Pith Number
pith:5M2LDDXC
pith:2025:5M2LDDXC3XHT7EDZDZEQN3SQIQ
not attested
not anchored
not stored
refs pending
CrosswalkNet: An Optimized Deep Learning Framework for Pedestrian Crosswalk Detection in Aerial Images with High-Performance Computing
arxiv:2506.07885 v1 · 2025-06-09 · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{5M2LDDXC3XHT7EDZDZEQN3SQIQ}
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 | 2026-07-05T11:18:34.898440Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
eb34b18ee2ddcf3f90791e4906ee504428430903f8dd64dfe4ae7e5aa9c8fc9d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/5M2LDDXC3XHT7EDZDZEQN3SQIQ \
| 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: eb34b18ee2ddcf3f90791e4906ee504428430903f8dd64dfe4ae7e5aa9c8fc9d
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "bca7549ef0554628dd5df942e7c8fa891e1016dd55f97a82591184257710af4c",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2025-06-09T15:56:24Z",
"title_canon_sha256": "26ff16a5b5f8bad6eca252236821d347c66ace3e01a39df75c09e4854eff6164"
},
"schema_version": "1.0",
"source": {
"id": "2506.07885",
"kind": "arxiv",
"version": 1
}
}