{"as_of":"2026-08-09T05:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1399ab00ce45d15cb89f751027ba76c1eeca2c5aeda755b2aa8df7f7054e5526","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:46:01.666201Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2507.10632/citation-record","integrity":"/paper/2507.10632/integrity","json":"/paper/2507.10632/citation-record.json","paper":"/paper/2507.10632"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:46:04.529626Z","title":"Joint modeling of multiple related time series via the beta process,","venue":null,"work_id":"f2f5e9c9-6909-473d-a394-85b95d183998","year":2011},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:45:59.617898Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:4f786c193d8895157ec0de26eb8b028c4f5683a07725e07bbe37d9188d3489e7","observation_id":"e71e1a89-87a5-475d-a084-53900004baaf","resolution":{"observed_at":"2026-08-06T17:46:04.576390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:04.413833Z","title":"Autoplait: Automatic mining of co-evolving time sequences,","venue":null,"work_id":"1851d8bc-d340-4755-a211-d7bb9e0478b0","year":2014},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:45:59.696471Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:421265377de143661e0fbd42ab78ce9212e10fbaf3deef8f0e99a9f40a5a31a3","observation_id":"58063204-76dd-42dc-b34d-107e8a4f2a27","resolution":{"observed_at":"2026-08-06T17:46:04.477566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:04.308267Z","title":"Unsupervised learning and segmentation of complex activities from video,","venue":null,"work_id":"70dc9fdc-8c0e-4ff1-8c50-6c7b6fb0099d","year":2018},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:45:59.825597Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:c893e8b53c42ebb6ffaebc4bf422bf2060799e3f9b492cea1981e1be1cc8baa3","observation_id":"c9f3b1ab-8b1c-46e5-a63e-b9e494778370","resolution":{"observed_at":"2026-08-06T17:46:04.361024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:04.189393Z","title":"Weakly supervised action labeling in videos under ordering constraints,","venue":null,"work_id":"f7ca37ac-b19d-46e2-be7c-22b057a646b8","year":2014},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:45:59.929070Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:fd2048d2463eec1c36bd02300fa441ea05eea0cde5d649b5a77c221bfb9d4f1d","observation_id":"62223f89-aa99-458e-9ed0-038858df03e3","resolution":{"observed_at":"2026-08-06T17:46:04.267445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:04.020722Z","title":"Connectionist temporal modeling for weakly supervised action labeling,","venue":null,"work_id":"93e26b96-fbbc-441e-b56a-6d440160b38e","year":2016},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.054732Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:e100e160f27586820c476331e946a04270a0ce749aa7966f26cdbc46e74858f9","observation_id":"1d28bd33-39e6-439c-b4a8-9a9502fb88cd","resolution":{"observed_at":"2026-08-06T17:46:04.081936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:03.907402Z","title":"Weakly supervised action learning with rnn based fine-to-coarse modeling,","venue":null,"work_id":"51999273-2f40-4ac5-a547-692e503dce5b","year":2017},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.187905Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:007b4dce744098439cd4684109a34e22b9569dcb138cb61a58fe8d97bed4a03b","observation_id":"8e4dc1ce-96ee-4bea-aa29-2812b6e925b8","resolution":{"observed_at":"2026-08-06T17:46:03.967216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:03.791552Z","title":"Segmenting continuous motions with hidden semi-markov models and gaussian processes,","venue":null,"work_id":"8d14a9bb-d318-4edc-8562-ed6e38f1bd24","year":2017},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.277291Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:b764fc55c6a2ab3214ef57513471b491c82d6bfcd86fb58565e97b61eff879a8","observation_id":"f478e2fc-0a29-4a32-b441-d30b10350af9","resolution":{"observed_at":"2026-08-06T17:46:03.842805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:03.503503Z","title":"Unsupervised work behavior analysis using hierarchical probabilistic segmentation,","venue":null,"work_id":"f1a8a10f-724c-4c5a-bab7-873f46a5f1fc","year":2023},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.336380Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:17815bd46623b94437eb62a092a7af4c7ee3bf5643df18a7014cbf2658943b95","observation_id":"9bec6024-8726-45b7-b9e5-4059d5a32f17","resolution":{"observed_at":"2026-08-06T17:46:03.671331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:03.173139Z","title":"Unsupervised decom- position of natural monkey behavior into a sequence of motion motifs,","venue":null,"work_id":"0bc6cb8e-2456-4967-9688-f93827b81ef0","year":2024},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.430407Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:77f0ad84d0bc9578b6338d456f8796643dc54178785e9ec146241f073ca3d3bd","observation_id":"2ea73286-8621-4fb4-ae2d-9ad32f2273cd","resolution":{"observed_at":"2026-08-06T17:46:03.260028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:03.071941Z","title":"Emergence