{"as_of":"2026-08-13T22:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c97de328099a9b7ee5fc9a1516ae756a1cc0b5b1e80ff2a4a458a0c1bd3d41ce","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T15:11:10.873791Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T21:11:07.380014Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-12T21:11:07.534628Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"cited_work":{"arxiv_id":"2411.14612","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.14612","snapshot_observed_at":"2026-08-12T21:11:07.534628Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","venue":"cs.LG","work_id":"47cfa672-74d4-4fc9-9127-bde75aa0d1f3","year":2024},"citing_paper":{"arxiv_id":"2411.09072","last_updated":"2025-01-14T00:21:51Z","snapshot_observed_at":"2026-08-13T14:47:11.498101Z","submitted_at":"2024-11-13T22:55:45Z","title":"Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T21:11:07.380014Z"},"links":{"cited_paper":"/paper/2411.14612","citing_paper":"/paper/2411.09072"},"observation_digest":"sha256:f7c940c409a8f7a080e120c63ce4f154171b140326924733ce5ae7a288984224","observation_id":"85ceaac9-8eb3-43eb-9971-facceb25842c","resolution":{"observed_at":"2026-08-12T21:11:07.542097Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.14612/citation-record","integrity":"/paper/2411.14612/integrity","json":"/paper/2411.14612/citation-record.json","paper":"/paper/2411.14612"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.11223","last_updated":"2024-02-17T08:41:37Z","snapshot_observed_at":"2026-08-13T07:13:38.981366Z","submitted_at":"2024-02-17T08:41:37Z","title":"HEAL: Brain-inspired Hyperdimensional Efficient Active Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11223","snapshot_observed_at":"2026-08-12T15:11:10.684501Z","title":"Heal: Brain-inspired hyperdimensional efficient active learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.684501Z"},"links":{"cited_paper":"/paper/2402.11223","citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:1fbabc84be43b654a365e82bbd08fd943c7040944b9ac40ca001cce97819113d","observation_id":"947a9f65-676f-4f76-af78-d1829cf2b8ce","resolution":{"observed_at":"2026-08-12T15:11:10.684501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.476959Z","title":"A framework for collaborative learning in secure high- dimensional space,","venue":null,"work_id":"dec29ffb-db76-459f-884f-4d366ce77655","year":2019},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.689990Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:760c0d66645350686a10d7d082e73ac491575ff13dbecb1f3e1b84955684be03","observation_id":"4ad0dfd1-d12b-46be-aba8-1ba680d4f1ff","resolution":{"observed_at":"2026-08-12T15:11:11.481503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.462349Z","title":"Biohd: an efficient genome sequence search platform using hyperdimensional memorization,","venue":null,"work_id":"b9dac26f-79ad-419e-aa74-f21f1b10a967","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.695026Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:65e97d7979b461e3b6e8f1abcfaac5d0419c9e89b331746eeec4c22d0f8b2a66","observation_id":"0e41c47c-2db9-4baf-8708-e780a615afe3","resolution":{"observed_at":"2026-08-12T15:11:11.466935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.447918Z","title":"Hdpg: Hyperdimensional policy-based reinforcement learning for continuous control,","venue":null,"work_id":"632e4952-7d7c-4978-9634-ba2a722abca9","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.700931Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:e7a233e63c132264891b1aa839d8bcc891f9e9123ce03eab71b8065d9cac68f1","observation_id":"c02ba037-a225-4038-853d-d77a39659b27","resolution":{"observed_at":"2026-08-12T15:11:11.452550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.433298Z","title":"Algorithm-hardware co-design for efficient brain-inspired hyperdimensional learning on edge. in 2022 design, automation & test in europe conference & exhibition (date),","venue":null,"work_id":"0ffe8bfb-0f31-480d-b584-08efef296165","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.706318Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:a301c91188aa5c97d767533fe9be141be67732c86cc51548eb510886e6048b92","observation_id":"908589be-0d84-403c-8a5e-97b093e68228","resolution":{"observed_at":"2026-08-12T15:11:11.438188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05763","last_updated":"2024-03-09T02:17:43Z","snapshot_observed_at":"2026-08-13T00:58:55.308866Z","submitted_at":"2024-03-09T02:17:43Z","title":"HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05763","snapshot_observed_at":"2026-08-12T15:11:10.711075Z","title":"Hdreason: Algorithm-hardware codesign for hyperdimensional knowledge graph reasoning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.711075Z"},"links":{"cited_paper":"/paper/2403.05763","citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:c21ab8f97f9c6e2f9349a8099642776ab3391dc62a3710183c7deb9ed2177efc","observation_id":"c8c7b0b8-6514-4688-bc6b-b35ff278bea6","resolution":{"observed_at":"2026-08-12T15:11:10.711075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.419218Z","title":"Scalable and interpretable brain-inspired hyper-dimensional computing intelligence with hardware- software co-design,","venue":null,"work_id":"4db6b247-9032-40f6-9c92-29256dceba6b","year":2024},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.716784Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:4f9a8d5d8f3edd7094261ea628d31035be856f972a99a99c5e5e47937bbc12f6","observation_id":"a402c473-d74a-42fe-a60d-18d2a58202a5","resolution":{"observed_at":"2026-08-12T15:11:11.424057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.387942Z","title":"Hyperdimensional computing for robust and efficient unsupervised learning,","venue":null,"work_id":"74bd7cee-fc73-4ef6-a3fc-66983d322419","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.725943Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:01a3473c45c0644ac43f5b2bf9783a15f7767b9c70623cd843070a7f1d43842e","observation_id":"67b03f46-80de-4faa-a215-da32c15761b5","resolution":{"observed_at":"2026-08-12T15:11:11.393186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11025","last_updated":"2024-05-22T16:59:14Z","snapshot_observed_at":"2026-08-13T00:28:39.269162Z","submitted_at":"2024-04-17T03:01:47Z","title":"NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval","version":3},"cited_work":{"arxiv_id":"2404.11025","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.11025","snapshot_observed_at":"2026-08-12T15:11:10.911819Z","title":"NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval","venue":"cs.CV","work_id":"8a1441f6-3912-4b5e-9b99-36b34f931fec","year":2024},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.730521Z"},"links":{"cited_paper":"/paper/2404.11025","citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:8be65277d3cf1a561066900449b64de21cbfad9b2afffb7f95bef4e2c1eaa814","observation_id":"6ad6e3e6-0918-4d18-9d2b-ea4b5a7f12e7","resolution":{"observed_at":"2026-08-12T15:11:10.919219Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.372458Z","title":"Neurally-inspired hyper- dimensional classification for efficient and robust biosignal processing,","venue":null,"work_id":"97908d1f-1b61-4a52-bb32-4c06cdb9d92a","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.735246Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:f6d32cde8389928c6d78f6a4388c3bed4d0df871e836cf4e04a69c46e8411040","observation_id":"d194e144-628f-4139-9216-89f27a469709","resolution":{"observed_at":"2026-08-12T15:11:11.378106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.357411Z","title":"Darl: Distributed reconfigurable accelerator for hyperdimensional reinforcement learning,","venue":null,"work_id":"c22f9301-405e-4300-89d4-d08e06710abe","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.740470Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:3751eada85c719cda8b327f22a58f0354845791e3057549d03d939d5aceb7fde","observation_id":"640dc227-342e-4f2c-ab62-f66ae8bafc18","resolution":{"observed_at":"2026-08-12T15:11:11.362232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.342356Z","title":"Brain-inspired computing for in-process melt pool characterization in additive manufacturing,","venue":null,"work_id":"55fd4989-adfa-494e-8b9e-b0e365e29cda","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.746049Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:3fe0c36e304fc973b7f7248969840139f2548720e3fd6a259eff26c2ab12afee","observation_id":"88d975b8-8a94-482f-8296-a01520144e73","resolution":{"observed_at":"2026-08-12T15:11:11.347090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.326518Z","title":"Real-time and robust hyperdimensional