{"as_of":"2026-08-16T15:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0071f479894c85f213d91c13bb09911be1de5276de17a66e63c9ad90b278732c","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:45:29.990789Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"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/1908.05349/citation-record","integrity":"/paper/1908.05349/integrity","json":"/paper/1908.05349/citation-record.json","paper":"/paper/1908.05349"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:45:29.654399Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.654399Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:2bfe0aa046a9bd84f24e5b93e072e354080e5c32dca1830e6517ab67385d5ecd","observation_id":"1868ba81-e25b-480d-8004-d2b020020729","resolution":{"observed_at":"2026-08-14T13:45:29.654399Z","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":"2026-08-14T13:45:31.519605Z","title":"Toward machine emotional in- telligence: Analysis of affective physiological state,","venue":null,"work_id":"81636c70-b918-4719-8139-6d1f6a97dbb0","year":2001},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.659621Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:d4f84a64432c5fbc0bdfffaf7324fa9c524008e31e109ec835a24cb9a6f45703","observation_id":"aaddcb35-7751-443e-a8cd-1d07dcefaa60","resolution":{"observed_at":"2026-08-14T13:45:31.525505Z","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":"2026-08-14T13:45:31.497357Z","title":"Survey on speech emotion recognition: Features, classification schemes, and databases,","venue":null,"work_id":"8d2aee0e-55f1-4864-a6df-2ff908484e97","year":2011},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.664413Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:cadcc55cf6d57903e71409a81e08d03a2c26792478376078e31034fd65d3fbc2","observation_id":"09f7fed6-cf4f-4c7c-8a09-f85e8176bf33","resolution":{"observed_at":"2026-08-14T13:45:31.505446Z","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":"2026-08-14T13:45:29.669493Z","title":"A brief review of facial emotion recognition based on visual information,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.669493Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:b13c7a02b70f721be809237131976a790851b630e4f5840697abed52e98b656f","observation_id":"7cee24f6-d54b-4841-a2df-12e1c764bd44","resolution":{"observed_at":"2026-08-14T13:45:29.669493Z","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":"2026-08-14T13:45:31.460404Z","title":"Current state of text sentiment analysis from opinion to emotion mining,","venue":null,"work_id":"7f51d0c4-a63e-4795-a222-6c0ef1d93cee","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.674608Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:5b444d3df48e8623f6720d0a7bf1d503e33a6289be5d646a5a509aeadefe6ddb","observation_id":"4dfe3eba-7118-47e3-9246-25282f9ac139","resolution":{"observed_at":"2026-08-14T13:45:31.466819Z","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":"2026-08-14T13:45:31.440933Z","title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks,","venue":null,"work_id":"cd517241-437e-4a4f-8e4f-5b13a11e64a6","year":2015},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.680752Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:a32877e7cbc4bd29654cb91fae6b3021c549af3340353672dcc2ec5df67b0d1f","observation_id":"874851fe-9861-44aa-9780-8915d35ec10f","resolution":{"observed_at":"2026-08-14T13:45:31.447513Z","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":"2026-08-14T13:45:29.686529Z","title":"Identifying stable patterns over time for emotion recognition from EEG,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.686529Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:3a1c63240f71634774eccf7c657e52cfc35a04c0f3bc5c77c4a29ece07562102","observation_id":"77e2814f-1e40-4369-9f9a-25b7c43235f4","resolution":{"observed_at":"2026-08-14T13:45:29.686529Z","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":"2026-08-14T13:45:31.419700Z","title":"EEG-based emotion recognition using hierarchical network with subnetwork