{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:54:20Z","timestamp":1776156860791,"version":"3.50.1"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003995","name":"Anhui Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2308085MF216"],"award-info":[{"award-number":["2308085MF216"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372153"],"award-info":[{"award-number":["62372153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476077"],"award-info":[{"award-number":["62476077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276242"],"award-info":[{"award-number":["62276242"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Image and Vision Computing"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.imavis.2026.105959","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T16:35:01Z","timestamp":1773765301000},"page":"105959","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MRTP: Multiscale video anomaly detection with Representative Text Prompt"],"prefix":"10.1016","volume":"169","author":[{"given":"Jun","family":"Yi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8646-8426","authenticated-orcid":false,"given":"Ye","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.imavis.2026.105959_b1","series-title":"2023 IEEE International Conference on Image Processing","first-page":"2540","article-title":"Exploring diffusion models for unsupervised video anomaly detection","author":"Tur","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b2","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14360","article-title":"Learning memory-guided normality for anomaly detection","author":"Park","year":"2020"},{"key":"10.1016\/j.imavis.2026.105959_b3","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2025.3568517","article-title":"A two-stage framework with memory for anomaly detection via video decomposition and bidirectional consistency","author":"Zhong","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.imavis.2026.105959_b4","doi-asserted-by":"crossref","first-page":"4505","DOI":"10.1109\/TIP.2021.3072863","article-title":"Localizing anomalies from weakly-labeled videos","volume":"30","author":"Lv","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.imavis.2026.105959_b5","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2024.3482007","article-title":"ReFLIP-VAD: Towards weakly supervised video anomaly detection via vision-language model","author":"Dev","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.imavis.2026.105959_b6","article-title":"Dual distillation fusion for weakly supervised anomaly detection in surveillance videos","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.imavis.2026.105959_b7","series-title":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"4020","article-title":"Learning spatio-temporal relations with multi-scale integrated perception for video anomaly detection","author":"Ye","year":"2024"},{"key":"10.1016\/j.imavis.2026.105959_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2023.104629","article-title":"Spatial-temporal graph attention network for video anomaly detection","volume":"131","author":"Chen","year":"2023","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.105959_b9","series-title":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","article-title":"Self-attention pyramidal convolutional network for weakly-supervised video anomaly detection","author":"Liu","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b10","series-title":"VadCLIP: Adapting vision-language models for weakly supervised video anomaly detection","author":"Wu","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b11","doi-asserted-by":"crossref","first-page":"4923","DOI":"10.1109\/TIP.2024.3451935","article-title":"Learning prompt-enhanced context features for weakly-supervised video anomaly detection","volume":"33","author":"Pu","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.imavis.2026.105959_b12","series-title":"International Conference on Machine Learning","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"issue":"9","key":"10.1016\/j.imavis.2026.105959_b13","doi-asserted-by":"crossref","first-page":"7046","DOI":"10.1109\/TII.2025.3574406","article-title":"Dual-detector reoptimization for federated weakly supervised video anomaly detection via adaptive dynamic recursive mapping","volume":"21","author":"Su","year":"2025","journal-title":"Ind. Inform. IEEE Trans. on"},{"key":"10.1016\/j.imavis.2026.105959_b14","series-title":"European Conference on Computer Vision","first-page":"55","article-title":"Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection","author":"Cao","year":"2024"},{"issue":"000","key":"10.1016\/j.imavis.2026.105959_b15","first-page":"12","article-title":"VPE-WSVAD: Visual prompt exemplars for weakly-supervised video anomaly detection","volume":"299","author":"Su","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.imavis.2026.105959_b16","series-title":"2021 IEEE\/CVF International Conference on Computer Vision","first-page":"4955","article-title":"Weakly-supervised video anomaly detection with robust temporal feature magnitude learning","author":"Tian","year":"2021"},{"key":"10.1016\/j.imavis.2026.105959_b17","series-title":"2022 IEEE International Conference on Data Mining","first-page":"1215","article-title":"Making reconstruction-based method great again for video anomaly detection","author":"Wang","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b18","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14592","article-title":"Video event restoration based on keyframes for video anomaly detection","author":"Yang","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b19","doi-asserted-by":"crossref","first-page":"10160","DOI":"10.1109\/TMM.2024.3405716","article-title":"Feature reconstruction with disruption for unsupervised video anomaly