Causal Consistency and Latency Optimality: Friend or Foe? [Extended Version]

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Epidemic mitigation by statistical inference from contact tracing data

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Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been ...

National Academy of Sciences2021

Hardware and Software Support for RPC-Centric Server Architecture

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Online services have become ubiquitous in technological society, the global demand for which has driven enterprises to construct gigantic datacenters that run their software. Such facilities have also recently become a substrate for third-party organizatio ...

EPFL2022

Do Children Adapt Their Perspective to a Robot When They Fail to Complete a Task?

Pierre Dillenbourg, Elmira Yadollahi

Spatial understanding and communication are essential skills in human interaction. An adequate understanding of others' spatial perspectives can increase the quality of the interaction, both perceptually and cognitively. In this paper, we take the first st ...

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When to Hedge in Interactive Services

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In online data-intensive (OLDI) services, each client request typically executes on multiple servers in parallel; as a result, “system hiccups”, although rare within a single server, can interfere with many client requests and cause violations of service-l ...

USENIX ASSOC2021

Simulation of High-Frequency Transients in Overhead Lines Including Frequency-Dependent Soil Parameters: an FDTD Aproach

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This paper investigates the effect of frequency-dependent soil parameters on the simulation of electromagnetic transients on overhead lines, based on the application of the FDTD method to solve the telegrapher’s equations. The ground return impedance is ca ...

2021

Genuinely distributed Byzantine machine learning

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Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

2022

Differentiated consistency for worldwide gossips

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Eventual consistency is a consistency model that favors liveness over safety. It is often used in large-scale distributed systems where models ensuring a stronger safety incur performance that are too low to be deemed practical. Eventual consistency tends ...

2022

Scalable Multi-agent Coordination and Resource Sharing

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A plethora of real world problems consist of a number of agents that interact, learn, cooperate, coordinate, and compete with others in ever more complex environments. Examples include autonomous vehicles, robotic agents, intelligent infrastructure, IoT de ...

EPFL2022

A Robotic Teacher Community to Foster the Integration of Educational Robotics in School

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The digital society requires the acquisition of skills related to computer sciences like computational thinking or competences in coding. Also transversal competences like communication, collaboration and creativity are increasingly important. Educational ...

Springer, Cham2021

Evaluation of Site Errors in LLS Magnetic Direction Finding Caused by Large Hills Using the 3D‐FDTD Technique

Farhad Rachidi-Haeri, Hannes Kohlmann

Angle and amplitude estimation errors in magnetic direction finding, called site errors, are important sensor-specific errors in lightning location systems (LLS). They are known to be caused by nearby cables and overhead lines due to induced currents. Due ...

2021

Robust Localization for Multi-robot Formations: An Experimental Evaluation of an Extended GM-PHD Filter

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This paper presents a thorough experimental evaluation of an extended Gaussian Mixture Probability Hypothesis Density filter which is able to provide state estimates for the maintenance of a multirobot formation, even when the communication fails and the t ...

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GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

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We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021