utc faculty recruitment workflow

 

Faculty Recruitment Workflow 

https://utc.teamdynamix.com/TDClient/2717/Portal/Requests/ServiceDet?ID=52650

Purpose

This workflow, which is housed within Dynamic Forms, provides a department head, or their designee, a path to request for new or vacant faculty or staff position to be advertised in our university's Taleo system. This workflow is Questions about concerning this process or form can be directed to academic-affairs@utc.edu. 

workflow for Seurat and Velocity

 

#workflow for Seurat and Velocity

#open Rstudio on VNC desktop

rstudio

library(Seurat)

#load in 10x data

sample1=Read10X(data.dir = 'path1')

sample2=Read10X(data.dir = 'path2')

#convert 10xdata into Seurat object

sample1.seurat=CreateSeuratObject(sample1, project = 'ID1')

sample2.seurat=CreateSeuratObject(sample2, project = 'ID2')

#calculate percent mito content

sample1.seurat[['percent.mt']]=PercentageFeatureSet(male5mo.seurat, pattern = 'mt-')

sample2.seurat[['percent.mt']]=PercentageFeatureSet(female5mo.seurat, pattern = 'mt-')

#show basic QC parameters for samples

VlnPlot(sample1.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(sample2.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

#can filter based on top/bottoms of violins to remove outliers e.g.

sample1.seurat=subset(sample1.seurat, subset = nFeature_RNA > 200 & nCount_RNA <20000 & percent.mt <5)

sample2.seurat=subset(sample2.seurat, subset = nFeature_RNA > 500 & nCount_RNA <20000 & percent.mt <5)

#remake vlnplots to compare vs before

VlnPlot(male5mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(female5mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(male22mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

VlnPlot(female22mo.seurat, c('nCount_RNA','nFeature_RNA','percent.mt'), pt.size=0)

#normalise data

sample1.seurat <- NormalizeData(sample1.seurat, verbose = FALSE)

sample2.seurat <- NormalizeData(sample2.seurat, verbose = FALSE)

sample1.seurat <- FindVariableFeatures(sample1.seurat)

sample2mo.seurat <- FindVariableFeatures(sample2.seurat)

integ.anchors <- FindIntegrationAnchors(object.list = list(sample1.seurat, sample2.seurat))

combo.seurat <- IntegrateData(anchorset = integ.anchors)

# Run the standard workflow for visualization and clustering

combo.seurat <- ScaleData(combo.seurat, verbose = FALSE, vars.to.regress=c('nCount_RNA', 'nFeature_RNA', 'percent.mt'))

combo.seurat <- RunPCA(combo.seurat, verbose = FALSE)

# UMAP and Clustering

combo.seurat <- RunUMAP(combo.seurat, dims = 1:10)

combo.seurat <- FindNeighbors(combo.seurat)

combo.seurat <- FindClusters(combo.seurat)

combo.seurat$cluster_by_genotype = paste(Idents(combo.seurat), sep='_', combo.seurat$orig.ident)

DefaultAssay(combo.seurat)='RNA'

combo.seurat=CellCycleScoring(combo.seurat, s.features = stringr::str_to_title(cc.genes.updated.2019$s.genes), g2m.features = stringr::str_to_title(cc.genes.updated.2019$g2m.genes))

#command to find top enriched genes in each cluster vs all other cells is FindAllMarkers()

FindAllMarkers(combo.seurat, assay='RNA', only.pos=T)

#output is pasted in terminal, either save it to an object e.g. x=FindAllMarkers() or write it out into a text file

write.csv(FindAllMarkers(combo.seurat, assay='RNA', only.pos=T), file='combo_allmarkers.csv')

#command to compare two specific clusters is FindMarkers()

write.csv(FindMarkers(combo.seurat, group.by = 'cluster_by_genotype', ident.1 = '16_UOmet', ident.2 = '16_UOcont'), file='combo_cluster16_UOmet_v_UOcont_DEG.csv')

