Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data
Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Forecasting the occurrence...
Deep Learning-based Extreme Heatwave Forecast
Because of the impact of extreme heat waves and heat domes on society and biodiversity, their study is a key challenge. We specifically study long-lasting extreme heat waves, which are among the...
Temporal network compression via network hashing
Pairwise temporal interactions between entities can be represented as temporal networks, which code the propagation of processes such as epidemic spreading or information cascades, evolving on top...
Clustering with Simplicial Complexes
In this work, we propose a new clustering algorithm to group nodes in networks based on second-order simplices (aka filled triangles) to leverage higher-order network interactions. We define a...
Studying Limits of Explainability by Integrated Gradients for Gene Expression Models
Understanding the molecular processes that drive cellular life is a fundamental question in biological research. Ambitious programs have gathered a number of molecular datasets on large...
Fast Multiscale Diffusion on Graphs
Diffusing a graph signal at multiple scales requires computing the action of the exponential of several multiples of the Laplacian matrix. We tighten a bound on the approximation error of...
Design of graph filters and filterbanks
Basic operations in graph signal processing consist in processing signals indexed on graphs either by filtering them, to extract specific part out of them, or by changing their domain of...
Solving NMF with smoothness and sparsity constraints using PALM
Non-negative matrix factorization is a problem of dimensionality reduction and source separation of data that has been widely used in many fields since it was studied in depth in 1999 by Lee and...
Scaling in Internet Traffic: a 14 year and 3 day longitudinal study, with multiscale analyses and random projections
In the mid-90's, it was shown that the statistics of aggregated time series from Internet traffic departed from those of traditional short range dependent models, and were instead characterized by...
Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
We propose a novel algorithm for unsupervised graph representation learning with attributed graphs. It combines three advantages addressing some current limitations of the literature: i) The model...
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis
We present a novel multiview canonical correlation analysis model based on a variational approach. This is the first nonlinear model that takes into account the available graph-based geometric...
Subgraph-based filterbanks for graph signals
We design a critically-sampled compact-support biorthogonal transform for graph signals, via graph filterbanks. Instead of partitioning the nodes in two sets so as to remove one every two nodes in...
A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation
Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori...
Evolutions of Individuals Use of Lyon's Bike Sharing System
Bike sharing systems (BSS) have been growing fast all over the world, along with the number of articles analyzing such systems. However the lack of temporally large trip databases has limited the...
Analytic signal in many dimensions
In this work we extend analytic signal theory to the multidimensional case when oscillations are observed in the $d$ orthogonal directions. First it is shown how to obtain separate phase-shifted...
From graphs to signals and back: Identification of network structures using spectral analysis
Many systems comprising entities in interactions can be represented as graphs, whose structure gives significant insights about how these systems work. Network theory has undergone further...
Discovering the structure of complex networks by minimizing cyclic bandwidth sum
Getting a labeling of vertices close to the structure of the graph has been proved to be of interest in many applications e.g., to follow smooth signals indexed by the vertices of the network....
Duality between Temporal Networks and Signals: Extraction of the Temporal Network Structures
We develop a framework to track the structure of temporal networks with a signal processing approach. The method is based on the duality between networks and signals using a multidimensional...