Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Abstract: This paper presents an optimized lightweight Super-Resolution Convolutional Neural Network (SRCNN) capable of reconstructing high-quality images with strong fidelity. The proposed framework ...

Abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented ...

ABSTRACT: Rainfall-induced landslides threaten mountainous regions globally, yet existing models face challenges in real-time, large-scale prediction due to dependency on post-event data. This study ...

The degradation of marine ecosystems - including coral reefs, oyster reefs, and deep-sea communities is accelerating due to ...

The significant contributions of this work are threefold. First, it leverages deep learning to extend in vivo imaging depth of two-photon excitation fluorescence microscopy, far beyond the depths ...