nodes,","venue":null,"work_id":"018419d0-8c34-47ab-97cc-c9b8a1ba9c3d","year":2018},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.691303Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:71b4de01371bf1294fd57914e1974c986c7b2e4095c3eff7f65b57715cb765c8","observation_id":"eb71e362-9855-4450-92e0-d94bac229a74","resolution":{"observed_at":"2026-08-14T13:45:31.425733Z","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":"2026-08-14T13:45:31.399949Z","title":"Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination,","venue":null,"work_id":"3e1304a7-95ad-452e-b074-cf1701ca6295","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.697707Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:54fecd7cb811072c72b8987e04fd89ba871633f9dd4f6fde6203a01c1eec1ad9","observation_id":"66b40d27-067e-4313-b204-4c2b3890eb94","resolution":{"observed_at":"2026-08-14T13:45:31.405641Z","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":"2026-08-14T13:45:31.379700Z","title":"Gen- eralizations of the subject-independent feature set for music-induced emotion recognition,","venue":null,"work_id":"4411c092-3d9f-4de0-8118-5f65896e11b4","year":2011},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.702899Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:3e69e4231b8e79f2f9b88166265394bfbe0ccf05e2bdfcb9583447d6e82c69a5","observation_id":"4d54e8f7-0600-47c9-85e0-c4f486673eda","resolution":{"observed_at":"2026-08-14T13:45:31.385590Z","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":"2026-08-14T13:45:31.358029Z","title":"Emotional state classification from eeg data using machine learning approach,","venue":null,"work_id":"cabaeab4-7057-4f9f-8e96-af96ac3dac38","year":2014},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.707913Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:584db2a35f5ec0cd56ca3315077b62c17529569b6c9461a4b8dc46d1094abd10","observation_id":"059039a0-eff5-436f-90f1-7d1fd83236ab","resolution":{"observed_at":"2026-08-14T13:45:31.365936Z","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":"2026-08-14T13:45:31.330914Z","title":"Emotion recognition based on physiological changes in music listening,","venue":null,"work_id":"9f9e160e-5fbc-4d7b-aa4c-2d98b13cda4e","year":2008},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.713034Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:0395af4ac7bd16c92a27c9ca7ac4ae778f37dcb3c2e56b6f015bb6a29288a2a7","observation_id":"2716086e-77d5-467c-a531-cbfdf3133777","resolution":{"observed_at":"2026-08-14T13:45:31.337494Z","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":"2026-08-14T13:45:31.311732Z","title":"The coupling of emotion and cognition in the eye: Introducing the pupil old/new effect,","venue":null,"work_id":"d9eb1e31-bf59-4eb8-bc95-60fd093226e5","year":2008},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.718104Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:e832ee696688ad43535be091b6ffd05961359a4a9c5c9dd70aa9d02f8a9afde8","observation_id":"a5b8827d-ae30-4904-b433-dcc18234d4fd","resolution":{"observed_at":"2026-08-14T13:45:31.317603Z","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":"2026-08-14T13:45:31.292140Z","title":"A review of affective computing: from unimodal analysis to multimodal fusion,","venue":null,"work_id":"1714071f-478a-4aa8-b8f4-c3580f026915","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.722945Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:886185c55865c08bb3b7604a485ff15fe3c2c5b39ed913435c968709951db148","observation_id":"10c7e76f-51ff-445a-b400-9e68ca1cf493","resolution":{"observed_at":"2026-08-14T13:45:31.296751Z","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":"2026-08-14T13:45:31.269915Z","title":"Emotion- meter: A multimodal framework for recognizing human emotions,","venue":null,"work_id":"f4a9e297-681f-4f8c-b9a7-933d6b3978e0","year":2019},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.727604Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:63f508f0b5052c5ec151e5440b3ea742a3798fc7585d7e71937c183f53582e6f","observation_id":"e63e1132-958b-47b7-b685-c52d85053f12","resolution":{"observed_at":"2026-08-14T13:45:31.278931Z","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":"2026-08-14T13:45:31.243575Z","title":"Multimodal emotion recognition in response to videos,","venue":null,"work_id":"e2ba746b-9a26-4121-a036-a432073429df","year":2012},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.732573Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:77c5002297f47493f96c9273d999d61609bbbd42f7357ea7cf2d4d7d2c3c3a21","observation_id":"cd201f92-4808-4b0c-9b9d-2eb31c6d2f08","resolution":{"observed_at":"2026-08-14T13:45:31.253148Z","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":"2026-08-14T13:45:31.215834Z","title":"Multimodal emotion