{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IYFHPUZR5QJ5377KPTZDJAGBSC","short_pith_number":"pith:IYFHPUZR","canonical_record":{"source":{"id":"1812.06247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NE","submitted_at":"2018-12-15T07:02:44Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b7ff51cb00ad23df6a086d3c5cf9884358cbee0b91e71478110fd4b7cdd1b9c3","abstract_canon_sha256":"151ccab38b724dbcf8570191ebff9ddb2c6ce65388faa50776fc3d29d5bbcf53"},"schema_version":"1.0"},"canonical_sha256":"460a77d331ec13ddffea7cf23480c1908c7ebff0ac097c25786ebf2cfd3fcb1d","source":{"kind":"arxiv","id":"1812.06247","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.06247","created_at":"2026-05-17T23:58:10Z"},{"alias_kind":"arxiv_version","alias_value":"1812.06247v1","created_at":"2026-05-17T23:58:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06247","created_at":"2026-05-17T23:58:10Z"},{"alias_kind":"pith_short_12","alias_value":"IYFHPUZR5QJ5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IYFHPUZR5QJ5377K","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IYFHPUZR","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IYFHPUZR5QJ5377KPTZDJAGBSC","target":"record","payload":{"canonical_record":{"source":{"id":"1812.06247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NE","submitted_at":"2018-12-15T07:02:44Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b7ff51cb00ad23df6a086d3c5cf9884358cbee0b91e71478110fd4b7cdd1b9c3","abstract_canon_sha256":"151ccab38b724dbcf8570191ebff9ddb2c6ce65388faa50776fc3d29d5bbcf53"},"schema_version":"1.0"},"canonical_sha256":"460a77d331ec13ddffea7cf23480c1908c7ebff0ac097c25786ebf2cfd3fcb1d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:10.806417Z","signature_b64":"2D3WyUBpvd15zRqtgIy+1c0F7gU/tzCQ3ycs7ae0UjENNpR99XQOOXss2ceS897cPMJaVZMurerhtO+qg8S5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"460a77d331ec13ddffea7cf23480c1908c7ebff0ac097c25786ebf2cfd3fcb1d","last_reissued_at":"2026-05-17T23:58:10.805886Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:10.805886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.06247","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":"2026-05-17T23:58:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q2OMiKxXa78DAf+unBkRo648a5XB+oEO8jVNPcjACFg9byakYT5fb+vW9WQk4/LUiRFUp5PDkwb4vSBk7YRMCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:38:15.530256Z"},"content_sha256":"930f43340eb400688503d1082c3931072d427fec802c81c7a11b6967360f20f6","schema_version":"1.0","event_id":"sha256:930f43340eb400688503d1082c3931072d427fec802c81c7a11b6967360f20f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IYFHPUZR5QJ5377KPTZDJAGBSC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"Hock Hung Chieng, Noorhaniza Wahid, Pauline Ong, Sai Raj Kishore Perla","submitted_at":"2018-12-15T07:02:44Z","abstract_excerpt":"Activation functions are essential for deep learning methods to learn and perform complex tasks such as image classification. Rectified Linear Unit (ReLU) has been widely used and become the default activation function across the deep learning community since 2012. Although ReLU has been popular, however, the hard zero property of the ReLU has heavily hindered the negative values from propagating through the network. Consequently, the deep neural network has not been benefited from the negative representations. In this work, an activation function called Flatten-T Swish (FTS) that leverage the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06247","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":"2026-05-17T23:58:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vVdS4/XrJFYQg+scxPrRlGIri59PdoMYdKO7D3HX/IQEJWmurAJk4fUBU+wLgHL3Ie6EP7heVwdrGDP6ovyXAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:38:15.530882Z"},"content_sha256":"41700ebdb30f202d1e10a234cc849b6e9e924d92835d5fded351160c15479c77","schema_version":"1.0","event_id":"sha256:41700ebdb30f202d1e10a234cc849b6e9e924d92835d5fded351160c15479c77"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IYFHPUZR5QJ5377KPTZDJAGBSC/bundle.json","state_url":"https://pith.science/pith/IYFHPUZR5QJ5377KPTZDJAGBSC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IYFHPUZR5QJ5377KPTZDJAGBSC/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":"2026-05-31T01:38:15Z","links":{"resolver":"https://pith.science/pith/IYFHPUZR5QJ5377KPTZDJAGBSC","bundle":"https://pith.science/pith/IYFHPUZR5QJ5377KPTZDJAGBSC/bundle.json","state":"https://pith.science/pith/IYFHPUZR5QJ5377KPTZDJAGBSC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IYFHPUZR5QJ5377KPTZDJAGBSC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IYFHPUZR5QJ5377KPTZDJAGBSC","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":"151ccab38b724dbcf8570191ebff9ddb2c6ce65388faa50776fc3d29d5bbcf53","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NE","submitted_at":"2018-12-15T07:02:44Z","title_canon_sha256":"b7ff51cb00ad23df6a086d3c5cf9884358cbee0b91e71478110fd4b7cdd1b9c3"},"schema_version":"1.0","source":{"id":"1812.06247","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.06247","created_at":"2026-05-17T23:58:10Z"},{"alias_kind":"arxiv_version","alias_value":"1812.06247v1","created_at":"2026-05-17T23:58:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06247","created_at":"2026-05-17T23:58:10Z"},{"alias_kind":"pith_short_12","alias_value":"IYFHPUZR5QJ5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IYFHPUZR5QJ5377K","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IYFHPUZR","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:41700ebdb30f202d1e10a234cc849b6e9e924d92835d5fded351160c15479c77","target":"graph","created_at":"2026-05-17T23:58:10Z","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":"Activation functions are essential for deep learning methods to learn and perform complex tasks such as image classification. Rectified Linear Unit (ReLU) has been widely used and become the default activation function across the deep learning community since 2012. Although ReLU has been popular, however, the hard zero property of the ReLU has heavily hindered the negative values from propagating through the network. Consequently, the deep neural network has not been benefited from the negative representations. In this work, an activation function called Flatten-T Swish (FTS) that leverage the","authors_text":"Hock Hung Chieng, Noorhaniza Wahid, Pauline Ong, Sai Raj Kishore Perla","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NE","submitted_at":"2018-12-15T07:02:44Z","title":"Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06247","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":"sha256:930f43340eb400688503d1082c3931072d427fec802c81c7a11b6967360f20f6","target":"record","created_at":"2026-05-17T23:58:10Z","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":"151ccab38b724dbcf8570191ebff9ddb2c6ce65388faa50776fc3d29d5bbcf53","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NE","submitted_at":"2018-12-15T07:02:44Z","title_canon_sha256":"b7ff51cb00ad23df6a086d3c5cf9884358cbee0b91e71478110fd4b7cdd1b9c3"},"schema_version":"1.0","source":{"id":"1812.06247","kind":"arxiv","version":1}},"canonical_sha256":"460a77d331ec13ddffea7cf23480c1908c7ebff0ac097c25786ebf2cfd3fcb1d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"460a77d331ec13ddffea7cf23480c1908c7ebff0ac097c25786ebf2cfd3fcb1d","first_computed_at":"2026-05-17T23:58:10.805886Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:10.805886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2D3WyUBpvd15zRqtgIy+1c0F7gU/tzCQ3ycs7ae0UjENNpR99XQOOXss2ceS897cPMJaVZMurerhtO+qg8S5BA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:10.806417Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.06247","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:930f43340eb400688503d1082c3931072d427fec802c81c7a11b6967360f20f6","sha256:41700ebdb30f202d1e10a234cc849b6e9e924d92835d5fded351160c15479c77"],"state_sha256":"8c5ee3c10e343702428ff0e4ef6a5646adaab6848fd7aa39017cc62e8cd47711"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8clut69e+4lFRlDQOrpelJGK1fF/bj/V3rM5yAfXCfVYmEcv3TLGAj3dBgEvXCnpdzZgbqGv4hm9ZSfuP06sCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T01:38:15.533948Z","bundle_sha256":"560e36805fa9dce3cbd0536bb9dc0a520320aa41e5da001f8b07f00d39e37638"}}