{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:IW6UDZV6ZAJWB7Y5FLFBJNSCUM","short_pith_number":"pith:IW6UDZV6","canonical_record":{"source":{"id":"1502.04187","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-14T10:58:53Z","cross_cats_sorted":[],"title_canon_sha256":"6f0d54d8fd7000d7663f7a2da1d1a465c0cda8b45450fa4174cdc889bccefbb0","abstract_canon_sha256":"ba14a3dcd8f21f5bf9806a2bac28a77e662a0a106e533fd79f81457f05506012"},"schema_version":"1.0"},"canonical_sha256":"45bd41e6bec81360ff1d2aca14b642a30ccef0b4607f950589ffc8d6e1569346","source":{"kind":"arxiv","id":"1502.04187","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.04187","created_at":"2026-05-18T01:37:11Z"},{"alias_kind":"arxiv_version","alias_value":"1502.04187v2","created_at":"2026-05-18T01:37:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.04187","created_at":"2026-05-18T01:37:11Z"},{"alias_kind":"pith_short_12","alias_value":"IW6UDZV6ZAJW","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"IW6UDZV6ZAJWB7Y5","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"IW6UDZV6","created_at":"2026-05-18T12:29:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:IW6UDZV6ZAJWB7Y5FLFBJNSCUM","target":"record","payload":{"canonical_record":{"source":{"id":"1502.04187","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-14T10:58:53Z","cross_cats_sorted":[],"title_canon_sha256":"6f0d54d8fd7000d7663f7a2da1d1a465c0cda8b45450fa4174cdc889bccefbb0","abstract_canon_sha256":"ba14a3dcd8f21f5bf9806a2bac28a77e662a0a106e533fd79f81457f05506012"},"schema_version":"1.0"},"canonical_sha256":"45bd41e6bec81360ff1d2aca14b642a30ccef0b4607f950589ffc8d6e1569346","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:11.036274Z","signature_b64":"OICCxOA0haB+Ocnj5I6jmui17Xmip90lLo1WaLhlRxMhrurtXbTLOSq0ZyCF771eDF+5tNieelMQ3rIYJ8jfCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45bd41e6bec81360ff1d2aca14b642a30ccef0b4607f950589ffc8d6e1569346","last_reissued_at":"2026-05-18T01:37:11.035708Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:11.035708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.04187","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":"2026-05-18T01:37:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zjsPHstq+UjvcFlhwsF+tanf6AHRRW5m5t1uzgbz64x08SatSSBPNQPsUHhlsVlnYr6pGWOOvLNpfrxBTK+1Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:28:12.704668Z"},"content_sha256":"c078c668da9657aa388cc847c261a0eece4a5c5f3bb7329c416edc12fba922d5","schema_version":"1.0","event_id":"sha256:c078c668da9657aa388cc847c261a0eece4a5c5f3bb7329c416edc12fba922d5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:IW6UDZV6ZAJWB7Y5FLFBJNSCUM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Application of Deep Neural Network in Estimation of the Weld Bead Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chee Khiang Pang, Chee Meng Chew, Soheil Keshmiri, Xin Zheng","submitted_at":"2015-02-14T10:58:53Z","abstract_excerpt":"We present a deep learning approach to estimation of the bead parameters in welding tasks. Our model is based on a four-hidden-layer neural network architecture. More specifically, the first three hidden layers of this architecture utilize Sigmoid function to produce their respective intermediate outputs. On the other hand, the last hidden layer uses a linear transformation to generate the final output of this architecture. This transforms our deep network architecture from a classifier to a non-linear regression model. We compare the performance of our deep network with a selected number of r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.04187","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":"2026-05-18T01:37:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sIbHxEPT73c2LjtAkccf8Fyf3R/Oe3wEouaPK7bpRKtG6YrsPaqrcIKWsaTIptMesR73UISagdFr8AXUZQj2AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:28:12.705060Z"},"content_sha256":"03339db283f599f859dc854b7d1a3ec642263822290f4f33a7d777cd0ee774c9","schema_version":"1.0","event_id":"sha256:03339db283f599f859dc854b7d1a3ec642263822290f4f33a7d777cd0ee774c9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IW6UDZV6ZAJWB7Y5FLFBJNSCUM/bundle.json","state_url":"https://pith.science