## Tuesday, August 27, 2019

## Monday, August 26, 2019

### Canvas gradebook, weighted final grade

Under "Assignments", "Groups" can be added, modified, deleted.

For weighted final grades,

"Assignment" -> "..." -> "weight final grade"

### Changes in Cellular Architecture During Aging (R01)

Changes in Cellular Architecture During Aging (R01)

https://grants.nih.gov/grants/guide/pa-files/PA-16-442.html

https://grants.nih.gov/grants/guide/pa-files/PA-16-442.html

### algebraic multiplicity and geometric multiplicity

https://people.math.carleton.ca/~kcheung/math/notes/MATH1107/wk10/10_algebraic_and_geometric_multiplicities.html

The

**algebraic multiplicity**of λ is the number of times λ is repeated as a root of the characteristic polynomial.
Let A be an n × n matrix with eigenvalue λ. The

**geometric multiplicity of λ**is the dimension of the eigenspace of λ.
In general,

**the algebraic multiplicity and geometric multiplicity of an eigenvalue can differ. However, the geometric multiplicity can never exceed the algebraic multiplicity**.
It is a fact that summing up the algebraic multiplicities of all the eigenvalues of an n×n $n\times n$ matrix A $A$ gives exactly n $n$.

**If for every eigenvalue of**A $A$, the geometric multiplicity equals the algebraic multiplicity, then

$A$ is said to be . As we will see, it is relatively easy to compute powers of a diagonalizable matrix.*diagonalizable*### yeast PIN nD

controllability for yPIN with Dang's data

```{r eigen-spectrum}

e=eig$values;

summary(e)

digits = c(1:30)

zeros = digits

debug = 0

for ( i in 1:length(digits ) ) {

tmp = sort(table(round(e, roundings[i])), decreasing = T)

if (debug > 0) { print(tmp[1:3]) }

zeros[i] = tmp[1]

}

cbind( digits , zeros)

```

Conclusion: frequency of zero eigen values stabilize between 5 and 14 at 135.

```{r eigen-spectrum}

e=eig$values;

summary(e)

digits = c(1:30)

zeros = digits

debug = 0

for ( i in 1:length(digits ) ) {

tmp = sort(table(round(e, roundings[i])), decreasing = T)

if (debug > 0) { print(tmp[1:3]) }

zeros[i] = tmp[1]

}

cbind( digits , zeros)

```

Min. 1st Qu. Median Mean 3rd Qu. Max. -59.919 -1.696 0.000 0.000 1.384 97.758 digits zeros [1,] 1 228 [2,] 2 158 [3,] 3 140 [4,] 4 136 [5,] 5 135 [6,] 6 135 [7,] 7 135 [8,] 8 135 [9,] 9 135 [10,] 10 135 [11,] 11 135 [12,] 12 135 [13,] 13 135 [14,] 14 135 [15,] 15 112 [16,] 16 45 [17,] 17 9 [18,] 18 3 [19,] 19 2 [20,] 20 1 [21,] 21 1 [22,] 22 1 [23,] 23 1 [24,] 24 1 [25,] 25 1 [26,] 26 1 [27,] 27 1 [28,] 28 1 [29,] 29 1 [30,] 30 1

Conclusion: frequency of zero eigen values stabilize between 5 and 14 at 135.

## Sunday, August 25, 2019

### embed youtube video in GitHub readme.md

embed youtube video in GitHub readme.md

http://sviridovserg.com/2017/05/22/embed-youtube-to-markdown/

http://embedyoutube.org/

http://sviridovserg.com/2017/05/22/embed-youtube-to-markdown/

http://embedyoutube.org/

## Saturday, August 24, 2019

### long term memory is a reliability model

long term memory is a reliability model

https://medicalxpress.com/news/2019-08-memories.html?fbclid=IwAR2WULqnQdMCcKaYrMYGjD0gvXwCgw18dWTL295995wtC8klh9kdHPjh5n4

https://medicalxpress.com/news/2019-08-memories.html?fbclid=IwAR2WULqnQdMCcKaYrMYGjD0gvXwCgw18dWTL295995wtC8klh9kdHPjh5n4

## Friday, August 23, 2019

### UTC work request

Everyone is able to put in a work request. Go here https://fpmis.utc.edu/. Put in your information and what room is having the problem.

## Thursday, August 22, 2019

### Thunor, HTS screen

Harris nature method, 2016, 13 . nmeth.3852

Hafner, Nature method, 2016 . nmeth.3853

https://www.nature.com/articles/nmeth.3853

### mass spec is a joint distribution

mass spec can generate 28 dimension joint distribution, then we need to figure out the mixtures of probiblity distrubitons of sub populations.

## Wednesday, August 21, 2019

### Vanderbilt cancer heterogeneity workshop

=> Ken Lau, single cell RNAseq

https://www.mc.vanderbilt.edu/vumcdept/cellbio/laulab/research.html

scanpy

AnnData

get high-quality cells,

feature selection

dpFeature, select EDG between clusters identified by density peak clusting, Qiu 2017

SCENIC, coordianted regulated TF target, Aibar, 2018

NVR, neighborhood variance ratio, Welch 2016, Chen 2019 (Lau lab).

trajectory reconstruction, many algorithms,

### phage PIN and aging simulation

first principle simulation of phage PIN: Can I regenerate the exponetial survival curves?

https://www.ncbi.nlm.nih.gov/pubmed/21943085

J Virol. 2013 Dec;87(23):12745-55. doi: 10.1128/JVI.02495-13. Epub 2013 Sep 18.

