Pith Number
pith:PFGXRYCC
pith:2019:PFGXRYCCTOP3MTWYDI5EOCH3QB
not attested
not anchored
not stored
refs pending
Industrial Robot Trajectory Tracking Using Multi-Layer Neural Networks Trained by Iterative Learning Control
arxiv:1903.00082 v2 · 2019-02-28 · cs.RO
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{PFGXRYCCTOP3MTWYDI5EOCH3QB}
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
· sign in to
claim
4
Citations
5
Replications
✓
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.
Cited by
Receipt and verification
| First computed | 2026-05-17T23:52:00.803229Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
794d78e0429b9fb64ed81a3a4708fb806475f1efdd48fad44dc9c368bc8e98bb
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/PFGXRYCCTOP3MTWYDI5EOCH3QB \
| 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: 794d78e0429b9fb64ed81a3a4708fb806475f1efdd48fad44dc9c368bc8e98bb
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "1eda70dec6545a5203a383992610e4f68b1bcf67f48ea2d044e5c849c76bc284",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.RO",
"submitted_at": "2019-02-28T21:57:30Z",
"title_canon_sha256": "24310943d882886134b6a59436c37edc9cf92d940c1863356304ac7c1a8cec11"
},
"schema_version": "1.0",
"source": {
"id": "1903.00082",
"kind": "arxiv",
"version": 2
}
}