Publications related to Computational anatomy

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Silvestro Micera, Simone Romeni, Elena Losanno, Luca Pierantoni

Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

IOP Publishing Ltd2023

Learning-based techniques for lensless reconstruction

Yohann Loïc Yann Perron

In this internship, I explore different optimization algorithms for lensless imaging. Lensless imaging is a new imaging technique that replaces the lens of a camera with a diffuser mask. This allows for simpler and cheaper camera hardware. However, the rec ...

2023

Stochastic adaptive resampling for the estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

2023

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

Lijing Xin, Daniel Wenz, Mark Stephan Widmaier, Songi Lim

A new and efficient magnetisation transfer P-31 magnetic resonance fingerprinting (MT-P-31-MRF) approach is introduced to measure the creatine kinase metabolic rate kCK

{k}_{\mathrm{CK}}

between phosphocreatine (PCr) and adenosine triphosphate (ATP) i ...

WILEY2023

Fast-Spiking Interneurons of the Premotor Cortex Contribute to Initiation and Execution of Spontaneous Actions

Silvestro Micera, Maria Pasquini

Planning and execution of voluntary movement depend on the contribution of distinct classes of neurons in primary motor and premotor areas. However, timing and pattern of activation of GABAergic cells during specific motor behaviors remain only partly unde ...

SOC NEUROSCIENCE2023

Extension, validation, and optimization of Serpent/DYN3D/ATHLET code system for SFR applications

Vincenzo Anthony Di Nora

The sequence of codes Serpent/DYN3D has been developed by the Helmholtz-Zentrum Dresden-Rossendorf and successfully applied to core static and transient analyses of sodium-cooled fast reactors (SFRs). The successful application of the sequence to SFRs was ...

EPFL2023

Practical computational imaging by use of spatiotemporal light modulation: from simulations to applications in biological microscopy

François Thierry M Marelli

Optical microscopy is an essential tool for biologists, who are often faced with the need to overcome the spatial and temporal resolution limitations of their devices to capture finer details. As upgrading imaging hardware is expensive, computational metho ...

EPFL2023

Robust optimization of control parameters for WEC arrays using stochastic methods

Tommaso Vanzan, Edie Miglio

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

MOX Modeling and Scientific Computing2023

MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency

Mathieu Salzmann, Jiancheng Yang, Kun Sun, Yunhua Zhang

3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle t ...

Ieee Computer Soc2023

How deep convolutional neural networks lose spatial information with training

Matthieu Wyart, Leonardo Petrini, Umberto Maria Tomasini, Francesco Cagnetta

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image da ...

Bristol2023

Computational anatomy

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Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Learning-based techniques for lensless reconstruction

Stochastic adaptive resampling for the estimation of discrete choice models

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

Fast-Spiking Interneurons of the Premotor Cortex Contribute to Initiation and Execution of Spontaneous Actions

Extension, validation, and optimization of Serpent/DYN3D/ATHLET code system for SFR applications

Practical computational imaging by use of spatiotemporal light modulation: from simulations to applications in biological microscopy

Robust optimization of control parameters for WEC arrays using stochastic methods

MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency

How deep convolutional neural networks lose spatial information with training

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

How deep convolutional neural networks lose spatial information with training

Practical computational imaging by use of spatiotemporal light modulation: from simulations to applications in biological microscopy

Learning-based techniques for lensless reconstruction

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Fast-Spiking Interneurons of the Premotor Cortex Contribute to Initiation and Execution of Spontaneous Actions

Extension, validation, and optimization of Serpent/DYN3D/ATHLET code system for SFR applications

Robust optimization of control parameters for WEC arrays using stochastic methods

MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency

Stochastic adaptive resampling for the estimation of discrete choice models