An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images

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A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccessible to communities, or focus on rib segmentation that neglects the clinical ...

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

The goal of this thesis is the development and the analysis of numerical methods for problems where the unknown is a curve on a smooth manifold. In particular, the thesis is structured around the three following problems: homotopy continuation, curve inter ...

Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

Context. We report the exploitation of a sample of Solar System observations based on data from the third Gaia Data Release (Gaia DR3) of nearly 157 000 asteroids. It extends the epoch astrometric solution over the time coverage planned for the Gaia DR4, w ...

T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry ...

Segmented meso-voids from a tomography scan of preforms impregnated under capillary dominated conditions, combined to 3D scans of the initial prefrom dry state. ...

In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...

3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics. It is an ill-posed inverse problem, since-without additional prior assumpti ...

This paper considers the multi-agent linear least-squares problem in a server-agent network architecture. The system comprises multiple agents, each with a set of local data points. The agents are connected to a server, and there is no inter-agent communic ...

We consider a least-squares/relaxation finite element method for the numerical solution of the prescribed Jacobian equation. We look for its solution via a least-squares approach. We introduce a relaxation algorithm that decouples this least-squares proble ...

The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, ...

This paper introduces a new method for solving the distributed AC power flow (PF) problem by further exploiting the problem formulation. We propose a new variant of the ALADIN algorithm devised specifically for this type of problem. This new variant is cha ...

Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ a ...

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

In this work we perform a neutron Bragg edge tomography of stainless steel 316L additive manufacturing samples, one as built via standard laser powder bed fusion, and one using the novel three-dimensional (3D) laser shock peening technique. First, we consi ...

An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images

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Spectral Estimators for High-Dimensional Matrix Inference

RibSeg v2: A Large-Scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Retraction-based numerical methods for continuation, interpolation and time integration on manifolds

Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Gaia Focused Product Release: Asteroid orbital solution: Properties and assessment

Brain morphometry in older adults with and without dementia using extremely rapid structural scans

3D representation of meso-voids location and morphology within the fibrous pore space of three carbon fabrics

Deep Learning for 3D Surface Modelling and Reconstruction

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Rapid Scalable Distributed Power Flow with Open-Source Implementation

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Momentum-Based Policy Gradient with Second-Order Information

Bragg edge tomography characterization of additively manufactured 316L steel