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Paper Citation Record · LEDGER

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition

As of 19 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 4 inbound Pith citation observations for arXiv:2505.21571.

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

pith.paper-citation-record.v1
2505.21571 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:50:58.243286Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:44:05.450427Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T12:53:05.833919Z

Reference resolution

68 of 68 outbound references displayed

  • verified exact0
  • verified fuzzy36
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c68aff61-e34c-45c5-81d6-8a87d2261427 · outbound

This paper cites Type of modulation identification using wavelet transform and neural network,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Type of modulation identification using wavelet transform and neural network,

Reference 1

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Observation 52c4e5da-c68a-4608-a5d9-770801dd832a · outbound

This paper cites Wavelet transform based modula- tion classification for 5g and uav communication in multipath fading channel,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Wavelet transform based modula- tion classification for 5g and uav communication in multipath fading channel,

Reference 2

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source=pdf_text observed=2026-08-07T13:50:51.587235Z digest=sha256:a9077fe319351b2bbad8cc13f09427c2b2bfec5d13cedb84772b76705ab841ef

Observation 77361fac-0a1c-4930-9ca4-b44f954f7f96 · outbound

This paper cites Phasma: An automatic modulation classification system based on random forest,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Phasma: An automatic modulation classification system based on random forest,

Reference 3

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source=pdf_text observed=2026-08-07T13:50:51.651188Z digest=sha256:81c638dfd06a455acf9c54c1d7aa10f35c68735ff86e2495b40cb7fc50b931a8

Observation c1ab6df0-98b9-449b-af6f-cb9e0aff506b · outbound

This paper cites Cyclic spectral analysis of ofdm/oqam signals,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Cyclic spectral analysis of ofdm/oqam signals,

Reference 4

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source=pdf_text observed=2026-08-07T13:50:51.730285Z digest=sha256:d857c1ec7b63defc95fe9b80248f84b0ec0783d023d7226f4f354b92e2d859d6

Observation 5c9342f9-e1a1-412c-ba7d-cc818e11e11d · outbound

This paper cites Automatic mod- ulation classification based on high order cumulants and hierarchical polynomial classifiers,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Automatic mod- ulation classification based on high order cumulants and hierarchical polynomial classifiers,

Reference 5

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source=pdf_text observed=2026-08-07T13:50:51.819634Z digest=sha256:30cf67a73cb07d50abb4ccd64db19343b0b91c156f178d4aa47db7f827d10e23

Observation 2c652554-48a0-4bf3-846e-93b4f06a3822 · outbound

This paper cites Over-the-air deep learning based radio signal classification,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Over-the-air deep learning based radio signal classification,

Reference 6

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source=pdf_text observed=2026-08-07T13:50:51.886538Z digest=sha256:d0d9da108c5d127c9a74ccc2f0cb5d722b534fb8bb501e471c2ac9b65d35498e

Observation b41b8275-5b4a-47ac-a897-d8ca1c62ce0d · outbound

This paper cites Convolutional radio mod- ulation recognition networks,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Convolutional radio mod- ulation recognition networks,

Reference 7

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source=pdf_text observed=2026-08-07T13:50:51.936228Z digest=sha256:a48c536aa5b6fa6966d0bee253a9b1eb87b538496c240d78fa5722fa5b3ad3ba

Observation 04fcb888-f502-4bfe-a9ba-e3ef36dbb170 · outbound

This paper cites An improved neural network pruning technology for automatic modulation classification in edge devices,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition An improved neural network pruning technology for automatic modulation classification in edge devices,

Reference 8

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source=pdf_text observed=2026-08-07T13:50:51.986819Z digest=sha256:fa3f010d436df8c8647e15b33e1007ad1dc075d7ba12bfde120cc1dd9e360915

Observation 175f86bf-abff-4149-b029-097d538ad087 · outbound

This paper cites Signet: A novel deep learning framework for radio signal classification,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Signet: A novel deep learning framework for radio signal classification,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:52.060490Z digest=sha256:af407441949bc2399457113336306fc07a0bb75c531f760358e7c24cc3030e84

