Piezoresistive AFM cantilevers surpassing standard optical beam deflection in low noise topography imaging

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Disentangling 1/f noise from confined ion dynamics

Ion transport through biological and solid-state nanochannels is known to be a highly noisy process. The power spectrum of current fluctuations is empirically known to scale like the inverse of frequency, following the long-standing yet poorly understood H ...

ROYAL SOC CHEMISTRY2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Michaël Unser, Stanislas Ducotterd, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

Zenodo2023

GTC Follow-up Observations of Very Metal-poor Star Candidates from DESI

Pascale Jablonka, Arjun Dey

The observations from the Dark Energy Spectroscopic Instrument (DESI) will significantly increase the numbers of known extremely metal-poor stars by a factor of similar to 10, improving the sample statistics to study the early chemical evolution of the Mil ...

Iop Publishing Ltd2023

Millimeter-wave to near-terahertz sensors based on reversible insulator-to-metal transition in VO2

Mihai Adrian Ionescu, Teodor Rosca, Fatemeh Qaderi Rahaqi

In the quest for low power bio-inspired spiking sensors, functional oxides like vanadium dioxide are expected to enable future energy efficient sensing. Here, we report uncooled millimeter-wave spiking detectors based on the sensitivity of insulator-to-met ...

SPRINGERNATURE2023

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

EPFL2023

Functional data analysis with rough sample paths?

Victor Panaretos, Neda Mohammadi Jouzdani

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

TAYLOR & FRANCIS LTD2024

Model-based constraints for trajectory determination of quad-copters: Design, calibration & merits for direct orientation

Jan Skaloud, Davide Antonio Cucci, Kenneth Joseph Paul

This paper proposes a novel method to improve georeferencing of airborne laser scanning by improved trajectory estimation using Vehicle Dynamic Model. In Vehicle Dynamic Model (VDM), the relationship between the dynamics of the platform and control inputs ...

2023

Observation of non-reciprocal harmonic conversion in real sounds

Hervé Lissek, Romain Christophe Rémy Fleury, Xinxin Guo

Reciprocity guarantees that in most media, sound transmission is symmetric between two points of space when the location of the source and receiver are interchanged. This fundamental law can be broken in non-linear media, often at the cost of detrimental i ...

2023

Euclid preparation XXVI. The Euclid Morphology Challenge: Towards structural parameters for billions of galaxies

Slobodan Ilic, Georges Meylan, Frédéric Courbin, Richard Massey, Maurizio Martinelli, Gianluca Castignani, Marcello Farina, Yi Wang

The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morpholo ...

2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

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