V ISUALIZATION (TDAV) study of approaches to EXTRACT structure from - - PowerPoint PPT Presentation

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V ISUALIZATION (TDAV) study of approaches to EXTRACT structure from - - PowerPoint PPT Presentation

Feb 02, 2024 156 likes •938 views

F EATURE E XTRACTION & D ATA C UBE V ISUALIZATION T HROUGH T OPOLOGICAL D ATA A NALYSIS Paul Rosen Assistant Professor University of South Florida In collaboration with: Bei Wang Phillips, University of Utah Chris Johnson, University of

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SLIDE 1

FEATURE EXTRACTION & DATA CUBE VISUALIZATION THROUGH TOPOLOGICAL DATA ANALYSIS

Paul Rosen Assistant Professor University of South Florida

In collaboration with: Bei Wang Phillips, University of Utah Chris Johnson, University of Utah Jeff Kern, NRAO Betsy Mills, San Jose State (formerly NRAO)

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SLIDE 2
  • INTRODUCTION TO THE TEAM

Chris Johnson Distinguished Professor U of Utah PhD in Physics Bei Wang Phillips Assistant Professor U of Utah PhD in CS Paul Rosen Assistant Professor U of South Florida PhD in CS Betsy Mills Assistant Professor San Jose State PhD in Astronomy Jeff Kern CASA Lead NRAO PhD in Astrophysics

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SLIDE 3
  • MO’ DATA MO’ PROBLEM
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SLIDE 4
  • MO’ DATA MO’ PROBLEM
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SLIDE 5
  • MO’ DATA MO’ PROBLEM
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SLIDE 6
  • MO’ DATA MO’ PROBLEM
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SLIDE 7
  • TOPOLOGICAL DATA ANALYSIS AND

VISUALIZATION (TDAV)

  • study of approaches to EXTRACT structure

from NOISY or COMPLEX data and REPRESENT that data in an actionable form

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SLIDE 8
  • PERSISTENT HOMOLOGY
  • a method for computing topological

FEATURES of a space at DIFFERENT spatial RESOLUTIONS

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SLIDE 9
  • HOW DOES THIS RELATE TO

RADIO ASTRONOMY?

  • TDAV represents a DIVERSE toolbox

capable of addressing analysis NEEDS in many contexts

  • Our development study addresses

these needs specifically via the CONTOUR TREE

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SLIDE 10
  • TOPOLOGICAL SKELETON: CONTOUR TREE
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SLIDE 11
  • CONTOUR TREES
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SLIDE 12
  • CONTOUR TREES
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SLIDE 13
  • CONTOUR TREES
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SLIDE 14
  • CONTOUR TREES
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SLIDE 15
  • CONTOUR TREES
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SLIDE 16
  • CONTOUR TREES
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SLIDE 17
  • CONTOUR TREES
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SLIDE 18
  • CONTOUR TREES
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SLIDE 19
  • A CLOSER LOOK AT THE CONTOUR TREE

Scalar Value

  • f Event
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SLIDE 20
  • A CLOSER LOOK AT THE CONTOUR TREE

Birth of the Feature Scalar Value

  • f Event
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SLIDE 21
  • A CLOSER LOOK AT THE CONTOUR TREE

Birth of the Feature Death of the Feature Scalar Value

  • f Event
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SLIDE 22
  • A CLOSER LOOK AT THE CONTOUR TREE

Persistence

  • f the

Feature Scalar Value

  • f Event
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SLIDE 23
  • FEATURE REMOVAL
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SLIDE 24
  • FEATURE REMOVAL
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SLIDE 25
  • FEATURE REMOVAL
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SLIDE 26
  • SCALARFIELD SIMPLIFICATION
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SLIDE 27
  • SCALARFIELD SIMPLIFICATION
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SLIDE 28
  • SCALARFIELD SIMPLIFICATION
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SLIDE 29
  • SCALARFIELD SIMPLIFICATION
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SLIDE 30
  • SCALARFIELD SIMPLIFICATION
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SLIDE 31
  • RESULTS
  • Simple Spinning Disk
  • from Anil Seth
  • Phys. & Astro.
  • University of Utah
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SLIDE 32
  • VARYING

PERSISTENT SIMPLIFICATION

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SLIDE 33
  • riginal

simplified

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SLIDE 34
  • riginal

simplified

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SLIDE 35
  • riginal

simplified

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SLIDE 36
  • riginal

simplified

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SLIDE 37
  • riginal

simplified

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SLIDE 38
  • STEPPING

THROUGH

SLICES

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SLIDE 39
  • riginal

simplified

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SLIDE 40
  • riginal

simplified

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SLIDE 41
  • riginal

simplified

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SLIDE 42
  • riginal

simplified

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SLIDE 43
  • riginal

simplified

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SLIDE 44
  • riginal

simplified

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SLIDE 45
  • riginal

simplified

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SLIDE 46
  • riginal

simplified

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SLIDE 47
  • riginal

simplified

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SLIDE 48
  • riginal

simplified

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SLIDE 49
  • riginal

simplified

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SLIDE 50

MOMENT 0 ANALYSIS

  • riginal

simplified

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SLIDE 51
  • VOLUME

RENDERED

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SLIDE 52
  • riginal

simplified

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SLIDE 53
  • riginal

simplified

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SLIDE 54
  • riginal

simplified

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SLIDE 55
  • SUMMARY
  • Early results convincing
  • Open questions remain

