The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning star

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We investigated the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we performed a systematic visual inspection of the 0.7 deg2 Euclid Early Release Observations data towards the Perseus cluster using both the high-re ...

Aircraft-based monitoring of wildlife is a popular way among conservation practitioners to obtain animal population counts over large areas. Nowadays, these aerial censuses are becoming increasingly scalable due to the advent of drone technology, which is ...

Context. The Pristine-Gaia synthetic catalogue of reliable photometric metallicities makes use of spectrophotometric information from Gaia DR3 XP spectra to calculate metallicity-sensitive CaHK magnitudes, which in turn provides photometric metallicities f ...

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today’s world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increas ...

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properti ...

Coffee production serves as an important source of income for millions of farmers across many tropical countries. Given the scale of the production, accurate and up-to-date maps of coffee plantations are needed to detect, monitor, and mitigate potential ne ...

According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...

The gravitationally lensed supernova Refsdal appeared in multiple images produced through gravitational lensing by a massive foreground galaxy cluster. After the supernova appeared in 2014, lens models of the galaxy cluster predicted that an additional ima ...

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models-a su ...

Wind farm modelling is an area of rapidly increasing interest with numerous analytical and computational-based approaches developed to extend the margins of wind farm efficiency and maximise power production. In this work, we present the novel ML framework ...

A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...

Recent traffic flow prediction methods are lacking abilities to determine predictive features. Thus, they will propagate the error in the next timestamps. In this paper, first, we assess the role of spatial and temporal features on the traffic speed predic ...

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

The expeditious proliferation of Internet connectivity and the growing adoption of digital products have transformed various spheres of our everyday lives. This increased digitization of society has led to the emergence of new applications, which are deplo ...

The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning star

Chattez avec Graph Search

Euclid: The Early Release Observations Lens Search Experiment

How to minimize the annotation effort in aerial wildlife surveys

The Pristine survey: XXV. The very metal-poor Galaxy: Chemodynamics through the follow-up of the Pristine- Gaia synthetic catalogue

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data

Probing the effects of broken symmetries in machine learning

Semantic Segmentation of Coffee Plantations from Sentinel-2 Time Series

Robust Training and Verification of Deep Neural Networks

Constraints on the Hubble constant from supernova Refsdal's reappearance

Automated image-based generation of finite element models for masonry buildings

Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models

A recipe for cracking the quantum scaling limit with machine learned electron densities

STATNet: Spatial-temporal attention in the traffic prediction

Context-Aware Image Super-Resolution Using Deep Neural Networks

Architecture Exploration and Optimization of Heterogeneous Many-Core Compute and Memory Architectures with Architectural Extensions