Paper Citation Record · LEDGER

Self-Supervised Dynamical System Representations for Physiological Time-Series

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2512.00239.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2512.00239 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:34:06.522251Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T04:36:51.588539Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T12:10:23.263196Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy0
unresolved32
parse uncertain0
malformed identifier1
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External citation measurements

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Outbound references

Observation fbbfa2f6-216e-40b4-ab14-c6240c345875 · outbound

This paper cites Following prior work (Sparrow, 2012; Kamiya et al., 2024), we fixβ= 8/3ands= 28, and sweepρacross the following 10 values: {28,41,55,69,83,96,110,124,138,152}.

Self-Supervised Dynamical System Representations for Physiological Time-Series Following prior work (Sparrow, 2012; Kamiya et al., 2024), we fixβ= 8/3ands= 28, and sweepρacross the following 10 values: {28,41,55,69,83,96,110,124,138,152}

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:06.057569Z

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Observation 9d5c76eb-e70a-44ab-8666-d420e4b6ae88 · outbound

This paper cites These results indicate that PULSE is robust to within-dataset variability and performs consistently across datasets with diverse signal characteristics.

Self-Supervised Dynamical System Representations for Physiological Time-Series These results indicate that PULSE is robust to within-dataset variability and performs consistently across datasets with diverse signal characteristics

Reference 2

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Observation 858e4e11-bf79-44b7-ab08-302040bc07a3 · outbound

This paper cites an unresolved cited work.

Self-Supervised Dynamical System Representations for Physiological Time-Series Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:06.522251Z

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Observation 0cd03453-c97c-4b33-9b7c-1d3daeea6f74 · outbound

This paper cites MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting.

Self-Supervised Dynamical System Representations for Physiological Time-Series MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting

Reference 7

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Observation e16a2504-7695-4e58-8ab7-390d7144b9e4 · outbound

This paper cites Weakly-supervised disentanglement without compromises.

Self-Supervised Dynamical System Representations for Physiological Time-Series Weakly-supervised disentanglement without compromises

Reference 9

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Observation 8cece707-5234-45ff-8356-a634bfb322d4 · outbound

This paper cites A review on the non- linear dynamical system analysis of electrocardiogram signal.Journal of healthcare engineering, 2018(1):6920420,.

Self-Supervised Dynamical System Representations for Physiological Time-Series A review on the non- linear dynamical system analysis of electrocardiogram signal.Journal of healthcare engineering, 2018(1):6920420,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:04.969335Z

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Observation 98595e19-a561-4b77-9822-7b4bf7573daa · outbound

This paper cites Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence.

Self-Supervised Dynamical System Representations for Physiological Time-Series Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 13

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Observation 5878a7e1-12ed-431e-a361-fa549816a738 · outbound

This paper cites lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems.

Self-Supervised Dynamical System Representations for Physiological Time-Series lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:05.285867Z

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Observation 9780c82f-8bac-4e4b-9a8f-6ad2a947bbd7 · outbound

This paper cites Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding.

Self-Supervised Dynamical System Representations for Physiological Time-Series Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

Reference 18

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Observation 1246f71d-e737-434b-b88f-574a51db30e0 · outbound

This paper cites What Should Not Be Contrastive in Contrastive Learning.

Self-Supervised Dynamical System Representations for Physiological Time-Series What Should Not Be Contrastive in Contrastive Learning

Reference 19

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Observation fa3d7bb2-c404-408b-821b-3c9fe5686b59 · outbound

This paper cites REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning.

Self-Supervised Dynamical System Representations for Physiological Time-Series REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:05.767220Z

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Observation b141f22e-8214-4d21-bb3b-425fe1ba21b1 · outbound

This paper cites We apply Algorithm 1 in Kong and Zhang (2023) to each case to determine what minimal set of shared latent variables are recovered.

