What was the motivation for the value 10,420? Is the ability to extract features pretty consistent (no the actual weights obviously but the behavior of being able to extract meaningful features) as long as the space is large enough?

Was the fourth layer chosen arbitrarily or did it have any particularly nice properties for visualization?

Is the discriminator learning something much more complicated than identifying something like how to identify MDH domains?

This seems to work well based on the results, was there previous work/concepts that lead to this as the architecture for the discriminator?

How computationally intensive was it to solve for all of the hyperparams for this model?

Do different values for percent of tokens masked have much of an impact on model performance?

Was there much variation around this?

Sorry if I missed this, but what was the motivation for choosing these particular discriminator models? They seem very reasonable given the results, but I'm curious how these two types of models were chosen based on the structure of the initial problem?

Would an "ideal" path be one where there is a single haplotype path that crosses every single \(a_{i}.u\) vertex? This would be something that generally exists for a given sample, but if present this would be the best path?

Just to verify I'm understanding this notation, \(a_{i}.u\) is being used like an accessor for components of the tuple \(a_{i}\)?

Sorry if this is clear, but I'm a little unclear on the notation. Is X the input data (so empirical results from a scRNA-seq experiment) and Y the generated dist? If so then are X' and Y' subsets of the respective distributions?

In this case does s denote tuning to how results are classified when calling true and false positives and negatives? Or weight terms in the F-measure score it self?

Is this a vector with only a value at position j? So a vector of size N with only position j having a value set, hence being different than x_{j}(t)?

It might be easier to see if the longest barcode was in a different color or had a dashed line overlayed on top of it.

It's hard to tell exactly which part of the structure is being referenced (at least for me). It might be helpful to add an annotation like a circle to show which area is being discussed.

The indexing of x_{ij} doesn't seem to be a unique element but a pair (u_{ij}, s_{ij}). Is this loss function calculating the difference between the spliced and unspliced differences combined?

This part is assuming that t has been set to 5, correct?

Wasn't scGPT v1 which out performed scGPT trained on a smaller pre-training data set?

Really interesting article!

Given the impact this had do you feel there are changes or criticisms needed around the review and publication process of the Bloom results? I'm also curious if you have any thoughts on how pre-print and open science can do a better job with contentious results and discussions around them.

Is this 1Mbp in either direction of a loci of interest? Just binning the human genome by 25 points gives about 1.7% of the genome within 1Mbp of these uniform bins. Depending on what percent of genes are associated with the traits of interest that could be very rare, or fairly common. Is there a way of viewing how impactful this result is in comparison to the size of the genome annotated as relevant to these traits?