{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T10:42:19Z","timestamp":1784284939927,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"TaiShan Scholars Program","award":["tsqn202211289"],"award-info":[{"award-number":["tsqn202211289"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006141"],"award-info":[{"award-number":["62006141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Oversea Innovation Team Project of the 20 Regulations for New Universities funding program of Jinan","award":["2021GXRC073"],"award-info":[{"award-number":["2021GXRC073"]}]},{"name":"Excellent Youth Scholars Program of Shandong Province","award":["2022HWYQ-048"],"award-info":[{"award-number":["2022HWYQ-048"]}]},{"name":"National Key R&D Program of China","award":["2021YFC3300203"],"award-info":[{"award-number":["2021YFC3300203"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612481","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:30Z","timestamp":1698391650000},"page":"3099-3107","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3433-7413","authenticated-orcid":false,"given":"Zhuang","family":"Qi","sequence":"first","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0273-5946","authenticated-orcid":false,"given":"Lei","family":"Meng","sequence":"additional","affiliation":[{"name":"Shandong University & Shandong Research Institute of Industrial Technology, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8422-4032","authenticated-orcid":false,"given":"Zitan","family":"Chen","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7532-0496","authenticated-orcid":false,"given":"Han","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0190-969X","authenticated-orcid":false,"given":"Hui","family":"Lin","sequence":"additional","affiliation":[{"name":"National Engineering Research Centerfor Risk Perception and Prevention, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7290-5659","authenticated-orcid":false,"given":"Xiangxu","family":"Meng","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Improving generalization in federated learning by seeking flat minima","author":"Caldarola Debora","unstructured":"Debora Caldarola, Barbara Caputo, and Marco Ciccone. 2022. Improving generalization in federated learning by seeking flat minima. In ECCV. Springer, 654--672."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Jingjing Chen and Chong-Wah Ngo. 2016. Deep-based ingredient recognition for cooking recipe retrieval. In MM. 32--41.","DOI":"10.1145\/2964284.2964315"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059295"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Pei Dong Lei Wu Lei Meng and Xiangxu Meng. 2022a. Disentangled Representations and Hierarchical Refinement of Multi-Granularity Features for Text-to-Image Synthesis. In ICMR. 268--276.","DOI":"10.1145\/3512527.3531389"},{"key":"e_1_3_2_1_5_1","volume-title":"HR-PrGAN: High-resolution story visualization with progressive generative adversarial networks. Information Sciences","author":"Dong Pei","year":"2022","unstructured":"Pei Dong, Lei Wu, Lei Meng, and Xiangxu Meng. 2022b. HR-PrGAN: High-resolution story visualization with progressive generative adversarial networks. Information Sciences (2022)."},{"key":"e_1_3_2_1_6_1","volume-title":"A Survey on Heterogeneous Federated Learning. preprint arXiv:2210.04505","author":"Gao Dashan","year":"2022","unstructured":"Dashan Gao, Xin Yao, and Qiang Yang. 2022b. A Survey on Heterogeneous Federated Learning. preprint arXiv:2210.04505 (2022)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Liang Gao Huazhu Fu Li Li Yingwen Chen Ming Xu and Cheng-Zhong Xu. 2022a. FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction. In CVPR. 10112--10121.","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3265645"},{"key":"e_1_3_2_1_9_1","volume-title":"One-shot federated learning. preprint arXiv:1902.11175","author":"Guha Neel","year":"2019","unstructured":"Neel Guha, Ameet Talwalkar, and Virginia Smith. 2019. One-shot federated learning. preprint arXiv:1902.11175 (2019)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3003648"},{"key":"e_1_3_2_1_11_1","volume-title":"FedX: Unsupervised Federated Learning with Cross Knowledge Distillation","author":"Han Sungwon","unstructured":"Sungwon Han, Sungwon Park, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xing Xie, and Meeyoung Cha. 2022. FedX: Unsupervised Federated Learning with Cross Knowledge Distillation. In ECCV. Springer, 691--707."