Scalar field simplification choice Scalability of software Related visualization needs Additional uses of the contour trees Scientific impact of simplification Other TDAV data structures

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SLIDE 56
  • SOFTWARE
  • Software will be publicly released

before the end of the year

  • We invite interested users to

contact us for early access

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SLIDE 57
  • QUESTIONS?
  • CONTACT
  • Paul Rosen <prosen@usf.edu>
  • PROJECT WEBSITE
  • http://alma-tda.cspaul.com
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SLIDE 58
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SLIDE 59
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SLIDE 60
  • ALTERNATIVE SIMPLIFICATIONS
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SLIDE 61
  • ALTERNATIVE SIMPLIFICATIONS
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SLIDE 62
  • ALTERNATIVE SIMPLIFICATIONS
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SLIDE 63
  • ALTERNATIVE SIMPLIFICATIONS
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SLIDE 64

azimuth( eleva,on( azimuth( eleva,on(

Data(Transforma,on( to(Scalar(Field(( Feature(Extrac,on(Using( Contour=Trees( Feature( Explora,on( Visualiza,on( Feature( Comparison( Visualiza,on(

  • PROCESSING PIPELINE

elevation azimuth

Data Transformation to Scalar Field Feature Extraction Using Contour Trees

Feature Comparison Visualization Feature Exploration Visualization

elevation azimuth

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SLIDE 65

azimuth( eleva,on( azimuth( eleva,on(

Data(Transforma,on( to(Scalar(Field(( Feature(Extrac,on(Using( Contour=Trees( Feature( Explora,on( Visualiza,on( Feature( Comparison( Visualiza,on(

  • PROCESSING PIPELINE

elevation azimuth

Data Transformation to Scalar Field Feature Extraction Using Contour Trees

Feature Comparison Visualization Feature Exploration Visualization

elevation azimuth

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SLIDE 66

azimuth( eleva,on( azimuth( eleva,on(

Data(Transforma,on( to(Scalar(Field(( Feature(Extrac,on(Using( Contour=Trees( Feature( Explora,on( Visualiza,on( Feature( Comparison( Visualiza,on(

  • PROCESSING PIPELINE

elevation azimuth

Data Transformation to Scalar Field Feature Extraction Using Contour Trees

Feature Comparison Visualization Feature Exploration Visualization

elevation azimuth

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SLIDE 67

azimuth( eleva,on( azimuth( eleva,on(

Data(Transforma,on( to(Scalar(Field(( Feature(Extrac,on(Using( Contour=Trees( Feature( Explora,on( Visualiza,on( Feature( Comparison( Visualiza,on(

  • PROCESSING PIPELINE

elevation azimuth

Data Transformation to Scalar Field Feature Extraction Using Contour Trees

Feature Comparison Visualization Feature Exploration Visualization

elevation azimuth

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SLIDE 68
  • RESEARCH QUESTIONS:

DATATRANSFORMATION

  • How are the spectral lines

represented in a 3D data cube

MEANINGFULLY converted to scalar

functions for contour tree-based analysis?

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SLIDE 69
  • RESEARCH QUESTIONS:

FEATURE EXTRACTION

  • Once a contour tree is generated,

there are many methods for selecting the important features of the tree. Therefore, how do we extract

MEANINGFUL features of the data via

contour tree simplification to suit the needs of astronomers?

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SLIDE 70
  • RESEARCH QUESTIONS:

FEATURE EXPLORATION

  • What is a MEANINGFUL visualization
  • f contour trees to enable feature

exploration of a single data cube by the astronomers?

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SLIDE 71
  • RESEARCH QUESTIONS:

FEATURE COMPARISON

  • Can contour tree representations be

used for MEANINGFUL feature comparisons among multiple data cubes to characterize secular changes with observed properties (for example, transition energy, molecular species and chemical families), or derived properties such as temperature and density?

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SLIDE 72
  • INTERDISCIPLINARY RESEARCH IS HARD

Me You

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SLIDE 73

Outstanding issue: multiple slices

  • How to co-simplify?
  • Multiple 2D vs 3D contour trees?
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SLIDE 74

Outstanding issue: Local vs. global contour tree

  • Precomputation?
  • Data storage and query?
  • Efficient computation on parallel machine(s)?
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SLIDE 75

Outstanding issue: Boundary Conditions

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SLIDE 76

Outstanding issues: Boundary Conditions

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SLIDE 77

Outstanding issues: Boundary Conditions

  • Are boundaries true critical points?