recognition in response to videos,","venue":null,"work_id":"b8cce3b4-68c1-4930-a782-a41361f22c85","year":2012},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.737255Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:e56634318c2769409e3a00753d140a6e8a856e902ee83bada17c3f4b93f6e985","observation_id":"bddc9d96-d00b-4bbb-a60e-17dee0491a74","resolution":{"observed_at":"2026-08-14T13:45:31.225702Z","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":"2026-08-14T13:45:31.188265Z","title":"Affectnet: A database for facial expression, valence, and arousal computing in the wild,","venue":null,"work_id":"1842ba9c-943f-4dd0-a590-e222332856ad","year":2019},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.742116Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:106b75ac6695b45e6ba27664a6c892931cea320cb3be43ef19c614b0fb71ac91","observation_id":"7141c63a-dd1a-433d-830f-1811297d8fc9","resolution":{"observed_at":"2026-08-14T13:45:31.198752Z","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":"2026-08-14T13:45:31.164901Z","title":"Analysis of eeg signals and facial expressions for continuous emotion detection,","venue":null,"work_id":"7e05ade9-98dc-440e-9790-6a12bbd3116c","year":2016},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.747366Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:5976e7ff3727c7ed81e6c91d801d4d554de0f52ae165ba78679c07dc06c6744f","observation_id":"5fed720e-6ba6-4878-97d7-f783a1e92ce1","resolution":{"observed_at":"2026-08-14T13:45:31.171702Z","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":"2026-08-14T13:45:31.138553Z","title":"Combining eye movements and EEG to enhance emotion recognition,","venue":null,"work_id":"e4ad0314-ea19-446d-b28d-ec1f4ecbbbff","year":2015},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.752472Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:0f90f33f0cb444c66dd0305e3d662d219ab7b6e8189da0d2bf93ddf1753b9534","observation_id":"5ba973ac-b726-4e8b-a8ae-a77db36ab4e6","resolution":{"observed_at":"2026-08-14T13:45:31.146595Z","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":"2026-08-14T13:45:31.118655Z","title":"DEAP: A database for emotion analysis; using physiological signals,","venue":null,"work_id":"d8c2f825-7928-4b83-83d6-6c0901e1984f","year":2012},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.757820Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:c0055eb164061e294fdd15d4224440428f0d90525e70c122bf5043dbdd8b3056","observation_id":"6d29b8a4-1f84-445c-8d2e-735b2abf54a1","resolution":{"observed_at":"2026-08-14T13:45:31.124791Z","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":"2026-08-14T13:45:31.094680Z","title":"Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild,","venue":null,"work_id":"57a83d17-4f1b-489b-8e99-bf222ae95d72","year":2016},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.762926Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:7aa430a8b355ffe3974757bd03786e7cb6f962ce2a96ac25033d63c5a257a14a","observation_id":"4545642a-aaab-4f00-803f-a96a5f6178a9","resolution":{"observed_at":"2026-08-14T13:45:31.102806Z","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":"2026-08-14T13:45:31.071092Z","title":"Multimodal machine learning: A survey and taxonomy,","venue":null,"work_id":"da6427a8-e832-46a5-832d-2de5d5e3a9d2","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.768018Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:416232884d414417700883b10497f611cc6ba496aa99bee298b823547069c773","observation_id":"9dfac22e-17af-43fb-a10b-1b42ec2d9fdb","resolution":{"observed_at":"2026-08-14T13:45:31.077858Z","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":"2026-08-14T13:45:31.040174Z","title":"Emotion recognition using multimodal deep learning,","venue":null,"work_id":"4bcfc9af-cfbe-435a-8bc2-bfeacb9e9db6","year":2016},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.772970Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:b05e9945cf04211ea04680f3b885a7f18e3297a1d447f8af9d83e1622dbebde1","observation_id":"a758aee2-6a10-4cf2-b3ec-99b8fbfda73e","resolution":{"observed_at":"2026-08-14T13:45:31.048390Z","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":"2026-08-14T13:45:31.010541Z","title":"Multimodal emotion recognition using deep neural