CPSim: Simulation Toolbox for Security Problems in Cyber-Physical Systems.

 

Liu, Mengyu, et al. "CPSim: Simulation Toolbox for Security Problems in Cyber-Physical Systems." ACM Transactions on Design Automation of Electronic Systems 29.5 (2024): 1-16.

https://github.com/lion-zhang/CPSim/tree/main

A2 consortium 2024 talk links

  Symposium below or share with interested colleagues.

 

1. Full list of Symposium talks
2. Welcome Remarks
3. Pattie Maes, PhD – Opportunities for AI and Wearables to Support Healthy Aging
4. Jianying Hu, PhD – Harnessing AI for Advancing Neurodegenerative Disease Therapeutics
5. Martin Sliwinski, PhD – Managing Cognitive Impairment – From Early Diagnosis to Supportive Care
6. Michael Kahana, PhD – Managing Cognitive Impairment – From Early Diagnosis to Supportive Care
7. Conor Walsh, PhD – Machine Learning and AI Methods for Aging and Dementia Care
8. Marzyeh Ghassemi, PhD -- Machine Learning and AI Methods for Aging and Dementia Care
9. Jordan Smoller, MD – Big Health Data: Challenges and Opportunities
10. Griffin Weber, MD, PhD -- Big Health Data: Challenges and Opportunities

 

 

national family survey of pregancy

  national family survey of pregancy

https://www.cdc.gov/nchs/nsfg/index.htm

todo: request to restrickted access variables. 

Fall 2025 schedule

August 23 - Dec 12, 2025, Thursday 6p - 8:40pm.  

Scheduled Meeting Times

Type	Time	Days	Where	Date Range	Schedule Type	Instructors
Scheduled In-Class Meetings	6:00 pm - 8:40 pm	R	ENGINEERING & COMP SCI BLDG 2120	Aug 23, 2025 - Dec 12, 2025	LECTURE

ZIP Code RUCA Approximation,

 

https://depts.washington.edu/uwruca/ruca-approx.php?utm_source=chatgpt.com

All of Us survey, data codebooks

 All of Us survey, data codebooks

https://docs.google.com/spreadsheets/d/1pODkE2bFN-kmVtYp89rtrJg7oXck4Fsex58237x47mA/edit?usp=sharing

A model for the assembly map of bordism-invariant functors

 The paper "A model for the assembly map of bordism-invariant functors" by Levin, Nocera, and Saunier (2025) develops advanced categorical frameworks for algebraic topology, particularly through oplax colimits of stable/hermitian/Poincaré categories and bordism-invariant functors123. While not directly addressing machine learning (ML) or large language models (LLMs), its contributions could indirectly influence these fields through three key pathways:

1. Enhanced Categorical Frameworks for ML

The paper's formalization of oplax colimits and Poincaré-Verdier localizing invariants13 provides new mathematical tools for structuring compositional systems. This could advance:

• Model Architecture Design: By abstracting relationships between components (e.g., neural network layers) as bordism-invariant functors, enabling more rigorous analysis of model behavior under transformations5.

• Geometric Deep Learning: Topological invariants and assembly maps could refine methods for learning on non-Euclidean data (e.g., graphs, manifolds) by encoding persistence of features under deformations5.

2. Invariance Learning and Equivalence

The bordism-invariance concept—where structures remain unchanged under continuous deformations—offers a mathematical foundation for invariance principles in ML:

• Data Augmentation: Formalizing "bordism equivalence" could guide the design of augmentation strategies that preserve semantic content (e.g., image rotations as "topological bordisms")5.

• Robust Feature Extraction: Kernels of Verdier projections13 might model noise subspaces to exclude during feature learning, improving adversarial robustness.

3. LLMs for Structured Reasoning

The paper’s explicit decomposition of complex functors (e.g., Shaneson splittings with twists13) parallels challenges in LLM-based reasoning:

• Program Invariant Prediction: LLMs that infer program invariants6 could adopt categorical decompositions to handle twisted or hierarchical constraints (e.g., loop invariants in code).

• Categorical Data Embeddings: LLM-generated numerical representations of categorical data4 might leverage bordism-invariance to ensure embeddings respect equivalence classes (e.g., "color" as a deformation-invariant attribute).

Limitations and Future Directions

The work is highly theoretical, with no direct ML/LLM applications in the paper. Bridging this gap requires:

• Translating topological bordisms into data-augmentation pipelines.

• Implementing Poincaré-Verdier invariants as regularization terms in loss functions.

• Extending LLM-based invariant predictors6 to handle categorical assembly maps.

While speculative, these connections highlight how advanced category theory could enrich ML’s theoretical foundations and LLMs’ reasoning capabilities.

1. https://arxiv.org/abs/2506.05238
2. https://arxiv.org/pdf/2506.05238.pdf
3. https://www.arxiv.org/pdf/2506.05238.pdf
4. https://pubmed.ncbi.nlm.nih.gov/39348252/
5. https://www.aimodels.fyi/papers/arxiv/category-theoretical-topos-theoretical-frameworks-machine-learning
6. https://openreview.net/pdf?id=mXv2aVqUGG
7. https://x.com/CTpreprintBot
8. https://keik.org/profile/mathat-bot.bsky.social
9. https://www.alphaxiv.org/abs/2506.05238
10. https://publications.mfo.de/bitstream/handle/mfo/4263/OWR_2024_47.pdf?sequence=1&isAllowed=y
11. https://x.com/CTpreprintBot/status/1930943445977518380
12. https://www.themoonlight.io/en/review/a-model-for-the-assembly-map-of-bordism-invariant-functors
13. https://library.slmath.org/books/Book69/files/wholebook.pdf
14. https://www.reed.edu/math-stats/thesis.html
15. https://math.mit.edu/events/talbot/2020/syllabus2020.pdf
16. https://webhomes.maths.ed.ac.uk/~v1ranick/papers/quinnass.pdf
17. https://msp.org/agt/2009/9-4/agt-v9-n4-p16-s.pdf
18. https://webhomes.maths.ed.ac.uk/~v1ranick/papers/owsem.pdf

Mendely preprint error

 In Mendely, if an article has unspecified type, it often list it as "preprint". 

