Final demo
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Transcript of Final demo
![Page 1: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/1.jpg)
infervote.org Democratizing democracy: a resource for political engagement
Robert Vogel
![Page 2: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/2.jpg)
Why?
• We are constantly bombarded with political rhetoric that shape our political views.
• How are we ACTUALLY represented by our elected officials?
• How does and will our congress vote on topics we care about?
• Do senator voting records exhibit polarized behavior? • How can we find misbehaving and polarized senators? • What action can we take?
![Page 4: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/4.jpg)
The Polarity Index
Senator i Votes
Republican Senator j
Votes
![Page 5: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/5.jpg)
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
![Page 6: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/6.jpg)
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
![Page 7: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/7.jpg)
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
Ji1 = ~1
![Page 8: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/8.jpg)
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
Ji1 = ~1
Ji2 =
+
~0
![Page 9: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/9.jpg)
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
+
Polarity=
Ji1 = ~1
Ji2 =
+
~0
![Page 10: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/10.jpg)
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
+
Polarity=
Ji1 = ~1
Ji2 =
+
~0
![Page 11: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/11.jpg)
Clustering Senator Voting with Jaccard distance
![Page 12: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/12.jpg)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
![Page 13: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/13.jpg)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
dMitch McConnell (KY)John McCain (AZ)
![Page 14: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/14.jpg)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)
dMitch McConnell (KY)John McCain (AZ)
![Page 15: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/15.jpg)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)Bernie Sanders (VT)
dMitch McConnell (KY)John McCain (AZ)
![Page 16: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/16.jpg)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)Bernie Sanders (VT)
dMitch McConnell (KY)John McCain (AZ)
Rand Paul (KY) Marco Rubio (FL) Ted Cruz (TX)
![Page 17: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/17.jpg)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)Bernie Sanders (VT)
dMitch McConnell (KY)John McCain (AZ)
Rand Paul (KY) Marco Rubio (FL) Ted Cruz (TX)
![Page 18: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/18.jpg)
Do votes align with bill sponsors?
Republican Sponsor
• The bill sponsor is the member of congress that introduces the document for consideration.
Democrat Sponsor
![Page 19: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/19.jpg)
Infer directionality of biochemical reactions using Langevin dynamics
Robert Vogel
Developed new parameterization of therapeutic drugs using insight from nonlinear dynamical systems
![Page 20: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/20.jpg)
Voting Distributions and the Simulated Senate• Sample 5000 experimental senates using parameters from data
• Data exhibit a more diverse distribution then simulation
• Potential next step, use the Ising model to model pairwise interactions
Republican SponsorDemocrat Sponsor
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The Jaccard Index and Political Polarity
![Page 22: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/22.jpg)
Jaccard Index for Measuring Polarity
• Jaccard Index measures the number identical votes between Senator i and Senator j normalized to total votes
• Polarity index is the average Jaccard index between Senator i and all Senators in party R.
Jij =|vi \ vj ||vi [ vj |
JiR =1
NR
X
j2R
Jij
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Distribution of polarity index
• If party politics were not a factor, these distributions would overlap
![Page 24: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/24.jpg)
Jaccard Distance for Senator Clustering
• Jij 1 the more similar Senator i votes to Senator j.
• Hierarchical clustering utilizes a dissimilarity measure. Standard solution 1 - Jij
dJ(i, j) = 1� |vi \ vj ||vi [ vj || {z }
Jij
![Page 25: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/25.jpg)
Votes are strictly partisan
• Fraction of votes along party line, most votes are partisan
![Page 26: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/26.jpg)
Topic Modeling
![Page 27: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/27.jpg)
Topic modeling of legislative summariesW
ord
spac
e pe
r bill
Topi
c Sp
ace
Bills
Topi
cs
T S = S’
Y N0
Congress person Topic Probabilities
P
T’ = P’
New bill in topic space Probability of vote
P
Y
N0
Prediction
Clustering
![Page 28: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/28.jpg)
Can we make predictions of senator votes from legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document NTopic M
![Page 29: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/29.jpg)
Can we make predictions of senator votes from legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document NTopic M
VoteSenator 2
![Page 30: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/30.jpg)
Can we make predictions of senator votes from legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document NTopic M Senator L Vote
VoteSenator 2
![Page 31: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/31.jpg)
Legislative document reduction to topics
• 2559 legislative summaries
• Constructed 6403 word basis from text by:
• removing stop words (e.g and, that, this, a)
• removing non-english words
• stemming (e.g. rested equal to rest)
• TF-IDF
• Cosine similarity to group topics
![Page 32: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/32.jpg)
Legislative document reduction to topics
• 2559 legislative summaries
• Constructed 6403 word basis from text by:
• removing stop words (e.g and, that, this, a)
• removing non-english words
• stemming (e.g. rested equal to rest)
• TF-IDF
• Cosine similarity to group topics
• Result: No structure in bill data, more data needed!
