{"as_of":"2026-08-16T19:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:80587fe8627634f6a1971514d456a2995abeb015e7239e2e9906220a6f98956b","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:40:14.135782Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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":"2026-08-10T14:30:40.910983Z","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":"2026-08-10T14:30:41.012351Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"cited_work":{"arxiv_id":"2501.01057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.01057","snapshot_observed_at":"2026-08-10T14:30:41.012351Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","venue":"cs.PF","work_id":"f7021fc2-a856-4b1e-9516-a8109ecfb819","year":2025},"citing_paper":{"arxiv_id":"2501.15266","last_updated":"2025-01-25T16:24:18Z","snapshot_observed_at":"2026-08-15T10:33:35.612667Z","submitted_at":"2025-01-25T16:24:18Z","title":"Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T14:30:40.910983Z"},"links":{"cited_paper":"/paper/2501.01057","citing_paper":"/paper/2501.15266"},"observation_digest":"sha256:dcfbc542657db830c2f23e982d753709f6fede81ff5f5caa69c2610edae9b9d0","observation_id":"a68c7276-b5d8-48ab-95ba-87979147dbcb","resolution":{"observed_at":"2026-08-10T14:30:41.018593Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.01057/citation-record","integrity":"/paper/2501.01057/integrity","json":"/paper/2501.01057/citation-record.json","paper":"/paper/2501.01057"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:40:14.867871Z","title":"Edge computing: Vision and challenges,","venue":null,"work_id":"0935de3e-35d5-4252-8765-7e44ca38a865","year":2016},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.914007Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:dc1da3f8db0a8df162e5ef576b5b613232ed8f8d9a5ebba60120dda710ffd6f9","observation_id":"c271856d-5129-4181-9c1d-eaac6210a2ef","resolution":{"observed_at":"2026-08-10T22:40:14.872455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.852954Z","title":"Pcie vs. 5g: The importance of hpc at the edge","venue":null,"work_id":"380ff4a0-5a6a-4d2a-931f-40d04d684674","year":2022},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.919806Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:db72ab607489518375126dc1848946d8d7d5f2d0f4e0c8e4da0ed7e645cdbe28","observation_id":"6a104d2d-6214-4b54-bd4e-a8bd314d1fe4","resolution":{"observed_at":"2026-08-10T22:40:14.857627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.837959Z","title":"5g enabled energy innovation: Advanced wireless networks for science,","venue":null,"work_id":"f14b3cf9-a44d-4a4a-bf74-32161840ff77","year":2020},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.925570Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:8b8047e2b46bcdf5c6f302a79c19e34845ceca40998408fd0431ea7c7c57560e","observation_id":"da9897d6-13b3-46d5-b8d6-6eed2322550f","resolution":{"observed_at":"2026-08-10T22:40:14.842818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.823892Z","title":"Auto-tuning full applications: A case study,","venue":null,"work_id":"8d0d7c8d-9451-4ea4-ba1e-af1f561f94ee","year":2011},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.930316Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:1355a2052cea4a5ccd6beadd2897db1de7eac50015dbfab6227252d92e53dc8f","observation_id":"df7b45ac-0d84-49e8-a6ba-dea5f78b4f33","resolution":{"observed_at":"2026-08-10T22:40:14.828530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.810029Z","title":"Automated reasoning and detection of specious configuration in large systems with symbolic execution,","venue":null,"work_id":"55e63a5d-5db1-453b-a568-022e7d066c3e","year":null},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.934883Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:5e0e66ffbb92d00cb3bb0d35fb7febdcbc1443453c4cccf7c490e4ffae3dcf17","observation_id":"9ed56c4c-4949-421c-a879-c3d03f19c13d","resolution":{"observed_at":"2026-08-10T22:40:14.814743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.796210Z","title":"Software challenges in extreme scale systems,","venue":null,"work_id":"927c61ba-d3d1-4732-a0ad-3c18234abd05","year":2009},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.939725Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:8ea9b82582b19570a2ade18224aa8081a7956b6605ac2ebe2ff8bbd813aa9235","observation_id":"9ebdf36e-67a8-4de2-869b-3831e795b692","resolution":{"observed_at":"2026-08-10T22:40:14.800720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.782167Z","title":"The antarex approach to autotuning and adaptivity for energy efficient hpc systems,","venue":null,"work_id":"bdd335b8-cfbe-47ab-8326-b143e82869ce","year":2016},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.944639Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:5b2e7eb133d2efe433cd61e00496ab93a916e56508bbba3def54e7994be26d77","observation_id":"bff6e93c-b748-4719-9e96-9b3c72cbe162","resolution":{"observed_at":"2026-08-10T22:40:14.786675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.767464Z","title":"Bestconfig: tapping the performance potential of systems via automatic configuration tuning,","venue":null,"work_id":"19d611d9-24bc-4616-b4a3-473b183f059d","year":2017},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.949634Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:b5aa54a7844014398e3036b7a2308529dd5f756d195f5b2c404853d72ee6fe06","observation_id":"e5d291f4-2483-4050-ae1f-0374ff7c768b","resolution":{"observed_at":"2026-08-10T22:40:14.772625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.752575Z","title":"d- simplexed: Adaptive delaunay triangulation for performance modeling and prediction on big data analytics,","venue":null,"work_id":"221a6e65-b287-45b9-aa6e-8a5e1126073b","year":2019},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.954124Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:37675147c762f10bf24416efe0e941d75f4d43277230c649b8892e6a825b3025","observation_id":"122474cd-0a33-476c-8cc6-4af950ad92e1","resolution":{"observed_at":"2026-08-10T22:40:14.757540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.738714Z","title":"Optimization by simulated annealing,","venue":null,"work_id":"00dd59cc-dd74-49a0-97b8-dd49616bcd1b","year":1983},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.958984Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:76d91d574747080bc2aac62016d25d73d0efaa9013409d3c07ad24c4fa5d088f","observation_id":"a4a51ee9-2c48-434f-be38-ceb0eaac8045","resolution":{"observed_at":"2026-08-10T22:40:14.743310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.723811Z","title":"Particle swarm optimization,","venue":null,"work_id":"70a9b09d-6247-4e12-8d52-97a4b9543b9f","year":null},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.963315Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:9a4460f3b313ee6672ef355d4be004d2fc9bb96e2893490d246752ba469ca80f","observation_id":"127be446-3b90-4868-a567-93e6ea9e589f","resolution":{"observed_at":"2026-08-10T22:40:14.728907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.709088Z","title":"{TVM}: An automated {End-to-End} optimizing compiler for deep learning,","venue":null,"work_id":"d5f78f01-f8f5-4c36-a7fc-b08e7df1cf80","year":2018},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.968282Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:fb79d92cd408b0916299e164483feea2e740198b1ed1efbb8e7001be2507255f","observation_id":"dc867ad2-d5c9-490f-9730-bb63c6dd0485","resolution":{"observed_at":"2026-08-10T22:40:14.713825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.694238Z","title":"Rfhoc: A random-forest approach to auto-tuning hadoop’s configura- tion,","venue":null,"work_id":"a62b4174-eea2-4d90-bfc0-8be5ebd1c0a2","year":2015},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.972688Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:05d8440afa92821c22c92c0d91da730b6baca3fc1518f411685e99724a070711","observation_id":"5b917c51-5a1e-433b-9479-d1b2244abfd9","resolution":{"observed_at":"2026-08-10T22:40:14.699172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.679192Z","title":"Efficient performance prediction for apache spark,","venue":null,"work_id":"389ef3be-88cd-4168-8511-f46bbbb37bea","year":2021},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.977288Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:a7de930e9943640de40b7189eab04957bde95b5ffdf0ad42bfe2c6dcc63b1f59","observation_id":"d2a73d00-3259-460e-8d29-2eafbcd11bed","resolution":{"observed_at":"2026-08-10T22:40:14.684013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.663954Z","title":"Datasize-aware high dimensional configu- rations auto-tuning of in-memory cluster