detection","volume":"26","author":"Tao","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.imavis.2026.105959_b20","article-title":"A novel unsupervised video anomaly detection framework based on optical flow reconstruction and erased frame prediction","volume":"23","author":"Huang","year":"2023","journal-title":"Sensors (Basel, Switzerland)"},{"key":"10.1016\/j.imavis.2026.105959_b21","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2025.3568517","article-title":"A two-stage framework with memory for anomaly detection via video decomposition and bidirectional consistency","author":"Zhong","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.imavis.2026.105959_b22","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1109\/TNNLS.2021.3083152","article-title":"Robust unsupervised video anomaly detection by multipath frame prediction","volume":"33","author":"Wang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.imavis.2026.105959_b23","doi-asserted-by":"crossref","DOI":"10.4218\/etrij.2024-0115","article-title":"AONet: Attention network with optional activation for unsupervised video anomaly detection","author":"Rakhmonov","year":"2024","journal-title":"ETRI J."},{"key":"10.1016\/j.imavis.2026.105959_b24","series-title":"2022 IEEE\/SICE International Symposium on System Integration","first-page":"870","article-title":"Unsupervised video anomaly detection in traffic and crowded scenes","author":"Hashimoto","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b25","series-title":"AAAI Conference on Artificial Intelligence","article-title":"Dual memory units with uncertainty regulation for weakly supervised video anomaly detection","author":"Zhou","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b26","doi-asserted-by":"crossref","first-page":"12372","DOI":"10.1109\/TITS.2024.3365820","article-title":"TA-NET: Empowering highly efficient traffic anomaly detection through multi-head local self-attention and adaptive hierarchical feature reconstruction","volume":"25","author":"Chen","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.imavis.2026.105959_b27","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18899","article-title":"Text prompt with normality guidance for weakly supervised video anomaly detection","author":"Yang","year":"2024"},{"key":"10.1016\/j.imavis.2026.105959_b28","series-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1237","article-title":"Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection","author":"Zhong","year":"2019"},{"key":"10.1016\/j.imavis.2026.105959_b29","series-title":"AAAI Conference on Artificial Intelligence","article-title":"MGFN: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection","author":"Chen","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b30","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/LSP.2022.3175092","article-title":"Weakly supervised video anomaly detection via transformer-enabled temporal relation learning","volume":"29","author":"Zhang","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.imavis.2026.105959_b31","series-title":"AAAI Conference on Artificial Intelligence","article-title":"Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection","author":"Li","year":"2022"},{"issue":"5","key":"10.1016\/j.imavis.2026.105959_b32","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1109\/TCYB.2022.3227044","article-title":"Weakly supervised video anomaly detection via self-guided temporal discriminative transformer","volume":"54","author":"Huang","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.imavis.2026.105959_b33","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/LSP.2022.3175092","article-title":"Weakly supervised video anomaly detection via transformer-enabled temporal relation learning","volume":"29","author":"Zhang","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.imavis.2026.105959_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110898","article-title":"Semantic-driven dual consistency learning for weakly supervised video anomaly detection","volume":"157","author":"Su","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.imavis.2026.105959_b35","doi-asserted-by":"crossref","first-page":"111093","DOI":"10.1109\/ACCESS.2023.3321801","article-title":"SwinAnomaly: Real-time video anomaly detection using video swin transformer and SORT","volume":"11","author":"Bajgoti","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.imavis.2026.105959_b36","series-title":"2023 IEEE International Conference on Image Processing","first-page":"3230","article-title":"CLIP-TSA: Clip-assisted temporal self-attention for weakly-supervised video anomaly detection","author":"Joo","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b37","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4724","article-title":"Quo vadis, action recognition? A new model and the kinetics dataset","author":"Carreira","year":"2017"},{"key":"10.1016\/j.imavis.2026.105959_b38","series-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"10.1016\/j.imavis.2026.105959_b39","series-title":"2017 IEEE International Conference on Computer Vision","first-page":"341","article-title":"A revisit of sparse coding based anomaly detection in stacked rnn framework","author":"Luo","year":"2017"},{"key":"10.1016\/j.imavis.2026.105959_b40","series-title":"Not only look, but also listen: Learning multimodal violence detection under weak supervision","author":"Wu","year":"2020"},{"key":"10.1016\/j.imavis.2026.105959_b41","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6479","article-title":"Real-world anomaly detection in surveillance videos","author":"Sultani","year":"2018"},{"key":"10.1016\/j.imavis.2026.105959_b42","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8022","article-title":"Unbiased multiple instance learning for weakly supervised video anomaly