/pith/IW6UDZV6ZAJWB7Y5FLFBJNSCUM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IW6UDZV6ZAJWB7Y5FLFBJNSCUM/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":"2026-05-28T16:28:12Z","links":{"resolver":"https://pith.science/pith/IW6UDZV6ZAJWB7Y5FLFBJNSCUM","bundle":"https://pith.science/pith/IW6UDZV6ZAJWB7Y5FLFBJNSCUM/bundle.json","state":"https://pith.science/pith/IW6UDZV6ZAJWB7Y5FLFBJNSCUM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IW6UDZV6ZAJWB7Y5FLFBJNSCUM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:IW6UDZV6ZAJWB7Y5FLFBJNSCUM","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":"ba14a3dcd8f21f5bf9806a2bac28a77e662a0a106e533fd79f81457f05506012","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-14T10:58:53Z","title_canon_sha256":"6f0d54d8fd7000d7663f7a2da1d1a465c0cda8b45450fa4174cdc889bccefbb0"},"schema_version":"1.0","source":{"id":"1502.04187","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.04187","created_at":"2026-05-18T01:37:11Z"},{"alias_kind":"arxiv_version","alias_value":"1502.04187v2","created_at":"2026-05-18T01:37:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.04187","created_at":"2026-05-18T01:37:11Z"},{"alias_kind":"pith_short_12","alias_value":"IW6UDZV6ZAJW","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"IW6UDZV6ZAJWB7Y5","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"IW6UDZV6","created_at":"2026-05-18T12:29:27Z"}],"graph_snapshots":[{"event_id":"sha256:03339db283f599f859dc854b7d1a3ec642263822290f4f33a7d777cd0ee774c9","target":"graph","created_at":"2026-05-18T01:37:11Z","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 present a deep learning approach to estimation of the bead parameters in welding tasks. Our model is based on a four-hidden-layer neural network architecture. More specifically, the first three hidden layers of this architecture utilize Sigmoid function to produce their respective intermediate outputs. On the other hand, the last hidden layer uses a linear transformation to generate the final output of this architecture. This transforms our deep network architecture from a classifier to a non-linear regression model. We compare the performance of our deep network with a selected number of r","authors_text":"Chee Khiang Pang, Chee Meng Chew, Soheil Keshmiri, Xin Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-14T10:58:53Z","title":"Application of Deep Neural Network in Estimation of the Weld Bead Parameters"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.04187","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:c078c668da9657aa388cc847c261a0eece4a5c5f3bb7329c416edc12fba922d5","target":"record","created_at":"2026-05-18T01:37:11Z","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":"ba14a3dcd8f21f5bf9806a2bac28a77e662a0a106e533fd79f81457f05506012","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-14T10:58:53Z","title_canon_sha256":"6f0d54d8fd7000d7663f7a2da1d1a465c0cda8b45450fa4174cdc889bccefbb0"},"schema_version":"1.0","source":{"id":"1502.04187","kind":"arxiv","version":2}},"canonical_sha256":"45bd41e6bec81360ff1d2aca14b642a30ccef0b4607f950589ffc8d6e1569346","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45bd41e6bec81360ff1d2aca14b642a30ccef0b4607f950589ffc8d6e1569346","first_computed_at":"2026-05-18T01:37:11.035708Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:37:11.035708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OICCxOA0haB+Ocnj5I6jmui17Xmip90lLo1WaLhlRxMhrurtXbTLOSq0ZyCF771eDF+5tNieelMQ3rIYJ8jfCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:37:11.036274Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.04187","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c078c668da9657aa388cc847c261a0eece4a5c5f3bb7329c416edc12fba922d5","sha256:03339db283f599f859dc854b7d1a3ec642263822290f4f33a7d777cd0ee774c9"],"state_sha256":"ef028d9c007a7687735afca2aacd51c0aeda70dbbcae287621850cec2ecda1a7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A5e5QJVcdS4mIYABnHVgU8bd0LE3a5k9PrBFa5nX2tX1RjkF1keKnplJurUpA+Waw0jDfJEC/wMNk3V5aDqeCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T16:28:12.707240Z","bundle_sha256":"878fdadcc65f03cefdf9461286ca5610a88d8b35f4555d8e01075edc019226b4"}}