classification,","venue":null,"work_id":"c0d429b3-97a0-4ca9-af15-3fce92644993","year":2021},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.750458Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:5a34923b2687dee8dfefe030894629512ef902b016ac19dc04e97ee516102f38","observation_id":"183f1726-d9a6-446d-aadc-75d7f8425d0a","resolution":{"observed_at":"2026-08-12T15:11:11.331977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.309849Z","title":"Efficient exploration in edge-friendly hyperdimensional reinforcement learning,","venue":null,"work_id":"cbecf2aa-a356-4549-bd8e-5113b1aa5655","year":2024},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.755605Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:129cd7792d08f04ecdd0399593053417a97e0d32a83974d448328c5250297bba","observation_id":"bac46be2-cc54-4c09-8e67-ef5b96154a48","resolution":{"observed_at":"2026-08-12T15:11:11.315002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.294741Z","title":"Density-aware parallel hyperdimensional genome sequence matching,","venue":null,"work_id":"c5b119c1-fc14-4d6c-8373-eee0bd17496a","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.760513Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:22bca10885948c812269cf0cb72c6fc38b5f46159d1b08560f30f7c38b5b2826","observation_id":"944b67cd-caa6-4576-bb97-952d0add9989","resolution":{"observed_at":"2026-08-12T15:11:11.299625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.278800Z","title":"Brain-inspired trustworthy hyperdimensional computing with efficient uncertainty quantification,","venue":null,"work_id":"ed2cd09d-64c0-4a86-9a42-4957c7186779","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.765740Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:c9dd2e5d4be7e892ad39d40af0f0c63a1b8ebcdb836189ccab8e3562bb58e271","observation_id":"e76aff67-849b-4ebc-a2dd-81abcf580417","resolution":{"observed_at":"2026-08-12T15:11:11.283933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.263250Z","title":"Onlinehd: Robust, efficient, and single-pass online learning using hyperdimensional system,","venue":null,"work_id":"f6e2cf34-7a72-4882-bb3d-3ae36420b6bc","year":2021},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.770216Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:91467a7501c6eabacb6413b36cbd5f273f2ada1068b0d3a8fc9ab88f481ea3c0","observation_id":"028d4df6-549e-4f0f-b239-11bfa9212b89","resolution":{"observed_at":"2026-08-12T15:11:11.268466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.248911Z","title":"An overview of overfitting and its solutions,","venue":null,"work_id":"e30915ac-7a28-4af4-9008-49006fe5f0bd","year":2019},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.775140Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:253c2c61ab681878fec3882b45d66aee27c1a337ab02d1583d84884d6f39bee1","observation_id":"5a995734-1537-4df9-80f7-1e134f9732f6","resolution":{"observed_at":"2026-08-12T15:11:11.253363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.780060Z","title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.780060Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:0f72b7fb0eb3329d5a1f917405a4f14b94d21878eacb63f54233d3f4a7ee5214","observation_id":"89a7cc04-7851-4812-a0d6-676adb380bed","resolution":{"observed_at":"2026-08-12T15:11:10.780060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.224203Z","title":"Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing frame- work and future research agenda,","venue":null,"work_id":"1ff9b9d1-8823-41b5-819a-2fa33afb1ea6","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.784736Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:c5a2e0a3665d68b197823918f6acf850e42614a7b86b4c6943c59ff54046b337","observation_id":"708122a1-f6ff-43c0-bb92-697a02ac8bb1","resolution":{"observed_at":"2026-08-12T15:11:11.229225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.208247Z","title":"Explaining adaboost,","venue":null,"work_id":"3a75439b-bdd7-4377-b0fe-2eee9015d66f","year":2013},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.789279Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:b3829834267248f682522b3884765bddc82138a9ced5441c66f2fe7bb8548f5d","observation_id":"db7b4dff-0b98-48a0-ab8f-0ba9ef194727","resolution":{"observed_at":"2026-08-12T15:11:11.213276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.403589Z","title":"A survey on hyperdimensional computing aka vector symbolic architectures, part ii: Applications, cognitive models, and