I'm not aware of any, but it wouldn't be insane to build a seekable deflate implementation by defining offsets in a zip comment. This would leave the zip file backwards compatible to usual decompression while allowing internal seeking within an individual file if the decompressor was aware of this index.
For seekable gzip indexes in zip, there SOZip: https://github.com/sozip/sozip-spec . However, it stores the indexes as files succeeding the actual file entry. To hide these index files and avoid extraction, they are not listed in the central directory, but a linear scan of the local headers, which some wrongly-behaved ZIP tools do, or which might be necessary for recovering broken ZIP files, would find those hidden indexes.
Just kidding, I don't see how the overhead of the directory entry is even remotely enough to warrant removal. Most of the magic can be left to efficient DEFLATE compatible blocks and removing entries not in the central directory in the first place (ZIP files can support concatenation of new data so long as you re-write the central directory at the end of the file).
Both linked in the Sources & Confessions modal at the bottom. Cover Your Tracks is the spiritual ancestor of this whole piece. amiunique is more rigorous; this is the editorial cousin.
I'm curious to know what you would rate as the most important features to make this work? It seems like calc+if do a lot of the heavy lifting, but the new function syntax is what makes instruction lookup tractable.
It does seem like the state of the art differs from popular understanding. Not only is mitochondrial DNA straight forward (although not especially useful for forensics as it is maternal), but with specialized extraction it is still possible to recover nuclear DNA, just exceedingly painful to do so.
It is the initial purpose of a microbiome to be at least commensal, in that it is usually prohibitively expensive to maintain a sterile environment so the odds of a true pathogen colonizing a system is greatly reduced if you simply have a crowded space of neutral participants.
Once that's true it does seem there's a lot of host and microbiome interactions we've only begun to explore, but it shouldn't be surprising that co-evolution of the microbiome and host begins to take over as soon as you have one. One great example is short-chain-fatty-acid (SCFA) producing bacteria in the human gut. [1] These seem to be essential, and if there was a general takeaway to improve health, it would be to eat your roughage so they can do their job.
This is also why high alpha-diversity (community richness in particular) is such a dead-ringer for healthy vs diseased states. And frustratingly, is often exactly where the story ends for a lot of observational studies.
Also, in case you are curious, artificially sterile mice (gnotobiotic mice) tend to act differently than other mice, which is pretty odd to be honest, and why the gut-brain axis is a plausible mechanism to research further. [2]
If anyone is interested in a more formal descriptions of these control-loops, with more testable mechanisms, check out the concept of reward-taxis. Here are two neat papers that I think are more closely related than might initially appear:
"Is Human Behavior Just Running and Tumbling?": https://osf.io/preprints/psyarxiv/wzvn9_v1
(This used to be a blog post, but its down, so here's a essentially identical preprint.)
A scale-invariant control-loop such as chemotaxis may still be the root algorithm we use, just adjusted for a dopamine gradient mediated by the prefrontal cortex.
Not an expert, but I have a bit of formal training on Bayesian stuff which handles similar problems.
Usually Gibbs is used when there's no directly straight-forward gradient (or when you are interested in reproducing the distribution itself, rather than a point estimate), but you do have some marginal/conditional likelihoods which are simple to sample from.
Since each visible node depends on each hidden node and each hidden node effects all visible nodes, the gradient ends up being very messy, so its much simpler to use Gibbs sampling to adjust based on marginal likelihoods.
https://thejenkinscomic.wordpress.com/2024/12/01/brady-bunch...
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