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[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
Informatics luis rocha 2017
I501introduction
to informatics
introduction to informaticslecture 18
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
introduction to informatics
Participation: 15%. class discussion, especially about readings engagement in class
Paper presentation and handout: 15% Covering key paper points, handling
discussion Think-Pair-Share
Black Box assignments: 40% 2 assignments during the semester.
15% , Assignment I: 20% , Assignment II: Due November 15
GRFP Research proposal: 30% Elevator pitch and proposal
due December 8, 2014
evaluation
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Due October 11th
Focus on uncovering quadrants using data
collection and induction.
Propose a formal model or algorithm of what each quadrant is doing. Analyze, using
deduction, the behavior of this algorithm.
Q1 Q2
Q3 Q4
The Black Box: Due October 11th, 2017
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Much observation And thinking And videos
overall observations
Q1 Q2
Q3 Q4
Clara, Andrew, Nicholas & StephenGroup 1
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Much observation And thinking And videos
Define language and terminology of observation Iterations/steps, cells, quadrants,
states/colors, etc.
overall observations
Q1 Q2
Q3 Q4
Clara, Andrew, Nicholas & StephenGroup 1
Sirag, Swapna, Vincent, LoganGroup 3
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Much observation And thinking And videos
Define language and terminology of observation Iterations/steps, cells, quadrants,
states/colors, etc.
overall observations
Q1 Q2
Q3 Q4
Clara, Andrew, Nicholas & StephenGroup 1
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Much observation And thinking And videos
Define language and terminology of observation Iterations/steps, cells, quadrants,
states/colors, etc. At start iteration
Different each time States (numbers/colors) uniformly
distributed All states in similar proportions
Chi-square or Kolmogorov–Smirnov test goodness of fit ???
overall observations
Q1 Q2
Q3 Q4
Clara, Andrew, Nicholas & StephenGroup 1
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
Cassie, Gianpaolo, Rosemary & VincentGroup 5
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Much observation And thinking And videos
Define language and terminology of observation Iterations/steps, cells, quadrants,
states/colors, etc. At start iteration
Different each time States (numbers/colors) uniformly
distributed All states in similar proportions
Chi-square or Kolmogorov–Smirnov test goodness of fit ???
With iterations Up to 1 change per quadrant
4 in total Randomness/stochasticity
“because moving back and forth a single time step can result in different pixel transitions.”
overall observations
Q1 Q2
Q3 Q4
Clara, Andrew, Nicholas & StephenGroup 1
Zackary, Patrick, & FilipGroup 4
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
Cassie, Gianpaolo, Rosemary & VincentGroup 5
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Results are published Anyone can now use shared
knowledge Citing sources
Data should be shared Collected up to now only Cited
Impressive data collection Beware of data overkill
Deduction from micro- and macro-level organization Direct visualization techniques
and observations powerful
Data collection
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step
Dynamics observations
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step state distributions in
time Aggregate
measure of activity per quadrant Quadrants very
distinguishable
Dynamics observations
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step state distributions in
time Aggregate
measure of activity per quadrant Quadrants very
distinguishable
Dynamics observations
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step state distributions in
time Aggregate
measure of activity per quadrant Quadrants very
distinguishable
Dynamics observations
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step state distributions in
time Aggregate
measure of activity per quadrant Quadrants very
distinguishable Converges
when? Better
statistics will help decide model alternatives
Varying observations about final behavior Attractors? Distributions of
cell states
Dynamics observations
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step state distributions in
time Aggregate
measure of activity per quadrant Quadrants very
distinguishable Converges
when? Better
statistics will help decide model alternatives
Varying observations about final behavior Attractors? Distributions of
cell states Entropy!
Dynamics observations
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step state distributions in
time Aggregate
measure of activity per quadrant Quadrants very
distinguishable Converges when?
