Analysis of heart failure patient trajectories using sequence modeling

A new study introduces the Unified Attention-Mamba (UAM) backbone, designed specifically for cell-level classification of tumor cells using radiomics features. This innovative approach enhances the diagnostic accuracy of hematoxylin and eosin (H&E) images by focusing on micro-level morphological and intensity patterns, which are crucial for precise tumor identification.

A comparative study has been conducted on UNet-based architectures for liver tumor segmentation in multi-phase contrast-enhanced computed tomography (CECT), revealing that ResNet-based models consistently outperform Transformer and Mamba alternatives across various metrics. The study also highlights the effectiveness of incorporating attention mechanisms, particularly the Convolutional Block Attention Module (CBAM), to enhance segmentation quality.

A new study introduces PREPO, a method that enhances data efficiency in reinforcement learning for large language models (LLMs) by utilizing intrinsic data properties. This approach aims to reduce the computational cost associated with training while maintaining competitive performance, particularly on models like Qwen and Llama.

A systematic review has been conducted on relation extraction (RE) research since the introduction of Transformer-based models, analyzing 34 surveys, 64 datasets, and 104 models published from 2019 to 2024. The study highlights advancements in methodologies, benchmark resources, and the integration of semantic web technologies, providing a comprehensive reference for the evolution of RE.

A new framework called Convolutional Nearest Neighbors (ConvNN) has been introduced, unifying convolutional neural networks and transformers within a k-nearest neighbor aggregation framework. This approach highlights that both convolution and self-attention can be viewed as methods of neighbor selection and aggregation, with ConvNN serving as a drop-in replacement for existing layers in neural networks.