{"as_of":"2026-08-16T10:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:569029fa90c22e729ee62ac6151288ef2fb5adeadb52d6cf9cc9f520705ba8cd","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:09:49.643716Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.01678/citation-record","integrity":"/paper/1908.01678/integrity","json":"/paper/1908.01678/citation-record.json","paper":"/paper/1908.01678"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:50.363030Z","title":"Chatter suppression techniques in metal cutting,","venue":null,"work_id":"18beee19-0dae-4501-929d-888a7f4c8213","year":2016},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.434403Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:ee6a4e5e3782d6d08aeb7f60a263e060c602cae22db88302e63f2f30176cf39d","observation_id":"478b03a8-3cc7-41c0-a3dc-6595b8e92851","resolution":{"observed_at":"2026-08-14T15:09:50.368212Z","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":"2026-08-14T15:09:50.347089Z","title":"Chatter detection in milling machines by neural network classification and feature selection,","venue":null,"work_id":"8758f9c7-582d-40ac-90fc-1661a4e51f13","year":2013},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.439765Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:caec92fdd2d71ebac5559e552a071f61750223b441097c5e2ccc9a0c4593924c","observation_id":"e185bc64-4bc6-4243-a13f-2354aba46c5a","resolution":{"observed_at":"2026-08-14T15:09:50.352794Z","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":"2026-08-14T15:09:50.329294Z","title":"Early chatter detection based on logistic regression with time and fre- quency domain features,","venue":null,"work_id":"0ee02d3a-7363-48a2-aca9-1bfc96fb0b97","year":2017},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.445135Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:0f4200e75ff90614f6fff243bcdcb8a204950bfde05b5f85267f93c7df73b851","observation_id":"d42f5c14-d956-4679-ab78-9fa4b84f89ad","resolution":{"observed_at":"2026-08-14T15:09:50.334930Z","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":"2026-08-14T15:09:50.313918Z","title":"Chatter detection in band sawing based on discriminant analysis of sound features,","venue":null,"work_id":"fb15e42d-072d-4a5d-9c33-f6988b94dbf3","year":2014},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.450428Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:db6e1ee1dec95dd828d6705d3da9ad8b664dcad2693fb120bb7c7cdac48fae8b","observation_id":"160d9c7a-49f8-48f0-ba88-e8aa7f682799","resolution":{"observed_at":"2026-08-14T15:09:50.318914Z","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":"2026-08-14T15:09:50.295475Z","title":"ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system,","venue":null,"work_id":"c7fc09f5-cc0c-4bf5-afc4-2066f2e46768","year":2016},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.455553Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:04f8049e556397a773fac857fecd74d416264feb1e71699b60dd38d86b57127f","observation_id":"206b353f-46a4-49a1-8f14-8355de50a630","resolution":{"observed_at":"2026-08-14T15:09:50.300687Z","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":"2026-08-14T15:09:50.278049Z","title":"Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination,","venue":null,"work_id":"46bfcda7-41c7-49a1-b338-64aee1af299b","year":2017},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.460432Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:9a7a456724a840b367bd2fd89dea0489c61738e8c2fa55fc15570bee6622d1d5","observation_id":"cd9e55f7-dd29-433e-af42-8dd15f183b77","resolution":{"observed_at":"2026-08-14T15:09:50.284602Z","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":"2026-08-14T15:09:50.260810Z","title":"Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation,","venue":null,"work_id":"973b284d-721c-4db8-8776-9300377d57bc","year":2018},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.465735Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:20c8b158db16c8d2fb078b4d3bfdb6b1e6b695ae7ccbdb4fcf8533f967dbe1e6","observation_id":"ae64972a-43a5-4393-9403-6456bc3327f6","resolution":{"observed_at":"2026-08-14T15:09:50.266895Z","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":"2026-08-14T15:09:50.245710Z","title":"The