Publications associées à Fast 3D reconstruction method for differential phase contrast X-ray CT

A ride time-oriented scheduling algorithm for dial-a-ride problems

Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

Pergamon-Elsevier Science Ltd2024

Impact of CO2-rich seawater injection on the flow properties of basalts

Lyesse Laloui, Eleni Stavropoulou, Cesare Griner

Permanent CO storage in basalts through mineralisation offers a promising solution for reducing carbon emissions and mitigating climate change. This study focuses on the impact of potential mineralisation on the flow properties of the basaltic material. Fl ...

2024

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

Yingying Ren

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

Association for Computing Machinery2024

In vivo X-ray microtomography locally affects stem radial growth with no immediate physiological impact

Pascal Turberg, Charlotte Grossiord, Laura Mekarni, Hervé Cochard

Microcomputed tomography (mu CT) is a nondestructive X-ray imaging method used in plant physiology to visualize in situ plant tissues that enables assessments of embolized xylem vessels. Whereas evidence for X-ray-induced cellular damage has been reported, ...

Oxford Univ Press Inc2024

Characterization of a flexible a-Si:H detector for in vivo dosimetry in therapeutic x-ray beams

Sylvain Dunand, Nicolas Würsch, Luca Massimiliano Antognini, Jonathan Emanuel Thomet, Matthew James Large

BackgroundThe increasing use of complex and high dose-rate treatments in radiation therapy necessitates advanced detectors to provide accurate dosimetry. Rather than relying on pre-treatment quality assurance (QA) measurements alone, many countries are now ...

Wiley2024

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Philippe Schwaller, Jeff Guo

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

Amer Chemical Soc2024

Direct observations of X-rays produced by upward positive lightning

Marcos Rubinstein, Antonio Sunjerga, Farhad Rachidi-Haeri, Thomas Chaumont

X-rays have been observed in natural downward cloud-to-ground lightning for over 20 years and in rocket-triggered lightning for slightly less. In both cases, this energetic radiation has been detected during the stepped and dart leader phases of downward n ...

Nature Publishing Group2024

Verification of the Fourier-enhanced 3D finite element Poisson solver of the gyrokinetic full-f code PICLS

Laurent Villard, Stephan Brunner, Moahan Murugappan, Alberto Bottino

We introduce and derive the Fourier -enhanced 3D electrostatic field solver of the gyrokinetic full -f PIC code PICLS. The solver makes use of a Fourier representation in one periodic direction of the domain to make the solving of the system easily paralle ...

Elsevier2024

Scalable constrained optimization

Maria-Luiza Vladarean

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Seyedbehzad Bozorgtabar

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Elsevier2024