Beyond Bouma's window: How to explain global aspects of crowding?

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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 desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

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Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

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Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

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Beyond Bouma's window: How to explain global aspects of crowding?

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Safe Deep Neural Networks

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

MEMS-based Tunable Metasurface for Reflective Display Applications

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Residual-based attention in physics-informed neural networks

Optimizing Dynamic Aperture Studies with Active Learning

Driving and suppressing the human language network using large language models

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Digital Twins of Stone Masonry Buildings for Damage Assessment