Observation 500e8d83-0ed9-4fbf-a007-611864acf92b · outbound

This paper cites Contour stella image and deep learning for signal recognition in the physical layer,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Contour stella image and deep learning for signal recognition in the physical layer,

Reference 10

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source=pdf_text observed=2026-08-07T13:50:52.113739Z digest=sha256:74bed6cfa60bc6011c8b882fe984534ec2c605befde79a4b38d5b998df3a5f10

Observation b9719354-7833-4835-b2f6-e9d5dd6a0683 · outbound

This paper cites Complex-valued networks for automatic modulation classification,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Complex-valued networks for automatic modulation classification,

Reference 11

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source=pdf_text observed=2026-08-07T13:50:52.177825Z digest=sha256:a4e53ab29b80b3bb2f72df380a097ce44c1a02bdebe6a98deefaa5355e14afd6

Observation 6cd51e07-9484-452f-a968-79029288157b · outbound

This paper cites Adversarial attacks in modulation recognition with convolutional neural networks,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Adversarial attacks in modulation recognition with convolutional neural networks,

Reference 12

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source=pdf_text observed=2026-08-07T13:50:52.249428Z digest=sha256:10851dd426c87a160f8a770cbf29b0c0e659ad178eb9a60f184cd0d4309f3b94

Observation bb9270ea-9093-4489-94a7-78efd82b05a9 · outbound

This paper cites Lightweight automatic modulation classification via progres- sive differentiable architecture search,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Lightweight automatic modulation classification via progres- sive differentiable architecture search,

Reference 13

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source=pdf_text observed=2026-08-07T13:50:52.308528Z digest=sha256:cb7303795c4177c79f75370e7cc92950a68e78412c430104ea9ec5ad27fe2359

Observation 42961009-f60b-49a6-b0f1-5900064c59bb · outbound

This paper cites Multi-view discriminant framework for automatic modulation open set recognition,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Multi-view discriminant framework for automatic modulation open set recognition,

Reference 14

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source=pdf_text observed=2026-08-07T13:50:52.373494Z digest=sha256:0edca74f8afe483f5cac11a60e5445d4100c0f69d595017b2257e39b10c11470

Observation 7e1c2389-d559-4f15-9788-0aa0c907e6f2 · outbound

This paper cites MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition

Reference 15

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source=pdf_text observed=2026-08-07T13:50:52.419819Z digest=sha256:469bbc14d5c675f1813226b8cb263ff80b3eba1dfaa581f99cb07d79f21be0d7

Observation 4631cb77-fd2a-4ce0-a828-635d0743bedf · outbound

This paper cites Data-driven deep learning for automatic modulation recognition in cognitive radios,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Data-driven deep learning for automatic modulation recognition in cognitive radios,

Reference 16

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:52.474307Z digest=sha256:a23fd32ec7504237a2681306e3957f1b781fd3ced5007f1fa2e9688de5dd947a

Observation c4a54990-a6d2-478e-b6e4-fbaa37078a13 · outbound

This paper cites Automatic modulation recognition for spectrum sensing using nonuniform compressive samples,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Automatic modulation recognition for spectrum sensing using nonuniform compressive samples,

Reference 17

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:52.519084Z digest=sha256:e6d97e1c74771070955bdb31bd642b3f16f4cb816d65514888e8941b557cff4e

Observation 317dc968-d954-4566-b404-2322d8b2fcc2 · outbound

This paper cites A feature weighted hybrid ica-svm approach to automatic modulation recognition,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition A feature weighted hybrid ica-svm approach to automatic modulation recognition,

Reference 18

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:52.584736Z digest=sha256:71a5fa30bd5b7b70f5b794d63f130e9e943dd0df0d5f9fdadb41b5252af1dfb0

Observation 485c6085-9734-43a7-9cc1-b9cc128bc4d3 · outbound

This paper cites Automatic modulation recognition of unknown interference signals based on graph model,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Automatic modulation recognition of unknown interference signals based on graph model,

