{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XKO3DYKMVIOKZM5HX6TSVZFV26","short_pith_number":"pith:XKO3DYKM","canonical_record":{"source":{"id":"2605.13105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:15:37Z","cross_cats_sorted":[],"title_canon_sha256":"974139390ca7ed35f9d6b6dc7187f4d9158adabc2396863129e1be71ce3dadfc","abstract_canon_sha256":"2216604d31c8e863c8e4a104c63fcf7c24903ce67f6a7380cbd2b52faf0e1fc4"},"schema_version":"1.0"},"canonical_sha256":"ba9db1e14caa1cacb3a7bfa72ae4b5d7ba02910f260d89f95f7fc67d2f8b6f37","source":{"kind":"arxiv","id":"2605.13105","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13105","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13105v1","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13105","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"pith_short_12","alias_value":"XKO3DYKMVIOK","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XKO3DYKMVIOKZM5H","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XKO3DYKM","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XKO3DYKMVIOKZM5HX6TSVZFV26","target":"record","payload":{"canonical_record":{"source":{"id":"2605.13105","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:15:37Z","cross_cats_sorted":[],"title_canon_sha256":"974139390ca7ed35f9d6b6dc7187f4d9158adabc2396863129e1be71ce3dadfc","abstract_canon_sha256":"2216604d31c8e863c8e4a104c63fcf7c24903ce67f6a7380cbd2b52faf0e1fc4"},"schema_version":"1.0"},"canonical_sha256":"ba9db1e14caa1cacb3a7bfa72ae4b5d7ba02910f260d89f95f7fc67d2f8b6f37","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:08:58.185618Z","signature_b64":"wjt18k2VdFeHoruV1e1jYr7vrUYkyrmVIq/O509yiE0Ek4dNIsPfYtyHvcFteAG80rU8tcqTbs3b8Vglt036CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba9db1e14caa1cacb3a7bfa72ae4b5d7ba02910f260d89f95f7fc67d2f8b6f37","last_reissued_at":"2026-05-18T03:08:58.185087Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:08:58.185087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.13105","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":"2026-05-18T03:08:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mFat5vLySlZRBZ0wZ2dJTXal7NuOdvZ+qepGE1flXnlFjY+Zauwq7UIzMAYQGV0MDyc/ZQ93ZyDKqJulVRbHCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T11:00:23.181054Z"},"content_sha256":"3c03b2f3d8392928e0da93a107bec820a8e14d5b005309ce1d42adbd8a1bcca6","schema_version":"1.0","event_id":"sha256:3c03b2f3d8392928e0da93a107bec820a8e14d5b005309ce1d42adbd8a1bcca6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XKO3DYKMVIOKZM5HX6TSVZFV26","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants to improve RL fine-tuning of VLA models under visual shifts.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chuheng Zhang, Jiang Bian, Jingjing Fu, Jun Zhang, Ling Zhang, Li Zhao, Mingyu Liu, Rui Wang, Yuanfang Peng","submitted_at":"2026-05-13T07:15:37Z","abstract_excerpt":"Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual shifts pose practical challenges. A key difficulty is that standard task rewards supervise task success, but offer limited guidance on whether a visual change is task-irrelevant or changes the behavior required for manipulation. We propose PAIR-VLA (Paired Action Invariance & Sensitivity for Visually Robust VLA), an RL fine-tuning framework to address this difficulty by adding two auxiliary objectives over paired visual variants during PPO optimi"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our method consistently improves over standard PPO, achieving average improvements of 16.62% on π0.5 and 9.10% on OpenVLA across diverse out-of-distribution visual shifts.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That paired visual variants (task-preserving and task-altering) can be reliably generated or labeled during training to provide accurate behavior-level supervision without introducing new biases.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants during PPO fine-tuning of VLA models, yielding 9-16% average gains on ManiSkill3 under distractors, textures, poses, viewpoints, and lighting shifts.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants to improve RL fine-tuning of VLA models under visual shifts.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"0d6da9a3407ba9d4c3a6557a00747a9cbcc78446906970bcaaf9a56db4009d45"},"source":{"id":"2605.13105","kind":"arxiv","version":1},"verdict":{"id":"7679d0dc-6da7-487d-b481-ed305e022076","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T18:41:43.923590Z","strongest_claim":"Our method consistently improves over standard PPO, achieving average improvements of 16.62% on π0.5 and 9.10% on OpenVLA across diverse out-of-distribution visual shifts.","one_line_summary":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants during PPO fine-tuning of VLA models, yielding 9-16% average gains on ManiSkill3 under distractors, textures, poses, viewpoints, and lighting shifts.