Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:57:21.810411Z
Paper Citation Record · LEDGER
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach
As of 16 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:1908.06168.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
16 of 16 outbound references displayed
- verified exact1
- verified fuzzy14
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 00c513e4-b569-4e0b-90e1-c1c8495ae0ed · outbound
This paper cites Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.947937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ef490468-572c-45bd-a911-5a973a1c69eb · outbound
This paper cites Machine learning in resting-state fMRI analysis.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Machine learning in resting-state fMRI analysis
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T12:57:21.836252Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5206bd3c-72e5-4971-a1ac-3f36057c94bd · outbound
This paper cites A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.940768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 31166a16-466a-4333-b06f-fbcb040ef34b · outbound
This paper cites Learning temporal regularity in video sequences.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Learning temporal regularity in video sequences
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.933704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f74e9f80-7839-425e-abd9-661368bc3347 · outbound
This paper cites Long short-term memory.Neural com- putation, 9(8):1735–1780, 1997.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Long short-term memory.Neural com- putation, 9(8):1735–1780, 1997
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.926664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 02980005-453c-4cd3-b3c8-a2d33a037154 · outbound
This paper cites Imagenet classification with deep convolutional neural networks.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Imagenet classification with deep convolutional neural networks
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.919758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation aed7a5d5-0061-481d-8247-af3932a6e549 · outbound
This paper cites Chronnectome fingerprinting: identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.Human brain mapping, 39(2):902–915, 2018.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Chronnectome fingerprinting: identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.Human brain mapping, 39(2):902–915, 2018
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.912331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8dd344bf-41d4-4892-a8cb-99e7f69b0a97 · outbound
This paper cites Future frame prediction for anomaly detection - a new baseline.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Future frame prediction for anomaly detection - a new baseline
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.904986Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 949680d4-6a91-49df-a7ac-df5494d55e77 · outbound
This paper cites The autism brain imaging data exchange:towards a large-scale evaluation of intrinsic brain architecture in autism.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach The autism brain imaging data exchange:towards a large-scale evaluation of intrinsic brain architecture in autism
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.897870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9e42e4fd-8ba4-4c0b-a077-83c11da8579e · outbound
This paper cites U-net: Convolutional networks for biomedical image seg- mentation.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach U-net: Convolutional networks for biomedical image seg- mentation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.890443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c4030ab9-81f4-4db5-8e86-1b467dedff53 · outbound
This paper cites Convolutional lstm network: A machine learning approach for precipitation nowcasting.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Convolutional lstm network: A machine learning approach for precipitation nowcasting
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.883272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1694e775-2735-4cb7-b189-040b3ed46c3c · outbound
This paper cites Salakhutdinov.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Salakhutdinov
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.875198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 312e4a81-b0cb-4f85-8c1e-30b569d4c502 · outbound
This paper cites A hybrid of deep network and hidden markov model for mci identification with resting-state fmri.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach A hybrid of deep network and hidden markov model for mci identification with resting-state fmri
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.867820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation aebedb4a-3b50-460d-b4ea-017717755533 · outbound
This paper cites Changes in dynamic functional connections with aging.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Changes in dynamic functional connections with aging
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.860012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e771979b-dbd4-4646-bf9e-1bc7f33ba1a9 · outbound
This paper cites Tzourio-Mazoyer et al.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Tzourio-Mazoyer et al
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:57:21.852120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2f31b02e-62eb-45d8-bb6c-08cb8e1bf787 · outbound
This paper cites an unresolved cited work.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach Unresolved cited work
Reference 16
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:57:21.844373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.