},{"key":"e_1_3_2_1_12_1","unstructured":"Weituo Hao Mostafa El-Khamy Jungwon Lee Jianyi Zhang Kevin J Liang Changyou Chen and Lawrence Carin Duke. 2021. Towards fair federated learning with zero-shot data augmentation. In CVPR. 3310--3319."},{"key":"e_1_3_2_1_13_1","volume-title":"Measuring the effects of non-identical data distribution for federated visual classification. preprint arXiv:1909.06335","author":"Harry Hsu Tzu-Ming","year":"2019","unstructured":"Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019. Measuring the effects of non-identical data distribution for federated visual classification. preprint arXiv:1909.06335 (2019)."},{"key":"e_1_3_2_1_14_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_15_1","volume-title":"Tiny imagenet visual recognition challenge. CS 231N","author":"Le Ya","year":"2015","unstructured":"Ya Le and Xuan Yang. 2015. Tiny imagenet visual recognition challenge. CS 231N, Vol. 7, 7 (2015), 3."},{"key":"e_1_3_2_1_16_1","unstructured":"Gihun Lee Minchan Jeong Yongjin Shin Sangmin Bae and Se-Young Yun. 2022. Preservation of the global knowledge by not-true distillation in federated learning. In NeurIPS."},{"key":"e_1_3_2_1_17_1","volume-title":"Fedmd: Heterogenous federated learning via model distillation. preprint arXiv:1910.03581","author":"Li Daliang","year":"2019","unstructured":"Daliang Li and Junpu Wang. 2019. Fedmd: Heterogenous federated learning via model distillation. preprint arXiv:1910.03581 (2019)."},{"key":"e_1_3_2_1_18_1","unstructured":"Qinbin Li Bingsheng He and Dawn Song. 2021a. Model-contrastive federated learning. In CVPR. 10713--10722."},{"key":"e_1_3_2_1_19_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. MLSys, Vol. 2 (2020), 429--450.","journal-title":"MLSys"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Xiangxian Li Haokai Ma Lei Meng and Xiangxu Meng. 2021b. Comparative Study of Adversarial Training Methods for Long-tailed Classification. In ADVM. 1--7.","DOI":"10.1145\/3475724.3483601"},{"key":"e_1_3_2_1_21_1","volume-title":"Dse-net: Artistic font image synthesis via disentangled style encoding","author":"Li Xiang","year":"2022","unstructured":"Xiang Li, Lei Wu, Xu Chen, Lei Meng, and Xiangxu Meng. 2022. Dse-net: Artistic font image synthesis via disentangled style encoding. In ICME. IEEE, 1--6."},{"key":"e_1_3_2_1_22_1","volume-title":"2023 a. Compositional Zero-Shot Artistic Font Synthesis. IJCAI","author":"Li Xiang","year":"2023","unstructured":"Xiang Li, Lei Wu, Changshuo Wang, Lei Meng, and Xiangxu Meng. 2023 a. Compositional Zero-Shot Artistic Font Synthesis. IJCAI (2023)."},{"key":"e_1_3_2_1_23_1","volume-title":"2023 b. Cross-modal Learning Using Privileged Information for Long-tailed Image Classification. CVM","author":"Li Xiangxian","year":"2023","unstructured":"Xiangxian Li, Yuze Zheng, Haokai Ma, Zhuang Qi, Xiangxu Meng, and Lei Meng. 2023 b. Cross-modal Learning Using Privileged Information for Long-tailed Image Classification. CVM (2023)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5651"},{"key":"e_1_3_2_1_25_1","unstructured":"Jinxing Liu Junjin Xiao Haokai Ma Xiangxian Li Zhuang Qi Xiangxu Meng and Lei Meng. 2022. Prompt Learning with Cross-Modal Feature Alignment for Visual Domain Adaptation. In CICAI."},{"key":"e_1_3_2_1_26_1","volume-title":"Cross-Training with Prototypical Distillation for improving the generalization of Federated Learning. ICME","author":"Liu Tianhan","year":"2023","unstructured":"Tianhan Liu, Zhuang Qi, Zitan Chen, Xiangxu Meng, and Lei Meng. 2023. Cross-Training with Prototypical Distillation for improving the generalization of Federated Learning. ICME (2023)."},{"key":"e_1_3_2_1_27_1","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-iid data","volume":"34","author":"Luo Mi","year":"2021","unstructured":"Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng. 2021. No fear of heterogeneity: Classifier calibration for federated learning with non-iid data. NeurIPS, Vol. 34 (2021), 5972--5984.