chatter identification in end milling based on combining EMD and WPD,","venue":null,"work_id":"277f3954-1795-4517-9f9e-360ce040572e","year":2017},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.471496Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:fadfa2dd7789897d434941a23b504470e9bb2d866d0085c663ab3fe9a264dbfd","observation_id":"5c318ea8-2f50-4e81-910d-812a29577590","resolution":{"observed_at":"2026-08-14T15:09:50.250851Z","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":"2026-08-14T15:09:50.228190Z","title":"An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals,","venue":null,"work_id":"37f9da70-8da8-4def-a2bd-d8b9cbf84e8c","year":2018},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.476125Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:a4e0f93f08096cf0505b01e25a4a865e41c80d3d50483c22935938379cfb228c","observation_id":"5d60336f-cfc7-4567-a771-87ee5b85a831","resolution":{"observed_at":"2026-08-14T15:09:50.233735Z","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":"1905.01982","last_updated":"2020-01-17T20:38:21Z","snapshot_observed_at":"2026-08-14T16:37:42.725053Z","submitted_at":"2019-05-03T14:11:23Z","title":"On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition","version":2},"cited_work":{"arxiv_id":"1905.01982","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.01982","snapshot_observed_at":"2026-08-14T15:09:49.746353Z","title":"On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition","venue":"eess.SP","work_id":"5b623adb-7090-41c6-aba0-c70e6890ba3e","year":2019},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.481703Z"},"links":{"cited_paper":"/paper/1905.01982","citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:b31dc0bb2150d9a97bbd0f1fb98c4243762a229792a7e8701e970016f28cc4ac","observation_id":"f36460e9-5abd-4340-8884-9d2bba153ecc","resolution":{"observed_at":"2026-08-14T15:09:49.752384Z","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":"2026-08-14T15:09:50.211634Z","title":"Mirror milling chatter identification using q-factor and SVM,","venue":null,"work_id":"97a564bc-2f6d-4567-a8eb-e8145bffc309","year":2018},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.487575Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:2a0302aba9a570f05952ee616f4812a447b6aa3e705cfd66940b46d536d31c79","observation_id":"93803682-8da0-4238-bc77-83e5c3e3ca0a","resolution":{"observed_at":"2026-08-14T15:09:50.216811Z","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":"2026-08-14T15:09:50.195110Z","title":"Machining chatter prediction using a data learning model,","venue":null,"work_id":"f9032148-03cf-4682-a4a0-d0e231297825","year":2019},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.492758Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:8b02de7c54dccf52281a3b326a0a628cfc93258c097720f2908ba1c4c0249217","observation_id":"63c179e6-2493-47c2-84d8-220350bdf390","resolution":{"observed_at":"2026-08-14T15:09:50.200885Z","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":"2026-08-14T15:09:50.179668Z","title":"Stability determination in turning using persistent homology and time series analysis,","venue":null,"work_id":"aceedae7-2aca-4449-b16c-72012c1ec178","year":2014},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.497432Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:66a51ef189e88f26538a4a548b9310c694b6b8feedfcf6518bd65f5a42a4f26c","observation_id":"2f772ec4-9e76-41ab-a714-3f183b408a80","resolution":{"observed_at":"2026-08-14T15:09:50.185072Z","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":"2026-08-14T15:09:50.164596Z","title":"Chatter detection in turning using persistent homology,","venue":null,"work_id":"afc625ef-1955-4674-9687-b7a3cad40e15","year":2016},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.503964Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:097dc17fe139c548f6a85509d0c6e5e765bee0a37e28726a8a43281f1674d580","observation_id":"975290ee-db80-4aa3-8616-5ee4751ca22f","resolution":{"observed_at":"2026-08-14T15:09:50.169468Z","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":"2026-08-14T15:09:50.148070Z","title":"Chatter