Applications of machine learning for studying local solvation environments

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Beyond Essentials: Nuanced and Diverse Text-to-video Retrieval

Yuchen Yang

The field of text-to-video retrieval has advanced significantly with the evolution of language models and large-scale pre-training on generated caption-video pairs. Current methods predominantly focus on visual and event-based details, making retrieval lar ...

Institute of Electrical and Electronics Engineers Inc.2024

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Guillaume André Jean Fraux, Arslan Mazitov, Nataliya Lopanitsyna

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

Scalable semantic 3D mapping of coral reefs with deep learning

Anders Meibom, Devis Tuia, Guilhem Maurice Louis Banc-Prandi, Jonathan Paul Sauder

Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existen ...

2024

Nissl_4, Raw images for Machine learning for histological annotation and quantification of cortical layers

Julie Meystre

This dataset contains some images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pi ...

Zenodo2024

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

Mohammad Khaja Nazeeruddin, Bin Ding, Xianfu Zhang

Utilization of small molecules as passivation materials for perovskite solar cells (PSCs) has gained significant attention recently, with hundreds of small molecules demonstrating passivation effects. In this study, a high-accuracy machine learning model i ...

Wiley-VCH Verlag Gmbh2024

Learning to Boost the Performance of Stable Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. Howev ...

2024

Live-cell imaging powered by computation

Suliana Manley

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Nature Portfolio2024

A journey toward generalizable trajectory forecasting models

Mohammadhossein Bahari

Autonomous driving is a revolutionary technology that has seen considerable advancements through the adoption of deep learning solutions. One of the major challenges in this field is the interaction with other road users. This interaction necessitates a "t ...

EPFL2024

Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

Dirk Grundler, Andrea Mucchietto, Mingran Xu, Kyongmo An

Raw data and Python code for the manuscript "Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution". All data files and source code are provided in a zip file. .ipynb file is a python jupyter notebook file whi ...

Zenodo2024

Normal Contact Force Estimation Using Deep Learning

Yves Perriard, Paolo Germano, Xinxin Liao, Marc Michel Charles Favier

Small scale ultrasonic piezoelectric actuators performance strongly depends on not well-known contact dynamics. Deep Neural Network (DNN) sees their use in physic simulation growing as their flexibility allows better performance especially when dynamics la ...

Institute of Electrical and Electronics Engineers Inc.2024

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

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

Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabling them to build a hierarchy of abstract, low-dimensional data representatio ...

American Physical Society2024

Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things

Olga Fink, Keivan Faghih Niresi

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intell ...

2024