# The protein interaction network of bacteriophage lambda with its host, Escherichia coli.

https://www.ncbi.nlm.nih.gov/pubmed/24049175
BMC Microbiol. 2011 Sep 26;11:213. doi: 10.1186/1471-2180-11-213.

# The protein interaction map of bacteriophage lambda.

https://www.ncbi.nlm.nih.gov/pubmed/21943085

## Tuesday, August 20, 2019

### counter factual model

Very nice blog on counterfactural models (related to aging and forbiden interactions).

https://www.inference.vc/causal-inference-3-counterfactuals/

https://www.ssc.wisc.edu/~felwert/causality/wp-content/uploads/2013/06/1-Elwert_Causal_Intro.pdf

### AI meeting notes

gradient descent is robust form of optimization. Exact optimization for training data probably do not generalize well for testing data.

gradient descent 'converge' to a global optimization point?

classification,

inverse problem in materials

transportation

mobility

AI only learn what data is (so memorization?)

AI is just a marketing term?

learning: physics based, foundational math, representation learning, reinforcement learning, adversary networks,

scalability: algorithms, convergence, parallelization, mix precision arithmetic, hardware,

Assurance: uncertainty quantification, explainability and interpretability, validation verification, causality,

workflow: edge computing, compression, online learning, federated learning, augmented intelligence,

AI learns the world is, not the way it should be. (bias)

AI leans the world from the data presented, not the way it is.

AI algorithm may needs FDA styled drug trails and approval.

AI/ML microscopy in materials: predicting crystal structure by merging data mining and quantum physics. Chemical space is non-differentiable. Chemical space is a graph. Functionalitties at the nodes are defined within the context, such biological context, water, etc. In many cases, chemical properties are hard to predict. Materials design can be thought as a search problem. Use mean field descriptors. Build precision micrscopy to map atoms?! Open data. Jupyter papers.

Localization: CNN, precision: Gaussian

theory-experiment matching.

Hypothesis driven science = forward mode P(dat/theory) P(theory)based on domain exptersize,

Q: how to get training data at atomic levels?

AI in health, Gina Tourassi

Johnson, KW, J Am Colle Cardiol, 2018, 7 23,

https://www.sciencedirect.com/science/article/pii/S0735109718344085

AI in memogram scan by MIT/MGH

basal carcinoma, by deep learning,

genes and biology are responsbble for 10% of our health and well being.

modeling health instead of disease.

current opintin in biotechnology, 2019, v58, by Eberhart Voit

https://www.sciencedirect.com/science/article/pii/S0958166918301915

## Thursday, August 8, 2019

## Wednesday, August 7, 2019

### Yuan method on adjacency matrix controllability

Yuan clearly used weighted matrix, and j->i as direction. So, column to row indicate direction?

Wikipedia, “In directed graphs, the in-degree of a vertex can be computed by summing the entries of the corresponding column, and the out-degree can be computed by summing the entries of the corresponding row.” The question now is does i->j and j->i matters? It can be tested using a star shaped network with outward and inwarding arrows. A quick exam on these show both star networks should have the same number of minimal control nodes. Well, eigen values of a matrix and its transpose are the same, see https://yutsumura.com/eigenvalues-of-a-matrix-and-its-transpose-are-the-same/.

So, i->j and j->i does not matter! This is somewhat shocking to me.

### Eigenvalues of a Matrix and its Transpose are the Same

https://yutsumura.com/eigenvalues-of-a-matrix-and-its-transpose-are-the-same/

Recall that the eigenvalues of a matrix are roots of its characteristic polynomial.

Hence if the matricesA $A$ and AT ${A}^{\mathrm{T}}$ have the same characteristic polynomial, then they have the same eigenvalues.

Hence if the matrices

So we show that the characteristic polynomial pA(t)=det(A−tI) ${p}_{A}(t)=det(A-tI)$ of A $A$ is the same as the characteristic polynomial pAT(t)=det(AT−tI) ${p}_{{A}^{\mathrm{T}}}(t)=det({A}^{\mathrm{T}}-tI)$ of the transpose AT ${A}^{\mathrm{T}}$.

We have

Therefore we obtain pAT(t)=pA(t) ${p}_{{A}^{\mathrm{T}}}(t)={p}_{A}(t)$, and we conclude that the eigenvalues of A $A$ and AT ${A}^{\mathrm{T}}$ are the same.

### Remark: Algebraic Multiplicities of Eigenvalues

Remark that since the characteristic polynomials of A $A$ and the transpose AT ${A}^{\mathrm{T}}$ are the same, it furthermore yields that the algebraic multiplicities of eigenvalues of A $A$ and AT ${A}^{\mathrm{T}}$ are the same.

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