Documents
Documents
![Page 33: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/33.jpg)
Document dimensionality reduction not sufficient with PCA
95% of the variablity corresponds to > 1000 dimensions
A small topic space, represents a small portionof the variability
![Page 34: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/34.jpg)
tSNE dimensionality reduction suggests no structure in bill data
• Each point is a document in the reduced space defined by tSNE
• t-distributed Stochastic Neighborhood Embedding maps points from a high to a low dimensional space by minimizing the Kullback-Leibler Divergence (minimize information loss).
![Page 35: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/35.jpg)
The data
![Page 36: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/36.jpg)
Why only choose Bills and Amendments?
• In general, these documents can become law
• Other votes are for approving nominations for office and resolutions.
• Resolutions can be very diverse as shown below.
![Page 37: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/37.jpg)
Graduate Research: An overview
• Langevin Dynamics to:
• figure out direction in biochemical reactions, and
• testing isolation of a network motif.
• Bifurcation analysis to identify:
• nodes in a network sensitive to therapeutic inhibition
![Page 38: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/38.jpg)
Biochemical Noise
• Flow cytometry measures the relative quantity of <= 12 biochemical species per cell at a rate of 20,000 cells per second.
• Fluorescent molecules are coupled to antibodies that specifically bind to a biochemical species.
• Quantity of molecules is proportional to fluorescent signal
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA 1
PMA 2
PMA 3
Log2 Normalized pMEK Log
2 N
orm
aliz
ed p
pER
K
PMA 1
PMA 2
PMA 3
PMA 1
PMA 2
PMA 3
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA : 1
Log2 pMEK
Log 2
ppE
RK
PMA : 1
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA : 2
Log2 pMEK
Log 2
ppE
RK
PMA : 2
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA : 3
Log2 pMEK
Log 2
ppE
RK
PMA : 3
![Page 39: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/39.jpg)
Fluctuations break symmetry of average measurements
Variance of Y > XY
X𝜉x
𝜉y
O
O
• Fluctuations from source node propagates to target
X
Y𝜉y
𝜉x
O
OVariance of X > Y
True Model False Model
![Page 40: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/40.jpg)
Fluctuations break symmetry of average measurements
Variance of Y > X
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
True Model
pMEKppERK
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
False Model
pMEKppERK
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
True Model
pMEKppERK
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
False Model
pMEKppERK
Y
X𝜉x
𝜉y
O
O
• Fluctuations from source node propagates to target
X
Y𝜉y
𝜉x
O
OVariance of X > Y
True Model False Model
![Page 41: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/41.jpg)
Nonlinear dynamics of biochemical inhibition
![Page 42: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/42.jpg)
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
![Page 43: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/43.jpg)
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
![Page 44: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/44.jpg)
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
![Page 45: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/45.jpg)
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
![Page 46: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/46.jpg)
Nonlinear dynamics of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic Inhibition
In preparation for publication
![Page 47: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/47.jpg)
Nonlinear dynamics of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic Inhibition
L c SRC
pMEK MEKi
ppERK
[MEKi] [MEKi]
In preparation for publication
![Page 48: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/48.jpg)
Nonlinear dynamics of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic InhibitionL c SRC
SRCi
pMEK
ppERK
[SRCi] [SRCi]
L c SRC
pMEK MEKi
ppERK
[MEKi] [MEKi]
In preparation for publication
![Page 49: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/49.jpg)
Finding dysfunctional components in tumor samples
![Page 50: Final demo](https://reader034.fdocuments.us/reader034/viewer/2022042602/55d2d9b0bb61ebdd4d8b465e/html5/thumbnails/50.jpg)
Single cell measurements find abnormalities in tumor patient profiles• Kullback-Leibler divergence measures the dissimilarity of the single cell
distribution of biochemical signaling features between patient and healthy donor samples.
Sjk =
X
i2HD
DKL (Pj(xk)||Pi(xk))
=
X
i2HD
Pj(xk) log
✓Pj(xk)
Pi(xk)
◆
• k = Biochemical species
• j = patient id
• i = Healthy donor