vi;
#define INF 1000000000
// 1 billion, safer than 2B for Floyd Warshall’s
// Common memset settings
//memset(memo, -1, sizeof memo);
// initialize DP memoization table with -1
//memset(arr, 0, sizeof arr);
// to clear array of integers
// Note that we abandon the usage of "REP" and "TRvii" in the second edition
// to reduce the confusion encountered by new programmers
The following shortcuts are frequently used in our C/C++/Java codes in this book:
// ans = a ? b : c;
// to simplify: if (a) ans = b; else ans = c;
// index = (index + 1) % n;
// from: index++; if (index >= n) index = 0;
// index = (index + n - 1) % n;
// from: index--; if (index < 0) index = n - 1;
// int ans = (int)((double)d + 0.5);
// for rounding to nearest integer
// ans = min(ans, new_computation)
// we frequently use this min/max shortcut
// some codes uses short circuit && (AND) and || (OR)
Problem Categorization
As of 1 August 2011, Steven and Felix – combined – have solved 1502 UVa problems (≈51% of
the entire UVa problems). About ≈1198 of them are discussed and categorized in this book.
These problems are categorized according to a ‘load balancing’ scheme: If a problem can be
classified into two or more categories, it will be placed in the category with a lower number of
problems. This way, you may find problems ‘wrongly’ categorized or problems whose category does
not match the technique you use to solve it. What we can guarantee is this: If you see problem X
in category Y, then you know that we have solved problem X with the technique mentioned in the
section that discusses category Y.
If you need hints for any of the problems, you may turn to the index at the back of this book and
save yourself the time needed to flip through the whole book to understand any of the problems.
The index contains a sorted list of UVa/LA problems number (do a binary search!) which will help
locate the pages that contains the discussion of those problems (and the required data structures
and/or algorithms to solve that problem).
Utilize this categorization feature for your training! To diversify your problem solving skill, it is
a good idea to solve at least few problems from each category, especially the ones that we highlight
as must try * (we limit ourself to choose maximum 3 highlights per category).
xii
####################
File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf
Page: 43
Context: 6.5.REMARKS316.5RemarksOneofthemainlimitationsoftheNBclassifieristhatitassumesindependencebe-tweenattributes(ThisispresumablythereasonwhywecallitthenaiveBayesianclassifier).Thisisreflectedinthefactthateachclassifierhasanindependentvoteinthefinalscore.However,imaginethatImeasurethewords,“home”and“mortgage”.Observing“mortgage”certainlyraisestheprobabilityofobserving“home”.Wesaythattheyarepositivelycorrelated.Itwouldthereforebemorefairifweattributedasmallerweightto“home”ifwealreadyobservedmortgagebecausetheyconveythesamething:thisemailisaboutmortgagesforyourhome.Onewaytoobtainamorefairvotingschemeistomodelthesedependenciesex-plicitly.However,thiscomesatacomputationalcost(alongertimebeforeyoureceiveyouremailinyourinbox)whichmaynotalwaysbeworththeadditionalaccuracy.Oneshouldalsonotethatmoreparametersdonotnecessarilyimproveaccuracybecausetoomanyparametersmayleadtooverfitting.6.6TheIdeaInaNutshellConsiderFigure??.Wecanclassifydatabybuildingamodelofhowthedatawasgenerated.ForNBwefirstdecidewhetherwewillgenerateadata-itemfromclassY=0orclassY=1.GiventhatdecisionwegeneratethevaluesforDattributesindependently.Eachclasshasadifferentmodelforgeneratingattributes.Clas-sificationisachievedbycomputingwhichmodelwasmorelikelytogeneratethenewdata-point,biasingtheoutcometowardstheclassthatisexpectedtogeneratemoredata.
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 474
Context: 446VII.InfiniteFieldExtensionsinI,andaisnotinI,thenbmisinIforsomeintegerm>0.Itisimmediatethateveryprimeidealisprimary.6.ProvethatanidealIofRisprimaryifandonlyifeveryzerodivisorinR/Iisnilpotent(inthesensethatsomepowerofitis0),ifandonlyif0isprimaryinR/I.7.(a)ProvethatifIisaprimaryideal,thenpIisaprimeideal.(Educationalnote:InthiscasetheprimeidealpIiscalledtheassociatedprimeidealtoI.)(b)ProvethatifIisanyidealandifI⊆JforaprimeidealJ,thenpI⊆J.8.(a)ShowthattheprimaryidealsinZare0and(pn)forpprimeandn>0.(b)LetR=C[x,y]andI=(x,y2).UseProblem6toshowthatIisprimary.ShowthatP=pIisgivenbyP=(x,y).DeducethatP2$I$Pandthataprimaryidealisnotnecessarilyapowerofaprimeideal.(c)LetKbeafield,letR=K[X,Y,Z]/(XY−Z2),andletx,y,zbetheimagesofX,Y,ZinR.ShowthatP=(x,z)isprimebyshowingthatR/Pisanintegraldomain.ShowthatP2isnotprimarybystartingfromthefactthatxy=z2liesinP2.9.ProvethatifIisanidealsuchthatpIismaximal,thenIisprimary.Deducethatthepowersofamaximalidealareprimary.10.Anidealisreducibleifitisthefiniteintersectionofidealsstrictlycontainingit;otherwiseitisirreducible.(a)Showthateveryprimeidealisirreducible.(b)LetR=C[x,y],andletIbethemaximalideal(x,y).ShowthatI2isprimaryandthattheequalityI2=(Rx+I2)∩(Ry+I2)exhibitsI2asreducible.11.ProvethatifRisNoetherian,theneveryidealisafiniteintersectionofproperirreducibleideals.(TheidealRisunderstoodtobeanemptyintersection.)12.SupposethatRisNoetherianandthatQisaproperirreducibleidealinR.Provethat0isprimaryinR/Q,anddeducethatQisprimaryinR.13.ProvethatifQ1,...,QnareprimaryidealsinRthatallhavepQi=P,thenQ=Tni=1QiisprimarywithpQ=P.14.(Lasker–NoetherDecompositionTheorem)TheexpressionI=Tni=1QiofanidealIasanintersectionofprimaryidealsQiissaidtobeirredundantif(i)noQicontainstheintersectionoftheotherones,and(ii)theQihavedistinctassociatedprimeideals.ProvethatifRisNoetherian,theneveryidealistheirredundantintersectionoffinitelymanyprimaryideals.
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 341
Context: toolsdevelopedinthepresentchapter,includingaStrongApproximationTheoremthatisprovedinSection8,acompleteproofisgivenfortheDedekindDiscriminantTheorem;onlyapartialproofhadbeenaccessibleinChapterV.Sections9–10specializetothecaseofnumberfieldsandtofunctionfieldsthatarefiniteseparableextensionsofFq(X),whereFqisafinitefield.Theadeleringandtheidelegroupareintroducedforeachofthesekindsoffields,anditisshownhowtheoriginalfieldembedsdiscretelyintheadelesandhowthemultiplicativegroupembedsdiscretelyintheideles.Themaintheoremsarecompactnesstheoremsaboutthequotientoftheadelesbytheembeddedfieldandaboutthequotientofthenormalizedidelesbytheembeddedmultiplicativegroup.Proofsaregivenonlyfornumberfields.InthefirstcasethecompactnessencodestheStrongApproximationTheoremofSection8andtheArtinproductformulaofSection9.InthesecondcasethecompactnessencodesboththefinitenessoftheclassnumberandtheDirichletUnitTheorem.313
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 92
Context: 78Chapter6.SavingSpaceProblemsSolutionsonpage154.1.CountthefrequenciesofthecharactersinthispieceoftextandassignthemtotheHuffmancodes,fillinginthefollowingtable.Thenencodethetextupto“morelightly.”.’IhaveatheorywhichIsuspectisratherimmoral,’Smileywenton,morelightly.’Eachofushasonlyaquantumofcompassion.Thatifwelavishourconcernoneverystraycat,wenevergettothecentreofthings.’LetterFrequencyCodeLetterFrequencyCode11111010010011001110111100100111110001011001011101000101010011010100000010010100010000010100101101101010011101010101100010100010110010001101011010110101010110112.Considerthefollowingfrequencytableandtext.Decodeit.LetterFrequencyCodeLetterFrequencyCodespace20111s200011e12100d2110101t91011T1110100h70111n1110011o70110w1110010m60100p1110001r50011b1010111
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 36
Context: Figure 2.8 IDA Pro workspace
Up to this point, you have been able to open the binary file within IDA Pro. This is
not a trivial task for people new to IDA Pro. That's why it's presented in a step-by-step
fashion. However, the output in the workspace is not yet usable. The next step is learning
the scripting facility that IDA Pro provides to make sense of the disassembly database that
IDA Pro generates.
2.3. IDA Pro Scripting and Key Bindings
Try to decipher the IDA Pro disassembly database shown in the previous section
with the help of the scripting facility. Before you proceed to analyzing the binary, you have
to learn some basic concepts about the IDA Pro scripting facility. IDA Pro script syntax is
similar to the C programming language. The syntax is as follows:
9
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 662
Context: HAN20-ch13-585-632-97801238147912011/6/13:26Page625#4113.6Summary625Furtherdevelopmentofprivacy-preservingdataminingmethodsisforeseen.Thecollaborationoftechnologists,socialscientists,lawexperts,governments,andcompaniesisneededtoproducearigorousprivacyandsecurityprotectionmech-anismfordatapublishinganddatamining.Withconfidence,welookforwardtothenextgenerationofdataminingtechnologyandthefurtherbenefitsthatitwillbring.13.6SummaryMiningcomplexdatatypesposeschallengingissues,forwhichtherearemanydedi-catedlinesofresearchanddevelopment.Thischapterpresentsahigh-leveloverviewofminingcomplexdatatypes,whichincludesminingsequencedatasuchastimeseries,symbolicsequences,andbiologicalsequences;mininggraphsandnetworks;andminingotherkindsofdata,includingspatiotemporalandcyber-physicalsystemdata,multimedia,textandWebdata,anddatastreams.Severalwell-establishedstatisticalmethodshavebeenproposedfordataanalysissuchasregression,generalizedlinearmodels,analysisofvariance,mixed-effectmod-els,factoranalysis,discriminantanalysis,survivalanalysis,andqualitycontrol.Fullcoverageofstatisticaldataanalysismethodsisbeyondthescopeofthisbook.Inter-estedreadersarereferredtothestatisticalliteraturecitedinthebibliographicnotes(Section13.8).Researchershavebeenstrivingtobuildtheoreticalfoundationsfordatamining.Sev-eralinterestingproposalshaveappeared,basedondatareduction,datacompression,probabilityandstatisticstheory,microeconomictheory,andpatterndiscovery–basedinductivedatabases.Visualdataminingintegratesdatamininganddatavisualizationtodiscoverimplicitandusefulknowledgefromlargedatasets.Visualdataminingincludesdatavisu-alization,dataminingresultvisualization,dataminingprocessvisualization,andinteractivevisualdatamining.Audiodataminingusesaudiosignalstoindicatedatapatternsorfeaturesofdataminingresults.Manycustomizeddataminingtoolshavebeendevelopedfordomain-specificapplications,includingfinance,theretailandtelecommunicationindustries,scienceandengineering,intrusiondetectionandprevention,andrecommendersystems
####################
File: Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf
Page: 27
Context: 1.2. TIPS TO BE COMPETITIVE
c
⃝Steven & Felix
2. For multiple input test cases, you should include two identical sample test cases consecutively.
Both must output the same known correct results.
This is to check whether you have forgotten to initialize some variables, which will be easily
identified if the 1st instance produces the correct output but the 2nd one does not.
3. Your test cases must include large cases.
Increase the input size incrementally up to the maximum possible stated in problem descrip-
tion. Sometimes your program works for small input size, but behave wrongly (or slowly)
when input size increases. Check for overflow, out of bounds, etc if that happens.
