Thesis Presentations

M.S. Thesis PresentationAlex Dekhtyarfor CSC 590

We will talk about...Logistics of M.S. Defense

Structure of Presentation

Presentation StyleDeliverySlides

Part I.M.S. Defense

M.S. Defense What? When? Who? How Long?

M.S. Defense What? Final step When? Who? How Long?

M.S. Defense What? When? When thesis is ready! Who? How Long?

M.S. Defense What? When? Who?You

Advisor

Committee

How Long?


Presentation: 30 45 minsQuestions and Answers: 10 30 mins Discussion: 5 15 minsTotal: 45 90 mins


Total: 45 90 minsPublicClosed doorsPresentation: 30 45 minsQuestions and Answers: 10 30 mins Discussion: 5 15 mins

Logistics Committee Selection Defense Scheduling Talk Preparation

Committee Selection

Selected by: You and AdvisorCommittee = Advisor + at least 2 morefaculty membersSelect: Those who know youThose who know the field

When: as early as possible

Scheduling Defense

Done with thesisSchedule defense around here

Talk Preparation

Think ... Memorize

first 2-5 mins Practice,

practice, practice

Talk Preparation

First rehearsal with advisorSecond rehearsal with advisor24-48 hours24-48 hoursDefense

Logistics Committee Selection Defense Scheduling Talk Preparation

We will talk about...Logistics of M.S. Defense

Structure of Presentation

Presentation StyleDeliverySlides

Part II.Presentation Structure

Presentation Outline Title Slide: backstory Teaser Outline Introduction/Motivation Problem Background Solution ImplementationValidationRelated work Future work and conclusions7 12 minutes5 20(!) minutes10 - 25 minutes5 - 10 minutes3 - 5 minutes

Title Slide & Backstory

Direct Extraction of Normal Maps from Volume Data

ByMark BarryFebruary 2007Masters ThesisTitleThesis mentionNameDateAdvisorDepartmentUniversity

Management of Concurrent XML using Distributed DOM Karthikeyan SethuramasubbuAdvisor: Dr. Alexander DekhtyarUniversity of KentuckyDepartment of Computer Science

Building An Operational Data Store For A Direct Marketing Application SystemChad SmithMarch, 2009Department of Computer ScienceCalifornia Polytechnic State University, SLO

Title Slide & Backstory

TitleNameAdvisorDepartmentThesis mentionDate

Who you areWhat you doHow you came across this project... a smooth transition to next slide...

SlideSpeak

Teaser

Distributed DOM Processor

XMLXMLXMLDistributed XML DocumentDOM ParserDOMDOMDOMDistributed DOMMulti-hierarchical XMLEXPath ProcessorKarthikeyan S.

Teaser

Slide(s) before OutlineOne-three slides screen shots output (e.g. In graphics) architecture diagram best experimental data Quick visual summary of your thesis

30-second version of your thesis talk

SlidesSpeak

Show your contribution right away

Why

Your Intro/Background part is long (15+ mins)

When(Optional)

Project Goal Developed front-end for an automated requirements tracing tool.Sravanthi Vadlamudi

In-memory datastructureConcurrent ParserXMLXMLXMLDistributed XML DocumentDriverBUVHDriverDriverOtherrepresentationsEditorUserToolsData Management FrameworkXPathExtendedExtendedXQueryDB DriverDB DriverEmil Iacob

Outline

OutlineIntroductionContributionsPrevious WorkInitial ExplorationDual Contouring With Normal Map ExtractionResultsConclusion and Future Work

Mark Barry

Outline

List of key milestones in talk

VERY LITTLE!

