Publications related to Method, apparatus and computer program product for locating a source of diffusion in a network

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

Verónica del Carmen Estrada Galiñanes, Arman Babaei

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

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

A cut-cell method for the numerical simulation of 3D multiphase flows with strong interfacial effects

Alexandre Caboussat, Alexandre Masserey, Marco Picasso, Julien Hess

We present a numerical model for the approximation of multiphase flows with free surfaces and strong interfacial effects. The model relies on the multiphase incompressible Navier-Stokes equations, and includes surface tension effects on the interfaces betw ...

Academic Press Inc Elsevier Science2024

Global, Passive Detection of Connection Tampering

Louis-Henri Manuel Jakob Merino

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

Assoc Computing Machinery2023

Equilibria in Network Constrained Energy Markets

Leonardo Massai

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Elsevier2023

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus

Henry Markram, Felix Schürmann, James Gonzalo King, Srikanth Ramaswamy, Juan Bautista Hernando Vieites, Alexis Arnaudon, Eilif Benjamin Muller, Michael Reimann, Huanxiang Lu, Ying Shi, Marwan Muhammad Ahmed Abdellah, Vishal Sood, Jean-Denis Georges Emile Courcol, Werner Alfons Hilda Van Geit, Lida Kanari, Juan Luis Riquelme Roman, Michael Emiel Gevaert, Pramod Shivaji Kumbhar, Liesbeth Maria L Vanherpe, Armando Romani, Cyrille Pierre Henri Favreau, Michele Migliore, Mohameth François Sy, Szabolcs Kali, Judit Planas Carbonell, Genrich Ivaska, Sàra Sàray, Carmen Alina Lupascu, Rosanna Migliore, Audrey Mercer, András Ecker, Alberto Antonietti, Cristina Colangelo, Anna-Kristin Kaufmann, Julian Martin Budd, Elisabetta Giacalone, Thomas Brice Delemontex, Davide Bella, Fabien Petitjean, , , , ,

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Florent Gérard Krzakala, Julien Marcel Daniel Emmanuel Launay

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

IEEE2023

Caching and Neutrality

Aikaterini Argyraki, Pavlos Nikolopoulos, Muhammad Abdullah

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

ACM Association for Computing Machinery2023

Short-term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Andrea Rinaldo, Izabela Bujak-Ozga, Jana Freiin von Freyberg

Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling i ...

Wiley2023

Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information

Isabel Haasler

Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In ...

Elsevier2023

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Ali H. Sayed, Michele Cirillo

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Melanie Nicole Zeilinger, Danilo Saccani

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

IEEE2023

Diffusion of connected and autonomous vehicles concerning mode choice, policy interventions and sustainability impacts: A system dynamics modelling study

Evangelos Paschalidis

Connected and autonomous vehicles (CAV) have seen a rapid development over the past decade. However, wide diffusion of high level CAVs is still decades to come, and will depend on many technological, policy and public acceptance factors. Merging a traditio ...

ELSEVIER SCI LTD2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

EPFL2023

Atomic scale volume and grain boundary diffusion elucidated by in situ STEM

Johann Michler, Amit Sharma, Laszlo Pethö, Xavier Maeder

Diffusion is one of the most important phenomena studied in science ranging from physics to biology and, in abstract form, even in social sciences. In the field of materials science, diffusion in crystalline solids is of particular interest as it plays a p ...

Nature Portfolio2023

Diffusion timescale conundrum in contact metamorphism: Oxygen isotope and trace element profiles in dolomite

Lukas Baumgartner, Anders Meibom, Stéphane Laurent Escrig, Florence Bégué

Successful application of diffusion chronometry is based on detailed knowledge of diffusion parameters obtained from laboratory experiments, which also need to be upscaled to be applied to natural samples. While generally applicable, several examples of di ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Low-Temperature Dynamics of Water Confined in Unidirectional Hydrophilic Zeolite Nanopores

Livia Eleonora Bove Kado, Umbertoluca Ranieri, Maria Rescigno

The dynamical properties of water molecules confinedin the unidirectionalhydrophilic nanopores of AlPO4-54 are investigated withquasi-elastic neutron scattering as a function of temperature downto 118 K. AlPO4-54 has among the largest pores known foralumin ...

AMER CHEMICAL SOC2023

Nonparametric estimation for SDE with sparsely sampled paths: An FDA perspective

Victor Panaretos, Neda Mohammadi Jouzdani

We consider the problem of nonparametric estimation of the drift and diffusion coefficients of a Stochastic Differential Equation (SDE), based on n independent replicates {Xi(t) : t is an element of [0 , 1]}13 d B(t), where alpha is an element of {0 , 1} a ...

Elsevier2024

Predicting nonlinear optical scattering with physics-driven neural networks

Demetri Psaltis, Ahmed Ayoub, Carlo Gigli

Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

AIP Publishing2023

Morphology and Transport Characterization of Catalyst Layers for CO2 Reduction

Sophia Haussener, Francesca Lorenzutti

Gas diffusion electrodes (GDEs) help to reduce transport limitations in devices for electrochemical CO2 reduction. Homogenized modeling of such devices requires input of morphological characteristics and effective transport properties of the porous structu ...

2023