩quadrature␈α∩to␈α∩evaluate␈α∩the
␈↓ ↓N␈↓form,␈αand␈αreturn␈αthe␈αentire␈αorder␈αform␈α
page        ␈↓ π∞␈↓Schwarz-Christoffel␈α=integral,␈α=making
␈↓ ↓N␈↓(including␈α
mailing␈α
label)␈α
by␈α
June␈α
29,␈α1979.       ␈↓ π∞␈↓possible␈αhigh␈αaccuracy␈αat␈αreasonable␈αcost;
␈↓ ↓N␈↓In␈α∞many␈α∞cases␈α∞we␈α∞can␈α∞print␈α∞only␈α∞a␈α∞limited           ␈↓ π∞␈↓and␈α∪the␈α∪elimination␈α∪of␈α∪constraints␈α∀in␈α∪the
␈↓ ↓N␈↓number␈α⊗of␈α∃copies,␈α⊗and␈α∃requests␈α⊗will␈α∃be            ␈↓ π∞␈↓nonlinear␈α∀system␈α∀by␈α∪a␈α∀simple␈α∀change␈α∪of
␈↓ ↓N␈↓filled␈α
on␈α
a␈α
first␈α
come,␈α
first␈α
serve␈α
basis.␈α If      ␈↓ π∞␈↓variables.
␈↓ ↓N␈↓the␈α∞code␈α∞(FREE)␈α∞is␈α∞printed␈α∞on␈α∞your␈α
mailing
␈↓ ↓N␈↓label,␈αyou␈α
will␈αnot␈α
be␈αcharged␈αfor␈α
hardcopy.       ␈↓ π∞␈↓    Application␈α⊗of␈α⊗the␈α∃Schwarz-Christoffel
␈↓ ↓N␈↓This␈α_exemption␈α↔from␈α_payment␈α_is␈α↔limited           ␈↓ π∞␈↓transformation␈α$may␈α$lead␈α$to␈α$practical
␈↓ ↓N␈↓primarily␈α⊗to␈α⊗libraries.␈α⊗ (The␈α⊗costs␈α∃shown        ␈↓ π∞␈↓methods␈α∨for␈α≡solving␈α∨the␈α∨Laplace␈α≡and
␈↓ ↓N␈↓include␈αall␈αapplicable␈αsales␈α
taxes.␈α PLEASE      ␈↓ π∞␈↓Poisson␈α!equations␈α!accurately␈α!in␈α two-
␈↓ ↓N␈↓SEND␈αNO␈αMONEY␈αNOW,␈αWAIT␈αUNTIL␈αYOU␈αGET              ␈↓ π∞␈↓cimensional␈α problems␈α with␈α irregular␈α∨or
␈↓ ↓N␈↓AN INVOICE.)                                ␈↓ π∞␈↓unbounded␈α~(but␈α~not␈α~curved␈α≠or␈α~multiply
                                            ␈↓ π∞␈↓connected)␈α!geometrices.␈α! Computational
␈↓ ↓N␈↓    ALTERNATIVELY:␈α2 Copies␈α2of␈α2most             ␈↓ π∞␈↓examples␈α(are␈α(presented.␈α( The␈α'time
␈↓ ↓N␈↓Stanford␈α⊂CS␈α⊂Reports␈α⊂may␈α⊂be␈α⊃obtained␈α⊂by            ␈↓ π∞␈↓required␈α⊃to␈α⊃solve␈α⊃the␈α⊃mapping␈α∩problem␈α⊃is
␈↓ ↓N␈↓writing␈α (about␈α 2␈α months␈α!after␈α MOST               ␈↓ π∞␈↓roughly␈α∞proportional␈α∞to␈α∞N␈↓#
3␈↓#,␈α∞where␈α∞N␈α∂is␈α∞the
␈↓ ↓N␈↓RECENT␈α⊃CS␈α⊃REPORTS␈α⊃listing)␈α⊃to␈α⊂NATIONAL           ␈↓ π∞␈↓number␈α→of␈α→vertices␈α→of␈α→the␈α→polygon.␈α→ A
␈↓ ↓N␈↓TECHNICAL␈α_INFORMATION␈α→SERVICE,␈α_5285            ␈↓ π∞␈↓typical␈α_set␈α_of␈α_computations␈α_to␈α_8-place
␈↓ ↓N␈↓Port␈α$Royal␈α$Road,␈α%Springfield,␈α$Virginia          ␈↓ π∞␈↓accuracy␈αwith␈αN␈α␈↓α␈↓β≤␈↓α␈↓␈α10␈αtakes␈α1-10␈αseconds
␈↓ ↓N␈↓22161.␈α) Stanford␈α)Ph.D.␈α)theses␈α)are               ␈↓ π∞␈↓on an IBM 370/168.