computing,","venue":null,"work_id":"d6188963-f79f-4976-8501-2d16bbc0c59a","year":2018},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.981731Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:2661cca277afceeba883375152919773cab8f24ceb5f8e250932e9bdb4a198b5","observation_id":"dbd947ee-2bc3-4819-8b87-62e7e41e924a","resolution":{"observed_at":"2026-08-10T22:40:14.668916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.649624Z","title":"Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models,","venue":null,"work_id":"d8ae4c64-9da5-4440-ba72-b9e577b81e98","year":2021},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.985952Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:109bb618cf53df0dcccf5c49de4cc4ae296da198d92274d1037960486425bd0f","observation_id":"9031c2a6-7f42-4098-88c1-fa5357a2fa0c","resolution":{"observed_at":"2026-08-10T22:40:14.654315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.634744Z","title":"Autotuning in High-Performance Computing Applications,","venue":null,"work_id":"89d133a3-9b10-4f8f-893d-05db91faf654","year":2018},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.990151Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:c00fd32c5e66d6cb55a40801b046fc73b24cb451529a7ef46af501ae7ebb1a52","observation_id":"03a2aafa-a707-4685-a12e-4b5ab5c49851","resolution":{"observed_at":"2026-08-10T22:40:14.639976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.05792","last_updated":"2019-08-15T23:14:54Z","snapshot_observed_at":"2026-08-16T03:34:29.983996Z","submitted_at":"2019-08-15T23:14:54Z","title":"Multitask and Transfer Learning for Autotuning Exascale Applications","version":1},"cited_work":{"arxiv_id":"1908.05792","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.05792","snapshot_observed_at":"2026-08-10T22:40:14.173213Z","title":"Multitask and Transfer Learning for Autotuning Exascale Applications","venue":"cs.LG","work_id":"7e4f458d-acfe-4bf1-a249-5325327c6ff6","year":2019},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.994617Z"},"links":{"cited_paper":"/paper/1908.05792","citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:72dccb4d4c8910cf645a3d5bf79352f1c377aea24634483e3d09fb821b3ad1d7","observation_id":"72cd41ac-cab9-4cd4-82d0-c71d1cd69078","resolution":{"observed_at":"2026-08-10T22:40:14.179846Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.619641Z","title":"Boot- strapping parameter space exploration for fast tuning,","venue":null,"work_id":"921bae80-878d-46c5-a450-68bb4112f0c4","year":2018},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:13.999592Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:941ec10992a4081f187f62d08398258fc91c626deec68b9f6c819cf6f54dc38c","observation_id":"059c1a68-6531-4121-a4f1-bf7a63d61528","resolution":{"observed_at":"2026-08-10T22:40:14.624559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.603124Z","title":"Artemis: Automatic runtime tuning using machine learning,","venue":null,"work_id":"90bb177f-aea5-40c1-809e-2041057627c9","year":2021},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.003919Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:61a3d724c34577296c00cd73fafded940fa1e8e08afb949a01efd15a4f40ca3c","observation_id":"fc36f280-5f6c-4b75-87ff-57c5f19d71f9","resolution":{"observed_at":"2026-08-10T22:40:14.608384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.588599Z","title":"Turbo: A cost- efficient configuration-based auto-tuning approach for cluster-based big data frameworks,","venue":null,"work_id":"c9ab4518-ba73-49e1-b2ff-838be9a83408","year":2023},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.008525Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:0887ab016d0aad64e91d91996f18b21f1e063b4c4bed7486f366c6b910b840e3","observation_id":"a3d4101c-d725-4fb1-bf06-25a8eb186461","resolution":{"observed_at":"2026-08-10T22:40:14.593289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.573824Z","title":"Conex: Efficient exploration of big-data system configurations for better performance,","venue":null,"work_id":"64d5c1f9-5de6-42a7-bf4e-9a887aec3732","year":2020},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.013462Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:85c9e569a90010bc9bea67e5541c0247051c029118f9d79f6a0dcae0dc207142","observation_id":"72fa40e0-1a82-4374-b022-55e9c7146f33","resolution":{"observed_at":"2026-08-10T22:40:14.578621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.557995Z","title":"Hdconfigor: automatically tuning high dimensional configuration parameters for log search