uvx demakein

I've finally updated my wind instrument design program to Python 3. It only took me 10 years to get around to. I was pleased to find there is now a fairly solid python library for 3D boolean operations (manifold3d).

github.com/pfh/demakein

Some conventional flow matching:

Happy little accidents...

I'm really liking this course on generative diffusion models. They seem to have boiled many years of confusing development of ideas down to a simple approach.

diffusion.csail.mit.edu

Second, sampling from the distribution with a Langevin Dynamics simulation. The algorithm is almost identical to gradient descent with momentum, but we add just the right amount of noise to the momentum at each step.

Comparison of optimization and sampling from a distribution defined by an energy function. I use a continuous version of the Ising model spin lattice energy.

First, optimization from a random initial state using gradient descent with momentum, using the SGD optimizer in PyTorch.

Here's a new version of the plot. There is one point per gene! The y axis shows the estimated log fold change, and the color tells about the confidence bound. I lose a little resolution by using color, but hopefully gain understandability. I am hoping it is less confusing and more conventional.

Here's the plot. It's looking at differential gene expression. There are two type of points. They gray dots show estimated log fold change on the y-axis. The colored points show a confidence bound on the log fold change on the y-axis. A significant gene is represented by two different points!

This was one of the important ideas:

This week the Monash Genomics and Bioinformatics Platform did a bulk RNA-Seq workshop, covering end-to-end from experimental design, through library preparations, running an analysis pipeline, and digging into differential expression.

I made a short video of the strange things UMAP and t-SNE can do to your data. The algorithms are shown mostly working as intended, yet with some surprising consequences.

#UMAP #tSNE #scRNAseq #wtf

www.youtube.com/watch?v=gwqU...