Pith Number
pith:2B22JDID
pith:2020:2B22JDIDVSNQHJPIGNXF7V6RDN
not attested
not anchored
not stored
refs pending
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques
arxiv:2001.07766 v1 · 2020-01-21 · cs.CV · cs.LG · eess.IV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{2B22JDIDVSNQHJPIGNXF7V6RDN}
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 | 2026-07-05T00:36:47.385681Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d075a48d03ac9b03a5e8336e5fd7d11b7f714bb9f13529af0beb6b036f964fa1
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2B22JDIDVSNQHJPIGNXF7V6RDN \
| 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: d075a48d03ac9b03a5e8336e5fd7d11b7f714bb9f13529af0beb6b036f964fa1
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "b10b6f88b913dedd75dd5b6b51ef68e31fdd8e47f3755fc1ce6a2d022df2a56f",
"cross_cats_sorted": [
"cs.LG",
"eess.IV"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CV",
"submitted_at": "2020-01-21T20:31:10Z",
"title_canon_sha256": "3d710269c49b727bbf2bc2c2c79edee42cdeaed09a7997278651d0cf65c4066e"
},
"schema_version": "1.0",
"source": {
"id": "2001.07766",
"kind": "arxiv",
"version": 1
}
}