Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for ...

Our brains build maps of the environment that help us understand the world around us, allowing us to think, recall, and plan. These maps not only help us to, say, find our room on the correct floor of ...

Many conventional computer architectures are ill-equipped to meet the computational demands of machine learning-based models. In recent years, some engineers have thus been trying to design ...

Adolphi, C. and Sosonkina, M. (2025) Machine Learning and Simulation Techniques for Detecting Buoy Types from LiDAR Data.

However, domain ... knowledge graph representations from semantic information remain limited. In this paper, we develop a natural language processing (NLP) approach to extract knowledge graphs ...

Methods: To address these challenges, we propose PoseGCN, a Graph Convolutional Network (GCN)-based model that integrates ... we introduce an adaptive learning strategy that incorporates ...

A cornerstone of neural network computation is the concept of weights, which represent the “strength” or “importance” of each neuron’s connection in the network. NPUs integrate these weights directly ...

Learn More A new neural-network architecture developed by researchers at Google might solve one of the great challenges for large language models (LLMs): extending their memory at inference time ...

These hairs convert vibrations from sound waves into neural signals that your auditory nerve carries to your brain. Exposure to sounds louder than 85 decibels can damage these hairs. Eighty-five ...