of continuous signals as shared symbols through emergent communication,","venue":null,"work_id":"ad0a577c-5c08-4437-8db4-ba40f8ade708","year":2024},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.524967Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:898ccb5318228f50d1d4944ef5331f05d7b13f802aa7f6f36342543b6d6707c0","observation_id":"0b068f89-44c6-43d8-affb-49426f58727d","resolution":{"observed_at":"2026-08-06T17:46:03.125184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:00.597181Z","title":"Multi-step motion learning by combining learning-from-demonstration and policy-search,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.597181Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:cf7bdca0120a19d97117b575832d01ec77cd54635807accf4527e3f99fd26f0d","observation_id":"b27e5544-c244-47fe-b333-e2ab25063225","resolution":{"observed_at":"2026-08-06T17:46:00.597181Z","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":"2026-08-06T17:46:02.938008Z","title":"Random features for large-scale kernel machines,","venue":null,"work_id":"9698b930-bc2e-4e36-b185-e14e91346699","year":2007},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.690731Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:2c29dbcd0570bd6650ed40e563b204695c82282290945b9a3b75d3d93cc2f344","observation_id":"83a45e37-a113-4a3e-acfd-3de18daaabac","resolution":{"observed_at":"2026-08-06T17:46:03.002084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:02.841947Z","title":"Fine- grained action recognition in assembly work scenes by drawing attention to the hands,","venue":null,"work_id":"483ade08-e458-47c8-b807-be7454ba59f0","year":2019},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.752092Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:af6337224ecd939b27de58ba8dd9c8aa22ae941c8402bc11702e1c1464c43836","observation_id":"349d09d2-61cd-4870-b68f-da6edffd78e6","resolution":{"observed_at":"2026-08-06T17:46:02.880012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:00.827358Z","title":"Tempo- ral convolutional networks for action segmentation and detection,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.827358Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:e3b8a918046f8d4f6e4a5d3cad9b0506922fae71b7b0f1f193113e0798ea2c65","observation_id":"c6d02dad-caea-4e3c-b6aa-2e2c0e9f215e","resolution":{"observed_at":"2026-08-06T17:46:00.827358Z","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":"2026-08-06T17:46:02.719184Z","title":"End-to-end learning of action detection from frame glimpses in videos,","venue":null,"work_id":"1c79aef2-7259-4a2f-bdff-408542d4f83c","year":2016},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.906318Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:dd8ce80f49ad075dbdfe1147c4a7af4b5e295ff97629c9806faf2edb380c246f","observation_id":"57111155-64f1-4221-835e-4ee92d302ac0","resolution":{"observed_at":"2026-08-06T17:46:02.772639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:02.577400Z","title":"Spatio-temporal channel correlation networks for action classification,","venue":null,"work_id":"099f91b4-6dd8-4aea-ad52-59acc4f07f1e","year":2018},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:00.977238Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:42e8fc613b825f225f8cffa61591c923dd461526432c474b34a693dcc7206c58","observation_id":"77782985-0a12-45f4-a680-b17dce5c6eab","resolution":{"observed_at":"2026-08-06T17:46:02.660178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13074","last_updated":"2023-02-25T13:05:57Z","snapshot_observed_at":"2026-07-06T14:55:45.711180Z","submitted_at":"2023-02-25T13:05:57Z","title":"Temporal Segment Transformer for Action Segmentation","version":1},"cited_work":{"arxiv_id":"2302.13074","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.13074","snapshot_observed_at":"2026-08-06T17:46:02.146351Z","title":"Temporal Segment Transformer for Action Segmentation","venue":"cs.CV","work_id":"b7147b2f-a9b0-4c54-ae4c-9558482bad50","year":2023},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.062929Z"},"links":{"cited_paper":"/paper/2302.13074","citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:7681b4021d279a1fe890f28da50afa91231efe15a44d02fb669059dbd9b5536e","observation_id":"28ba5618-3826-4d9c-8477-a0d4c763f8a5","resolution":{"observed_at":"2026-08-06T17:46:02.197758Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07125","last_updated":"2023-05-11T21:47:52Z","snapshot_observed_at":"2026-08-06T09:46:00.800448Z","submitted_at":"2022-02-15T01:43:27Z","title":"Transformers in Time Series: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.07125","snapshot_observed_at":"2026-08-06T17:46:01.137428Z","title":"Trans- formers in time series: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.137428Z"},"links":{"cited_paper":"/paper/2202.07125","citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:e355aff10d6e503f0b8eba107ac9ab6755bcd5401e804fc1d6de9ab9ae0a65e4","observation_id":"b45d3fbe-d455-4043-8fe8-7e45e41c0aa3","resolution":{"observed_at":"2026-08-06T17:46:01.137428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08424","last_updated":"2024-04-04T16:24:19Z","snapshot_observed_at":"2026-08-06T08:23:57.165934Z","submitted_at":"2023-04-17T16:46:48Z","title":"Long-term