Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The

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Zero-Shot Heterogeneous Graph Embedding via Semantic Extraction advantage of labeled data, showing promising performance. However, real-world datasets are frequently completely-imbalanced (i.e., zero-shot), wherein certain node types have no labeled instances. This scenario poses a formidable challenge for conventional graph embedding models, resulting in subop

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SCBC: A Supervised Single-Cell Classification Method Based on Batch Correction for ATAC-Seq DataL) to cell classification tailored for scATAC-seq data. However, scATAC-seq data possess ambiguous feature spaces and sparse expression levels, and existing cell-typing methods typically either align modalities in the latent space or perform transfer learning based on scRNA-seq data. In this study,

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Federated Prompt Tuning: When is it Necessary?work of federated learning. This paper aims to answer “whether it is necessary to seek federation when clients already possess strong few-shot learning abilities with local prompt tuning” through experimental studies. We simulated various types of data distribution shifts that may exist among client

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Dirichlet-Based Local Inconsistency Query Strategy for Active Domain Adaptationarget domain. In this process, uncertainty and representativeness are two crucial principles. Strategies focused on uncertainty seek to choose samples where the model’s predictions are less certain, whereas those centered on representativeness aim to pick samples that better reflect the overall data

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FedSD: Cross-Heterogeneous Federated Learning Based on Self-distillationer, in practical applications, IoT devices often train different sizes of models for different tasks. The heterogeneity among client model significantly affects the convergence and generalization performance of model. To enhance robustness in such heterogeneous scenarios, we introduce a novel FL fra