Lightweight HI source finding for next generation radio surveys

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Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data st ...

This data package supports the publication 'Complexity of crack front geometry enhances toughness of brittle solids' by Xinyue Wei, Chenzhuo Li, Cían McCarthy, and John M. Kolinski Nature physics (2024) - https://doi.org/10.1038/s41567-024-02435-x DOI: 1 ...

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, ...

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

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 ...

Despite the widespread use of self-tracking technologies for promoting personal wellbeing, there is limited research on the monitoring of intimate data, particularly urine. To shed light on the design possibilities within this unexplored domain we designed ...

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 ...

The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in ne ...

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 ...

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 ...

Numerous conductor designs for pulsed magnets based on High Temperature Superconductors (HTS), featuring stacks of tapes are currently being proposed. A major contribution to the AC losses is expected to be given by hysteresis losses. Several numerical mod ...

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 ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Masonry aggregates, which emerged as layouts of cities and villages became denser, make up historical centres all over the world. In these aggregates, neighbouring structures may share structural walls that are joined at the interfaces by mortar or interlo ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Lightweight HI source finding for next generation radio surveys

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Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Orchestrating chromosome conformation capture analysis with Bioconductor

Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"

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

Robust time series denoising with learnable wavelet packet transform

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

Designing for Intimate Wellbeing: Aidee, a Qualitative Approach To Urine Home Monitoring

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

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

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

Safe Deep Neural Networks

Validation and Application of Hysteresis Loss Model for HTS Stacks and Conductors for Fusion Applications

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

Random matrix methods for high-dimensional machine learning models

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Seismic Testing of Adjacent Interacting Masonry Structures – Analysis of a Blind Prediction

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks