Publications associées à Nonlinear Effects in Fluctuation Short Pulse Reflectometry

Publications associées (19)

Machine Learning-assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength-cost-co2 Emissions Trade-offs

Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance econom ...

MDPI2025

Automated processing of eXplainable Artificial Intelligence outputs in deep learning models for fault diagnostics of large infrastructures

Olga Fink

Deep Learning (DL) models processing images to recognize the health state of large infrastructure components can exhibit biases and rely on non-causal shortcuts. eXplainable Artificial Intelligence (XAI) can address these issues but manually analyzing expl ...

2025

Machine learning-aided hysteretic response prediction of double skin composite wall under earthquake loads

Yongtao Wu

Accurately and rapidly simulating the hysteretic behavior of double skin composite wall (DSCW) under earthquake loads enhance the efficiency of seismic performance assessments for high-rise steel-concrete composite structures. The application of artificial ...

2025

Emotion information recovery potential of wav2vec2 network fine-tuned for speech recognition task

Tilak Purohit

Fine-tuning has become a norm to achieve state-of-the-art performance when employing pre-trained networks like foundation models. These models are typically pre-trained on large-scale unannotated data using self-supervised learning (SSL) methods. The SSL-b ...

Institute of Electrical and Electronics Engineers Inc.2025

Fusing Machine Learning Methods with Computational Fluid Dynamics and Sensor Data for Indoor Air Quality Monitoring

Athanasios Nenes

Monitoring indoor air quality is important nowdays, as noted by many researchers, since pollution potentially introduces a major impact on the health of people. There are many factors that increase the concentration of a pollutant in a room and there may e ...

Institute of Electrical and Electronics Engineers Inc.2025

SINDER: Repairing the Singular Defects of DINOv2

Mathieu Salzmann, Haoqi Wang

Vision Transformer models trained on large-scale datasets, although effective, often exhibit artifacts in the patch token they extract. While such defects can be alleviated by re-training the entire model with additional classification tokens, the underlyi ...

Springer Nature2025

VIVALDI: steel billet tracking with advanced computer vision

Liza Carol Andrea Dunbar

Our advanced computer vision system allows for the precise tracking of serial numbers on steel billets in challenging industrial settings. It combines cutting-edge hardware and machine learning, excelling in character recognition (99.8%) and localization w ...

SPIE2024

Self-Supervised Underwater Caustics Removal and Descattering via Deep Monocular SLAM

Devis Tuia, Jonathan Paul Sauder

Underwater scenes are challenging for computer vision methods due to color degradation caused by the water column and detrimental lighting effects such as caustic caused by sunlight refracting on a wavy surface. These challenges impede widespread use of co ...

Springer Nature (Switzerland)2024

Non-negative matrix factorization-aided phase unmixing and trace element quantification of STEM-EDXS data

Cécile Hébert, Duncan Thomas Lindsay Alexander, James Badro, Farhang Nabiei, Hui Chen

Energy-dispersive X-ray spectroscopy (EDXS) mapping with a scanning transmission electron microscope (STEM) is commonly used for chemical characterization of materials. However, STEM-EDXS quantification becomes challenging when the phases constituting the ...

Elsevier2024

Machine Understanding of Architectural Space: From Analytical to Generative Applications

Mikhael Johanes

The unprecedented development of machine learning (ML) and artificial intelligence (AI) has opened new ways of capturing architectural quality, where large neural networks have demonstrated remarkable capabilities compared to traditional rule-based approac ...

EPFL2024

Performance of the CMS high-level trigger during LHC Run 2

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 x 10(34) cm(-2)s(-1), twice the initial design value, at root s = 13 TeV. The ...

IOP Publishing Ltd2024

Second Edition Frcsyn Challenge at Cvpr 2024: Face Recognition Challenge in the Era of Synthetic Data

Alexander Carl Unnervik

Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases p ...

IEEE2024

Evidence for tWZ production in proton-proton collisions at √s=13 TeV in multilepton final states

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk

The first evidence for the standard model production of a top quark in association with a W boson and a Z boson is reported. The measurement is performed in multilepton final states, where the Z boson is reconstructed via its decays to electron or muon pai ...

ELSEVIER2024

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

Georg Michael Joachim Starke

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Mu ...

2023

ShapeNet: Shape constraint for galaxy image deconvolution

Utsav Akhaury

Deep learning (DL) has shown remarkable results in solving inverse problems in various domains. In particular, the Tikhonet approach is very powerful in deconvolving optical astronomical images. However, this approach only uses the l(2) loss, which does no ...

2022

what If...? Pandemic Policy-decision-support to Guide a Cost-benefit-optimised, Country- Specific Response

Francesco Posa, Lucas Christophe Massemin, Giorgio Mannarini, Martin Jaggi, Mary-Anne Hartley, Tatjana Chavdarova, Prakhar Gupta, Thierry Bossy

Background After 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiol ...

PUBLIC LIBRARY SCIENCE2022

Minimax rate for optimal transport regression between distributions

Victor Panaretos, Laya Ghodrati

Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...

ELSEVIER2022

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

Nicola Marzari, Michele Ceriotti, Giovanni Pizzi, Michele Kotiuga, Max David Veit, Lorenzo Gigli

Modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that driv ...

NATURE PORTFOLIO2022

MulT: An End-to-End Multitask Learning Transformer

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Deblina Bhattacharjee

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edg ...

IEEE COMPUTER SOC2022