SlideSpeak Use throughout the talk to keep track of where you are

Presentation Outline Title Slide: backstory Teaser Outline Introduction/Motivation Problem Background Solution ImplementationValidationRelated work Future work and conclusions

Introduction/Motivation

Explain the subject areaMotivate your problemState your contributionsYour GoalsBy minute 10 of the talk your contribution(s) MUST be stated/described5-10 minutes

Introduction (contd)My Contributions

Signature filesAbstractionStorage requirementsSearch spaceNetwork trafficBackend load sharing

Cooperative I.S. daemonTransparencyUpdate independence

Query managerBuilding SQL statementsQuery shipment decisions

Saad Ijad

ContributionsDirect extraction of low-resolution meshes with normal maps from volume dataOne integrated stepExcellent visual resultsFastBenefits:Shortcuts the current multi-step processHigh-resolution mesh never generatedNo extra high- to low-resolution simplification processEfficient search generating normal maps

Mark Barry

Problem DefinitionMay be fully covered in IntroductionMay be fully covered in BackgroundMay need to be formally stated separatelyFormal Problem statement must be found in your talk

IntroductionProblem:High-resolution meshes = slow to renderUse low-resolution meshesFast to renderStill look good

One of a number of slides

Articulate the problemUse stress, inflection

SpeakMark Barry

BackgroundCommittee members must understand what your work is about

BackgroundNon-Functional Requirements (Relatively) short Explain all necessary things Sufficient to explain/introduce/define your problem Should assumeGeneral CS knowledge within curriculumNo special topic knowledge

What is XML?

Karthikeyan Sethuramasubbu College of EngineeringComputer Science

Attribute nameAttribute valueMarkupcontent

XML schema to Validate XMLKarthikeyan S.

Document Object Model (DOM)

id=123456

College of Engineering

Computer SciencerootText nodeXXXYYYelement nodeattribute nodeKarthikeyan S.

Path Expressions

Find the major of the student:student college major/student/college/major is called the path expressionKarthikeyan S.

XPath To access data from XML

XPathExpression:= step1/step2/step3/../stepnstepi := axis :: node-test Predicate*Predicate := [expression]Example:/ child ::college [position()=1] / descendant::* Location stepaxisNode-testpredicateKarthikeyan S.

XPath

Context Node : current node in the treecontext nodechildXPath Axeschilddescendantancestorparentpreceding following attribute

Took about 10 mins Introduced 2-3 weeks

worth of course materialKarthikeyan S.

Solution and Implementation

Your time to shine!


DO:Think about it...Come up with a narrativeConcentrate on ideasExplainDONT:Get bogged in minutiaJump from point to pointLeave cruicial pieces out


Remember:Highlight that this is your work!Formal description of your work is called thesisPresentation = high level descriptionYou get (at most) one chance to go technical Use it wiselyA picture is worth a thousand words

Specific things Definitions Example/Illustration Formal statement

Se Boetius ws ore naman haten Seuerinus se ws heretoga Romana Extended Axis Definitionsxdescendantxancestorxdescendant xancestorSwati Tata

Extended XPath [TR394-04]Semantics:xancestor(n) := {x | start-index(x) start-index(n) andend-index(x) end-index(x)} Algorithms for linear evaluation of axes

XPathExpression ::= LocationStep*LocationStep ::= Axis ::nodetest [predicates]New function: documents(String[,String]*)New return type: ICollectionSetNew axes: xancestor xdescendant xfollowing xpreceding overlapping preceding-overlapping following-overlapping and their combinations

Specific things Definitions Example/Illustration Formal statement

You may include formal statementsBut: spend your time on examples

Specific things Algorithms/Methods/Techniques Example/Illustration Pseudocode Code Math

Surface Extraction From Volume DataMarching Cubes algorithm

Mark Barry

Surface Extraction From Volume DataExtended Marching Cubes algorithmCaptures features better

Contour verticeswith normalsMarching Cubescontour surfaceExtended Marching Cubescontour surfaceMark Barry

Surface Extraction From Volume DataExtended Marching Cubes algorithmCaptures features better

Contour verticeswith normalsMarching Cubescontour surfaceExtended Marching Cubescontour surface Might not explain

much by itself

But remember

you get to talkMark Barry

xdescendant (Pseudo-code)evaluateXdescendant (n, hname, result){if n is leaf-node return nullevaluateDescendant (n, hname, result)append result to a Vector Vfor each element p in Vector Vif Start index of p is in between the start and end index of nappend p to resultreturn result}Karthikeyan S.

