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Paper Citation Record · LEDGER

Assessing Quantum Advantage for Gaussian Process Regression

As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.22502.

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

pith.paper-citation-record.v1
2505.22502 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:15:37.597202Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:10:12.428509Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T01:10:12.644797Z

Reference resolution

46 of 46 outbound references displayed

  • verified exact3
  • verified fuzzy23
  • unresolved19
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1dab3da8-7032-4256-9b3f-b79d88605dce · outbound

This paper cites Quantum machine learning.

Assessing Quantum Advantage for Gaussian Process Regression Quantum machine learning

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3b5f0d62-9a10-4309-aeb3-95e2f455d106 · outbound

This paper cites Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J.

Assessing Quantum Advantage for Gaussian Process Regression Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J

Reference 2

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Observation 6a3de4f6-6dee-4ef5-adc0-acf79146f88e · outbound

This paper cites Quantum machine learning: a classical perspective.

Assessing Quantum Advantage for Gaussian Process Regression Quantum machine learning: a classical perspective

Reference 3

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Observation f8a928d9-3cc7-4fd4-a9ac-fdbc49cb4ea4 · outbound

This paper cites Quantum algorithms for supervised and unsupervised machine learning.

Assessing Quantum Advantage for Gaussian Process Regression Quantum algorithms for supervised and unsupervised machine learning

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 58d8b3cb-2943-4b33-918c-bb3b8fb2788d · outbound

This paper cites Quantum principal compo- nent analysis.

Assessing Quantum Advantage for Gaussian Process Regression Quantum principal compo- nent analysis

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b45f5b9c-2c32-48b1-8d57-25b39683d237 · outbound

This paper cites Quantum Recommendation Systems.

Assessing Quantum Advantage for Gaussian Process Regression Quantum Recommendation Systems

Reference 6

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Source-reported events for the cited work

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Observation e877ed34-19d8-4b12-a991-01b8249f412c · outbound

This paper cites Quantum algorithm for solving linear systems of equations.

Assessing Quantum Advantage for Gaussian Process Regression Quantum algorithm for solving linear systems of equations

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:33.003099Z digest=sha256:9c920956c4e6583a0ca871da4b59d3ceb29d0412663ad7fafc19dc92437f7a95

Observation a4895fd3-5fcf-496d-aa41-eac68330a33b · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 8

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cd1de0c6-c26a-48c2-81db-1a626ac5c75c · outbound

This paper cites A quantum-inspired classical algorithm for recommendation systems, May.

Assessing Quantum Advantage for Gaussian Process Regression A quantum-inspired classical algorithm for recommendation systems, May

Reference 9

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Observation 7f9ee1ad-3a97-4736-bb82-283608332630 · outbound

This paper cites Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions.

Assessing Quantum Advantage for Gaussian Process Regression Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions

Reference 10

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9cfc6851-24ba-45ad-9278-5c9348fefd9b · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 11

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Observation c06d3364-e46c-4467-a4c4-3b896b17a351 · outbound

This paper cites Fitzsimons, and Joseph F.

Assessing Quantum Advantage for Gaussian Process Regression Fitzsimons, and Joseph F

Reference 12

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Observation 4c16720a-91a3-4448-a75f-24632945e934 · outbound

This paper cites Quantum algorithm for Gaussian process regression.

Assessing Quantum Advantage for Gaussian Process Regression Quantum algorithm for Gaussian process regression

Reference 13

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a2a1b793-e797-4a65-a6e1-d9170514d3e6 · outbound

This paper cites Galvis-Florez, and Simo S¨ arkk¨ a.

Assessing Quantum Advantage for Gaussian Process Regression Galvis-Florez, and Simo S¨ arkk¨ a

Reference 14

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Observation 4bd7830a-a592-4cf0-b433-096171f656c3 · outbound

This paper cites An introduction to the conjugate gradient method with- out the agonizing pain, 1994.

Assessing Quantum Advantage for Gaussian Process Regression An introduction to the conjugate gradient method with- out the agonizing pain, 1994

Reference 15

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Observation 2e72ca44-6e94-4b7b-8685-36ea35d846e4 · outbound

This paper cites Faster quantum-inspired algorithms for solving linear systems.

Assessing Quantum Advantage for Gaussian Process Regression Faster quantum-inspired algorithms for solving linear systems

Reference 16

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8bf17589-bc23-4491-92aa-5322077e9c2f · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 17

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7e1b8820-c0c2-43b4-983b-2e5c161c3506 · outbound

This paper cites Eigenvalues and Condition Numbers of Random Matrices.

Assessing Quantum Advantage for Gaussian Process Regression Eigenvalues and Condition Numbers of Random Matrices

Reference 18

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cf0933b0-9402-46c6-b904-f4c707b6a4e7 · outbound

This paper cites The condition number of a randomly perturbed matrix.

Assessing Quantum Advantage for Gaussian Process Regression The condition number of a randomly perturbed matrix

Reference 19

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Observation 3cf7434e-30b4-44c8-a7ff-305e0c4d64d2 · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 20

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation bb5dde40-225d-4153-9da6-7d3d73b204ec · outbound

This paper cites Zimmermann.

Assessing Quantum Advantage for Gaussian Process Regression Zimmermann

Reference 21

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e971260f-b4a1-4d29-a48c-095bbca88904 · outbound

This paper cites Chaitin-Chatelin.

Assessing Quantum Advantage for Gaussian Process Regression Chaitin-Chatelin

Reference 22

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Observation 8783eb95-7da5-416e-b0e3-b54ff6cfe292 · outbound

This paper cites Dunford and J.T.

