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Paper Citation Record · LEDGER

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2506.07099.

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

pith.paper-citation-record.v1
2506.07099 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:47:00.171736Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

  • verified exact0
  • verified fuzzy37
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 04c58217-ce72-46db-914e-25b35f555282 · outbound

This paper cites Traffic flow prediction using graph convolution neural networks.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Traffic flow prediction using graph convolution neural networks

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.705473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.022813Z digest=sha256:0f94fe59d505488fbeb803e1efdacf8d5f6c91a0f367bb2b7982b67eae1db1c7

Observation 0ae3b743-121c-4b1c-bee5-7270da1f7cbf · outbound

This paper cites Missing data imputation using fuzzy-rough methods.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Missing data imputation using fuzzy-rough methods

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.689626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.027237Z digest=sha256:a888a01d9d10d29622620257414579c5e1881a6e83f379d91ae2592f2df7ab6c

Observation 67f38359-b740-46e0-ac50-7513a22a24ab · outbound

This paper cites Learning spatiotemporal latent factors of traffic via regularized tensor factorization: Imputing missing values and forecasting.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Learning spatiotemporal latent factors of traffic via regularized tensor factorization: Imputing missing values and forecasting

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.679454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.030987Z digest=sha256:5d32cd362e444f1629fa2d7a65e7cf7d0ae8b6b4055f93acadbaaa3573acf405

Observation 3c9fe767-d63d-4ded-945b-a31d376b1cb4 · outbound

This paper cites Argusdroid: detecting android malware variants by mining permission-api knowledge graph.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Argusdroid: detecting android malware variants by mining permission-api knowledge graph

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.667004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.035056Z digest=sha256:41ae283ed234ac3600adc20496a29d25f622b078c3d48a0dc8848e8863a4c55a

Observation 1e422045-2e52-4c2f-bf93-eac1bbf99cbd · outbound

This paper cites E3m: zero-shot spatio-temporal video grounding with expectation-maximization multimodal modulation.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion E3m: zero-shot spatio-temporal video grounding with expectation-maximization multimodal modulation

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.655103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.039022Z digest=sha256:9ab32d632c73086279febb807b0bd1b3826ebfafae64e16c125d6bd19f60ce86

Observation f6a7755f-eb0f-48c7-8f9f-4f2b4d8a25c3 · outbound

This paper cites Crps learning.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Crps learning

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.644749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.042699Z digest=sha256:385b43bf00b0034508ac0a854a6634e0dd925485ad2d48bde6eab28b86cfbe4c

Observation d885237a-389f-4210-9660-c43eebfdd77b · outbound

This paper cites Brits: Bidirectional recurrent imputation for time series.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Brits: Bidirectional recurrent imputation for time series

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.634575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.046630Z digest=sha256:3454cbbccc713e52e669822c88364d597eacc58f2451979e8f158cc43bd8c64e

Observation fdf09f79-8167-47fb-83a3-2f45aa5361c4 · outbound

This paper cites Nhits: Neural hierarchical interpolation for time series forecasting.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Nhits: Neural hierarchical interpolation for time series forecasting

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.621360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.049505Z digest=sha256:414b64237a5f837e010efea857f8d8d4b599e97af5c7a83ca172a4e94c3919f6

Observation 7a11d1da-44fd-44ce-8c80-f818595d0e68 · outbound

This paper cites Missing traffic data imputation and pattern discovery with a bayesian augmented tensor factorization model.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Missing traffic data imputation and pattern discovery with a bayesian augmented tensor factorization model

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.609825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.053057Z digest=sha256:4374a7b644b9f676e4bf90500632f50957c7f6c5b663e4dc6ae9c4bdec0f0fa9

Observation 4aa0c5da-85c7-4596-8f5e-9cd5a304927e · outbound

This paper cites A bayesian tensor decomposition approach for spatiotemporal traffic data imputation.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion A bayesian tensor decomposition approach for spatiotemporal traffic data imputation

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.598287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.056225Z digest=sha256:240cd29bb67847e9e6323aa50be91fe6bf1465b6d76517cea3a86164dcfb3cb0