networks,","venue":null,"work_id":"0cb1f2d7-548e-4978-98a3-8cf5c8d6f07e","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.777880Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:9572f58cf943da39659b2ef42c20e66fc8ce173ab05d4f9ebe48669cfe3fadfc","observation_id":"378c0490-8205-48d5-a14e-361f075356dd","resolution":{"observed_at":"2026-08-14T13:45:31.018390Z","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":"2026-08-14T13:45:30.979148Z","title":"Emotion recogni- tion from multi-channel EEG data through convolutional recurrent neural network,","venue":null,"work_id":"211646d3-7fde-485d-a8b1-e699ac4266c5","year":2016},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.782972Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:b17e99c5e7f56f48248937534be5dcf9599b4b21d4af6e7013c30cebbc04be42","observation_id":"a6700002-359c-46b6-a136-0a607e57095e","resolution":{"observed_at":"2026-08-14T13:45:30.984682Z","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":"2026-08-14T13:45:30.950121Z","title":"Recognition of emotions using multimodal physiological signals and an ensemble deep learning model,","venue":null,"work_id":"cdf69cb5-dbe4-4367-9a4c-9d55bdd49378","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.788571Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:31a9f76f35c3f923470a19a7cbfbde4ffa3ffada23c0a1d5db1517434818bb88","observation_id":"9ac65190-5320-413a-9659-c2821b84a2fa","resolution":{"observed_at":"2026-08-14T13:45:30.967217Z","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":"2026-08-14T13:45:30.923185Z","title":"Deep canonical correlation analysis,","venue":null,"work_id":"72e55823-570b-4e47-898e-ce2163bc498e","year":2013},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.793764Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:692449d1517b99084bf984e06a7d12abf8e129b26ce15d177782581bb5ba7611","observation_id":"b0ce750a-c11d-4e91-989a-e7ca228a6ad6","resolution":{"observed_at":"2026-08-14T13:45:30.930132Z","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":"2026-08-14T13:45:30.899504Z","title":"Multi-view emotion recognition using deep canonical correlation analysis,","venue":null,"work_id":"4cdcc08e-9b02-424e-b1a7-48c46742aa0f","year":2018},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.798733Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:8d51d92038c38d55b872404b0a2281c15bb30e5a3d698803ec4a1cf03693a950","observation_id":"0fc1c040-1456-45d6-9bd5-a42eefbc2931","resolution":{"observed_at":"2026-08-14T13:45:30.906270Z","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":"2026-08-14T13:45:29.803771Z","title":"Multimodal data fusion: an overview of methods, challenges, and prospects,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.803771Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:7256aa32122a2311b04be5ffea92acb41a783442b9c0270a1a12e9f775162c43","observation_id":"fdc3f0ba-b8d6-485c-8def-2c59ea3db5e3","resolution":{"observed_at":"2026-08-14T13:45:29.803771Z","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":"2026-08-14T13:45:30.852433Z","title":"A review and meta-analysis of multimodal affect detection systems,","venue":null,"work_id":"d59c05ab-90c3-4d05-ab27-02df279b177b","year":2015},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.808642Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:fbe7d5d359095005ad8d9be8f0734e5486868e3b737929f6fa931447567d5b57","observation_id":"3937e3bd-4a3c-463f-80bc-6f2cd0333bbc","resolution":{"observed_at":"2026-08-14T13:45:30.861530Z","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":"2026-08-14T13:45:30.826146Z","title":"Self- attentive feature-level fusion for multimodal emotion detection,","venue":null,"work_id":"ba6da4eb-6e24-433b-a9c6-b7646ba0e2f4","year":2018},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.813925Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:88d321af88b85618fc5ed383f5b888f078dcb06df3e735303f063d5ae37e5d90","observation_id":"3a409406-7d2e-48bd-92ce-a31e1ee30a96","resolution":{"observed_at":"2026-08-14T13:45:30.835940Z","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":"2026-08-14T13:45:30.795694Z","title":"Multimodal deep learning,","venue":null,"work_id":"c09b255c-503b-4938-bae8-b4f73a591e5b","year":2011},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.819333Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:0e9affcb261617151a207b8fd4879be77d49c075a8674680b6d72240ff72c106","observation_id":"36a8c5c1-b239-41bb-ab7f-a85d3f81d1be","resolution":{"observed_at":"2026-08-14T13:45:30.805799Z","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":"2026-08-14T13:45:30.768407Z","title":"Classification