Self-Supervised Dynamical System Representations for Physiological Time-Series We apply Algorithm 1 in Kong and Zhang (2023) to each case to determine what minimal set of shared latent variables are recovered

Reference 23

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Observation d4dec668-261e-4af5-abe9-739399d1306f · outbound

This paper cites To obtain a representative embedding for each time window, we apply a global max pooling layer to aggregate features across the temporal dimension.

Self-Supervised Dynamical System Representations for Physiological Time-Series To obtain a representative embedding for each time window, we apply a global max pooling layer to aggregate features across the temporal dimension

Reference 27

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Observation 17ef4b87-4762-42e7-81f2-37210df19efa · outbound

This paper cites We include this baseline in our work since it’s generative process resembles PULSE and our results show it is a competitive baseline when ap- plied onto physiological time-series.

Self-Supervised Dynamical System Representations for Physiological Time-Series We include this baseline in our work since it’s generative process resembles PULSE and our results show it is a competitive baseline when ap- plied onto physiological time-series

Reference 28

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Observation d18fc527-f748-4875-a31d-8c00450ee4b3 · outbound

This paper cites In total, 76,590 distinct subsequences are extracted from 23 recordings, each lasting approximately 9.25 hours and sampled at 250 Hz with two channels.

Self-Supervised Dynamical System Representations for Physiological Time-Series In total, 76,590 distinct subsequences are extracted from 23 recordings, each lasting approximately 9.25 hours and sampled at 250 Hz with two channels

Reference 29

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Observation ec121c81-61b5-426f-ad9f-cdfc08a07269 · outbound

This paper cites The results reported in Table 1 are the average result for all three systems.

Self-Supervised Dynamical System Representations for Physiological Time-Series The results reported in Table 1 are the average result for all three systems

Reference 100

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Observation 232dcd59-fdee-4384-9294-94b7af37c07b · outbound

This paper cites Epilepsy.

Self-Supervised Dynamical System Representations for Physiological Time-Series Epilepsy

Reference 200

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Observation 0f275086-a754-4e31-8649-07bf99e75da8 · outbound

This paper cites Decoupled Weight Decay Regularization.

Self-Supervised Dynamical System Representations for Physiological Time-Series Decoupled Weight Decay Regularization

Reference 1963

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:04.859300Z

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Observation e322edf0-fef3-40ac-a55a-a50676379129 · outbound

This paper cites Koopman operator based dynamical sim- ilarity analysis for data-driven quantification of distance between dynamics.

Self-Supervised Dynamical System Representations for Physiological Time-Series Koopman operator based dynamical sim- ilarity analysis for data-driven quantification of distance between dynamics

Reference 1984

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:04.216193Z

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Observation 73f52d9c-02cc-4649-b7fe-e5a67c9e968f · outbound

This paper cites an unresolved cited work.

Self-Supervised Dynamical System Representations for Physiological Time-Series Unresolved cited work

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:06.313860Z

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Observation f47cd915-baa7-41f3-8dc4-ed4acc0d0122 · outbound

This paper cites Heart Rate Variability Series is the Output of a non-Chaotic System driven by Dynamical Noise.

Self-Supervised Dynamical System Representations for Physiological Time-Series Heart Rate Variability Series is the Output of a non-Chaotic System driven by Dynamical Noise

Reference 2001

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:03.788539Z

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Observation 064b7999-93ba-461f-a468-4f86f4fd3282 · outbound

This paper cites Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification.

Self-Supervised Dynamical System Representations for Physiological Time-Series Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:04.637643Z

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Observation 5dbe37a0-4c92-41fb-a30f-a5e3308e008f · outbound

This paper cites This is the canonical 3D nonlinear attractor used to study chaotic behavior in dynamical systems, with a state-space trajectory that resembles butterfly wings.