␈↓ ↓N␈↓available␈α≥from␈α≥UNIVERSITY␈α≤MICROFILMS,
␈↓ ↓N␈↓300␈α∞North␈α∞Zeeb␈α
Road,␈α∞Ann␈α∞Arbor,␈α
Michigan          ␈↓ π∞␈↓STAN-CS-78-711
␈↓ ↓N␈↓48106.                                      ␈↓ π∞␈↓    VERSION SPACES: AN APPROACH TO
␈↓ ↓N␈↓--␈↓ ε∩--                                        ␈↓ π∞␈↓CONCEPT LEARNING
␈↓ ↓N␈↓--␈↓ ε∩--                                        ␈↓ π∞␈↓Author:  Tom Michael Mitchell␈↓ 
|(Thesis)
␈↓ ↓N␈↓STAN-CS-79-710                              ␈↓ π∞␈↓216 pages␈↓ 
∀Microfiche only.
␈↓ ↓N␈↓    NUMERICAL COMPUTATION OF THE
␈↓ ↓N␈↓SCHWARZ-CHRISTOFFEL TRANSFORMATION          ␈↓ π∞␈↓    ABSTRACT:␈α⊃ A␈α⊃method␈α⊃is␈α⊃presented␈α⊂for
␈↓ ↓N␈↓Author:  Lloyd Trefethen                    ␈↓ π∞␈↓learning␈α⊃general␈α⊃descriptions␈α⊃of␈α⊃concepts
␈↓ ↓N␈↓42 pages␈↓ ¬εCost: $2.90                         ␈↓ π∞␈↓from␈α∞a␈α∞sequence␈α∞of␈α∞positive␈α∂and␈α∞negative
                                            ␈↓ π∞␈↓training␈α∃instances.␈α∀ This␈α∃method␈α∀involves
␈↓ ↓N␈↓    ABSTRACT:␈α≡ A␈α≥program␈α≡is␈α≥described           ␈↓ π∞␈↓examining␈α#a␈α#predetermined␈α#space␈α#or
␈↓ ↓N␈↓which␈α;computes␈α:Schwarz-Christoffel            ␈↓ π∞␈↓language␈α
of␈αpossible␈α
concept␈αdescriptions,
␈↓ ↓N␈↓transformations␈α→that␈α~map␈α→the␈α~unit␈α→disk           ␈↓ π∞␈↓finding␈αthose␈α
which␈αare␈α
consistent␈αwith␈α
the
␈↓ ↓N␈↓conformally␈α∞onto␈α∞the␈α∞interior␈α∞of␈α∂a␈α∞bounded        ␈↓ π∞␈↓observed␈α⊃training␈α⊃instances.␈α∩ Rather␈α⊃than
␈↓ ↓N␈↓or␈α≤unbounded␈α≥polygon␈α≤in␈α≥the␈α≤complex              ␈↓ π∞␈↓use␈α)heuristic␈α(search␈α)techniques␈α(to
␈↓ ↓N␈↓plane.␈α∞ The␈α∞inverse␈α∞map␈α∞is␈α∂also␈α∞computed.         ␈↓ π∞␈↓examine␈α∀this␈α∀concept␈α∃description␈α∀space,
␈↓ ↓N␈↓The␈α∩computational␈α∩problem␈α∪is␈α∩approached         ␈↓ π∞␈↓the␈α subspace␈α (version␈α space)␈α of␈α∨all
␈↓ ↓N␈↓by␈α∪setting␈α∪up␈α∪a␈α∪nonlinear␈α∪system␈α∩whose            ␈↓ π∞␈↓plausible␈α<concept␈α<descriptions␈α<is
␈↓ ↓N␈↓α␈↓ V2


␈↓ ↓N␈↓represented␈α%and␈α$updated␈α%with␈α$each               ␈↓ π∞␈↓was␈α∩completely␈α∩revised␈α⊃and␈α∩put␈α∩into␈α⊃the
␈↓ ↓N␈↓training␈α instance.␈α∨ This␈α version␈α∨space          ␈↓ π∞␈↓TEX␈α=typesetting␈α>language;␈α=since
␈↓ ↓N␈↓approach␈α=determines␈α=all␈α<concept                ␈↓ π∞␈↓publication␈α∩of␈α⊃this␈α∩new␈α⊃edition␈α∩is␈α∩not␈α⊃far
␈↓ ↓N␈↓descriptions␈α∃consistent␈α∃with␈α⊗the␈α∃training       ␈↓ π∞␈↓off,␈α∃no␈α∃changes␈α∃to␈α∃Volume␈α∃2␈α∃are␈α∀listed
␈↓ ↓N␈↓instances,␈α4without␈α4backtracking␈α3to             ␈↓ π∞␈↓here.
