Pith. sign in

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 16 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 2026-08-16T06:30:59.297886+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 exact1
  • verified fuzzy10
  • unresolved33
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:40.311246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.331665Z digest=sha256:1089267d62802ff31ca828db475272fc6385bcdb39db0729e894ecf467060bb2

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
raw_fallback, observed 2026-08-06T19:48:40.039136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.399430Z digest=sha256:bae56ed937983462c528170a586bba5304f9e020c137bee40d34f1b8dade6f23

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:39.756998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.505398Z digest=sha256:067ed0930abc8b09254d79821582738bd508a6df227cbae37a29c049c0c33b7c

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:39.507274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.608194Z digest=sha256:189cad76aa2185cd3030d9bad7e704300bde5bf916ae1c9e2e45894e4089df05

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
unresolved
raw_fallback, observed 2026-08-06T19:48:39.256118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.705470Z digest=sha256:ed8e038e4e8edb29fc2c3b31d2bff620ae7d89b16713a9b62b33c5790d4be1dd

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
raw_fallback, observed 2026-08-06T19:48:39.041786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.799585Z digest=sha256:0189dc0a7b5a97ae65516394b0dcbcbc5d9730e7b0cae962babab0f885193bad

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:38.806949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.903427Z digest=sha256:691ac4ae93f7a780f5725e647dfd379e669ba235fbd230ffc7ab0a8a73a4410d

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
unresolved
raw_fallback, observed 2026-08-06T19:48:38.642874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:26.991324Z digest=sha256:027648e97f46c12ee231840328a1da1c87712dc3abb3eadb908de1afe31b9058

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:38.470441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.114405Z digest=sha256:a44fa2884873736586b3c047e5bbb8bd15c26a10cce102602618dc4cf24257ae

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:38.292157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.259871Z digest=sha256:bff9ecd2140626b0eee7d64ba3435a97ad866fa815686b2eefba85bf47e922fb

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:38.112926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.387452Z digest=sha256:6c07d53add73340072be0845e30ab997a8a6df1fdf082d93de614f98414628b9

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
unresolved
raw_fallback, observed 2026-08-06T19:48:37.908512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.488653Z digest=sha256:78edd97d65fbfb46e973b1bc51e228679596ada66f68b7b72b0de23d679de68a

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
unresolved
raw_fallback, observed 2026-08-06T19:48:37.681311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.592809Z digest=sha256:ecaae0f6d99054ebb726853fef50841d20a6150e57d6864873318d033c0d4592

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:37.470746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.724551Z digest=sha256:9154d60f8f666bc0f68334207d2eb1a2219dd7dbc2a53c32bbfced5da2e7e2a1

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:37.236828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.828115Z digest=sha256:eb7eceeb827336bc24ef83b8b8c3d94f020cde53f2297236eb82a34eab62c954

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:37.040772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:27.994317Z digest=sha256:6c1be2bf9df369b8ec9d81d4f76a22bf8b0e9a2590df28a219b56f8ad0017704

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:36.819791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.124724Z digest=sha256:77153d212219ab5c1b6effd7fb517d55b5cc23cdd4428d18d3221374de3213d2

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:36.624227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.251127Z digest=sha256:23536c2ad4987d04677a67e0d456756d5fcdec1e4358ecc57cd1eb0f635de64b

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:36.417231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.335980Z digest=sha256:4d0f7342264f350641eb8e87f066b1f0588cb8a30414b30ff9d58b1e2562b2de

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:36.223300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.485925Z digest=sha256:478dd8b34da7f01ae35bebbbb57976cf04dfc3e024daeb0917dbca0bbacc5bf4

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:36.014334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.586176Z digest=sha256:b973609755a8926dbe8ae0bcab0d5db504c1faaf41cbb9021582cb5ab16e49e3

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:35.789554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.687181Z digest=sha256:1fbc8e8999491c5e17cf8fd3ecfa3a3b5468d700d6226006608236112e3448e0

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
unresolved
raw_fallback, observed 2026-08-06T19:48:35.547168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.858966Z digest=sha256:4000903a1ebfecc74eaf49fd54b4fd005183f87728ec64f396dc9acf97e0ad3b

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:35.333980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:28.998073Z digest=sha256:1a9692dd2ee38ea386ec0478a8fc7b7a4e9e9e91f2b48b36e77bc3155f1a8590

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:35.185833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:29.103056Z digest=sha256:7b1dee248027d497591cc3db1c33e5a82f1d54386954156be7244fc381e216be

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:34.981757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:29.233557Z digest=sha256:2b0ec66e7c67910cd9924e8eabc5c582b5ae4259ffe2707f8a407f353d138a00

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:34.817898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:29.377849Z digest=sha256:8a6e82f25ed411f11b72f21eaa0a40e6ea53ea5f4e2f17af2537dda9cb30c085

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:34.609085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:29.485466Z digest=sha256:2726cd6736e99b24b5a7fb236ab389053c61e4b41034b30c257f4d618d17ed2b

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:34.410300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:29.605704Z digest=sha256:dfa9863ad687d7b7d4e984facf0fdae295d48d1f4fdcdfc58ba36c13c6c87815

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:34.099621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:29.737401Z digest=sha256:4db399fadce23cad9742ad38371c30dc194c7f110a8dff3bfe3f6210c8969513

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

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:33.835075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:29.855271Z digest=sha256:a8c9f58020daaf7b1eaad52cff167c667704de700a2e12616deab4ee9c0c313a

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:29.963508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:48:29.963508Z digest=sha256:af9f90b5f749d980be352bb31189c1b23fc8b56caaf325b8237053e7ec22c94b

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:33.490689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.075494Z digest=sha256:3280a82f38933d615afc7b044e499465e50ba997dac115173896b936e08aa833

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:30.168002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:48:30.168002Z digest=sha256:2dc4e3e93a9de5b00cbf1b5aa902f7c7f716f0742840eebcf173eb91b1c96ac2

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

Resolution
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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.274962Z digest=sha256:9782104614eab7aec8cc2cb7bbae85bb57f40c205327c5337888cc6db6185cfb

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
raw_fallback, observed 2026-08-06T19:48:32.911418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.369890Z digest=sha256:cf5cac82da6e3b7300d4cd343493f074f83f6488b4690295ef590a806ee150f6

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.478518Z digest=sha256:04632908030f78b29efd990a5cdb65e8a82063bf812c9f6fb704ed4be78a887f

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.618816Z digest=sha256:578ed164332e8a4b171dd998cdc21fdd2455fe9e727fff2fe9520e342d0f1f6d

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
raw_fallback, observed 2026-08-06T19:48:32.217994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.729954Z digest=sha256:a94685bafc6eb1e2f376b3c49db0d28942f0771e6f138a3dd7da4682b1bc402f

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.831201Z digest=sha256:7fddd5cfb5f7e48c7258ab7bd7cdb5f906ec4e2db32e93a01a761c3da9707284

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:30.949783Z digest=sha256:67eb6baf94765a2c549fda8c01e3220e7c6c9405210cfcf695c4bc665570e86f

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:31.093580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:48:31.093580Z digest=sha256:f19ec2c8f83178b077d50358c1b18916c6f415370b19d0f701ce0feef12ec68f

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:31.208212Z

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T19:48:31.308145Z digest=sha256:e1e5ebb2013025b2099ecc0ba4da1159eef31b1446ac0c4d73350e6373afc92b

Pith citing papers

No inbound Pith citation observations are available.