Pith Number
pith:BQU2HIHY
pith:2022:BQU2HIHYHMPICF56WZTD2UB6IJ
not attested
not anchored
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refs pending
Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models
arxiv:2201.04042 v2 · 2022-01-11 · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{BQU2HIHYHMPICF56WZTD2UB6IJ}
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
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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 | 2026-07-05T03:51:12.585534Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0c29a3a0f83b1e8117beb6663d503e424ff1ba0ccfd21e31ff21373e551a2089
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BQU2HIHYHMPICF56WZTD2UB6IJ \
| 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: 0c29a3a0f83b1e8117beb6663d503e424ff1ba0ccfd21e31ff21373e551a2089
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "fa5e0698b4d6b42193ef95a87fae921615712db9cc23b5e10c7a6f3bd455895c",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2022-01-11T16:39:32Z",
"title_canon_sha256": "9201b29be99ca87dd4bc3b1bf5e5428875d74cf2ea1a35fecaf0f5aba66955d3"
},
"schema_version": "1.0",
"source": {
"id": "2201.04042",
"kind": "arxiv",
"version": 2
}
}