Publications associées à Joint Multiband Deconvolution for Euclid and Vera C. Rubin Images

Publications associées (16)

Digital Transformation in African Heritage Preservation: A Digital Twin Framework for a Sustainable Bab Al-Mansour in Meknes City, Morocco

Highlights What are the main findings? - The study develops a Digital Twin Heritage Building (DTHB) framework based on digital transformation, integrating HBIM, IoT sensors, and predictive analytics to address structural and environmental challenges. - The ...

MDPI2025

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Fernando Perez Cruz

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

2025

Structured Random Model for Fast and Robust Phase Retrieval

Michaël Unser, Jonathan Yuelin Dong

Phase retrieval, a nonlinear problem prevalent in imaging applications, has been extensively studied using random models, some of which with i.i.d. sensing matrix components. While these models offer robust reconstruction guarantees, they are computational ...

Institute of Electrical and Electronics Engineers Inc.2025

Computational approaches for spatialomics data: construction, integration and analysis of biomolecular atlases

Halima Hannah Emilie Schede

Cellular function and behavior are governed not only by molecular composition but also by a cell's native environmental context. By leveraging in-depth molecular quantification and high-resolution imaging methods to study microenvironmental composition and ...

EPFL2025

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Alexandre Massoud Alahi, Yuejiang Liu, Po-Chien Luan, Ahmad Rahimi

Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the cau ...

2025

Controlled Training Data Generation with Diffusion Models

Aleksandr Alekseev, Shuqing Teresa Yeo, Andrei Atanov

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

2025

Unravelling the components of diffuse scattering using deep learning

Lucas Sebastian Powell-Rudden

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

Int Union Crystallography2024

Morse theoretic signal compression and reconstruction on chain complexes

Kelly Spry Maggs, Celia Camille Hacker, Stefania Ebli

At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal on the cells of a complex is processed using the combinatorial Laplacian ...

2024

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Christian Leinenbach, Antonios Nikolaos Baganis

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

Taylor & Francis Ltd2024

Stripping the Swiss discount curve using kernel ridge regression

Nicolas Etienne Camenzind, Damir Filipovic

We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curve-a robust machine learning approach. Swiss Finance Institute Research Paper No. 22-24. SSRN. https://ssrn.com/abstract=4058150, 2022 ...

Springer Heidelberg2024

PARAMETRIC SHAPE HOLOMORPHY OF BOUNDARY INTEGRAL OPERATORS WITH APPLICATIONS

Fernando José Henriquez Barraza

We consider a family of boundary integral operators supported on a collection of parametrically defined bounded Lipschitz boundaries. Consequently, the boundary integral operators themselves also depend on the parametric variables, thus leading to a parame ...

2024

Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics

Xiaotian Su, Seyed Parsa Neshaei

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diver ...

Association for Computing Machinery2024

Multitask methods for predicting molecular properties from heterogeneous data

Michael Herbst

Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we co ...

2024

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Filippo Bigi

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

2024

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

Jérôme Waser, Anne-Clémence Corminboeuf, Matthew Wodrich, Rubén Laplaza Solanas, Alexandre Alain Schöpfer

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the poten ...

Amer Chemical Soc2024

Point-process-based Representation Learning for Electronic Health Records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

IEEE2023