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Models of 802.11 Multi-Hop Networks: Theoretical Insights and Experimental Validation

Related publications (32)

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Benign Overfitting in Deep Neural Networks under Lazy Training

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...

2023

The DESI N-body Simulation Project - II. Suppressing sample variance with fast simulations

Andrei Variu, Cheng Zhao, Yu Yu, Hanyu Zhang

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches similar to 20 h(-3) Gpc(3). It is a great challenge to prepare high-resolution simulations with a much ...

OXFORD UNIV PRESS2022

The Neurorobotic Platform: A simulation environment for brain-inspired robotics

Marc-Oliver Gewaltig, Paul Levi

The aim of the neurorobotic platform of the Human Brain Project is to offer scientists and technology developers a soft - ware and hardware infrastructure that allows them to connect pre-validated brain models to detailed simulations of ro- bot bodies and ...

VDE Verlag2021

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Michaël Unser, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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We present AGGREGATHOR, a framework that implements state-of-the-art robust (Byzantine-resilient) distributed stochastic gradient descent. Following the standard parameter server model, we assume that a minority of worker machines can be controlled by an a ...

2019

Integration of Perimeter Control and Route Guidance in Large-scale Urban Networks via Model Predictive Control

Nikolaos Geroliminis, Isik Ilber Sirmatel

Traffic congestion is a substantial problem plaguing modern society. Researchers and practitioners working on transportation systems turn to methods from automatic control to decrease congestion and improve urban mobility. Considering the sheer physical si ...

2017

This report gives an overview of Swiss climate policy and existing simulations of decarbonisation strategies. Report on results from Task A and B in the DDPP related mandate from FOEN. ...

INFRAS2015

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We propose a novel regularization method for compressive imaging in the context of the CS theory with coherent and redundant dictionaries. The approach relies on the conjecture that natural images exhibit strong average sparsity over multiple coherent fram ...

2013

An Adaptive Field Estimation Algorithm for Sensor Networks in Dynamic Environments

Alcherio Martinoli, William Christopher Evans, Amanda Stella Markowska Prorok

The efficiency of distributed sensor networks depends on an optimal trade-off between the usage of resources and data quality. This workshop paper addresses the problem of optimizing this trade-off in self-configured distributed sensor networks. In our cas ...

2011

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Wireless ad hoc networks are inherently vulnerable, as any node can disrupt the communication of potentially any other node in the network. Many solutions to this problem have been proposed. In this paper, we take a fresh and comprehensive approach that ad ...

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