Publications associées à Detection of Floods In Sar Images with Non-Linear Kernel Clustering and Topographic Prior

Publications associées (29)

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Julian Thomas Blackwell, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Oxidation processes and me

Urs von Gunten

This publication summarizes my journey in the field of chemical oxidation processes for water treatment over the last 30+ years. Initially, the efficiency of the application of chemical oxidants for micropollutant abatement was assessed by the abatement of ...

Pergamon-Elsevier Science Ltd2024

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Marc Conrad Russwurm, Thiên-Anh Claris Nguyen, Gaston Jean Lenczner

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

Fairness and Explainability in Clustering Problems

Xinrui Jia

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

EPFL2023

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Yichen Xu, Qiang Liu

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

Assoc Computing Machinery2023

Oxidative Water Treatment: The Track Ahead

Urs von Gunten

Chemical oxidation has been applied in municipal water treatment for more than a century, initially for disinfection. In the early decades, chlorine disinfection was adopted in the fight against waterborne disease. However, the oxidative properties of chlo ...

2023

Sunshine and a pinch of tropical soils: A natural, low-cost photo-Fenton variation for safer water, assisted by H2O2 or percabonate

César Pulgarin, Michaël Bensimon, Stefanos Giannakis, Thomas Guillaume

Clean water is essential for human survival, but access to safe drinking water remains a challenge in resource -limited regions. Herein, we explored a low-cost but effective solution for water potabilization using natural iron sources from soils along with ...

ELSEVIER2023

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

Transfer learning application of self-supervised learning in ARPES

Gabriel Aeppli

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

IOP Publishing Ltd2023

swissALPS: a Dataset for Imbalanced Land Cover Mapping in Alpine Regions

Valérie Zermatten

Our dataset consists of very high-resolution aerial images (50cm) and a digital elevation model (50cm) that covers approx. 2300 km2 of land above 2000m altitude in the southwestern part of Switzerland. Our land cover labels focus on alpine land cover that ...

IEEE DataPort2023

Why five decades of massive research on heterogeneous photocatalysis, especially on TiO2, has not yet driven to water disinfection and detoxification applications? Critical review of drawbacks and challenges

César Pulgarin, Julian Andrés Rengifo Herrera

For fifty years, heterogeneous photocatalysis has been considered as having potential to remove organic and microbiological pollutants from water under either artificial UV light or sunlight irradiation. However, after tens of thousands of published resear ...

Elsevier Science Sa2023

Inactivation of coxsackievirus B5 by free chlorine under conditions relevant to drinking water treatment

Shotaro Torii

Chlorine disinfection is commonly applied to inactivate pathogenic viruses in drinking water treatment plants. However, the role of water quality in chlorine disinfection of viruses has not been investigated thoughtfully. In this study, we investigated the ...

IWA PUBLISHING2023

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Danya Li

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-av ...

Elsevier2023

Predicting Transformation Products during Aqueous Oxidation Processes: Current State and Outlook

Urs von Gunten

Water quality and its impacts on human and ecosystem health presents tremendous global challenges. While oxidative water treatment can solve many of these problems related to hygiene and micropollutants, identifying and predicting transformation products f ...

2023

Formation of carbonyl compounds during ozonation of lake water and wastewater: Development of a non-target screening method and quantification of target compounds

Urs von Gunten, Joanna Maria Houska

Ozonation of natural waters is typically associated with the formation of carbonyl compounds (aldehydes, ketones and ketoacids), a main class of organic disinfection byproducts (DBPs). However, the detection of carbonyl compounds in water and wastewater is ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Snowfall microphysics: a dual-frequency and Doppler spectral radar perspective

Anne-Claire Marie Billault--Roux

Snowfall is an essential component of the hydrological cycle, as it is involved in most precipitation on Earth, either directly as snow falling to the ground or indirectly as rain melted from snow. At the same time, the ice phase of clouds and precipitatio ...

EPFL2023

Sparse Autoencoders for Speech Modeling and Recognition

Selen Hande Kabil

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

EPFL2023

Scalable maximal subgraph mining with backbone-preserving graph convolutions

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

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

ELSEVIER SCIENCE INC2023

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Alexis Berne, Anne-Claire Marie Billault--Roux, Gionata Ghiggi

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

COPERNICUS GESELLSCHAFT MBH2023

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Luca Pesce

A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

2022