{"as_of":"2026-08-20T19:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a83ca34d2fe8021b08f840b87998deaf68702d35ec33f74754c2c323a309ff47","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:15:05.534464Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T23:57:10.155575Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T15:47:23.188694Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07022","snapshot_observed_at":"2026-08-10T23:57:10.155575Z","title":"Dense cross-connected ensemble convolutional neural networks for enhanced model robustness,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19747","last_updated":"2024-12-27T17:14:52Z","snapshot_observed_at":"2026-08-15T00:34:24.516526Z","submitted_at":"2024-12-27T17:14:52Z","title":"Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T23:57:10.155575Z"},"links":{"cited_paper":"/paper/2412.07022","citing_paper":"/paper/2412.19747"},"observation_digest":"sha256:069621464152ee326fad35b20f134f2a3d4b3304e911ff06ca0e1dc436763c77","observation_id":"d30cfbcc-753e-46a7-8295-39db115af3b4","resolution":{"observed_at":"2026-08-10T23:57:10.155575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07022","snapshot_observed_at":"2026-07-11T17:32:24.547251Z","title":"Dense cross-connected ensemble convolutional neural networks for enhanced model robustness.arXiv preprint arXiv:2412.07022, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.04548","last_updated":"2026-07-05T23:36:36Z","snapshot_observed_at":"2026-08-16T14:09:48.731362Z","submitted_at":"2026-07-05T23:36:36Z","title":"Explainable Novel Category Discovery in Semantic Concept Space","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-11T17:32:24.547251Z"},"links":{"cited_paper":"/paper/2412.07022","citing_paper":"/paper/2607.04548"},"observation_digest":"sha256:ad04381888a75d46167f29a0833676795ed6d9776afc99491fad246c5a8ff5f6","observation_id":"9b47b93a-e0c4-4bd6-a843-269bf9195ad0","resolution":{"observed_at":"2026-07-11T17:32:24.547251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"cited_work":{"arxiv_id":"2412.07022","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.07022","snapshot_observed_at":"2026-07-10T15:47:23.188694Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","venue":"cs.CV","work_id":"f48beca7-3a98-4e83-95cb-3e32f1ed136b","year":2024},"citing_paper":{"arxiv_id":"2607.07903","last_updated":"2026-07-08T20:31:06Z","snapshot_observed_at":"2026-08-07T02:48:01.295589Z","submitted_at":"2026-07-08T20:31:06Z","title":"Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-10T15:42:46.392593Z"},"links":{"cited_paper":"/paper/2412.07022","citing_paper":"/paper/2607.07903"},"observation_digest":"sha256:ba017a46c695957a667699d1a489f063d79ccf02fbbcd57369406409e2bbb82a","observation_id":"65310eba-0524-4ea7-98df-0a2f9476d6ff","resolution":{"observed_at":"2026-07-10T15:47:23.189844Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07022","snapshot_observed_at":"2026-08-01T12:21:51.616392Z","title":"Dense cross-connected ensemble convolutional neural networks for enhanced model robustness,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.19590","last_updated":"2026-07-21T21:31:14Z","snapshot_observed_at":"2026-08-14T13:06:40.694700Z","submitted_at":"2026-07-21T21:31:14Z","title":"Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T12:21:51.616392Z"},"links":{"cited_paper":"/paper/2412.07022","citing_paper":"/paper/2607.19590"},"observation_digest":"sha256:e94268077ba7e2b9212ee4726940c893a76e95ee4512a012f711a5015e05aa80","observation_id":"72e7e636-a05c-4b82-881f-5ca7f1eb5449","resolution":{"observed_at":"2026-08-01T12:21:51.616392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07022","snapshot_observed_at":"2026-08-08T16:59:10.624519Z","title":"Dense cross-connected ensemble convolutional neural networks for enhanced model robustness,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05258","last_updated":"2026-08-05T16:36:06Z","snapshot_observed_at":"2026-08-18T12:58:19.684736Z","submitted_at":"2026-08-05T16:36:06Z","title":"Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T16:59:10.624519Z"},"links":{"cited_paper":"/paper/2412.07022","citing_paper":"/paper/2608.05258"},"observation_digest":"sha256:5b3c16d146172831023f1e1ea1a6705a33ac53bc41b9fe24b81b3f55f5cd49d4","observation_id":"4187ea7e-2c05-4ca1-aa10-17fbc28ccd8d","resolution":{"observed_at":"2026-08-08T16:59:10.624519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.07022/citation-record","integrity":"/paper/2412.07022/integrity","json":"/paper/2412.07022/citation-record.json","paper":"/paper/2412.07022"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.813378Z","title":"Wein- berger","venue":null,"work_id":"9f476ca2-6cc3-43f9-82d9-1509228da2da","year":2017},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.451904Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:e08cb3d5453483fefb46cbb23c5c267b740a72e1c129372fba7f0c6f1c8f3ce3","observation_id":"af555d96-56a6-48ca-bcc5-a4f6237ce48a","resolution":{"observed_at":"2026-08-11T19:15:05.817300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.800360Z","title":"Y ., Zhou, X., Lin, M., Sun, J","venue":null,"work_id":"90165588-3d2a-41bd-9401-41463d0f89da","year":2017},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.456085Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:eb4b256ab95b82647949609ffb6794abaa8271c42eeae7d9b061df19d32bb996","observation_id":"26676f62-9c78-4065-a3c7-aa9a020fec1f","resolution":{"observed_at":"2026-08-11T19:15:05.804637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.785911Z","title":"”Ensemble methods in machine learning.” In Multiple classifier systems, pp","venue":null,"work_id":"a7943f9a-f763-4d3d-9164-a0bbdcb2185f","year":2000},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.460112Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:e2eb2eb267d2b523c5beac4e9ec67c21ff126cbcc99d4cebcfe44a7a524b084c","observation_id":"3adcd821-1067-435a-b41d-d86c47673cba","resolution":{"observed_at":"2026-08-11T19:15:05.791198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.772288Z","title":"”Simple and scalable predictive uncertainty estimation using deep en- sembles.” In Advances in neural information processing systems, pp","venue":null,"work_id":"42b35cea-2983-4841-8cb1-10b9be329735","year":2017},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.463962Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:4269b04e2ef41c0ef47cda7153f1303bbb9c623e1a0831630c118e32171014c3","observation_id":"3ca161af-2f0c-44c8-abb0-d8d8561a7eba","resolution":{"observed_at":"2026-08-11T19:15:05.776640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.757077Z","title":"”Explaining the behavior of neuron activations in deep neural networks.” Ad Hoc Networks, vol","venue":null,"work_id":"c28f76d5-f911-4ad2-bdc0-0beb711971b2","year":2021},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.468030Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:d3e2212c356e3ac86bfc7508b93a0905ca4a398c05dd44ee105234b24a13c655","observation_id":"97a248b6-577c-4d92-9147-303456bad563","resolution":{"observed_at":"2026-08-11T19:15:05.761991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.745149Z","title":"”DFT-Spread Based PAPR Reduction of OFDM for Short Reach Communication Systems.” In Communications, Signal Processing, and Systems, pp","venue":null,"work_id":"a2246629-d04d-441b-a1e1-187c61e95c08","year":2020},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.471875Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:4268af60220806313991a8e74cc77989a43bd995984c3bdeee68526bb7334bbf","observation_id":"3a0b24cc-12f1-4484-9abf-91f78fc43f3b","resolution":{"observed_at":"2026-08-11T19:15:05.749049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.734005Z","title":"”Deep reinforcement learning based computation offloading for mobility-aware edge computing.” In Communications and Network- ing, 2019, pp","venue":null,"work_id":"b2b9719d-22ea-4b7e-8414-e2edf839154a","year":2019},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.476519Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:4674feeb1053699cd824fc2cfc570055b9ccb82ad1263ceb733eb23143180efe","observation_id":"95bacb7e-8afb-4c81-ae24-c63e55498bb3","resolution":{"observed_at":"2026-08-11T19:15:05.737652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.722300Z","title":"”Improving robustness of deep neural networks via large-difference transformation.” Neurocomputing, vol","venue":null,"work_id":"2f6f005a-0885-402f-b87d-13aedf62efd1","year":2021},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.480971Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:ab58cd9779d94d83f9d217ee70935ae299164005d4204f5a7219b98a4ef53360","observation_id":"0b70dda9-099f-4db5-8a17-03c63554b2c9","resolution":{"observed_at":"2026-08-11T19:15:05.725712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.709821Z","title":"”Partial interference alignment for heterogeneous cellular networks.” IEEE Access, vol","venue":null,"work_id":"c6b1eddd-aecc-4ee7-9016-0e990b837c05","year":2018},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.484412Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:094259f3c7aa82a8a7299c759d20f9779528b1dfb08aad9faf1a4adc5c41db1a","observation_id":"96e62053-f992-44f1-99d3-11b9050cd556","resolution":{"observed_at":"2026-08-11T19:15:05.713263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.699263Z","title":"”Representation learning and nature encoded fusion for heterogeneous sensor networks.” IEEE Access, vol","venue":null,"work_id":"3907a58f-5b13-40a6-a9ed-649b47017235","year":2019},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.488311Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:5db2334d98ddc1d07fa1fa780629c81472638ee5c4e5327141608a6321f5ce4d","observation_id":"9033789e-11ba-4f4b-a6a6-2b1e2d281eb7","resolution":{"observed_at":"2026-08-11T19:15:05.702756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.687090Z","title":"”Performance analysis of cooperative multicell precoding with global CSI and local individual CSI in the large dimensional regime.” IEEE Transactions on Vehicular Technology, vol","venue":null,"work_id":"e0c8d321-1ae3-42c4-9233-ed9a02dc7d64","year":2017},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.492589Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:7786d748149bb3a1513167f4661502983a2ac388a5d719c972f395bbbd49be8e","observation_id":"9246667f-ac3b-4221-a146-92709d581ba5","resolution":{"observed_at":"2026-08-11T19:15:05.691381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.673931Z","title":"”Optimization for user centric mas- sive mimo cell free networks via large system analysis.” In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), 2016","venue":null,"work_id":"2e32bc9b-ebb6-4e8e-a766-76a4b91ebbc5","year":2016},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.496294Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:17486bbd4f022e183982701e79b0b0394c54817c4a65482188af1ebbe5c00a47","observation_id":"ed328fa9-9e43-494b-a56e-58136c4e86ba","resolution":{"observed_at":"2026-08-11T19:15:05.678154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.660916Z","title":null,"venue":null,"work_id":"5a062736-ebb2-4194-9b50-86a5c45f3dd1","year":2014},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.501003Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:be310d5614a70a98ed302ff1fda7858456600784c2e01e974880cad82557711b","observation_id":"245be100-7be2-463b-96b9-c4420e028b52","resolution":{"observed_at":"2026-08-11T19:15:05.664558Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.647772Z","title":null,"venue":null,"work_id":"d501b020-71c5-4d1b-9b76-098ec52abdd7","year":2011},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.505778Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:0182f2051918f6f9477030c6e5fe0d829a04ce3c478a7766bf51b829914a11f0","observation_id":"bd7b3d0b-b282-49f9-95ff-617e32fbf61e","resolution":{"observed_at":"2026-08-11T19:15:05.652364Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.634254Z","title":"”Low complexity optimization for user centric cellular networks via large dimensional analysis.” Physical Communication, vol","venue":null,"work_id":"bbe49e1d-e544-4538-b328-619588bdebd1","year":2017},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.510191Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:a922e5edf1dd573e2e7818848ac6262b697ef5831deb1183c080fb65d33b44ca","observation_id":"e10d9859-0126-438d-aa05-66e75368d4fe","resolution":{"observed_at":"2026-08-11T19:15:05.639321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-19T07:17:20.004918Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-11T19:15:05.514010Z","title":"”Explaining and harnessing adversarial examples.” arXiv preprint arXiv:1412.6572 (2014)","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.514010Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:1f33d6b135630310a95812b80dac82c20b9d0d7fa31a86428cbd493b5ece3e07","observation_id":"d693af0b-baf5-400b-b7e3-433d2e38c565","resolution":{"observed_at":"2026-08-11T19:15:05.514010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.621026Z","title":"”Fully convolu- tional networks for semantic segmentation.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp","venue":null,"work_id":"22279be3-a6b4-4123-a4fe-51b453204a82","year":2015},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.518763Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:27f1de9b31e7662de8e8cf2a5082d2f73c9a9a75c8c6b07cfc06cdf7fa430d9d","observation_id":"09ced281-b6b5-4198-8c9e-d83f3c4956db","resolution":{"observed_at":"2026-08-11T19:15:05.624701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.608664Z","title":"”Improving deep learning with generic data augmentation.” In 2018 IEEE symposium series on com- putational intelligence (SSCI), pp","venue":null,"work_id":"5b9b0168-9e25-4607-ace4-cbdb5e330f1c","year":2018},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.523241Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:c91a30f8d3fc8c51517e8a71b664d34a9ddb502641c6aeba7f89634f02733cc1","observation_id":"3f2b87ff-2f62-4c43-988d-c1dee79a44b9","resolution":{"observed_at":"2026-08-11T19:15:05.612387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.596854Z","title":"”Group equivariant convolutional net- works.” In International conference on machine learning, pp","venue":null,"work_id":"eafe9299-0039-4842-a6dc-f17d2df82ee1","year":2016},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.527323Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:cb450ecbaf31d815248b974fe03e72479b754631ac214484e843febe4a372826","observation_id":"b4a7c080-56f2-4841-801d-6a5c24e9ac1b","resolution":{"observed_at":"2026-08-11T19:15:05.600242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.584779Z","title":"”Spatial pyramid pooling in deep convolutional networks for visual recognition.” IEEE transactions on pattern analysis and machine intelligence 37, no","venue":null,"work_id":"203365ea-7dd0-4616-bc5e-2895df6776f1","year":2015},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.530798Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:890401d485212c70253339ec5d2aaa945483b85c651e1814f6d43d5ee7a4efec","observation_id":"7b7d3bbe-d22b-4cab-b5e5-8b2b49943058","resolution":{"observed_at":"2026-08-11T19:15:05.589681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:15:05.571187Z","title":"”Benchmarking neural network robustness to common corruptions and perturbations.” In Pro- ceedings of the International Conference on Learning Representations (ICLR), 2019","venue":null,"work_id":"f4e61445-cc6c-4c67-b60e-f9d6f956ddc2","year":2019},"citing_paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T19:15:05.534464Z"},"links":{"citing_paper":"/paper/2412.07022"},"observation_digest":"sha256:d8053d27b39e24ecf231d56db07985914ff2c825b7b1efb4bdeb3c7a3aa6c6f5","observation_id":"4dfbaebc-4a4d-4aa0-98a7-b943552d5eaf","resolution":{"observed_at":"2026-08-11T19:15:05.576645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.07022","last_updated":"2024-12-09T22:09:13Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T08:20:30.697137Z","submitted_at":"2024-12-09T22:09:13Z","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":18},"total_outbound_references":21},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 5 inbound Pith citation observations for arXiv:2412.07022."}