{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:BWGWMJDCVAJDP5T7UXN7BYXW2G","short_pith_number":"pith:BWGWMJDC","canonical_record":{"source":{"id":"1809.03314","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-05T02:14:50Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"c88ff08355a6e544abe3f30713031db9a32af743606242214c4dcffa9c13dc1d","abstract_canon_sha256":"6a65ba7c242fb83c9f9e19dcdb6a54e8c0e0566aaca0c180bc1d8e7045ccbabc"},"schema_version":"1.0"},"canonical_sha256":"0d8d662462a81237f67fa5dbf0e2f6d1964ce3551b91d5470f74aed83a7da020","source":{"kind":"arxiv","id":"1809.03314","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.03314","created_at":"2026-05-18T00:06:07Z"},{"alias_kind":"arxiv_version","alias_value":"1809.03314v1","created_at":"2026-05-18T00:06:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03314","created_at":"2026-05-18T00:06:07Z"},{"alias_kind":"pith_short_12","alias_value":"BWGWMJDCVAJD","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BWGWMJDCVAJDP5T7","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BWGWMJDC","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:BWGWMJDCVAJDP5T7UXN7BYXW2G","target":"record","payload":{"canonical_record":{"source":{"id":"1809.03314","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-05T02:14:50Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"c88ff08355a6e544abe3f30713031db9a32af743606242214c4dcffa9c13dc1d","abstract_canon_sha256":"6a65ba7c242fb83c9f9e19dcdb6a54e8c0e0566aaca0c180bc1d8e7045ccbabc"},"schema_version":"1.0"},"canonical_sha256":"0d8d662462a81237f67fa5dbf0e2f6d1964ce3551b91d5470f74aed83a7da020","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:07.235833Z","signature_b64":"eZ8JjzFqhkEpYyzjbg8IhSnb53mA3yomHYJiEwVv4Wc/C6ijzmlTinsISbpHhO+ZazP/Y10zolTKfK/46Q9pDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d8d662462a81237f67fa5dbf0e2f6d1964ce3551b91d5470f74aed83a7da020","last_reissued_at":"2026-05-18T00:06:07.235163Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:07.235163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.03314","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J+oPeTOBtdQz1OKFIwzzAJjB8oPTRbdDe1Wrv0L2qy3LbsmaHOCRIQF6JTgFvvJHsX2gI66amQUF/1cDmo2qDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:30:40.468097Z"},"content_sha256":"26cf405d819447529bc42e86417be045bbade91843e5d823e85828c5afcd6daa","schema_version":"1.0","event_id":"sha256:26cf405d819447529bc42e86417be045bbade91843e5d823e85828c5afcd6daa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:BWGWMJDCVAJDP5T7UXN7BYXW2G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Robotic Auto-Focus System based on Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"cs.CV","authors_text":"Jingsong Yang, Runze Yu, Xiaofan Yu, Xiaohui Duan","submitted_at":"2018-09-05T02:14:50Z","abstract_excerpt":"Considering its advantages in dealing with high-dimensional visual input and learning control policies in discrete domain, Deep Q Network (DQN) could be an alternative method of traditional auto-focus means in the future. In this paper, based on Deep Reinforcement Learning, we propose an end-to-end approach that can learn auto-focus policies from visual input and finish at a clear spot automatically. We demonstrate that our method - discretizing the action space with coarse to fine steps and applying DQN is not only a solution to auto-focus but also a general approach towards vision-based cont"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03314","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DA/3Z8C0/3W3+UH1xzIKdcY5a6jpPpVn+KWBAwholscqIgZ4BZMiEmLgMbpXYKC8k+wwC1RIRWqCEFWFsAyKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:30:40.468939Z"},"content_sha256":"1955046e9a7eb9df22af3cfae59b4ed220b02580f7e24d319525e342d6d4d8b0","schema_version":"1.0","event_id":"sha256:1955046e9a7eb9df22af3cfae59b4ed220b02580f7e24d319525e342d6d4d8b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BWGWMJDCVAJDP5T7UXN7BYXW2G/bundle.json","state_url":"https://pith.science/pith/BWGWMJDCVAJDP5T7UXN7BYXW2G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BWGWMJDCVAJDP5T7UXN7BYXW2G/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T06:30:40Z","links":{"resolver":"https://pith.science/pith/BWGWMJDCVAJDP5T7UXN7BYXW2G","bundle":"https://pith.science/pith/BWGWMJDCVAJDP5T7UXN7BYXW2G/bundle.json","state":"https://pith.science/pith/BWGWMJDCVAJDP5T7UXN7BYXW2G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BWGWMJDCVAJDP5T7UXN7BYXW2G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:BWGWMJDCVAJDP5T7UXN7BYXW2G","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6a65ba7c242fb83c9f9e19dcdb6a54e8c0e0566aaca0c180bc1d8e7045ccbabc","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-05T02:14:50Z","title_canon_sha256":"c88ff08355a6e544abe3f30713031db9a32af743606242214c4dcffa9c13dc1d"},"schema_version":"1.0","source":{"id":"1809.03314","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.03314","created_at":"2026-05-18T00:06:07Z"},{"alias_kind":"arxiv_version","alias_value":"1809.03314v1","created_at":"2026-05-18T00:06:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03314","created_at":"2026-05-18T00:06:07Z"},{"alias_kind":"pith_short_12","alias_value":"BWGWMJDCVAJD","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BWGWMJDCVAJDP5T7","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BWGWMJDC","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:1955046e9a7eb9df22af3cfae59b4ed220b02580f7e24d319525e342d6d4d8b0","target":"graph","created_at":"2026-05-18T00:06:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Considering its advantages in dealing with high-dimensional visual input and learning control policies in discrete domain, Deep Q Network (DQN) could be an alternative method of traditional auto-focus means in the future. In this paper, based on Deep Reinforcement Learning, we propose an end-to-end approach that can learn auto-focus policies from visual input and finish at a clear spot automatically. We demonstrate that our method - discretizing the action space with coarse to fine steps and applying DQN is not only a solution to auto-focus but also a general approach towards vision-based cont","authors_text":"Jingsong Yang, Runze Yu, Xiaofan Yu, Xiaohui Duan","cross_cats":["cs.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-05T02:14:50Z","title":"A Robotic Auto-Focus System based on Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03314","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:26cf405d819447529bc42e86417be045bbade91843e5d823e85828c5afcd6daa","target":"record","created_at":"2026-05-18T00:06:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6a65ba7c242fb83c9f9e19dcdb6a54e8c0e0566aaca0c180bc1d8e7045ccbabc","cross_cats_sorted":["cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-05T02:14:50Z","title_canon_sha256":"c88ff08355a6e544abe3f30713031db9a32af743606242214c4dcffa9c13dc1d"},"schema_version":"1.0","source":{"id":"1809.03314","kind":"arxiv","version":1}},"canonical_sha256":"0d8d662462a81237f67fa5dbf0e2f6d1964ce3551b91d5470f74aed83a7da020","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0d8d662462a81237f67fa5dbf0e2f6d1964ce3551b91d5470f74aed83a7da020","first_computed_at":"2026-05-18T00:06:07.235163Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:07.235163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eZ8JjzFqhkEpYyzjbg8IhSnb53mA3yomHYJiEwVv4Wc/C6ijzmlTinsISbpHhO+ZazP/Y10zolTKfK/46Q9pDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:07.235833Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.03314","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:26cf405d819447529bc42e86417be045bbade91843e5d823e85828c5afcd6daa","sha256:1955046e9a7eb9df22af3cfae59b4ed220b02580f7e24d319525e342d6d4d8b0"],"state_sha256":"9ad92a04ca3d240470e2767f66ba30c7dca98595ef738b7b98cb63bd7e10ad0b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"isaHhHs+dzobigEJA8LQJy2VN7tx73Oq1veH4fO3gwTxEkuAmRKwRcplhPRX2V+d4DjoChxILkZbuaMuStGQCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T06:30:40.474979Z","bundle_sha256":"46c70cfb38bd4b07129d27ba7898b05b1420dd2afa350c2d0de25e5aa33c31e4"}}