Molecular property prediction is crucial for drug discovery when labeled data are scarce. This work presents CardinalGraphFormer, a graph transformer augmented with a query-conditioned cardinality-preserving attention (CPA) channel that retains dynamic support-size signals complementary to static centrality embeddings. The approach combines structured sparse attention with Graphormer-inspired biases (shortest-path distance, centrality, direct-bond features) and unified dual-objective self-supervised pretraining (masked reconstruction and contrastive alignment of augmented views). Evaluation on 11 public benchmarks spanning MoleculeNet, OGB, and TDC ADMET demonstrates consistent improvements over protocol-matched baselines under matched pretraining, optimization, and hyperparameter tuning. Rigorous ablations confirm CPA's contributions and rule out simple size shortcuts. Code and reproducibility artifacts are provided.
Function graph transformers universally approximate operators between function spaces
Doshisha University（大阪市立大学）；RIKEN AIP（日本科学技术厅Advanced Institute for Photonics and Electron器件）；Rice University（里士满大学）；University of Basel（巴塞尔大学）；Simons Chair in Computational and Applied Mathematics and Earth Science（Simons计算与应用数学及地球科学主席职位）；University of Helsinki（赫尔辛基大学）
Volume-Preserving Transformers for Learning Time Series Data with Structure
Max-Planck-Institut für Plasmaphysik（马克斯·普朗克等离子体物理研究所）；ONERA（法国航空航天研究院）；Université Paris Saclay（巴黎萨克雷大学）；Arts et Métiers Institute of Technology（国立巴黎高等机械学院）；Zentrum Mathematik, Technische Universität München（慕尼黑工业大学数学中心）
Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
Institute of Computing (IComp), Federal University of Amazonas (UFAM)（亚马逊联邦大学计算研究所）；Brazilian Army Research Institute in the Amazon (IPEAM)（巴西陆军亚马逊研究所）；Military Institute of Engineering (IME)（军事工程学院）
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；Fudan University（复旦大学）；Shanghai Academy of AI for Science（上海人工智能科学研究院）；Southwestern University of Finance and Economics（西南财经大学）
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
Chengdu Medical College（成都医学院）；Kunming Medical University（昆明医科大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Southern California（南加州大学）；Yanshan University（燕山大学）
On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning
School of Electronics and Information, Northwestern Polytechnical University（电子工程学院，西北工业大学）；DecoreX Intelligent Technologies Co., Ltd.（DecoreX智能科技有限公司）；Samsung AI Center（三星人工智能中心）；th Research Institute of China North Industries Group Corporation（中国北方工业集团208研究所）；Department of Electrical and Computer Engineering, University of Houston（电子与计算机工程系，休斯顿大学）
Physics Transformer: Tailoring Transformer for General PDE Prediction
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Mechanics and Engineering Science, Peking University（北京大学力学与工程科学学院）
Accelerating Transformers with Spectrum-Preserving Token Merging
University of Science - VNUHCM（胡志明市国家大学自然科学大学）；Max Planck Research School for Intelligent Systems (IMPRS-IS)（马克斯·普朗克智能系统研究院（IMPRS-IS））；University of Stuttgart（斯图加特大学）；Dublin City University（都柏林城市大学）；University of Queensland（昆士兰大学）；University of Arkansas（阿肯色大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；UC San Diego（加利福尼亚大学圣地亚哥分校）；Oldenburg University（奥尔登堡大学）；Stanford University（斯坦福大学）
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
Yonsei University（延世大学）；Duke University（杜克大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Oulu（奥卢大学）
本文提出 DP-CutMixSL 框架，通过在分割学习中注入高斯噪声并跨客户端随机混合粉碎数据块，以差分隐私机制增强 ViT 训练时的隐私保护，同时提升分类准确性。
