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We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Poisoning attacks compromise the training data utilized to train machine learning (ML) models, diminishing their overall performance, manipulating predictions on specific test samples, and implanting backdoors. This article thoughtfully explores these atta ...

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 upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

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 ...

We demonstrate the first EDWA-enabled Terabit-class coherent optical communication with 1.6-Tb/s net bit rate per channel and 16 -channel WDM transmission over 81 -km fiber, proving the potential of such on-chip amplification for future coherent applicatio ...

What training should engineering students receive to enable them to contribute to reducing bias, discrimination and the persistent lack of diversity in engineering? Collaboration is central to professional engineering work and, consequently, teamwork and g ...

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Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

On the Generalization of Stochastic Gradient Descent with Momentum

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Machine Learning Security Against Data Poisoning: Are We There Yet?

Safe Deep Neural Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Predicting the long-term collective behaviour of fish pairs with deep learning

Driving and suppressing the human language network using large language models

First Demonstration of Erbium -Doped Waveguide Amplifier Enabled Multi-Tb/s (16x1.6T) Coherent Transmission

Educating Engineering Students to Address Bias and Discrimination Within Their Project Teams