␈↓ ↓N␈↓reexamine␈α"past␈α"training␈α"instances␈α"or
␈↓ ↓N␈↓previously rejected concept descriptions.   ␈↓ π∞␈↓STAN-CS-79-713
                                            ␈↓ π∞␈↓    A HESSENBERG-SCHUR METHOD FOR THE
␈↓ ↓N␈↓    The␈α?␈αβcomputed␈α?␈αβversion␈α?␈ααspace                 ␈↓ π∞␈↓PROBLEM AX + XB = C
␈↓ ↓N␈↓summarizes␈α the␈α∨information␈α within␈α∨the           ␈↓ π∞␈↓Authors:  Gene Golub, Stephen Nash &
␈↓ ↓N␈↓training␈α⊃instances␈α⊃concerning␈α⊃the␈α⊂identity      ␈↓ π∞␈↓Charles Van Loan
␈↓ ↓N␈↓of␈α↔the␈α⊗concept␈α↔to␈α⊗be␈α↔learned.␈α⊗ Version            ␈↓ π∞␈↓50 pages␈↓ 
∀Microfiche only.
␈↓ ↓N␈↓spaces␈α⊗are␈α⊗therefore␈α⊗useful␈α↔for␈α⊗making
␈↓ ↓N␈↓reliable␈αclassifications␈αbased␈αupon␈αpartially   ␈↓ π∞␈↓    ABSTRACT:␈α⊃ One␈α⊂of␈α⊃the␈α⊃most␈α⊂effective
␈↓ ↓N␈↓learned␈α$concepts,␈α$and␈α%for␈α$proposing             ␈↓ π∞␈↓methods␈αfor␈αsolving␈αthe␈αmatrix␈αequation␈αAX
␈↓ ↓N␈↓informative␈α!new␈α!training␈α!instances␈α to           ␈↓ π∞␈↓+␈α
XB␈α∞=␈α
C␈α∞is␈α
the␈α∞Bartels-Stewart␈α
algorithm.
␈↓ ↓N␈↓direct␈α further␈α∨learning.␈α  The␈α uses␈α∨of            ␈↓ π∞␈↓Key␈α∃to␈α∃this␈α∃technique␈α∃is␈α∃the␈α∃orthogonal
␈↓ ↓N␈↓version␈αspaces␈α
for␈αdetecting␈α
inconsistency      ␈↓ π∞␈↓reduction␈α∃of␈α∃A␈α∃and␈α∃B␈α∃to␈α∃triangular␈α∃form
␈↓ ↓N␈↓in␈α
the␈α
training␈αinstances,␈α
and␈α
for␈αlearning␈α
in     ␈↓ π∞␈↓using␈α∂the␈α∂QR␈α∞algorithm␈α∂for␈α∂eigenvalues.␈α∞ A
␈↓ ↓N␈↓the␈α⊗presence␈α⊗of␈α⊗inconsistency␈α⊗are␈α⊗also           ␈↓ π∞␈↓new␈α⊗method␈α⊗is␈α⊗proposed␈α⊗which␈α⊗differes
␈↓ ↓N␈↓described.                                  ␈↓ π∞␈↓from␈α∂the␈α∂Bartels-Stewart␈α∂algorithm␈α∂in␈α∞that
                                            ␈↓ π∞␈↓A␈αis␈αonly␈αreduced␈αto␈αHessenberg␈αform.␈α
 The
␈↓ ↓N␈↓    Proofs␈α
are␈α
given␈α
for␈α
the␈α
correctness␈αof       ␈↓ π∞␈↓resulting␈α⊂algorithm␈α∂is␈α⊂between␈α∂30␈α⊂and␈α∂70
␈↓ ↓N␈↓the␈α$method␈α$for␈α%representing␈α$version             ␈↓ π∞␈↓percent␈α(faster␈α(depending␈α(upon␈α'the
␈↓ ↓N␈↓spaces,␈α⊗and␈α⊗of␈α⊗the␈α⊗associated␈α∃concept            ␈↓ π∞␈↓dimensions␈α⊂of␈α⊂the␈α⊂matrices␈α⊂A␈α⊂and␈α⊂B.␈α⊂ The
␈↓ ↓N␈↓learning␈α$algorithm,␈α$for␈α$any␈α#countably           ␈↓ π∞␈↓stability␈α0of␈α1the␈α0new␈α1method␈α0is
␈↓ ↓N␈↓infinite␈α&concept␈α&description␈α%language.         ␈↓ π∞␈↓demonstrated␈α≤through␈α≤a␈α≥roundoff␈α≤error
␈↓ ↓N␈↓Empirical␈α∃results␈α∃obtained␈α∃from␈α∀computer        ␈↓ π∞␈↓analysis␈αand␈αsupported␈αby␈αnumerical␈αtests.
␈↓ ↓N␈↓implementations␈α'in␈α'two␈α(domains␈α'are              ␈↓ π∞␈↓Finally,␈α⊗it␈α⊗is␈α∃shown␈α⊗how␈α⊗the␈α∃techniques
␈↓ ↓N␈↓presented.␈α∃ The␈α∀version␈α∃space␈α∀approach          ␈↓ π∞␈↓described␈α∂can␈α∂be␈α∂applied␈α∂and␈α∞generalized
␈↓ ↓N␈↓has␈α⊃been␈α⊃implemented␈α⊃as␈α∩one␈α⊃component            ␈↓ π∞␈↓to other matrix equation problems.
