m.s. thesis presentation
DESCRIPTION
M.S. Thesis Presentation. Alex Dekhtyar for CSC 590. We will talk about. Logistics of M.S. Defense Structure of Presentation Presentation Style Delivery Slides. Part I. M.S. Defense. M.S. Defense. What? When? Who? How Long?. M.S. Defense. What? Final step When? Who? - PowerPoint PPT PresentationTRANSCRIPT
M.S. Thesis PresentationM.S. Thesis Presentation
Alex DekhtyarAlex Dekhtyar
for CSC 590for CSC 590
We will talk about...We will talk about...• Logistics of M.S. Defense
• Structure of Presentation
• Presentation Style
–Delivery
–Slides
Part I.Part I.M.S. DefenseM.S. Defense
M.S. Defense
+ What?
+ When?
+ Who?
+ How Long?
M.S. Defense
- What?- Final stepFinal step
+ When?
+ Who?
+ How Long?
M.S. Defense
+ What?
- When? - When thesis is ready!
+ Who?
+ How Long?
M.S. Defense+ What?+ When? - Who?
- YouYou
- AdvisorAdvisor
- CommitteeCommittee
+ How Long?
M.S. Defense
+ What?
+ When?
+ Who?
- How Long?
Presentation: Presentation: 30– 45 mins30– 45 minsQuestions and Answers: Questions and Answers: 10 – 3010 – 30 minsmins
Discussion: Discussion: 5 – 15 mins5 – 15 mins
Total: 45 – 90 mins45 – 90 mins
M.S. Defense
+ What?
+ When?
+ Who?
- How Long?
Total: 45 – 90 mins45 – 90 mins
PublicPublic
Closed doorsClosed doors
Presentation: Presentation: 30– 45 mins30– 45 minsQuestions and Answers: Questions and Answers: 10 – 3010 – 30 minsmins
Discussion: Discussion: 5 – 15 mins5 – 15 mins
LogisticsLogistics
Committee SelectionCommittee Selection Defense SchedulingDefense Scheduling Talk PreparationTalk Preparation
Committee Selection
Selected bySelected by: You and Advisor: You and Advisor
CommitteeCommittee = Advisor + at least 2 more = Advisor + at least 2 morefaculty membersfaculty members
Select:Select: (a)(a)Those who know Those who know youyou(b)(b)Those who know Those who know the fieldthe field
WhenWhen: as early as possible: as early as possible
Scheduling Defense
Three weeks Three weeks
ahead of timeahead of time
After thesis is After thesis is
completecompleteDone with
Done with
thesisthesis
Schedule defense around here
Talk Preparation
You speakYou speakYou show props slides
Think ... Memorize first 2-5 mins Practice, practice, practiceFirst set : 24 hours
Second set:12 hours Third set : 6 hours
Alex’s rulesAlex’s rulesFor 1 hour talk:For 1 hour talk:
Talk Preparation
First set : 24 hours Second set:12 hours Third set : 6 hours
Alex’s rulesAlex’s rulesFor 1 hour talk:For 1 hour talk:
First rehearsal with advisor
Second rehearsal with advisor
24-48 hours
24-48 hoursDefense
LogisticsLogistics
Committee SelectionCommittee Selection Defense SchedulingDefense Scheduling Talk PreparationTalk Preparation
We will talk about...We will talk about...• Logistics of M.S. Defense
Structure of Presentation
• Presentation Style
–Delivery
–Slides
Part II.Part II.Presentation StructurePresentation Structure
Presentation Outline
Title Slide: «backstory» Teaser Outline Introduction/Motivation Problem Background Solution ImplementationValidationRelated work Future work and conclusions
7 – 12 minutes7 – 12 minutes
5 – 20(5 – 20(!!) minutes) minutes
10 - 25 minutes10 - 25 minutes
5 - 10 minutes5 - 10 minutes
3 - 5 minutes3 - 5 minutes
Title Slide & Backstory
By
Mark Barry
Direct Extraction of Normal Maps from Volume Data
February 2007
Master’s ThesisTitleTitle
Thesis mentionThesis mention
NameName
DateDate
AdvisorAdvisor
DepartmentDepartment
UniversityUniversity
Management of Concurrent XML Management of Concurrent XML using Distributed DOMusing Distributed DOM
Karthikeyan SethuramasubbuKarthikeyan Sethuramasubbu
Advisor: Dr. Alexander DekhtyarAdvisor: Dr. Alexander Dekhtyar
University of University of KentuckyKentucky
Department of Computer ScienceDepartment of Computer Science
Building An Operational Data Store For A Direct Marketing Application
System
Chad Smith
March, 2009
Department of Computer Science
California Polytechnic State University, SLO
Title Slide & Backstory
• Title• Name• Advisor• Department• Thesis mention• Date
• Who you are• What you do• How you came across
this project• ... a smooth transition
to next slide...
