Publications related to Improved Estimation of the Specific Differential Phase Shift Using a Compilation of Kalman Filter Ensembles

Related publications (28)

Data and scripts for the RaFSIP scheme

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

Zenodo2024

Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"

Alexis Berne, Athanasios Nenes, Satoshi Takahama, Anne-Claire Marie Billault--Roux, Georgia Sotiropoulou, Paraskevi Georgakaki, Kunfeng Gao, Romanos Foskinis

This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the pa ...

Zenodo2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Data from: Optimal blade pitch control for enhanced vertical-axis wind turbine performance

Karen Ann J Mulleners, Sébastien Le Fouest

This directory contains open-source data obtained using a single-bladed H-type vertical-axis wind turbine prototype with individual blade pitching. This data results from the optimisation of the blade's pitching kinematics using a genetic algorithm at two ...

Zenodo2024

Spectral Estimators for High-Dimensional Matrix Inference

Farzad Pourkamali

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

Validating functional redundancy with mixed generative adversarial networks

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Thanh Trung Huynh

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

ELSEVIER2023

Data-driven modeling of beam loss in the LHC

Tatiana Pieloni, Michael Schenk, Ekaterina Krymova, Loïc Thomas Davies Coyle

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

FRONTIERS MEDIA SA2023

Dataset for "Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid"

John Martin Kolinski, Lebohang Molefe

This dataset supports the publication 'Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid' by Lebo Molefe and John M. Kolinski, Physical Review E, (2023). The data are surface profiles of textured surf ...

Zenodo2023

Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-Based Filters

Colin Neil Jones, Yingzhao Lian, Jicheng Shi

Data-driven control can facilitate the rapid development of controllers, offering an alternative to conventional approaches. In order to maintain consistency between any known underlying physical laws and a data-driven decision-making process, preprocessin ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Bayesian reconstruction of memories stored in neural networks from their connectivity

Lenka Zdeborová, Florent Gérard Krzakala

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

2023

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Panayiotis Danassis, Ljubomir Rokvic

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Paolo Ienne, Kubilay Atasu, Jovan Blanusa

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

arXiv2023

1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History

Isabella Di Lenardo, Rémi Guillaume Petitpierre, Lucas Arnaud André Rappo, Marion Kramer

This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 mi ...

Zenodo2023

Pollution mask for the continuous corrected particle number concentration data in 1 min time resolution measured in the Swiss aerosol container using a whole air inlet during MOSAiC 2019/2020

Julia Schmale, Andrea Baccarini, Ivo Fabio Beck, Hélène Paule Angot

This dataset contains particle number concentrations and a pollution flag in 1 min time resolution. It is derived by the pollution detection algorithm (PDA, doi:10.5281/zenodo.5761101) based on the corrected particle number concentration data of the CPC377 ...

Zenodo2023

Data-driven Methods for Control: from Linear to Lifting

Yingzhao Lian

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

EPFL2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Jiancheng Yang, Amos Sironi, Jian Yang

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in m ...

Nature Publishing Group2023

Adaptive Symplectic Model Order Reduction Of Parametric Particle-Based Vlasov-Poisson Equation

Jan Sickmann Hesthaven, Cecilia Pagliantini, Nicolò Ripamonti

. High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of para ...

AMER MATHEMATICAL SOC2023

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Christophe Ballif, Nicolas Würsch, Laure-Emmanuelle Perret Aebi, Marine Dominique Cauz

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

2023

Shared metadata for data-centric materials science

Giovanni Pizzi, Gian-Marco Rignanese

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data prin ...

Nature Portfolio2023