Publications associées à Impact of Data Processing on Fairness in Supervised Learning

Publications associées (24)

Slack-variable Approach for Variational Quantum Semidefinite Programming

Solving optimization problems is a key task for which quantum computers could possibly provide a speedup over the best known classical algorithms. Particular classes of optimization problems including semidefinite programming (SDP) and linear programming ( ...

AMER PHYSICAL SOC2025

Robust design optimization of gas-lubricated herringbone grooved journal bearings: Surrogate modeling and experimental validation

Cameron John Bush, Jürg Alexander Schiffmann, Soheyl Massoudi

This study experimentally validates robust designs for herringbone grooved journal bearings (HGJBs) in micro-turbocompressors, addressing manufacturing deviations that lead to rotor instabilities. Experiments on a Pelton-driven rotor system focused on maxi ...

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

Distributionally Robust Optimization

Daniel Kuhn, Wolfram Wiesemann

Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a fami ...

Cambridge University Press2025

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

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

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

Lei Xu

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

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

Multi-Period Optimal Power Flow: Convex Relaxations and Parallel Algorithms

Daniel Kuhn, Tianshu Yang

Ensuring reliable operation in power grids given the complexity of modern electricity networks with increasing demands, varying generation and flows present significant computational challenges. Addressing these challenges, we develop efficient methods for ...

Institute of Electrical and Electronics Engineers Inc.2024

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

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

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

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

New Equivalences between Interpolation and SVMs: Kernels and Structured Features

Andrew Duncan Mc Rae

The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain sufficien ...

SIAM PUBLICATIONS2024

Auction algorithm sensitivity for multi-robot task allocation

Tony Alan Wood

We consider the problem of finding a low-cost allocation and ordering of tasks between a team of robots in a d-dimensional, uncertain, landscape, and the sensitivity of this solution to changes in the cost function.Various algorithms have been shown to giv ...

2023

Carbonized wood impregnated with bimetallic nanoparticles as a monolithic continuous-flow microreactor for the reduction of 4-nitrophenol

Qingtong Zhang, Paul Joseph Dyson, Zhaofu Fei, Rosemarie Janet Somerville

Porous monolithic microreactors show great promise in catalytic applications, but are usually based on non-renewable materials. Herein, we demonstrate a Ni/Au nanoparticle-decorated carbonized wood (Ni/Au-CW) monolithic membrane microreactor for the effici ...

ELSEVIER2023

Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market

Sidi-Rachid Cherkaoui

Finding Nash equilibrium in an imperfect market with active and strategic resources is key to the successful operation of the electricity market. A hierarchical Nash distributed reinforcement learning (HNDRL) framework is proposed to realize the interactio ...

ELSEVIER SCI LTD2023

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

Alfio Quarteroni, Luca Dede'

We propose a mathematical and numerical model for the simulation of the heart function that couples cardiac electrophysiology, active and passive mechanics and hemodynamics, and includes reduced models for cardiac valves and the circulatory system. Our mod ...

Wiley2023

Distributionally Robust Chance Constrained Optimization for Providing Flexibility in an Active Distribution Network

Sidi-Rachid Cherkaoui

In this paper, we propose a distributionally robust chance constrained (DRCC) optimization problem for the operation of an active distribution network (ADN). The ADN's operator uses the proposed problem to centrally optimize the dispatch plan of his resour ...

2022