Publications related to Machine Learning-assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength-cost-co<sub>2</sub> Emissions Trade-offs

Related publications (19)

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

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

Nonlinear Effects in Fluctuation Short Pulse Reflectometry

Stefano Coda, Stephan Brunner, Oleg Krutkin

The fluctuation short pulse reflectometry diagnostic is simulated with the full-wave CUWA code for ad-hoc turbulence and simplified geometry. The nonlinear effects on the measurements are investigated while accounting for the plasma curvature, probing angl ...

IOP Publishing Ltd2025

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

Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics

Francesco Croce

Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse training sets, e.g. CLIP, have become popular. At the same time, the metrics extrac ...

IEEE2025

Meteorological information prediction using images from webcams

Demetri Psaltis, Christophe Moser, Luisa Lambertini, Roy Sarkis

One aspect of the present invention proposes a method of predicting meteorological information by using an artificial deep neural network. The present invention according to one example focuses on image processing, specifically on fusing a plurality of ima ...

2025

On the Conflict Between Robustness and Learning in Collaborative Machine Learning

Mathilde Aliénor Raynal

Collaborative Machine Learning (CML) allows participants to jointly train a machine learning model while keeping their training data private. In many scenarios where CML is seen as the solution to privacy issues, such as health-related applications, safety ...

IEEE2025

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

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

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

Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains

Jean-Philippe Thiran, Meritxell Bach Cuadra, Rizhong Lin

Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, ...

IEEE2024

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

Power Shift: Quantifying the Role of Actors in the Multi-Actor Swiss Energy System Decentralization

François Maréchal, Julia Granacher, Jonas Schnidrig, Cédric Terrier, Manuele Domenico Margni, Arthur Antoine Chuat

The global transition to decentralized energy systems signifies a fundamental transformation toward sustainable energy paradigms. This study specifically focuses on the Swiss energy system, analyzing how dynamic pricing influences the strategic decisions o ...

Frontiers Media SA2024

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

Numerical Approach to Determine the Resistance of Threaded Anchors in Ultra-High-Performance Fiber-Reinforced Cementitious Composite

Eugen Brühwiler, Antonina Hochuli

Due to their relatively high tensile strength and dense matrix, UHPFRCs have proven to be a highly effective building material for both strengthening existing reinforced concrete structures and constructing new ones. In both cases, the use of fasteners is ...

MDPI2024

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

Knowledge Distillation with Graph Neural Networks for Epileptic Seizure Detection

Pascal Frossard, Arun Venkitaraman

Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for ...

Springer International Publishing Ag2023