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_28_1","unstructured":"Haokai Ma Xiangxian Li Lei Meng and Xiangxu Meng. 2021. Comparative study of adversarial training methods for cold-start recommendation. In ADVM."},{"key":"e_1_3_2_1_29_1","volume-title":"2023 a. Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation. IJCNN","author":"Ma Haokai","year":"2023","unstructured":"Haokai Ma, Zhuang Qi, Xinxin Dong, Xiangxian Li, Yuze Zheng, and Xiangxu Mengand Lei Meng. 2023 a. Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation. IJCNN (2023)."},{"key":"e_1_3_2_1_30_1","unstructured":"Haokai Ma Ruobing Xie Lei Meng Xin Chen Xu Zhang Leyu Lin and Jie Zhou. 2023 b. Exploring False Hard Negative Sample in Cross-Domain Recommendation. In Recsys."},{"key":"e_1_3_2_1_31_1","volume-title":"2023 c. Triple Sequence Learning for Cross-domain Recommendation. preprint arXiv:2304.05027","author":"Ma Haokai","year":"2023","unstructured":"Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, and Jie Zhou. 2023 c. Triple Sequence Learning for Cross-domain Recommendation. preprint arXiv:2304.05027 (2023)."},{"key":"e_1_3_2_1_32_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage and et al. 2017. Communication-efficient learning of deep networks from decentralized data. In AISTATS. PMLR 1273--1282."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Lei Meng Long Chen Xun Yang Dacheng Tao Hanwang Zhang Chunyan Miao and Tat-Seng Chua. 2019. Learning using privileged information for food recognition. In MM. 557--565.","DOI":"10.1145\/3343031.3350870"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Lei Meng Fuli Feng Xiangnan He Xiaoyan Gao and Tat-Seng Chua. 2020. Heterogeneous fusion of semantic and collaborative information for visually-aware food recommendation. In MM. 3460--3468.","DOI":"10.1145\/3394171.3413598"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.01.019"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-216257"},{"key":"e_1_3_2_1_37_1","volume-title":"Clustering-based Curriculum Construction for Sample-Balanced Federated Learning","author":"Qi Zhuang","unstructured":"Zhuang Qi, Yuqing Wang, Zitan Chen, Ran Wang, Xiangxu Meng, and Lei Meng. 2022. Clustering-based Curriculum Construction for Sample-Balanced Federated Learning. In CICAI. Springer, 155--166."},{"key":"e_1_3_2_1_38_1","volume-title":"Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, and Daniel Rubin.","author":"Qu Liangqiong","year":"2022","unstructured":"Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, and Daniel Rubin. 2022. Rethinking architecture design for tackling data heterogeneity in federated learning. In CVPR. 10061--10071."},{"key":"e_1_3_2_1_39_1","volume-title":"Grad-cam: Visual explanations from deep networks via gradient-based localization. In ICCV. 618--626.","author":"Selvaraju Ramprasaath R","year":"2017","unstructured":"Ramprasaath R Selvaraju, Michael Cogswell, and et al. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In ICCV. 618--626."},{"key":"e_1_3_2_1_40_1","volume-title":"Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features. preprint arXiv:2204.13399","author":"Shang Xinyi","year":"2022","unstructured":"Xinyi Shang, Yang Lu, Gang Huang, and Hanzi Wang. 2022. Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features. preprint arXiv:2204.13399 (2022)."},{"key":"e_1_3_2_1_41_1","volume-title":"Vincent YF Tan, and Song Bai","author":"Shi Yujun","year":"2023","unstructured":"Yujun Shi, Jian Liang, Wenqing Zhang, Vincent YF Tan, and Song Bai. 2023. Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. In ICLR."},{"key":"e_1_3_2_1_42_1","volume-title":"Overcoming forgetting in federated learning on non-iid data. preprint arXiv:1910.07796","author":"Shoham Neta","year":"2019","unstructured":"Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, and Itai Zeitak. 2019. Overcoming forgetting in federated learning on non-iid data. preprint arXiv:1910.07796 (2019)."},{"key":"e_1_3_2_1_43_1","volume-title":"Sequential Fusion of Multi-view Video Frames for 3D Scene Generation","author":"Sun Weilin","unstructured":"Weilin Sun, Xiangxian Li, Manyi Li, Yuqing Wang, Yuze Zheng, Xiangxu Meng, and Lei Meng. 2022. Sequential Fusion of Multi-view Video Frames for 3D Scene Generation. In CICAI. Springer, 597--608."