␈↓ ↓N␈↓of␈α∞the␈α∞Meta-DENDRAL␈α∞program␈α∞for␈α
learning
␈↓ ↓N␈↓production␈α∞rules␈α∞in␈α∞the␈α∞domain␈α∞of␈α∞chemical        ␈↓ π∞␈↓AIM-322
␈↓ ↓N␈↓spectroscopy.␈α∃ Its␈α∃implementation␈α∃in␈α∃this       ␈↓ π∞␈↓    A FRAMEWORK FOR CONTROL IN
␈↓ ↓N␈↓program is described in detail.             ␈↓ π∞␈↓PRODUCTION SYSTEMS
                                            ␈↓ π∞␈↓Author:  Michael Georgeff
␈↓ ↓N␈↓STAN-CS-79-712                              ␈↓ π∞␈↓35 pages␈↓ 
FCost: $2.70
␈↓ ↓N␈↓    THE ERRATA OF COMPUTER
␈↓ ↓N␈↓PROGRAMMING                                 ␈↓ π∞␈↓    Abstract:␈α5 A␈α4formal␈α5model␈α4for
␈↓ ↓N␈↓Author:  Donald E. Knuth                    ␈↓ π∞␈↓representing␈α
control␈α
in␈α
production␈α
systems
␈↓ ↓N␈↓58 pages␈↓ ¬εCost: $3.35                         ␈↓ π∞␈↓is␈αdefined.␈α
 The␈αformalism␈αallows␈α
control␈αto
                                            ␈↓ π∞␈↓be␈α∞directly␈α
specified␈α∞independently␈α∞of␈α
the
␈↓ ↓N␈↓    ABSTRACT:␈α, This␈α,report␈α,lists␈α,all            ␈↓ π∞␈↓conflict␈αresolution␈αscheme,␈αand␈αthus␈αallows
␈↓ ↓N␈↓corrections␈α
and␈α
changes␈αof␈α
Volumes␈α
1␈αand          ␈↓ π∞␈↓the␈α
issues␈α
of␈α
control␈α
and␈α
nondeterminism␈α
to
␈↓ ↓N␈↓3␈α
of␈α
␈↓↓The␈α
Art␈α
of␈α
Computer␈α
Programming␈↓,␈α
as␈α
of         ␈↓ π∞␈↓be␈α→treated␈α→separately.␈α→ Unlike␈α→previous
␈↓ ↓N␈↓January␈α!5,␈α 1979.␈α! This␈α!updates␈α the               ␈↓ π∞␈↓approaches,␈α#it␈α#allows␈α#control␈α#to␈α#be
␈↓ ↓N␈↓previous␈α∞list␈α
in␈α∞report␈α
CS-551,␈α∞May␈α
1976.         ␈↓ π∞␈↓examined␈α⊂within␈α⊃a␈α⊂uniform␈α⊃and␈α⊂consistent
␈↓ ↓N␈↓The␈α∂second␈α∂edition␈α∂of␈α∂Volume␈α∂2␈α∂has␈α∞been            ␈↓ π∞␈↓framework.
␈↓ ↓N␈↓delayed␈α∞two␈α
years␈α∞due␈α
to␈α∞the␈α
fact␈α∞that␈α
it
␈↓ ↓N␈↓α␈↓ U3


␈↓ ↓N␈↓    It␈α
is␈α
shown␈α
that␈α
the␈α
formalism␈α
provides␈α
a      ␈↓ π∞␈↓operation␈α*at␈α*the␈α+Stanford␈α*Artificial
␈↓ ↓N␈↓basis␈α∪for␈α∪implementing␈α∪control␈α∩constructs       ␈↓ π∞␈↓Intelligence␈α∀Laboratory,␈α∀and␈α∀teaches␈α∪the
␈↓ ↓N␈↓which,␈α⊃unlike␈α∩existing␈α⊃schemes,␈α∩retain␈α⊃all       ␈↓ π∞␈↓reader␈αhow␈αto␈αuse␈αit.␈α The␈αsystem␈αconsists
␈↓ ↓N␈↓the␈α_properties␈α→desired␈α_of␈α→a␈α_knowledge            ␈↓ π∞␈↓of␈α∩AL,␈α∩a␈α∩high-level␈α∩programming␈α∩language
␈↓ ↓N␈↓based␈α_system␈α↔---␈α_modularity,␈α↔flexibility,       ␈↓ π∞␈↓for␈α⊃manipulator␈α⊂control␈α⊃useful␈α⊃in␈α⊂industrial
␈↓ ↓N␈↓extensibility␈α≡and␈α≡explanatory␈α≥capacity.        ␈↓ π∞␈↓assembly␈α∞research;␈α
POINTY,␈α∞an␈α
interactive
␈↓ ↓N␈↓Most␈α∪importantly,␈α∪it␈α∪is␈α∪shown␈α∪that␈α∪these          ␈↓ π∞␈↓system␈α↔for␈α↔specifying␈α↔representation␈α↔of
␈↓ ↓N␈↓properties␈αare␈αnot␈αa␈αfunction␈αof␈αthe␈αlack␈α
of        ␈↓ π∞␈↓parts;␈α∂and␈α∂ALAID,␈α∂an␈α∂interactive␈α∞debugger
␈↓ ↓N␈↓control␈α→constrains,␈α→but␈α→of␈α→the␈α→type␈α→of            ␈↓ π∞␈↓for AL.
