Quasi-local mass integrals and the total mass

On asymptotically flat and asymptotically hyperbolic manifolds, by evaluating the total mass via the Ricci tensor, we show that the limits of certain Brown-York type and Hawking type quasi-local mass integrals equal the total mass of the manifold in all dimensions.Comment: References updated, introduction revise

A Roadmap Toward a Unified Space Communication Architecture

In recent years, the number of space exploration missions has multiplied. Such an increase raises the question of effective communication between the multitude of human-made objects spread across our solar system. An efficient and scalable communication architecture presents multiple challenges, including the distance between planetary entities, their motion and potential obstruction, the limited available payload on satellites, and the high mission cost. This paper brings together recent relevant specifications, standards, mission demonstrations, and the most recent proposals to develop a unified architecture for deep-space internetworked communication. After characterizing the transmission medium and its unique challenges, we explore the available communication technologies and frameworks to establish a reliable communication architecture across the solar system. We then draw an evolutive roadmap for establishing a scalable communication architecture. This roadmap builds upon the mission-centric communication architectures in the upcoming years towards a fully interconnected network or InterPlanetary Internet (IPN). We finally discuss the tools available to develop such an architecture in the short, medium, and long terms. The resulting architecture cross-supports space agencies on the solar system-scale while significantly decreasing space communication costs. Through this analysis, we derive the critical research questions remaining for creating the IPN regarding the considerable challenges of space communication.Peer reviewe

Neural Unsupervised Domain Adaptation in NLP—A Survey

Deep neural networks excel at learning from labeled data and achieve state-of-the-art results on a wide array of Natural Language Processing tasks. In contrast, learning from unlabeled data, especially under domain shift, remains a challenge. Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data. This is a more challenging yet a more widely applicable setup. We outline methods, from early approaches in traditional non-neural methods to pre-trained model transfer. We also revisit the notion of domain, and we uncover a bias in the type of Natural Language Processing tasks which received most attention. Lastly, we outline future directions, particularly the broader need for out-of-distribution generalization of future intelligent NLP