},{"key":"e_1_3_2_1_44_1","unstructured":"Zhenheng Tang Yonggang Zhang Shaohuai Shi Xin He Bo Han and Xiaowen Chu. 2022. Virtual homogeneity learning: Defending against data heterogeneity in federated learning. In ICML. PMLR 21111--21132."},{"key":"e_1_3_2_1_45_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J MACH LEARN RES, Vol. 9, 11 (2008).","journal-title":"J MACH LEARN RES"},{"key":"e_1_3_2_1_46_1","volume-title":"Federated learning with matched averaging. preprint arXiv:2002.06440","author":"Wang Hongyi","year":"2020","unstructured":"Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni. 2020b. Federated learning with matched averaging. preprint arXiv:2002.06440 (2020)."},{"key":"e_1_3_2_1_47_1","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume":"33","author":"Wang Jianyu","year":"2020","unstructured":"Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor. 2020a. Tackling the objective inconsistency problem in heterogeneous federated optimization. NeurIPS, Vol. 33 (2020), 7611--7623.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_48_1","volume-title":"Causal Inference with Sample Balancing for Out-of-Distribution Detection in Visual Classification","author":"Wang Yuqing","unstructured":"Yuqing Wang, Xiangxian Li, Haokai Ma, Zhuang Qi, Xiangxu Meng, and Lei Meng. 2022a. Causal Inference with Sample Balancing for Out-of-Distribution Detection in Visual Classification. In CICAI. Springer, 572--583."},{"key":"e_1_3_2_1_49_1","volume-title":"Meta-causal feature learning for out-of-distribution generalization","author":"Wang Yuqing","unstructured":"Yuqing Wang, Xiangxian Li, Zhuang Qi, Jingyu Li, Xuelong Li, Xiangxu Meng, and Lei Meng. 2022b. Meta-causal feature learning for out-of-distribution generalization. In ECCVW. Springer, 530--545."},{"key":"e_1_3_2_1_50_1","volume-title":"Multi-channel Attentive Weighting of Visual Frames for Multimodal Video Classification. IJCNN","author":"Wang Yuqing","year":"2023","unstructured":"Yuqing Wang, Zhuang Qi, Xiangxian Li, Jinxing Liu, Xiangxu Meng, and Lei Meng. 2023. Multi-channel Attentive Weighting of Visual Frames for Multimodal Video Classification. IJCNN (2023)."},{"key":"e_1_3_2_1_51_1","volume-title":"Multitask adversarial learning for Chinese font style transfer","author":"Wu Lei","unstructured":"Lei Wu, Xi Chen, Lei Meng, and Xiangxu Meng. 2020. Multitask adversarial learning for Chinese font style transfer. In IJCNN. IEEE, 1--8."},{"key":"e_1_3_2_1_52_1","unstructured":"Y Xun W Meng Z Luming and Dacheng Tao. 2016. Empirical risk minimization for metric learning using privileged information. In IJCAI."},{"key":"e_1_3_2_1_53_1","volume-title":"Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval. SIGIR","author":"Yang Xun","year":"2020","unstructured":"Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, and Tat-Seng Chua. 2020. Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval. SIGIR (2020)."},{"key":"e_1_3_2_1_54_1","volume-title":"Deconfounded Video Moment Retrieval with Causal Intervention. SIGIR","author":"Yang Xun","year":"2021","unstructured":"Xun Yang, Fuli Feng, Wei Ji, Meng Wang, and Tat-Seng Chua. 2021. Deconfounded Video Moment Retrieval with Causal Intervention. SIGIR (2021)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2765836"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Lin Zhang Yong Luo Yan Bai Bo Du and Ling-Yu Duan. 2021. Federated learning for non-iid data via unified feature learning and optimization objective alignment. In ICCV. 4420--4428.","DOI":"10.1109\/ICCV48922.2021.00438"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Lin Zhang Li Shen Liang Ding Dacheng Tao and Ling-Yu Duan. 2022. Fine-tuning global model via data-free knowledge distillation for non-iid federated learning. In CVPR. 10174--10183","DOI":"10.1109\/CVPR52688.2022.00993"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612481","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612481","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T23:59:51Z","timestamp":1755820791000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":57,"alternative-id":["10.1145\/3581783.3612481","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612481","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}