of affects using head movement, skin color features and physiological signals,","venue":null,"work_id":"7c07a94d-bed7-471b-9cf5-d9844d832511","year":2012},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.824371Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:95ecf2b65cbe3b51516d120e1d94ea27ef6d7fab0916511020eb0ef2187e3af9","observation_id":"3ddc807a-4423-4f12-ad10-a7bba6c46e79","resolution":{"observed_at":"2026-08-14T13:45:30.774216Z","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":"2026-08-14T13:45:30.745279Z","title":"A hybrid fuzzy cognitive map/support vector machine approach for EEG- based emotion classification using compressed sensing,","venue":null,"work_id":"113b45ea-6700-4a00-911b-da487e96b520","year":2019},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.829242Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:856941814edae42558f2218c453678351e3ef4cc282806c7b4880aa973d08770","observation_id":"8a36679d-8ab1-42d3-a957-c257f7d164ab","resolution":{"observed_at":"2026-08-14T13:45:30.752976Z","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":"2026-08-14T13:45:30.722362Z","title":"Unsupervised alignment of natural language instructions with video segments,","venue":null,"work_id":"366cd518-007e-493a-aba5-1a874bdd62f2","year":2014},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.834296Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:50f1025251ab3e18e94b946764d3a4a6200330612e86e8ad918be0e9e4c36a66","observation_id":"06b44e5c-34fc-4ee6-acd5-a701cf190d0d","resolution":{"observed_at":"2026-08-14T13:45:30.730696Z","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":"2026-08-14T13:45:30.701544Z","title":"Devise: A deep visual-semantic embedding model,","venue":null,"work_id":"fc5c8f7a-0018-4196-bb5d-8b37b5693a6d","year":2013},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.839698Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:88d05791bdaffc29a3059d7b8b7e3b1133ca6eed9b17755dd6cbec0a80ff36d6","observation_id":"ce5a1be4-bd15-4502-b4aa-8ab59cfd07d4","resolution":{"observed_at":"2026-08-14T13:45:30.708416Z","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":"2026-08-14T13:45:30.682683Z","title":"Relations between two sets of variates,","venue":null,"work_id":"7a5050cf-3c8a-4592-bd31-7b30c78256fb","year":1992},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.845002Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:1430e530750472e40599c0e55e04875424a89b95b50e4f2d1085b4c8fc6d0007","observation_id":"d17c30d0-9b1b-457f-9eb9-6051cbc5858b","resolution":{"observed_at":"2026-08-14T13:45:30.688034Z","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":"2026-08-14T13:45:29.849963Z","title":"Canonical correlation analysis: An overview with application to learning methods,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.849963Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:8c0ea1ececfa8ae682c2cb2f393acdf11a5cae4bddba7d74987a39fe2f49b8ba","observation_id":"d2f65ad8-a714-42fc-b04d-de1ca0a75ef1","resolution":{"observed_at":"2026-08-14T13:45:29.849963Z","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":"2026-08-14T13:45:30.656358Z","title":"Kernel and nonlinear canonical correlation analysis,","venue":null,"work_id":"f282e7fd-f334-4d7b-b326-7b8817fa591a","year":2000},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.854438Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:0c60afae9dc6240c5abbb6f4464e8729db7c77f69d809f3af40c03146f0c3732","observation_id":"b04a73de-d068-4ef6-a2ef-e3c96532b2d2","resolution":{"observed_at":"2026-08-14T13:45:30.661043Z","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":"2026-08-14T13:45:30.637667Z","title":"Probabilistic approach to detecting dependen- cies between data sets,","venue":null,"work_id":"5917f350-6da0-4385-8a6e-1a04fa467700","year":2008},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.859667Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:87bd5f615b39d5400af601e010c198ffc654adfc49f74dac470e6fb2b4cb6bc5","observation_id":"e76bc62b-91d5-4d3d-9750-2955cbc51f60","resolution":{"observed_at":"2026-08-14T13:45:30.643677Z","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":"2026-08-14T13:45:30.620283Z","title":"Bayesian canonical correlation