classification in turning using machine learning and topological data analysis,","venue":null,"work_id":"c7f7159d-2fea-4868-9fe6-e24f14bc5cad","year":2018},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.508616Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:289946e3667f023c64fe8ece8786873f0f77cd1f9d74758b59d1e700ba6ff367","observation_id":"9e3aaf76-430c-4028-80ab-d92742711ba7","resolution":{"observed_at":"2026-08-14T15:09:50.152965Z","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":"1905.08671","last_updated":"2021-06-01T21:35:27Z","snapshot_observed_at":"2026-08-14T16:29:02.234758Z","submitted_at":"2019-05-21T14:36:52Z","title":"Topological Feature Vectors for Chatter Detection in Turning Processes","version":3},"cited_work":{"arxiv_id":"1905.08671","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.08671","snapshot_observed_at":"2026-08-14T15:09:49.724221Z","title":"Topological Feature Vectors for Chatter Detection in Turning Processes","venue":"eess.SP","work_id":"8fb02445-70d0-4bda-9338-7ee16f69c469","year":2019},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.513365Z"},"links":{"cited_paper":"/paper/1905.08671","citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:13dd254ad1e48b5f967c298934f65b663a5bf21433e5794ac2d52015852d2bea","observation_id":"07c839ab-88ed-4eed-9938-78d7e695e300","resolution":{"observed_at":"2026-08-14T15:09:49.729862Z","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":"2026-08-14T15:09:50.132213Z","title":"The ring of algebraic functions on persistence bar codes,","venue":null,"work_id":"07fcf502-abcc-442a-a128-0f6e6854b2ca","year":2016},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.518833Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:12edd41f2e9f1d6446d534626a791e15e82c28931dc592f5cd29840a229ee1d9","observation_id":"da44d52f-a511-4c39-9f77-ebc9f31d7640","resolution":{"observed_at":"2026-08-14T15:09:50.137775Z","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":"1902.07190","last_updated":"2022-04-12T18:03:27Z","snapshot_observed_at":"2026-08-14T17:14:15.304360Z","submitted_at":"2019-02-19T18:43:14Z","title":"Approximating Continuous Functions on Persistence Diagrams Using Template Functions","version":3},"cited_work":{"arxiv_id":"1902.07190","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.07190","snapshot_observed_at":"2026-08-14T15:09:49.700877Z","title":"Approximating Continuous Functions on Persistence Diagrams Using Template Functions","venue":"cs.CG","work_id":"3a0b372f-cc79-49e3-a688-e86ae47de4e4","year":2019},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.523539Z"},"links":{"cited_paper":"/paper/1902.07190","citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:6eeff9a4c24c35ace9ce948e116be5c002ff6ece164d615edb041462c56851af","observation_id":"d5138fb5-5cb8-456c-8b81-53272983b6bb","resolution":{"observed_at":"2026-08-14T15:09:49.707164Z","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":"2026-08-14T15:09:50.116972Z","title":"A stable multi-scale kernel for topological machine learning,","venue":null,"work_id":"bd2a1b53-9d8c-4779-b958-9258fb36f497","year":null},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.529079Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:6e201dcaa47945dfb9fc3fd3b87ffc6bf5e3ebc6955329597f4c65458670d863","observation_id":"10a103bb-1ceb-4090-b48d-9d99ab207261","resolution":{"observed_at":"2026-08-14T15:09:50.122223Z","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":"2026-08-14T15:09:50.099890Z","title":"Persistence paths and signature features in topological data analysis,","venue":null,"work_id":"be32113c-216b-42f8-b8e1-366459e3382d","year":2018},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.533568Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:fa7f026613f42fd49f3054e2478aec03e92a9d3c4e82a002e05b2a4554f6ccbb","observation_id":"c6129492-8299-4370-8e07-283e89640b36","resolution":{"observed_at":"2026-08-14T15:09:50.105570Z","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":"2026-08-14T15:09:50.083948Z","title":"Persistence