Self-Supervised Dynamical System Representations for Physiological Time-Series This is the canonical 3D nonlinear attractor used to study chaotic behavior in dynamical systems, with a state-space trajectory that resembles butterfly wings

Reference 2011

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Observation 2d1e8745-5d78-4f37-8700-ccb0c5d9b3b6 · outbound

This paper cites LFADS - Latent Factor Analysis via Dynamical Systems.

Self-Supervised Dynamical System Representations for Physiological Time-Series LFADS - Latent Factor Analysis via Dynamical Systems

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:05.481477Z

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Observation 78bead6f-c1ea-453e-8568-62f71c5705bf · outbound

This paper cites Jie Gui, Tuo Chen, Jing Zhang, Qiong Cao, Zhenan Sun, Hao Luo, and Dacheng Tao.

Self-Supervised Dynamical System Representations for Physiological Time-Series Jie Gui, Tuo Chen, Jing Zhang, Qiong Cao, Zhenan Sun, Hao Luo, and Dacheng Tao

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:04.096641Z

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Observation f63574c7-66fd-4537-b0af-94937c416a06 · outbound

This paper cites Deterministic chaos seen in terms of feedback circuits: Analysis, synthesis,” labyrinth chaos”.International Journal of Bifurcation and Chaos, 9(10):1889–1905,.

Self-Supervised Dynamical System Representations for Physiological Time-Series Deterministic chaos seen in terms of feedback circuits: Analysis, synthesis,” labyrinth chaos”.International Journal of Bifurcation and Chaos, 9(10):1889–1905,

Reference 2016

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Observation c2dc223d-1d65-43ed-9a90-4334e5a7b698 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Self-Supervised Dynamical System Representations for Physiological Time-Series A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:05.077739Z

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Observation a1fbb2bc-a057-4243-83c1-e5101f34db0b · outbound

This paper cites Infinite Bifurcations in Thomas system.

Self-Supervised Dynamical System Representations for Physiological Time-Series Infinite Bifurcations in Thomas system

Reference 2019

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Observation 45193b71-998b-4621-8a6d-06f9baed5edb · outbound

This paper cites Disentangled Sequential Autoencoder.

Self-Supervised Dynamical System Representations for Physiological Time-Series Disentangled Sequential Autoencoder

Reference 2020

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Observation 19993664-db19-4c95-be6f-770d9a9c3258 · outbound

This paper cites WY k=1 p(Yn,tk |Xn,tk ) #.

Self-Supervised Dynamical System Representations for Physiological Time-Series WY k=1 p(Yn,tk |Xn,tk ) #

Reference 2022

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Observation 14936476-2941-4828-a00a-9ac7f92bafca · outbound

This paper cites 4 under the full-sample masking scheme, it recoversC={Θ (s)}.

Self-Supervised Dynamical System Representations for Physiological Time-Series 4 under the full-sample masking scheme, it recoversC={Θ (s)}

Reference 2023

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Observation d8d8d465-ba80-46b1-a6b1-e9cc474121de · outbound

This paper cites Timemae: Self- supervised representations of time series with decoupled masked autoencoders.arXiv preprint arXiv:2303.00320,.

Self-Supervised Dynamical System Representations for Physiological Time-Series Timemae: Self- supervised representations of time series with decoupled masked autoencoders.arXiv preprint arXiv:2303.00320,

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:03.889020Z

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Observation 40b23d6a-f46d-4a6c-b4b7-5e5029081005 · outbound

This paper cites Perturbations of hindmarsh-rose neuron dynamics by fractional operators: Bifurcation, firing and chaotic bursts.

Self-Supervised Dynamical System Representations for Physiological Time-Series Perturbations of hindmarsh-rose neuron dynamics by fractional operators: Bifurcation, firing and chaotic bursts

Reference 2025

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Pith citing papers

Observation af5eb51d-9b2c-468f-8c1a-a66b54c59827 · inbound

Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity cites this paper.

Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity Self-Supervised Dynamical System Representations for Physiological Time-Series

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:06.771225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.