SlideSlide SpeakSpeak
Teaser
Distributed DOM Processor
XML XML XML…Distributed XML Document
DOM Parser
DOM DOM DOM…Distributed DOM
Multi-hierarchical XML
EXPath Processor
Karthikeyan S.
Teaser
• Slide(s) before OutlineSlide(s) before Outline• One-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 30-second version of your thesis talkthesis talk
SlidesSlides SpeakSpeak
• Show your your contribution right away
WhyWhy
• Your Intro/Background part is long long ((15+ mins15+ mins))
WhenWhen
(Optional)(Optional)
Project Goal Developed front-end for an automated
requirements tracing tool.
Sravanthi Vadlamudi
GODDAGGODDAG
In-memory datastructure
Concurrent Parser
XML XML XML…Distributed XML Document
DriverXML(TEI)
BUVHDriver
JITTSDriver
Otherrepresentations
SpecialDBMS
RDBMS
Persistentsupport
Editor
User
Tools
Data Management Framework
XPathExtended Extende
dXQuery
DB Driver
DB Driver
Processor
Query
Processor
Query
Emil Iacob
Outline
Outline
• Introduction• Contributions• Previous Work• Initial Exploration• Dual Contouring With Normal Map Extraction• Results• Conclusion and Future Work
Mark Barry
Outline
List of key List of key «milestones» in talk«milestones» in talk
• VERY LITTLE!VERY LITTLE!
SlideSlide SpeakSpeak
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
1. Explain the subject area
2. Motivate your problem
3. State your contributions
Your GoalsYour Goals
By By minute 10minute 10 of the talk your of the talk your contribution(s) MUST be stated/describedcontribution(s) MUST be stated/described
5-10 minutes5-10 minutes
Introduction (cont’d)My Contributions
– Signature files• Abstraction• Storage requirements• Search space• Network traffic• Backend load sharing
– Cooperative I.S. daemon• Transparency• Update independence
– Query manager• Building SQL statements• Query shipment decisions
Saad Ijad
Contributions
• Direct extraction of low-resolution meshes with normal maps from volume data– One integrated step– Excellent visual results– Fast
• Benefits:– Shortcuts the current multi-step process
• High-resolution mesh never generated• No extra high- to low-resolution simplification process• Efficient “search” generating normal maps
Mark Barry
Problem Definition
May be fully covered in Introduction May be fully covered in Background May need to be formally stated separately
Formal Problem statement Formal Problem statement must be found in your talkmust be found in your talk
Introduction
• Problem:– High-resolution meshes = slow to render
• Use low-resolution meshes– Fast to render– Still look good
One of a number of
slides • Articulate the problem• Use stress, inflection
SpeakSpeak
Mark Barry
Background
Committee members Committee members must understand what must understand what your work is aboutyour work is about
Background
Non-Functional Requirements
1. (Relatively) short
2. Explain all necessary things
3. Sufficient to explain/introduce/define your problem
4. Should assume
General CS knowledge within curriculum
No special topic knowledge
What is XML?
<student id=“123456”>
<firstname> Karthikeyan </firstname>
<lastname> Sethuramasubbu </lastname>
<college> College of Engineering
<major>Computer Science</major>
</college>
</student>
Attribute name
Attribute value
Markup
content
<!ELEMENT Student (firstname, lastname, college)<!ELEMENT college (#PCDATA | major)*><!ATTLIST Student id ID #REQUIRED><!ELEMENT firstname #PCDATA>
XML schema to Validate XML
Karthikeyan S.
Document Object Model (DOM)
<student>
id=“123456” <firstname> <lastname> <college>
College of Engineering
<major>
Computer Science
root
Text node
XXX YYY
element node
attribute node
Karthikeyan S.
Path Expressions
Find the major of the student:
student college major
/student/college/major is called the path expression
<college>
<student>
id=“123456” <firstname> <lastname>
College of Engineering
<major>
Computer Science
XXXYYY
Karthikeyan S.
XPath – To access data from XML
XPathExpression:= step1/step2/step3/……../stepn
stepi := axis :: node-test Predicate*
Predicate := [expression]
Example:
/ child ::college [position()=1] / descendant::*
Location step
axis Node-testpredicate
Karthikeyan S.
XPath
Context Node : current node in the tree
<college>
<student>
id=“123456” <firstname> <lastname>
College of Engineering
<major>
Computer Science
XXXYYY
context node
child
XPath Axes
•child
•descendant
•ancestor
•parent
•preceding
• following
•attribute
Took about 10 mins Introduced 2-3 weeks worth of course material
Karthikeyan S.
Presentation Outline
Title Slide: «backstory» Teaser Outline Introduction/Motivation Problem Background Solution ImplementationValidationRelated work Future work and conclusions
Solution and Implementation
Your time to shine!