in sampled dynamical systems faster,","venue":null,"work_id":"e7605dcc-38aa-41f9-abdf-80d657946ace","year":null},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.538552Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:46b557c089d259879604f64450938169dba176a13b017326d0334fc92b9316f4","observation_id":"c7e87dde-79f0-4ae0-ade9-b9cb3efcff41","resolution":{"observed_at":"2026-08-14T15:09:50.088740Z","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":"2026-08-14T15:09:50.066912Z","title":"Performance tradeoffs in dynamic time warping algorithms for isolated word recognition,","venue":null,"work_id":"95f3779f-d184-4aec-bf9d-22538ff3bca2","year":1980},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.543408Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:88f9324ec51c864006314f435bff6dfd6a1964e06c9ce92b3587e9addf1931f1","observation_id":"be6d15ce-0f83-4a39-b84c-2a3e824304aa","resolution":{"observed_at":"2026-08-14T15:09:50.072786Z","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":"2026-08-14T15:09:50.051369Z","title":"A comparative study of several dynamic time-warping algorithms for connected-word recognition,","venue":null,"work_id":"0180b6c5-fe30-464a-8c37-0ae7e0f23933","year":1981},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.547867Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:b231c9bd34389d78f75dc3f1d6aba67bb729bdd504aa6d0e9d0bab8e1ad9b649","observation_id":"8c90d158-afcc-4941-8ed5-5b8aaa935d5a","resolution":{"observed_at":"2026-08-14T15:09:50.056288Z","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":"2026-08-14T15:09:50.035879Z","title":"Dynamic programming algorithm optimization for spoken word recognition,","venue":null,"work_id":"eea7b98a-dc76-4b1b-afaa-9fde83ed8626","year":1990},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.552540Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:fc2ca4cc09de1f97999a4660df1c80f9a2193b161896446c772da65fa54f67fd","observation_id":"d66a02f8-060e-47cf-9017-1fe23dba6924","resolution":{"observed_at":"2026-08-14T15:09:50.040995Z","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":"2026-08-14T15:09:50.019134Z","title":"On the hidden markov model and dynamic time warping for speech recognition-a unified view,","venue":null,"work_id":"9ae6b5cb-f642-4783-89b0-0ae87004d643","year":1984},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.557262Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:0d95e48e48bd7d2448a099df3be88d67ec684eb5d116f9c164e497640b6a5bdd","observation_id":"9eedb67b-920d-4bbe-b91e-e858d5aba5a6","resolution":{"observed_at":"2026-08-14T15:09:50.025306Z","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":"2026-08-14T15:09:50.003518Z","title":"Minimum prediction residual principle applied to speech recognition,","venue":null,"work_id":"1133a473-0727-4465-bf31-075ab69c4edc","year":1975},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.561966Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:c078addb0151b762b16b78c9ad2557d2d6b207121a485245b7f6519b6448776a","observation_id":"57ee0384-8d7c-4747-aa18-7a5e9ab63896","resolution":{"observed_at":"2026-08-14T15:09:50.008551Z","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":"2026-08-14T15:09:49.986808Z","title":"On clustering multimedia time series data using k- means and dynamic time warping,","venue":null,"work_id":"b5dbeebb-baaf-496d-a402-a13d5b2b4c94","year":2007},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.566980Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:d019064cb1d741b61ca7d8faf13a6b5ed87b47288dd667a643ca14b40fa97730","observation_id":"2262be86-b2b1-4228-b48a-50b3cc5c8244","resolution":{"observed_at":"2026-08-14T15:09:49.992306Z","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":"2026-08-14T15:09:49.971424Z","title":"Clustering time series with granular dynamic time warping