Better statistics will help decide model alternatives
Varying observations about final behavior Attractors? Distributions of
cell states Entropy! “Inter-run Cell
Entropy”! Surprise as to
what other state cells change to
Dynamics observations
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Final state Does not depend
of n step state distributions in
time Aggregate
measure of activity per quadrant Quadrants very
distinguishable Converges when?
Better statistics will help decide model alternatives
Varying observations about final behavior Attractors? Distributions of
cell states Entropy! “Inter-run Cell
Entropy”! Surprise as to
what other state cells change to
Average in time demonstrates initial random state
Dynamics observations
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Does not die off Favors lower states (0, 1 , 2…)
Quadrant 3
Q3
Zackary, Patrick, & FilipGroup 4
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Does not die off Favors lower states (0, 1 , 2…)
but state 9 more prevalent than others (7th most prevalent)
Quadrant 3
Q3
Zackary, Patrick, & FilipGroup 4
Clara, Andrew, Nicholas & StephenGroup 1
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Does not die off Favors lower states (0, 1 , 2…)
but state 9 more prevalent than others (7th most prevalent)
All cells doing same thing?
Quadrant 3
Q3
Zackary, Patrick, & FilipGroup 4
Clara, Andrew, Nicholas & StephenGroup 1
Sirag, Swapna, Vincent, LoganGroup 3
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Does not die off Favors lower states (0, 1 , 2…)
but state 9 more prevalent than others (7th most prevalent)
All cells doing same thing? A proposed model
Quadrant 3
Q3
Zackary, Patrick, & FilipGroup 4
Clara, Andrew, Nicholas & StephenGroup 1
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Does not die off Favors lower states (0, 1 , 2…)
but state 9 more prevalent than others (7th most prevalent)
All cells doing same thing? A proposed model
What does it do? Statistics from data What can cause this type of random behavior? Does it fit known processes?
Serial correlation and goodness of fit tests
Quadrant 3
Q3
Zackary, Patrick, & FilipGroup 4
Clara, Andrew, Nicholas & StephenGroup 1
Sirag, Swapna, Vincent, LoganGroup 3
Jayati, Lucas, Stephen & KaichengGroup 2
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Random probability of cell being picked for transition?
Not yet validated No transitions from 0
P(0 -> j)=0 Best to report the support for this assertion
Ratio of transitions to 0 to transitions to other states changes day to day !!!???
Statistically significant? Test further?
states behave differently Transition to 0 most frequent
Quadrant dies off When? Compute statistics to differentiate models
Even numbers can only transition to other even numbers Odd numbers transition to any other numbers
Except 5 only transitions to 0 And to itself?
Conclusion must be justified from observation How does it do this?
Q4
Quadrant 4
Clara, Andrew, Nicholas & StephenGroup 1
Jayati, Lucas, Stephen & KaichengGroup 2
Zackary, Patrick, & FilipGroup 4
Cassie, Gianpaolo, Rosemary & VincentGroup 5
1. 0 02. { 5} {0, 5}3. {2, 4, 6, 8} {0, 2, 4, 6, 8}4. {1, 3, 7, 9}
{0 , 1, 2, 3, 4, 5, 6, 7, 8, 9}
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Random probability of cell being picked for transition?
Not yet validated No transitions from 0
P(0 -> j)=0 Best to report the support for this assertion
Q4
Quadrant 4
Clara, Andrew, Nicholas & StephenGroup 1
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Random probability of cell being picked for transition?
Not yet validated No transitions from 0
P(0 -> j)=0 Best to report the support for this assertion
Ratio of transitions to 0 to transitions to other states changes day to day !!!???
Statistically significant? Test further?
Q4
Quadrant 4
Clara, Andrew, Nicholas & StephenGroup 1
Zackary, Patrick, & FilipGroup 4
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Random probability of cell being picked for transition?
Not yet validated No transitions from 0
P(0 -> j)=0 Best to report the support for this assertion
Ratio of transitions to 0 to transitions to other states changes day to day !!!???