Extended XPath to XQuery /xdescendant-or-self::*/parent::*for $u in ( (for $x in doc(doc1) /descendant-or-self::* where local:startIndex ($x) >= startIndex (doc(doc1)) and local:endIndex($x) < =endIndex (doc(doc1)) return if ($x intersect $R) $x union $R else $x) union (for $x in doc(docn) /descendant-or-self::* where local:startIndex ($x) >= startIndex (doc(docn)) and local:endIndex($x)

Evaluation of startIndex and endIndexEnd index computed as sum of start index and total length of the descendant text nodes.

declare function local: endIndex ($node as node()) as xs: integer{ let $st:=local: startIndex ($node) let $nodeText:=fn: string-join ((for $u in $node/descendant-or-self::* return $u/text()),'') let $len:=fn: string-length ($nodeText) let $end:=$st+$len return($end)};Swati Tata

Evaluation of startIndex and endIndexEnd index computed as sum of start index and total length of the descendant text nodes.

declare function local: endIndex ($node as node()) as xs: integer{ let $st:=local: startIndex ($node) let $nodeText:=fn: string-join ((for $u in $node/descendant-or-self::* return $u/text()),'') let $len:=fn: string-length ($nodeText) let $end:=$st+$len return($end)};Swati TataThis was Swatisone technical moment

Applying Normal Maps to the Implicit Surface

Mark Barry

Specific things Algorithms/Methods/Techniques Example/Illustration Pseudocode Code Math

You may include math/pseudocodeBut: spend your time on examples

Specific thingsSoftware Architecture Diagram Component-by-component coverage Implementation Info Screenshots/Walkthroughs Output Demo

In-memory datastructureConcurrent ParserXMLXMLXMLDistributed XML DocumentDriverBUVHDriverDriverOtherrepresentationsEditorUserToolsData Management FrameworkXPathExtendedExtendedXQueryDB DriverDB DriverEmil IacobArchitecture Diagram

Start a new projectSravanthi VadlamudiSoftware Screenshots/ Walkthrough

Advanced mode Sravanthi Vadlamudi

Trace tabSravanthi Vadlamudi

4.bin

RETRO Trace tabSravanthi Vadlamudi

RETRO Browse tabSravanthi Vadlamudi

Browse tabSravanthi Vadlamudi

5.bin

RETRO Trace tabSravanthi Vadlamudi

RETRO View tabSravanthi Vadlamudi

Applying Normal Maps to the Implicit Surface

138,632triangles8,216trianglesMark BarryOutput

Results

Adaptive Contouring of Volume Data With Normal Map ExtractionMark Barry

ImplementationEmulationJava 2 Micro EditionSun Wireless ToolkitOracle, SQL Server 2000, MS AccessJava Database Connectivity

Saad IjadImplementation Details

Validation How did you evaluate? What did you do? What results did you obtain? What do results mean?

Validation How did you evaluate?ExperimentCase StudySoftware V&VTestimonyWhat did you do? What results did you obtain? What do results mean?

Validation How did you evaluate? What did you do?Hypothesis/Objective of studyExperimental/Case study designValidation activities, ... What results did you obtain? What do results mean?

Validation How did you evaluate? What did you do? What results did you obtain? Graphs, charts, tables, ... Program outputWhat do results mean?

Validation How did you evaluate? What did you do? What results did you obtain? What do results mean?Hypothesis confirmed?What worked?What didnt?


At this point you are probably running out of time...

Evaluation Outline

Original text is taken from James Joyces Ulysses (project Gutenberg)Used 10 hierarchies Markup generated randomly for these 10 hierarchies

Karthikeyan S.

