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Paper Citation Record · LEDGER

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction

As of 17 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2507.04665.

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

pith.paper-citation-record.v1
2507.04665 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:48:31.308145Z

measured 44 of 44 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 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

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  • verified fuzzy10
  • unresolved33
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8cf3314-26d8-4da5-805e-4910170b76d7 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 1

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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 c59c2617-3a71-4a6c-8873-a2a47b2a7ab6 · outbound

This paper cites B., & Cheung, B.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction B., & Cheung, B

Reference 2

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.

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Observation dc8c3ced-d744-48d2-8214-51ef25800783 · outbound

This paper cites N., & Bissacco, G.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction N., & Bissacco, G

Reference 3

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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 bfe964d0-f5a4-49bc-81d1-c8a6f575a954 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 4

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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 c78b9ecc-c917-4dc5-a15b-92b3567f7ec1 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 5

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 b234be2c-9f06-4be5-8a13-2cf7bb83ffad · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 6

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 3df8ae63-fab0-4d87-93b1-25c2fc3a07a8 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 7

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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 93e998a3-0238-48d0-b462-9225f99899db · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 8

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 9c700a76-8932-43fb-92e5-02bf8cf686c2 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 9

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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 441c8dba-5c09-44e7-b1d9-e608b137d13a · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 10

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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 823ed993-29be-4f81-8c52-053a9e6e5708 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 11

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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 57549353-7417-4cbd-b5ec-26c595442cb1 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 12

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 0305266d-8914-4c7c-8fb2-9778c094de09 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 13

Resolution
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Source-reported events for the cited work

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Observation 260dc1b3-6280-42ad-92ee-388a992f870c · outbound

This paper cites F., & Zheng, P.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction F., & Zheng, P

Reference 14

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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 c19b4b9e-bdc5-4f2f-8326-ef241db98a21 · outbound

This paper cites T., & Komanduri, R.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction T., & Komanduri, R

Reference 15

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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 e904c863-1a37-4320-b35c-5ad21613cb9c · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 16

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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 161d84ee-5600-4228-9a2f-ec45a3ec8c57 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 17

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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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Reference 18

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Source-reported events for the cited work

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Observation 9c1bbc9d-4877-4370-b795-07f6b13b7458 · outbound

This paper cites ( 2024) A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction ( 2024) A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining

Reference 19

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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 69474e07-80e7-4926-a397-6763f901afc9 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 20

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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 060ebf96-6445-4b43-ab81-559e758a7d3f · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 21

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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 6810a85a-7543-4edf-ae97-8285915c3e3a · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 22

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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 dd4ed005-c887-43f8-8489-2e2479e2d831 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 23

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 4fd43539-ce63-4c83-9577-2db8da26316b · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 24

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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 77344705-9ba5-4b47-85cd-c40d12cf7996 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 25

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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 faccdb1d-8b8c-429e-95d5-e137c1918c43 · outbound

This paper cites & Bengio, Y.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction & Bengio, Y

Reference 26

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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 d1db59d3-5878-46a5-af0d-046603a4b5c0 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 27

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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 ef560080-c9db-4e38-a948-4966f6f6ed71 · outbound

This paper cites P., & Welling, M.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction P., & Welling, M

Reference 28

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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.

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Observation fcdda9a7-293c-400c-99ac-ec6bc479e460 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 29

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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 8f49d1e3-4402-482a-a287-619b3188b0d6 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 30

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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 241a9a28-53d2-4833-96c6-9f7fb7e988dd · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 31

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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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Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5b7578f2-ba5e-4425-89f2-ef45c6a5d252 · outbound

This paper cites Towards Principled Methods for Training GANs.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Towards Principled Methods for Training GANs

Reference 33

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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.

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Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e1907a68-80da-4b91-b87c-a67aa4046c9d · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 35

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unresolved
raw_fallback, observed 2026-08-06T19:48:33.197157Z

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 2f6bf607-d6b3-41c4-920b-701136cf394e · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 36

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 d3e6b6d5-dd4d-4831-9fb7-5e957a56b751 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:32.616233Z

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 7cc518ab-c6f6-4837-b5e1-d4ac608ae70b · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:32.397943Z

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 a680181e-5378-4004-953d-a7ede0ad664a · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 39

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 88465c26-a39e-4d8d-ac6a-761bc693b348 · outbound

This paper cites Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:48:31.551510Z

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=2026-08-06T19:48:30.831201Z digest=sha256:d89db3c83562bdc9ae493a7bea34313c1aae75d239fa7bea160c5d91cae5bd04

Observation 14a77780-0729-4e62-aef4-171319e4a696 · outbound

This paper cites (2024) Roughness prediction of end milling surface for behavior mapping of digital twined machine tools [version 2; peer review: 2 approved, 1 approved with reservations].

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction (2024) Roughness prediction of end milling surface for behavior mapping of digital twined machine tools [version 2; peer review: 2 approved, 1 approved with reservations]

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:32.069205Z

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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Reference 42

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference 43

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:48:31.208212Z digest=sha256:ae897f101f23d2b4c11d95bbf3991c786a2bc8ffb1393ddcbd05d8113e3ae046

Observation c654296c-73c2-478b-9b62-ed9ff6bad8c1 · outbound

This paper cites an unresolved cited work.

Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:31.794814Z

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

No inbound Pith citation observations are available.