#group.by can be column name in combo.seurat$ 

#ident.1 and ident.2 must be set, if only ident.1 set then it compares against all other cells 

#calculate Otsu threshold for gene, autothresholdr must use integers so multiple out all decimal places then divide back down

OtsuThresh = function(seurat_object, gene_name) {

     library(autothresholdr)

     array=as.integer(seurat_object@assays$RNA@data[gene_name,]*10000000)

     auto_thresh(array, method='Otsu')[1]/10000000

 }

UTC EMCS Room capacities

EMCS 201/101, Card Auditorium, 141 students

EMCS 301, 302, lecture, 50 students

ECS 404 has 58 computers

ECS 423 has 48 computers

UTC graduate tuition, fall 2023

tuition for a Ph.D. student at CSE, UTC

I entered a total of $7,302 for your tuition waiver which includes 9 credit hours ($5,394), 9 differential hours ($540), 3 online hours ($156) and $1,200 for fall insurance. 

Faculty SSB Training through UTC Learn (Canvas)

 

Hello XXX

I'm including the instructions below:

2.Complete Faculty SSB Training through UTC Learn (Canvas)
Log into Canvas or select the ‘View in Canvas’ link once you have logged into MyMocsNet.
Select course ‘Records Training Class’.
Select Faculty SSB training module.  At the end of the training, you will need to score 80% or higher on the quiz.  If you will also be advising students, please take the Faculty SSB training with Advising quiz.

3.Complete the Training Verification Form
Once all trainings have been completed, a Training Verification Form for Faculty and Staff will need to be submitted.  You will need to enter the following:

Supervisor’s information
Your Department
 
Select ‘Next’
This form will enter a workflow and go to your supervisor for approval.  Once your supervisor approves the form, it will route to Registrar Training where your training is verified and documented. Lastly, it will route to the Faculty Records Specialist.
Thank you
Joanne

Quantum computing lec 3, complex vector space, part 2

zoom recording. 

book chapter 3.  review hilbert space and inner product, represented by trianle brackets < >

conjugate transpose, also known as Hermitian transpose,  Hermitian conjugate, adjoint matrix or transjugate, see Conjugate transpose - Wikipedia

show github

https://github.com/hongqin/quantum_sandbox/blob/main/complex_numbers.ipynb 

There is a discussion on visualization of the tensor product, and how to interpret the visualziation. 

ket and bra symbol in quantum computing

classical computing, a Boolean circuit is a mathematical mode

 In classical computing, a Boolean circuit is a mathematical model used to describe logical operations. It consists of gates, inputs, and outputs, all of which can have the value of 0 or 1 (true or false). Let's explore some common gates and their meanings in a classical Boolean circuit:
1. **AND Gate** (Symbol: ∧):
   - Description: Outputs true (1) if and only if both inputs are true.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   0
     0 | 1 |   0
     1 | 0 |   0
     1 | 1 |   1
     ```
2. **OR Gate** (Symbol: ∨):
   - Description: Outputs true (1) if at least one of the inputs is true.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   0
     0 | 1 |   1
     1 | 0 |   1
     1 | 1 |   1
     ```
3. **NOT Gate** (Symbol: ¬):
   - Description: Outputs the opposite value of the input (inverts the input).
   - Truth Table:
     ```
     A | Output
     0 |   1
     1 |   0
     ```
4. **NAND Gate** (Symbol: ⊼):
   - Description: Outputs false (0) if and only if both inputs are true; otherwise, it outputs true.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   1
     0 | 1 |   1
     1 | 0 |   1
     1 | 1 |   0
     ```
5. **NOR Gate** (Symbol: ⊽):
   - Description: Outputs true (0) if and only if both inputs are false.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   1
     0 | 1 |   0
     1 | 0 |   0
     1 | 1 |   0
     ```
6. **XOR Gate** (Exclusive OR) (Symbol: ⊕):
   - Description: Outputs true (1) if the inputs are different, false (0) if they are the same.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   0
     0 | 1 |   1
     1 | 0 |   1
     1 | 1 |   0
     ```
7. **XNOR Gate** (Exclusive NOR) (Symbol: ⊙):
   - Description: Outputs true (1) if the inputs are the same, false (0) if they are different.
   - Truth Table:
     ```
     A | B | Output
     0 | 0 |   1
     0 | 1 |   0
     1 | 0 |   0
     1 | 1 |   1
     ```
These gates form the building blocks of classical Boolean circuits and can be combined in various ways to perform complex logical operations. They provide a foundational understanding of how digital logic and classical computation operate.