4. Your test cases must include the tricky corner cases.
Think like the problem setter! Identify cases that are ‘hidden’ in the problem description.
Some typical corner cases: N = 0, N = 1, N = maximum values allowed in problem description,
N = negative values, etc. Think of the worst possible input for your algorithm.
5. Do not assume the input will always be nicely formatted if the problem description does not
say so (especially for a badly written problem). Try inserting white spaces (spaces, tabs) in
your input, and check whether your code is able to read in the values correctly (or crash).
6. Finally, generate large random test cases to see if your code terminates on time and still give
reasonably ok output (the correctness is hard to verify here – this test is only to verify that
your code runs within the time limit).
However, after all these careful steps, you may still get non-AC responses. In ICPC6, you and your
team can actually use the judge’s response to determine your next action. With more experience
in such contests, you will be able to make better judgment. See the next exercises:
Exercise 1.2.4: Situation judging (Mostly in ICPC setting. This is not so relevant in IOI).
1. You receive a WA response for a very easy problem. What should you do?
(a) Abandon this problem and do another.
(b) Improve the performance of your solution (optimize the code or use better algorithm).
(c) Create tricky test cases and find the bug.
(d) (In team contest): Ask another coder in your team to re-do this problem.
2. You receive a TLE response for an your O(N3) solution. However, maximum N is just 100.
What should you do?
(a) Abandon this problem and do another.
(b) Improve the performance of your solution (optimize the code or use better algorithm).
(c) Create tricky test cases and find the bug.
3. Follow up question (see question 2 above): What if maximum N is 100.000?
4. You receive an RTE response. Your code runs OK in your machine. What should you do?
5. One hour to go before the end of the contest. You have 1 WA code and 1 fresh idea for
another problem. What should you (your team) do?
(a) Abandon the problem with WA code, switch to that other problem in attempt to solve
one more problem.
(b) Insist that you have to debug the WA code. There is not enough time to start working
on a new code.
(c) (In ICPC): Print the WA code. Ask two other team members to scrutinize the code
while you switch to that other problem in attempt to solve two more problems.
6In IOI 2010-2011, contestants have limited tokens that they can use sparingly to check the correctness of their
submitted code. The exercise in this section is more towards ICPC style contest.
11
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 580
Context: tion,youwilllearnaboutminingcontextualandcollectiveoutliers(Section12.7)andoutlierdetectioninhigh-dimensionaldata(Section12.8).c(cid:13)2012ElsevierInc.Allrightsreserved.DataMining:ConceptsandTechniques543
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 351
Context: HAN14-ch07-279-326-97801238147912011/6/13:21Page314#36314Chapter7AdvancedPatternMiningPattern:“{frequent,pattern}”contextindicators:“mining,”“constraint,”“Apriori,”“FP-growth,”“rakeshagrawal,”“jiaweihan,”...representativetransactions:1)miningfrequentpatternswithoutcandidate...2)...miningclosedfrequentgraphpatternssemanticallysimilarpatterns:“{frequent,sequential,pattern},”“{graph,pattern}”“{maximal,pattern},”“{frequent,closed,pattern},”...Figure7.12Semanticannotationofthepattern“{frequent,pattern}.”Ingeneral,thehiddenmeaningofapatterncanbeinferredfrompatternswithsim-ilarmeanings,dataobjectsco-occurringwithit,andtransactionsinwhichthepatternappears.Annotationswithsuchinformationareanalogoustodictionaryentries,whichcanberegardedasannotatingeachtermwithstructuredsemanticinformation.Let’sexamineanexample.Example7.15Semanticannotationofafrequentpattern.Figure7.12showsanexampleofasemanticannotationforthepattern“{frequent,pattern}.”Thisdictionary-likeannotationpro-videssemanticinformationrelatedto“{frequent,pattern},”consistingofitsstrongestcontextindicators,themostrepresentativedatatransactions,andthemostsemanticallysimilarpatterns.Thiskindofsemanticannotationissimilartonaturallanguagepro-cessing.Thesemanticsofawordcanbeinferredfromitscontext,andwordssharingsimilarcontextstendtobesemanticallysimilar.Thecontextindicatorsandtherepre-sentativetransactionsprovideaviewofthecontextofthepatternfromdifferentanglestohelpusersunderstandthepattern.Thesemanticallysimilarpatternsprovideamoredirectconnectionbetweenthepatternandanyotherpatternsalreadyknowntotheusers.“Howcanweperformautomatedsemanticannotationforafrequentpattern?”Thekeytohigh-qualitysemanticannotationofafrequentpatternisthesuccessfulcontextmodelingofthepattern.Forcontextmodelingofapattern,p,considerthefollowing.Acontextunitisabasicobjectinadatabase,D,thatcarriessemanticinformationandco-occurswithatleastonefrequentpattern,p,inatleastonetransactioninD.Acontextunitcanbeanitem,apattern,orevenatransaction,dependingonthespeci
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 70
Context: HAN08-ch01-001-038-97801238147912011/6/13:12Page33#331.8Summary33Invisibledatamining:Wecannotexpecteveryoneinsocietytolearnandmasterdataminingtechniques.Moreandmoresystemsshouldhavedataminingfunc-tionsbuiltwithinsothatpeoplecanperformdataminingorusedataminingresultssimplybymouseclicking,withoutanyknowledgeofdataminingalgorithms.Intelli-gentsearchenginesandInternet-basedstoresperformsuchinvisibledataminingbyincorporatingdataminingintotheircomponentstoimprovetheirfunctionalityandperformance.Thisisdoneoftenunbeknownsttotheuser.Forexample,whenpur-chasingitemsonline,usersmaybeunawarethatthestoreislikelycollectingdataonthebuyingpatternsofitscustomers,whichmaybeusedtorecommendotheritemsforpurchaseinthefuture.Theseissuesandmanyadditionalonesrelatingtotheresearch,development,andapplicationofdataminingarediscussedthroughoutthebook.1.8SummaryNecessityisthemotherofinvention.Withthemountinggrowthofdataineveryappli-cation,dataminingmeetstheimminentneedforeffective,scalable,andflexibledataanalysisinoursociety.Dataminingcanbeconsideredasanaturalevolutionofinfor-mationtechnologyandaconfluenceofseveralrelateddisciplinesandapplicationdomains.Dataminingistheprocessofdiscoveringinterestingpatternsfrommassiveamountsofdata.Asaknowledgediscoveryprocess,ittypicallyinvolvesdatacleaning,datainte-gration,dataselection,datatransformation,patterndiscovery,patternevaluation,andknowledgepresentation.Apatternisinterestingifitisvalidontestdatawithsomedegreeofcertainty,novel,potentiallyuseful(e.g.,canbeactedonorvalidatesahunchaboutwhichtheuserwascurious),andeasilyunderstoodbyhumans.Interestingpatternsrepresentknowl-edge.Measuresofpatterninterestingness,eitherobjectiveorsubjective,canbeusedtoguidethediscoveryprocess.Wepresentamultidimensionalviewofdatamining.Themajordimensionsaredata,knowledge,technologies,andapplications.Dataminingcanbeconductedonanykindofdataaslongasthedataaremeaningfulforatargetapplication,suchasdatabasedata,datawarehousedata,transactionaldata,andadvanceddatatypes.Advanceddatatyp
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 21
Context: GuidefortheReaderxxiouttobeacohomologygroupindegree2.ThisdevelopmentrunsparalleltothetheoryoffactorsetsforgroupsasinChapterVIIofBasicAlgebra,andsomefamiliaritywiththattheorycanbehelpfulasmotivation.ThecasethattherelativeBrauergroupiscyclicisofspecialimportance,andthetheoryisusedintheproblemstoconstructexamplesofdivisionringsthatwouldnothavebeenotherwiseavailable.ThechaptermakesuseofmaterialfromChapterXofBasicAlgebraonthetensorproductofalgebrasandoncomplexesandexactsequences.ChapterIVisabouthomologicalalgebra,withemphasisonconnectinghomo-morphisms,longexactsequences,andderivedfunctors.Allbutthelastsectionisdoneinthecontextof“good”categoriesofunitalleftRmodules,Rbeingaringwithidentity,whereitispossibletoworkwithindividualelementsineachobject.Thereaderisexpectedtobefamiliarwithsomeexampleformotivation;thiscanbeknowledgeofcohomologyofgroupsatthelevelofSectionIII.5,oritcanbesomeexperiencefromtopologyorfromthecohomologyofLiealgebrasastreatedinotherbooks.KnowledgeofcomplexesandexactsequencesfromChapterXofBasicAlgebraisprerequisite.Homologicalalgebraproperlybelongsinthisbookbecauseitisfundamentalintopologyandcomplexanalysis;inalgebraitsrolebecomessignificantjustbeyondthelevelofthecurrentbook.Importantapplicationsarenotlimitedinpracticeto“good”categories;“sheaf”cohomologyisanexamplewithsignificantapplicationsthatdoesnotfitthismold.Section8sketchesthetheoryofhomologicalalgebrainthecontextof“abelian”categories.Inthiscaseonedoesnothaveindividualelementsathand,butsomesubstituteisstillpossible;sheafcohomologycanbetreatedinthiscontext.ChaptersVandVIareanintroductiontoalgebraicnumbertheory.ThetheoryofDedekinddomainsfromChaptersVIIIandIXofBasicAlgebraistakenasknown,alongwithknowledgeoftheingredientsofthetheory—Noetherianrings,integralclosure,andlocalization.Bothchaptersdealwiththreetheorems—theDedekindDiscriminantTheorem,theDirichletUnitTheorem,andthefinitenessoftheclassnumber.ChapterVattacksthesedirectly,usingnoadditionaltools,anditcomesupalittleshortinthecaseoftheDedekindDiscrimin
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 16
Context: diamturpis,molestievitae,placerata,molestienec,leo.Maecenaslacinia.Namipsumligula,eleifendat,accumsannec,suscipita,ipsum.Morbiblanditligulafeugiatmagna.Nunceleifendconsequatlorem.Sedlacinianullavitaeenim.Pellentesquetinciduntpurusvelmagna.Integernonenim.Praesenteuismodnunceupurus.Donecbibendumquamintellus.Nullamcursuspulvinarlectus.Donecetmi.Namvulputatemetuseuenim.Vestibulumpellentesquefeliseumassa.102004006000200400600800xyYoucanseethatthechapterheading“Chapter1”beginsatabout(80,630).Noticethatthecoordinatesofthebottomleftofthepage(calledtheorigin)are,ofcourse,(0,0).Thechoiceofthebottomleftasouroriginissomewhatarbitrary–onecouldmakeanargumentthatthetopleftpoint,withverticalpositionsmeasureddownwards,isamoreappropriatechoice,atleastintheWestwherewereadtoptobottom.Ofcourse,onecouldalsohavetheoriginatthetoprightorbottomright,withhorizontalpositionsmeasuringleftward.Weshallbeusingsuchcoordinatestodescribethepositionandshapeofeachpartofeachletter,eachword,andeachparagraph,aswellasanydrawingsorphotographstobeplacedonthepage.Wewillseehowlinescanbedrawnbetweencoordinates,andhowtomaketheelegantcurveswhichformthelettersinatypeface.Oncewehavedeterminedwhatshapeswewishtoputoneachpage,wemustconsiderthefinalformofourdocument.Youmay
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 400
Context: emostrecentlyaddedconjunctwhencon-sideringpruning.Conjunctsareprunedoneatatimeaslongasthisresultsinanimprovement.
