CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification and matrix completion tasks

Network Slicing

Network slicing is emerging as a key enabling technology to support new service needs, business cases, and the evolution of programmable networking. As an end-to-end concept involving network functions in different domains and administrations, network slicing calls for new standardization efforts, design methodologies, and deployment strategies. This chapter aims at addressing the main aspects of network slicing with relevant challenges and practical solutions

September 11, 1986

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia

Exploiting Multiword Expressions to Solve “La Ghigliottina”

Il contributo descrive il sistema UNIOR4NLP, sviluppato per risolvere il gioco “La Ghigliottina”, che ha partecipato alla sfida NLP4FUN della campagna di valutazione Evalita 2018. Il sistema risulta il migliore della competizione e ha prestazioni più elevate rispetto agli umani.The paper describes UNIOR4NLP a system developed to solve “La Ghigliottina” game which took part in the NLP4FUN task of the Evalita 2018 evaluation campaign. The system is the best performing one in the competition and achieves better results than human players

DialettiBot: a Telegram Bot for Crowdsourcing Recordings of Italian Dialects

In this paper we describe DialettiBot, a Telegram based chatbot for crowdsourcing geo-referenced voice recordings of Italian dialects. The system enables people to listen to previously recorded audio and encourages them to contribute to building a collective linguistic resource by sending voice recordings of their own spoken dialects. The project aims at collecting a large sample of voice recordings in order to promote knowledge of linguistic variation and preserve proverbs or idioms typical for different local dialects. Moreover, the collected data can contribute to several voice-based Natural Language Processing (NLP) applications in helping them understand utterances in non-standard Italian.In questo articolo descriviamo DialettiBot, un chatbot basato su Telegram per raccogliere registrazioni audio georeferenziate di dialetti italiani. Il sistema permette alle persone di ascoltare le registrazioni precedentemente inserite, e le incoraggia a contribuire alla costruzione di questa risorsa linguistica collettiva, attraverso l’invio di registrazioni audio nel proprio dialetto. Il progetto mira a raccogliere una grande mole di registrazioni che possono aiutare a promuovere la conoscenza delle variazioni linguistiche e la salvaguardia dei proverbi o modi di dire tipici di ogni dialetto locale. I dati raccolti possono inoltre contribuire a diverse applicazioni del trattamento automatico del linguaggio (TAL) che hanno bisogno di essere adattate per comprendere espressioni dialettali