Pith Number
pith:MMXDX7TG
pith:2019:MMXDX7TGXSZI66WO5SCX2D2Y64
not attested
not anchored
not stored
refs pending
A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer
arxiv:1903.11712 v1 · 2019-03-27 · cs.NE
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{MMXDX7TGXSZI66WO5SCX2D2Y64}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-05-17T23:50:01.959057Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
632e3bfe66bcb28f7aceec857d0f58f73ded7aef1c418615c8e7e7fa17f7ae1c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/MMXDX7TGXSZI66WO5SCX2D2Y64 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 632e3bfe66bcb28f7aceec857d0f58f73ded7aef1c418615c8e7e7fa17f7ae1c
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "5251edb3d39f42b7eddfbba539c547861d683023674928e16774055df9781ced",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.NE",
"submitted_at": "2019-03-27T21:46:34Z",
"title_canon_sha256": "1c47faa98afab93064c3986e5baea308b34bdc260ea01d63cb2cfc6680cedba7"
},
"schema_version": "1.0",
"source": {
"id": "1903.11712",
"kind": "arxiv",
"version": 1
}
}