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Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

Publications associées (31)

Transfer learning application of self-supervised learning in ARPES

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

IOP Publishing Ltd2023

Predicting party switching through machine learning and open data

Silvestro Micera, Emanuele Rossi

Parliament dynamics might seem erratic at times. Predicting future voting patterns could support policy design based on the simulation of voting scenarios. The availability of open data on legislative activities and machine learning tools might enable such ...

CELL PRESS2023

JET D-T scenario with optimized non-thermal fusion

Haomin Sun, Michele Marin, Javier García Hernández, Mikhail Maslov

In JET deuterium-tritium (D-T) plasmas, the fusion power is produced through thermonuclear reactions and reactions between thermal ions and fast particles generated by neutral beam injection (NBI) heating or accelerated by electromagnetic wave heating in t ...

IOP Publishing Ltd2023

Validation of D-T fusion power prediction capability against 2021 JET D-T experiments

Michele Marin, Javier García Hernández, Mikhail Maslov

JET experiments using the fuel mixture envisaged for fusion power plants, deuterium and tritium (D-T), provide a unique opportunity to validate existing D-T fusion power prediction capabilities in support of future device design and operation preparation. ...

IOP Publishing Ltd2023

Full-discharge simulation and optimization with the RAPTOR code, from present tokamaks to ITER and DEMO

Simon Van Mulders

Tokamak devices aim to magnetically confine a hydrogen plasma at sufficiently high pressure to achieve net energy production from nuclear fusion of light isotopes. Predictive modeling and optimization is crucial for reliable operation of tokamak reactors, ...

EPFL2023

The JET hybrid scenario in Deuterium, Tritium and Deuterium-Tritium

Matteo Fontana, Haomin Sun, Javier García Hernández, Mikhail Maslov

The JET hybrid scenario has been developed from low plasma current carbon wall discharges to the record-breaking Deuterium-Tritium plasmas obtained in 2021 with the ITER-like Be/W wall. The development started in pure Deuterium with refinement of the plasm ...

IOP Publishing Ltd2023

Object Priors for Volumetric Image Segmentation

Pamuditha Udaranga Wickramasinghe

Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotat ...

EPFL2022

Micromechanically inspired investigation of cemented granular materials: part I-from X-ray micro tomography to measurable model variables

Gioacchino Viggiani, Edward Carlo Giorgio Andò

Cemented granular materials are abundant in nature and are often artificially produced. Their macroscopic behaviour is driven by small-scale material processes, which are generally classified as: grain breakage, cement damage and fragment rearrangement. Th ...

SPRINGER HEIDELBERG2022

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Isinsu Katircioglu

Detecting people from 2D images and analyzing their motion in 3D have been long standing computer vision problems central to numerous applications such as autonomous driving and athletic training. Recently, with the availability of large amounts of trainin ...

EPFL2022

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars

Giovanni De Cesare, Christian Mörtl

In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...

International Association for Hydro-Environment Engineering and Research (IAHR)2022