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 112
Context: in compressed state. The compressed component preceding awardext.rom is the compressed system BIOS, and the byte highlighted in pink is a custom checksum that follows the end-of-file marker for this compressed system BIOS. Other compressed components always end up with an end-of-file marker, and no checksum byte precedes the next compressed component in the BIOS binary. Proceed to the pure binary component of the Foxconn BIOS. The mapping of this pure binary component inside the hex editor as follows: 1. 6_A9C0h–6_BFFEh: The decompression block. This routine contains the LZH decompression engine 2. 7_E000h–7_FFFFh: This area contains the boot block code. Between of the pure binary components lay padding bytes. Some padding bytes re FFh bytes, and some are 00h bytes. Reverse Engineering e engineering. The boot BIOS. Understanding the reverse boot block is valuable, because these ifferent vendors. From this point on, I assemble the boot block routines. Now, I'll present some obscure and important areas of of the Foxconn 955X7AA-8EKRS2 you learned how to start ation here. All you have t the initial load address to 8_0000h–FFFh. Then, create new segments at FFF8_0000h–FFFD_FFFFh and relocate the h to that newly created segment to mimic the mapping of the dress map. You can use the IDA Pro script in listing 5.1 to e IDA Pro add the o make it a standalone script in an ASCII file, . a 5.1.2. Award Boot Block This section delves into the mechanics of boot block reversblock is the key into overall insight of the motherboard engineering tricks needed to reverse engineer thehniques tend to be applicable to BIOS from dtecisdthe BIOS code in the disassembled boot block motherboard BIOS dated November 11, 2005. In section 2.3 assembling a BIOS file with IDA Pro. I won't repeat that informdisto do is open the 512-KB file in IDA Pro and seF_Fcontents of 8_0000h–D_FFFFstem adBIOS binary in the syaccomplish this operation. The script in listing 5.1 must be executed directly in thrkspace scripting window that's called with Shift+F2 shortcut. You canwoappropriate include statements if you wish tas you learned in chapter 2 Listing 5.1 IDA Pro Relocation Script for Award BIOS with a 512-KB File auto ea, ea_src, ea_dest; /* Create segments for the currently loaded binary */ for(ea=0x80000; ea<0x100000; ea = ea+0x10000) { SegCreate(ea, ea+0x10000, ea>>4, 0,0,0); } /* Create new segments for relocation */ 6
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 8
Context: viiiContentsIII.BRAUERGROUP1231.DefinitionandExamples,RelativeBrauerGroup1242.FactorSets1323.CrossedProducts1354.Hilbert’sTheorem901455.DigressiononCohomologyofGroups1476.RelativeBrauerGroupwhentheGaloisGroupIsCyclic1587.Problems162IV.HOMOLOGICALALGEBRA1661.Overview1672.ComplexesandAdditiveFunctors1713.LongExactSequences1844.ProjectivesandInjectives1925.DerivedFunctors2026.LongExactSequencesofDerivedFunctors2107.ExtandTor2238.AbelianCategories2329.Problems250V.THREETHEOREMSINALGEBRAICNUMBERTHEORY2621.Setting2622.Discriminant2663.DedekindDiscriminantTheorem2744.CubicNumberFieldsasExamples2795.DirichletUnitTheorem2886.FinitenessoftheClassNumber2987.Problems307VI.REINTERPRETATIONWITHADELESANDIDELES3131.p-adicNumbers3142.DiscreteValuations3203.AbsoluteValues3314.Completions3425.Hensel’sLemma3496.RamificationIndicesandResidueClassDegrees3537.SpecialFeaturesofGaloisExtensions3688.DifferentandDiscriminant3719.GlobalandLocalFields38210.AdelesandIdeles38811.Problems397
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 422
Context: HAN15-ch08-327-392-97801238147912011/6/13:21Page385#598.7Summary385usesoversamplingwheresynthetictuplesareadded,whichare“closeto”thegivenpositivetuplesintuplespace.Thethreshold-movingapproachtotheclassimbalanceproblemdoesnotinvolveanysampling.Itappliestoclassifiersthat,givenaninputtuple,returnacontinuousoutputvalue(justlikeinSection8.5.6,wherewediscussedhowtoconstructROCcurves).Thatis,foraninputtuple,X,suchaclassifierreturnsasoutputamapping,f(X)→[0,1].Ratherthanmanipulatingthetrainingtuples,thismethodreturnsaclas-sificationdecisionbasedontheoutputvalues.Inthesimplestapproach,tuplesforwhichf(X)≥t,forsomethreshold,t,areconsideredpositive,whileallothertuplesarecon-siderednegative.Otherapproachesmayinvolvemanipulatingtheoutputsbyweighting.Ingeneral,thresholdmovingmovesthethreshold,t,sothattherareclasstuplesareeas-iertoclassify(andhence,thereislesschanceofcostlyfalsenegativeerrors).Examplesofsuchclassifiersincludena¨ıveBayesianclassifiers(Section8.3)andneuralnetworkclas-sifierslikebackpropagation(Section9.2).Thethreshold-movingmethod,althoughnotaspopularasover-andundersampling,issimpleandhasshownsomesuccessforthetwo-class-imbalanceddata.Ensemblemethods(Sections8.6.2through8.6.4)havealsobeenappliedtotheclassimbalanceproblem.Theindividualclassifiersmakinguptheensemblemayincludeversionsoftheapproachesdescribedheresuchasoversamplingandthresholdmoving.Thesemethodsworkrelativelywellfortheclassimbalanceproblemontwo-classtasks.Threshold-movingandensemblemethodswereempiricallyobservedtooutper-formoversamplingandundersampling.Thresholdmovingworkswellevenondatasetsthatareextremelyimbalanced.Theclassimbalanceproblemonmulticlasstasksismuchmoredifficult,whereoversamplingandthresholdmovingarelesseffective.Althoughthreshold-movingandensemblemethodsshowpromise,findingasolutionforthemulticlassimbalanceproblemremainsanareaoffuturework.8.7SummaryClassificationisaformofdataanalysisthatextractsmodelsdescribingdataclasses.Aclassifier,orclassificationmodel,predictscategoricallabels(classes).Nu
####################
File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf
Page: 8
Context: viPREFACE
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 746
Context: 718IndexofNotationfv,533GP,368Gal(F2/F1),434≤GLEX,≤GREVLEX,494g,538gx,538H(s,a),633Ha(s,a),621,626H(s,a),633Ha(s,a),625,628Hj,620Hn(X),153,172Hn(X),153,174H∗(X),172H∗(X),174Hn(G,M),209Hn(G,M),147HomR(A,B),169h(D),7,14hK,299I,IK,390I1,390I,330,393,576eI,576I=(r1,r2),38I=hr1,r1i,38I(E),560I(P),571I(P,F∩G),474I(P,L∩F),467imagef,240J(ξ),272K(S),409K(E),412k,528,559k(V),580,585k0,531L(A),544L(A),535L(s,χ),63LCM(Xα,Xβ),501Log,289LM(f),LC(f),LT(f),496LT(I),497≤LEX,493`(A),536lim√,439M,493,620MP,600Mx,431mP,600mx,431mP(F),474N(I),39,273NA/F(·),165NK/F(·),norm,xxviNrdA/F(·),165O(U),580,582,587,641OP(U),582,587OP(V),580,585Ro,oppositering,xxivordv(A),532P2,456Pn,457,570PnK,457P,330,393PF,532,549Pv,322,533Qp,316,318R(f,g),451R(f,g),451R(f1,F),514Rp,346Rv,322,533Rx,431Residue,542Residuep(v),541r1,r2,348,383radA,78S(f1,f2),502
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 341
Context: CHAPTERVIReinterpretationwithAdelesandIdelesAbstract.ThischapterdevelopstoolsforamorepenetratingstudyofalgebraicnumbertheorythanwaspossibleinChapterVandconcludesbyformulatingtwoofthemainthreetheoremsofChapterVinthemodernsettingof“adeles”and“ideles”commonlyusedinthesubject.Sections1–5introducediscretevaluations,absolutevalues,andcompletionsforfields,alwayspayingattentiontoimplicationsfornumberfieldsandforcertainkindsoffunctionfields.Section1containsaprototypeforallthesenotionsintheconstructionofthefieldQpofp-adicnumbersformedoutoftherationals.DiscretevaluationsinSection2areageneralizationoftheorder-of-vanishingfunctionaboutapointinthetheoryofonecomplexvariable.AbsolutevaluesinSection3arereal-valuedmultiplicativefunctionsthatgiveametriconafield,andthepairconsistingofafieldandanabsolutevalueiscalledavaluedfield.InequivalentabsolutevalueshaveacertainindependencepropertythatiscapturedbytheWeakApproximationTheorem.CompletionsinSection4arefunctionsmappingvaluedfieldsintotheirmetric-spacecompletions.Section5concernsHensel’sLemma,whichinitssimplestformallowsonetoliftrootsofpolynomialsoverfiniteprimefieldsFptorootsofcorrespondingpolynomialsoverp-adicfieldsQp.Section6containsthemaintheoremforinvestigatingthefundamentalquestionofhowprimeidealssplitinextensions.LetKbeafiniteseparableextensionofafieldF,letRbeaDedekinddomainwithfieldoffractionsF,andletTbetheintegralclosureofRinK.ThequestionconcernsthefactorizationofanidealpTinTwhenpisanonzeroprimeidealinR.IfFpdenotesthecompletionofFwithrespecttop,thetheoremexplainshowthetensorproductK⊗FFpsplitsuniquelyasadirectsumofcompletionsofvaluedfields.ThetheoremineffectreducesthequestionofthesplittingofpTinTtothesplittingofFpinacompletefieldinwhichonlyoneoftheprimefactorsofpTplaysarole.Section7isabriefasidementioningadditionalconclusionsonecandrawwhentheextensionK/FisaGaloisextension.Section8appliesthemaintheoremofSection6toananalysisofthedifferentofK/Fandultimatelytotheabsolutediscriminantofanumberfield.Withthenewsharptoolsdevelopedinthep
####################
File: Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf
Page: 7
Context: CONTENTS
c
⃝Steven & Felix
Topic
In This Book
Data Structures: Union-Find Disjoint Sets
Section 2.3.2
Graph: Finding SCCs, Max Flow, Bipartite Graph
Section 4.2.1, 4.6.3, 4.7.4
Math: BigInteger, Probability, Nim Games, Matrix Power
Section 5.3, 5.6, 5.8, 5.9
String Processing: Suffix Tree/Array
Section 6.6
More Advanced Topics: A*/IDA*
Section 8.3
Table 1: Not in IOI Syllabus [10] Yet
We know that one cannot win a medal in IOI just by mastering the current version of this book.
While we believe many parts of the IOI syllabus have been included in this book – which should
give you a respectable score in future IOIs – we are well aware that modern IOI tasks requires more
problem solving skills and creativity that we cannot teach via this book. So, keep practicing!
Specific to the Teachers/Coaches
This book is used in Steven’s CS3233 - ‘Competitive Programming’ course in the School of Com-
puting, National University of Singapore. It is conducted in 13 teaching weeks using the following
lesson plan (see Table 2). The PDF slides (only the public version) are given in the companion web
site of this book. Hints/brief solutions of the written exercises in this book are given in Appendix
A. Fellow teachers/coaches are free to modify the lesson plan to suit your students’ needs.
Wk
Topic
In This Book
01
Introduction
Chapter 1
02
Data Structures & Libraries
Chapter 2
03
Complete Search, Divide & Conquer, Greedy
Section 3.2-3.4
04
Dynamic Programming 1 (Basic Ideas)
Section 3.5
05
Graph 1 (DFS/BFS/MST)
Chapter 4 up to Section 4.3
06
Graph 2 (Shortest Paths; DAG-Tree)
Section 4.4-4.5; 4.7.1-4.7.2
-
Mid semester break
-
07
Mid semester team contest
-
08
Dynamic Programming 2 (More Techniques)
Section 6.5; 8.4
09
Graph 3 (Max Flow; Bipartite Graph)
Section 4.6.3; 4.7.4
10
Mathematics (Overview)
Chapter 5
11
String Processing (Basic skills, Suffix Array)
Chapter 6
12
(Computational) Geometry (Libraries)
Chapter 7
13
Final team contest
All, including Chapter 8
-
No final exam
-
Table 2: Lesson Plan
To All Readers
Due to the diversity of its content, this book is not meant to be read once, but several times. There
are many written exercises and programming problems (≈1198) scattered throughout the body
text of this book which can be skipped at first if the solution is not known at that point of time,
but can be revisited later after the reader has accumulated new knowledge to solve it. Solving
these exercises will strengthen the concepts taught in this book as they usually contain interesting
twists or variants of the topic being discussed. Make sure to attempt them once.