challenges,","venue":null,"work_id":"877c4203-b362-4d76-8237-370956c2e6eb","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.794494Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:adfd24a630b6a43246062e89edec4d93e47f2e96064844d3d0f7f6dcb659abc9","observation_id":"9540e021-60e1-421d-93cc-3c36a6d02e4b","resolution":{"observed_at":"2026-08-12T15:11:11.409388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.192373Z","title":"A theoretical perspective on hyperdimensional computing,","venue":null,"work_id":"0bc8e833-38d0-492a-8de5-3e7e7028b79e","year":2021},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.800056Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:4ad907313207b1395d7547b88abb0788dd79965863d5dc5ef2b78accee66e3cf","observation_id":"8e40a0ea-860f-4aa9-9540-d34bbeaac251","resolution":{"observed_at":"2026-08-12T15:11:11.197350Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.805296Z","title":"Greedy function approximation: a gradient boosting machine,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.805296Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:09a6373e4a30ed7acf39f0528a09a6b41f3581010957866b624112d4af7a7cd7","observation_id":"f382c9bf-f0f7-43f3-8d6f-950824a3ea6e","resolution":{"observed_at":"2026-08-12T15:11:10.805296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.810175Z","title":"Lightgbm: A highly efficient gradient boosting decision tree,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.810175Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:342dac3d0bec9f88816c7f859716a4e7d636c6818197ecffbeeafbfa601680ed","observation_id":"3ac925da-c509-4bde-bd05-b0afd1f7c4e7","resolution":{"observed_at":"2026-08-12T15:11:10.810175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.157392Z","title":"Xgboost: extreme gradient boosting,","venue":null,"work_id":"add9cfc0-bdb3-44c1-85eb-d1a80fa94e89","year":2015},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.814464Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:d79f9f4c57e21a7d3ec0dc90dd7383fabf593ea0ceb0d3f88cb51966086ada9a","observation_id":"7bd6dd2e-0652-43cb-8552-14ac2101eb3d","resolution":{"observed_at":"2026-08-12T15:11:11.162244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.819402Z","title":"Introducing wesad, a multimodal dataset for wearable stress and affect detection,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.819402Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:cc10432cfc7ae8bce07864b708d9618dd82d6d4bd1b3cefa2607a812963e13df","observation_id":"242f6a3f-fd99-49df-afdb-5700866a285a","resolution":{"observed_at":"2026-08-12T15:11:10.819402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.130453Z","title":"Deep PPG: Large-Scale heart rate estimation with convolutional neural networks,","venue":null,"work_id":"bd22564f-ae0e-4040-a735-62493f3655ee","year":2019},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.823932Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:f215a383d8ef381216a07210c23a8ef3e77fb616c398448e88db011ee8c9accd","observation_id":"245c8110-9cf1-4f45-b20a-84a4e5624961","resolution":{"observed_at":"2026-08-12T15:11:11.135574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.115448Z","title":"Hyperdimensional computing for resilient edge learning,","venue":null,"work_id":"c90d7e4b-acb2-4257-af98-01cef81b982a","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.828539Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:1886062c4c2fb4a3f3d614c987894fe9eec446981d662c58fabf982e8e4cd3de","observation_id":"886fb3bf-cfc3-463a-8fa6-3b41e5fb7e81","resolution":{"observed_at":"2026-08-12T15:11:11.120320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.100066Z","title":"Reliable hyperdimensional reasoning on unreliable emerging technologies,","venue":null,"work_id":"88c11e9d-a854-4207-a478-c1e8d73d165c","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.832902Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:1b0797c0d0077eab3995cf51391108eb86053a9404968ba816bb7f640ea49645","observation_id":"5053c509-8de6-4e51-8a9c-65a00bc4f536","resolution":{"observed_at":"2026-08-12T15:11:11.105047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.084947Z","title":"Comprehensive analysis of hyperdimensional computing against gra- dient based attacks,","venue":null,"work_id":"3fb73b48-c1e0-4f83-be8a-ce3ae96297a2","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.837210Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:af5f4a44ff80f910ef50f7c29ad42b0ab852261728f35b9c2064d7b596acde78","observation_id":"3c4acf89-dbc9-40b3-b298-f067b9066c0c","resolution":{"observed_at":"2026-08-12T15:11:11.089972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.069109Z","title":"Hyper- graf: Hyperdimensional graph-based reasoning acceleration on fpga,","venue":null,"work_id":"f670bb0b-d9d8-48e8-bc33-0478a0bdedcb","year":2023},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.841793Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:b1f386636cc668a7423f9a49ac0b406fc8a1ba83602e864076857b288180c877","observation_id":"8bf3f466-bc4d-4db1-874b-1d64df1b49ea","resolution":{"observed_at":"2026-08-12T15:11:11.074174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.846115Z","title":"V oicehd: Hyperdi- mensional computing for efficient speech recognition,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.846115Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:bf821360668fabdb0139f0d4bdfd91c9fa319430ccab7da0de04c661dbdc253c","observation_id":"4ce2b3d2-cce8-4b11-9bd7-0d59d22d50af","resolution":{"observed_at":"2026-08-12T15:11:10.846115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.041667Z","title":"Hyperdimensional hybrid learning on end- edge-cloud networks,","venue":null,"work_id":"b0d7db90-3f90-4288-a232-d0835022aede","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.851082Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:7158bfb3aa67d23af6c9a36f12f0a0e1be830d7c29a279ed95eeec7df3c4230c","observation_id":"96c77398-5b5a-4c42-8553-7b20e2fb7df4","resolution":{"observed_at":"2026-08-12T15:11:11.047304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.024754Z","title":"Robust in-memory computing with hyperdimensional stochastic repre- sentation. in 2021 ieee,","venue":null,"work_id":"d200dc80-998a-4677-8f89-a6b37f52eb0f","year":2021},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.855712Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:b20644de1f384e388e6af891c3d61c7c0b5db95dd24b7c5d5a5e4b5ccfe37508","observation_id":"825c117a-422b-4038-8a0a-e3162cb3158b","resolution":{"observed_at":"2026-08-12T15:11:11.029706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:11.008695Z","title":"Stochd: Stochastic hyperdimensional system for efficient and robust learning from raw data,","venue":null,"work_id":"68703154-4fc6-438f-bd1b-7491f17cf60a","year":2021},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.860297Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:aa97b763333550dfba6a76624c50529fb7da5ffc834e53a64064b79ff5fa3a7f","observation_id":"52c5e696-f1e1-4554-8ded-5acca41776fe","resolution":{"observed_at":"2026-08-12T15:11:11.013615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.992484Z","title":"Rate of convergence in probability to the marchenko-pastur law,","venue":null,"work_id":"99c4b06c-a84b-4491-a04d-d1a5d039c9a3","year":2004},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.864805Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:b255632f8944fce1548b06e0e0bcb3e35f71bc583259e75c0c183d5022b29189","observation_id":"448e04c5-294f-4df2-955a-fdbb11320dee","resolution":{"observed_at":"2026-08-12T15:11:10.997934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.977353Z","title":"A multimodal sensor dataset for continuous stress detection of nurses in a hospital,","venue":null,"work_id":"3fbe3f68-1f34-4e05-988b-c955266922d7","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.869391Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:ffd8d302cce25b130ff4640ebf5365363d28fd1a0e878727681ace02f37064f5","observation_id":"d42278ba-c11b-4891-8494-edb9ce418234","resolution":{"observed_at":"2026-08-12T15:11:10.982244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:11:10.961652Z","title":"Stress monitoring using wearable sensors: A pilot study and stress-predict dataset,","venue":null,"work_id":"9f11209d-9c4f-4366-8e1a-4e1cadb869be","year":2022},"citing_paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T15:11:10.873791Z"},"links":{"citing_paper":"/paper/2411.14612"},"observation_digest":"sha256:90f91dc1b651deb8f34f6f7a6181ba4b1cbf88f34142337f5a7f0fd0422c1727","observation_id":"ba87ba22-d4ad-4d6b-bc91-a65c1c6d1d84","resolution":{"observed_at":"2026-08-12T15:11:10.966788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.14612","last_updated":"2025-01-14T00:20:32Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T15:03:45.685702Z","submitted_at":"2024-11-21T22:28:45Z","title":"Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":30},"total_outbound_references":39},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2411.14612."}