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 23 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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.331665Z digest=sha256:33b9332659a9efdb8b8551edd6829e33825c006c58e236c803ac03b78e4c312f

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.399430Z digest=sha256:a8538e5672abe6d5161291b0bcfe29b1510325b543921b453ce512651a02cd74

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.505398Z digest=sha256:a64d5dfd838a1fa40fc8abf9fa7474e3bc8236be40b0351341e461d3b99d4a74

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.608194Z digest=sha256:4d8943758eb593c396606089dd7a95285648b3da53ed60533a359a351c50ce68

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.705470Z digest=sha256:f7ff9beca83a89b6624d06db7aabae2c9dd908ae80577c1afbd4a876e4a57c03

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.799585Z digest=sha256:0256ef7e9ec85563c7df57118339fb146ce02cb5ebee2cc129a4c39b48c4d1e9

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.903427Z digest=sha256:632b1c5df9a43bdb7777f9a5e253927dfaf4b0dee296f38317000ad5f665584d

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:26.991324Z digest=sha256:125db9722bf6a4378d953fbebf046620dae0fb8fd681716a08c951eb61bd4192

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.114405Z digest=sha256:fbd1411070da2c850a6647b2dd33fcc70a9d30e41e7db1a4eb5d01a088297f8d

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.259871Z digest=sha256:cab9ee3829cfbadffb08bcc4d76e7eaa8a82913eb5c040e38af90b4a0442fe5a

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.387452Z digest=sha256:e3c4a9063efe284fc3a6a81ccea5995500e505a7502a5ffd90888747fc47418b

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.488653Z digest=sha256:10db90d67e9b14efc4f65c3faa454430cff0252062548cd70516f3cf45228535

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.592809Z digest=sha256:f82f72eabe1b55a123f0533ffe477c372a3ae99b3290d778452e9d01df2833c9

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.724551Z digest=sha256:5965895c35e5d645829ceb86d50f2595f60951437884e52e3579bd65a5422d8f

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.828115Z digest=sha256:e841d0269c84202eda1a2f6dbbffde25452cb29f575adfb7180ca421077e3e47

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:27.994317Z digest=sha256:cdae9d7b5a88ce25a6778d897879a4eac36cdce60836cee7388e022e6c401167

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.124724Z digest=sha256:88d54ce29dc77409071a3c85a1ade7fa656edfc570f0017224a754d172476d73

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.251127Z digest=sha256:d5fc11abe7e0a9d7b991126a46b5e95baaa2166111e11384678ee0909b2f4939

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.335980Z digest=sha256:a745a8b1801f2cb386a6e78f40bfee07a0127a5dcd272b4e80e6f1e918a6fd63

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.485925Z digest=sha256:064844aabf356e4635fc6bf8a4cbd8ee3ebfac82195ba6869e627919c8943bad

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.586176Z digest=sha256:2f2c628b9f10d066a2c848c8ac9970a99208356698bf406f976c4434b2709af8

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.687181Z digest=sha256:251a604322a440bf9b8bfd65d6a0ebc7a9f2639fa368004c457091a3d18a8fbc

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.858966Z digest=sha256:4efe9e9c648a6a660c0bc901dbf8ff53748a3cb86a2acc6ca6bbb5c0b4f907b7

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:28.998073Z digest=sha256:9e6dc48dfcd350a532587a6ebca38b8ae2f4a5348e938a168a04f9f7a4784153

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:29.103056Z digest=sha256:dce626d82e70499d23a89fe49d878b2bedcb9a6099c4120a08a380b911ed5f88

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:29.233557Z digest=sha256:dca17e3f2844065100ef18be8ed6c82a1ce9242ee05073847dd84528dea5294a

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:29.377849Z digest=sha256:9659a34e9c1b29c27f778970a42a733598a75a79644fff5f8a8a999695674d82

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:29.485466Z digest=sha256:85c723c7e5cbc1ab599253ad2cc829cd028d1bf4f5486a03046129ca3284cebc

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:29.605704Z digest=sha256:6b4b8d6e6e3d47e30ad6509ce48cbde9b14703912420cf281d930dcc722c7aea

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:29.737401Z digest=sha256:cc25be81d95a35761d62c91402ffb45bd2c49b5e7670eefbc96f995167f9a97c

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:29.855271Z digest=sha256:87002488ce9395baa06a3eb6dfc84dee0eef525f9b5c4d9296b258fca38219d1

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=sha256:0ae8bb95ea94f5731b83caa5dd0b4ecb2523d169ed139a3e7f801d5c70abe363

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.075494Z digest=sha256:1b7ef29814a08f50629cd9d9b7c6130c7b685a7459de8fada02b04fc7491a614

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:df544e7a02fecfc8d3d941ea4b4167f3daee554eee60dd8da47215df9e0dfb4d

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.274962Z digest=sha256:a85d70272d7b2ae5a0fa9c5b9c650f4693266a5cb6f87e86f648fb7f84b76566

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.369890Z digest=sha256:60c7f44e3296992aacd648886cf632f5496f9d05feb767b9d5e7e84253d970e9

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.478518Z digest=sha256:1f147a63b475962994cddc8cde1ed09c2fbac63aedcb011162d1a20bc55c9c94

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.618816Z digest=sha256:f22929a8f52d4515ecdb0e0fde2c3a5db6f2b8c4290f7283610cc1732a1da1d4

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.729954Z digest=sha256:a834b99e9ebdadffe6048aaba39810ef65b2627f65bc191019793b376660d412

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.831201Z digest=sha256:527a943ed4013a89dcb56153ae59b17a5873a0ddcc17c17ec56a0763947c9e04

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:30.949783Z digest=sha256:80f38761452b74446c63d1544f289e89ed2962657706deb213d6263197bff8f3

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=sha256:19305c65e89a5be3be783d8eaab3e615f54230313aa097870b6496e49be21068

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=sha256:4d15223d367dfff6c16cc455f9950caa47bba07323e3ec51cca4bd60f6bbed77

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T19:48:31.308145Z digest=sha256:e40386c5c8a2b286fef636e3aa3d6f8941232402443bd11e975d94ed5000eb38

Pith citing papers

No inbound Pith citation observations are available.