method,","venue":null,"work_id":"469b0b86-98f3-4470-b64a-cc74b73ca882","year":2007},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.572766Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:0ebd667a04143f19d3b94ca05a4f1745faf1ea94656a66b330a13695101137c4","observation_id":"1aaa7604-0360-49b5-ba02-b0ee446700c0","resolution":{"observed_at":"2026-08-14T15:09:49.976582Z","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":"2026-08-14T15:09:49.956611Z","title":"Dynamic time warping averaging of time series allows faster and more accurate classification,","venue":null,"work_id":"37869d0b-d07d-40da-9192-140d18f8fd56","year":2014},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.578436Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:14ab88918dee2ef1b9d9d18900c8c42864d78d419ca9ce6724b2310e5e1d8480","observation_id":"71f8d467-7b4e-4a80-a058-dc4710587b8e","resolution":{"observed_at":"2026-08-14T15:09:49.961398Z","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":"2026-08-14T15:09:49.941017Z","title":"Off-line signature verification using DTW,","venue":null,"work_id":"0b819e96-c740-49dc-88e0-ad2baf5b9bd5","year":2007},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.582835Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:7fd270390e0b7f2a40db0f6683815496a0d14a6c8c02273b7c7a91055ac02b57","observation_id":"6d478e9b-b1e4-4f97-8d92-ca1e0d201c33","resolution":{"observed_at":"2026-08-14T15:09:49.946059Z","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":"2026-08-14T15:09:49.925929Z","title":"Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification,","venue":null,"work_id":"4382ceb6-5dd5-49ad-b4f1-73bca66ee58a","year":1999},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.587400Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:2ac5f080d3c93426b553ee93df9073ad98bfe01fb3dd2d8fc9ebf7a09a5a2977","observation_id":"06f188c8-48da-45a1-a9c1-a90baba28dbd","resolution":{"observed_at":"2026-08-14T15:09:49.931048Z","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":"2026-08-14T15:09:49.909310Z","title":"A comparative analysis of regional correlation, dynamic time warping, and skeletal tree matching for signature verification,","venue":null,"work_id":"bf926f8d-945a-4ad9-aefd-e4b5fb22332b","year":1990},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.592311Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:c8767360c8bbdd4c7786d239b171050f5451abe2f499836660a35f2f533f3902","observation_id":"6ee5a406-7fba-41f9-b466-98ec893a71ee","resolution":{"observed_at":"2026-08-14T15:09:49.915533Z","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":"2026-08-14T15:09:49.891918Z","title":"On-line signature verification by dynamic time-warping,","venue":null,"work_id":"bbd17a17-fd29-469c-a983-83c143aeeaf8","year":1996},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.597014Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:77d4213416a2f399aa07b35466143afa181934a2565a23d45b75d442381af9c7","observation_id":"ec8d2334-640d-47f0-917f-1de00e3d1b1b","resolution":{"observed_at":"2026-08-14T15:09:49.897013Z","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":"10.17632/hvm4wh3jzx.1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:09:49.676146Z","title":"Turning dataset for chatter diagnosis using machine learning","venue":null,"work_id":"444082ff-a6de-4598-8ed7-3ceabdf2f74e","year":2019},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.602676Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:4759769d650c736836b946a86b043161d8bae89fa62ae213dda9f17886be2016","observation_id":"3f03ec6f-fd08-4fc9-8f22-eb3bea64909c","resolution":{"observed_at":"2026-08-14T15:09:49.683932Z","resolver_source":"doi","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":"2026-08-14T15:09:49.875487Z","title":"On the chatter frequencies of milling processes with runout,","venue":null,"work_id":"804f1a4e-6a0a-4ad3-b236-04ff9fa8e268","year":2008},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.607904Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:990733000ea5d966cf9bae19a4ddd51e4284825aec4e1d44c7bb143ae24dfba5","observation_id":"bea3365c-0957-43c3-896f-18bf39acd48a","resolution":{"observed_at":"2026-08-14T15:09:49.880926Z","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":"2026-08-14T15:09:49.859057Z","title":"Using dynamic