Subframework-based Bearing Rigidity Maintenance Control in Multirobot Networks

This work presents a novel approach for analyzing and controlling bearing rigidity in multi-robot networks with dynamic topology. By decomposing the system's framework into subframeworks, we express bearing rigidity, a global property, as a set of local properties, with rigidity eigenvalues serving as natural local rigidity metrics. We propose a decentralized, scalable, gradient-based controller that uses only bearing measurements to execute mission-specific commands. The controller preserves bearing rigidity by maintaining rigidity eigenvalues above a threshold, and also avoids inter-robot collisions. Simulations confirm the scheme's effectiveness, with information exchange confined to subframeworks, underscoring its scalability and practicality.

Learning language variations in news corpora through differential embeddings

There is an increasing interest in the NLP community in capturing variations in the usage of language, either through time (i.e., semantic drift), across regions (as dialects or variants) or in different social contexts (i.e., professional or media technolects). Several successful dynamical embeddings have been proposed that can track semantic change through time. Here we show that a model with a central word representation and a slice-dependent contribution can learn word embeddings from different corpora simultaneously. This model is based on a star-like representation of the slices. We apply it to The New York Times and The Guardian newspapers, and we show that it can capture both temporal dynamics in the yearly slices of each corpus, and language variations between US and UK English in a curated multi-source corpus. We provide an extensive evaluation of this methodology.