analysis,","venue":null,"work_id":"69b12d95-5c27-4970-ba43-780e49951f8d","year":2013},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.864473Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:c4906ba082d3d733ddd2f1488a419c547f7c7409dc4a1a4694abe19daca348e9","observation_id":"d9c10649-8ee0-4265-8a49-01aa02f47954","resolution":{"observed_at":"2026-08-14T13:45:30.625570Z","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":"2026-08-14T13:45:30.602220Z","title":"Tensor canonical correlation analysis for action classification,","venue":null,"work_id":"982dc6ac-3c00-4864-a058-a46065b4ba20","year":2007},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.868859Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:30a49aeb9577eae6c8d5e4feca5c63808c052a254166013be9ce7137c98b34cf","observation_id":"b245edd5-7044-40d8-9b19-074871f964c5","resolution":{"observed_at":"2026-08-14T13:45:30.608111Z","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":"2026-08-14T13:45:30.580521Z","title":"Sparse canonical correlation analysis,","venue":null,"work_id":"0d3e909b-c89e-4c96-b667-680ae4a729e2","year":2011},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.873925Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:c9e1bec0ce5c2ae51869c12fb4a42c0674bb24fa036e1d8af0dedaa7450243df","observation_id":"b43492d3-ec95-494a-a6a6-3a2f3e73cf81","resolution":{"observed_at":"2026-08-14T13:45:30.587443Z","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":"2026-08-14T13:45:30.561689Z","title":"Cluster canonical correlation analysis,","venue":null,"work_id":"0e173b11-16c2-4b73-9e6d-b990520eb521","year":2014},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.878815Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:2126dd3a736922bf3fb48256fd73f63bcaab2e7ad9412dd8ccef11aaa283c028","observation_id":"e776400d-f13d-4352-88a3-5961a8a6bbc1","resolution":{"observed_at":"2026-08-14T13:45:30.567828Z","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":"2026-08-14T13:45:30.542624Z","title":"Application of the choquet integral in multicriteria decision making,","venue":null,"work_id":"19d86fd9-43e7-4ef4-b175-45bf104b47bb","year":2000},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.884065Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:c085f949c8d5a1b27e531387015fa453b5f67f23e43d04506f5f09304d362a6a","observation_id":"cfe9ffdf-584a-469a-86b8-5f796559f31f","resolution":{"observed_at":"2026-08-14T13:45:30.549163Z","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":"2026-08-14T13:45:30.516185Z","title":"Gender classification by combin- ing clothing, hair and facial component classifiers,","venue":null,"work_id":"09134a3e-8eaa-4e44-b37b-d30a51d60a16","year":2012},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.889547Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:f8570c86953a5aa4cf93a01598154d4b0f292e495087c1ff8a374df5cb26f7a2","observation_id":"ffe4c250-c9b6-44bd-96a9-925b15d0f05b","resolution":{"observed_at":"2026-08-14T13:45:30.525130Z","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":"2026-08-14T13:45:30.494346Z","title":"A study on subjective evaluations of printed color images,","venue":null,"work_id":"100a8593-2e7b-4f28-a39a-132fa42fb96e","year":1991},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.894543Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:658377c351bd74a30c7069e899b096a25beb953e9f146e8e11613bad57033090","observation_id":"e0d230ae-6ae4-4eff-b5bb-8f786defabcf","resolution":{"observed_at":"2026-08-14T13:45:30.499568Z","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":"2026-08-14T13:45:29.899354Z","title":"Scikit-learn: Machine learning in Python,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.899354Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:01b22257325e22733657baff60d703c0efd88280aafadae1c39a0a909ed3fa3a","observation_id":"bda34282-cec4-4778-9d57-89e8856c0629","resolution":{"observed_at":"2026-08-14T13:45:29.899354Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.04062","last_updated":"2021-08-14T18:35:05Z","snapshot_observed_at":"2026-08-14T19:55:44.931753Z","submitted_at":"2018-01-12T05:42:58Z","title":"MINE: Mutual Information Neural Estimation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.04062","snapshot_observed_at":"2026-08-14T13:45:29.904041Z","title":"Mine: mutual information neural