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

 

DELTA New Department Leaders Institute

 

I found information about the DELTA New Department Leaders Institute, which is designed to provide new department chairs with the knowledge and tools needed to positively launch and navigate their leadership role. The program is an intermediate-level, instructor-led program that takes place over two days for a total of 12 hours of instruction. The program offers opportunities for participants to interact with facilitators and each other, with ample time dedicated to breakout activities and discussion².

Is this what you were looking for?

Source: Conversation with Bing, 6/25/2023

(1) DELTA New Department Leaders Institute – ASEE Learning. https://learning.asee.org/course_catalog/delta-new-department-leaders-institute/.

(2) Delta Leadership Program 2023 - Delta Protection Commission. https://delta.ca.gov/delta-leadership-program/.

(3) Leader Bios | Delta News Hub. https://news.delta.com/leader-bios.

food desert and health disparity

Food deserts are indicators of more than just socioeconomic injustice; they indicate public health and safety concerns for those living within their borders. Residents with a chronic lack of access to adequate food resources are shown to have higher rates of diabetes, obesity, and cardiovascular disease¹. Restricted access to healthy foods may magnify health disparities that lead to higher rates of diabetes, hypertension, and cardiovascular disease⁴.

To research how food deserts affect health disparities, you can look at studies that examine the relationship between food insecurity and chronic health conditions such as diabetes, obesity, heart disease, mental health disorders, and other chronic diseases². You can also look at studies that examine the impact of food deserts on different populations, such as low-income communities and communities of color².

(1) https://sites.duke.edu/lit290s-1_02_s2017/2017/03/04/health-and-socioeconomic-disparities-of-food-deserts/#:~:text=Food%20deserts%20are%20indicators%20of%20more%20than%20just,rates%20of%20diabetes%2C%20obesity%2C%20and%20cardiovascular%20disease%20%28Corapi%29.. https://sites.duke.edu/lit290s-1_02_s2017/2017/03/04/health-and-socioeconomic-disparities-of-food-deserts/.

(2) Along With Health Care Disparity, Effects Of Food Deserts Likely Making .... https://blockclubchicago.org/2020/04/10/along-with-health-care-disparity-and-poverty-effects-of-food-deserts-likely-making-covid-19-more-deadly-for-black-chicagoans/.

(3) Food Accessibility, Insecurity and Health Outcomes - NIMHD. https://www.nimhd.nih.gov/resources/understanding-health-disparities/food-accessibility-insecurity-and-health-outcomes.html.

(4) The changing landscape of food deserts - PMC - National Center for .... https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299236/.

(5) Food insecurity in the US: An explainer and research roundup. https://journalistsresource.org/home/food-insecurity-health/.

(6) Food Deserts: Causes, Effects, and Solutions - Healthline. https://www.healthline.com/nutrition/food-deserts.

AGE

 promoter methylation is associate social factor in dogs

Yuan 2011 longevity between mouse and human Framingham study. 

George Williams, evolutionary theory of aging. antagnistic pleotropic genes. 

Hongjie

Lu Wang, UT San Antonio

Alex UW Seattle

=>single cell aging: 

worm

https://c.elegans.aging.atlas.research.calicolabs.com/

fly 

=> Hevolution

GPT for few-shot genomics analysis across species

=> women in Japan and immigrant in US have different breast-cancer risks, suggesting environmental factors on age-dependent risks, city of hope presentation. 