We believe this book is and will be relevant to many university and high school students as
ICPC and IOI will be around for many years ahead. New students will require the ‘basic’ knowledge
presented in this book before hunting for more challenges after mastering this book. But before
you assume anything, please check this book’s table of contents to see what we mean by ‘basic’.
vii
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 294
Context: edwith“null.”ScandatabaseDasecondtime.TheitemsineachtransactionareprocessedinLorder(i.e.,sortedaccordingtodescendingsupportcount),andabranchiscreatedforeachtransaction.Forexample,thescanofthefirsttransaction,“T100:I1,I2,I5,”whichcontainsthreeitems(I2,I1,I5inLorder),leadstotheconstructionofthefirstbranchofthetreewiththreenodes,(cid:104)I2:1(cid:105),(cid:104)I1:1(cid:105),and(cid:104)I5:1(cid:105),whereI2islinkedasachildtotheroot,I1islinkedtoI2,andI5islinkedtoI1.Thesecondtransaction,T200,containstheitemsI2andI4inLorder,whichwouldresultinabranchwhereI2islinkedtotherootandI4islinkedtoI2.However,thisbranchwouldshareacommonprefix,I2,withtheexistingpathforT100.Therefore,weinsteadincrementthecountoftheI2nodeby1,andcreateanewnode,(cid:104)I4:1(cid:105),whichislinkedasachildto(cid:104)I2:2(cid:105).Ingeneral,
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 190
Context: 176TemplatesProblem2.1
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 183
Context: FurtherReadingTherefollowsalistofinterestingbooksforeachchapter.Somearecloselyrelatedtothechaptercontents,sometangentially.Thelevelofexpertiserequiredtounderstandeachofthemvariesquiteabit,butdonotbeafraidtoreadbooksyoudonotunderstandallof,especiallyifyoucanobtainorborrowthematlittlecost.Chapter1ComputerGraphics:PrinciplesandPracticeJamesD.Foley,AndriesvanDam,StevenK.Fiener,andJohnF.Hughes.PublishedbyAddisonWesley(secondedition,1995).ISBN0201848406.ContemporaryNewspaperDesign:ShapingtheNewsintheDigitalAge–Typography&ImageonModernNewsprintJohnD.BerryandRogerBlack.PublishedbyMarkBatty(2007).ISBN0972424032.Chapter2ABookofCurvesE.H.Lockwood.PublishedbyCambridgeUniver-sityPress(1961).ISBN0521044448.FiftyTypefacesThatChangedtheWorld:DesignMuseumFiftyJohnL.Waters.PublishedbyConran(2013).ISBN184091629X.ThinkingwithType:ACriticalGuideforDesigners,Writers,Editors,andStudentsEllenLupton.PublishedbyPrincetonArchitecturalPress(secondedition,2010).ISBN1568989695.169
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 357
Context: onglength)byaPattern-Fusionmethod.Toreducethenumberofpatternsreturnedinmining,wecaninsteadminecom-pressedpatternsorapproximatepatterns.Compressedpatternscanbeminedwithrepresentativepatternsdefinedbasedontheconceptofclustering,andapproximatepatternscanbeminedbyextractingredundancy-awaretop-kpatterns(i.e.,asmallsetofk-representativepatternsthathavenotonlyhighsignificancebutalsolowredundancywithrespecttooneanother).Semanticannotationscanbegeneratedtohelpusersunderstandthemeaningofthefrequentpatternsfound,suchasfortextualtermslike“{frequent,pattern}.”Thesearedictionary-likeannotations,providingsemanticinformationrelatingtotheterm.Thisinformationconsistsofcontextindicators(e.g.,termsindicatingthecontextofthatpattern),themostrepresentativedatatransactions(e.g.,fragmentsorsentences
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 9
Context: ContentsixVII.INFINITEFIELDEXTENSIONS4031.Nullstellensatz4042.TranscendenceDegree4083.SeparableandPurelyInseparableExtensions4144.KrullDimension4235.NonsingularandSingularPoints4286.InfiniteGaloisGroups4347.Problems445VIII.BACKGROUNDFORALGEBRAICGEOMETRY4471.HistoricalOriginsandOverview4482.ResultantandBezout’sTheorem4513.ProjectivePlaneCurves4564.IntersectionMultiplicityforaLinewithaCurve4665.IntersectionMultiplicityforTwoCurves4736.GeneralFormofBezout’sTheoremforPlaneCurves4887.Gr¨obnerBases4918.ConstructiveExistence4999.UniquenessofReducedGr¨obnerBases50810.SimultaneousSystemsofPolynomialEquations51011.Problems516IX.THENUMBERTHEORYOFALGEBRAICCURVES5201.HistoricalOriginsandOverview5202.Divisors5313.Genus5344.Riemann–RochTheorem5405.ApplicationsoftheRiemann–RochTheorem5526.Problems554X.METHODSOFALGEBRAICGEOMETRY5581.AffineAlgebraicSetsandAffineVarieties5592.GeometricDimension5633.ProjectiveAlgebraicSetsandProjectiveVarieties5704.RationalFunctionsandRegularFunctions5795.Morphisms5906.RationalMaps5957.Zariski’sTheoremaboutNonsingularPoints6008.ClassificationQuestionsaboutIrreducibleCurves6049.AffineAlgebraicSetsforMonomialIdeals61810.HilbertPolynomialintheAffineCase626
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 582
Context: HAN19-ch12-543-584-97801238147912011/6/13:25Page545#312.1OutliersandOutlierAnalysis545justifywhytheoutliersdetectedaregeneratedbysomeothermechanisms.Thisisoftenachievedbymakingvariousassumptionsontherestofthedataandshowingthattheoutliersdetectedviolatethoseassumptionssignificantly.Outlierdetectionisalsorelatedtonoveltydetectioninevolvingdatasets.Forexample,bymonitoringasocialmediawebsitewherenewcontentisincoming,noveltydetectionmayidentifynewtopicsandtrendsinatimelymanner.Noveltopicsmayinitiallyappearasoutliers.Tothisextent,outlierdetectionandnoveltydetectionsharesomesimilarityinmodelinganddetectionmethods.However,acriticaldifferencebetweenthetwoisthatinnoveltydetection,oncenewtopicsareconfirmed,theyareusuallyincorporatedintothemodelofnormalbehaviorsothatfollow-upinstancesarenottreatedasoutliersanymore.12.1.2TypesofOutliersIngeneral,outlierscanbeclassifiedintothreecategories,namelyglobaloutliers,con-textual(orconditional)outliers,andcollectiveoutliers.Let’sexamineeachofthesecategories.GlobalOutliersInagivendataset,adataobjectisaglobaloutlierifitdeviatessignificantlyfromtherestofthedataset.Globaloutliersaresometimescalledpointanomalies,andarethesimplesttypeofoutliers.Mostoutlierdetectionmethodsareaimedatfindingglobaloutliers.Example12.2Globaloutliers.ConsiderthepointsinFigure12.1again.ThepointsinregionRsignifi-cantlydeviatefromtherestofthedataset,andhenceareexamplesofglobaloutliers.Todetectglobaloutliers,acriticalissueistofindanappropriatemeasurementofdeviationwithrespecttotheapplicationinquestion.Variousmeasurementsarepro-posed,and,basedonthese,outlierdetectionmethodsarepartitionedintodifferentcategories.Wewillcometothisissueindetaillater.Globaloutlierdetectionisimportantinmanyapplications.Considerintrusiondetec-tionincomputernetworks,forexample.Ifthecommunicationbehaviorofacomputerisverydifferentfromthenormalpatterns(e.g.,alargenumberofpackagesisbroad-castinashorttime),thisbehaviormaybeconsideredasaglobaloutlierandthecorrespondingcomputerisasuspectedvictimofhacking
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 87
Context: Chapter6.SavingSpace73problemofhavingtogatherfrequencydataforthewholepage,apre-preparedmastercodetableisused,uponwhicheveryoneagrees.Thetablehasbeenbuiltbygatheringfrequenciesfromthousandsoftextdocumentsinseverallanguagesandtypefaces,andthencollatingthefrequenciesofthevariousblackandwhiteruns.Hereisthetableofcodesforblackandwhiterunsoflengths0to63.(Weneedlength0becausealineisalwaysassumedtobeginwhite,andazero-lengthwhiterunisrequiredifthelineactuallybeginsblack.)RunWhiteBlackRunWhiteBlack000110101000011011132000110110000011010101000011101033000100100000011010112011111340001001100001101001031000103500010100000011010011410110113600010101000011010100511000011370001011000001101010161110001038000101110000110101107111100011390010100000001101011181011000101400010100100000110110091010000010041001010100000011011011000111000010042001010110000110110101101000000010143001011000000110110111200100000001114400101101000001010100130000110000010045000001000000010101011411010000000111460000010100000101011015110101000011000470000101000000101011116101010000001011148000010100001100100171010110000011000490101001000000110010118010011100000010005001010011000001010010190001100000011001115101010100000001010011200001000000011010005201010101000000100100210010111000011011005300100100000000110111220000001100000110111540010010100000011100023000010000000101000550101100000000010011124010100000000010111560101100100000010100025010101100000011000570101101000000101100026001001100001100101058010110110000010110012701001000000110010115901001010000000101011
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 441
Context: fedintothenetwork,andthenetinputandoutputofeachunit
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 21
Context: theDedekindDiscriminantTheorem.ChapterVIintroducestoolstogetaroundtheweaknessofthedevelopmentinChapterV.Thesetoolsarevaluations,completions,anddecompositionsoftensorproductsoffieldswithcompletefields.ChapterVImakesextensiveuseofmetricspacesandcompleteness,andcompactnessplaysanimportantroleinSections9–10.AsnotedinremarkswithProposition6.7,SectionVI.2takesforgrantedthatTheorem8.54ofBasicAlgebraaboutextensionsofDedekinddomainsdoesnotneedseparabilityasahypothesis;theactualproofoftheimprovedtheoremwithoutahypothesisofseparabilityisdeferredtoSectionVII.3.ChapterVIIsuppliesadditionalbackgroundneededforalgebraicgeometry,partlyfromfieldtheoryandpartlyfromthetheoryofcommutativerings.Knowl-edgeofNoetherianringsisneededthroughoutthechapter.Sections4–5assume
####################
File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf
Page: 3
Context: ContentsPrefaceiiiLearningandIntuitionvii1DataandInformation11.1DataRepresentation.........................21.2PreprocessingtheData.......................42DataVisualization73Learning113.1InaNutshell.............................154TypesofMachineLearning174.1InaNutshell.............................205NearestNeighborsClassification215.1TheIdeaInaNutshell........................236TheNaiveBayesianClassifier256.1TheNaiveBayesModel......................256.2LearningaNaiveBayesClassifier.................276.3Class-PredictionforNewInstances.................286.4Regularization............................306.5Remarks...............................316.6TheIdeaInaNutshell........................317ThePerceptron337.1ThePerceptronModel.......................34i
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 613
Context: HAN19-ch12-543-584-97801238147912011/6/13:25Page576#34576Chapter12OutlierDetectionAswithcontextualoutlierdetection,collectiveoutlierdetectionmethodscanalsobedividedintotwocategories.Thefirstcategoryconsistsofmethodsthatreducetheprob-lemtoconventionaloutlierdetection.Itsstrategyistoidentifystructureunits,treateachstructureunit(e.g.,asubsequence,atime-seriessegment,alocalarea,orasubgraph)asadataobject,andextractfeatures.Theproblemofcollectiveoutlierdetectionisthustransformedintooutlierdetectiononthesetof“structuredobjects”constructedassuchusingtheextractedfeatures.Astructureunit,whichrepresentsagroupofobjectsintheoriginaldataset,isacollectiveoutlierifthestructureunitdeviatessignificantlyfromtheexpectedtrendinthespaceoftheextractedfeatures.Example12.23Collectiveoutlierdetectionongraphdata.Let’sseehowwecandetectcollectiveout-liersinAllElectronics’onlinesocialnetworkofcustomers.Supposewetreatthesocialnetworkasanunlabeledgraph.Wethentreateachpossiblesubgraphofthenetworkasastructureunit.Foreachsubgraph,S,let|S|bethenumberofverticesinS,andfreq(S)bethefrequencyofSinthenetwork.Thatis,freq(S)isthenumberofdifferentsubgraphsinthenetworkthatareisomorphictoS.Wecanusethesetwofeaturestodetectoutliersubgraphs.Anoutliersubgraphisacollectiveoutlierthatcontainsmultiplevertices.Ingeneral,asmallsubgraph(e.g.,asinglevertexorapairofverticesconnectedbyanedge)isexpectedtobefrequent,andalargesubgraphisexpectedtobeinfrequent.Usingtheprecedingsimplemethod,wecandetectsmallsubgraphsthatareofverylowfrequencyorlargesubgraphsthataresurprisinglyfrequent.Theseareoutlierstructuresinthesocialnetwork.Predefiningthestructureunitsforcollectiveoutlierdetectioncanbedifficultorimpossible.Consequently,thesecondcategoryofmethodsmodelstheexpectedbehav-iorofstructureunitsdirectly.Forexample,todetectcollectiveoutliersintemporalsequences,onemethodistolearnaMarkovmodelfromthesequences.Asubsequencecanthenbedeclaredasacollectiveoutlierifitsignificantlydeviatesfromthemodel.Insummary,collectiveoutlierdetectionissubtledue
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 147
Context: | | (8 KB) | the temporary result of the decompression
process before being copied to the destination
address. |
| -------- | -------- | -------- |
| | | |
| 571Ch | 1 | LHA header length. |
| 571Dh | 1 | LHA header sum (8-bit sum). |
| ... | ... | ... |
Table 5.4 Memory map of scratch-pad used by the decompression engine
3. In t
segm
com
ts are not decompressed yet. However, their original header
information was stored at 0000:6000h–0000:6xxxh in RAM. Among this
information were the starting addresses10 of the compressed component.
