In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Deep network architectures struggle to continually learn new tasks without forgetting the
previous tasks. A recent trend indicates that dynamic architectures based on an expansion …

Humans perceive the world by concurrently processing and fusing high-dimensional inputs
from multiple modalities such as vision and audio. Machine perception models, in stark …

Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural
costs and environmental footprints, and therefore is attracting ever-increasing interests in …

While semantic communications have shown the potential in the case of single-modal single-
users, its applications to the multi-user scenario remain limited. In this paper, we investigate …

This article presents a comprehensive survey of deep learning applications for object
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …

Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …