Publications associées à Deep learning assisted image transmission in multimode fibers

Publications associées (51)

Search for Pair Production of Heavy Particles Decaying to a Top Quark and a Gluon in the Lepton Plus Jets Final State in Proton-proton Collisions at √s=13 Tev

Jan Steggemann, Anna Mascellani, Joanna Malgorzata Wanczyk

A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV ...

SPRINGER2025

Engineering Symmetry Breaking Interfaces by Nanoscale Structural-Energetics in Orthorhombic Perovskite Thin Films

Duncan Thomas Lindsay Alexander, Bernat Mundet

The atomic configuration of phases and their interfaces is fundamental to materials design and engineering. Here, we unveil a transition metal oxide interface, whose formation is driven by energetic influences─epitaxial tensile strain versus oxygen octahed ...

2025

Bioanode engineering for living solar cells

Charlotte Elisabeth Marie Roullier

Whole-cell biophotovoltaic (BPV) devices leverage photoautotrophic microorganisms like cyanobacteria to convert sunlight and water into bioelectricity. They are capable of growing on sequestered carbon dioxide (CO2), eliminating the need to add exogenous o ...

EPFL2025

Significant reduction of cell invasiveness in nanoneedle insertion into a living cell with an electron-beam-deposited probe: impacts of probe geometry, speed and vibration

Marcos Penedo Garcia

Intracellular probing of living cells using atomic force microscopy (AFM) has advanced significantly, but it requires specially designed nanoprobes to achieve precision and minimize damage. The development of focused ion beam (FIB)-milled nanoprobes enable ...

2025

Learning to Reason with Neural Networks: Principles and Methods

Aryo Lotfi Jandaghi

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

EPFL2025

Passivating oxidation behavior of Ti0.12Al0.21B0.67 coatings investigated by scanning transmission electron microscopy and chemical environment dependent density functional theory simulations

Johann Michler

The oxidation behavior of stoichiometric Ti0.12Al0.21B0.67 coatings is investigated by scanning transmission electron microscopy (STEM) after oxidizing for 1, 4 and 8 h at 700 degrees C and at 800 and 900 degrees C. In the as deposited state, a similar to ...

PERGAMON-ELSEVIER SCIENCE LTD2025

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Giuseppe Carleo, Jannes Willy E. Nys, Imelda Romero

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

NATURE PORTFOLIO2025

Sequential Reasoning with Socially Caused Beliefs

Mert Kayaalp

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

EPFL2025

Advanced imaging of gas diffusion layers: A comparison between focused ion beam scanning electron microscopy and X-ray computed tomography

Jan Van Herle, Hossein Pourrahmani

With the advances in clean technologies, the role of fuel cells and batteries is becoming increasingly vital. Proton exchange membrane fuel cells are known to be the best candidates for low-temperature and portable operations, such as hydrogen cars. The ef ...

2025

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Anaïs Betsabeh Haget, Maria Boulougouri

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

OXFORD UNIV PRESS2025

Lamellar Schwann Cells in the Pacinian Corpuscle Potentiate Vibration Perception

Graham Knott, Mary-Claude Croisier-Coeytaux, Jérôme Blanc, Stéphanie Clerc

Pacinian corpuscles are among the most sensitive mechanoreceptors found in vertebrates, and they are tuned to vibrations in the highest perceptible frequency range (100 to 2000 Hz). One of their anatomical hallmarks is the onion-like cell layers surroundin ...

AMER ASSOC ADVANCEMENT SCIENCE2025

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Maude Girardin, Marco Picasso, Alexandre Caboussat

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Elsevier Science Sa2024

A Multiscale Bayesian Approach to Quantification and Denoising of Energy-dispersive X-ray Data

Cécile Hébert, Duncan Thomas Lindsay Alexander, Pau Torruella Besa

Energy dispersive x-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detecte ...

IOP Publishing Ltd2025

FADE-CTP: A Framework for the Analysis and Design of Educational Computational Thinking Problems

Alberto Piatti, Francesco Mondada, Engin Walter Bumbacher

In recent years, the emphasis on computational thinking (CT) has intensified as an effect of accelerated digitalisation. While most researchers are concentrating on defining CT and developing tools for its instruction and assessment, we focus on the charac ...

Springer Science and Business Media LLC2025

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Pengbo Yu

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

IEEE2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Hard x-ray grazing-incidence ptychography: large field-of-view nanostructure imaging with ultra-high surface sensitivity

Manuel Guizar Sicairos

The morphology and distribution of nanoscale structures, such as catalytic active nanoparticles and quantum dots on surfaces, have a significant impact on their function. Thus, the capability of monitoring these properties during manufacturing and operatio ...

Optica Publishing Group2024

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Tatiana Pieloni, Mike Seidel, Davide Di Croce

The Dynamic Aperture is an important concept for the study of non-linear beam dynamics in a circular accelerator. The DA is defined as the extent of the phase-space region in which the particle's motion remains bounded over a finite number of turns. Such a ...

Institute of Physics2024

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