{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KNQNDBHYDMUHXNPAF3EHON26IB","short_pith_number":"pith:KNQNDBHY","canonical_record":{"source":{"id":"2307.03353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T02:22:17Z","cross_cats_sorted":[],"title_canon_sha256":"357886d2f5316a438e78f84c01a4ed33787a9eebda1b26a8a90b39e5ac4b28c5","abstract_canon_sha256":"22a4c1b217596ac7953fdfc08e9c0faad9f088af9738ff5c54d82ed5c95e2483"},"schema_version":"1.0"},"canonical_sha256":"5360d184f81b287bb5e02ec877375e4060035191f443ae4f77fa726622416341","source":{"kind":"arxiv","id":"2307.03353","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.03353","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"arxiv_version","alias_value":"2307.03353v1","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.03353","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"pith_short_12","alias_value":"KNQNDBHYDMUH","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"KNQNDBHYDMUHXNPA","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"KNQNDBHY","created_at":"2026-07-05T06:28:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KNQNDBHYDMUHXNPAF3EHON26IB","target":"record","payload":{"canonical_record":{"source":{"id":"2307.03353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T02:22:17Z","cross_cats_sorted":[],"title_canon_sha256":"357886d2f5316a438e78f84c01a4ed33787a9eebda1b26a8a90b39e5ac4b28c5","abstract_canon_sha256":"22a4c1b217596ac7953fdfc08e9c0faad9f088af9738ff5c54d82ed5c95e2483"},"schema_version":"1.0"},"canonical_sha256":"5360d184f81b287bb5e02ec877375e4060035191f443ae4f77fa726622416341","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:28:41.657427Z","signature_b64":"TQ/+xy/GMiSbUqmOfXdj1ZSA4rRkh43IDVdsyO8x4UgN/bI/o0zfii6pvPjLVR1DnCVxkaPDjJDTDpdWdjs0AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5360d184f81b287bb5e02ec877375e4060035191f443ae4f77fa726622416341","last_reissued_at":"2026-07-05T06:28:41.656952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:28:41.656952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.03353","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":"2026-07-05T06:28:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XgdRwa3Z28lZogb9DIycItDQ/YemmaQ8ete8otfA8l3V1nwf6YGl4YXyrhlrpWMbPqG7f4c85rkk+k4d1XcVBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:55:51.104459Z"},"content_sha256":"4376688911d27aaa687a3fb1450a56c5719987e0d83f577bb1e6ba24312ebcd9","schema_version":"1.0","event_id":"sha256:4376688911d27aaa687a3fb1450a56c5719987e0d83f577bb1e6ba24312ebcd9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KNQNDBHYDMUHXNPAF3EHON26IB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gaoang Wang, Guanhong Wang, Jenq-Neng Hwang, Mingli Song, Shengyu Hao, Shidong Cao, Wenhao Chai, Wenhao Hu, Zhonghan Zhao","submitted_at":"2023-07-07T02:22:17Z","abstract_excerpt":"Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their cha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.03353","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.03353/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T06:28:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sMmKwyXnQtNIYj4T5R7YIZ6swe2to2zbLFELB8USGi3MLQ6gKVbJUPfEvAMxEVnWT6Ja0Xa4BAOIQZkA/feKDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:55:51.105373Z"},"content_sha256":"5fae804d9c8b6f50459c78be5a9c7b24ba38f25f0038f2cdae45b837494edf9e","schema_version":"1.0","event_id":"sha256:5fae804d9c8b6f50459c78be5a9c7b24ba38f25f0038f2cdae45b837494edf9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KNQNDBHYDMUHXNPAF3EHON26IB/bundle.json","state_url":"https://pith.science/pith/KNQNDBHYDMUHXNPAF3EHON26IB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KNQNDBHYDMUHXNPAF3EHON26IB/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":"2026-08-23T03:55:51Z","links":{"resolver":"https://pith.science/pith/KNQNDBHYDMUHXNPAF3EHON26IB","bundle":"https://pith.science/pith/KNQNDBHYDMUHXNPAF3EHON26IB/bundle.json","state":"https://pith.science/pith/KNQNDBHYDMUHXNPAF3EHON26IB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KNQNDBHYDMUHXNPAF3EHON26IB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KNQNDBHYDMUHXNPAF3EHON26IB","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":"22a4c1b217596ac7953fdfc08e9c0faad9f088af9738ff5c54d82ed5c95e2483","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T02:22:17Z","title_canon_sha256":"357886d2f5316a438e78f84c01a4ed33787a9eebda1b26a8a90b39e5ac4b28c5"},"schema_version":"1.0","source":{"id":"2307.03353","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.03353","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"arxiv_version","alias_value":"2307.03353v1","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.03353","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"pith_short_12","alias_value":"KNQNDBHYDMUH","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"pith_short_16","alias_value":"KNQNDBHYDMUHXNPA","created_at":"2026-07-05T06:28:41Z"},{"alias_kind":"pith_short_8","alias_value":"KNQNDBHY","created_at":"2026-07-05T06:28:41Z"}],"graph_snapshots":[{"event_id":"sha256:5fae804d9c8b6f50459c78be5a9c7b24ba38f25f0038f2cdae45b837494edf9e","target":"graph","created_at":"2026-07-05T06:28:41Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2307.03353/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their cha","authors_text":"Gaoang Wang, Guanhong Wang, Jenq-Neng Hwang, Mingli Song, Shengyu Hao, Shidong Cao, Wenhao Chai, Wenhao Hu, Zhonghan Zhao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T02:22:17Z","title":"A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.03353","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":"sha256:4376688911d27aaa687a3fb1450a56c5719987e0d83f577bb1e6ba24312ebcd9","target":"record","created_at":"2026-07-05T06:28:41Z","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":"22a4c1b217596ac7953fdfc08e9c0faad9f088af9738ff5c54d82ed5c95e2483","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-07T02:22:17Z","title_canon_sha256":"357886d2f5316a438e78f84c01a4ed33787a9eebda1b26a8a90b39e5ac4b28c5"},"schema_version":"1.0","source":{"id":"2307.03353","kind":"arxiv","version":1}},"canonical_sha256":"5360d184f81b287bb5e02ec877375e4060035191f443ae4f77fa726622416341","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5360d184f81b287bb5e02ec877375e4060035191f443ae4f77fa726622416341","first_computed_at":"2026-07-05T06:28:41.656952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:28:41.656952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TQ/+xy/GMiSbUqmOfXdj1ZSA4rRkh43IDVdsyO8x4UgN/bI/o0zfii6pvPjLVR1DnCVxkaPDjJDTDpdWdjs0AA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:28:41.657427Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.03353","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4376688911d27aaa687a3fb1450a56c5719987e0d83f577bb1e6ba24312ebcd9","sha256:5fae804d9c8b6f50459c78be5a9c7b24ba38f25f0038f2cdae45b837494edf9e"],"state_sha256":"4ff6a682f6764e742eebe95eed19b41691ab330a3c0e2bc23f1ff277adb21d7e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lQAQUmD3Ee4ClkH6ZUI2LQtTgPpe93Pp7iCo4nA7L4AubUzLIH79JBvjTIvqBVzLnKX9IClneBckECEOCZqBDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T03:55:51.110449Z","bundle_sha256":"2f42884db32c080d42213603d59726b3d66fb2423ffea3838792817a5e6ebdc8"}}