Observation 0f40bfbe-6674-4e7e-86c8-00bb985a2ad0 · outbound

This paper cites Multitask offloading strategy optimization based on directed acyclic graphs for edge computing.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Multitask offloading strategy optimization based on directed acyclic graphs for edge computing

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.586887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.059566Z digest=sha256:33b6f0b4b4ada04f8131a06021894acfa3c33bd5cceec9ddbe0f3364403bd315

Observation 5b75b59c-5c4b-4ced-89d1-3b543cd0d014 · outbound

This paper cites Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

Reference 12

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unresolved
no resolver link, observed 2026-08-07T05:47:00.063032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:47:00.063032Z digest=sha256:3ed61e1332753ae8e14172ddb8c236e4372b3a193329d4a21dd5530de72b33e1

Observation 76ca0ad0-485a-4213-94db-8fa9b7b11d80 · outbound

This paper cites Big learning expectation maximization.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Big learning expectation maximization

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.575648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.066617Z digest=sha256:c7cba0dc7332c95c55dde7d5232291c174a1270939a9caa24bab402c3427a4fb

Observation d10b299e-395e-40fb-a5ed-496f93460013 · outbound

This paper cites Gp-vae: Deep probabilistic time series imputation.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Gp-vae: Deep probabilistic time series imputation

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.560720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.069819Z digest=sha256:614d341123d2e343e6068ebe29ad4339e0008c0cbeaadfece62775f62be6aa84

Observation 48ef15ec-007b-4623-83b0-88bba92fa47e · outbound

This paper cites Generative adversarial networks.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Generative adversarial networks

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T05:47:00.072993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:47:00.072993Z digest=sha256:86e637205967952ddba70d44e37b378698797c0d6c7eb08c91a33311e3aa0bbc

Observation 1ed84669-78c7-44e9-b780-3a7da0df6213 · outbound

This paper cites Muse: A deep learning model based on multi-feature fusion for super-enhancer prediction.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Muse: A deep learning model based on multi-feature fusion for super-enhancer prediction

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.542354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.076572Z digest=sha256:3245c9319135a2e95169e44e6ff91b1e4ca8e829328c35fd82f6165a793b4d82

Observation 9e8953f1-fbbc-4670-826f-83fa59963d41 · outbound

This paper cites Xiong, Guangquan Xu, and Fei Guo.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Xiong, Guangquan Xu, and Fei Guo

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.529365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.079443Z digest=sha256:118cf5d8316f2decd873614b03c2b430c56aa3f3659e5aaee714478b3543c233

Observation 24deae18-07eb-46c3-8e9d-9ba668c8d2a6 · outbound

This paper cites Ensemblese: identification of super-enhancers based on ensemble learning.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Ensemblese: identification of super-enhancers based on ensemble learning

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.517166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.082869Z digest=sha256:3000ab78c570a366936b1b47e3e9af717a6e77d6ad530e76691ba8dbbc0da5e8

Observation 35e55922-8186-49dd-b118-40bad37c5f44 · outbound

This paper cites Fecam: Frequency enhanced channel attention mechanism for time series forecasting.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Fecam: Frequency enhanced channel attention mechanism for time series forecasting

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.505859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.086507Z digest=sha256:28074cb30de72a4d4737dc102424855bb03df1c096cd339fa42c01b0c62bee3d

Observation c857f79f-418d-4a02-8e09-68612a8b7b65 · outbound

This paper cites Local-global defense against unsupervised adversarial attacks on graphs.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Local-global defense against unsupervised adversarial attacks on graphs

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.495178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.090398Z digest=sha256:f0d20b27dadfe10acfd5558dc7ac68a0d6cbf968cac022223de9399653ef033e

Observation e64bfbee-44b3-4bf2-b845-9a8d66d098f4 · outbound

This paper cites Auto-Encoding Variational Bayes.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Auto-Encoding Variational Bayes

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T05:47:00.093652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:47:00.093652Z digest=sha256:2e15e6d281f54d102e7c5b11e34697368532bccdb289ffc623878413b4f0c3ca

Observation deb92747-ac0e-4179-81d4-05b3d550e604 · outbound

This paper cites Missing traffic data: comparison of imputation methods.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Missing traffic data: comparison of imputation methods

