The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...
The main strengthening mechanism for Inconel 718 (IN718), a Ni-based superalloy, is precipitation hardening by gamma ' and gamma '' particles. It is thus essential, for good alloy performance, that precipitates with the desired chemical composition have ad ...
Understanding metal surface reconstruction is of the utmost importance in electrocatalysis, as this phenomenon directly affects the nature of available active sites. However, its dynamic nature renders surface reconstruction notoriously difficult to study. ...
The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...
To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...
Dense image-based prediction methods have advanced tremendously in recent years. Their remarkable development has been possible due to the ample availability of real-world imagery. While these methods work well on photographs, their abilities do not genera ...
Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...
State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...
The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...
Fatigue damage in materials results in localized strain at the microstructural level. In many engineering components of the cooling circuits of nuclear power plants, where austenitic steels are used, the material experiences multiaxial cyclic loading, eith ...
