Publications related to The challenges of merging two similar structured overlays: A tale of two networks

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

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

Reliable Microsecond-Scale Distributed Computing

Athanasios Xygkis

The landscape of computing is changing, thanks to the advent of modern networking equipment that allows machines to exchange information in as little as one microsecond. Such advancement has enabled microsecond-scale distributed computing, where entire dis ...

EPFL2023

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

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

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

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

Verónica del Carmen Estrada Galiñanes

Peer-to-peer (p2p) networks are not independent of their peers, and the network efficiency depends on peers contributing resources. Because shared resources are not free, this contribution must be rewarded. Peers across the network may share computation po ...

IEEE COMPUTER SOC2022

Policy-based Exploration of Equilibrium Representations (PEER): A topology grammar for generative conceptual structural design

Ioannis Mirtsopoulos

Design exploration is a creative process that consists of the incremental generation of design candidates. Supported by digital means or not, the process handles the ill-structured nature of design and allows creativity to flourish through diversity of des ...

EPFL2022

An automatized signal timing detection at signalized intersections with detailed vehicle trajectory data

Nikolaos Geroliminis, Jasso Espadaler Clapés, Emmanouil Barmpounakis

Signalized intersections are a fundamental part of urban networks. Their understanding is crucial to identify congestion patterns, queues, delays, and safety issues in local and network level. In this work, we analyze multimodal vehicle trajectories and pr ...

2022

Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"

Vassily Hatzimanikatis, Homa Mohammadi Peyhani, Anastasia Sveshnikova

Supplementary datasets accompanying the manuscript "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds" published in the Metabolic Engineering Journal (https://doi.org/10.1016/j.ymben.2022.03.013). In line w ...

Zenodo2022

The Universal Gossip Fighter

Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafaël Benjamin Pinot

The notion of adversary is a staple of distributed computing. An adversary typically models “hostile” assumptions about the underlying distributed environment, e.g., a network that can drop messages, an operating system that can delay processes or an attac ...

IEEE2022

The kinectome: A comprehensive kinematic map of human motion in health and disease

Enrico Amico, Emahnuel Troisi Lopez, Antonella Romano

Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body ...

WILEY2022

Critical node selection method for efficient Max-Pressure traffic signal control in large-scale congested networks

Nikolaos Geroliminis, Anastasios Kouvelas, Dimitrios Tsitsokas

Decentralized signal control of congested traffic networks based on the Max-Pressure (MP) controller is theoretically proven to maximize throughput, stabilize the system and balance queues for single intersections under specific conditions. However, its pe ...

2022

Investigating cognitive ability using action-based models of structural brain networks

Enrico Amico

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...

OXFORD UNIV PRESS2022

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

2021

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Martin Jaggi, Sebastian Urban Stich, Hossein Shokri Ghadikolaei

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

MICROTOME PUBLISHING2021

Hardware-Software Co-Design of an RPC Processor

Arash Pourhabibi Zarandi

The booming popularity of online services has led to a major evolution in the way these services are built and deployed. To cope with such online data-intensive services, service providers deploy several massive-scale datacenters, also referred to as wareh ...

EPFL2021

A Distributed Luenberger Observer for Linear State Feedback Systems With Quantized and Rate-Limited Communications

Colin Neil Jones, Andrea Alessandretti, Ye Pu, Francisco Fernandes Castro Rego

This article addresses the problem of simultaneous distributed state estimation, and control of linear systems with linear state feedback, subjected to process, and measurement noise, under the constraints of quantized, and rate-limited network data transm ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Immunizing Systems from Distant Failures by Limiting Lamport Exposure

Bryan Alexander Ford, Cristina Basescu

Failures far away from a user should intuitively be less likely to affect that user. Today's ecosystem miserably fails this test, however, despite high-availability best practices. Correlated and cascading failures – triggered by misconfigurations, bugs, a ...

Association for Computing Machinery2021

Consensus Control for Decentralized Deep Learning

Tao Lin, Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Lingjing Kong

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021