Reference 19

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:52.651880Z digest=sha256:9a7726853ba6d533743ccf134716ef65f77703e8fd0afe11e5fd7ecaa07fa3b7

Observation 23c5deff-0e81-4e6e-8a04-406aa5b7d469 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 20

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source=pdf_text observed=2026-08-07T13:50:52.698347Z digest=sha256:6049e036c679f6b57c18fd6790eb65738cc6d12f2908544e66b79e2a18c6d68f

Observation 013c65c9-26e8-4c4a-b332-27a3e9f20722 · outbound

This paper cites Amc-net: An effective network for automatic modulation classification,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Amc-net: An effective network for automatic modulation classification,

Reference 21

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Observation f060e421-61c3-44f0-9cbd-5d75a8022d58 · outbound

This paper cites Efficient automatic modulation classification in non-terrestrial networks with snn-based transformer,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Efficient automatic modulation classification in non-terrestrial networks with snn-based transformer,

Reference 22

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:52.838801Z digest=sha256:d44598fb694782012a5dfc5ef07956e02f870c503438d88310cd300bfa02bc76

Observation 3876cba6-d8e8-4033-ae8e-b66420220456 · outbound

This paper cites Towards building a high-performance intelligent radio network through deep learning: Addressing data privacy, adver- sarial robustness, network structure, and latency requirements.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Towards building a high-performance intelligent radio network through deep learning: Addressing data privacy, adver- sarial robustness, network structure, and latency requirements

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:52.900538Z digest=sha256:e461ec7c0ccd7bda4c6cbe3ec2b9c3d8ff7289c45356437738e4a77962ebb55d

Observation 0fe8b002-1c32-4300-9d6f-7af2c57c7003 · outbound

This paper cites Edge learning for b5g networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Edge learning for b5g networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,

Reference 24

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source=pdf_text observed=2026-08-07T13:50:52.954912Z digest=sha256:e13ab1bccb918fcefe5bc71323ef4f01b7456072db1eba3125ac5e0ab49e51d1

Observation dbaabaf2-af25-45a5-9ad9-02998646f9ef · outbound

This paper cites Task- oriented communications for 6g: Vision, principles, and technologies,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Task- oriented communications for 6g: Vision, principles, and technologies,

Reference 25

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source=pdf_text observed=2026-08-07T13:50:53.029958Z digest=sha256:50b0c1d3038103ca51f924d8d3d943edd0fbd8098fda0280ee096fca2cd1935d

Observation cff199ea-b03a-4271-ac11-0b29806e8d52 · outbound

This paper cites A systematic dnn weight pruning framework using alternating direction method of multipliers,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition A systematic dnn weight pruning framework using alternating direction method of multipliers,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.059227Z digest=sha256:4b0835c36ca273500fd150c5efddceabc7d28cce9e44540dcf53c08a059c0249

Observation a254bd20-1362-4822-98e4-06fc96af366e · outbound

This paper cites Pconv: The missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Pconv: The missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices,

Reference 27

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raw_fallback, observed 2026-08-07T13:51:02.950381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.063714Z digest=sha256:264e05409f54765565808725b31bec55f3b789a84e95ceb87eb93e0751ea0131

Observation eef041e7-dc92-4374-8e3a-3408f454daf0 · outbound

This paper cites Combining weight pruning and knowledge distillation for cnn compression,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Combining weight pruning and knowledge distillation for cnn compression,

Reference 28

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raw_fallback, observed 2026-08-07T13:51:02.760688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.078915Z digest=sha256:fbcc2238a5eaaa064f2dc8a521f31a4fb11e46e4ae7fc7d6a9a1e3655be08cf6

Observation af218beb-44c7-4b26-87cb-5512fc89133d · outbound

This paper cites Pruning filter in filter,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Pruning filter in filter,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.183974Z digest=sha256:52d15bff5932ca373bc74c771213cc997a868d98194afc5db86808d219013842

Observation c6b80503-a383-4f9c-ab8f-ddd07fd6ab67 · outbound

This paper cites Importance estimation for neural network pruning,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Importance estimation for neural network pruning,

