{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T21:13:47Z","timestamp":1788556427806,"version":"build-2803163510"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"The Marie Skłodowska-Curie Postdoctoral Individual Fellowship","award":["101154277"],"award-info":[{"award-number":["101154277"]}]},{"name":"The European Union’s Horizon Europe Research and Innovation Programme","award":["101118217"],"award-info":[{"award-number":["101118217"]}]},{"name":"The China Scholarship Council","award":["202208410132"],"award-info":[{"award-number":["202208410132"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3818194","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:47Z","timestamp":1786036547000},"page":"3354-3365","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["TRIAD: Ternary Information Routing for Multimodal Time-Series Sensing Signals"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0731-9215","authenticated-orcid":false,"given":"Yusen","family":"Liu","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4964-4323","authenticated-orcid":false,"given":"Shouxing","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1199-6678","authenticated-orcid":false,"given":"Hua","family":"Lu","sequence":"additional","affiliation":[{"name":"Aalborg University, Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4724-5748","authenticated-orcid":false,"given":"Xu","family":"Cheng","sequence":"additional","affiliation":[{"name":"Tianjin University of Technology, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5133-6688","authenticated-orcid":false,"given":"Xiufeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Technical University of Denmark, Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-714X","authenticated-orcid":false,"given":"Huan","family":"Huo","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"3","author":"Anguita Davide","year":"2013","unstructured":"Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al., 2013. A public domain dataset for human activity recognition using smartphones.. In Esann, Vol. 3. 3-4.","journal-title":"Esann"},{"key":"e_1_3_2_2_2_1","volume-title":"The im algorithm: a variational approach to information maximization. Advances in neural information processing systems","author":"Barber David","year":"2004","unstructured":"David Barber and Felix Agakov. 2004. The im algorithm: a variational approach to information maximization. Advances in neural information processing systems, Vol. 16, 320 (2004), 201-208."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/e16042161"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714931"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00054"},{"key":"e_1_3_2_2_6_1","volume-title":"International conference on machine learning. PmLR, 1597-1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PmLR, 1597-1607."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736952"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550316"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1306"},{"key":"e_1_3_2_2_10_1","volume-title":"Forty-first International Conference on Machine Learning.","author":"Duan Jufang","year":"2024","unstructured":"Jufang Duan, Wei Zheng, Yangzhou Du, Wenfa Wu, Haipeng Jiang, and Hongsheng Qi. 2024. MF-CLR: Multi-frequency contrastive learning representation for time series. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2018.09.003"},{"key":"e_1_3_2_2_12_1","first-page":"5408","volume-title":"International Conference on Learning Representations","volume":"2025","author":"Dufumier Benoit","year":"2025","unstructured":"Benoit Dufumier, Javiera Castillo Navarro, Devis Tuia, and Jean-Philippe Thiran. 2025. What to align in multimodal contrastive learning?. In International Conference on Learning Representations, Vol. 2025. 5408-5432."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"e_1_3_2_2_14_1","volume-title":"Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems","author":"Franceschi Jean-Yves","year":"2019","unstructured":"Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi. 2019. Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_15_1","volume-title":"Multimodal masked autoencoders learn transferable representations. arXiv preprint arXiv:2205.14204","author":"Geng Xinyang","year":"2022","unstructured":"Xinyang Geng, Hao Liu, Lisa Lee, Dale Schuurmans, Sergey Levine, and Pieter Abbeel. 2022. Multimodal masked autoencoders learn transferable representations. arXiv preprint arXiv:2205.14204 (2022)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2528282.2528302"},{"key":"e_1_3_2_2_19_1","volume-title":"International conference on machine learning. PMLR, 4904-4916","author":"Jia Chao","year":"2021","unstructured":"Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning. PMLR, 4904-4916."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00740"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645346"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714853"},{"key":"e_1_3_2_2_23_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Lee Seunghan","year":"2024","unstructured":"Seunghan Lee, Taeyoung Park, and Kibok Lee. 2024. Learning to Embed Time Series Patches Independently. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_2_24_1","volume-title":"International conference on machine learning. PMLR","author":"Li Junnan","year":"2023","unstructured":"Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning. PMLR, 19730-19742."