Your time to shine!
Solution and Implementation
DO:Think about it...Come up with a narrativeConcentrate on ideasExplain
DON’T:Get bogged in minutiaJump from point to pointLeave cruicial pieces out
Solution and Implementation
Remember:Highlight that this is your work!your work!
Formal description of your work is called thesis
Presentation = high level description
You get (at most) one chance to go technical
Use it wisely
A picture is worth a thousand words
Specific «things»
• Definitions– Example/Illustration– Formal statement
Se Boetius wæs ođre naman haten Seuerinus se wæs heretoga Romana
Extended Axis DefinitionsExtended Axis Definitions
xdescendant
xancestor
xdescendant
xancestor
Swati 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: ICollectionSet
New axes:
• xancestor
• xdescendant
• xfollowing
• xpreceding
• overlapping
• preceding-overlapping
• following-overlapping
• and their combinations
Specific «things»
• Definitions– Example/Illustration– Formal statement
• You may include formal statements
• But: spend your time on examplesspend your time on examples
Specific «things»
• Algorithms/Methods/Techniques– Example/Illustration– Pseudocode– Code– Math
Surface Extraction From Volume Data
• Marching Cubes algorithm
Mark Barry
Surface Extraction From Volume Data
• Marching Cubes algorithm
Mark Barry
Surface Extraction From Volume Data
• Extended Marching Cubes algorithm– Captures features better
Contour verticeswith normals
Marching Cubescontour surface
Extended Marching Cubescontour surface
Mark Barry
Surface Extraction From Volume Data
• Extended Marching Cubes algorithm– Captures features better
Contour verticeswith normals
Marching Cubescontour surface
Extended Marching Cubescontour surface
• Might not explain much by itself
• But remember – you get to talk
Mark Barry
xdescendant (Pseudo-code)
evaluateXdescendant (n, hname, result)
{
if n is leaf-node
return null
evaluateDescendant (n, hname, result)
append result to a Vector V
for each element p in Vector V
if Start index of p is in between the start and end index of n
append p to result
return 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($x) <= endIndex (doc(“docn”)) return if ($x intersect $R) then $x union $R else $x) )return ((for $u1 in doc(“doc1”)/$u/parent::* return if $x intersect $R then $x union $R else $R)union ….(for $u1 in doc(“docn”)/$u/parent::* return if $x intersect $R then $x union $R else $R))
Swati Tata
Evaluation of startIndex and endIndex
• End index computed as sum of start index and total length of the descendant text nodes.
declare function local: endIndex ($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 endIndex
• End 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
This was Swati’s«one technical moment»
Applying Normal Maps to the Implicit Surface
)sin(2
)sin(2
)sin(2
),,(
bzabz
byaby
bxabx
zyxf
y
zx
y
zx
y
zx
y
x
Mark Barry
Specific «things»
• Algorithms/Methods/Techniques– Example/Illustration– Pseudocode– Code– Math
• You may include math/pseudocode
• But: spend your time on examplesspend your time on examples
Specific «things»
• Software – Architecture Diagram– Component-by-component coverage– Implementation Info– Screenshots/Walkthroughs– Output– Demo
GODDAGGODDAG
In-memory datastructure
Concurrent Parser
XML XML XML…Distributed XML Document
DriverXML(TEI)
BUVHDriver
JITTSDriver
Otherrepresentations
SpecialDBMS
RDBMS
Persistentsupport
Editor
User
Tools
Data Management Framework
XPathExtended Extende
dXQuery
DB Driver
DB Driver
Processor
Query
Processor
Query
Emil IacobArchitecture Diagram
Start a new projectSravanthi Vadlamudi
Software Screenshots/Software Screenshots/ WalkthroughWalkthrough
Advanced mode Sravanthi Vadlamudi
Trace tabSravanthi Vadlamudi
RETRO Trace tabSravanthi Vadlamudi
RETRO Browse tabSravanthi Vadlamudi
Browse tabSravanthi Vadlamudi
RETRO Trace tabSravanthi Vadlamudi
RETRO View tabSravanthi Vadlamudi
Applying Normal Maps to the Implicit Surface
138,632triangles
8,216triangles
Mark BarryOutputOutput
• Results
Adaptive Contouring of Volume Data With Normal Map Extraction
Mark Barry
Implementation
• Emulation• Java 2 Micro Edition• Sun Wireless Toolkit• Oracle, SQL Server 2000, MS
Access• Java Database Connectivity
Saad IjadImplementation Details
Presentation Outline
Title Slide: «backstory» Teaser Outline Introduction/Motivation Problem Background Solution ImplementationValidationValidationRelated work Future work and conclusions
Validation
+ How did you evaluate?
+ What did you do?
+ What results did you obtain?
+ What do results mean?