cloudBank

 CI_login use federated login, with University login portal. 

UTC CSE adjunct position and training

 ===begin ===

Subject: Adjunct Professor Position: Computer Science & Engineering at UTC

 

The Computer Science and Engineering Department at the University of Tennessee at Chattanooga seeks industry professionals to join as Adjunct Professors. Those with experience in computer science who can provide insights into current industry practices, technology trends, and problem-solving techniques are encouraged to apply.

 

This position offers a chance to teach and mentor in an academic setting. As an Adjunct Professor, you will interact with students interested in the latest industry practices and technologies. Your experience will offer them a perspective grounded in real-world applications.

 

Compensation starts at $1500 per credit hour, and experts with PhD often receive $2000 per credit hour. The role demands clear communication, teaching skills, and a commitment to student learning and development.

 

Interested in contributing to the education of future computer science and IT professionals? Submit your application for the Adjunct Faculty, Computer Science & Engineering, position 16000000D6 at https://ut.taleo.net/careersection/utc_faculty/jobsearch.ftl.

 

adjunct teaching training

 

You will need to complete the FERPA and SSB training.  Link provided below.

 

https://www.utc.edu/enrollment-management-and-student-affairs/registrar/faculty-and-staff-resources/training

 

 

Let me know if you have any questions.

 

Thank you.

Your request for FERPA and Faculty SSB training has been received and the trainings have been assigned to you through Canvas. An email from Canvas has been sent to your UTC email account. Please check your Junk / Spam folder if you do not see it in your Inbox.  You may also access training via the link below. 

Follow the steps below to complete FERPA and Faculty SSB Training: 

1.Complete  FERPA training 

Log into Canvas or select the ‘View in Canvas’ link once you have logged into MyMocsNet

Select course ‘FERPA Training’ 
Select the FERPA Training Module
Select the ‘Load FERPA: Family Educational Rights and Privacy Act in a new window
Start the training

2.Complete Faculty SSB Training through UTC Learn (Canvas)
Log into Canvas or select the ‘View in Canvas’ link once you have logged into MyMocsNet.
Select course ‘Records Training Class’. 
Select Faculty SSB training module.  At the end of the training, you will need to score 80% or higher on the quiz.  If you will also be advising students, please take the Faculty SSB training with Advising quiz.

 

3.Complete the Training Verification Form
Once all trainings have been completed, a Training Verification Form for Faculty and Staff will need to be submitted.  You will need to enter the following:

Supervisor’s information
Your Department
 
Select ‘Next’ 

This form will enter a workflow and go to your supervisor for approval.  Once your supervisor approves the form, it will route to Registrar Training where your training is verified and documented. Lastly, it will route to the Faculty Records Specialist.  

 

 

tax exempt documents UTC

UTC tax id: employer identification number 62-6001636

tax exempt document  for Tennessee:

 

http://controller.tennessee.edu/wp-content/uploads/sites/7/2023/06/Tennessee-Sales-and-Use-Tax-Exemption-Certificate-Signed-7.1.23-6.30.27-1.pdf

For other state’s sales tax, here is a list of other exemptions we have:

 

https://controller.tennessee.edu/tax/sales-and-local-tax-exemptions/

 

 

Here is a link to UTC's income tax exempt IRS letter:

 

https://www.utc.edu/sites/default/files/2020-05/inst-fiscal-data-taxletter.pdf

 

It should not matter, but some donors demand to see that UTC are a 501(c)(3) charitable organization. Since UTC is government, we are tax exempt under a different section of the internal revenue code, 107(c)(1). 

