Publications related to Overlay network

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

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

Optimal routing and design of CO2 based district anergy networks

Andrea Valle

One of the main global goals is the reduction of the energy consumption and the most appropriate way for achieving this aim is to target one of most critical sectors, the residential. The selected technology for this work is the district energy network, wh ...

2022

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Sébastien Marcel, Hatef Otroshi Shahreza

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of ...

IEEE2022

Physics-informed neural networks for diffraction tomography

Demetri Psaltis, Carlo Gigli, Amirhossein Saba Shirvan, Ahmed Ayoub

We propose a physics-informed neural network (PINN) as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field ...

SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS2022

Byzantine Consensus is Θ(n^2): The Dolev-Reischuk Bound is Tight even in Partial Synchrony!

Rachid Guerraoui, Jovan Komatovic, Pierre Philippe Civit, Manuel José Ribeiro Vidigueira, Vincent Gramoli, Seth Gilbert

The Dolev-Reischuk bound says that any deterministic Byzantine consensus protocol has (at least) quadratic communication complexity in the worst case. While it has been shown that the bound is tight in synchronous environments, it is still unknown whether ...

Dagstuhl Publishing2022

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

Switchover phenomenon induced by epidemic seeding on geometric networks

Gergely Odor

It is a fundamental question in disease modeling how the initial seeding of an epidemic, spreading over a network, determines its final outcome. One important goal has been to find the seed configuration, which infects the most individuals. Although the id ...

2021

Railway timetable rescheduling with capacity constraint

Benoit Pahud

The increasing demand for travelling forces railway operators to use their network at maximum capacity. When unforeseen events occur, the time needed to make the proper adjustments before reaching a gridlocked state is too short. The timetable rescheduling ...

2021

Social Learning Under Inferential Attacks

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries a ...

IEEE2021

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Qian Wang, Jan Sickmann Hesthaven

A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the ...

2021

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Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

Optimal routing and design of CO2 based district anergy networks

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Physics-informed neural networks for diffraction tomography

Byzantine Consensus is Θ(n^2): The Dolev-Reischuk Bound is Tight even in Partial Synchrony!

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

Switchover phenomenon induced by epidemic seeding on geometric networks

Railway timetable rescheduling with capacity constraint

Social Learning Under Inferential Attacks

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

Byzantine Consensus is Θ(n^2): The Dolev-Reischuk Bound is Tight even in Partial Synchrony!

Physics-informed neural networks for diffraction tomography

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Optimal routing and design of CO2 based district anergy networks

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

Switchover phenomenon induced by epidemic seeding on geometric networks

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Railway timetable rescheduling with capacity constraint

Social Learning Under Inferential Attacks