Publications related to Telecommunications network

DeepNC: Deep Generative Network Completion

Most network data are collected from partially observable networks with both missing nodes and missing edges, for example, due to limited resources and privacy settings specified by users on social media. Thus, it stands to reason that inferring the missin ...

IEEE COMPUTER SOC2022

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

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

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

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

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

The Impact of Collaborative Scheduling and Routing for Interconnected Logistics: A European Case Study

Michel Bierlaire, Shadi Sharif Azadeh, Mohammad Yousef Maknoon, Jianghang Chen

Interconnected logistics system can play an important role towards having a more sustainable green freight transport. Recently, after introducing the concept of Physical Internet (PI), researchers have started to explore the opportunities and challenges th ...

Springer, Cham2021

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

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

Telecommunications network

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DeepNC: Deep Generative Network Completion

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

Optimal routing and design of CO2 based district anergy networks

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Physics-informed neural networks for diffraction tomography

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

The Impact of Collaborative Scheduling and Routing for Interconnected Logistics: A European Case Study

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

Railway timetable rescheduling with capacity constraint

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

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

DeepNC: Deep Generative Network Completion

Optimal routing and design of CO2 based district anergy networks

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

Physics-informed neural networks for diffraction tomography

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

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

The Impact of Collaborative Scheduling and Routing for Interconnected Logistics: A European Case Study

Railway timetable rescheduling with capacity constraint