estimation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.904041Z"},"links":{"cited_paper":"/paper/1801.04062","citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:2eb6fcaf65babdf382a36c6a6b42418e45ddc242da7e47967d582d8236967a93","observation_id":"67d98ffd-8321-4b7f-a8cc-116c69cfde2a","resolution":{"observed_at":"2026-08-14T13:45:29.904041Z","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":"2026-08-14T13:45:30.462871Z","title":"Classification of five emotions from eeg and eye movement signals: Discrimination ability and stability over time,","venue":null,"work_id":"951f72aa-ad04-466c-a585-f47b960a181b","year":2019},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.909247Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:4effa570d08521db914566d6eb1116eee9c6dde967ee7abbe07a06659f816d6d","observation_id":"21e6b96c-229c-4f84-948a-42d769d9e3bc","resolution":{"observed_at":"2026-08-14T13:45:30.467972Z","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":"2026-08-14T13:45:30.445476Z","title":"DREAMER: A database for emotion recognition through eeg and ecg signals from wireless low-cost off- the-shelf devices,","venue":null,"work_id":"bd92e85a-6038-408a-8747-8aff9d982c4e","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.914415Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:3b00565ed2309e2f76b4ba88beaa5a9e40319c365b453f439b5332ce0011bb32","observation_id":"7f24be59-7fb7-43db-a5ee-10652501f1cc","resolution":{"observed_at":"2026-08-14T13:45:30.451037Z","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":"2026-08-14T13:45:30.428388Z","title":"Differential entropy fea- ture for EEG-based emotion classification,","venue":null,"work_id":"3e69c22e-e321-4caf-b083-6e10f994d287","year":2013},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.919866Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:95351d19c315a6365a9952ac5c16bb79dfbf4a662cd7c355fc933d2b8396e280","observation_id":"3ef8e4a6-1ba1-4f14-bc51-737febc29299","resolution":{"observed_at":"2026-08-14T13:45:30.433543Z","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":"2026-08-14T13:45:30.411934Z","title":"Differential entropy feature for EEG-based vigilance estimation,","venue":null,"work_id":"83365f6f-3061-4c33-90ac-e54cf2ab5dfd","year":2013},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.925845Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:596aba20738dc52a7da752d288b390f2440c26f162d8d80e620aec317a92b475","observation_id":"8fa3c444-7a94-4f26-bbdf-6d30c3918fa6","resolution":{"observed_at":"2026-08-14T13:45:30.417460Z","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":"2026-08-14T13:45:30.394024Z","title":"Off-line and on-line vigilance estimation based on linear dynamical system and manifold learning,","venue":null,"work_id":"6a89c9cf-323c-4bec-b5d4-32d01c109efb","year":2010},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.930208Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:a58f4407d4114be48606c318efb5ebafcf8ca544c3e069473f505edb821b8c17","observation_id":"baf3078f-4307-4423-aa8b-d7d280eb5f4d","resolution":{"observed_at":"2026-08-14T13:45:30.400250Z","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":"2026-08-14T13:45:30.375348Z","title":"Automatic ecg-based emotion recognition in music listening,","venue":null,"work_id":"4e41b224-4147-4683-83b0-ef44f0d5f72c","year":2018},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.938259Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:5a0c1be0d2d73fc00ecccce86a24eabea2af945bfa645b3818641a90b9297e1a","observation_id":"daeeceb5-8933-49d4-ba8a-136ebf896152","resolution":{"observed_at":"2026-08-14T13:45:30.381363Z","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":"2026-08-14T13:45:30.351848Z","title":"Emotion recognition using wireless signals,","venue":null,"work_id":"e54c068c-dde1-41b2-b2e4-935be86d45a4","year":2016},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.943664Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:d6c6fa7a9b9c45886b6a67f2039d9eec8a3b35b280993a5efeed058d4d986260","observation_id":"5c8d6660-b404-48a8-a25a-9ba445539e29","resolution":{"observed_at":"2026-08-14T13:45:30.358421Z","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":"2026-08-14T13:45:30.329610Z","title":"Frequency analysis of the electro- cardiogram,","venue":null,"work_id":"9c292b54-1c8b-4686-bcb9-a2aad20bb2c8","year":1960},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.948659Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:5231f52df9661d6c514b7070a5c99ffdf1969831f17f631cc709993667f0822c","observation_id":"6a51ac94-3ca2-441b-848c-57f5e7f8529c","resolution":{"observed_at":"2026-08-14T13:45:30.336452Z","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":"2026-08-14T13:45:30.309291Z","title":"Ecg