time warping to find patterns in time series.,","venue":null,"work_id":"68d6da1f-a5ae-4bdc-9ef6-01f73e620d08","year":1994},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.613818Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:272f6ac019ba375ee742da49b007dfb46a24dd21554b27110b49ef802d3fe77c","observation_id":"a58a8b56-0740-41c8-932a-dc9b5d70ec34","resolution":{"observed_at":"2026-08-14T15:09:49.864355Z","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":"2026-08-14T15:09:49.844238Z","title":"Dynamic programming algorithm optimization for spoken word recognition,","venue":null,"work_id":"a5c15301-82cc-4a24-b4a4-e55db294babb","year":1978},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.618514Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:e5558c9e558ca5ed5ccf1071f617632483ab3a40dfb8363dc80935a904a26496","observation_id":"b5258a7c-3da9-4958-bf63-ed9fa2e2979f","resolution":{"observed_at":"2026-08-14T15:09:49.849011Z","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":"2026-08-14T15:09:49.827630Z","title":"Toward accurate dynamic time warping in linear time and space,","venue":null,"work_id":"40a51e40-b582-46d5-a4ef-09fdf608d2e4","year":2007},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.623098Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:e3d7af7c4840e76fdda90ca034fab887c4dccb4bfff8ce19e9f3f52767bd6071","observation_id":"4d9f4457-2a07-43a3-9fa6-dd05449c136d","resolution":{"observed_at":"2026-08-14T15:09:49.833366Z","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":"2026-08-14T15:09:49.811997Z","title":"Feature selection,","venue":null,"work_id":"362ea825-f1bf-4a2f-9ca3-7ed208ac53e0","year":2009},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.627227Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:b20dec35a9c2c5cc7db5fdfc570ae07738d0e909e4589f426055c27c1ad6f5cb","observation_id":"c4ba3350-9eae-43af-afcf-a0afcbfb60ba","resolution":{"observed_at":"2026-08-14T15:09:49.817302Z","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":"2026-08-14T15:09:49.796243Z","title":"The distance-weighted k-nearest-neighbor rule,","venue":null,"work_id":"5cb4ab29-e3a1-4a10-b81a-b0b6c1f8220a","year":1976},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.632729Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:b45005f6c3f0d0fcafad062e526cfe89270510cb92cb0d11585fb5fc342f9835","observation_id":"0fb2c933-e399-4bfe-ab6f-c1cb878fd497","resolution":{"observed_at":"2026-08-14T15:09:49.801286Z","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":"2026-08-14T15:09:49.779161Z","title":"Relational instance-based learning with lists and terms,","venue":null,"work_id":"3e88cee2-754d-40c5-ad76-32bf02adaf26","year":2001},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.637793Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:d06190a826b62bb424f2d1b9dfc48867c627d38ec3fc303cf1b2f58a0b519819","observation_id":"c87dba60-1a42-4730-a229-2c01ada94688","resolution":{"observed_at":"2026-08-14T15:09:49.785021Z","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":"2026-08-14T15:09:49.763185Z","title":"Searching and mining trillions of time series subsequences under dynamic time warping,","venue":null,"work_id":"d70e0493-09b3-46d1-97f4-ba6c7fe2f158","year":2012},"citing_paper":{"arxiv_id":"1908.01678","last_updated":"2019-08-05T15:09:49Z","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T15:09:49.643716Z"},"links":{"citing_paper":"/paper/1908.01678"},"observation_digest":"sha256:01b8ee9c5d60533e2a83e06f4f6233f9e55fae50d87271dfd65e4ad77efef06f","observation_id":"c015dbdb-5d66-41c9-ad24-373243979709","resolution":{"observed_at":"2026-08-14T15:09:49.768386Z","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":"1908.01678","last_updated":"2019-08-05T15:09:49Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-14T15:04:04.508983Z","submitted_at":"2019-08-05T15:09:49Z","title":"Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":4,"verified_fuzzy":38},"total_outbound_references":42},"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 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:1908.01678."}