Evaluation OutlineFour sets of queriesQueries that test individual axes /xdescendant:: line/ancestor::*Queries with recursive predicates / xdescendant:: line [xancestor:: fol]Queries with varying number of hierarchies /child::* (condition, navigation)Queries with varying length/overlapping:: (condition)/overlapping:: (condition) / overlapping:: (navigation)

Karthikeyan S.

Experimental ResultsKarthikeyan S.

Results

225,467quads

360 ms99.8% fewer polygons

360x faster to render558quads

1 msMark Barry

Results

225,467quads


1200x faster to render65quads

0.3 msMark Barry

Results

150,823quads


11.1x faster to render10,950quads

22 msMark Barry

Results

64,896quads



6 msMark Barry

Results

56,637quads



3 msMark Barry

Results of SurveySimple experiment to trace 22 high level with 52 low level requirements is assigned.Experiment was done on 30 students of class cs617. Group1 had 15 students for manual tracing.Group 2 had 15 students for tracing using RETRO.A Survey with 7 questions is given to

each group and answers were on 5-point scale. 5 is strongly agree and 1 is strongly disagree.Sravanthi Vadlamudi

Questions of SurveyQuestions common to both groups.

The project could be completed quickly.The project was tedious.If I were The project was simple to complete.performing a similar task in the future, I would want to use a software tool to assist. MEANS for questions: 1 2 3 4

Manual Group 3.4 2.3 3.6 4.5 RETRO Group 3.6 3.4 2.5 3.8 Sravanthi Vadlamudi

Questions Specific to RETRORETRO was easy to use.I would rather have completed the project by hand than use RETRO.It probably took less time to use RETRO than it would have to complete the project by hand.Means for questions: 5 6 7

3.8 2.2 3.6Sravanthi Vadlamudi

Questions specific to manual groupI would rather have completed the project by hand than use a software tool.It probably would have taken less time to use a software tool to complete the project than it did by hand.Means for questions: 5 6

2 4.4Sravanthi Vadlamudi

Results of survey(Contd)From the analysis of the result :

Students liked using RETRO.Students of manual group preferred using some software tool.Sravanthi Vadlamudi

Presentation Outline Title Slide: backstory Teaser Outline Introduction/Motivation Problem Background Solution Implementation ValidationRelated work Future work and conclusions

Related WorkTerse:List of papers nothing elseVerboseOverview Detailed description of one-two approaches Compare-and-contrast

Previous WorkContour surface (mesh) extraction from volumesAdaptive contouringDual contouringGenerating normal maps

Mark BarryTerse, but no citations!

Concurrent Hierarchies

Representation of non-well-formed features within the same XML

document

TEI Guidelines (P4) Milestone (empty) elements

Splits

Durusau, ODonnel ( XML Europe 2002) Separate DTDs One XML document Xpath expressions encode markup of atomic pieces

Se Boetius ws ore naman ha ten Seuerinus se ws heretoga Romana Se Boetius ws ore naman ha ten Seuerinus se ws heretoga Romana Emil IacobHere, drawbacks of existing work are used to motivate research

Future WorkPromises, promises:

Fix known weaknesses/incompletnessAdd new featuresApply to something else

Conclusion and Future WorkFuture WorkApplication to games?Determine good simplification error metricOptimal placement of fine details in normal map vs. meshFaster and high-quality normal interpolationOptimize code

32Mark Barry

Future Enhancements Re-write the back end to java. Display the keywords used in tracing to the analyst. Color-code the keywords in both the high level and low level elements Enable analyst to modify the

keywords used for tracing.Sravanthi Vadlamudi1122

Future WorkPromises, promises:

Fix known weaknesses/incompletnessAdd new featuresApply to something else

Who?Not necessarily youBe bold!

Conclusions What you did What you achieved What you learned What you published

Part III. Presentation Style

Next Time!

Documents

Thesis Presentations