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That paired visual variants (task-preserving and task-altering) can be reliably generated or labeled during training to provide accurate behavior-level supervision without introducing new biases.","pith_extraction_headline":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants to improve RL fine-tuning of VLA models under visual shifts."},"references":{"count":43,"sample":[{"doi":"","year":2024,"title":"Open x- embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0","work_id":"846c44cc-0874-4a3c-90cb-b86f68157e99","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset","work_id":"13253de2-3d89-415c-8c2f-3adb25d4c337","ref_index":2,"cited_arxiv_id":"2403.12945","is_internal_anchor":true},{"doi":"","year":2023,"title":"Rt-2: Vision-language-action models transfer web knowledge to robotic control, 2023","work_id":"f08c7a53-d673-4be5-b955-d92443959ebf","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Octo: An open-source generalist robot policy, 2024","work_id":"9644ac38-9a21-4d5e-9386-5460ec7456ea","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","work_id":"f790abdc-a796-482f-a40d-f8ee035ecfc2","ref_index":5,"cited_arxiv_id":"2410.24164","is_internal_anchor":true}],"resolved_work":43,"snapshot_sha256":"ec062a49971027b94d2df6dce5fbe087e484745e564ade7f770ea2cdc4c7f442","internal_anchors":9},"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":"7679d0dc-6da7-487d-b481-ed305e022076"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:08:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RyooBzeAw4w1qrc+YEnuYuP8R4HAFpFh3EPFQ93c8jQxSs0mM4sy/qpNDleBkdA32ZUBC9CZd4FS4/p1eZPjDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T11:00:23.181679Z"},"content_sha256":"e6da02523cff4e8601a29d9916a53760ce937f59a449afdcb67c76a5a11c0b66","schema_version":"1.0","event_id":"sha256:e6da02523cff4e8601a29d9916a53760ce937f59a449afdcb67c76a5a11c0b66"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XKO3DYKMVIOKZM5HX6TSVZFV26/bundle.json","state_url":"https://pith.science/pith/XKO3DYKMVIOKZM5HX6TSVZFV26/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XKO3DYKMVIOKZM5HX6TSVZFV26/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":"2026-06-01T11:00:23Z","links":{"resolver":"https://pith.science/pith/XKO3DYKMVIOKZM5HX6TSVZFV26","bundle":"https://pith.science/pith/XKO3DYKMVIOKZM5HX6TSVZFV26/bundle.json","state":"https://pith.science/pith/XKO3DYKMVIOKZM5HX6TSVZFV26/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XKO3DYKMVIOKZM5HX6TSVZFV26/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XKO3DYKMVIOKZM5HX6TSVZFV26","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":"2216604d31c8e863c8e4a104c63fcf7c24903ce67f6a7380cbd2b52faf0e1fc4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:15:37Z","title_canon_sha256":"974139390ca7ed35f9d6b6dc7187f4d9158adabc2396863129e1be71ce3dadfc"},"schema_version":"1.0","source":{"id":"2605.13105","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13105","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13105v1","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13105","created_at":"2026-05-18T03:08:58Z"},{"alias_kind":"pith_short_12","alias_value":"XKO3DYKMVIOK","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XKO3DYKMVIOKZM5H","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XKO3DYKM","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:e6da02523cff4e8601a29d9916a53760ce937f59a449afdcb67c76a5a11c0b66","target":"graph","created_at":"2026-05-18T03:08:58Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Our method consistently improves over standard PPO, achieving average improvements of 16.62% on π0.5 and 9.10% on OpenVLA across diverse out-of-distribution visual shifts."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That paired visual variants (task-preserving and task-altering) can be reliably generated or labeled during training to provide accurate behavior-level supervision without introducing new biases."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants during PPO fine-tuning of VLA models, yielding 9-16% average gains on ManiSkill3 under distractors, textures, poses, viewpoints, and lighting shifts."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants to improve RL fine-tuning of VLA models under visual shifts."