engines,","venue":null,"work_id":"8b50e6ea-5fa9-4fa4-9f08-363f827f7566","year":null},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.017939Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:a94838d412462b803ceb13e3d21416affa7ec8c151f75de594bca67b8f480a5a","observation_id":"08455729-affb-4e0d-8337-e270fcd1daa0","resolution":{"observed_at":"2026-08-10T22:40:14.562754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.542539Z","title":"Locat: Low-overhead online configuration auto-tuning of spark sql applications,","venue":null,"work_id":"d7694aec-4c43-4b5d-b4cb-c4fbeb4e4e51","year":2022},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.022656Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:594dce069e8bbfd7778e7d29f2e2f4b8d294582b3c21aec8ca4afd5f89bc4db3","observation_id":"08e02977-144a-466c-9b42-c9d281da79d6","resolution":{"observed_at":"2026-08-10T22:40:14.547573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.027425Z","title":"Introduction to multi-armed bandits,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.027425Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:ec8015da9e4a106126aeb5e125a540a91101534fe9eab6cbc5bdc94c7ec6e7cc","observation_id":"b69f25fe-9c13-4aa9-979c-8c0d5f6378b2","resolution":{"observed_at":"2026-08-10T22:40:14.027425Z","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":"2026-08-10T22:40:14.517804Z","title":"Pure exploration in finitely-armed and continuous-armed bandits,","venue":null,"work_id":"79bb2745-dbb2-4b55-a9d5-3699ad97d684","year":2011},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.032302Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:828676570826bd4a1a08cd48130edb2ceb7179d360cd5307c834221bd9371573","observation_id":"3262ab0e-2b92-4518-8d7b-ed7199f09627","resolution":{"observed_at":"2026-08-10T22:40:14.522298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.503931Z","title":"Non-stochastic best arm identification and hyperparameter optimization,","venue":null,"work_id":"245dfa56-8012-49d6-bd17-ad0bce67e534","year":2016},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.036831Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:089c804e0c73c3ec07e15cebf8e75c339244f4164bdcd270ab738544c7fb8dee","observation_id":"a2dba2cb-541a-401a-b1ea-25ae1d76a751","resolution":{"observed_at":"2026-08-10T22:40:14.508607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.489242Z","title":"Simple regret for infinitely many armed bandits,","venue":null,"work_id":"8c90d6f9-b9ed-4256-86b2-07b40cd2abee","year":2015},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.041306Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:9be1f18b6115a59c2bc3d86807189f154c88e510653896124f5db310f6e4941a","observation_id":"4ac18c44-4084-4209-8090-00462c54150d","resolution":{"observed_at":"2026-08-10T22:40:14.494535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.474906Z","title":"Hyperband: A novel bandit-based approach to hyperparameter opti- mization,","venue":null,"work_id":"25644773-7b36-40cd-80dd-306fb4b9e310","year":2017},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.045796Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:454fb8d06dc8abaae224185ca61e88828ec3f2fa5f492d8ca5f49aa0e2fafc0a","observation_id":"5d8c687e-58d7-449b-9081-298ba653a799","resolution":{"observed_at":"2026-08-10T22:40:14.479744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.460651Z","title":"Portfolio choices with orthogonal bandit learning,","venue":null,"work_id":"520d6f4f-2a9e-4738-aafa-3bafe8661b50","year":2015},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.050421Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:a8206fbcc875094f6f78b21d6687028f8fd40b8a6e8bdbe53a6dafd90a694d4a","observation_id":"71d85d51-41a2-4d5c-858d-cb8359f44ebd","resolution":{"observed_at":"2026-08-10T22:40:14.465521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.445533Z","title":"Input warping for bayesian optimization of non-stationary functions,","venue":null,"work_id":"404cb1ed-f32b-47a6-979a-ae0f3258aec5","year":2014},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.055237Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:c1a0f9cf77321157eb6f8b2c95b306473a1209f43da05cb9b1db542508954526","observation_id":"79f42406-5aea-4a8d-9f79-9305d3629dea","resolution":{"observed_at":"2026-08-10T22:40:14.450534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.431536Z","title":"Waggle: An open sensor platform for edge