detection","author":"Lv","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b43","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"5549","article-title":"TEVAD: Improved video anomaly detection with captions","author":"Chen","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b44","doi-asserted-by":"crossref","first-page":"4135","DOI":"10.1109\/TCSVT.2023.3321235","article-title":"Towards video anomaly detection in the real world: A binarization embedded weakly-supervised network","volume":"34","author":"Yang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.imavis.2026.105959_b45","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14724","article-title":"Generative cooperative learning for unsupervised video anomaly detection","author":"Zaheer","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b46","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"22846","article-title":"Hierarchical semantic contrast for scene-aware video anomaly detection","author":"Sun","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b47","series-title":"2020 IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"Weakly supervised video anomaly detection via center-guided discriminative learning","author":"Wan","year":"2020"},{"key":"10.1016\/j.imavis.2026.105959_b48","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14004","article-title":"MIST: Multiple instance self-training framework for video anomaly detection","author":"Feng","year":"2021"},{"key":"10.1016\/j.imavis.2026.105959_b49","series-title":"2021 IEEE\/CVF International Conference on Computer Vision","first-page":"4955","article-title":"Weakly-supervised video anomaly detection with robust temporal feature magnitude learning","author":"Tian","year":"2021"},{"key":"10.1016\/j.imavis.2026.105959_b50","series-title":"European Conference on Computer Vision","article-title":"Self-supervised sparse representation for video anomaly detection","author":"Wu","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b51","series-title":"2024 IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops","first-page":"212","article-title":"Overlooked video classification in weakly supervised video anomaly detection","author":"Tan","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b52","series-title":"2023 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"2664","article-title":"Normality guided multiple instance learning for weakly supervised video anomaly detection","author":"Park","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b53","doi-asserted-by":"crossref","first-page":"5575","DOI":"10.1109\/TMM.2023.3336576","article-title":"Abnormal ratios guided multi-phase self-training for weakly-supervised video anomaly detection","volume":"26","author":"Shi","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.imavis.2026.105959_b54","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2024.3450734","article-title":"Batchnorm-based weakly supervised video anomaly detection","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.imavis.2026.105959_b55","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1109\/TIP.2021.3062192","article-title":"Learning causal temporal relation and feature discrimination for anomaly detection","volume":"30","author":"Wu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.imavis.2026.105959_b56","series-title":"AAAI Conference on Artificial Intelligence","article-title":"MGFN: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection","author":"Chen","year":"2022"},{"key":"10.1016\/j.imavis.2026.105959_b57","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18297","article-title":"Open-vocabulary video anomaly detection","author":"Wu","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b58","doi-asserted-by":"crossref","first-page":"4922","DOI":"10.1109\/TMM.2022.3184533","article-title":"Audiovisual dependency attention for violence detection in videos","volume":"25","author":"Pang","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.imavis.2026.105959_b59","series-title":"2024 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"6834","article-title":"Real-time weakly supervised video anomaly detection","author":"Karim","year":"2024"},{"key":"10.1016\/j.imavis.2026.105959_b60","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18899","article-title":"Text prompt with normality guidance for weakly supervised video anomaly detection","author":"Yang","year":"2024"},{"key":"10.1016\/j.imavis.2026.105959_b61","series-title":"AAAI Conference on Artificial Intelligence","article-title":"Dual memory units with uncertainty regulation for weakly supervised video anomaly detection","author":"Zhou","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b62","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18297","article-title":"Open-vocabulary video anomaly detection","author":"Wu","year":"2023"},{"key":"10.1016\/j.imavis.2026.105959_b63","article-title":"Dual distillation fusion for weakly supervised anomaly detection in surveillance videos","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["Image and Vision Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626000661?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626000661?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:00:33Z","timestamp":1776153633000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0262885626000661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":63,"alternative-id":["S0262885626000661"],"URL":"https:\/\/doi.org\/10.1016\/j.imavis.2026.105959","relation":{},"ISSN":["0262-8856"],"issn-type":[{"value":"0262-8856","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MRTP: Multiscale video anomaly detection with Representative Text Prompt","name":"articletitle","label":"Article Title"},{"value":"Image and Vision Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.imavis.2026.105959","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"105959"}}