Self-supervised foundation models have achieved remarkable success across domains, including time series. However, the potential of non-contrastive methods, a paradigm that has driven significant advances in computer vision, remains underexplored for time series. In this work, we adapt DINOv2-style self-distillation to pretrain a time series foundation model, building on the Mantis tokenizer and transformer encoder architecture as our backbone. Through a student-teacher framework, our method Utica learns representations that capture both temporal invariance via augmented crops and fine-grained local structure via patch masking. Our approach achieves state-of-the-art classification performance on both UCR and UEA benchmarks. These results suggest that non-contrastive methods are a promising and complementary pretraining strategy for time series foundation models.
Diffusion-Guided Pretraining for Brain Graph Foundation Models
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA（电气与计算机工程系，莱维大学）；Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA（计算机科学与工程系，莱维大学）；Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学医学院）
HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models
National Engineering Research Center for Mobile Network Technologies, Beijing University of Posts and Telecommunications（中国移动网络技术国家工程研究中心，北京邮电大学）；Department of Broadband Communication, Pengcheng Laboratory（宽带通信系，鹏城实验室）；Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China（民族语言智能分析与安全治理重点实验室，中央民族大学）；China Telecom Corporation Limited Gansu Branch（中国电信集团甘肃分公司）
Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking
School of Software, Shandong University（山东大学软件学院）；Joint SDU–NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学-南洋理工大学人工智能联合研究中心（C-FAIR））；Nanyang Technological University（南洋理工大学）
REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
IMT Atlantique Lab-STICC UMR CNRS 6285（IMT Atlantique 实验室）；Psychology Department Université de Montréal（蒙特利尔大学心理学系）；Mila (Quebec AI research institute)（魁北克人工智能研究院）；UNIQUE (Quebec Neuro-AI research center)（魁北克神经人工智能研究中心）
BarcodeMAE+: Rethinking Masked Pretraining and Global Representations for DNA Barcode Foundation Models
University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）；Simon Fraser University（西蒙弗雷泽大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所（Amii））；University of Guelph（圭尔夫大学）
A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, 1 Canada（儿童健康评估科学计划，多伦多医院儿童医学中心，多伦多，加拿大）；Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA, USA（斯坦福大学生物医学信息学研究中心，斯坦福大学，帕洛阿尔托，美国）；Universidad del Norte, Barranquilla, Columbia（北方大学，巴兰基利亚，哥伦比亚）；Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada（血液肿瘤科，多伦多医院儿童医学中心，多伦多，加拿大）
TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation
SAIVT Group, School of Electrical Engineering and Robotics, Queensland University of Technology（澳大利亚昆士兰科技大学电气工程与机器人学院SAIVT组）；CSIRO Robotics, CSIRO（澳大利亚联邦科学与工业研究组织机器人部）
DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining
Fordham University（福特汉姆大学）；Rutgers University（罗格斯大学）；Pioneer Centre for AI（先锋人工智能中心）；Cold Spring Harbor Laboratory（冷泉港实验室）；Brown University（布朗大学）；University of Copenhagen（哥本哈根大学）
STEP: Scientific Time-Series Encoder Pretraining via Cross-Domain Distillation
Shanghai Aritificial Intelligence Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；Harbin Institute of Technology（哈尔滨工业大学）；Sichuan University（四川大学）；University of Cambridge（剑桥大学）
