Using a recently introduced method [D. Bini, T. Damour, and A. Geralico, Phys. Rev. Lett. 123, 231104 (2019)], which splits the conservative dynamics of gravitationally interacting binary systems into a nonlocal-in-time part and a local-in-time one, we compute the local part of the dynamics at the sixth post-Newtonian (6PN) accuracy. Our strategy combines several theoretical formalisms: post-Newtonian, post-Minkowskian, multipolar-post-Minkowskian, effective-field-theory, gravitational self-force, effective one-body, and Delaunay averaging.

An efficient approach to the calculation of the E-entropy is proposed. The method is based on the idea of looking at the information content of a string nf data hv annalyzing the signal only nt thp instants when the fluctuations are larger than a certain threshold is an element of, i.e., by looking at the exit-time statistics. The practical and theoretical advantages of our method with respect to the usual one are shown by the examples of a deterministic map and a self-affine stochastic process.

Topic Modelling (TM) is a widely adopted generative model used to infer the thematic organization of text corpora. When document-level covariate information is available, so-called Structural Topic Modelling (STM) is the state-of-the-art approach to embed this information in the topic mining algorithm. Usually, TM algorithms rely on unigrams as the basic text generation unit, whereas the quality and intelligibility of the identified topics would significantly benefit from the detection and usage of topical phrasemes.

Bioventing is a technology used to abate the presence of pollutants in the subsoil. Microorganisms biodegrade the pollutant but the biochemical reaction requires oxygen and so an airflow is induced in the subsoil by means of injection and/or extraction wells. Costs, final result and decontamination time are reliant on contaminant type, soil permeability and several other factors, but oxygen subsoil concentration plays a very important role.

Motivated by the upcoming Internet of Things, designing light-weight authentication protocols for resource constrained devices is among the main research directions of the last decade. Current solutions in the literature attempt either to improve the computational efficiency of cryptographic authentication schemes, or to build a provably-secure scheme relying on the hardness of a specific mathematical problem.

Linear solvers for large and sparse systems are a key element of scientific applications, and their efficient implementation is necessary to harness the computational power of current computers. Algebraic Multigrid (AMG) Preconditioners are a popular ingredient of such linear solvers; this is the motivation for the present work where we examine some recent developments in a package of AMG preconditioners to improve efficiency, scalability, and robustness on extreme-scale problems.