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Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Alireza Modirshanechi

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

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

Paul Scharnhorst

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

Models implemented in the methodological approach to design the initial STEP first wall contour

Kevin Henricus Annemarie Verhaegh, Davide Galassi, Filippo Bagnato, Dario Vaccaro

The official Spherical Tokamak for Energy Production mission aims to demonstrate the ability to generate net electricity from fusion with the STEP Prototype Power plant. One of the key technological and engineering challenges in fusion power plants is mana ...

Elsevier Science Sa2024

Technology-Aware Logic Synthesis for Superconducting Electronics

Giovanni De Micheli, Alessandro Tempia Calvino, Siang-Yun Lee, Dewmini Sudara Marakkalage, Mingfei Yu, Rassul Bairamkulov

Superconducting electronics provide us with cryogenic digital circuits that can rival established technologies in performance and energy consumption. Today, the lack of tools for the design of large-scale integrated superconducting circuits is a major obst ...

2024

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

Alireza Karimi, Nathan Russell Powell, Mert Eyuboglu

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

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Guillaume André Jean Fraux, Arslan Mazitov, Nataliya Lopanitsyna

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants

Mohammad Javad Karimi

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

EPFL2024

Improving Group Testing via Gradient Descent

Pavlos Nikolopoulos

We study the problem of group testing with non-identical, independent priors. So far, the pooling strategies that have been proposed in the literature take the following approach: a hand-crafted test design along with a decoding strategy is proposed, and g ...

2024

FIRST COMPARISON STUDIES IN DYNAMIC APERTURE FOR NESTED MAGNETS AND BASELINE LATTICE IN THE FCC-ee

Tatiana Pieloni, Mike Seidel, Léon Van Riesen-Haupt, Cristobal Miguel Garcia Jaimes

A significant project such as the FCC-ee (with 91.17 km circumference) entails numerous challenges to ensure the stability and performance of the machine. In the pursuit of contributing to the improvement of energy consumption during its operation, the exp ...

JACoW2024

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

Robust machine learning for neuroscientific inference

Steffen Schneider

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024