computing,","venue":null,"work_id":"6fa2ec92-7b3a-4015-9ab5-e1af34e56152","year":2016},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.059682Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:69a6978c535937c3a415bf708d3a0d839df9bb7ab1bdf33ad71b87e01f7da6ea","observation_id":"2bca65d5-be03-46fb-9a7d-3c14f47bdb47","resolution":{"observed_at":"2026-08-10T22:40:14.436340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.417620Z","title":"Sage: A distributed software-defined sensor network,","venue":null,"work_id":"35d4ecd0-e7ac-4d4c-aafb-78086b648fa1","year":2013},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.064149Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:9c05779a23d472429776041559a7ece8d06b93d6ff1afa0791d00dba8f882224","observation_id":"ea8405ff-cc9c-4766-bc56-bd8de978f442","resolution":{"observed_at":"2026-08-10T22:40:14.422198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.403022Z","title":"Optimizing cloud motion estimation on the edge with phase correlation and optical flow,","venue":null,"work_id":"69666fba-3d8f-4c8e-a8a2-0fef82472aa7","year":2023},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.068589Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:d4bf2ba19784fa5685e2f891aed8642ac9c319b28b723ba1d12573a912920dc1","observation_id":"4601cd7f-00ee-4aed-b52f-308b7d98fe1a","resolution":{"observed_at":"2026-08-10T22:40:14.407849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.388525Z","title":"Goal-driven scheduling model in edge computing for smart city applications,","venue":null,"work_id":"d7c638c0-f357-476f-ad6e-c7df952e8bab","year":2022},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.073463Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:b557f37565fe4d2ffe2c2d87b45da8c1ba5401bc92365ea0906d09761ea3171d","observation_id":"b01a02de-f2e3-4316-a61b-1704e773ab86","resolution":{"observed_at":"2026-08-10T22:40:14.393510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.373867Z","title":"Intersecting needs and challenges in scalable operating system research","venue":null,"work_id":"6a5a2648-5c35-4757-b2b1-63afbdae534f","year":2022},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.079043Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:17c3225946870ff344ec16df73700dffd391ae0d0fdc6afaf4633efb7b397466","observation_id":"b07790e5-9e5f-4111-b1de-97ded1859252","resolution":{"observed_at":"2026-08-10T22:40:14.378858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.359632Z","title":"Automating hpc model selection on edge devices,","venue":null,"work_id":"f73e0d87-457a-48fc-a4bb-76175765fe0e","year":2023},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.083409Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:a45d6d3145ffa3ac314d8c940ec5cc684292b3e1e5713a921b7a966eb9d8fa03","observation_id":"c29c4c7d-ea21-4474-b337-95e2113fe740","resolution":{"observed_at":"2026-08-10T22:40:14.364316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.343698Z","title":"Kripke-a massively parallel transport mini-app,","venue":null,"work_id":"fd71e74f-a2b6-4524-8fdb-8dc241642e7c","year":2015},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.088173Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:4563e49e379a55d60071dedebe34abf7ee1cb18dffd20ddca128fbd21d9a9897","observation_id":"3444c971-cc54-4b25-8994-5b075f29bf86","resolution":{"observed_at":"2026-08-10T22:40:14.348623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.328406Z","title":"Quantitative performance assessment of proxy apps and parents,","venue":null,"work_id":"07ea87db-d9c2-45cc-a0a5-a65f6f1cfb05","year":2018},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.092504Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:da371fe61cd4893d657a8f189eea481e8edab11ed3415377cbe5869b10164f90","observation_id":"1d5283cc-08a7-40b4-8056-f4b3a650032a","resolution":{"observed_at":"2026-08-10T22:40:14.333290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.313857Z","title":"Using confidence bounds for exploitation-exploration trade- offs,","venue":null,"work_id":"772a55bb-a68c-4249-83b9-134a09a12376","year":2002},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.096981Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:99118e7639ff3e48f25e321f292a4e0eb33325bf0fcd8a58cdef68c5520c35ed","observation_id":"c1004eb8-cc9d-416f-8838-8054b3a007f7","resolution":{"observed_at":"2026-08-10T22:40:14.318525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.299420Z","title":"Security analysis of iot protocols: A focus in