Latest AI and machine learning research in transplantation for healthcare professionals.
As one of the most detrimental code smells, code clones significantly increase software maintenance costs and heighten vulnerability risks, making their detection a critical challenge in software engineering. Abstract Syntax Trees (ASTs) dominate deep learning-based code clone detection due to their precise syntactic structure representation, but they inherently lack semantic depth. Recent studi...
Adapting text-to-image (T2I) latent diffusion models for video editing has shown strong visual fidelity and controllability, but challenges remain in maintaining causal relationships in video content. Edits affecting causally dependent attributes risk generating unrealistic or misleading outcomes if these relationships are ignored. In this work, we propose a causally faithful framework for count...
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotatio...
Real-time adaptive control of nonlinear systems with unknown dynamics and time-varying disturbances demands precise modeling and robust parameter ad...
Adaptive Curriculum Sequencing (ACS) is essential for personalized online learning, yet current approaches struggle to balance complex educational c...
Existing segmentation models trained on a single medical imaging dataset often lack robustness when encountering unseen organs or tumors. Developing...
Our research addresses the critical challenge of managing blood transfusions and optimizing allocation in resource-constrained regions. We present h...
Disentangling user intentions from implicit feedback has become a promising strategy to enhance recommendation accuracy and interpretability. Prior ...
Autoregressive visual generation has garnered increasing attention due to its scalability and compatibility with other modalities compared with diff...
This paper presents a joint system modeling approach for fault simulation of all-electric auxiliary power unit (APU), integrating starter/generator ...
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for class...
As textual reasoning with large language models (LLMs) has advanced significantly, there has been growing interest in enhancing the multimodal reaso...
Open-set domain generalization(OSDG) for hyperspectral image classification presents significant challenges due to the presence of unknown classes i...
Achieving consistent color reproduction across multiple cameras is essential for seamless image fusion and Image Processing Pipeline (ISP) compatibi...
The performance and reliability of machine learning (ML)-quantitative structure-property relationship (QSPR) models depend on the quality, size, and d...
Designing model architectures requires decisions such as selecting operators (e.g., attention, convolution) and configurations (e.g., depth, width)....
Cued Speech (CS) enhances lipreading through hand coding, providing precise speech perception support for the hearing-impaired. CS Video-to-Speech g...
Current large language model (LLM) agents lack authentic human psychological processes necessary for genuine digital twins and social AI application...
Modern recommendation systems rely on exploration to learn user preferences for new items, typically implementing uniform exploration policies (e.g....
Purpose: This study aims to evaluate the effectiveness of large language models (LLMs) in automating disease annotation of CT radiology reports. We ...