{"as_of":"2026-08-10T17:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:081edb0dc70de1807627f38d03243660b45a31fe9f329e7b620b1966b1fe66b2","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T23:09:38.303269Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2501.18538/citation-record","integrity":"/paper/2501.18538/integrity","json":"/paper/2501.18538/citation-record.json","paper":"/paper/2501.18538"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T23:09:38.227387Z","title":"Deep Residual Learning for Image Recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.227387Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:e54f6be624e8400cecb2e0dfc7add56248c5efe3f6bb0fde9c9c0ad3ea66ad99","observation_id":"858d8210-6064-4fe7-87a7-7eff6c4ac546","resolution":{"observed_at":"2026-08-09T23:09:38.227387Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T23:09:39.210421Z","title":"Squeeze-and-Excitation Networks,","venue":null,"work_id":"5a6b35aa-d742-4034-8945-7edd3f644439","year":2018},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.232107Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:31100b520e9fb0a8863476ebb4f593bb6d20c2ba25bbbafa31909978f7088fa2","observation_id":"524dc688-00ac-46a0-8697-ab15fdc60975","resolution":{"observed_at":"2026-08-09T23:09:39.213952Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-09T23:09:38.236843Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.236843Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:9aceac89c5aa33a8ddf47b05ac7540f037a6e92e9789566655d323979f977842","observation_id":"9e01c920-9b8e-4162-b57c-f7ef64dd46da","resolution":{"observed_at":"2026-08-09T23:09:38.236843Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T23:09:39.027039Z","title":"Transformer -Based Multimodal Emotional Perception for Dynamic Facial Expression Recognition in the Wild,","venue":null,"work_id":"45e41d6d-431c-4744-933c-83fe95d30c41","year":2024},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.241442Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:155a95b18c2c252571e9945d8c54944f32fade91aaa265b766add344bcb15b1b","observation_id":"e83cce2a-4906-4602-9aa3-75a20a0bf5d5","resolution":{"observed_at":"2026-08-09T23:09:39.030826Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.12148","last_updated":"2023-09-26T04:55:31Z","snapshot_observed_at":"2026-08-03T09:03:43.222838Z","submitted_at":"2023-01-28T10:21:37Z","title":"Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis","version":3},"cited_work":{"arxiv_id":"2301.12148","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.12148","snapshot_observed_at":"2026-08-09T23:09:38.775712Z","title":"Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis","venue":"cs.NE","work_id":"04ca6c04-b18e-493b-8e8c-51cc417869fb","year":2023},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.246127Z"},"links":{"cited_paper":"/paper/2301.12148","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:f9ce5d607537ef91dc7424b391acfcc9cfd5b8332f9aba141248423914c6ccc0","observation_id":"a65deb79-36c4-40a7-9ecd-3373fdc73b28","resolution":{"observed_at":"2026-08-09T23:09:38.779807Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01234","last_updated":"2026-04-20T17:04:19Z","snapshot_observed_at":"2026-07-06T16:42:12.176043Z","submitted_at":"2023-11-02T13:40:21Z","title":"Velocity averaging under minimal conditions for deterministic and stochastic kinetic equations with irregular drift","version":2},"cited_work":{"arxiv_id":"2311.01234","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.01234","snapshot_observed_at":"2026-08-09T23:09:38.759922Z","title":"Velocity averaging under minimal conditions for deterministic and stochastic kinetic equations with irregular drift","venue":"math.AP","work_id":"15fd3712-9f30-4466-acc3-8a03f951757a","year":2023},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.250625Z"},"links":{"cited_paper":"/paper/2311.01234","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:57a27dca73dc6548b2ef35794c3088daf47a037214f0d15b00d0ede9bc19984c","observation_id":"6f331c33-748e-4b64-852a-b8a25ab3ea04","resolution":{"observed_at":"2026-08-09T23:09:38.764399Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12345","last_updated":"2024-07-17T06:39:52Z","snapshot_observed_at":"2026-08-10T14:40:01.622558Z","submitted_at":"2024-07-17T06:39:52Z","title":"VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.12345","snapshot_observed_at":"2026-08-09T23:09:38.255341Z","title":"Representation learning and identity adversarial training for facial behavior understanding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.255341Z"},"links":{"cited_paper":"/paper/2407.12345","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:a725029c80684cfe6e96a323ce859af438ce3ac0ad7213eec85a77ab5bbdccac","observation_id":"795b7a08-de6a-499b-845a-cc59d93500ec","resolution":{"observed_at":"2026-08-09T23:09:38.255341Z","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":"10.1007/s41870-023-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T23:09:38.330563Z","title":"A novel facial emotion recognition model using segmentation VGG-19 architecture,","venue":null,"work_id":"2d4b1692-60a1-4168-90f7-08c345010ae3","year":2023},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.260033Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:f3ab9c612189f786c4c34c801c515f2b3f4c4f3944899637a1e7dc8d7e8e5dc2","observation_id":"71a308b5-270a-41a5-b5c2-e6d195e0ccd7","resolution":{"observed_at":"2026-08-09T23:09:38.336962Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T23:09:38.734546Z","title":"EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition,","venue":null,"work_id":"b7fe7fc8-75a5-407c-8f8b-bf5610e874f4","year":2023},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.264013Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:3dc087c6aa8a93178b710b410631b1fa13bfe2b0c029328819dc299ed2495685","observation_id":"56a95ce1-e0cd-495f-aca9-7257421dc371","resolution":{"observed_at":"2026-08-09T23:09:38.738391Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T23:09:38.582422Z","title":"Facial