Publication

Reduced order modelling of nonaffine problems on parameterized NURBS multipatch geometries

Publications associées (29)

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

Nature Portfolio2023

Understanding the role of Hubbard corrections in the rhombohedral phase of BaTiO3

Nicola Marzari, Iurii Timrov, Michele Kotiuga, Lorenzo Bastonero

We present a first-principles study of the low-temperature rhombohedral phase of BaTiO3 using Hubbardcorrected density-functional theory. By employing density-functional perturbation theory, we compute the onsite Hubbard U for Ti(3d) states and the intersi ...

Amer Physical Soc2023

A method and system for enforcing smoothness constraints on surface meshes from a graph convolutional neural network

Pascal Fua, Pamuditha Udaranga Wickramasinghe

A method for enforcing smoothness constraints on surface meshes produced by a Graph Convolutional Neural Network (GCNN) including the steps of reading image data from a memory, the image data including two-dimensional image data representing a three-dimens ...

2023

Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures

Marianne Liebi, Manuel Guizar Sicairos

The development of small-angle scattering tensor tomography has enabled the study of anisotropic nanostructures in a volume-resolved manner. It is of great value to have reconstruction methods that can handle many different nanostructural symmetries. For s ...

Int Union Crystallography2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Laterally unconfined neutral or negative buoyancy inflow into a quiescent ambient over sloping bottom

Haoran Shi

This work studies the nearshore hydrodynamics of a shallow turbulent flow entering a laterally unconfined quiescent ambient with a sloping bottom boundary. Examples of such flow are neutrally buoyant ebb tidal jets and hyperpycnal river plumes entering ope ...

EPFL2023

Bioengineering human mini-intestines with in vivo-like complexity and function

Olga Mitrofanova

Biological research heavily relies on the use of animal models, which has made it difficult to answer specific questions about human biology and disease. However, with the advent of human organoids - miniature versions of tissues generated in 3D human stem ...

EPFL2023

Enhancement of brain atlases with region-specific coordinate systems: flatmaps and barrel column annotations

Michael Reimann, Armando Romani, Aleksandra Zuzanna Teska, Felix Schürmann, Juan Bautista Hernando Vieites, Huanxiang Lu, Sirio Bolaños Puchet

Digital brain atlases define a hierarchy of brain regions and their locations in three-dimensional space. They provide a standard coordinate system in which diverse datasets can be integrated for visualization and analysis. They also enable building of dat ...

2024

Salt Bridge Structure of Microhydrated Arginine Kinetically Trapped in the Gas Phase by Evaporative Cooling

Oleg Boyarkine, Andrei Zviagin

Aminoacids and peptides generally exhibit zwitterionic forms withsalt bridge (SB) structures in solution but charge-solvated (CS) motifsin the gas phase. Here, we report a study of non-covalent complexesof the protonated amino acid arginine, ArgH(+)(H2O)( ...

2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023