}],"snapshot_sha256":"0d6da9a3407ba9d4c3a6557a00747a9cbcc78446906970bcaaf9a56db4009d45"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual shifts pose practical challenges. A key difficulty is that standard task rewards supervise task success, but offer limited guidance on whether a visual change is task-irrelevant or changes the behavior required for manipulation. We propose PAIR-VLA (Paired Action Invariance & Sensitivity for Visually Robust VLA), an RL fine-tuning framework to address this difficulty by adding two auxiliary objectives over paired visual variants during PPO optimi","authors_text":"Chuheng Zhang, Jiang Bian, Jingjing Fu, Jun Zhang, Ling Zhang, Li Zhao, Mingyu Liu, Rui Wang, Yuanfang Peng","cross_cats":[],"headline":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants to improve RL fine-tuning of VLA models under visual shifts.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:15:37Z","title":"What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models"},"references":{"count":43,"internal_anchors":9,"resolved_work":43,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Open x- embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0","work_id":"846c44cc-0874-4a3c-90cb-b86f68157e99","year":2024},{"cited_arxiv_id":"2403.12945","doi":"","is_internal_anchor":true,"ref_index":2,"title":"DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset","work_id":"13253de2-3d89-415c-8c2f-3adb25d4c337","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Rt-2: Vision-language-action models transfer web knowledge to robotic control, 2023","work_id":"f08c7a53-d673-4be5-b955-d92443959ebf","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Octo: An open-source generalist robot policy, 2024","work_id":"9644ac38-9a21-4d5e-9386-5460ec7456ea","year":2024},{"cited_arxiv_id":"2410.24164","doi":"","is_internal_anchor":true,"ref_index":5,"title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","work_id":"f790abdc-a796-482f-a40d-f8ee035ecfc2","year":2024}],"snapshot_sha256":"ec062a49971027b94d2df6dce5fbe087e484745e564ade7f770ea2cdc4c7f442"},"source":{"id":"2605.13105","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T18:41:43.923590Z","id":"7679d0dc-6da7-487d-b481-ed305e022076","model_set":{"reader":"grok-4.3"},"one_line_summary":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants during PPO fine-tuning of VLA models, yielding 9-16% average gains on ManiSkill3 under distractors, textures, poses, viewpoints, and lighting shifts.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"PAIR-VLA adds invariance and sensitivity objectives over paired visual variants to improve RL fine-tuning of VLA models under visual shifts.","strongest_claim":"Our method consistently improves over standard PPO, achieving average improvements of 16.62% on π0.5 and 9.10% on OpenVLA across diverse out-of-distribution visual shifts.","weakest_assumption":"That paired visual variants (task-preserving and task-altering) can be reliably generated or labeled during training to provide accurate behavior-level supervision without introducing new biases."}},"verdict_id":"7679d0dc-6da7-487d-b481-ed305e022076"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3c03b2f3d8392928e0da93a107bec820a8e14d5b005309ce1d42adbd8a1bcca6","target":"record","created_at":"2026-05-18T03:08:58Z","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":"2216604d31c8e863c8e4a104c63fcf7c24903ce67f6a7380cbd2b52faf0e1fc4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T07:15:37Z","title_canon_sha256":"974139390ca7ed35f9d6b6dc7187f4d9158adabc2396863129e1be71ce3dadfc"},"schema_version":"1.0","source":{"id":"2605.13105","kind":"arxiv","version":1}},"canonical_sha256":"ba9db1e14caa1cacb3a7bfa72ae4b5d7ba02910f260d89f95f7fc67d2f8b6f37","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ba9db1e14caa1cacb3a7bfa72ae4b5d7ba02910f260d89f95f7fc67d2f8b6f37","first_computed_at":"2026-05-18T03:08:58.185087Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:08:58.185087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wjt18k2VdFeHoruV1e1jYr7vrUYkyrmVIq/O509yiE0Ek4dNIsPfYtyHvcFteAG80rU8tcqTbs3b8Vglt036CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:08:58.185618Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13105","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c03b2f3d8392928e0da93a107bec820a8e14d5b005309ce1d42adbd8a1bcca6","sha256:e6da02523cff4e8601a29d9916a53760ce937f59a449afdcb67c76a5a11c0b66"],"state_sha256":"d5c0146d8ea5d0c3470ea723c3ed8ff6348f50a100325a6719da3cb50cc24f71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y1oqJL1xVGUuJpVF6IwcI747s+vlb8zweYfpjFGKqMMpEcObxTwbaiDwU6CbRk7eqd9FNQGHCHcjDHh/9xEBBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T11:00:23.184782Z","bundle_sha256":"93a746e573de829691d76427c5b60922f4b5329415670509eeab89277ab98854"}}