d to 4000h by the
Decompression_Ngine procedure in the BIOS binary image at 30_0000h–
needed.
4. The 40xxh in the header behaves as an ID that works as follows:
•
(hi-byte) is an identifier that marks it as an "Extension BIOS" to be
•
xx is an identifier that will be used in system BIOS execution to refer to the
decompressed. This will be explained more thoroughly in the system BIOS
explanation later.
Engineering
previous section: I'll just highlight the places
here the "code execution path" is obscure. By now, you're looking at the disassembly of
erboard.
his stage, only the system BIOS that is decompressed. It is decompressed to
ent 5000h and later will be relocated to segment E000h–F000h. Other
pressed componen
Subsequently, their destination segments were patche
37_FFFFh. This can be done because not all of those components will be
decompressed at once. They will be decompressed one by one during system
BIOS execution and relocated from segment 4000h as
11
40
decompressed later during original.tmp execution.
component's starting address within the image of the BIOS binary12 to be
5.1.3. Award System BIOS Reverse
I'll proceed as in the boot block in the
w
the decompressed system BIOS of the Foxconn moth
5.1.3.1. Entry Point from the "Boot Block in RAM"
This is where the boot block jumps after relocating and write-protecting the system
BIOS.
10 The starting address is in the form of a physical address.
11 The 40xxh value is the destination segment of the LHA header of the compressed component.
12 This image of the BIOS binary is already copied to RAM at 30_0000h–37_FFFFh.
41
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 705
Context: ChapterIV677converseisimmediatebecauseker((1⊗f)ØØMF)⊆ker(1⊗f)forallF.19.ThelongexactsequencefortensorproductoverRisoftheform···→TorR1(A,F)→TorR1(A,B)→A⊗RK→A⊗RF→A⊗RB→0,andTorR1(A,F)=0becauseFisprojectiveforCR.Thisestablishestheexactnessofthesequenceintheproblem.IfAisflat,then0→TorR1(A,B)→A⊗RK→A⊗RF→A⊗RB→0isexactforeachB,andTorR1(A,B)mustbe0foreachB.ConverselyifTorR1(A,B)is0foreachB,thenA⊗R(·)isanexactfunctorbyProposition4.3.HenceAisflatbydefinition.20.Ontheonehand,thelongexactsequenceassociatedtotensoringtheshortexactsequencegivenin(a)byBisoftheform0→TorR1(M,B)→TorR1(T(M),B)→F⊗RB→M⊗RB→T(M)⊗RB→0,sinceFfreeimpliesTorR1(F,B)=0.Ontheotherhand,thegivenshortexactsequencesplits,andtensoringitbyBmustdirectlyproduceashortexactsequence0→F⊗RB→M⊗RB→T(M)⊗RB→0.Thusker(F⊗RB→M⊗RB)=0,andwemustthereforehaveimage(TorR1(T(M),B)→F⊗RB)=ker(F⊗RB→M⊗RB)=0.Consequently0→TorR1(M,B)→TorR1(T(M),B)→0isexact.Thisproves(a).For(b),Problem18showsthatMisflatifandonlyifeachMFisflat,and(a)incombinationwithProblem19showsthateachMFisflatifandonlyifeachT(MF)isflat.NowsupposethatMisflat,sothatT(MF)isflatforeachfinitesubsetFofM.ThisistrueinparticularforeachfinitesubsetF0ofT(M),andT(MF0)=MF0=(T(M))F0.HenceProblem18showsthatT(M)isflat.ConverselysupposethatT(M)isflat.ThenT(M)F0isflatforeachfinitesubsetF0ofT(M).LetFbeafinitesubsetofM.ThenMFisafinitelygeneratedRsubmodule,andthestructuretheoremshowsthatT(MF)isfinitelygenerated.LetF0beasetofgeneratorsforit.ThenT(MF)=MF0=T(M)F0.ThisisflatbyProblem18,sinceT(M)isflat,andthefirstsentenceofthisparagraphallowsustoconcludethatMisflat.For(c),T(M)6=0meansthatam=0forsomenonzeroa∈Randm∈M.Leti:(a)→Rbetheinclusion,whichisone-one.Theni⊗1:(a)⊗RM→R⊗RM∼=Mhas(i⊗1)(a⊗m)=am=0.Thustheone-onemapiiscarriedtothemapi⊗1thatisnotone-one,andtensoringwithMisnotexact.SoMisnotflat.For(d),ifMisflat,thenT(M)=0by(c).ConverselyifT(M)=0,thenT(M)isflat,and(b)showsthatMisflat.
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 48
Context: 34Chapter3.StoringWordsWemight,forexample,extendoursystemofspecialcharactersinthefollowingfashion:!SectionTitle!Thisisthe$first$paragraph,whichis*important*.Inthelanguageusedforwebpages,thestartingandendingsignifiers(theyarecalled“tags”)arenotsymmetrical.Atagsuchasbeginsbold,thetagendsit.Wealsouseandforitalic,and
fortheheading,andand
toexplicitlymarkparagraphs.(Inthepreviousmethod,wehadjustusedCarriageReturnsandLineFeedstomarkthem.)Wemaywrite:SectionTitle
Thisisthefirst,whichisimportant.
Inthetypesettinglanguageusedforwritingthisbook,mark-upisintroducedwiththebackslashescapecharacter,followedbyadescriptivenameofthechangebeingmade,withthecontentsenclosedincurlybrackets{and}:\section{SectionTitle}Thisisthe\textit{first}paragraph,whichis\textbf{important}.Here,wehaveused\section{}forthesectiontitle,\textit{}foritalic,and\textbf{}forbold.Thesedifferingmark-upsystemsarenotjusthistoricalartefacts:theyservedifferentpurposes.Therequirementsmaybewhollydifferentforadocumenttobeprinted,tobeputontheweb,ortobeviewedonaneBookreader.Wepromisedtotalkaboutrepresentingtheworld’smanylan-guagesandwritingsystems.Since1989,therehasbeenaninter-nationalindustrialeffort,undertheUnicodeinitiative,toencodemorethanonehundredthousandcharacters,givingeachanumber,anddefininghowtheymaybecombinedinvalidways.Therearemorethanamilliontotalslotsavailableforfutureuse.ItisimportanttosaythattheUnicodesystemisconcernedonlywithassigningcharacterstonumbers.Itdoesnotspecifytheshapesthosecharacterstake:thatisamatterfortypefacedesigners.Theprincipleisoneofseparationofconcerns:thateachpartofacom-putersystemshoulddoonejobwellandallowinteractionwiththeother,similarlywell-designedcomponents.ThisisparticularlydifficultfortheUnicodesystem,whichmustnavigateinnumerableculturaldifferencesandawidevarietyofpossibleuses.ThefollowingfivepagesgivesomeexamplesdrawnfromthehugeUnicodestandard.
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 354
Context: 7.6 Pattern Exploration and Application
317
Table 7.4 Annotations Generated for Frequent Patterns in the DBLP Data Set
Pattern
Type
Annotations
| christos faloutsos | Context indicator Representative
transactions
Representative
transactions
Representative
transactions | spiros papadimitriou multi-attribute hash use gray code
recovery latent time-series observe sum
network tomography particle filter
index multimedia database tutorial |
| |Semantic similar
patterns | spiros papadimitriou&christos faloutsos;
spiros papadimitriou; flip korn;
timos k selli;
ramakrishnan srikant;
ramakrishnan srikant&rakesh agrawal |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| informationretrieval | Context indicator | w bruce croft; web information;monika rauch henzinger;james p callan; full-text |
| |Representative
transactions
Representative
transactions | web information retrieval
language model information retrieval |
| |Semantic similar
patterns | information use; web information;
probabilistic information; information
filter;
text information |
In both scenarios, the representative transactions extracted give us the titles of papers
that effectively capture the meaning of the given patterns. The experiment demonstrates
the effectiveness of semantic pattern annotation to generate a dictionary-like annota-
tion for frequent patterns, which can help a user understand the meaning of annotated
patterns.
The context modeling and semantic analysis method presented here is general and
can deal with any type of frequent patterns with context information. Such semantic
annotations can have many other applications such as ranking patterns, categorizing
and clustering patterns with semantics, and summarizing databases. Applications of
the pattern context model and semantical analysis method are also not limited to pat-
tern annotation; other example applications include pattern compression, transaction
clustering, pattern relations discovery, and pattern synonym discovery.
