Publications related to Machine Learning Uncovers Aerosol Size Information From Chemistry and Meteorology to Quantify Potential Cloud-Forming Particles

Related publications (18)

Observations of high-time-resolution and size-resolved aerosol chemical composition and microphysics in the central Arctic: implications for climate-relevant particle properties

Julia Schmale, Andrea Baccarini, Ivo Fabio Beck, Hélène Paule Angot, Lubna Dada, Jakob Boyd Pernov, Benjamin Jérémy Laurent Heutte, Nora Bergner, Markus Frey

Aerosols play a critical role in the Arctic's radiative balance, influencing solar radiation and cloud formation. Limited observations in the central Arctic leave gaps in understanding aerosol dynamics year-round, affecting model predictions of climate-rel ...

Copernicus GmbH2025

Assessing the Global Contribution of Marine Aerosols, Terrestrial Bioaerosols, and Desert Dust to Ice-nucleating Particle Concentrations

Athanasios Nenes

Aerosol-cloud interactions, particularly ice processes in mixed-phase clouds (MPCs), remain a key source of uncertainty in climate change assessments. This study introduces state-of-the-art laboratory-based parameterizations into a global chemistry-transpo ...

COPERNICUS GESELLSCHAFT MBH2025

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Thomas Grégoire Stegmüller

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

EPFL2025

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Boi Faltings, Zeki Doruk Erden

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Springer Science and Business Media Deutschland GmbH2025

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

2025

Characteristics and effects of aerosols during blowing snow events in the central Arctic

Martin Schneebeli, Jian Wang, Julia Schmale, Ivo Fabio Beck, Hélène Paule Angot, Jakob Boyd Pernov, Benjamin Jérémy Laurent Heutte, Nora Bergner, Michael Lonardi

Sea salt aerosol (SSaer) significantly impacts aerosol-radiation and aerosol-cloud interactions, and sublimated blowing snow is hypothesized to be an important SSaer source in polar regions. Understanding blowing snow and other wind-sourced aerosols’ clima ...

University of California Press2025

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Irem Boybat Kara, Flavio Ponzina, Joshua Alexander Harrison Klein, Yuxuan Wang, Abu Sebastian

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Computational design of complex protein folds and soluble analogs of membrane proteins

Casper Alexander Goverde

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

EPFL2025

A multidomain approach to neighbourhood typology for urban environmental studies

Jan Wienold, Dolaana Khovalyg, Dusan Licina, Kun Lyu, Hanieh Khodaei Tehrani

Cities are facing unprecedented challenges to their environments and residents due to continuous growth, including urban overheating, daylight accessibility, noise, and air pollution. This requires a holistic approach to the research and implementation of ...

2025

On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds

Athanasios Nenes, Kunfeng Gao, Romanos Foskinis

We report the drivers of spatiotemporal variability of ice nucleating particles (INPs) for mixed-phase orographic clouds (~−25 °C) in the Eastern Mediterranean. In the planetary boundary layer, pronounced INP diurnal periodicity is observed, which is mainl ...

Springer Science and Business Media LLC2025

Growth Onset Rather Than Photosynthesis Strongly Regulates Autumn Senescence Termination Besides Climate Change

Charlotte Grossiord

AimPlant senescence largely influences the global carbon cycle by regulating the growing season length. However, the driving mechanisms of plant senescence remain unclear, particularly the role of developmental factors. This study aims to investigate how e ...

Wiley2025

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Nathan Moïse De Sadeleer, Christian Heinis, Alexander Lund Nielsen, Anne Sofie Luise Zarda, Rebecca Manuela Neeser

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

Wiley2025

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Ismail Nejjar

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

EPFL2025

A robust asymptotic control model to analyze climate policy with CDR options

Marc Vielle

A three-region optimal economic growth model is proposed to represent the global energy transition to net-zero emissions when carbon dioxide removal (CDR) technologies are available. The main features of the model are (i) the representation of the economy ...

2025

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Minh Hieu Lê

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

Springer Science and Business Media Deutschland GmbH2025

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Anne-Marie Kermarrec, Rafael Pereira Pires, Marinus Abraham de Vos, Akash Balasaheb Dhasade, Tuan Anh Nguyen

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

ACM2025

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

Stéphane Joost, Anaïs Laurence Ladoy, David Nicolas De Ridder

Introduction Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynam ...

FRONTIERS MEDIA SA2024

Good enough: Intermediate complexity atmospheric models improve the representation of processes affecting seasonal snow

Dylan Stewart Reynolds

Snow plays a crucial role in processes regulating ecosystems, the climate, and human development. Mountain snowpack in particular has great relevance for downstream communities. Knowledge about the distribution and properties of the snowpack thus help in p ...

EPFL2024