{"as_of":"2026-08-15T11:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c9102d5c20a87c75a74cab9759f07f303dd6d036504914d39699bc8c96ce0675","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:48:55.118338Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.04355/citation-record","integrity":"/paper/1908.04355/integrity","json":"/paper/1908.04355/citation-record.json","paper":"/paper/1908.04355"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:48:55.267079Z","title":"and Wagner, D","venue":null,"work_id":"38db2299-f904-4491-8d32-29872d3dc78d","year":2017},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.073008Z"},"links":{"citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:f853e2c142e9b3c4dcda88d0d9fbda8ff001c6a7d10f070553b14522d83a08ee","observation_id":"0b6e30c0-26fa-4dfb-aaf6-8d9017d1347b","resolution":{"observed_at":"2026-08-14T13:48:55.270121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.10272","last_updated":"2018-11-23T19:07:57Z","snapshot_observed_at":"2026-08-14T18:47:20.839941Z","submitted_at":"2018-07-26T17:58:26Z","title":"Evaluating and Understanding the Robustness of Adversarial Logit Pairing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.10272","snapshot_observed_at":"2026-08-14T13:48:55.080994Z","title":"Evaluating and understanding the robustness of adversarial logit pairing","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.080994Z"},"links":{"cited_paper":"/paper/1807.10272","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:03c67ce782e61c0ef5ccc5ece65a5810aaba66ad9a53fa387811eb9f9c1eda20","observation_id":"54daab14-6fe8-4f42-ad02-2d113d8cefbd","resolution":{"observed_at":"2026-08-14T13:48:55.080994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.06763","last_updated":"2017-04-17T21:54:30Z","snapshot_observed_at":"2026-08-15T08:44:59.490553Z","submitted_at":"2017-02-22T11:48:35Z","title":"DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples","version":8},"cited_work":{"arxiv_id":"1702.06763","doi":null,"metadata_source":"pith","pith_arxiv_id":"1702.06763","snapshot_observed_at":"2026-08-14T13:48:55.198417Z","title":"DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples","venue":"cs.LG","work_id":"2c4b6402-631a-4802-9a73-0aee8bb27c2e","year":2017},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.085509Z"},"links":{"cited_paper":"/paper/1702.06763","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:7a72259a87382be434c172325f090268cca1641143ff6a0118cf088879c0955b","observation_id":"a97bef24-8c94-43a6-85e0-f528e8af62a7","resolution":{"observed_at":"2026-08-14T13:48:55.203931Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.06197","last_updated":"2017-04-03T22:36:33Z","snapshot_observed_at":"2026-08-14T21:56:34.558727Z","submitted_at":"2016-05-20T02:20:13Z","title":"Stick-Breaking Variational Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.06197","snapshot_observed_at":"2026-08-14T13:48:55.096314Z","title":"and Smyth, P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.096314Z"},"links":{"cited_paper":"/paper/1605.06197","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:6999549379cc23001ee847e26bf5a1e1dc334d4ace1b0224eb9a67bb5c515109","observation_id":"427efe01-9e8f-465b-ae0e-fec1192b5f94","resolution":{"observed_at":"2026-08-14T13:48:55.096314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-14T23:20:42.336514Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-14T13:48:55.102813Z","title":"and Zisserman, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.102813Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:205b67a5d55769a2fc4c7992fef8d2b4d91934dbea8a25ae797b0dd7b744c3f7","observation_id":"c6d158bf-56d7-4e37-9095-9f4f689c2e11","resolution":{"observed_at":"2026-08-14T13:48:55.102813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.01155","last_updated":"2017-12-05T23:45:08Z","snapshot_observed_at":"2026-08-14T21:08:18.764548Z","submitted_at":"2017-04-04T18:56:53Z","title":"Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.01155","snapshot_observed_at":"2026-08-14T13:48:55.110152Z","title":"Feature squeezing: Detecting adversarial examples in deep neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.110152Z"},"links":{"cited_paper":"/paper/1704.01155","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:04efb0fe39405f8cdf0259d7ca5355869853d2180b622b6cf3392e36cf1df37d","observation_id":"04f19e54-c79c-4a1a-980e-76870c963e4c","resolution":{"observed_at":"2026-08-14T13:48:55.110152Z","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":"2026-08-14T13:48:55.246663Z","title":"Letp(hi|hi−) define the conditional prob- ability andI(hi;hi−) define the mutual information be- tween hidden layer activationshi andhi− for every hidden layer in the network","venue":null,"work_id":"67bad495-ae70-449c-b530-472341882307","year":2000},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.113836Z"},"links":{"citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:dd6a1010eb11f39deba668f113301b27b0621c2c8a742da3dfe7dd5a8d73daed","observation_id":"cb2929bd-74de-47fd-a277-6be636487447","resolution":{"observed_at":"2026-08-14T13:48:55.249983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.10896","last_updated":"2019-03-04T03:27:59Z","snapshot_observed_at":"2026-08-15T08:47:29.575025Z","submitted_at":"2018-05-28T12:50:02Z","title":"Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.10896","snapshot_observed_at":"2026-08-14T13:48:55.093013Z","title":"B., Yang, E., and Hwang, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.093013Z"},"links":{"cited_paper":"/paper/1805.10896","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:06a33f10de28aa5fca7b6acba1ce77e6ff073e52fdf52bc03f492c59407f4e8d","observation_id":"bb795ec0-d9a7-4bff-b2ca-3bd44c9a5ed0","resolution":{"observed_at":"2026-08-14T13:48:55.093013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"physics/0004057","last_updated":"2000-04-24T15:22:30Z","snapshot_observed_at":"2026-08-12T16:03:20.142135Z","submitted_at":"2000-04-24T15:22:30Z","title":"The information bottleneck method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"physics/0004057","snapshot_observed_at":"2026-08-14T13:48:55.106547Z","title":"C., and Bialek, W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.106547Z"},"links":{"cited_paper":"/paper/physics/0004057","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:ba6d9020b950cad80dd00bf17e1d6aa3ba3d6c68355f1f0c62ce9913872f8766","observation_id":"e8c7aa04-d78b-4717-86b7-7e7fd4a99f5f","resolution":{"observed_at":"2026-08-14T13:48:55.106547Z","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":"2026-08-14T13:48:55.237121Z","title":null,"venue":null,"work_id":"ee995225-410d-4c0b-b57f-e2e13ef5872e","year":2017},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.118338Z"},"links":{"citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:1d2c5822742f98059ec157a0b105a9993d2f9a15d8c984abf1cb3b64e499da52","observation_id":"d9d5df5c-db25-4285-aacf-938afcaf6169","resolution":{"observed_at":"2026-08-14T13:48:55.240503Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T13:48:55.256913Z","title":"B., and Swami, A","venue":null,"work_id":"87b1e1a0-9947-4e39-828d-764c44d0d2bf","year":2017},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.099633Z"},"links":{"citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:2a1ab2b5ee3a45470f02341c4acade9838502419136c2c6da82b5beb60d9961c","observation_id":"62d9a053-8782-441b-930f-9662def89a2f","resolution":{"observed_at":"2026-08-14T13:48:55.260339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.1442","last_updated":"2014-12-03T19:08:38Z","snapshot_observed_at":"2026-08-14T23:09:04.583544Z","submitted_at":"2014-12-03T19:08:38Z","title":"Memory Bounded Deep Convolutional Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.1442","snapshot_observed_at":"2026-08-14T13:48:55.076727Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.076727Z"},"links":{"cited_paper":"/paper/1412.1442","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:dae0717718b3a90e234b15e521e6d3e553a0ad8c18a9ace0b4bb8ec5cf9845cb","observation_id":"850071a6-725e-45ca-8913-03f068dda9bb","resolution":{"observed_at":"2026-08-14T13:48:55.076727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1604.07316","last_updated":"2016-04-25T16:03:56Z","snapshot_observed_at":"2026-07-06T04:53:59.754200Z","submitted_at":"2016-04-25T16:03:56Z","title":"End to End Learning for Self-Driving Cars","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.07316","snapshot_observed_at":"2026-08-14T13:48:55.068049Z","title":"D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.068049Z"},"links":{"cited_paper":"/paper/1604.07316","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:862f46279045579d3bc5e54d7e4175dc13ff01cced2bf7d2e7c520db4b9b5a8f","observation_id":"08c7e284-991b-4b36-8ba8-b9015f9e9d2b","resolution":{"observed_at":"2026-08-14T13:48:55.068049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T13:48:55.089649Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression","version":4},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-14T13:48:55.089649Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.04355"},"observation_digest":"sha256:e5fb47083ba941eb6d58a65f0e55ab1d991f914b297f5610d66ea57ff680bfb5","observation_id":"413e4a32-b824-4252-8a07-a050b7b123c5","resolution":{"observed_at":"2026-08-14T13:48:55.089649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.04355","last_updated":"2020-07-02T13:47:36Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T08:44:35.767979Z","submitted_at":"2019-08-12T19:33:58Z","title":"Adversarial Neural Pruning with Latent Vulnerability Suppression"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":14},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:1908.04355."}