But the UC Foundation is a 501(c)(3) charitable organization and we can receive funds through them if the donor requires it. If that is the case, let me know and I will give you more information.

Shapley Attributed Ablation with Augmented Learning (ShapAAL)

 One new method that improves Shapley values for deep learning explanation is **Shapley Attributed Ablation with Augmented Learning (ShapAAL)**. It is a novel push-pull deep architecture where the subset selection through Shapley value attribution pushes the model to lower dimension while augmented training augments the learning capability of the model over unseen data¹.

ShapAAL demonstrates that a deep learning algorithm with a suitably selected subset of the seen examples or ablating the unimportant ones from the given limited training dataset can ensure consistently better classification performance under augmented training¹.

Would you like more information on this topic?

Source: Conversation with Bing, 7/2/2023

(1) When less is more powerful: Shapley value attributed ablation with .... https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277975.

(2) Explaining a series of models by propagating Shapley values. https://www.nature.com/articles/s41467-022-31384-3.

(3) [2104.02297] Shapley Explanation Networks - arXiv.org. https://arxiv.org/abs/2104.02297.

(4) GitHub - slundberg/shap: A game theoretic approach to explain the .... https://github.com/slundberg/shap.

flash attention

 FlashAttention is an **IO-aware exact attention algorithm** that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM. It is designed to be **fast and memory-efficient**¹.

It is optimal for a range of SRAM sizes and requires fewer HBM accesses than standard attention¹. FlashAttention trains Transformers faster than existing baselines and enables longer context in Transformers, yielding higher quality models¹.

Would you like more information on this topic?

Source: Conversation with Bing, 7/2/2023

(1) [2205.14135] FlashAttention: Fast and Memory-Efficient Exact Attention .... https://arxiv.org/abs/2205.14135.

(2) arXiv:2205.14135v2 [cs.LG] 23 Jun 2022. https://arxiv.org/pdf/2205.14135.pdf.

(3) Introducing Lightning Flash — From Deep Learning Baseline To ... - Medium. https://medium.com/pytorch/introducing-lightning-flash-the-fastest-way-to-get-started-with-deep-learning-202f196b3b98.

(4) Attention in Neural Networks - 1. Introduction to attention mechanism. https://buomsoo-kim.github.io/attention/2020/01/01/Attention-mechanism-1.md/.

PETs Prize Challenge Track B, Dataset use

 Hello PETs Prize Challenge Track B participants,

Thank you for your participation in the challenge! In case you missed it, the winners were announced last week at the Summit for Democracy. You can learn more about the winners in the U.S. winners blog post or the U.K. winners blog post.

The pandemic forecasting dataset from the challenge has been released online with an accompanying paper. You can find all of that on the dataset website, including instructions for how to cite the dataset. 

You are welcome to share any work that you produced as part of the challenge. Please be sure to acknowledge the challenge and appropriately cite the challenge dataset. You should link to the official release of the dataset rather than making any data available yourselves. 

If you also participated in Track A: Financial Crime Prevention of the challenge, please note that guidelines for sharing Track A work are more expansive and have been sent out separately to Track A participants. 

If you have any further questions, please send them to NIST at PrivacyEng@nist.gov.

Thanks again for your engagement with the challenge. We look forward to following your continued work in privacy-enhancing technologies research. 

Best,

The DrivenData Team and the PETs Prize Challenge organizers

PH AWS quotes

 I have calculated the price for instances suitable for our project.

Based on the information in the following link:

AWS EC2 Instance Types  (section of Accelerated Computing)

I have created an Excel spreadsheet to provide you with a detailed breakdown and comparison. You can access it using the following link:

Google Sheets - Instance Price Comparison

 

The following link contains PDF files of all the estimations, which I downloaded from AWS:
AWS - PDF