␈↓ ↓N␈↓information␈α≡allowed␈α≥to␈α≡establish␈α≥these
␈↓ ↓N␈↓constraints.                                ␈↓ π∞␈↓STAN-CS-79-719
                                            ␈↓ π∞␈↓    EXTRAPOLATION OF ASYMPTOTIC
␈↓ ↓N␈↓    Within␈α⊂the␈α⊂formalism␈α⊂it␈α⊂is␈α⊃also␈α⊂possible      ␈↓ π∞␈↓EXPANSIONS BY A MODIFIED AITKEN
␈↓ ↓N␈↓to␈α
provide␈α
a␈α
meaningful␈α
notion␈α
of␈αthe␈α
power        ␈↓ π∞␈↓␈↓∧d␈↓␈↓#
2␈↓#-FORMULA
␈↓ ↓N␈↓of␈α∃control␈α⊗constructs.␈α∃ This␈α⊗enables␈α∃the         ␈↓ π∞␈↓Authors:  Petter Bj␈↓αO␈↓rstad,
␈↓ ↓N␈↓types␈α_of␈α→control␈α_required␈α→in␈α_production          ␈↓ π∞␈↓Germund Dahlquist & Eric Grosse
␈↓ ↓N␈↓systems␈α
to␈α
be␈α
examined␈α
and␈α∞the␈α
capacity           ␈↓ π∞␈↓54 pages␈↓ 
∀Microfiche only.
␈↓ ↓N␈↓of␈α$various␈α$schemes␈α$to␈α%meet␈α$these
␈↓ ↓N␈↓requirements to be determined.              ␈↓ π∞␈↓    Abstract:␈α≤ A␈α≤modified␈α≤Aitken␈α≠formula
                                            ␈↓ π∞␈↓permits␈α=iterted␈α=extrapolations␈α<to
␈↓ ↓N␈↓    Schemes␈α:for␈α;improving␈α:system               ␈↓ π∞␈↓efficiently␈α∀estimate␈α∀␈↓∧d␈↓β␈↓#v∞␈↓#␈↓␈α∀from␈α∀␈↓∧d␈↓↓␈↓#vn␈↓#␈↓␈α∀when␈α∪an
␈↓ ↓N␈↓efficiency␈α≠and␈α≠resolving␈α~nondeterminism        ␈↓ π∞␈↓asymptotic expansion
␈↓ ↓N␈↓are␈α8examined,␈α7and␈α8devices␈α7for
␈↓ ↓N␈↓representing␈α↔such␈α↔meta-level␈α↔knowledge         ␈↓ π∞␈↓␈↓ π5␈↓∧d␈↓↓␈↓#vn␈↓# = ␈↓∧d␈↓β␈↓#v∞␈↓#␈↓↓ + n␈↓#
-k␈↓#(c␈↓#v0␈↓# + c␈↓#v1␈↓#n␈↓#
-1␈↓# + c␈↓#v2␈↓#n␈↓#
-2␈↓# + ...)␈↓
␈↓ ↓N␈↓are␈α1described.␈α1 In␈α1particular,␈α0the
␈↓ ↓N␈↓objectification␈α≤of␈α≠control␈α≤information␈α≠is       ␈↓ π∞␈↓holds␈α∩for␈α⊃some␈α∩(unknown)␈α∩coefficients␈α⊃␈↓↓c␈↓#vj␈↓#␈↓.
␈↓ ↓N␈↓shown␈α∀to␈α∃provide␈α∀a␈α∀better␈α∃paradigm␈α∀for            ␈↓ π∞␈↓We␈αstudy␈αthe␈αtruncation␈αand␈αirregular␈αerror
␈↓ ↓N␈↓problem␈α≥solving␈α≥and␈α≥for␈α≡talking␈α≥about            ␈↓ π∞␈↓and␈α∂compare␈α∂the␈α∞method␈α∂with␈α∂other␈α∞forms
␈↓ ↓N␈↓problem␈α∞solving.␈α∞ It␈α∞is␈α∞also␈α∞shown␈α∞that␈α
the        ␈↓ π∞␈↓of extrapolation.
