{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VXR3DWDHXNNSHA6WM33WVVJL3K","short_pith_number":"pith:VXR3DWDH","canonical_record":{"source":{"id":"1709.08073","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-23T16:42:34Z","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"title_canon_sha256":"69d75265815b510502ee535c3030c757859141a027adf357e32ab8d7ea69a8c5","abstract_canon_sha256":"1186c728e7e5a4e82ee5d18084fc7b80ed7a0630626d296a2befb94297735761"},"schema_version":"1.0"},"canonical_sha256":"ade3b1d867bb5b2383d666f76ad52bda95e41e240b780ee2572f4c6a2d6e972d","source":{"kind":"arxiv","id":"1709.08073","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.08073","created_at":"2026-05-18T00:05:56Z"},{"alias_kind":"arxiv_version","alias_value":"1709.08073v2","created_at":"2026-05-18T00:05:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08073","created_at":"2026-05-18T00:05:56Z"},{"alias_kind":"pith_short_12","alias_value":"VXR3DWDHXNNS","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VXR3DWDHXNNSHA6W","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VXR3DWDH","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VXR3DWDHXNNSHA6WM33WVVJL3K","target":"record","payload":{"canonical_record":{"source":{"id":"1709.08073","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-23T16:42:34Z","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"title_canon_sha256":"69d75265815b510502ee535c3030c757859141a027adf357e32ab8d7ea69a8c5","abstract_canon_sha256":"1186c728e7e5a4e82ee5d18084fc7b80ed7a0630626d296a2befb94297735761"},"schema_version":"1.0"},"canonical_sha256":"ade3b1d867bb5b2383d666f76ad52bda95e41e240b780ee2572f4c6a2d6e972d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:56.599091Z","signature_b64":"1uJbSwVMa2tnsiGfk/faw6E/Ake9HUAn7JDGQ+xUwZdtGgNiVH4vtCrDt7vvmMwXYPNsN0kOQXxOA6X0AC4/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ade3b1d867bb5b2383d666f76ad52bda95e41e240b780ee2572f4c6a2d6e972d","last_reissued_at":"2026-05-18T00:05:56.598506Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:56.598506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.08073","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":"2026-05-18T00:05:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gdmNESzIJYNO4lV3YXNDxyPiiouG3lUV3HRuOJyMYVmW43YytC1ga/JNlgcSVLOQaXGHvXG6qc4xQ7ExOvMHAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T16:37:49.711669Z"},"content_sha256":"15deffe306bb88faa5d0e10e37ce27bbeba293c268f26a17a006991fccdb2f96","schema_version":"1.0","event_id":"sha256:15deffe306bb88faa5d0e10e37ce27bbeba293c268f26a17a006991fccdb2f96"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VXR3DWDHXNNSHA6WM33WVVJL3K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","q-bio.QM"],"primary_cat":"stat.ML","authors_text":"Angela Chieh, Edgar Liberis, Laurynas Karazija, Matthieu Vegreville, Nicholas D. Lane, Otmane Bellahsen, Petar Veli\\v{c}kovi\\'c, Pietro Li\\`o, Sourav Bhattacharya","submitted_at":"2017-09-23T16:42:34Z","abstract_excerpt":"We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LS"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08073","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":"2026-05-18T00:05:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7EFypQdhHngmx0KgZZxjtI9dJ40/rjEPi4mE+a12d4QB9fCTm/jHfydde3YS4g3XxwxjfLa+Px3mTAM2oNOvBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T16:37:49.712022Z"},"content_sha256":"81d49a33d43f0b4ffd0b3b7cf108be53592cac233dbcaf7e5935b4cabfac70c2","schema_version":"1.0","event_id":"sha256:81d49a33d43f0b4ffd0b3b7cf108be53592cac233dbcaf7e5935b4cabfac70c2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VXR3DWDHXNNSHA6WM33WVVJL3K/bundle.json","state_url":"https://pith.science/pith/VXR3DWDHXNNSHA6WM33WVVJL3K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VXR3DWDHXNNSHA6WM33WVVJL3K/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":"2026-06-03T16:37:49Z","links":{"resolver":"https://pith.science/pith/VXR3DWDHXNNSHA6WM33WVVJL3K","bundle":"https://pith.science/pith/VXR3DWDHXNNSHA6WM33WVVJL3K/bundle.json","state":"https://pith.science/pith/VXR3DWDHXNNSHA6WM33WVVJL3K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VXR3DWDHXNNSHA6WM33WVVJL3K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VXR3DWDHXNNSHA6WM33WVVJL3K","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":"1186c728e7e5a4e82ee5d18084fc7b80ed7a0630626d296a2befb94297735761","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-23T16:42:34Z","title_canon_sha256":"69d75265815b510502ee535c3030c757859141a027adf357e32ab8d7ea69a8c5"},"schema_version":"1.0","source":{"id":"1709.08073","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.08073","created_at":"2026-05-18T00:05:56Z"},{"alias_kind":"arxiv_version","alias_value":"1709.08073v2","created_at":"2026-05-18T00:05:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08073","created_at":"2026-05-18T00:05:56Z"},{"alias_kind":"pith_short_12","alias_value":"VXR3DWDHXNNS","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VXR3DWDHXNNSHA6W","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VXR3DWDH","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:81d49a33d43f0b4ffd0b3b7cf108be53592cac233dbcaf7e5935b4cabfac70c2","target":"graph","created_at":"2026-05-18T00:05:56Z","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":"We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LS","authors_text":"Angela Chieh, Edgar Liberis, Laurynas Karazija, Matthieu Vegreville, Nicholas D. Lane, Otmane Bellahsen, Petar Veli\\v{c}kovi\\'c, Pietro Li\\`o, Sourav Bhattacharya","cross_cats":["cs.AI","cs.LG","q-bio.QM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-23T16:42:34Z","title":"Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08073","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":"sha256:15deffe306bb88faa5d0e10e37ce27bbeba293c268f26a17a006991fccdb2f96","target":"record","created_at":"2026-05-18T00:05:56Z","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":"1186c728e7e5a4e82ee5d18084fc7b80ed7a0630626d296a2befb94297735761","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-23T16:42:34Z","title_canon_sha256":"69d75265815b510502ee535c3030c757859141a027adf357e32ab8d7ea69a8c5"},"schema_version":"1.0","source":{"id":"1709.08073","kind":"arxiv","version":2}},"canonical_sha256":"ade3b1d867bb5b2383d666f76ad52bda95e41e240b780ee2572f4c6a2d6e972d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ade3b1d867bb5b2383d666f76ad52bda95e41e240b780ee2572f4c6a2d6e972d","first_computed_at":"2026-05-18T00:05:56.598506Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:56.598506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1uJbSwVMa2tnsiGfk/faw6E/Ake9HUAn7JDGQ+xUwZdtGgNiVH4vtCrDt7vvmMwXYPNsN0kOQXxOA6X0AC4/CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:56.599091Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.08073","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15deffe306bb88faa5d0e10e37ce27bbeba293c268f26a17a006991fccdb2f96","sha256:81d49a33d43f0b4ffd0b3b7cf108be53592cac233dbcaf7e5935b4cabfac70c2"],"state_sha256":"a23f2fd505b24a62a1297a2056794a868cb94749c4e9ed23b7990dd1e2735775"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yO9mNgR4+uNh4wZzLvaUnIZyB1eP23Xrhix/fl45g5/JUcnR9It3Dg9MpD1t63zGHIXSiaA492h9CxKBVBylAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T16:37:49.714079Z","bundle_sha256":"e30dfa14c09966c51c2b1589ddfe8f6e0419977251a78ba214058efef35d2f93"}}