Publications associées à Estimating network travel time reliability with network partitioning

Publications associées (29)

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model ...

2024

Gaia's brightest very metal-poor (VMP) stars Metallicity catalogue of a thousand VMP stars from Gaia's radial velocity spectrometer spectra

Nicolas Lawrence Etienne Longeard

Context. Gaia DR3 has offered the scientific community a remarkable dataset of approximately one million spectra acquired with the radial velocity spectrometer (RVS) in the calcium II triplet region, which is well suited to identify very metal-poor (VMP) s ...

Edp Sciences S A2024

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Verónica del Carmen Estrada Galiñanes

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

2024

The Influence of the Planetary Boundary Layer on the Atmospheric State at an Orographic Site at the Eastern Mediterranean

Athanasios Nenes, Kunfeng Gao, Romanos Foskinis

We studied the influence of the Planetary Boundary Layer (PBL) on the air masses sampled at the mountaintop Hellenic Atmospheric Aerosol and Climate Change station ((HAC) 2 ) at Mount Helmos (Greece) during the Cloud-AerosoL InteractionS in the Helmos back ...

Stockholm Univ Press2024

Measurement of the Higgs boson production via vector boson fusion and its decay into bottom quarks in proton-proton collisions at √s=13 TeV

Jian Wang, Matthias Finger, Qian Wang, Yiming Li, João Miguel das Neves Duarte, Matthias Wolf, Varun Sharma, Yi Zhang, Tian Cheng, Yixing Chen, Alexis Kalogeropoulos, Ioannis Papadopoulos, Hua Zhang, Siyuan Wang, Xin Chen, Michele Bianco, Sebastiana Gianì, Sun Hee Kim, Rakesh Chawla, Jan Steggemann, Konstantin Androsov, Anna Mascellani, Federica Legger, Matteo Galli, Gabriele Grosso

A measurement of the Higgs boson (H) production via vector boson fusion (VBF) and its decay into a bottom quark-antiquark pair (b (b) over bar) is presented using proton-proton collision data recorded by the CMS experiment at root s = 13TeV and correspondi ...

Springer Nature2024

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

2024

Generation of entangled photon pairs from a silicon bichromatic photonic crystal cavity

Matteo Galli, Marco Clementi

Integrated quantum photonics leverages the on-chip generation of nonclassical states of light to realize key functionalities of quantum devices. Typically, the generation of such nonclassical states relies on whispering gallery mode resonators, such as int ...

Aip Publishing2024

High-throughput parallel testing of ten photoelectrochemical cells for water splitting: case study on the effects of temperature in hematite photoanodes

Sophia Haussener, Isaac Thomas Holmes-Gentle, Franky Esteban Bedoya Lora, Roberto Valenza

High-throughput testing of photoelectrochemical cells and materials under well-defined operating conditions can accelerate the discovery of new semiconducting materials, the characterization of the phenomena occurring at the semiconductor-electrolyte inter ...

Royal Soc Chemistry2024

Graph generative deep learning models with an application to circuit topologies

Igor Krawczuk

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

EPFL2024

The Strong Integral Input-to-State Stability Property in Dynamical Flow Networks

Nils Gustav Nilsson

Dynamical flow networks serve as macroscopic models for, e.g., transportation networks, queuing networks, and distribution networks. While the flow dynamics in such networks follow the conservation of mass on the links, the outflow from each link is often ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

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

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Jérôme Chenal

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak ...

MDPI2024

Safe Deep Neural Networks

Kyle Michael Matoba

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Zenodo2024

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Optimal Depth of Discharge for Electric Batteries with Robust Capacity-Shrinkage Estimator

Thomas Alois Weber

A user’s benefit from the energy stored in a battery over its lifetime depends on the time-varying characteristics of the battery, which are in turn affected by the chosen usage behavior. Both the capacity shrinkage and the number of lifetime cycles are st ...

2024

Cloud Condensation Nuclei (CCN) concentrations measured in the Swiss container during MOSAiC 2019/2020

Julia Schmale, Ivo Fabio Beck, Hélène Paule Angot, Lubna Dada, Benjamin Jérémy Laurent Heutte, Nora Bergner

This dataset contains CCN concentrations at five supersaturation levels, averaged to 1 min time resolution, measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to Septem ...

2023

Bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols measured in the Swiss container during MOSAiC 2019/2020

Julia Schmale, Ivo Fabio Beck, Hélène Paule Angot, Lubna Dada, Benjamin Jérémy Laurent Heutte

This dataset contains the bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols (NR-PM1) measured during the MOSAiC expedition from October 2019 to July 2020. These include the mass concentrations of sulfate (S ...

Zenodo2023