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Paul Pu Liang Zihao Deng Martin Ma James Zou Louis-Philippe Morency and Ruslan Salakhutdinov. 2023a. Factorized Contrastive Learning: Going Beyond Multi-view Redundancy. In Advances in Neural Information Processing Systems.","DOI":"10.52202\/075280-1430"},{"key":"e_1_3_2_2_26_1","volume-title":"High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning. Transactions on Machine Learning Research","author":"Liang Paul Pu","year":"2023","unstructured":"Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, and Russ Salakhutdinov. 2023b. High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning. Transactions on Machine Learning Research (2023)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i2.25253"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2049"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25575"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26098"},{"key":"e_1_3_2_2_31_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3560519"},{"key":"e_1_3_2_2_33_1","volume-title":"International Conference on Machine Learning. PMLR, 17782-17800","author":"Poklukar Petra","year":"2022","unstructured":"Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S Melo, Ana Paiva, and Danica Kragic. 2022. Geometric multimodal contrastive representation learning. In International Conference on Machine Learning. PMLR, 17782-17800."},{"key":"e_1_3_2_2_34_1","volume-title":"International conference on machine learning. PmLR, 8748-8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PmLR, 8748-8763."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3242969.3242985"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671673"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736969"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Learning Representations","author":"Tonekaboni Sana","year":"2021","unstructured":"Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. 2021. Unsupervised representation learning for time series with temporal neighborhood coding. International Conference on Learning Representations (2021)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1160"},{"key":"e_1_3_2_2_42_1","first-page":"15529","article-title":"InfMasking: Unleashing Synergistic Information by Contrastive Multimodal Interactions","volume":"38","author":"Wen Liangjian","year":"2026","unstructured":"Liangjian Wen, Qun Dai, Jianzhuang Liu, Jiangtao Zheng, Yong Dai, Dongkai Wang, Zhao Kang, Jun Wang, Zenglin Xu, and Jiang Duan. 2026. InfMasking: Unleashing Synergistic Information by Contrastive Multimodal Interactions. Advances in Neural Information Processing Systems, Vol. 38 (2026), 15529-15555.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_43_1","volume-title":"Nonnegative decomposition of multivariate information. arXiv preprint arXiv:1004.2515","author":"Williams Paul L","year":"2010","unstructured":"Paul L Williams and Randall D Beer. 2010. Nonnegative decomposition of multivariate information. arXiv preprint arXiv:1004.2515 (2010)."},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Learning Representations","author":"Woo Gerald","year":"2022","unstructured":"Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi. 2022. Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting. International Conference on Learning Representations (2022)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681095"},{"key":"e_1_3_2_2_46_1","volume-title":"International conference on machine learning. PMLR, 25038-25054","author":"Yang Ling","year":"2022","unstructured":"Ling Yang and Shenda Hong. 2022. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In International conference on machine learning. PMLR, 25038-25054."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Kexin Zhang Qingsong Wen Chaoli Zhang Rongyao Cai Ming Jin Yong Liu James Y Zhang Yuxuan Liang Guansong Pang Dongjin Song et al. 2024. Self-supervised learning for time series analysis: Taxonomy progress and prospects. IEEE transactions on pattern analysis and machine intelligence Vol. 46 10 (2024) 6775-6794.","DOI":"10.1109\/TPAMI.2024.3387317"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0288"},{"key":"e_1_3_2_2_51_1","volume-title":"Simts: Rethinking contrastive representation learning for time series forecasting. arXiv preprint arXiv:2303.18205","author":"Zheng Xiaochen","year":"2023","unstructured":"Xiaochen Zheng, Xingyu Chen, Manuel Sch\u00fcrch, Amina Mollaysa, Ahmed Allam, and Michael Krauthammer. 2023. Simts: Rethinking contrastive representation learning for time series forecasting. arXiv preprint arXiv:2303.18205 (2023)."},{"key":"e_1_3_2_2_52_1","volume-title":"Parametric Augmentation for Time Series Contrastive Learning. International Conference on Learning Representations","author":"Zheng Xu","year":"2024","unstructured":"Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma, Haifeng Chen, Mo Sha, and Dongsheng Luo. 2024. Parametric Augmentation for Time Series Contrastive Learning. International Conference on Learning Representations (2024)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770855.3818194","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T20:33:11Z","timestamp":1788553991000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3818194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":52,"alternative-id":["10.1145\/3770855.3818194","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3818194","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}