Publications related to A cell fitness selection model for neuronal survival during development

At what spatial scale does natural selection operate? Using multiscale frameworks to calibrate the effects of environmental factors across landscape continuums

Patterns in nature arise from processes interacting across a continuum of spatial scales, where new relationships emerge at each level of investigation. These patterns are nested features encompassing fine-scale local patterns, such as topography and geolo ...

EPFL2024

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Wenlong Liao, Yi Zhang, Zhe Yang

Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...

Piscataway2024

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Frédéric Courbin, Georges Meylan, Gianluca Castignani, Maurizio Martinelli, Matthias Wiesmann, Yi Wang, Richard Massey, Fabio Finelli, Marcello Farina

Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for gala ...

Edp Sciences S A2024

Reproductive isolation arises during laboratory adaptation to a novel hot environment

Ana Marija Jaksic

Background: Reproductive isolation can result from adaptive processes (e.g., ecological speciation and mutation-order speciation) or stochastic processes such as "system drift" model. Ecological speciation predicts barriers to gene flow between populations ...

BMC2024

The neural correlates of topographical disorientation-a lesion analysis study

Olaf Blanke, Lukas Heydrich, Eva Blondiaux

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

Hoboken2024

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Silvestro Micera, Daniela De Luca

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

A cell fitness selection model for neuronal survival during development

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At what spatial scale does natural selection operate? Using multiscale frameworks to calibrate the effects of environmental factors across landscape continuums

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Reproductive isolation arises during laboratory adaptation to a novel hot environment

The neural correlates of topographical disorientation-a lesion analysis study

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Deep Learning Theory Through the Lens of Diagonal Linear Networks

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Reproductive isolation arises during laboratory adaptation to a novel hot environment

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

At what spatial scale does natural selection operate? Using multiscale frameworks to calibrate the effects of environmental factors across landscape continuums

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

The neural correlates of topographical disorientation-a lesion analysis study

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks