{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:EZTOJ4ZJEYEMZRHP5Y52REVCTH","short_pith_number":"pith:EZTOJ4ZJ","canonical_record":{"source":{"id":"1710.04211","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-11T11:22:26Z","cross_cats_sorted":["cs.DM","cs.NE","stat.ML"],"title_canon_sha256":"0da01e15ca5205546a38e474f4b268c0ef4f6a114eca67d2d8b2b4d6b37fb5ef","abstract_canon_sha256":"e396d536341a4f6f7489df7f4c4e5a30b4095c06ac675613e661fd6fd450fe1e"},"schema_version":"1.0"},"canonical_sha256":"2666e4f3292608ccc4efee3ba892a299d2ab2089c525f1623db14313604e0855","source":{"kind":"arxiv","id":"1710.04211","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04211","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04211v2","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04211","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"pith_short_12","alias_value":"EZTOJ4ZJEYEM","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EZTOJ4ZJEYEMZRHP","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EZTOJ4ZJ","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:EZTOJ4ZJEYEMZRHP5Y52REVCTH","target":"record","payload":{"canonical_record":{"source":{"id":"1710.04211","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-11T11:22:26Z","cross_cats_sorted":["cs.DM","cs.NE","stat.ML"],"title_canon_sha256":"0da01e15ca5205546a38e474f4b268c0ef4f6a114eca67d2d8b2b4d6b37fb5ef","abstract_canon_sha256":"e396d536341a4f6f7489df7f4c4e5a30b4095c06ac675613e661fd6fd450fe1e"},"schema_version":"1.0"},"canonical_sha256":"2666e4f3292608ccc4efee3ba892a299d2ab2089c525f1623db14313604e0855","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:41.701590Z","signature_b64":"2RJdICOIY6QCd3nuelQKh6qLxCbYMAulwn1l3s7DoYxiGkvmhqzhpLeX350iMiMvvndit2XuXrEbi6ZtjhdOCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2666e4f3292608ccc4efee3ba892a299d2ab2089c525f1623db14313604e0855","last_reissued_at":"2026-05-18T00:25:41.701064Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:41.701064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.04211","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:25:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"io/AA/BqA7rNyV+DjhSxEsCAOxo2r4+nmEp5AgoGSoAUVBiOtRYhp430BofEgLU8kRHRrQQUCue10AeiEUtSCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T21:38:51.782909Z"},"content_sha256":"12401851836565ee1571ac6a6c25cf69622591402d5dfe54dcd1fe8ebc4e282b","schema_version":"1.0","event_id":"sha256:12401851836565ee1571ac6a6c25cf69622591402d5dfe54dcd1fe8ebc4e282b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:EZTOJ4ZJEYEMZRHP5Y52REVCTH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DM","cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alessandro Bay, Biswa Sengupta","submitted_at":"2017-10-11T11:22:26Z","abstract_excerpt":"A widely studied non-deterministic polynomial time (NP) hard problem lies in finding a route between the two nodes of a graph. Often meta-heuristics algorithms such as $A^{*}$ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence-2-Sequence (Seq2Seq) model, widely used, for instance in text translation. Particularly, we illustrate that utilising a context vector that has been learned from two different recurrent networks enables increased accuracies in learning the shortest route of a graph. Additionally, we sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04211","kind":"arxiv","version":2},"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":"2026-05-18T00:25:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F4ppKHzTspdHOLt2MgGwg7z8vzb6sqSWZw/31U9MHLSU6OWUL0F4IFCzehasXqxN/ySXJ1u4wFi/Jn71lbPGDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T21:38:51.783428Z"},"content_sha256":"db41d9163b90e0687b36ce747cb521a646b23f836c36893c55aa4ac0ce574a5b","schema_version":"1.0","event_id":"sha256:db41d9163b90e0687b36ce747cb521a646b23f836c36893c55aa4ac0ce574a5b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EZTOJ4ZJEYEMZRHP5Y52REVCTH/bundle.json","state_url":"https://pith.science/pith/EZTOJ4ZJEYEMZRHP5Y52REVCTH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EZTOJ4ZJEYEMZRHP5Y52REVCTH/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":"2026-05-24T21:38:51Z","links":{"resolver":"https://pith.science/pith/EZTOJ4ZJEYEMZRHP5Y52REVCTH","bundle":"https://pith.science/pith/EZTOJ4ZJEYEMZRHP5Y52REVCTH/bundle.json","state":"https://pith.science/pith/EZTOJ4ZJEYEMZRHP5Y52REVCTH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EZTOJ4ZJEYEMZRHP5Y52REVCTH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:EZTOJ4ZJEYEMZRHP5Y52REVCTH","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":"e396d536341a4f6f7489df7f4c4e5a30b4095c06ac675613e661fd6fd450fe1e","cross_cats_sorted":["cs.DM","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-11T11:22:26Z","title_canon_sha256":"0da01e15ca5205546a38e474f4b268c0ef4f6a114eca67d2d8b2b4d6b37fb5ef"},"schema_version":"1.0","source":{"id":"1710.04211","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04211","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04211v2","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04211","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"pith_short_12","alias_value":"EZTOJ4ZJEYEM","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"EZTOJ4ZJEYEMZRHP","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"EZTOJ4ZJ","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:db41d9163b90e0687b36ce747cb521a646b23f836c36893c55aa4ac0ce574a5b","target":"graph","created_at":"2026-05-18T00:25:41Z","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":"A widely studied non-deterministic polynomial time (NP) hard problem lies in finding a route between the two nodes of a graph. Often meta-heuristics algorithms such as $A^{*}$ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence-2-Sequence (Seq2Seq) model, widely used, for instance in text translation. Particularly, we illustrate that utilising a context vector that has been learned from two different recurrent networks enables increased accuracies in learning the shortest route of a graph. Additionally, we sho","authors_text":"Alessandro Bay, Biswa Sengupta","cross_cats":["cs.DM","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-11T11:22:26Z","title":"StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04211","kind":"arxiv","version":2},"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":"sha256:12401851836565ee1571ac6a6c25cf69622591402d5dfe54dcd1fe8ebc4e282b","target":"record","created_at":"2026-05-18T00:25:41Z","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":"e396d536341a4f6f7489df7f4c4e5a30b4095c06ac675613e661fd6fd450fe1e","cross_cats_sorted":["cs.DM","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-11T11:22:26Z","title_canon_sha256":"0da01e15ca5205546a38e474f4b268c0ef4f6a114eca67d2d8b2b4d6b37fb5ef"},"schema_version":"1.0","source":{"id":"1710.04211","kind":"arxiv","version":2}},"canonical_sha256":"2666e4f3292608ccc4efee3ba892a299d2ab2089c525f1623db14313604e0855","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2666e4f3292608ccc4efee3ba892a299d2ab2089c525f1623db14313604e0855","first_computed_at":"2026-05-18T00:25:41.701064Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:25:41.701064Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2RJdICOIY6QCd3nuelQKh6qLxCbYMAulwn1l3s7DoYxiGkvmhqzhpLeX350iMiMvvndit2XuXrEbi6ZtjhdOCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:25:41.701590Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.04211","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:12401851836565ee1571ac6a6c25cf69622591402d5dfe54dcd1fe8ebc4e282b","sha256:db41d9163b90e0687b36ce747cb521a646b23f836c36893c55aa4ac0ce574a5b"],"state_sha256":"d609f0a6d4d4c8ae35514fd9d6249acb7bb7c8df3b0065be4944b50d64b05b53"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4LyZIrqWzgt86UJcsKkYrH6FKpH3WF8t2IECYRK4g8/p5AjiRBhBLIQ/ieYQVigztMMeK+LhUua7u9+UGAswBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T21:38:51.786360Z","bundle_sha256":"66579c8172009043e6724119b06f6cbfea7499f230016a4d1fdd4b2b77cbf419"}}