coap,","venue":null,"work_id":"3b94896b-f032-4fa3-a503-79d08c3925ff","year":2016},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.101661Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:4c4f55690f4cc018d309bc55e35416dc67409c383c7746ce2ebf9e7739c019b3","observation_id":"1465b60a-b92f-4da1-abd4-e6042d9cebac","resolution":{"observed_at":"2026-08-10T22:40:14.304192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.284856Z","title":"Characterizing the per- formance of accelerated jetson edge devices for training deep learning models,","venue":null,"work_id":"b4403b70-91f9-4049-93e5-bcb99e75aa91","year":2022},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.107294Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:8c74f825d53d86dd18830df8ff859f9a6d3c4e57fe1057d7efee91f694301829","observation_id":"a61becec-b5b4-4eb1-a960-f756863266cd","resolution":{"observed_at":"2026-08-10T22:40:14.289739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.268792Z","title":"Clustering algo- rithms on low-power and high-performance devices for edge computing environments,","venue":null,"work_id":"e70e5cfc-ad85-47a3-97e0-d751afeed1b6","year":2021},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.111666Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:c234975990edf8e47944a38f6cf839dfd4d4218d4ca6ef103e8cd7aa4182faf6","observation_id":"960962b9-fba1-4214-a73e-fb345ffe4ce0","resolution":{"observed_at":"2026-08-10T22:40:14.274835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.252433Z","title":"End-to-end energy models for edge cloud-based iot platforms: Application to data stream analysis in iot,","venue":null,"work_id":"a9767528-9f43-437c-ac35-a26fe9353880","year":null},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.116328Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:ccb0521cb8abf274726b72d5051d3dc355a25df223eeb745dd8fe150e8be5d83","observation_id":"c3ab9b3f-654c-481e-83b9-a71fff4dbcfc","resolution":{"observed_at":"2026-08-10T22:40:14.257868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.237341Z","title":"Performance modeling under resource constraints using deep transfer learning,","venue":null,"work_id":"f6be03c5-94ff-4525-8a96-ed8f198b7155","year":2017},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.121978Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:15b6e8db2b8bcfabba05dcf3ad4255236ae841f02f14d497e9153ce880174f3c","observation_id":"85ded9d6-ebba-49ba-9620-63cfaf5c9fe5","resolution":{"observed_at":"2026-08-10T22:40:14.242227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.221906Z","title":"hypre: A library of high performance preconditioners,","venue":null,"work_id":"a89bf291-766b-4797-baaa-fd2a1da1b26d","year":2002},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.126676Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:2b424641b7ae43993769ec89d3f317be72af88d5858b0a8fee49c28b312b8a1f","observation_id":"cc40ce2d-9bb0-4332-94df-6efb72f3a1d7","resolution":{"observed_at":"2026-08-10T22:40:14.226891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.204992Z","title":"Clomp: Ac- curately characterizing openmp application overheads,","venue":null,"work_id":"de57b396-f295-4728-9ac2-3a9d4b84b054","year":2009},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.130990Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:880494ae0d6b81b9e4009513f16949a8b0351e0a031e213f893fffef8b468c38","observation_id":"14aad446-451e-49cc-ab8b-e2a2f4c2f0b0","resolution":{"observed_at":"2026-08-10T22:40:14.210503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T22:40:14.190542Z","title":"Lulesh 2.0 updates and changes,","venue":null,"work_id":"7af1306a-6666-4f57-b470-933e92c1f7e7","year":2013},"citing_paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:14.135782Z"},"links":{"citing_paper":"/paper/2501.01057"},"observation_digest":"sha256:609d63b1018e3ee715025d66cc21ff3f3a8db71b1092516b6a8479fa487e41ea","observation_id":"615cf6be-f0d7-4dbc-ae81-e83d7aa3a8c1","resolution":{"observed_at":"2026-08-10T22:40:14.195141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.01057","last_updated":"2025-01-02T04:59:32Z","latest_version":1,"primary_category":"cs.PF","snapshot_observed_at":"2026-08-16T15:14:37.400614Z","submitted_at":"2025-01-02T04:59:32Z","title":"HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":1,"verified_fuzzy":46},"total_outbound_references":48},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2501.01057."}