To fix this, just change the document type to 'jounral article' or 'conference proceedings' or other appropriate type. 

Beyond Attention: Toward Machines with Intrinsic Higher Mental States

 

Beyond Attention: Toward Machines with Intrinsic Higher Mental States

https://techxplore.com/news/2025-05-architecture-emulates-higher-human-mental.html#google_vignette

USDA Rural-Urban Commuting Area Codes

 USDA

Rural-Urban Commuting Area Codes

https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes?utm_source=chatgpt.com

three digit 360 zipcode in Alabama

 Based on our discussion, I have looked further into the ‘360’ zipcode region, and found it contain 14 counties below:

 

counties = [    "Autauga County", "Barbour County", "Bullock County", "Butler County",

    "Chilton County", "Coosa County", "Covington County", "Crenshaw County",

    "Elmore County", "Lowndes County", "Macon County", "Montgomery County",

    "Pike County", "Tallapoosa County"]

 

Amon them, there are “Barbour”, “Bullock”, and “Macon” counties.

 

So, not sure how useful the three-digit zip code of ‘360’ might be relevant to TU MCH project. 

pastbin

 https://pastebin.com/uBcFUXCA

1. import pandas as pd
2. dataset = %env WORKSPACE_CDR
3. query = """
4. SELECT
5. p.person_id AS person_id,
6. c.concept_name AS state_name
7. FROM `{dataset}.person` AS p
8. LEFT JOIN `{dataset}.concept` AS c ON p.state_of_residence_concept_id = c.concept_id
9. WHERE c.concept_name LIKE 'PII State: %'
10. """
11.  
12. state_df = pd.read_gbq(query.format(dataset = dataset))
13.  
14. state_df['state_of_residence'] = state_df['state_name'].str.replace('PII State: ', '')
15.  
16. state_df.head()
17. state_df.shape
18.  
19. homeless_state_df=state_df[state_df['person_id'].isin(homeless_respiratory_status['person_id'])]
20. homeless_state_df['state_of_residence'].value_counts()

All of us research platform, data explore for rural health research.

 All of us research platform, data explore for rural health research.

Zip3, observational table

Workspaces > Beginner Intro to AoU Data and the Workbench > Analysis >

00. Overview of Data Analysis Steps in AoU

In ‘test-state_data_v060523’, found sample on state, county, and zip3.

 

 

ODU CS and DSC courses taught by Hong Qin

 

https://catalog.odu.edu/courses/cs/#graduatecoursestext

https://catalog.odu.edu/courses/dasc/

CS 781  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

CS 782  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

CS 881  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

CS 882  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

DASC 781  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

DASC 782  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

DASC 881  AI for Health Sciences  (3 Credit Hours)  

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

Prerequisites: Prior programming experience  

DASC 882  Generative AI  (3 Credit Hours)  

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Prerequisites: Prior programming experience  

logit, sigmoid function, and probability

step-by-step through how the sigmoid function turns a logit into a probability, with both math and plain English at each step.

---

✅ Step 1: What is a logit?

The logit of a probability $p$ is defined as:

$$\text{logit}(p) = \log\left(\frac{p}{1 - p}\right)$$

This is called the log-odds, because it’s the natural log of the odds.

---

✅ Step 2: What if we are given the logit and want to get the probability?

Let’s say we are given a number $x$, and we know it’s a logit.

That means:

$$x = \log\left(\frac{p}{1 - p}\right)$$

We want to solve for $p$, the probability.

---

✅ Step 3: Exponentiate both sides

To get rid of the logarithm, we exponentiate both sides:

$$e^x = \frac{p}{1 - p}$$

This tells us the odds in terms of $x$.

---

✅ Step 4: Solve for $p$

Now we solve this equation for $p$.

Start with:

$$e^x = \frac{p}{1 - p}$$

Multiply both sides by $1 - p$:

$$e^x (1 - p) = p$$

Distribute the left-hand side:

$$e^x - e^x p = p$$

Now get all the $p$'s on one side:

$$e^x = p + e^x p$$

Factor out $p$ on the right-hand side:

$$e^x = p(1 + e^x)$$

Now divide both sides by $1 + e^x$:

$$p = \frac{e^x}{1 + e^x}$$

---

✅ Step 5: Simplify into the sigmoid form

We now simplify that expression:

$$p = \frac{e^x}{1 + e^x} = \frac{1}{\frac{1 + e^x}{e^x}} = \frac{1}{1 + e^{-x}}$$

That’s the sigmoid function:

$$p = \sigma(x) = \frac{1}{1 + e^{-x}}$$

---

✅ Final Result

So if you’re given a logit value $x$, the sigmoid function:

$$\sigma(x) = \frac{1}{1 + e^{-x}}$$

turns it into a probability $p \in (0, 1)$.

---

🔁 Why this is useful

• In logistic regression, we compute:

  $$x = w^T x + b \quad \text{(a linear score)}$$

• Then use the sigmoid to convert that score (logit) into a probability of class 1:

  $$p = \frac{1}{1 + e^{-x}}$$

This way, the model’s output is interpretable as a probability — suitable for binary classification.

Let me know if you want a plot or visualization to make it even clearer!