Pith Number
pith:IS3QVRYK
pith:2018:IS3QVRYKU6TKXAKSEW4NZBAAIZ
not attested
not anchored
not stored
refs pending
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
arxiv:1802.04822 v1 · 2018-02-13 · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{IS3QVRYKU6TKXAKSEW4NZBAAIZ}
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
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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 | 2026-05-18T00:23:22.550160Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
44b70ac70aa7a6ab815225b8dc840046406a4736e1000145c2b5fbec9983f2bc
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/IS3QVRYKU6TKXAKSEW4NZBAAIZ \
| 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: 44b70ac70aa7a6ab815225b8dc840046406a4736e1000145c2b5fbec9983f2bc
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "db64310c95eb415d4007ab944f4091a5b09013ef06e6c60ea42f3111a516b37b",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2018-02-13T19:10:12Z",
"title_canon_sha256": "31509cd2ab0e57fda28039868bb16ee7e0157af3dbd0d2d6615d352ed4fdb0fb"
},
"schema_version": "1.0",
"source": {
"id": "1802.04822",
"kind": "arxiv",
"version": 1
}
}