Inverse Rendering for PET Image Reconstruction

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Diagnostics play a pivotal role in nuclear fusion experimental reactors, supporting physical studies, modelling, and plasma control. However, most diagnostics provide limited and partial information about the plasma's status. For instance, magnetic probes ...

Research in artificial intelligence (AI) and neural network (NN) technologies has been boosted by recent hardware advances, while the emergence of self-synthesized materials such as nanocarbons has attracted much attention to overcome the limitations of Si ...

Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Background and objectives: Parameter estimation and uncertainty quantification are crucial in computa-tional cardiology, as they enable the construction of digital twins that faithfully replicate the behavior of physical patients. Many model parameters reg ...

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of ...

Animal behavior emerges from an interaction between neural network dynamics, musculoskeletal properties and the physical environment. Accessing and understanding the interplay between these elements requires the development of integrative and morphological ...

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recen ...

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 ...

This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods' shortcomings such as low-density spatial distribution and time dela ...

An assembly refers to a collection of parts joined together to achieve a specific form and/or functionality. Designing assemblies is a non-trivial task as a slight local modification on a part's geometry or its joining method could have a global impact on ...

Purpose: The morphological and hemodynamic evaluations of neurovascular diseases treated with stents would benefit from noninvasive imaging techniques such as 3D time-of-flight MRI (3D-TOF) and 3D phase contrast MRI (3D-PCMRI). For this purpose, a comprehe ...

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we provide a quantitative analysi ...

Deep neural networks (DNN) have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image or speech recognition. Training of large DNNs, however, is a computationally in ...

Automatic transcription of handwritten texts has made important progress in the recent years. This increase in performance, essentially due to new architectures combining convolutional neural networks with recurrent neutral networks, opens new avenues for ...

Inverse Rendering for PET Image Reconstruction

Chattez avec Graph Search

Time-resolved, physics-informed neural networks for tokamak total emission reconstruction and modelling

Split weight distribution learning for binary neural networks exploiting nano-memristive graphene/sumanene/graphene devices

What can be learnt with wide convolutional neural networks?

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Fast and robust parameter estimation with uncertainty quantification for the cardiac function

Polynomial-time universality and limitations of deep learning

Adversarial training with informed data selection

NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity

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

PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

State of the Art on Computational Design of Assemblies with Rigid Parts

Neurovascular stent artifacts in 3D-TOF and 3D-PCMRI: Influence of stent design on flow measurement

Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

Mixed-precision architecture based on computational memory for training deep neural networks

Comparing human and machine performances in transcribing 18th century handwritten Venetian script