=> 

Karen Guerrero Vazquez

Authors: Karen Guerrero Vazquez, Pilib Ó Broin, Katarzyna Goljanek-Whysall

Selection of miRNA candidates for the treatment of sarcopenia using network-based analysis and differential expression scoring. Karen Guerrero Vazquez 1, Pilib Ó Broin 1, Katarzyna Goljanek-Whysall 2. 1 School of Mathematical & Statistical Sciences, National University of Ireland Galway, 2 School of Medicine, National University of Ireland Galway. Sarcopenia is a natural consequence of aging and leads to progressive muscle wasting. Currently, there is no cure for this condition, and target identification and validation remain pressing challenges. Many potential therapeutic targets have failed in clinical trials or shown poor association with the disease. This project aims to address these challenges by creating a model of microRNA:target interactions for more efficient in silico selection of potential therapeutic targets for sarcopenia. We conducted a novel network-based analysis of microRNA involvement in aging using RNAseq and microarray data from five studies, with a total of 246 samples of skeletal muscle from healthy participants with ages ranging from 19 to 85 years old. We analyzed young, middle age and older adults and determined interactions between genes coming from co-expression, colocalization, genetic interaction, physical interaction, predicted and shared protein domain calculated with GeneMania. Next, we predicted microRNAs that are putative regulators of shortlisted genes using target prediction tools, such as TargetScan, mirDB and mirTarbase. Finally, we add tissue expression from Diana-miTed and miRNATissue Atlas2. After identifying the network of gene:gene and microRNA:gene interactions, the relevance of each node was calculated using multiple scoring metrics and measures of centrality in order to identify the most likely key regulatory microRNAs and their targets during aging. Our model of microRNA-target interactions is specifically tailored to the context of muscle aging and offers a more comprehensive and accurate representation of the complex regulatory mechanisms involved in muscle aging than existing tools, as it integrates multiple layers of biological information. By identifying a few tens of microRNAs and genes with potential therapeutic power, our model offers a valuable and efficient approach to target identification and validation for the treatment of sarcopenia. This abstract has emanated from research supported in part by a research grant from Science Foundation Ireland under Grant number [18/CRT/6214]

=> 

Explaining the asynchrony of aging through cell population dynamicsMing Yang1, Benjamin R. Harrison1, Daniel E.L. Promislow1,21 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195 USA 2 Department of Biology, University of Washington, Seattle, WA 98195 USA Different tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cell lifespan explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis, and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.This work was supported by NIH R01 AG063371 (to D. Promislow and S. Pletcher) and Norn Group Impetus Grant (to D. Promislow).

https://www.biorxiv.org/content/10.1101/2023.05.31.543091v1

fair AI high school camp

 

https://www.bloomberg.com/graphics/2023-generative-ai-bias/

greek letters in math with latex input

 Greek letters in Math can be pronounced quite differently than the way in Greek languages. 

https://www.youtube.com/watch?v=CnotqG06t_A

algebra, Fibonecci series

 

https://www.youtube.com/watch?v=z4cP8aDYt0g

SHAP tutorial from BING

 

There are several popular tutorials on using SHAP for ML. Some of them include:

1. "SHAP: How to Interpret Machine Learning Models With Python" by Dario Radečić on Better Data Science ¹.

2. "An introduction to explainable AI with Shapley values" on the SHAP documentation website ².

3. "Interpretable Machine Learning using SHAP — theory and applications" by Towards Data Science ³.

4. "Tutorial: Explainable Machine Learning with Python and SHAP" by Natalie Beyer on ML Conference ⁴.

These tutorials provide a step-by-step guide on how to use the SHAP package in Python to interpret machine learning models.

Is there anything else you would like to know?

Source: Conversation with Bing, 6/1/2023

(1) SHAP: How to Interpret Machine Learning Models With Python. https://betterdatascience.com/shap/ .

(2) An introduction to explainable AI with Shapley values — SHAP latest .... https://shap.readthedocs.io/en/latest/example_notebooks/overviews/An%20introduction%20to%20explainable%20AI%20with%20Shapley%20values.html.

(3) Interpretable Machine Learning using SHAP — theory and applications .... https://towardsdatascience.com/interpretable-machine-learning-using-shap-theory-and-applications-26c12f7a7f1a.

(4) Tutorial: Explainable Machine Learning with Python and SHAP. https://mlconference.ai/blog/tutorial-explainable-machine-learning-with-python-and-shap/.

SHAP tutorial

 

KernelSHAP vs TreeSHAP

Comparing SHAP approximation methods based on speed, complexity and other considerations

Conor O'Sullivan

·

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medium-articles/kernelSHAP_vs_treeSHAP.ipynb at master · conorosully/medium-articles · GitHub