Validation
- How did you evaluate?- Experiment
- Case Study
- Software V&V
- Testimony
+What did you do?
+ What results did you obtain?
+ What do results mean?
Validation
+ How did you evaluate?
+ What did you do?
+ What results did you obtain?
+ What do results mean?
Validation
+ How did you evaluate?
- What did you do?
- Hypothesis/Objective of study
- Experimental/Case study design
- Validation activities, ...
+ What results did you obtain?
+ What do results mean?
Validation
+ How did you evaluate?
+ What did you do?
+ 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 output
+What do results mean?
Validation
+ How did you evaluate?
+ What did you do?
+ What results did you obtain?
+ What 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 didn’t?
Validation
+ How did you evaluate?
+ What did you do?
+ What results did you obtain?
+ What do results mean?
+ At this point you are probably running out of time...
Evaluation Outline
• Original text is taken from James Joyce’s Ulysses (project Gutenberg)
• Used 10 hierarchies • Markup generated randomly for these 10
hierarchies
Karthikeyan S.
Evaluation Outline
• Four sets of queries– Queries 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 Results
Karthikeyan S.
Experimental Results
Karthikeyan S.
Experimental Results
Karthikeyan S.
Results
225,467quads
360 ms
99.8% fewer polygons
360x faster to render
558quads
1 ms
Mark Barry
225,467quads
360 ms
99.97% fewer polygons
1200x faster to render
65quads
0.3 ms
Results
Mark Barry
150,823quads
245 ms92.7% fewer polygons
11.1x faster to render
10,950quads
22 ms
Results
Mark Barry
64,896quads
103 ms
95.3% fewer polygons
17.2x faster to render
3,035quads
6 ms
Results
Mark Barry
56,637quads
91 ms 97.5% fewer polygons
30.3x faster to render
1,406quads
3 ms
Results
Mark Barry
Results of Survey
• Simple 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 Survey
• Questions 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 4Manual Group 3.4 2.3 3.6 4.5 RETRO Group 3.6 3.4 2.5 3.8
Sravanthi Vadlamudi
Questions Specific to RETRO
• RETRO 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 3.6
Sravanthi Vadlamudi
Questions specific to manual group
• I 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 4.4
Sravanthi 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 Related work Future work and conclusionsFuture work and conclusions
Related Work
Terse: List of papers nothing else
Verbose Overview Detailed description of one-two approaches Compare-and-contrast
Previous Work
• Contour surface (mesh) extraction from volumes
• Adaptive contouring• Dual contouring• Generating normal maps
Mark Barry
Terse, but no citations!
Concurrent HierarchiesConcurrent Hierarchies• Representation of non-well-formed features within the same XMLRepresentation of non-well-formed features within the same XML documentdocument
• TEI Guidelines (P4)TEI Guidelines (P4)• Milestone (empty) elementsMilestone (empty) elements
• SplitsSplits
•Durusau, O’Donnel ( XML Europe 2002) Durusau, O’Donnel ( XML Europe 2002) • Separate DTDsSeparate DTDs• One XML documentOne XML document• Xpath expressions encode markup of “atomic pieces”Xpath expressions encode markup of “atomic pieces”
<line/><line/> Se Boetius wæs o Se Boetius wæs ođđre naman re naman <w>ha<w>ha <line/><line/> ten</w>ten</w> <w><w>SeuerinSeuerin<dmg-start/>us<dmg-start/>us</w></w> <w><w>s<dmg-end/>s<dmg-end/>ee</w></w> wæs heretoga wæs heretoga <line/><line/>Romana Romana
<line><line> Se Boetius wæs o Se Boetius wæs ođđre naman re naman <w id=“1”>ha</w><w id=“1”>ha</w> </line></line><line><line> <w id=“1”>ten</w><w id=“1”>ten</w> <w><w>SeuerinSeuerin<dmg id=“2”>us</dmg><dmg id=“2”>us</dmg></w></w> <w><w><dmg id=“2”> s</dmg><dmg id=“2”> s</dmg>ee</w></w> wæs wæs heretoga heretoga </line> </line> <line><line>Romana Romana </line></line>
Emil Iacob
Here, drawbacks of existing work are used to motivate research
Future Work
• Promises, promises:
1. Fix known weaknesses/incompletness
2. Add new features
3. Apply to something else
Conclusion and Future Work
• Future Work– Application to games?– Determine good simplification error metric
• Optimal placement of fine details in normal map vs. mesh
– Faster and high-quality normal interpolation
– Optimize code
33
22
Mark 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 Vadlamudi
11
11
22
22
Future Work
• Promises, promises:
1. Fix known weaknesses/incompletness2. Add new features3. Apply to something else
– Who?– Not necessarily you– Be bold!
Conclusions
What you did What you achieved What you learned What you published
Part III.Part III. Presentation StylePresentation Style
Next Time!Next Time!