Concept

Structural alignment

Related publications (478)

Structure-Property Relationships in Complex Materials by Combining Supervised and Unsupervised Machine Learning

The work presented in this thesis combines supervised and unsupervised machine learning to examine structure-property relationships in databases of materials. While either supervised learning or unsupervised learning alone can be a powerful tool for assess ...

EPFL2021

Studying Alignment in Spontaneous Speech via Automatic Methods: How Do Children Use Task-specific Referents to Succeed in a Collaborative Learning Activity?

Barbara Bruno, Utku Norman

A dialogue is successful when there is alignment between the speakers, at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, and explore how performance and learning ...

2021

Fast end-to-end learning on protein surfaces

Bruno Emanuel Ferreira De Sousa Correia, Freyr Sverrisson

Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional s ...

IEEE COMPUTER SOC2021

3D architecture and structural flexibility revealed in the subfamily of large glutamate dehydrogenases by a mycobacterial enzyme

Luciano Andres Abriata

Glutamate dehydrogenases (GDHs) are widespread metabolic enzymes that play key roles in nitrogen homeostasis. Large glutamate dehydrogenases composed of 180kDa subunits (L-GDHs(180)) contain long N- and C-terminal segments flanking the catalytic core. Desp ...

2021

Cyclic thermomechanical behaviour of pile-soil interface and design methods for energy pile foundations

Elena Ravera

Energy piles are shallow geothermal systems integrated into structures already required for structural functions of civil works that provide both geothermal energy supply and structural support to the built environments. This research work is driven by the ...

EPFL2021

Structural design methodology in Integrally-Attached Timber Plate structures

Petras Vestartas, Aryan Rezaei Rad

The immediate application of research discoveries requires a global and exact understanding of the structure under consideration. To gain insight into the complex behavior of structures, numerical simulations are carried out in parallel to physical experim ...

Routledge, Taylor & Francis Group2021

Control and Characterization of the Compactness of Single-Chain Nanoparticles

Francesco Stellacci, Luciano Andres Abriata, Lixia Wei, Suiyang Liao

Polymers when self-cross-linked into single-chain nanoparticles bear some resemblance to folded proteins; yet proteins have clear energy landscapes that determine precisely folded structures, while single-chain polymer nanoparticles (SCNPs) have more undef ...

AMER CHEMICAL SOC2021

Structural Order of the Molecular Adlayer Impacts the Stability of Nanoparticle-on-Mirror Plasmonic Cavities

Christophe Marcel Georges Galland, Magalí Alejandra Lingenfelder, Sachin Suresh Verlekar, Fernando Pablo Cometto, Karla Banjac, Aqeel Ahmed

Immense field enhancement and nanoscale confinement of light are possible within nanoparticle-on-mirror (NPoM) plasmonic resonators, which enable novel optically activated physical and chemical phenomena and render these nanocavities greatly sensitive to m ...

AMER CHEMICAL SOC2021

Optical diffraction tomography from single-molecule localization microscopy

Michaël Unser, Thanh-An Michel Pham, Emmanuel Emilien Louis Soubies, Ferréol Arnaud Marie Soulez

Single-molecule localization microscopy (SMLM) is a powerful method for the imaging of cellular structures. This modality delivers nanoscale resolution by sequentially activating a subset of fluorescent molecules and by extracting their super-resolved posi ...

2021

Optimal radial basis for density-based atomic representations

Michele Ceriotti, Sergey Pozdnyakov, Jigyasa Nigam, Félix Benedito Clément Musil, Alexander Jan Goscinski

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

AIP Publishing2021

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