We introduce the first “learned” scheme for implementing a compressed rank/select dictionary. We prove theoretical bounds on its time and space performance both in the worst...

This webinar was organised by the Network Medicine Exploratory (WP10) and hosted Enrico Maiorino. Enrico Maiorino is a very esteemed researcher at Brigham Women's Hospital,...

This course developed by Universitat Politècnica de Catalunya and Barcelona Supercomputing Center provides an applied approach to Deep Learning. It chooses to present an...

This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ The...

In this seminar cycle, students are guided in the direct usage of a powerful C++ library implementing many state-of-the-art compressed data structures for big data. Other than...

Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a...

Artificial intelligence, and machine learning (ML) in particular, is being used for different purposes that are critical for human life. To avoid an algorithm-based...

The emergency of the Coronavirus imposes a cultural debate on the balancing of rights, freedoms and social responsibilities, finalized to the protection of individual and...

Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the...

Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached...

Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model....