Forecasting with TiDE: Time-series Dense Encoder","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08424","snapshot_observed_at":"2026-08-06T17:46:01.214106Z","title":"Long- term forecasting with tide: Time-series dense encoder,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.214106Z"},"links":{"cited_paper":"/paper/2304.08424","citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:bda0488b90efce2db6c212ad72b18cde39476986d859b25dc903b1762f8721c8","observation_id":"1ce2bffb-043a-438f-b519-d2e066a6f176","resolution":{"observed_at":"2026-08-06T17:46:01.214106Z","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":"2026-08-06T17:46:02.459665Z","title":"Sequence pattern extraction by segmenting time series data using gp-hsmm with hierarchical dirichlet process,","venue":null,"work_id":"1d358dae-dc42-46ea-a50e-2e32d43a3e59","year":2018},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.311124Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:a52b764b94b7fa351e4ba0f1729969e7deca205608850a6dd568013dcee939fb","observation_id":"bb7df3c1-2163-4ef9-8c61-7b3f675fbc49","resolution":{"observed_at":"2026-08-06T17:46:02.517790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.08352","last_updated":"2023-06-14T08:42:10Z","snapshot_observed_at":"2026-08-07T19:54:34.668679Z","submitted_at":"2023-06-14T08:42:10Z","title":"Bayesian Non-linear Latent Variable Modeling via Random Fourier Features","version":1},"cited_work":{"arxiv_id":"2306.08352","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.08352","snapshot_observed_at":"2026-08-06T17:46:02.015638Z","title":"Bayesian Non-linear Latent Variable Modeling via Random Fourier Features","venue":"stat.ML","work_id":"438da4b6-c2b7-4582-9bcd-8312d731d478","year":2023},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.398281Z"},"links":{"cited_paper":"/paper/2306.08352","citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:70a659b4e6fa6b849e1a79381b6a7d08a03852b448d7d3dc29b6e6984c8a704b","observation_id":"4077ca5f-2fad-4b64-a01e-11a321ab7eb7","resolution":{"observed_at":"2026-08-06T17:46:02.073219Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01697","last_updated":"2024-06-18T08:56:13Z","snapshot_observed_at":"2026-08-03T00:13:01.597485Z","submitted_at":"2024-04-02T06:58:41Z","title":"Preventing Model Collapse in Gaussian Process Latent Variable Models","version":2},"cited_work":{"arxiv_id":"2404.01697","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.01697","snapshot_observed_at":"2026-08-06T17:46:01.902747Z","title":"Preventing Model Collapse in Gaussian Process Latent Variable Models","venue":"stat.ML","work_id":"76102bd2-b9c2-45eb-9970-4faccbd7906b","year":2024},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.493178Z"},"links":{"cited_paper":"/paper/2404.01697","citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:3b0044a00585cd55d3af179a200fc39ba2d1bdabec3e467a1e43923b92ecfd6c","observation_id":"d7f35a14-4a48-42b7-94b6-9276d5cd05ce","resolution":{"observed_at":"2026-08-06T17:46:01.955928Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.01917","last_updated":"2020-01-07T07:28:21Z","snapshot_observed_at":"2026-07-06T08:48:56.242657Z","submitted_at":"2020-01-07T07:28:21Z","title":"Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering","version":1},"cited_work":{"arxiv_id":"2001.01917","doi":null,"metadata_source":"pith","pith_arxiv_id":"2001.01917","snapshot_observed_at":"2026-08-06T17:46:01.765792Z","title":"Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering","venue":"cs.LG","work_id":"ea734916-c50a-4e18-a338-239d8107fe0b","year":2020},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.589402Z"},"links":{"cited_paper":"/paper/2001.01917","citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:a0c143a01e9c2e48263458ab298eecf4c33a4cddda9bf228090fd6c9a928a3cb","observation_id":"c77fe043-8e54-47da-9602-53139c907e28","resolution":{"observed_at":"2026-08-06T17:46:01.814367Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T17:46:02.336877Z","title":"A high-speed method of segmenting human body motions with regular time interval sensor data based on gaussian process hidden semi- markov model,","venue":null,"work_id":"4b6095e9-3ba9-4bb3-8caf-877a71badbee","year":2023},"citing_paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:01.666201Z"},"links":{"citing_paper":"/paper/2507.10632"},"observation_digest":"sha256:dea2e998a16a49688f4ac352e857b80831225f543757084773062780e15704b1","observation_id":"5c05feff-a567-4945-ab01-becf60cf5b5b","resolution":{"observed_at":"2026-08-06T17:46:02.380788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.10632","last_updated":"2025-07-14T08:41:03Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T10:30:01.968833Z","submitted_at":"2025-07-14T08:41:03Z","title":"Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":4,"verified_fuzzy":16},"total_outbound_references":24},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2507.10632."}