Reference 30

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.284175Z digest=sha256:b44bbe2bfc3a927558ec48d3fdfc6520443ff9b09fb27f5e08444ca19599c1f4

Observation 455cf491-4d39-417c-ad31-2456c58273d4 · outbound

This paper cites Channel pruning for accelerating very deep neural networks,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Channel pruning for accelerating very deep neural networks,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.414338Z digest=sha256:5af80fb66fd29eeadcb6822155671df0ce61beea070e30869502f4c3345ce3aa

Observation c9d1a78c-d8bc-4f8a-8850-a1dcacdc36a3 · outbound

This paper cites Discrimination-aware channel pruning for deep neural networks,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Discrimination-aware channel pruning for deep neural networks,

Reference 32

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raw_fallback, observed 2026-08-07T13:51:02.045970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.512326Z digest=sha256:423ad1ddd726addce994a38a82d6c9489d654be108cc96015969bf852c18305f

Observation 48c8f65b-d748-4387-9a5b-86b118091be9 · outbound

This paper cites Understanding the dynamics of dnns using graph modularity,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Understanding the dynamics of dnns using graph modularity,

Reference 33

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raw_fallback, observed 2026-08-07T13:51:01.877329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.630663Z digest=sha256:9719876865e137a239f0ec6326a4ba8acd3c1bd7c2f87bc71d91ce5f640f5608

Observation dbef18bb-f16c-4916-8a51-ca37b609f34f · outbound

This paper cites Sr-init: An interpretable layer pruning method,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Sr-init: An interpretable layer pruning method,

Reference 34

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raw_fallback, observed 2026-08-07T13:51:01.739550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.799105Z digest=sha256:263ba2ccdeb5cfaf3d77c8e778dcb7d5e78d362f45fd5e84e33e8241adf792bd

Observation 3d8a1e3f-53d2-4de5-a962-0a48db4ea70b · outbound

This paper cites Shallowing deep networks: Layer-wise pruning based on feature representations,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Shallowing deep networks: Layer-wise pruning based on feature representations,

Reference 35

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raw_fallback, observed 2026-08-07T13:51:01.563759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:53.932136Z digest=sha256:5a40bbceb3b78d2da01c709a341fd6ba3aa1d91fec68815ed095bdd677c7b4d4

Observation d81140c0-3918-4f3f-96d9-9fca8a133c74 · outbound

This paper cites Data-driven sparse structure selection for deep neural networks,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Data-driven sparse structure selection for deep neural networks,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-07T13:51:01.414482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:54.080724Z digest=sha256:7a0e4a7ffdf68875d4dbb09f2075bf5e55d299ff0a3a1c5e35069bd4bd63d722

Observation 73d32736-377f-4e6f-9899-b105dff1953a · outbound

This paper cites A generic layer pruning method for signal modulation recognition deep learning models,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition A generic layer pruning method for signal modulation recognition deep learning models,

Reference 37

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no resolver link, observed 2026-08-07T13:50:54.324154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:54.324154Z digest=sha256:bef1b408e964ae183fd08c0c4eb423d85becfd10831369312a3f1666765d7907

Observation a77fbc9f-3a5c-47b5-a901-9a8369f35e12 · outbound

This paper cites Reassessing Layer Pruning in LLMs: New Insights and Methods.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:54.535902Z digest=sha256:19e2c2447374f965e53a68d46a74f42525eed2b7e45654afe3be18056d00c3d0

Observation 9f5a7474-aa12-4bc9-b5b7-358bbf7b59ca · outbound

This paper cites Rgp: Neural network pruning through regular graph with edges swap- ping,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Rgp: Neural network pruning through regular graph with edges swap- ping,

Reference 39

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no resolver link, observed 2026-08-07T13:50:54.731261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:54.731261Z digest=sha256:a32f1cea933908a30796dd7ffd0b044ca99c6e18dd4101352427ff6340b36496