␈↓ ↓N␈↓notion␈α∀of␈α∪control␈α∀provides␈α∪a␈α∀basis␈α∀for␈α∪a
␈↓ ↓N␈↓theory␈α≥of␈α≥transformation␈α≡of␈α≥production          ␈↓ π∞␈↓STAN-CS-79-720
␈↓ ↓N␈↓systems,␈α∂and␈α∂that␈α∂this␈α∂provides␈α⊂a␈α∂uniform         ␈↓ π∞␈↓    ON GRID OPTIMIZATION FOR BOUNDARY
␈↓ ↓N␈↓and␈α!consistent␈α!approach␈α"to␈α!problems             ␈↓ π∞␈↓VALUE PROBLEMS
␈↓ ↓N␈↓involving subgoal protection.               ␈↓ π∞␈↓Author:  R. Glowinski
                                            ␈↓ π∞␈↓22 pages␈↓ 
∀Microfiche only.
␈↓ ↓N␈↓STAN-CS-79-717
␈↓ ↓N␈↓                                            ␈↓ π∞␈↓    Abstract:␈α We␈α
discuss␈αin␈αthis␈α
report␈αthe
␈↓ ↓N␈↓Authors:                                    ␈↓ π∞␈↓numerical␈α
procedures␈αwhich␈α
can␈α
be␈αused␈α
to
␈↓ ↓N␈↓   pages␈↓ ¬GCost: $                             ␈↓ π∞␈↓obtain␈α∂the␈α⊂optimal␈α∂grid␈α⊂when␈α∂solving␈α⊂by␈α∂a
                                            ␈↓ π∞␈↓finite␈α∃element␈α∀method␈α∃a␈α∃model␈α∀boundary
␈↓ ↓N␈↓    Abstract:                               ␈↓ π∞␈↓value␈αproblem␈αof␈αelliptic␈αtype␈αmodelling␈αthe
                                            ␈↓ π∞␈↓potential␈αflow␈αof␈αan␈αincompressible␈αinviscid
␈↓ ↓N␈↓AIM-323                                     ␈↓ π∞␈↓fluid.␈α
 Results␈α
of␈α
numerical␈αexperiments␈α
are
␈↓ ↓N␈↓    AL USERS' MANUAL                        ␈↓ π∞␈↓presented.
␈↓ ↓N␈↓Authors:  Shahid Mujtaba & Ron Goldman
␈↓ ↓N␈↓136 pages␈↓ ¬εCost: $5.55                        ␈↓ π∞␈↓STAN-CS-79-721
                                            ␈↓ π∞␈↓    ON FAULT-TOLERANT NETWORKS FOR
␈↓ ↓N␈↓    Abstract:␈α∞ This␈α∞document␈α∂describes␈α∞the      ␈↓ π∞␈↓SORTING
␈↓ ↓N␈↓current␈α∃state␈α∃of␈α∃the␈α∃AL␈α∃system␈α⊗now␈α∃in              ␈↓ π∞␈↓Authors:  Andrew C. Yao & F. Frances Yao
                                            ␈↓ π∞␈↓20 pages␈↓ 
FCost: $2.30
␈↓ ↓N␈↓α␈↓ W4


␈↓ ↓N␈↓    Abstract:␈α→ The␈α→study␈α→of␈α_constructing        ␈↓ π∞␈↓overlapping␈α∞data␈α∞models,␈α
or␈α∞user␈α∞views,␈α
is
␈↓ ↓N␈↓reliable␈α?␈ααsystems␈α?␈αβfrom␈α?␈ααunreliable               ␈↓ π∞␈↓associated␈α∞with␈α∂the␈α∞model.␈α∂ The␈α∞database
␈↓ ↓N␈↓components␈α
goes␈α
back␈α
to␈α
the␈α
work␈α∞of␈α
von            ␈↓ π∞␈↓model,␈α%or␈α%conceptual␈α&schema,␈α%which
␈↓ ↓N␈↓Neumann,␈α∞and␈α
of␈α∞Moore␈α
and␈α∞Shannon.␈α
 The           ␈↓ π∞␈↓represents␈α∩the␈α⊃integrated␈α∩database,␈α⊃may
␈↓ ↓N␈↓present␈α'paper␈α'studies␈α'the␈α'use␈α&of                 ␈↓ π∞␈↓thus␈α∪be␈α∩derived␈α∪from␈α∩the␈α∪individual␈α∩data
␈↓ ↓N␈↓redundancy␈α"to␈α!enhance␈α"reliability␈α!for           ␈↓ π∞␈↓models␈α
of␈α∞the␈α
users.␈α∞ We␈α