Assessing Quantum Advantage for Gaussian Process Regression Dunford and J.T

Reference 23

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Observation d4852ed5-4ace-4834-8ba6-ae97cb3cc004 · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 24

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Observation 5025e20e-a758-4a24-b2ad-b033b60c848e · outbound

This paper cites A Survey on Graph Kernels.

Assessing Quantum Advantage for Gaussian Process Regression A Survey on Graph Kernels

Reference 25

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Observation 652f0c8b-7994-4fdd-b094-5eb25a042288 · outbound

This paper cites String kernels construction and fusion: a survey with bioinformatics application.

Assessing Quantum Advantage for Gaussian Process Regression String kernels construction and fusion: a survey with bioinformatics application

Reference 26

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Observation 36bfbc07-5182-459f-8f3c-472f805d1ec7 · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 27

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Observation d63df0d0-b3c3-420c-8f21-20910cee24db · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:35.383328Z digest=sha256:5afe3ac8810ea78b196c8b024fd28c7d0d1788ae4d8a1568d455e29a96826a85

Observation 7382bb3e-cee2-45a7-ab1d-07b58a198e9e · outbound

This paper cites Hilbert space methods for reduced-rank Gaussian process regression.

Assessing Quantum Advantage for Gaussian Process Regression Hilbert space methods for reduced-rank Gaussian process regression

Reference 29

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7a4a012f-ca3b-4ad6-a888-983b9c00b295 · outbound

This paper cites When Gaussian Process Meets Big Data: A Review of Scalable GPs.

Assessing Quantum Advantage for Gaussian Process Regression When Gaussian Process Meets Big Data: A Review of Scalable GPs

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 109052b6-5143-41c0-b5d0-ca15917c1e47 · outbound

This paper cites Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra.

Assessing Quantum Advantage for Gaussian Process Regression Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra

Reference 31

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Observation 96d92622-f47a-48e1-aa68-b461bc30815c · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 33aa58a7-9c52-42a5-bfc0-80c5250b5bc5 · outbound

This paper cites Quantum circulant preconditioner for a linear system of equations.

Assessing Quantum Advantage for Gaussian Process Regression Quantum circulant preconditioner for a linear system of equations

Reference 33

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 29a206cc-39be-4393-8492-dfa1f39e7359 · outbound

This paper cites Preconditioning for Scalable Gaussian Process Hyperparameter Optimization.

Assessing Quantum Advantage for Gaussian Process Regression Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

Reference 34

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1fc79f8c-42e2-4006-9c4c-78dbb2a8dbb7 · outbound

This paper cites Quantum Support Vector Ma- chine for Big Data Classification.

Assessing Quantum Advantage for Gaussian Process Regression Quantum Support Vector Ma- chine for Big Data Classification

Reference 35

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T13:15:36.043478Z digest=sha256:0d768e891268b3ca706eeb593ce2726a28a360364cbfc0c62f145d7f9b034103

Observation e629e42a-754f-4f2f-9c8c-92db3f499e1f · outbound

This paper cites C´ orcoles, Kristan Temme, Aram W.

Assessing Quantum Advantage for Gaussian Process Regression C´ orcoles, Kristan Temme, Aram W

Reference 36

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raw_fallback, observed 2026-08-07T13:15:39.995273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 48ae4340-2f89-4145-b832-5aceb8728624 · outbound

This paper cites Quantum Machine Learning in Feature Hilbert Spaces.

Assessing Quantum Advantage for Gaussian Process Regression Quantum Machine Learning in Feature Hilbert Spaces

Reference 37

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3fcddc55-da66-4c9c-a005-ad1ed1e35e23 · outbound

This paper cites A rigorous and robust quantum speed-up in supervised machine learning.

Assessing Quantum Advantage for Gaussian Process Regression A rigorous and robust quantum speed-up in supervised machine learning

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 00a856c8-0409-4e37-b874-1a3ae67b3bfa · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 26098587-6591-4937-b6dc-06953e5fa1f2 · outbound

This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

Assessing Quantum Advantage for Gaussian Process Regression Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 40

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 211ee424-5b2c-40b0-a697-b066df62bbae · outbound

This paper cites Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Assessing Quantum Advantage for Gaussian Process Regression Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Reference 41

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 595179f1-af74-4138-aa6d-58e71585a684 · outbound

This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Assessing Quantum Advantage for Gaussian Process Regression Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 42

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 622d69f6-0cc2-4b5e-ad92-5c650fec9015 · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 43

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation df29c00c-ec11-4df7-8936-f5ccf8d15f02 · outbound

This paper cites Random matrix approximation of spectra of integral operators.

Assessing Quantum Advantage for Gaussian Process Regression Random matrix approximation of spectra of integral operators

Reference 44

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 35acd12a-fa66-40a5-a87c-0306fb331df1 · outbound

This paper cites A very short proof of cauchy’s interlace theorem for eigenvalues of hermitian matrices, 2005.

Assessing Quantum Advantage for Gaussian Process Regression A very short proof of cauchy’s interlace theorem for eigenvalues of hermitian matrices, 2005

Reference 45

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T13:15:37.597202Z digest=sha256:0514ef17cafd25e7dd54f126a790dd0967d1e2e5f3b1fbdaca2664ebbf02a8c8

Observation c9fec235-a543-44a6-a97c-bcb95f6bf1a1 · outbound

This paper cites an unresolved cited work.

Assessing Quantum Advantage for Gaussian Process Regression Unresolved cited work

Reference 2014

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Pith citing papers

Observation 78c1fe1e-426a-43cc-aedf-b521e2046091 · inbound

Spectral Estimation with Free Decompression cites this paper.

Spectral Estimation with Free Decompression Assessing Quantum Advantage for Gaussian Process Regression

Reference 42

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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