7.6.2 Applications of Pattern Mining
We have studied many aspects of frequent pattern mining, with topics ranging from effi-
cient mining algorithms and the diversity of patterns to pattern interestingness, pattern
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 202
Context: 0000:001A0044 dd 40000h ; dest seg = 4000h; size = 5D56h (relocated) 0000:001A0048 dd 80005D56h 0000:001A004C dd 0A8530h ; dest seg = A853h; size = 82FCh (relocated) 0000:001A0050 dd 800082FCh 0000:001A0054 dd 49A90h ; dest seg = 49A9h; size = A29h (relocated) 0000:001A0058 dd 80000A29h 0000:001A005C dd 45D60h ; dest seg = 45D6h; size = 3D28h (relocated) 0000:001A0060 dd 80003D28h 0000:001A0064 dd 0A0000h ; dest seg = A000h; size = 55h (relocated) 0000:001A0068 dd 80000055h 0000:001A006C dd 0A0300h ; dest seg = A030h; size = 50h (relocated) 0000:001A0070 dd 80000050h 0000:001A0074 dd 400h ; dest seg = 40h; size = 110h (NOT relocated) 0000:001A0078 dd 110h 0000:001A007C dd 510h ; dest seg = 51h; size = 13h (NOT relocated) 0000:001A0080 dd 13h 0000:001A0084 dd 1A8E0h ; dest seg = 1A8Eh; size = 7AD0h (relocated) 0000:001A0088 dd 80007AD0h 0000:001A008C dd 0 ; dest seg = 0h; size = 400h (NOT relocated) 0000:001A0090 dd 400h 0000:001A0094 dd 266F0h ; dest seg = 266Fh; size = 101Fh (relocated) 0000:001A0098 dd 8000101Fh 0000:001A009C dd 2EF60h ; dest seg = 2EF6h; size = C18h (relocated) 0000:001A00A0 dd 80000C18h 0000:001A00A4 dd 30000h ; dest seg = 3000h; size = 10000h 0000:001A00A4 ; (NOT relocated) 0000:001A00A8 dd 10000h 0000:001A00AC dd 4530h ; dest seg = 453h; size = EFF0h 0000:001A00AC ; (NOT relocated) 0000:001A00B0 dd 0EFF0h 0000:001A00B4 dd 0A8300h ; dest seg = A830h; size = 230h (relocated) 0000:001A00B8 dd 80000230h 0000:001A00BC dd 0E8000h ; dest seg = E800h; size = 8000h 0000:001A00BC ; (NOT relocated) 0000:001A00C0 dd 8000h 0000:001A00C4 dd 0A7D00h ; dest seg = A7D0h; size = 200h 0000:001A00C4 ; (NOT relocated) 0000:001A00C8 dd 200h 0000:001A00CC dd 0B0830h ; dest seg = B083h; size = F0h (relocated) 0000:001A00D0 dd 800000F0h 0000:001A00D4 dd 0A8000h ; dest seg = A800h; size = 200h 0000:001A00D4 ; (NOT relocated) 0000:001A00D8 dd 200h 0000:001A00DC dd 530h ; dest seg = 53h; size = 4000h 0000:001A00DC ; (NOT relocated) 0000:001A00E0 dd 4000h 0000:001A00E4 dd 0A7500h ; dest seg = A750h; size = 800h 0000:001A00E4 ; (NOT relocated) 0000:001A00E8 dd 800h 0000:001A00EC dd 0C0000h ; dest seg = C000h; size = 20000h 0000:001A00EC ; (NOT relocated) 96
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 122
Context: ning,dataintegration,datareduction,anddatatransformation.Datacleaningroutinesworkto“clean”thedatabyfillinginmissingvalues,smooth-ingnoisydata,identifyingorremovingoutliers,andresolvinginconsistencies.Ifusersbelievethedataaredirty,theyareunlikelytotrusttheresultsofanydataminingthathasbeenapplied.Furthermore,dirtydatacancauseconfusionfortheminingprocedure,resultinginunreliableoutput.Althoughmostminingroutineshavesomeproceduresfordealingwithincompleteornoisydata,theyarenotalwaysrobust.Instead,theymayconcentrateonavoidingoverfittingthedatatothefunctionbeingmodeled.Therefore,ausefulpreprocessingstepistorunyourdatathroughsomedatacleaningroutines.Section3.2discussesmethodsfordatacleaning.GettingbacktoyourtaskatAllElectronics,supposethatyouwouldliketoincludedatafrommultiplesourcesinyouranalysis.Thiswouldinvolveintegratingmultipledatabases,datacubes,orfiles(i.e.,dataintegration).Yetsomeattributesrepresentinga
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 475
Context: CHAPTERVIIIBackgroundforAlgebraicGeometryAbstract.Thischapterintroducesaspectsofthealgebraictheoryofsystemsofpolynomialequationsinseveralvariables.Section1givesabriefhistoryofthesubject,treatingitasoneoftwoearlysourcesofquestionstobeaddressedinalgebraicgeometry.Section2introducestheresultantasatoolforeliminatingoneofthevariablesinasystemoftwosuchequations.AfirstformofBezout’sTheoremisanapplication,sayingthatiff(X,Y)andg(X,Y)arepolynomialsofrespectivedegreesmandnwhoselocusofcommonzeroshasmorethanmnpoints,thenfandghaveanontrivialcommonfactor.Thisversionofthetheoremmayberegardedaspertainingtoapairofaffineplanecurves.Section3passestoprojectiveplanecurves,whicharenonconstanthomogeneouspolynomialsinthreevariables,twosuchbeingregardedasthesameiftheyaremultiplesofoneanother.VersionsoftheresultantandBezout’sTheoremarevalidinthiscontext,andtwoprojectiveplanecurvesdefinedoveranalgebraicallyclosedfieldalwayshaveacommonzero.Sections4–5introduceintersectionmultiplicityforprojectiveplanecurves.Section4treatsalineandacurve,andSection5treatsthegeneralcaseoftwocurves.ThetheoryinSection4iscompletelyelementary,andaversionofBezout’sTheoremisprovedthatsaysthatalineandacurveofdegreedhaveexactlydcommonzeros,providedtheunderlyingfieldisalgebraicallyclosed,thezerosarecountedasoftenastheirintersectionmultiplicities,andthelinedoesnotdividethecurve.Section5makesmoreserioususeofalgebraicbackground,particularlylocalizationsandtheNullstellensatz.Itgivesanindicationthatostensiblysimplephenomenainthesubjectcanrequiresophisticatedtoolstoanalyze.Section6provesaversionofBezout’sTheoremappropriateforthecontextofSection5:ifFandGaretwoprojectiveplanecurvesofrespectivedegreesmandnoveranalgebraicallyclosedfield,theneithertheyhaveanontrivialcommonfactorortheyhaveexactlymncommonzeroswhentheintersectionmultiplicitiesofthezerosaretakenintoaccount.Sections7–10concernGr¨obnerbases,whicharefinitegeneratingsetsofaspecialkindforidealsinapolynomialalgebraoverafield.Section7setsthestage,introducingmonomialordersandde
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 345
Context: HAN14-ch07-279-326-97801238147912011/6/13:21Page308#30308Chapter7AdvancedPatternMiningpattern/ruleinterestingnessandcorrelation(Section6.3)canalsobeusedtohelpconfinethesearchtopatterns/rulesofinterest.Inthissection,welookattwoformsof“compression”offrequentpatternsthatbuildontheconceptsofclosedpatternsandmax-patterns.RecallfromSection6.2.6thataclosedpatternisalosslesscompressionofthesetoffrequentpatterns,whereasamax-patternisalossycompression.Inparticular,Section7.5.1exploresclustering-basedcompressionoffrequentpatterns,whichgroupspatternstogetherbasedontheirsimilar-ityandfrequencysupport.Section7.5.2takesa“summarization”approach,wheretheaimistoderiveredundancy-awaretop-krepresentativepatternsthatcoverthewholesetof(closed)frequentitemsets.Theapproachconsidersnotonlytherepresentativenessofpatternsbutalsotheirmutualindependencetoavoidredundancyinthesetofgener-atedpatterns.Thekrepresentativesprovidecompactcompressionoverthecollectionoffrequentpatterns,makingthemeasiertointerpretanduse.7.5.1MiningCompressedPatternsbyPatternClusteringPatterncompressioncanbeachievedbypatternclustering.ClusteringtechniquesaredescribedindetailinChapters10and11.Inthissection,itisnotnecessarytoknowthefinedetailsofclustering.Rather,youwilllearnhowtheconceptofclusteringcanbeappliedtocompressfrequentpatterns.Clusteringistheautomaticprocessofgroupinglikeobjectstogether,sothatobjectswithinaclusteraresimilartooneanotheranddis-similartoobjectsinotherclusters.Inthiscase,theobjectsarefrequentpatterns.Thefrequentpatternsareclusteredusingatightnessmeasurecalledδ-cluster.Arepresenta-tivepatternisselectedforeachcluster,therebyofferingacompressedversionofthesetoffrequentpatterns.Beforewebegin,let’sreviewsomedefinitions.AnitemsetXisaclosedfrequentitemsetinadatasetDifXisfrequentandthereexistsnopropersuper-itemsetYofXsuchthatYhasthesamesupportcountasXinD.AnitemsetXisamaximalfrequentitemsetindatasetDifXisfrequentandthereexistsnosuper-itemsetYsuchthatX⊂YandYisfrequentinD.Usingtheseconceptsaloneisnotenoughtoobt
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 197
Context: 1.Overview169orexactcomplex,passingtoanothercomplexbymeansofafunctorwithsomespecialproperties,andthenextractingthehomologyorcohomologyoftheimagecomplex.Twocategoriesarethusinvolved,onefortheresolutionandoneforthevaluesofthefunctor.Fromanexpositorypointofview,itseemswisetostartwithconcretecategoriesandnottotrytoidentifythemostgeneralcategoriesforwhichthetheorymakessense.Formuchofthechapter,weshallworkwithacategorynotmuchmoregeneralthanthecategoryCRofallunitalleftRmodules,whereRisaringwithidentity,andourfunctorswillpassfromonesuchcategorytoanother.UseofcategoriesCRsubsumesthefollowingapplications:(i)manipulationswithbasichomologyandcohomologyintopology,inwhichonebeginswiththeringR=Zofintegers.Formoreadvancedapplicationsintopology,onemovesfromZtomoregeneralrings.(ii)homologyandcohomologyofgroups,inwhichoneinitiallyusesgroupringsoftheformZG,whereGisanygroupandZistheringofintegers.(iii)homologyandcohomologyofLiealgebras.IfgisaLiealgebraoverafieldsuchasC,thenghasa“universalenvelopingalgebra”U(g)andacanonicalmapping∂:g→U(g).HereU(g)isacomplexassociativealgebrawithidentity,∂isaLiealgebrahomomorphism,andthepair(U(g),∂)hasthefollowinguniversalmappingproperty:when-everϕ:g→AisaLiealgebrahomomorphismintoacomplexasso-ciativealgebraAwithidentity,thenthereisauniquehomomorphism8:U(g)→Aofassociativealgebraswithidentitysuchthatϕ=8◦∂.Liealgebrahomologyandcohomologyarethetheoryfortheset-upinwhichtheinitialunderlyingringsareU(g)andC.Inotherwords,ineachofthethreeapplicationsabove,manyderivedfunctorsofimportancepassfromthecategoryCRforaringRwithidentitytothecategoryCSforanotherringSwithidentity.TheslightgeneralizationofcategoriesCRthatweshalluseformuchofthechapterisasfollows:LetRbearingwithidentity.AgoodcategoryCofRmodulesconsistsof(i)somenonemptyclassofunitalleftRmodulesclosedunderpassagetosubmodules,quotients,andfinitedirectsums(themodulesofthecategory),(ii)thefullsetsHomR(A,B)ofallRlinearhomomorphismsfromAtoBforeachAandBasin(i)(themorphisms,ormaps,ofthecategory).Forexamplethecoll
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 425
Context: 11.Problems39711.Problems1.IfFisacompletefieldwithanonarchimedeanabsolutevalueandifP∞n=1anisaninfiniteserieswhosetermsanareinF,provethattheseriesconvergesinFifandonlyiflimnan=0.2.Letthe2-adicabsolutevaluebeimposedonQ.Theorem6.5showsthatZisdenseinthesubringofQconsistingofallrationalswithodddenominator.(a)Findasequenceofintegersconverginginthismetricto13.(b)Generalizetheresultof(a)byfindinganexplicitsequenceofintegersconverginginthismetrictoanygivenrationalab−1,whereaandbarenonzerointegerswithbodd.3.FortheDedekinddomainR=ZanditsfieldoffractionsK=Q,theringofunitsR×isjust{±1},andthesetofarchimedeanplacesisjustS∞={∞}.Theformula∂(R×)=∂(K×)∩I(S∞)ofSection10thereforebecomes{∂(±1)}=∂(Q×)∩°R××QpZ×p¢.(a)Verifythisformuladirectly.(b)SinceZisaprincipalidealdomain,thetheoryofSection10andtheaboveremarksshowthatI=∂(Q×)°R××QpZ×p¢.Provethisformulabyanexplicitconstructionwhoseonlyallowablechoice,inviewof(a),isacertainsign.4.LetRbetheDedekinddomainZ[p−5].(a)Verifyforeachchoiceofsignthattheideals(1±p−5,3)and(1±p−5,2)areprimeandthat(1+p−5,2)=(1−p−5,2).(b)Findtheprimefactorizationsoftheprincipalideals(1+p−5)and(3).(c)LetPbetheprimeidealP=(1+p−5,3),andletvPbethevaluationofRdeterminedbyP.ProvethatvP°(1+p−5)/3¢=0.(d)Lemma6.3showsthat(1+p−5)/3canbewrittenasthequotientoftwomembersaandbofRwithvP(a)=vP(b)=0.Findsuchachoiceofaandb.5.LetvbeadiscretevaluationofafieldF,letRvbethevaluationring,andletPvbethevaluationideal.ItwasobservedafterProposition6.2that1+Pnvisagroupundermultiplicationforanyn∏1.Proveforn∏1thatthemultiplicativegroup(1+Pnv)/(1+Pn+1v)isisomorphictotheadditivegroupPnv/Pn+1vunderthemappinginducedby1+x7→x+Pn+1v.6.DerivethefinitenessoftheclassnumberofanumberfieldKfromthecompact-nessofI1K/∂(K×)givenasTheorem6.53.Problems7–8comparethetopologyontheidelesI=IKofanumberfieldKwiththetopologyoftheadelesA=AK.ThenotationisasinSection10.