believe␈α∞that␈α
the
␈↓ ↓N␈↓sorting␈α⊗and␈α⊗related␈α⊗networks␈α↔build␈α⊗from          ␈↓ π∞␈↓structural␈α$model␈α$could␈α$be␈α$used␈α$for
␈↓ ↓N␈↓unreliable␈α≠comparators.␈α~ Two␈α≠models␈α~of          ␈↓ π∞␈↓representation␈α↔of␈α⊗the␈α↔data␈α⊗relationships
␈↓ ↓N␈↓fault-tolerant␈α!networks␈α!are␈α!discussed.         ␈↓ π∞␈↓within␈α≡the␈α≡conceptual␈α≡schema␈α≡of␈α≥the
␈↓ ↓N␈↓The␈αfirst␈αmodel␈αpatterns␈αafter␈αthe␈αconcept        ␈↓ π∞␈↓ANSI/SPARC␈α≤DBMS␈α≠model␈α≤since␈α≤it␈α≠can
␈↓ ↓N␈↓of␈α→error-dorrecting␈α→codes␈α→in␈α_information        ␈↓ π∞␈↓support␈α∩database␈α∩submodels␈α∩(or␈α⊃external
␈↓ ↓N␈↓theory,␈α(and␈α'the␈α(other␈α(follows␈α'the                ␈↓ π∞␈↓schema),␈α∞and␈α∞maintain␈α∞the␈α∞integrity␈α∞of␈α
the
␈↓ ↓N␈↓stochastic␈α∪criterion␈α∪used␈α∪byvon␈α∩Neumann         ␈↓ π∞␈↓submodels␈α∃with␈α∃respect␈α∃to␈α⊗the␈α∃integrity
␈↓ ↓N␈↓and␈α≡Moore-Shannon.␈α≥ It␈α≡is␈α≡shown,␈α≥for             ␈↓ π∞␈↓constraints␈α∪expressable␈α∩in␈α∪the␈α∩structural
␈↓ ↓N␈↓example,␈α&that␈α&an␈α&additional␈α&k(2n-3)             ␈↓ π∞␈↓model.
␈↓ ↓N␈↓comparators␈α_are␈α→sufficient␈α_to␈α→render␈α_a
␈↓ ↓N␈↓sorting␈α⊂network␈α⊃reliable,␈α⊂provided␈α⊃that␈α⊂no       ␈↓ π∞␈↓    We␈α∞then␈α∞briefly␈α∞discuss␈α∞the␈α∞use␈α∞of␈α
the
␈↓ ↓N␈↓more␈α∀than␈α∀k␈α∀of␈α∀its␈α∀comparators␈α∃may␈α∀be              ␈↓ π∞␈↓structural␈α∀model␈α∀in␈α∀database␈α∀design␈α∪and
␈↓ ↓N␈↓faulty.                                     ␈↓ π∞␈↓implementation.␈α( The␈α(structural␈α(model
                                            ␈↓ π∞␈↓provides␈αa␈αtool␈α
to␈αdeal␈αeffectively␈αwith␈α
the
␈↓ ↓N␈↓STAN-CS-79-722                              ␈↓ π∞␈↓complexity of large, real-world databases.
␈↓ ↓N␈↓    A STRUCTURAL MODEL FOR DATABASE
␈↓ ↓N␈↓SYSTEMS                                     ␈↓ π∞␈↓STAN-CS-79-723
␈↓ ↓N␈↓Authors:  Gio Wiederhold & Ramez El-Masri   ␈↓ π∞␈↓    KNOWLEDGE ENGINEERING FOR MEDICAL
␈↓ ↓N␈↓57 pages␈↓ ¬εCost: $3.30                         ␈↓ π∞␈↓DECISION MAKING:  A REVIEW OF
                                            ␈↓ π∞␈↓COMPUTER-BASED CLINICAL DECISION AIDS
␈↓ ↓N␈↓    Abstract:␈α⊃ A␈α⊃structural␈α⊃database␈α⊂model      ␈↓ π∞␈↓Authors:  Edward Shortliffe,
␈↓ ↓N␈↓is␈α
presented.␈α The␈α
model␈αuses␈α
relations␈αas        ␈↓ π∞␈↓Bruce Buchanan &
␈↓ ↓N␈↓building␈α_blocks␈α↔of␈α_the␈α↔data␈α_model,␈α↔and            ␈↓ π∞␈↓& Edward Feigenbaum
␈↓ ↓N␈↓classifies␈α∀each␈α∀relation␈α∀into␈α∀a␈α∀structural       ␈↓ π∞␈↓52 pages␈↓ 
FCost: $3.20
␈↓ ↓N␈↓type.␈α This␈α
model␈αcan␈α
be␈αconsidered␈α
as␈αan
␈↓ ↓N␈↓extension␈α#of␈α#Codd's␈α#relational␈α#model            ␈↓ π∞␈↓    Abstract:␈α→ Computer-based␈α~models␈α→of
