Publications related to The stability of deep learning for 21cm foreground removal across various sky models and frequency-dependent systematics

Related publications (24)

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Jérôme Chenal, Baraka Jean-Claude Munyaka

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

AIP Publishing2025

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Michael Lehning, Devis Tuia, Yael Frischholz

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

2025

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Ramin Mohammadi, Nikolaos Stergiopoulos, Georgios Rovas, Lydia Aslanidou, Sokratis Anagnostopoulos

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

2025

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Alfio Quarteroni

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

2025

On the added value of sequential deep learning for the upscaling of evapotranspiration

Devis Tuia

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

2025

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Pascal Fua

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Springer Science and Business Media Deutschland GmbH2025

One Code to Predict Them All: Universal Encoding for Inquiry Modeling

Valentine Marie Delevaux, Tanja Christina Käser Jacober, Jade Maï L Cock

Interactive simulations enhance science education and foster inquiry skills, but their open-ended nature can be cognitively overloading. While adaptive systems offer timely support, research on predicting conceptual understanding in these environments is l ...

Springer Science and Business Media Deutschland GmbH2025

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Su Yeon Chang

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

IOP Publishing Ltd2025

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Zaffar Ahmed Shaikh

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

PeerJ2025

Euclid preparation: LXVII. Deep learning true galaxy morphologies for weak lensing shear bias calibration

Frédéric Courbin, Georges Meylan

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double Sérsic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified param ...

2025

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Yung-Cheng Chiang

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

2025

Search for Ηc (2s) →ωω and Ωφ and Measurements of Χcj → Ωω and Ωφ in Ψ(2s) Radiative Processes

Yunxuan Song

Using (2712 +/- 14) x 10(6) psi(2S) events collected with the BESIII detector at the BEPCII collider, we search for the decays eta(c) (2S) -> omega omega and eta(c) (2S) -> omega phi via the process psi(2S) -> gamma eta(c)(2S). No statistically significant ...

AMER PHYSICAL SOC2025

Observation of the ${{{\varLambda } ^0_{b}} \!\rightarrow {{J \hspace{-1.66656pt}/\hspace{-1.111pt}\psi }} {{\varXi } ^-} {{K} ^+} }$ and ${{{{\varXi } ^0_{b}} \!\rightarrow {{J \hspace{-1.66656pt}/\hspace{-1.111pt}\psi }} {{\varXi } ^-} {{\pi } ^+} }}$ decays

Olivier Schneider, Aurelio Bay, Tatsuya Nakada, Frédéric Blanc, Lesya Shchutska, Elena Graverini, Marie Theres Christin Bachmayer, Ettore Zaffaroni, Aravindhan Venkateswaran, Luis Miguel Garcia Martin, Yunxuan Song, Vitalii Lisovskyi, Federico Ronchetti, Radoslav Marchevski, Anni Matilda Kauniskangas, Dimitrios Kaminaris, Raphaël van Laak, Pierre Paul Louis Mayencourt, Brianna Leililani Thielen, Arnaud Merlin Gauthey, Gianluca Zunica, Abdul-Kerim Guseinov, Roberto Ribatti, Louis André Marie Jean Henry, Spencer Edward Collaviti, Julius Christian Daniel Schulte

The first observation of the

{{{{\varXi } ^0_{b}} \!\rightarrow {{J \hspace{-1.66656pt}/\hspace{-1.111pt}\psi }} {{\varXi } ^-} {{\pi } ^+} }}

decay and the most precise measurement of the branching fraction of the ${{{\varLambda } ^0_{b}} !\rightarrow ...

Springer Science and Business Media LLC2025

Neural Garment Modeling

Ren Li

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

EPFL2025

Probabilistic Load Forecasting of distribution power systems based on empirical copulas

Mario Paolone, Fabio Nobile, Pål Forr Austnes, Celia Garcia Pareja

Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. Distribution System Operators (DSOs) are called to accurately forecast their production and consumption to ...

Elsevier BV2025

GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim

Chenghao Xu

Photorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be easily applied to robotics because they typically lack physics-related informa ...

SAGE Publications2025

t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials

Konstantinos Karapiperis

More attention has been paid to integrating existing knowledge with data to understand the complex mechanical behaviour of geomaterials, but it incurs scepticism and criticism on its generalizability and robustness. Moreover, a common mistake in current da ...

2025

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Jan Weinreich, Matthieu Nicolas Haeberle

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

IOP Publishing Ltd2025

Image steganalysis using active learning and hyperparameter optimization

Zaffar Ahmed Shaikh

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

Springer Science and Business Media LLC2025

Multimodal Prediction Framework for Total Shoulder Arthroplasty Outcomes: Integrating Morphological, Musculoskeletal, Finite Element, and Bayesian Causal Models

Pezhman Eghbalishamsabadi

This thesis aimed to estimate the effect of demographic and biomechanical preoperative variables on total shoulder arthroplasty (TSA) outcomes. To achieve this, various biomechanical variables were calculated using a clinical database comprising normal and ...

EPFL2025