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 157
Context: Chapter 10. Words to Paragraphs
143
The finished paragraphs of type are arranged in a galley. This
will be used to make prints of the page (or pages – two or four may
be printed from one galley, then folded and cut). You can imagine
how long it takes to make up the galleys for a book, and how much
time is required to justify each line by inserting exactly the right
spaces and hyphenating by hand. Mistakes found after test prints
can be very costly to fix, since they necessitate taking apart the
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 93
Context: Chapter 6. Saving Space
79
a
4
0010
l
1
010101
f
4
0000
v
1
01010000
c
4
11011
y
1
01010001
u
4
10101
.
1
01010010
i
3
10100
1101000111100001110011100100011100111010001100100
1001100110110001111111001001111010011011011111100
1000111001110100001011010110011110101110001111011
0000001110110110011011101001010101110110111111000
1101110101000000001110000011000111110110111100010
0111011011011101011110001010110100010100001001101
0111100101011111101101111001111011101000100100111
1011011110001010001111011011011110111010100110101
0010
3. Encode the following fax image. There is no need to use zero-
length white runs at the beginning of lines starting with a
black pixel.
4. Decode the following fax image to the same 37x15 grid. There
are no zero-length white runs at the beginning of lines starting
with a black pixel.
0001011000001110001111110001111000001110000001001
0110000100100000010001111111001010001011001001111
1110010000011111111011011110111111011111111011000
0111111100100111111011110111111100100000111000100
1000111011110111000100011100010010001110111101110
0010001111111001001111110111101111111001000001111
1111011011111101111011111111011000011111111011011
1101110100111111110110000111111110110111011110011
1000111110110000111000010010000000100100000010001
110000111000111111001011100010101100010110
####################
File: Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf
Page: 55
Context: Chapter3ProblemSolvingParadigmsIfallyouhaveisahammer,everythinglookslikeanail—AbrahamMaslow,19623.1OverviewandMotivationInthischapter,wehighlightfourproblemsolvingparadigmscommonlyusedtoattackproblemsinprogrammingcontests,namelyCompleteSearch,Divide&Conquer,Greedy,andDynamicProgramming.BothIOIandICPCcontestantsneedtomasteralltheseproblemsolvingparadigmssothattheycanattackthegivenproblemwiththeappropriate‘tool’,ratherthan‘hammering’everyproblemwiththebrute-forcesolution(whichisclearlynotcompetitive).Ouradvicebeforeyoustartreading:Donotjustrememberthesolutionsfortheproblemsdiscussedinthischapter,butremembertheway,thespiritofsolvingthoseproblems!3.2CompleteSearchCompleteSearch,alsoknownasbruteforceorrecursivebacktracking,isamethodforsolvingaproblembysearching(upto)theentiresearchspacetoobtaintherequiredsolution.Inprogrammingcontests,acontestantshoulddevelopaCompleteSearchsolutionwhenthereisclearlynocleveralgorithmavailable(e.g.theproblemofenumeratingallpermutationsof{0,1,2,...,N−1},whichclearlyrequiresO(N!)operations)orwhensuchcleveralgorithmsexist,butoverkill,astheinputsizehappenstobesmall(e.g.theproblemofansweringRangeMinimumQueryasinSection2.3.3butonastaticarraywithN≤100–solvablewithanO(N)loop).InICPC,CompleteSearchshouldbethefirstsolutiontobeconsideredasitisusuallyeasytocomeupwiththesolutionandtocode/debugit.Rememberthe‘KISS’principle:KeepItShortandSimple.Abug-freeCompleteSearchsolutionshouldneverreceiveWrongAnswer(WA)responseinprogrammingcontestsasitexplorestheentiresearchspace.However,manyprogrammingproblemsdohavebetter-than-Complete-Searchsolutions.ThusaCompleteSearchsolutionmayreceiveaTimeLimitExceeded(TLE)verdict.Withproperanalysis,youcandeterminewhichisthelikelyoutcome(TLEversusAC)beforeattemptingtocodeanything(Table1.4inSection1.2.2isagoodgauge).IfCompleteSearchcanlikelypassthetimelimit,thengoahead.ThiswillthengiveyoumoretimetoworkontheharderproblemswhereCompleteSearchistooslow.InIOI,weusuallyneedbetterproblemsolvingtechniquesasCompleteSearchsolutionsareusu
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 216
Context: HAN11-ch04-125-186-97801238147912011/6/13:17Page179#554.6Summary179Adatacubeconsistsofalatticeofcuboids,eachcorrespondingtoadifferentdegreeofsummarizationofthegivenmultidimensionaldata.Concepthierarchiesorganizethevaluesofattributesordimensionsintogradualabstractionlevels.Theyareusefulinminingatmultipleabstractionlevels.Onlineanalyticalprocessingcanbeperformedindatawarehouses/martsusingthemultidimensionaldatamodel.TypicalOLAPoperationsincluderoll-up,anddrill-(down,across,through),slice-and-dice,andpivot(rotate),aswellasstatisticaloperationssuchasrankingandcomputingmovingaveragesandgrowthrates.OLAPoperationscanbeimplementedefficientlyusingthedatacubestructure.Datawarehousesareusedforinformationprocessing(queryingandreporting),analyticalprocessing(whichallowsuserstonavigatethroughsummarizedanddetaileddatabyOLAPoperations),anddatamining(whichsupportsknowledgediscovery).OLAP-baseddataminingisreferredtoasmultidimensionaldatamin-ing(alsoknownasexploratorymultidimensionaldatamining,onlineanalyticalmining,orOLAM).Itemphasizestheinteractiveandexploratorynatureofdatamining.OLAPserversmayadoptarelationalOLAP(ROLAP),amultidimensionalOLAP(MOLAP),orahybridOLAP(HOLAP)implementation.AROLAPserverusesanextendedrelationalDBMSthatmapsOLAPoperationsonmultidimensionaldatatostandardrelationaloperations.AMOLAPservermapsmultidimensionaldataviewsdirectlytoarraystructures.AHOLAPservercombinesROLAPandMOLAP.Forexample,itmayuseROLAPforhistoricdatawhilemaintainingfrequentlyaccesseddatainaseparateMOLAPstore.Fullmaterializationreferstothecomputationofallofthecuboidsinthelatticedefiningadatacube.Ittypicallyrequiresanexcessiveamountofstoragespace,particularlyasthenumberofdimensionsandsizeofassociatedconcepthierarchiesgrow.Thisproblemisknownasthecurseofdimensionality.Alternatively,partialmaterializationistheselectivecomputationofasubsetofthecuboidsorsubcubesinthelattice.Forexample,anicebergcubeisadatacubethatstoresonlythosecubecellsthathaveanaggregatevalue(e.g.,count)abovesomeminimumsupportthreshold.O
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 737
Context: ChapterX70911.Continuityisnoproblem.Fortheconditioninvolvingregularity,weuseProblem10.LetEbearelativelyopensetinV,andletfbeinO(E).Wearetoshowthatf◦ϕisinO(ϕ−1(E)).ThusletPbeinϕ−1(E)⊆U;thenϕ(P)isinE⊆V.SincefisinO(E),Problem10producesarelativelyopenneighborhoodE0ofϕ(P),anopensubseteE0ofYwitheE0∩V=E0,andafunctionFinO(eE0)suchthatFØØE0=fØØE0.Sinceϕ:X→Yisamorphism,F◦ϕisinO(ϕ−1(eE0)).Sinceϕ(ϕ−1(eE0)∩U)⊆eE0∩V=E0,F◦ϕagreeswithf◦ϕonϕ−1(eE0)∩U.Thusf◦ϕhasanextensionF◦ϕfromϕ−1(eE0)∩Utoϕ−1(eE0)thatisinO(eE0).ThequotientsthatexhibitF◦ϕasdefinedatpointsofϕ−1(eE0)∩Uexhibitf◦ϕasdefinedthere.Theinclusionϕ−1(E0)=ϕ−1(eE0∩V)=ϕ−1(eE0)∩ϕ−1(V)⊆ϕ−1(eE0)∩Ushowsthatf◦ϕisinO(ϕ−1(E0)).ThisbeingtrueforallPinϕ−1(E),f◦ϕisinO(ϕ−1(E)).12.Part(a)followsbyapplyinginstancesofProblem11toϕandϕ−1.Then(b)followsbyanotherapplicationofProblem11.Part(c)followsbyinductiveapplicationof(b).13.LetdibethedegreeofhomogeneityofFi.Thentheithrowoftheright-handmatrixis∏di−1timestheithrowoftheleft-handmatrix.Hencethedimensionofthespanoftherowsisthesameforthetwomatrices,andthisnumberistherank.14.ThiscomesdowntothefactthatdifferentiatingwithrespecttoXjforj>0andthensettingX0equalto1isthesameassettingX0equalto1andthendifferentiatingwithrespecttoXj.15.ForanyofthefunctionsFi,therightsideoftheformulainEuler’sTheoremis0at(x0,...,xn)byassumption.HenceEuler’sTheoremgivesx0@Fi@X0(x0,...,xn)=−Pnj=1xj@Fi@Xj(x0,...,xn).Thissaysthatx0×0thcolumnofJ(F)(x0,...,xn)=−nPj=1xj×jthcolumnofJ(F)(x0,...,xn).Sincex06=0,thisisarelationoftherequiredtype.16.Problem13showsthattheleftsideequalsrankJ(F)(1,x1/x0,...,xn/x0),whichProblem15showstobeequaltotherankofthematrixformedfromthelastncolumns,whichProblem14showstobeequaltotherankofJ(f)(x1/x0,...,xn/x0).18.Regardtheelementswijastheentriesofamatrix.Thegivenconditionisthatevery2-by-2subdeterminantofthismatrixequals0.Thematrixisnot0,andconsequentlyitsrankis1.Everymatrixoverkofrank1isoftheformxytforcolumnvectorsxandy,andthen[{wij}]isexhibiteda
####################
File: Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf
Page: 426
Context: 398VI.ReinterpretationwithAdelesandIdeles7.ForeachfinitesetSofplacescontainingthearchimedeanplaces,exhibitthemappingsI(S)→Kvforv∈SandI(S)→Rvforv/∈Sascontinuous,anddeducethattheinclusionI→Aiscontinuous.8.LetpnbethenthpositiveprimeinZ,andletxn=(xn,v)vbetheadeleinAQwithxn,v=pnifv=pnandxn,v=1ifv6=pn.Theresultisasequence{xn}ofidelesinIQ.Showthatthissequenceconvergestotheidele(1)vinthetopologyoftheadelesbutdoesnotconvergeinthetopologyoftheideles.Problems9–10belowassumeknowledgefrommeasuretheoryofelementaryprop-ertiesofmeasuresandoftheexistence–uniquenesstheoremfortranslation-invariantmeasures(Haarmeasures)onlocallycompactabeliangroups.ThecontinuityinProblem10arequiresmakingestimatesofintegrals.9.LetGbealocallycompactabeliantopologicalgroupwithaHaarmeasurewrittenasdx,andlet8beanautomorphismofGasatopologicalgroup,i.e.,anautomorphismofthegroupstructurethatisalsoahomeomorphismofG.Provethatthereisapositiveconstanta(8)suchthatd(8(x))=a(8)dx.10.LetFbealocallycompacttopologicalfield,andletF×bethegroupofnonzeroelements,thegroupoperationbeingmultiplication.(a)LetcbeinF×,anddefine|c|Ftobetheconstanta(8)fromthepreviousproblemwhenthemeasureisanadditiveHaarmeasureand8ismultipli-cationbyc.Define|0|F=0.Provethatc7→|c|FisacontinuousfunctionfromFinto[0,+∞)suchthat|c1c2|F=|c1|F|c2|F.(b)IfdxisaHaarmeasureforFasanadditivelocallycompactgroup,provethatdx/|x|FisaHaarmeasureforF×asamultiplicativelocallycompactgroup.(c)LetF=Rbethelocallycompactfieldofrealnumbers.Computethefunctionx7→|x|F.DothesamethingforthelocallycompactfieldF=Cofcomplexnumbers.(d)LetF=Qpbethelocallycompactfieldofp-adicnumbers,wherepisaprime.Computethefunctionx7→|x|F.(e)ForthefieldF=Qpofp-adicnumbers,supposethattheringZpofp-adicintegershasadditiveHaarmeasure1.WhatistheadditiveHaarmeasureofthemaximalidealIofZp?Problems11–14analyzethestructureofcompletevaluedfieldswhoseresidueclassfieldsarefinite,showingthattheonlykindsarep-adicfieldsandfieldsofformalLaurentseriesoverafinitefield.LetFbeacompletevaluedfieldwithadiscretenonarchime
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 118
Context: 2.7 Bibliographic Notes
81
(c) Numeric attributes
(d) Term-frequency vectors
2.6 Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8):
(a) Compute the Euclidean distance between the two objects.
