PTHash and other minimum perfect hash functions return an arbitrary value if the query key did not exist when building the MPHF, so they can be a lot smaller. B-field can identify query keys that don't exist in the set (with high probability?).
What I'm wondering is why the Kraken2 probabilistic hash table doesn't work. It uses 32 bits per element in an open addressing hash table. For 1 billion k-mers and 19 bits for the value, 32 - 19 = 13 bits of the key hash can be stored alongside the value, helping disambiguate hash collisions. If the load factor is 1.25x, then that's 4 * 10^9 * 1.25 = 5GB total, better than ~7GB. Also, this is only one cache miss (+ linear probing that can be SIMD accelerated) per lookup.
Adversarial attacks is a super interesting field, but unfortunately I feel that a lot of papers are just incremental attack or defense improvements like a cat-and-mouse game. I originally did some research on 3D point cloud attacks, but later stopped because making super successful attacks (eg., attacks with higher success rates than all the previous techniques for some very specific task) don't really help us understand that much more about neural nets, its just optimizing a metric for publishing papers. This kind of research is quite common, even at top conferences.
Despite this, recently, we made a 1 minute explainer video introducing adversarial attacks on neural nets as a submission for the Veritasium contest: https://youtu.be/hNuhdf-fL_g Give it a watch!
Yes, this will uwuify your text at high speeds. It reached 2.3 GB/s on my 8-core macbook pro, while uwu-ing the first 1 GB of english Wikipedia. This Rust command-line tool takes advantage of SSE4.1 SIMD vectorization and multithreading (exploit all your CPU cores for this!) to be almost as fast as simply copying a file. Installing it is simply cargo install uwuify, assuming you already have Rust installed. It is on crate.io too: https://crates.io/crates/uwuify
In case you don't know what uwu'd text looks like, here's an example:
hey... i think i w-weawwy wuv you. (⑅˘꒳˘) d-do you want a headpat?
Yes, this will uwuify your text at high speeds. It reached 2.3 GB/s on my 8-core macbook pro, while uwu-ing the first 1 GB of english Wikipedia. This Rust command-line tool takes advantage of SSE4.1 SIMD vectorization and multithreading to be almost as fast as simply copying a file. Installing it is simply cargo install uwuify, assuming you already have Rust installed. It is on crate.io: https://crates.io/crates/uwuify
For binary trees, indexing can be done by saving the subtree size of each node and doing a sort of binary search. Not sure if this is fast for B-trees that have more than 2 children nodes, though.
What I'm wondering is why the Kraken2 probabilistic hash table doesn't work. It uses 32 bits per element in an open addressing hash table. For 1 billion k-mers and 19 bits for the value, 32 - 19 = 13 bits of the key hash can be stored alongside the value, helping disambiguate hash collisions. If the load factor is 1.25x, then that's 4 * 10^9 * 1.25 = 5GB total, better than ~7GB. Also, this is only one cache miss (+ linear probing that can be SIMD accelerated) per lookup.