{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:YPS5RV4RHTCXLWVHXJVTL5JTMI","short_pith_number":"pith:YPS5RV4R","canonical_record":{"source":{"id":"1206.4116","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-06-19T03:35:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"951a133ee9d099cb3fb260a38cb80fe820675ff794dc457bf7344b826d6ef693","abstract_canon_sha256":"150d9ddb7352814977e3a3b3a692997b4b361c930e3a878da8d8d3c3ce3f3478"},"schema_version":"1.0"},"canonical_sha256":"c3e5d8d7913cc575daa7ba6b35f533621eae0b0f4b5bcc31b687053a31379df8","source":{"kind":"arxiv","id":"1206.4116","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.4116","created_at":"2026-05-18T03:53:16Z"},{"alias_kind":"arxiv_version","alias_value":"1206.4116v1","created_at":"2026-05-18T03:53:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.4116","created_at":"2026-05-18T03:53:16Z"},{"alias_kind":"pith_short_12","alias_value":"YPS5RV4RHTCX","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"YPS5RV4RHTCXLWVH","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"YPS5RV4R","created_at":"2026-05-18T12:27:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:YPS5RV4RHTCXLWVHXJVTL5JTMI","target":"record","payload":{"canonical_record":{"source":{"id":"1206.4116","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-06-19T03:35:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"951a133ee9d099cb3fb260a38cb80fe820675ff794dc457bf7344b826d6ef693","abstract_canon_sha256":"150d9ddb7352814977e3a3b3a692997b4b361c930e3a878da8d8d3c3ce3f3478"},"schema_version":"1.0"},"canonical_sha256":"c3e5d8d7913cc575daa7ba6b35f533621eae0b0f4b5bcc31b687053a31379df8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:53:16.735372Z","signature_b64":"SEh7shjz3jB2tnKBIUwysIQkHkZprGV8OPlogFWs6UxLNwNWzGI/aFSlcqNAmQv2K947IEF9xczNp6TE9kFzBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3e5d8d7913cc575daa7ba6b35f533621eae0b0f4b5bcc31b687053a31379df8","last_reissued_at":"2026-05-18T03:53:16.734547Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:53:16.734547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1206.4116","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":"2026-05-18T03:53:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yWNK1G8vVbFkcqq5mvfTHCfF/9BJpno7d3ufeanPadqeEFyQTJlgX28muddq24z8NcHWjJKOgc1ymG55KX1+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:03:23.400774Z"},"content_sha256":"53e074ebc18ec9ee1f6581f2c558f37a0b62153a862d60ca8ce530c31c1c99e7","schema_version":"1.0","event_id":"sha256:53e074ebc18ec9ee1f6581f2c558f37a0b62153a862d60ca8ce530c31c1c99e7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:YPS5RV4RHTCXLWVHXJVTL5JTMI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"stat.ML","authors_text":"Leonid Sigal, Makoto Yamada, Masashi Sugiyama, Michalis Raptis","submitted_at":"2012-06-19T03:35:52Z","abstract_excerpt":"The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time warping (LSDTW). LSDTW finds an alignment that maximizes statistical dependency between sequences, measured by a squared-loss variant of mutual information. The benefit of this novel information-theoretic formulation is that LSDTW can align sequences with different lengths, differen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.4116","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":"2026-05-18T03:53:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0nz6Qod1DkOA3UAeqZlUDJZJNX9aSahgpdNC7ir7YFzNuUMkOmVLyy/Ansy1qthuyLnoueXMaONJGHgRl6mPCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:03:23.401174Z"},"content_sha256":"bf457db074915e0121b11904bae0d6cc5403ab70f277fee5e7058ef8b9d77dd8","schema_version":"1.0","event_id":"sha256:bf457db074915e0121b11904bae0d6cc5403ab70f277fee5e7058ef8b9d77dd8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YPS5RV4RHTCXLWVHXJVTL5JTMI/bundle.json","state_url":"https://pith.science/pith/YPS5RV4RHTCXLWVHXJVTL5JTMI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YPS5RV4RHTCXLWVHXJVTL5JTMI/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":"2026-05-28T05:03:23Z","links":{"resolver":"https://pith.science/pith/YPS5RV4RHTCXLWVHXJVTL5JTMI","bundle":"https://pith.science/pith/YPS5RV4RHTCXLWVHXJVTL5JTMI/bundle.json","state":"https://pith.science/pith/YPS5RV4RHTCXLWVHXJVTL5JTMI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YPS5RV4RHTCXLWVHXJVTL5JTMI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:YPS5RV4RHTCXLWVHXJVTL5JTMI","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":"150d9ddb7352814977e3a3b3a692997b4b361c930e3a878da8d8d3c3ce3f3478","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-06-19T03:35:52Z","title_canon_sha256":"951a133ee9d099cb3fb260a38cb80fe820675ff794dc457bf7344b826d6ef693"},"schema_version":"1.0","source":{"id":"1206.4116","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.4116","created_at":"2026-05-18T03:53:16Z"},{"alias_kind":"arxiv_version","alias_value":"1206.4116v1","created_at":"2026-05-18T03:53:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.4116","created_at":"2026-05-18T03:53:16Z"},{"alias_kind":"pith_short_12","alias_value":"YPS5RV4RHTCX","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"YPS5RV4RHTCXLWVH","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"YPS5RV4R","created_at":"2026-05-18T12:27:27Z"}],"graph_snapshots":[{"event_id":"sha256:bf457db074915e0121b11904bae0d6cc5403ab70f277fee5e7058ef8b9d77dd8","target":"graph","created_at":"2026-05-18T03:53:16Z","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":"The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time warping (LSDTW). LSDTW finds an alignment that maximizes statistical dependency between sequences, measured by a squared-loss variant of mutual information. The benefit of this novel information-theoretic formulation is that LSDTW can align sequences with different lengths, differen","authors_text":"Leonid Sigal, Makoto Yamada, Masashi Sugiyama, Michalis Raptis","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-06-19T03:35:52Z","title":"Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.4116","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":"sha256:53e074ebc18ec9ee1f6581f2c558f37a0b62153a862d60ca8ce530c31c1c99e7","target":"record","created_at":"2026-05-18T03:53:16Z","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":"150d9ddb7352814977e3a3b3a692997b4b361c930e3a878da8d8d3c3ce3f3478","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-06-19T03:35:52Z","title_canon_sha256":"951a133ee9d099cb3fb260a38cb80fe820675ff794dc457bf7344b826d6ef693"},"schema_version":"1.0","source":{"id":"1206.4116","kind":"arxiv","version":1}},"canonical_sha256":"c3e5d8d7913cc575daa7ba6b35f533621eae0b0f4b5bcc31b687053a31379df8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c3e5d8d7913cc575daa7ba6b35f533621eae0b0f4b5bcc31b687053a31379df8","first_computed_at":"2026-05-18T03:53:16.734547Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:53:16.734547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SEh7shjz3jB2tnKBIUwysIQkHkZprGV8OPlogFWs6UxLNwNWzGI/aFSlcqNAmQv2K947IEF9xczNp6TE9kFzBA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:53:16.735372Z","signed_message":"canonical_sha256_bytes"},"source_id":"1206.4116","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53e074ebc18ec9ee1f6581f2c558f37a0b62153a862d60ca8ce530c31c1c99e7","sha256:bf457db074915e0121b11904bae0d6cc5403ab70f277fee5e7058ef8b9d77dd8"],"state_sha256":"bf24345f3cb127ecad6712a3cb7e1969e225f39713c55fb6f10f23ccc11360d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lxmxTcOsX16L+GTXLERE4VmgT6HSSLe9vSwTdDkyDht89zH80XNg4eUfRo66WxCfnYRy8342MTUVSW4qeI3RAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T05:03:23.403307Z","bundle_sha256":"49ed69f0aa48aeaa8a1a877fd0085607910a986fa13ca38a142bae4025e16176"}}