(b) Compute the Manhattan distance between the two objects.
(c) Compute the Minkowski distance between the two objects, using q = 3.
(d) Compute the supremum distance between the two objects.
2.7 The median is one of the most important holistic measures in data analysis. Pro-
pose several methods for median approximation. Analyze their respective complexity
under different parameter settings and decide to what extent the real value can be
approximated. Moreover, suggest a heuristic strategy to balance between accuracy and
complexity and then apply it to all methods you have given.
2.8 It is important to define or select similarity measures in data analysis. However, there
is no commonly accepted subjective similarity measure. Results can vary depending on
the similarity measures used. Nonetheless, seemingly different similarity measures may
be equivalent after some transformation.
Suppose we have the following 2-D data set:
| | A
1 | A
2 |
| -------- | -------- | -------- |
| x
1 | 1.5 | 1.7 |
| x
2 | 2 | 1.9 |
| x3 | 1.6 | 1.8 |
| x
4 | 1.2 | 1.5 |
| x
5 | 1.5 | 1.0 |
(a) Consider the data as 2-D data points. Given a new data point, x = (1.4,1.6) as a
query, rank the database points based on similarity with the query using Euclidean
distance, Manhattan distance, supremum distance, and cosine similarity.
(b) Normalize the data set to make the norm of each data point equal to 1. Use Euclidean
distance on the transformed data to rank the data points.
2.7 Bibliographic Notes
Methods for descriptive data summarization have been studied in the statistics literature
long before the onset of computers. Good summaries of statistical descriptive data min-
ing methods include Freedman, Pisani, and Purves [FPP07] and Devore [Dev95]. For
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 132
Context: The last thing to note
the
normal boot block code
tion
i
that takes place if the system BIO
As promised, I now delv
e d
f the decompression routine for the
system BIOS, mentioned in point
ompressed c
po
LZH le
header for
Th
ill be
located after decompression are
t. The format is provided in
table 5.2. Remember that it applies t
is that the
path, wh
S is corrupt
e into th
boot block explanation here only covers
ch means it didn't explain the boot block POST
ed.
etails o
execu
5. Start by learn
nent in an
e address ra
contained with
o all com
ing the prerequisites.
Award BIOS uses a modified version of the
nges where these BIOS components w
in this forma
The c
vel-1
om
mat.
pressed components.
| | Starting | | |
| -------- | -------- | -------- | -------- |
| Starting Offset | | | |
| |Offset in | Size in | |
| from First Byte | | | Contents |
| |LZH Basic | Bytes | |
| (from Preheader) | | | |
| |Header | | |
| | | 1 for | The header length of the component. It
depends on the file/component name. The
formula is header_length = filename_length +
25. |
| | | preheader, | |
| 00h | N/A | N/A for | |
| | | LZH basic | |
| | | header | |
| | | 1 for | The header 8-bit checksum, not including the
first 2 bytes (header length and header
checksum byte). |
| | | preheader, | |
| 01h | N/A | N/A for | |
| | | LZH basic | |
| | | header | |
| | | | LZH method ID (ASCII string signature). In
Award BIOS, it's "-lh5-," which means: 8-KB
sliding dictionary (max 256 bytes) + static
Huffman + improved encoding of position and
trees. |
| 02h | 00h | 5 | |
| | | | Compressed file or component size in little
endian dword value, i.e., MSB8 at 0Ah, and so
forth. |
| 07h | 05h | 4 | |
| | | | Uncompressed file or component size in little
endian dword value, i.e., MSB at 0Eh, and so
forth. |
| 0Bh | 09h | 4 | |
| | | | Destination offset address in little endian word
value, i.e., MSB at 10h, and so forth. The
component will be decompressed into this
offset address (real-mode addressing is in
effect here). |
| 0Fh | 0Dh | 2 | |
| | | | Destination segment address in little endian
word value, i.e., MSB at 12h, and so forth. The |
| 11h | 0Fh | 2 | |
8 MSB stands for most significant bit.
26
##########
"""QUERY: Please summarize the whole context. It is important that you include a summary for each file. All files should be included, so please make sure to go through the entire context"""
Consider the chat history for relevant information. If query is already asked in the history double check the correctness of your answer and maybe correct your previous mistake. If you find information separated by a | in the context, it is a table formatted in Markdown - the whole context is formatted as md structure.
Final Files Sources: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 82, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 353, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 19, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 584, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 351, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 352, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 4, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 612, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 717, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 10, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 17, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 167, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 618, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 287, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 583, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 52, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 611, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 228, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 80, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 136, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 166, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 149, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 18, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 273, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 36, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 107, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 153, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 76, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 610, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 212, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 66, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 187, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 81, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 14, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 349, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 151, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 528, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 27, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 194, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 324, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 716, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 582, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 155, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 363, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 4, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 337, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 108, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 28, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 157, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 9, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 257, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 494, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 22, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 19, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 8, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 154, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 53, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 5, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 104, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 16, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 427, Analytic%20Geometry%20%281922%29%20-%20Lewis%20Parker%20Siceloff%2C%20George%20Wentworth%2C%20David%20Eugene%20Smith%20%28PDF%29.pdf - Page 1, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 237, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 12, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 43, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 474, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 341, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 92, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 36, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 662, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 27, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 580, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 70, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 21, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 400, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 112, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 8, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 422, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 8, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 746, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 7, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 294, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 190, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 183, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 357, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 9, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 87, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 441, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 3, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 613, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 147, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 705, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 48, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 354, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 202, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 122, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 475, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 345, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 197, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 425, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 157, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 93, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 55, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 216, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 737, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf - Page 426, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 118, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 132
==================================================
**Elapsed Time: 8.44 seconds**
==================================================
FINAL ANSWER
Answer: The provided context is a collection of excerpts from various PDF files, each focusing on different aspects of computer science and related fields.
**File: A MACHINE MADE THIS BOOK ten sketches of computer science - JOHN WHITINGTON (PDF).pdf**
This file explores the process of creating a book using computers, covering topics like typography, layout, and compression. It delves into the challenges of representing different languages and writing systems, and how computers handle them. The excerpt provided focuses on data compression, specifically using Huffman codes to reduce the size of text.
**File: Data Mining Concepts and Techniques - Jiawei Han, Micheline Kamber, Jian Pei (PDF).pdf**
This file is a textbook on data mining, covering various techniques and concepts. The excerpts provided focus on advanced pattern mining, specifically semantic pattern annotation. This involves understanding the context of a pattern and using that information to generate annotations that help users understand its meaning. The file also discusses contextual and collective outliers, which are data points that deviate significantly from the rest of the dataset based on specific contexts or group behavior.
**File: Advanced Algebra - Anthony W. Knapp (PDF).pdf**
This file is a textbook on advanced algebra, focusing on algebraic number theory and algebraic geometry. The excerpts provided offer a guide for the reader, outlining the key concepts and dependencies between chapters. It highlights the importance of understanding the relationship between number theory and geometry, and how these concepts are used in other fields of mathematics.
**File: Competitive Programming, 2nd Edition - Steven Halim (PDF).pdf**
This file is a guide for competitive programmers, covering various data structures, algorithms, and problem-solving paradigms. The excerpts provided focus on string processing, specifically basic string processing skills and how to use them in competitive programming contests. It also includes a section on string processing with dynamic programming.
**File: BIOS Disassembly Ninjutsu Uncovered 1st Edition - Darmawan Salihun (PDF) BIOS_Disassembly_Ninjutsu_Uncovered.pdf**
This file is a guide to reverse engineering BIOS, focusing on understanding the structure and functionality of BIOS code. The excerpts provided focus on using tools like ctags and vi to navigate and understand source code, as well as the process of decompressing and relocating BIOS components. It also discusses the concept of trusted computing and how it is implemented in PC architecture.
**File: A First Encounter with Machine Learning - Max Welling (PDF).pdf**
This file is an introduction to machine learning, covering various concepts and techniques. The excerpts provided focus on the Naive Bayesian classifier, which is a simple yet effective method for classifying data. It also discusses the limitations of this classifier and how to address them.
**File: Analytic Geometry (1922) - Lewis Parker Siceloff, George Wentworth, David Eugene Smith (PDF).pdf**
This file is a textbook on analytic geometry, published in 1922. The excerpt provided is the table of contents, outlining the various topics covered in the book, including geometric magnitudes, loci, straight lines, circles, parabolas, ellipses, hyperbolas, and surfaces.
==================================================
**Elapsed Time: 0.55 seconds**
==================================================