Statistically significant? Test further?
states behave differently Transition to 0 most frequent
Quadrant dies off When? Compute statistics to differentiate models
Q4
Quadrant 4
Clara, Andrew, Nicholas & StephenGroup 1
Zackary, Patrick, & FilipGroup 4
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Random probability of cell being picked for transition?
Not yet validated No transitions from 0
P(0 -> j)=0 Best to report the support for this assertion
Ratio of transitions to 0 to transitions to other states changes day to day !!!???
Statistically significant? Test further?
states behave differently Transition to 0 most frequent
Quadrant dies off When? Compute statistics to differentiate models
Q4
Quadrant 4
Clara, Andrew, Nicholas & StephenGroup 1
Jayati, Lucas, Stephen & KaichengGroup 2
Zackary, Patrick, & FilipGroup 4
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Random probability of cell being picked for transition?
Not yet validated No transitions from 0
P(0 -> j)=0 Best to report the support for this assertion
Ratio of transitions to 0 to transitions to other states changes day to day !!!???
Statistically significant? Test further?
states behave differently Transition to 0 most frequent
Quadrant dies off When? Compute statistics to differentiate models
Even numbers can only transition to other even numbers Odd numbers transition to any other numbers
Except 5 only transitions to 0 And to itself?
Conclusion must be justified from observation How does it do this?
Q4
Quadrant 4
Clara, Andrew, Nicholas & StephenGroup 1
Jayati, Lucas, Stephen & KaichengGroup 2
Zackary, Patrick, & FilipGroup 4
Cassie, Gianpaolo, Rosemary & VincentGroup 5
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Random probability of cell being picked for transition?
Not yet validated No transitions from 0
P(0 -> j)=0 Best to report the support for this assertion
Ratio of transitions to 0 to transitions to other states changes day to day !!!???
Statistically significant? Test further?
states behave differently Transition to 0 most frequent
Quadrant dies off When? Compute statistics to differentiate models
Even numbers can only transition to other even numbers Odd numbers transition to any other numbers
Except 5 only transitions to 0 And to itself?
Conclusion must be justified from observation How does it do this?
Q4
Quadrant 4
Clara, Andrew, Nicholas & StephenGroup 1
Jayati, Lucas, Stephen & KaichengGroup 2
Zackary, Patrick, & FilipGroup 4
Cassie, Gianpaolo, Rosemary & VincentGroup 5
1. 0 02. { 5} {0, 5}3. {2, 4, 6, 8} {0, 2, 4, 6, 8}4. {1, 3, 7, 9}
{0 , 1, 2, 3, 4, 5, 6, 7, 8, 9}
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Groups of black and
white cells Stable frequency
Quadrant 1
Q1Zackary, Patrick, & Filip
Group 4
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Groups of black and
white cells Stable frequency Measure of cell “self-
information” How surprising is the state
a cell adopts What does it do and how?
How does clustering occur?
What processes are at play? From data
Investigate correlations , mutual information, etc. study self-transitions in first
iterations
Quadrant 1
Q1
Sirag, Swapna, Vincent, LoganGroup 3
Zackary, Patrick, & FilipGroup 4
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Behavior without borders more similar for every cell
Markov chain prediction more possible
Quadrant 2
Q2
Sirag, Swapna, Vincent, LoganGroup 3
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Behavior without borders more similar for every cell
Markov chain prediction more possible A proposed (descriptive) model
Quadrant 2
Zackary, Patrick, & FilipGroup 4
Q2
Sirag, Swapna, Vincent, LoganGroup 3
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment I
Observations Behavior without borders more similar for every cell
Markov chain prediction more possible A proposed (descriptive) model Transition constraints
How does it do this? Think of causal models! Specific rules that govern change Stochastic, deterministic, both? Serial correlation and goodness of fit tests
Quadrant 2
Zackary, Patrick, & FilipGroup 4
Q2
Sirag, Swapna, Vincent, LoganGroup 3
1. 9 {9,0}2. 0 {1,2,3,4}3. Most transitions
to {4,5,6,7,8,9}
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment 1
Aggregate observations
Digging into the structure of quadrants
Cassie, Gianpaolo, Rosemary & VincentGroup 5
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment 1
Aggregate observations
Mutual Information Reveals structure
of correlations between cells and neighborhoods
Repeating Patterns of correlations exist!
