Publications associées à Predicting climate-change impacts on the global glacier-fed stream microbiome

Publications associées (24)

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

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

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

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Yves Rychener

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

EPFL2025

A journey toward generalizable trajectory forecasting models

Mohammadhossein Bahari

Autonomous driving is a revolutionary technology that has seen considerable advancements through the adoption of deep learning solutions. One of the major challenges in this field is the interaction with other road users. This interaction necessitates a "t ...

EPFL2024

Data-driven models in fusion exhaust: AI methods and perspectives

Yoeri Renier Johannes Poels

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

IOP Publishing Ltd2024

Intelligent prediction framework for axial compressive capacity of FRP-RACFST columns

Lei Xu

Fiber reinforced polymer confined-recycled aggregate concrete filled steel tube (FRP-RACFST) not only proposes excellent load-bearing capacity and corrosion resistance, but is also an environmentally friendly structure due to the incorporation of recycled ...

ELSEVIER2024

Completeness of Atomic Structure Representations

Michele Ceriotti, Jigyasa Nigam, Sergey Pozdnyakov, Kevin Kazuki Huguenin-Dumittan

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with ...

AIP Publishing2024

Live-cell imaging powered by computation

Suliana Manley

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Nature Portfolio2024

Coupled carbon and nitrogen cycling regulates the cnidarian–algal symbiosis

Anders Meibom, Stéphane Laurent Escrig, Nils Rädecker

Efficient nutrient recycling underpins the ecological success of cnidarian-algal symbioses in oligotrophic waters. In these symbioses, nitrogen limitation restricts the growth of algal endosymbionts in hospite and stimulates their release of photosynthates ...

Nature Research2023

Machine Learning in Protein Science De Novo Design of Site-Specific Protein Binders Using Surface Fingerprints

Henning Paul-Julius Stahlberg, Didier Trono, Priscilla Turelli, Anthony Marchand, Bruno Emanuel Ferreira De Sousa Correia, Zander Harteveld, Pablo Gainza Cirauqui, Andreas Scheck, Sarah Wehrle, Alexandra Krina Van Hall-Beauvais, Casper Alexander Goverde, Dongchun Ni

WILEY2023

Finding Actual Descent Directions For Adversarial Training

Volkan Cevher, Igor Krawczuk, Fabian Ricardo Latorre Gomez, Thomas Michaelsen Pethick, Leello Tadesse Dadi

Adversarial Training using a strong first-order adversary (PGD) is the gold standard for training Deep Neural Networks that are robust to adversarial examples. We show that, contrary to the general understanding of the method, the gradient at an optimal ad ...

2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification

Daniel Probst

Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed a ...

2023

Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes

Ashok Vardhan Makkuva

Reed-Muller (RM) codes achieve the capacity of general binary-input memoryless symmetric channels and are conjectured to have a comparable performance to that of random codes in terms of scaling laws. However, such results are established assuming maximum- ...

2023

Interpretable temporal-spatial graph attention network for multi-site PV power forecasting

Pascal Frossard, Rafael Eduardo Carrillo Rangel, Jelena Simeunovic

Accurate forecasting of photovoltaic (PV) and wind production is crucial for the integration of more renewable energy sources into the power grid. To address the limited resolution and costs of methods based on numerical weather predictions (NWP), we take ...

ELSEVIER SCI LTD2022

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Michaël Unser, Joaquim Gonçalves Garcia Barreto Campos, Shayan Aziznejad

We develop a novel 2D functional learning framework that employs a sparsity-promoting regularization based on second-order derivatives. Motivated by the nature of the regularizer, we restrict the search space to the span of piecewise-linear box splines shi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Fast Quench Detection in SFCL Pancake Using Optical Fibre Sensing and Machine Learning

Bertrand Dutoit

A fast hotspot detection technique has been implemented and patented at ecole Polytechnique Federale de Lausanne (EPFL) under the European Union project FastGrid. The optical fibre sensing based technique uses the Mach-Zehnder Interferometer (MZI) and can ...

IEEE Institute of Electrical and Electronics Engineers2022

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

EPFL2021

Federated Generative Privacy

Boi Faltings, Aleksei Triastcyn

We propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw private artificial data samples and empirica ...

IEEE COMPUTER SOC2020

Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions

David Atienza Alonso, Fabio Isidoro Tiberio Dell'Agnola, Adriana Arza Valdes, Niloofar Momeni

High levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observabl ...

IEEE2019

Data Mining the C-C Cross-Coupling Genome

Anne-Clémence Corminboeuf, Matthew Wodrich, Alberto Fabrizio, Benjamin André René Meyer, Boodsarin Sawatlon

The speed and precision of machine-learning (ML) techniques in determining quantum chemical properties has resulted in a considerable computational speed up in comparison to traditional quantum chemical methods, and now allows a desired property of thousan ...

Wiley-VCH Verlag2019