Pith Number
pith:XMVJZENS
pith:2018:XMVJZENSZ24PFNVGMQSXQBNYPT
not attested
not anchored
not stored
refs pending
When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning frawmework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware
arxiv:1811.07672 v1 · 2018-11-19 · cs.NE · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{XMVJZENSZ24PFNVGMQSXQBNYPT}
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 | 2026-05-18T00:00:23.803786Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
bb2a9c91b2ceb8f2b6a664257805b87cc647b2a04ecc6613be6ad15913fc88c0
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/XMVJZENSZ24PFNVGMQSXQBNYPT \
| 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: bb2a9c91b2ceb8f2b6a664257805b87cc647b2a04ecc6613be6ad15913fc88c0
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "aab8bfb3adf6810b0d11eb3638da039e9f6f75405e1dfbd9c15bf4f0848a8d2c",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://creativecommons.org/publicdomain/zero/1.0/",
"primary_cat": "cs.NE",
"submitted_at": "2018-11-19T13:32:30Z",
"title_canon_sha256": "666514f89dd0e3d690148386fa81b5bed3788f6666ceedccbe5f24b1559a3de6"
},
"schema_version": "1.0",
"source": {
"id": "1811.07672",
"kind": "arxiv",
"version": 1
}
}