Posts Tagged ‘from’
Bouba/kiki effect – Wikipedia
March 29, 2026Should We All Be ‘House Burping’? – The New York Times
March 29, 2026https://www.nytimes.com/2026/01/29/realestate/what-is-house-burping-luften.html
QT:{{”
In the last few weeks, an unfamiliar German term may have surfaced on your social media feeds. “Lüften” roughly translates to “ventilate” or “airing out” and involves just that — opening windows in your home once or twice a day, regardless of the outside temperature, to eliminate stale air.
“}}
Transformer youtube video
March 6, 2026youtube links to the transformer video that is helpful for
understanding the transformer:
https://www.youtube.com/watch?v=wjZofJX0v4M
https://www.youtube.com/watch?v=eMlx5fFNoYc
3Blue1Brown. (2024, April 1). Transformers, the tech behind LLMs | Deep Learning Chapter 5 [Video]. YouTube.
https://www.youtube.com/watch?v=wjZofJX0v4M
Attention in transformers, step-by-step | Deep Learning Chapter 6
Hook for 11am oped
March 5, 2026Ortiz, A. (2026, March 3). British Columbia moving to permanent daylight saving time, changing clocks for the last time Sunday. The New York Times.
https://www.nytimes.com/2026/03/03/world/canada/daylight-savings-bc-time.html
Should we all be “house burping”?
March 5, 2026https://www.nytimes.com/2026/01/29/realestate/what-is-house-burping-luften.html
It may improve your home air quality.
February 18, 2026
The German practice of “lüften” is gaining traction on social media.
If famous artists painted you | twoworlds.co
March 5, 2026LDA resources
February 28, 2026Ganegedara, T. (2025, February 2). Intuitive Guide to Latent Dirichlet Allocation. Towards Data Science.
https://towardsdatascience.com/light-on-math-machine-learning-intuitive-guide-to-latent-dirichlet-allocation-437c81220158/
some blogs
https://johaupt.github.io/blog/Topic_modeling_with_Gibbs_sampling_in_R.html https://agustinus.kristia.de/blog/lda-gibbs/
RFA – MPI/PI NIH limit
February 28, 2026the answer… from OSP…
The NIH limitation is by council round, rather than submission date. Council rounds can be found on the NIH standard due dates page: https://grants.nih.gov/grants-process/submit/submission-policies/standard-due-dates
Here is the policy info on the limitation:
https://grants.nih.gov/grants/guide/notice-files/NOT-OD-25-132.html
Some additional guidance (including a table to show which deadlines fit into which year) can be found on the OSP website: https://research-support.yale.edu/sponsored-projects/office-of-sponsored-projects/proposals
Calendar Year 2026
| Advisory Council Dates | Application Due Date | Notes |
|---|---|---|
| January | 5/25/25-9/7/25 | Not included in the count |
| May | 9/25/25-1/7/26 | Included in CY 26 count |
| August or October | 1/25/26-5/7/26 | Included in CY 26 count |
Calendar Year 2027
| Advisory Council Dates | Application Due Dates | Notes |
|---|---|---|
| January | 5/25/26-9/7/26 | Included in CY 27 count |
| May | 9/25/26-1/7/27 | Included in CY 27 count |
| August or October | 1/25/27-5/7/27 | Included in CY 27 count |
Receipt dates:
9/25/2025-5/7/26 6 max
Receipt dates:
5/25-2026 -5/7/27 6 max
MCB111 Mathematics in Biology
February 22, 2026http://mcb111.org/w06/w06-lecture.html
Has some nice textbook downloads – e.g.
http://mcb111.org/w06/KollerFriedman.pdf
QT:{{”
There are many good books to learn about probabilistic models. “Probabilistic graphical models: principles and techniques” (by Koller & Friedman) is a comprehensive source about more general probabilistic models than the one we are going to study here.
“}}
Subset of chap 7 focuses on GMRF
Send ppt from our chat
February 22, 2026good tutorial/textbook chaper/review paper on Poisson regression :
Below are a few readings that discuss how to fit a generalized linear mixed model.
1. Breslow & Clayton (1993), JASA, Approximate Inference in
Generalized Linear Mixed Models.
https://doi.org/10.2307/2290687
A classic statistical paper introducing the Laplace approximation and penalized quasi-likelihood for GLMMs
2. Bates (2011), Mixed models in R using the lme4 package Part 5: Generalized linear mixed models
https://lme4.r-forge.r-project.org/slides/2011-03-16-Amsterdam/5GLMMH.pdf 3. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of statistical software, 67, 1-48.
https://doi.org/10.18637/jss.v067.i01
4. Bates (2025), Computational methods for mixed models
https://cran.r-project.org/web/packages/lme4/vignettes/Theory.pdf
These are written by the authors of the lme4 package, discussing the details of how a mixed-effects model (more specifically, a generalized linear mixed-effects model) is trained using the PIRLS approach.