Observation 018e89b4-335d-4127-ad05-620910b535bb · outbound

This paper cites Radio machine learning dataset generation with gnu radio,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Radio machine learning dataset generation with gnu radio,

Reference 40

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no resolver link, observed 2026-08-07T13:50:54.865404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:54.865404Z digest=sha256:edb0561535acafc9489f677cabd9942b738975b03d6f3bef497b04d52625db1b

Observation d1e7457a-a635-45a0-924c-5157c7bbb174 · outbound

This paper cites Filter pruning via measuring feature map information,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Filter pruning via measuring feature map information,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:01.258598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:54.962882Z digest=sha256:16c4cb0c287f327d2a33f66526056553fce96daeafcca00534148f543f25ae58

Observation a2c808e7-0619-40c2-ab50-d4aaf89b6478 · outbound

This paper cites Filter pruning via geometric median for deep convolutional neural networks acceleration,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Filter pruning via geometric median for deep convolutional neural networks acceleration,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:01.088450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:55.069726Z digest=sha256:aacb91e693ddb2d94329104987f4e8be37266a4de935ca3c72471a5e2ba33d12

Observation a3268216-00ff-436a-be2d-1e4631ba6476 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Pruning Filters for Efficient ConvNets

Reference 43

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no resolver link, observed 2026-08-07T13:50:55.240309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:55.240309Z digest=sha256:2dee9b51cadaf3e26833d6da51d33b2a1e80b0141b02e8a178d28eb6d4239089

Observation 4f32501d-91f8-4542-9392-4cfa764aa0f0 · outbound

This paper cites Soft filter pruning for accelerating deep convolutional neural networks,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Soft filter pruning for accelerating deep convolutional neural networks,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:00.929437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:55.371087Z digest=sha256:52816e655530cb4e1f5951d8f64d98adbe376b3618cb9edf3cba306e04d40354

Observation 75287790-a218-435c-b517-067a61a4b42a · outbound

This paper cites Channel pruning method for signal modulation recognition deep learning models,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Channel pruning method for signal modulation recognition deep learning models,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:00.724880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:55.490579Z digest=sha256:9b345eed44adcc3dbe0410ce26ab4187d00693b26fbfacb80d9c1bb99e518e86

Observation f0eb0332-431a-4efc-a843-a1915b1a33ee · outbound

This paper cites DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

Reference 46

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no resolver link, observed 2026-08-07T13:50:55.590562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:55.590562Z digest=sha256:6aaf80464de764e7480696dbbe20d837461bf36d0f3dde006ef5df4bd73a4ad6

Observation 517e8739-2649-4ea0-bd81-2ea9983c3988 · outbound

This paper cites Lightweight automatic modulation classification based on decentralized learning,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Lightweight automatic modulation classification based on decentralized learning,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:00.538374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:55.652609Z digest=sha256:f8e4f3ff3d06db9f203cb580ca3a6e2178ca495a0dbc731a9135efeb6acf70f0

Observation cbea6626-3db5-4276-8ce5-30d2ae9ff169 · outbound

This paper cites Nas-amr: Neural architecture search-based automatic mod- ulation recognition for integrated sensing and communication systems,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Nas-amr: Neural architecture search-based automatic mod- ulation recognition for integrated sensing and communication systems,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:00.377586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:55.781963Z digest=sha256:ac8ada86146177052f1a37fbbce872b9d6b2690a38cd840f4203ddec7613b2b7

Observation 18e13ad6-1730-42ab-8c3d-26feaa2f3af8 · outbound

This paper cites Ultra lite convolutional neural network for automatic modulation classification in internet of unmanned aerial vehicles,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Ultra lite convolutional neural network for automatic modulation classification in internet of unmanned aerial vehicles,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:00.203547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:55.970047Z digest=sha256:0f6835b11428fa3d10c54b8a66b28caa09d17a60474f7a59b82782ee39c72f8c

Observation 19c93ac3-ff32-491a-b17a-aa3afce05f3d · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Pruning neural networks without any data by iteratively conserving synaptic flow,

Reference 50

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unresolved
no resolver link, observed 2026-08-07T13:50:56.105469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:56.105469Z digest=sha256:d2b083c2d02a16cf58640a57b0091c3e6bf7ba2148237f32ad58cfe696455802

Observation 5f6daf25-4f50-437f-8787-27b32bbab15c · outbound

This paper cites Deep Model Fusion: A Survey.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Deep Model Fusion: A Survey

Reference 51

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no resolver link, observed 2026-08-07T13:50:56.276806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:56.276806Z digest=sha256:980b06ff26107f881d6947350b939f3909e93e5ce845610c17971e527b890952

Observation 12299d5d-8c5a-4d15-b242-552fbd81aa29 · outbound

This paper cites Merging models with fisher-weighted averaging,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Merging models with fisher-weighted averaging,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:00.091492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:56.429707Z digest=sha256:01efa82265f0830e05a8480d7662d099ebfbd218168a8cf474ca74f8b9cbd374

Observation 61809271-4cd2-4657-be0f-8c96ccf2f541 · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 53

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no resolver link, observed 2026-08-07T13:50:56.554806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:56.554806Z digest=sha256:569915f5366eeee5e2b29b22b5c7ac33cccd8dcd2c3eb251723b7881eb0ada71

Observation c36648d1-2e00-4059-b460-a3884b6a0ad7 · outbound

This paper cites PopulAtion Parameter Averaging (PAPA).

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition PopulAtion Parameter Averaging (PAPA)

Reference 54

Resolution
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no resolver link, observed 2026-08-07T13:50:56.696309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:56.696309Z digest=sha256:5bcabd793e3bcb74d3b2655bcd46992f0700cfec8605b55092a12dbbc5d99834

Observation 66e090fb-77e9-4c1b-bb0a-a3b87da061ac · outbound

This paper cites Model ratatouille: Recycling diverse models for out-of-distribution gen- eralization,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Model ratatouille: Recycling diverse models for out-of-distribution gen- eralization,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:59.944912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:56.869908Z digest=sha256:8d8158d7218cb57662e65bfffca93a2071075c58b81f32767d4f00136bc6c541

Observation ffc1c792-5b4c-42e9-870c-81572af719d4 · outbound

This paper cites Faster cnns with direct sparse convolutions and guided pruning,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Faster cnns with direct sparse convolutions and guided pruning,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:59.818189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:57.200733Z digest=sha256:16068d2a0032109ac868089b6fe33b7d6c11697d70a29216ba539a0781945c5d

Observation 478082e7-5d8e-4b45-a7e7-8170a6579ecf · outbound

This paper cites Eie: Efficient inference engine on compressed deep neural network,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Eie: Efficient inference engine on compressed deep neural network,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:59.670042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:57.298887Z digest=sha256:a465a2f1bedc0e206382c47eab477beebe8d43240dce8c82e11a8587428c8b27

Observation 6a6903e7-cb7b-4d40-930a-095c7a5a2774 · outbound

This paper cites Robust statistics on riemannian manifolds via the geometric median,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Robust statistics on riemannian manifolds via the geometric median,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:59.518083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:57.403111Z digest=sha256:653d0a0faf717500cae467c5138ee16c7bd77718a9c03679eb56e92026f7354c

Observation b127322e-7874-4f0d-b313-0b248179428b · outbound

This paper cites To filter prune, or to layer prune, that is the question,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition To filter prune, or to layer prune, that is the question,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:59.349165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:57.506374Z digest=sha256:6ac9abfd2fe5b9765921897d329f35a0a9e458bc7f9bcbfe208606c6fc9fbac3

Observation fe76a514-356d-4e75-9566-df5d19ad7546 · outbound

This paper cites Hierarchical clustering schemes,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Hierarchical clustering schemes,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:59.188862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:57.620813Z digest=sha256:a8a038d7c22cb27fd4c4b0748c2c3f7c57b03a8bffd975cb63546010a6b0705c

Observation 6aa05946-a461-43f7-a992-53930973a26e · outbound

This paper cites Emr- merging: Tuning-free high-performance model merging,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Emr- merging: Tuning-free high-performance model merging,