␈↓ ↓N␈↓[Codd70].␈α⊃ Both␈α⊂entities␈α⊃and␈α⊂associations       ␈↓ π∞␈↓medical␈α~decision␈α~making␈α~account␈α~for␈α→a
␈↓ ↓N␈↓among␈α)entities␈α)are␈α)represented␈α)as               ␈↓ π∞␈↓large␈α"proportion␈α"of␈α#clinical␈α"computing
␈↓ ↓N␈↓relations,␈α≡and␈α≡three␈α≡further␈α∨types␈α≡of            ␈↓ π∞␈↓efforts.␈α?␈α⊂ This␈α?␈α⊃article␈α?␈α⊂reviews
␈↓ ↓N␈↓relations␈α⊂are␈α⊂introduced.␈α⊂ A␈α⊂limited␈α⊃set␈α⊂of       ␈↓ π∞␈↓representative␈α≤examples␈α≤from␈α≤each␈α≠of
␈↓ ↓N␈↓possible␈α↔structural␈α_connections␈α↔between        ␈↓ π∞␈↓several␈α>major␈α>medical␈α>computing
␈↓ ↓N␈↓the␈α&different␈α%types␈α&of␈α&relations␈α%is              ␈↓ π∞␈↓paradigms.␈α  These␈α include␈α!(1)␈α clinical
␈↓ ↓N␈↓specified.␈α/ The␈α/model,␈α/which␈α/was                ␈↓ π∞␈↓algorithms,␈α≡(2)␈α≡clinical␈α≡databanks␈α≡that
␈↓ ↓N␈↓introduced␈α/informally␈α0by␈α/Wiederhold            ␈↓ π∞␈↓include␈α?␈αanalytic␈α?␈αfunctions,␈α?␈α
(3)
␈↓ ↓N␈↓[Wiederhold77],␈αimplicityly␈αdefines␈αa␈α
basic,    ␈↓ π∞␈↓mathematical␈α?models␈α?of␈α?physical
␈↓ ↓N␈↓limited␈α∞set␈α∞of␈α∞integrity␈α∞constraints.␈α
 These     ␈↓ π∞␈↓processes,␈α~(4)␈α~pattern␈α≠recognition,␈α~(5)
␈↓ ↓N␈↓integrity␈α≥constraints␈α≡identify␈α≥existence       ␈↓ π∞␈↓Bayesian␈α⊂statistics,␈α⊂(6)␈α⊃decision␈α⊂analysis,
␈↓ ↓N␈↓dependencies␈α∞among␈α∞tuples␈α∂from␈α∞different        ␈↓ π∞␈↓and␈α_(7)␈α→symbolic␈α_reasoning␈α→or␈α_artificial
␈↓ ↓N␈↓relations.␈α
 Rules␈α
for␈α
the␈α
maintenance␈αof␈α
the      ␈↓ π∞␈↓intelligence.␈α Because␈αthe␈αtechniques␈αused
␈↓ ↓N␈↓structural␈α→integrity␈α→of␈α→the␈α→model␈α→under          ␈↓ π∞␈↓in␈α
the␈α
various␈α
systems␈α
cannot␈αbe␈α
examined
␈↓ ↓N␈↓insertion and deletion of tuples are given. ␈↓ π∞␈↓exhaustively,␈α↔the␈α⊗case␈α↔studies␈α↔in␈α⊗each
                                            ␈↓ π∞␈↓category␈α
are␈α
used␈α
as␈α
a␈α
basis␈α
for␈αstudying
␈↓ ↓N␈↓    A␈α↔methodology␈α↔for␈α↔combining␈α↔multiple,       ␈↓ π∞␈↓general␈α_strengths␈α_and␈α_limitations.␈α_ It␈α_is
␈↓ ↓N␈↓α␈↓ W5


␈↓ ↓N␈↓noted␈α⊃that␈α⊃no␈α⊃one␈α⊂method␈α⊃is␈α⊃best␈α⊃for␈α⊂all
␈↓ ↓N␈↓applications.␈α⊃ However,␈α⊃emphasis␈α∩is␈α⊃given
␈↓ ↓N␈↓to␈α∩thelimitations␈α∩of␈α∩early␈α∩work␈α∩that␈α⊃have
␈↓ ↓N␈↓made␈α∂artificial␈α∞intelligence␈α∂techniques␈α∞and
␈↓ ↓N␈↓knowledge␈α?␈α→engineering␈α?␈α_research
␈↓ ↓N␈↓particularly␈α~attractive.␈α~ We␈α~stress␈α→that
␈↓ ↓N␈↓considerable␈α≠basic␈α~research␈α≠in␈α~medical
␈↓ ↓N␈↓computing␈α→remains␈α→to␈α→bedone␈α~and␈α→that
␈↓ ↓N␈↓powerful␈α∪new␈α∪approaches␈α∪may␈α∪lie␈α∪in␈α∩the
␈↓ ↓N␈↓melding␈α"of␈α#two␈α"or␈α#more␈α"established
␈↓ ↓N␈↓techniques.