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.484787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.098570Z digest=sha256:8e32f438b2286834265475a6fe39957d31172a367c8088e41edb075cf49617d9

Observation 7608f51d-388a-48b0-8cf1-3ee32b586808 · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-07T05:47:00.102017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:47:00.102017Z digest=sha256:a2d3435d1870f8655aa3070cc8474c7cf620af7388b96de9b3c0ac3040c6b2da

Observation 79a7c0d4-c27a-463e-b166-6941ff1d6f85 · outbound

This paper cites Auto-gas: automated proxy discovery for training-free generative architecture search.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Auto-gas: automated proxy discovery for training-free generative architecture search

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.472954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.106009Z digest=sha256:e133df8c0efdba63eb99b25292d380fdf9f3a5162583e27aa17e46e83f62aba1

Observation db168e8f-29fb-46be-88a9-6977831a5f75 · outbound

This paper cites Gcnet: Graph completion network for incomplete multimodal learning in conversation.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Gcnet: Graph completion network for incomplete multimodal learning in conversation

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.462465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.109280Z digest=sha256:22d3bff2e9c3a80ccedf5808e77383c19635e60d3505b9cb5cc2b689acab875b

Observation 2ab6945e-f647-4978-bd48-5cbe8f66f254 · outbound

This paper cites Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.451734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.112311Z digest=sha256:d08bd04f8b69da2cf6b5d90ffcec5872891f5e55223168cc4868a6c5ed65dc14

Observation bed07cc9-cc95-4e6b-9db2-ee31ed648809 · outbound

This paper cites Pristi: A conditional diffusion framework for spatiotemporal imputation.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Pristi: A conditional diffusion framework for spatiotemporal imputation

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.440930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.115612Z digest=sha256:8fe3b8c637bf2f0bb84565f294ce3f4c9c0a56a13d4b575b35e5c105f329a122

Observation efdfd2ea-5a68-47df-aad2-f43e46e45ef2 · outbound

This paper cites Learning to reconstruct missing data from spatiotemporal graphs with sparse observations.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Learning to reconstruct missing data from spatiotemporal graphs with sparse observations

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.430036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.119190Z digest=sha256:c801bfeb6112ec7fd5191f3107d81d52111997a10de0ece9869581b4f83647b6

Observation a69c93f0-1bc0-4456-8984-0e42f7034bf1 · outbound

This paper cites Uncertainty-aware variational-recurrent imputation network for clinical time series.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Uncertainty-aware variational-recurrent imputation network for clinical time series

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.417700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.122440Z digest=sha256:7d525224fe8359d6fd41fca9b176dec0fd7571ea4b6cafe176a3e99f1a65c918

Observation baf69535-c8b3-4154-98e0-c686055c03d9 · outbound

This paper cites Enhancing risk prediction in mental health using ensemble hybrid models and administrative healthcare data with irregular intervals.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Enhancing risk prediction in mental health using ensemble hybrid models and administrative healthcare data with irregular intervals

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.407960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.125505Z digest=sha256:8ab532cd299fba008d38e4ef9e39c8b35139be81cb36d149625d7f0ab2b3f850

Observation 971cc901-2554-4655-be4d-8758157e5701 · outbound

This paper cites New rnn algorithms for different time-variant matrix inequalities solving under discrete-time framework.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion New rnn algorithms for different time-variant matrix inequalities solving under discrete-time framework

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.397693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.128598Z digest=sha256:ad1f74de576aaaa3e76f7d0693af8c19a385223b2b83e2de5cde7689303f62bf

Observation 9d772257-9252-4d04-9e83-516b21198fa1 · outbound

This paper cites Incomplete multi-view weak-label learning.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Incomplete multi-view weak-label learning

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.385341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.131444Z digest=sha256:ed6b1b906374da1f066694cb751cc6582c10b0f9d171fbc1945baa081f4277e9

Observation fbf98e13-fb26-4d7b-a385-3dc33133cbb3 · outbound

This paper cites Csdi: Conditional score-based diffusion models for probabilistic time series imputation.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Csdi: Conditional score-based diffusion models for probabilistic time series imputation