Digging into the structure of quadrants
Sirag, Swapna, Vincent, LoganGroup 3
Cassie, Gianpaolo, Rosemary & VincentGroup 5
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment 1
Aggregate observations
Mutual Information Reveals structure
of correlations between cells and neighborhoods
Repeating Patterns of correlations exist!
Reveals correlations (interactions?) between Q1 and Q2
Digging into the structure of quadrants
Sirag, Swapna, Vincent, LoganGroup 3
Cassie, Gianpaolo, Rosemary & VincentGroup 5
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment 1
Aggregate observations Mutual Information
Reveals structure of correlations between cells and neighborhoods
Repeating Patterns of correlations exist!
Reveals correlations (interactions?) between Q1 and Q2
Cells in Q2 most dependent on neighbors Contrast with Q3
Q3 and Q4 more uniform (less structures MI)
Digging into the structure of quadrants
Sirag, Swapna, Vincent, LoganGroup 3
Cassie, Gianpaolo, Rosemary & VincentGroup 5
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Black Box
Remember “published” facts Statistical behavior in Q3 Odd/Even behavior in Q4 Clustering and intricate dependencies, different processes in Q1 Rules of transitions in Q2
Are there quadrant dependencies? Focus on smaller grid subsets Think of neighborhoods and boundary conditions Move from descriptive to predictive models Induction and deduction
Data and reasoning Given a model, are things you have never seen possible?
Questions and suggestions
1. 0 02. { 5} {0, 5}3. {2, 4, 6, 8} {0, 2, 4, 6, 8}4. {1, 3, 7, 9} {0 , 1, 2, 3, 4, 5, 6, 7, 8, 9}
.....??, 1 ttjicellstate
1. 9 {9,0}2. 0 {1,2,3,4}3. Most transitions
to {4,5,6,7,8,9}
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Black Box
Data-driven analysis Klir’s GSPS
Mask analysis of smaller grids
E.g. predict behavior of a given cell in Q1
Correlations Information theory
Description model Statistical
Predictive model Causal
Validate Check distributions
observed against those predicted
Make predictions given models
Methods to employ
.....??, 1 ttjicellstate
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
Assignment II Due November
15th
Focus descriptive models of quadrant behavior using data
collection, induction, deduction and validation.
Propose a formal model or algorithm of what each quadrant is doing. Analyze, using
deduction, the behavior of this algorithm.
Q1 Q2
Q3 Q4
[email protected]/rocha/academics/i501/
INDIANAUNIVERSITY
I501introduction
to informatics
Informatics luis rocha 2017
next class
Next classes Lecture
Klir, G.J. and D. Elias [2003]. Architecture of Systems Problem Solving. Springer. Chapters: 1,2, 3.1, 3.2, 3.10, 4.1, 4.2
Optional: Chapters 3, 4 Coutinho, A. [2003]. "On doing science: a speech by Professor
Antonio Coutinho". Economia, 4(1): 7-18, jan./jun. 2003. Knapp B, Bardenet R, Bernabeu MO, Bordas R, Bruna M, et
al. (2015) ”Ten Simple Rules for a Successful Cross-Disciplinary Collaboration”. PLoS Comput Biol 11(4): e1004214.
Schwartz, M.A. [2008]. "The importance of stupidity in scientific research". Journal of Cell Science, 121: 1771.
Presentations Thomas S. Kuhn (1970). Logic of discovery or Psychology of
Research. Misevic, Filip:
Karl Popper (1963). Science: Conjecture and refutations. Kempe-Cook, Lucas
Readings (available in OnCourse)