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Paper Citation Record · LEDGER

Deterministic matrix sketches for low-rank compression of high-dimensional simulation data

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2105.01271.

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

pith.paper-citation-record.v1
2105.01271 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:34:43.991086Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T01:15:35.886687Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ffd91b71-f5e3-42cd-a9b8-601cb68a6dcf · inbound

In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization cites this paper.

In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization Deterministic matrix sketches for low-rank compression of high-dimensional simulation data

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:15:35.894006Z

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=pdf_text observed=2026-05-18T01:14:53.558234Z digest=sha256:db4cfbd57d1a9fef9308c0e16233fd0d77981a2c121f41c8f16705ac7ecbd5ac

Observation 6ba52b2b-fb2f-4455-a404-fb304fd79679 · inbound

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization cites this paper.

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization Deterministic matrix sketches for low-rank compression of high-dimensional simulation data

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-02T23:34:43.991086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:34:43.991086Z digest=sha256:6dfea2da750cb8677ba4a724b95780a77ac5b4e9bd23475bf3e72741c8f2ecde