Publications related to Fréchet means and Procrustes analysis in Wasserstein space

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

Paolo De Los Rios, Pierre Goloubinoff, Satyam Tiwari, Mathieu Rebeaud, Bruno Claude Daniel Fauvet, Adélaïde Alice Mohr

The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in ne ...

Elsevier Science Inc2024

Reliable data-driven decision-making through optimal transport

Bahar Taskesen

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks the ...

EPFL2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Alireza Karimi, Mert Eyuboglu, Nathan Russell Powell

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Search for the lepton flavour violating decays B0 -> emu and B0s -> emu with LHCb Run 2 data

Sebastian Schulte

A large variety of new physics models suggest that the rates for lepton flavour violating

b

-hadron decays may be much higher than predicted in the Standard Model, which leads to a high interest in the search for such decays. This thesis presents the sear ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Thin-laminate composites with thicknesses below 200 mu m hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusi ...

2024

Waterborne Virus Transport and Risk Assessment in Lake Geneva Under Climate Change

Tamar Kohn, Chaojie Li

Climate changes influence lake hydrodynamics and radiation levels and thus may affect the fate and transport of waterborne pathogens in lakes. This study examines the impact of climate change on the fate, transport, and associated risks of four waterborne ...

2024

Fréchet means and Procrustes analysis in Wasserstein space

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Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

Reliable data-driven decision-making through optimal transport

Random matrix methods for high-dimensional machine learning models

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Search for the lepton flavour violating decays B0 -> emu and B0s -> emu with LHCb Run 2 data

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Waterborne Virus Transport and Risk Assessment in Lake Geneva Under Climate Change

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Reliable data-driven decision-making through optimal transport

Random matrix methods for high-dimensional machine learning models

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Waterborne Virus Transport and Risk Assessment in Lake Geneva Under Climate Change

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

Search for the lepton flavour violating decays B0 -> emu and B0s -> emu with LHCb Run 2 data

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models