Reference 61

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unresolved
no resolver link, observed 2026-08-07T13:50:57.733402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:57.733402Z digest=sha256:4ebed90cba0e61fa33a58b626ab69971b3e15acdaedc39bf1043be88884e6fa4

Observation 2c9c3835-7874-466d-932b-c971570b856c · outbound

This paper cites Ties- merging: Resolving interference when merging models,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Ties- merging: Resolving interference when merging models,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:58.948664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:57.784523Z digest=sha256:b5b6802ea0fea9fb754d68720acbcb4671b7d35daeb0f74d8af10a54db4e4351

Observation 469d4e00-55d5-4c23-90b4-41aadd2d2ebc · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Averaging Weights Leads to Wider Optima and Better Generalization

Reference 63

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no resolver link, observed 2026-08-07T13:50:57.861124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:57.861124Z digest=sha256:98182a3461a9002b5a23f74cf54ae1f86668b8c961bd58710db8d486440f46e0

Observation e191e467-5cf5-4df3-b9cf-2bc812617a3a · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:58.805394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:57.940560Z digest=sha256:fa5e184692f267cc66bc511b851b3f2eac84e5bf214d6c9f3c2020d54fb2c45b

Observation 45444254-76e9-49b6-b583-06b517df23c6 · outbound

This paper cites Revisiting Checkpoint Averaging for Neural Machine Translation.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Revisiting Checkpoint Averaging for Neural Machine Translation

Reference 65

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no resolver link, observed 2026-08-07T13:50:57.996475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:57.996475Z digest=sha256:a3c4b21be8bd8af97e484bdce74702808e1453c55a0540b7a2071fccfbed12d6

Observation 095fbfbd-e801-49e5-a949-3e5358f0274b · outbound

This paper cites Seasoning model soups for robustness to adversarial and natural distribution shifts,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Seasoning model soups for robustness to adversarial and natural distribution shifts,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:58.666245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:58.078172Z digest=sha256:742db1ee55fbde81506ba123bb43de6030c308213dcbb92c991a6df12e0b63b7

Observation 6dffdc4d-65d1-4542-9b43-98ea387e360a · outbound

This paper cites Gnu radio: tools for exploring the radio frequency spec- trum,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Gnu radio: tools for exploring the radio frequency spec- trum,

Reference 67

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unresolved
no resolver link, observed 2026-08-07T13:50:58.168289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:58.168289Z digest=sha256:3cb552a7d35f02f32c147b81afdf22c4e7bf3115f12475b9f2efa0cc333b3309

Observation c0284f58-7af2-4c4e-b21f-9f166017758a · outbound

This paper cites Deep residual learning for image recognition,.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Deep residual learning for image recognition,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:58.537777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:50:58.243286Z digest=sha256:2612175509954c1d9a2afdf1e4b10ba0239e7226c53704b41676256e8ba28bce

Pith citing papers

Observation cdcccab2-eb25-42d3-9cea-3434fc5a8c19 · inbound

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices cites this paper.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition

Reference 15

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no resolver link, observed 2026-08-07T05:44:05.450427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:44:05.450427Z digest=sha256:94c976561a14281ec1d0290951f59182a9917a94ae4650f73bbc1ba845bba309

Observation b20c43bb-d855-495f-938f-9a25d85662ea · inbound

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning cites this paper.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T17:02:28.271299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:02:28.271299Z digest=sha256:dbe1f2e6895eb008c49bd065d35f7a443bbc313fa6b0095c4d0fb099fab6d02b

Observation a8ee956e-c199-4e85-95e8-7bc84059e658 · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition

Reference 59

Resolution
verified exact
local_arxiv, observed 2026-08-05T12:53:05.838858Z

Source-reported events for the cited work

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A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition

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source=pdf_text observed=2026-08-05T12:53:14.905406Z digest=sha256:9a3c6a0033dd4c0d9201ed4f3cd134065ae743d5fcdca015422d0db9157de3e8