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.374515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.135345Z digest=sha256:ef5da6fe0772727b072ecd354cd8db9d58bde7d352116c8f3c1b8ce688577e26

Observation 576c0b28-2448-4b9b-8287-7ecfa09f3165 · outbound

This paper cites Attention is all you need.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Attention is all you need

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.360006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.138540Z digest=sha256:4886d7b758464d7d8c19d77431da08d913c02cc7e2ef4f8820931c6937637e82

Observation 47aa9b96-3a79-48b5-ab99-b9ef6f8ed1ef · outbound

This paper cites Traffic data reconstruction via adaptive spatial-temporal correlations.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Traffic data reconstruction via adaptive spatial-temporal correlations

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.345178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.141769Z digest=sha256:384b8b68da2e068de2476f51cd1c504ac92cc21c7cbff5a7e5d1af79f69ba879

Observation b8a77ebf-123e-466e-92fb-8c3f08b013ac · outbound

This paper cites Multiple imputation using chained equations: issues and guidance for practice.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Multiple imputation using chained equations: issues and guidance for practice

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.325770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.145330Z digest=sha256:832732a321664ccd5345f4924e92f64d965823383bf7b9ed47c3e7fdf3fe6860

Observation 42f3a1b7-a17d-4d6a-9c3f-843728b4b23c · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T05:47:00.148477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:47:00.148477Z digest=sha256:0b6d2e8428cc03d364e5732ae56ad60b34957d6a64c890427e3e31204e1a43a5

Observation 13f89511-8cb8-4db8-a25f-d2b02cca8987 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T05:47:00.152244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:47:00.152244Z digest=sha256:422895e555f82806ec05b8980e5c31c4ea9a5153997715bf4f915a818d5db830

Observation 916da26d-883d-4d8a-805d-a23e44d44a33 · outbound

This paper cites Ultrahigh thermal stability and piezoelectricity of lead-free knn-based texture piezoceramics.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Ultrahigh thermal stability and piezoelectricity of lead-free knn-based texture piezoceramics

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.311390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.155927Z digest=sha256:0d23e9bd856f347b51d1c4945860808bbfbcda1710f198525a991ecd0f4b2a72

Observation 5a0dedcc-80d3-4e0c-aeac-bc6c21111835 · outbound

This paper cites St-mvl: Filling missing values in geo-sensory time series data.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion St-mvl: Filling missing values in geo-sensory time series data

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.296953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.158844Z digest=sha256:130bb97da4e6d739a946b3670997d6b7f01794839adb2b63f8a5dae342eecd20

Observation f073f370-c9e8-42f0-8d61-b5a1515d270a · outbound

This paper cites Gain: Missing data imputation using generative adversarial nets.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Gain: Missing data imputation using generative adversarial nets

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.283383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.162055Z digest=sha256:5805cd4fe1c7338389275e4a2c517209e5009ba0b9afdb019ecd22a991f24ee2

Observation 32d914d7-a74b-47d4-be24-9be46532cf4a · outbound

This paper cites Temporal regularized matrix factorization for high-dimensional time series prediction.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Temporal regularized matrix factorization for high-dimensional time series prediction

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.270716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.165401Z digest=sha256:4bced77dcfa48df33433449bd156b6dbf96a44120975b5464c66a8b9f4482fdb

Observation 6baa8494-7502-464e-a7e6-b8418021446c · outbound

This paper cites Urban computing: concepts, methodologies, and applications.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion Urban computing: concepts, methodologies, and applications

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:47:00.257392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T05:47:00.168614Z digest=sha256:81455b6da7ac84a5df543aa386ffcaef7842a005f8bf2f8bbd3945e31aa7b337

Observation 8b19ccf8-f10c-4c5a-891f-8b6981919c72 · outbound

This paper cites write newline.

Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion write newline

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T05:47:00.171736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:47:00.171736Z digest=sha256:c55789f9c9633efe1ce3d4282256f9477ecfaf4d7b3fb98075b9c2ae605737bb

Pith citing papers

No inbound Pith citation observations are available.