Researchers have developed a dynamic range compression dual-domain attention network for enhancing tunnel images under extreme exposure conditions, a problem that continues to challenge transportation ...

Abstract: Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional ...

ABSTRACT: The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant ...

The start-up Function will send practically anyone to a lab for extensive medical testing, no physical required. Is that a good thing? By Kristen V. Brown As Kimberly Crisp approached middle age, ...

Seismic inversion is vital for reservoir characterization but faces significant challenges in complex fluvial-deltaic systems due to strong heterogeneity and thin-bedded formations. Current methods, ...

To address the issues of low accuracy, high dependence on prior knowledge, and poor adaptability in fusing multi-channel features in existing plunger pump fault diagnosis methods, a new method based ...

A new brain-inspired AI method called Lp-Convolution enhances image recognition by dynamically reshaping CNN filters, combining biological realism with improved performance and efficiency. Credit: ...

Large Language Models (LLMs) significantly benefit from attention mechanisms, enabling the effective retrieval of contextual information. Nevertheless, traditional attention methods primarily depend ...

Event-based cameras are bio-inspired vision sensors that mimic the sparse and asynchronous activation of the animal retina, offering advantages such as low latency and low computational load in ...