{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:C4TDXTTRV5AGY4RLCCAD6IEJ2A","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":"5ac380164d0c8e9b187d59a70f81a5c7f64a6cc4d9909e79ddac3b8d32814f57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-02T03:37:15Z","title_canon_sha256":"b4c6c02ebbb11463185ce2ed2725e0890a3bf39767e94d43e11a65714ffb5c28"},"schema_version":"1.0","source":{"id":"2108.07787","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.07787","created_at":"2026-07-05T03:06:42Z"},{"alias_kind":"arxiv_version","alias_value":"2108.07787v1","created_at":"2026-07-05T03:06:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.07787","created_at":"2026-07-05T03:06:42Z"},{"alias_kind":"pith_short_12","alias_value":"C4TDXTTRV5AG","created_at":"2026-07-05T03:06:42Z"},{"alias_kind":"pith_short_16","alias_value":"C4TDXTTRV5AGY4RL","created_at":"2026-07-05T03:06:42Z"},{"alias_kind":"pith_short_8","alias_value":"C4TDXTTR","created_at":"2026-07-05T03:06:42Z"}],"graph_snapshots":[{"event_id":"sha256:202b53d77320dc5303f5c4972efcf98f6237bffaa2338be4f21ea2a870d359c4","target":"graph","created_at":"2026-07-05T03:06:42Z","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/2108.07787/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time Delay Neural Networks (TDNN)-based methods are widely used in dialect identification. However, in previous work with TDNN application, subtle variant is being neglected in different feature scales. To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling. Dynamic kernel convolution captures features between short-term and long-term context adaptively. Local multi-scale learning, which represents multi-scale features at a granular level, is able to in","authors_text":"Dandan Song, Dawei Zhang, Huiyu Shi, Jinwen Huang, Shouyi Yin, Tianlong Kong, Wang Geng, Xiaorui Wang, Xin Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-02T03:37:15Z","title":"Dynamic Multi-scale Convolution for Dialect Identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.07787","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:f7c2847d1d0a7ae5d11cde2e59a68339d17e414295f86443225e6080bba766c0","target":"record","created_at":"2026-07-05T03:06:42Z","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":"5ac380164d0c8e9b187d59a70f81a5c7f64a6cc4d9909e79ddac3b8d32814f57","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-02T03:37:15Z","title_canon_sha256":"b4c6c02ebbb11463185ce2ed2725e0890a3bf39767e94d43e11a65714ffb5c28"},"schema_version":"1.0","source":{"id":"2108.07787","kind":"arxiv","version":1}},"canonical_sha256":"17263bce71af406c722b10803f2089d01b4b12ccbcbcf63c06427bcd25d9d1ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17263bce71af406c722b10803f2089d01b4b12ccbcbcf63c06427bcd25d9d1ea","first_computed_at":"2026-07-05T03:06:42.773719Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:06:42.773719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5zNGcMmXBW1q36TK2g07kKZVlFKr6tTu/KA3HQmtRJPRXaRWKhtaSLMer9oVmOuqaa73x4w3xjmt1QwEYMNCBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:06:42.774244Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.07787","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f7c2847d1d0a7ae5d11cde2e59a68339d17e414295f86443225e6080bba766c0","sha256:202b53d77320dc5303f5c4972efcf98f6237bffaa2338be4f21ea2a870d359c4"],"state_sha256":"23cad27dd49ecd006851ca1d3638d979fbee574b5f41352f6a34a9a18ca9d4e6"}