Publications associées à Lack-of-fit sum of squares

Augmenting locomotor perception by remapping tactile foot sensation to the back

Olaf Blanke, Mohamed Bouri, Oliver Alan Kannape, Atena Fadaeijouybari, Selim Jean Habiby Alaoui

Background :Sensory reafferents are crucial to correct our posture and movements, both reflexively and in a cognitively driven manner. They are also integral to developing and maintaining a sense of agency for our actions. In cases of compromised reafferen ...

2024

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

Jan Skaloud, Davide Antonio Cucci, Aurélien Arnaud Brun, Kyriaki Mouzakidou

Tightly-coupled sensor orientation, i.e. the simultaneous processing of temporal (GNSS and raw inertial) and spatial (image and lidar) constraints in a common adjustment, has demonstrated significant improvement in the quality of attitude determination wit ...

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An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Adrien Michel, Tobias Jonas

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model a ...

COPERNICUS GESELLSCHAFT MBH2023

Timing and spatial selection bias in rapid extreme event attribution

Anthony Christopher Davison, Ophélia Mireille Anna Miralles

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

ELSEVIER2023

TOPO-Loss for continuity-preserving crack detection using deep learning

Pascal Fua, Katrin Beyer, Bryan German Pantoja Rosero

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

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

Nicolas Henri Bernard Flammarion, Etienne Patrice Boursier, Loucas Pillaud-Vivien

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, ...

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Resolution and penetration depth of reflection-mode time-domain near infrared optical tomography using a ToF SPAD camera

Edoardo Charbon, Martin Wolf, Jingjing Jiang

In a turbid medium such as biological tissue, near-infrared optical tomography (NIROT) can image the oxygenation, a highly relevant clinical parameter. To be an efficient diagnostic tool, NIROT has to have high spatial resolution and depth sensitivity, fas ...

Optica Publishing Group2022

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Jean-Philippe Thiran, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Marco Pizzolato, Gian Franco Piredda, Thomas Yu, Alessandro Daducci, Nicolas Kunz

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is ...

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Lack-of-fit sum of squares

Augmenting locomotor perception by remapping tactile foot sensation to the back

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Timing and spatial selection bias in rapid extreme event attribution

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

TOPO-Loss for continuity-preserving crack detection using deep learning

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

Resolution and penetration depth of reflection-mode time-domain near infrared optical tomography using a ToF SPAD camera

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI

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Chattez avec Graph Search

Augmenting locomotor perception by remapping tactile foot sensation to the back

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

Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

TOPO-Loss for continuity-preserving crack detection using deep learning

Resolution and penetration depth of reflection-mode time-domain near infrared optical tomography using a ToF SPAD camera

Timing and spatial selection bias in rapid extreme event attribution

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

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