signals classification based on dis- crete wavelet transform, time domain and frequency domain features,","venue":null,"work_id":"755a5652-e6ec-4cf5-a0ec-30c269f98c0f","year":2015},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.954130Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:81fd6623ae82fa3f57df682de4da9b1be40de986db51c24b1d0c4828e165a3e5","observation_id":"da53ceca-b194-46be-b86c-d5c441649777","resolution":{"observed_at":"2026-08-14T13:45:30.315305Z","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":"2026-08-14T13:45:30.280845Z","title":"Frequency content and characteristics of ventricular conduction,","venue":null,"work_id":"029774e5-7190-4292-9db7-17083bf2842c","year":2015},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.960820Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:1c315072c70c50eaab940d87277249d3496322033092cec696f2af322ca339a7","observation_id":"a6e91a2e-e1ba-4ff3-b137-ff598f27f380","resolution":{"observed_at":"2026-08-14T13:45:30.288176Z","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":"2026-08-14T13:45:30.253491Z","title":"Classification of five emotions from eeg and eye movement signals: Complementary representation properties,","venue":null,"work_id":"08c49ca5-4fc8-4013-b593-53de913aa22a","year":2019},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.967479Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:b2367296e122d87acb86e79aae11bd7fa88dff5b7e68e7b206778533879b05a6","observation_id":"755ce9a6-8265-4568-9e2b-5ca775044e98","resolution":{"observed_at":"2026-08-14T13:45:30.261170Z","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":"2026-08-14T13:45:30.234386Z","title":"EEG emotion recognition using dynamical graph convolutional neural networks,","venue":null,"work_id":"49e43696-1ea3-4d88-bc13-c6aa7c614b01","year":2018},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.972946Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:9a90d26e83b95f2e7cc2d90ca9faa75a5f96f30be3cf122903b059f031c7ec85","observation_id":"48acf3e6-eaae-418b-9a28-16b743888326","resolution":{"observed_at":"2026-08-14T13:45:30.240910Z","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":"2026-08-14T13:45:30.211945Z","title":"A three-stage decision framework for multi-subject emotion recognition using physi- ological signals,","venue":null,"work_id":"d1dc53eb-6e29-4f8a-af19-096e25274dbb","year":2017},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.979144Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:335b89c0c3901dbdb10549a2ba6a8aa37bbc5eb577da8a517a3c8985e5f9d945","observation_id":"08dafdc9-a472-4246-bfe0-027ad21675f7","resolution":{"observed_at":"2026-08-14T13:45:30.218575Z","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":"2026-08-14T13:45:30.187264Z","title":"Robust kernel density estimation,","venue":null,"work_id":"7faeecde-86d6-4ffc-8ad4-21fa8ce0201e","year":2012},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.985081Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:9aee88aff3fa2d9ab7dafddbf39e4a039d7628aa32c0a7f683429837ffaab731","observation_id":"bf0d34f3-aab0-48ad-8043-a2b058cf8aca","resolution":{"observed_at":"2026-08-14T13:45:30.196717Z","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":"2026-08-14T13:45:30.166478Z","title":"Bao-Liang Lu (M’94–SM’10) received his B.S","venue":null,"work_id":"7f637cab-cfef-4ed3-a3ac-9011ec9e74b7","year":1982},"citing_paper":{"arxiv_id":"1908.05349","last_updated":"2019-08-13T09:22:23Z","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-14T13:45:29.990789Z"},"links":{"citing_paper":"/paper/1908.05349"},"observation_digest":"sha256:aba3ab86bcf63bf4d39e69155d43dcb6f518d9ea9d286568b90981e314e81cbb","observation_id":"a1ebee1c-a9fc-45f5-a963-4d5a0de2d0a9","resolution":{"observed_at":"2026-08-14T13:45:30.173230Z","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":"1908.05349","last_updated":"2019-08-13T09:22:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T07:21:36.395475Z","submitted_at":"2019-08-13T09:22:23Z","title":"Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":58},"total_outbound_references":65},"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 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:1908.05349."}