Expression Recognition Using Residual Masking Network,","venue":null,"work_id":"4ce39d3e-b14c-4031-a329-514d1d734879","year":2020},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.268193Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:7dbf5ecbe436535cec542a36ced43114bc979e473b2f0b441f7096014f40afde","observation_id":"133a6e5f-91f2-45de-8d72-64ff54148ce4","resolution":{"observed_at":"2026-08-09T23:09:38.586470Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10545","last_updated":"2024-11-23T19:32:32Z","snapshot_observed_at":"2026-08-01T16:22:30.342103Z","submitted_at":"2024-09-01T16:50:24Z","title":"ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition","version":2},"cited_work":{"arxiv_id":"2409.10545","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.10545","snapshot_observed_at":"2026-08-09T23:09:38.401574Z","title":"ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition","venue":"cs.CV","work_id":"d88f8b0d-4477-4e74-8a90-efb1e0356e89","year":2024},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.273045Z"},"links":{"cited_paper":"/paper/2409.10545","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:1051e33325af95fc3afded93a4b7bfcf982f9d4f4708aa11117bfc47cbca39bb","observation_id":"34824372-fcb3-4694-9258-18dbc659b84f","resolution":{"observed_at":"2026-08-09T23:09:38.406769Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-09T23:09:38.277682Z","title":"MobileNets: Efficient convolutional neural networks for mobile vision applications,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.277682Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:3d269ec858d90b26ca62c7234e5e8fbd309764341394ecd0c3409aff72d618f2","observation_id":"06add19c-a505-4694-84bd-6b307f634f58","resolution":{"observed_at":"2026-08-09T23:09:38.277682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11946","last_updated":"2020-09-11T05:08:01Z","snapshot_observed_at":"2026-08-06T22:36:56.473526Z","submitted_at":"2019-05-28T17:05:32Z","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11946","snapshot_observed_at":"2026-08-09T23:09:38.282278Z","title":"EfficientNet: Rethinking model scaling for convolutional neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.282278Z"},"links":{"cited_paper":"/paper/1905.11946","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:c0843abc8cecb0387cca92b2316d56c7e6344afc71618a8f5367dcf209f3c0bc","observation_id":"b65da0f4-33b4-4b2b-b187-c1b245134fff","resolution":{"observed_at":"2026-08-09T23:09:38.282278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-09T23:09:38.286857Z","title":"Distilling the knowledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.286857Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:1251a6eb7317af7fffa0fd346b0fae38e09065866186dbe55159942b887ae6ba","observation_id":"edeb9492-da05-43c7-bb63-165709b0eeff","resolution":{"observed_at":"2026-08-09T23:09:38.286857Z","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":"2026-08-09T23:09:39.245953Z","title":"Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection,","venue":null,"work_id":"f2fa1e5b-57dd-49c0-8b94-57eb94b6a036","year":2024},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.290965Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:a91dbbdd2cfa61f0f234a49f46ebeb0c8c88b2c1e22189e75234d58531e6500d","observation_id":"d8180fbc-8897-4f41-8d12-985fb5ed718b","resolution":{"observed_at":"2026-08-09T23:09:39.249858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T23:09:39.233544Z","title":"Challenges in representation learning: A report on three machine learning contests,","venue":null,"work_id":"a8a7014a-20d2-4b4a-99f9-f90996052d96","year":2013},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.295426Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:35c05d9681e769891c6b9443cf16806ac258010c1dd091a12f690a5acae754e3","observation_id":"37a7bb92-076d-4477-81cc-f8281f1627b4","resolution":{"observed_at":"2026-08-09T23:09:39.237990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T23:09:39.222418Z","title":"Reliable crowdsourcing and deep locality- preserving learning for expression recognition in the wild,","venue":null,"work_id":"3fc7ff4b-7a12-4381-9516-e69333898c7f","year":2017},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.299413Z"},"links":{"citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:66e4637aafa77a38806342c475fc04e276516cf08ab555ff4fb6780de965a498","observation_id":"2183f978-b0fe-4aa1-89aa-f8919fa1af09","resolution":{"observed_at":"2026-08-09T23:09:39.226476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.01219","last_updated":"2024-01-03T15:00:34Z","snapshot_observed_at":"2026-08-10T08:11:20.595925Z","submitted_at":"2024-01-02T14:18:11Z","title":"Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.01219","snapshot_observed_at":"2026-08-09T23:09:38.303269Z","title":"Distribution matching for multi-task learning of classification tasks: A large -scale study o n faces & beyond,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T23:09:38.303269Z"},"links":{"cited_paper":"/paper/2401.01219","citing_paper":"/paper/2501.18538"},"observation_digest":"sha256:adaefd9e7555b58598520a212b893a6d1f5b0b9c4d46861bb1e46c4abc57b917","observation_id":"09a94ef2-5cc5-4404-88f0-751870b39395","resolution":{"observed_at":"2026-08-09T23:09:38.303269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.18538","last_updated":"2025-01-30T18:06:44Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T23:02:32.094408Z","submitted_at":"2025-01-30T18:06:44Z","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":1,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":7,"verified_exact":5,"verified_fuzzy":3},"total_outbound_references":18},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2501.18538."}