arXiv subjects
Publications and source records attributed to M. Fiore.
We show that the bicategory of operads and bimodules can be embedded into the bicategory of symmetric 2-rigs, a categorification of commutative rings. In order to do this, we introduce the notion of an operadic 2-rig and show that the full sub-bicategory of symmetric 2-rigs spanned by operadic 2-rigs has the universal property of being a completion under Eilenberg-Moore-Kleisli objects.
We develop further the theory of monoidal bicategories by introducing and studying bicategorical counterparts of the notions of a linear exponential comonad, as considered in the study of linear logic, and of a codereliction transformation, introduced to study differential linear logic via differential categories. As an application, we extend the differential calculus of Joyal's analytic functors to analytic functors between presheaf categories, just as ordinary calculus extends from a single variable to many variables.
Big data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data with little to no need of prior knowledge. As continuous developments in experimental and numerical methods improve our ability to collect high-quality data, machine learning tools become increasingly viable and promising also in disciplines rooted in physical principles. These notes explore how machine learning can be integrated and combined with more classic methods in fluid dynamics. After a brief review of the machine learning landscape, we show how many problems in fluid mechanics can be framed as machine learning problems and we explore challenges and opportunities. We consider several relevant applications: aeroacoustic noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control. While this list is by no means exhaustive, the presentation will provide enough concrete examples to offer perspectives on how machine learning might impact the way we do research and learn from data.
A method is described which allows to deduce the dead-time of the front-end electronics of the LHCb muon detector from a series of measurements performed at different luminosities at a bunch-crossing rate of 20 MHz. The measured values of the dead-time range from 70 ns to 100 ns. These results allow to estimate the performance of the muon detector at the future bunch-crossing rate of 40 MHz and at higher luminosity.
Astronomical data are gathered through a very large number of heterogeneous techniques and stored in very diversified and often incompatible data repositories. Moreover in the e-science environment, it is needed to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise and/or external resource sharing and service provider relationships. The DAME/VONeural project, run jointly by the University Federico II, INAF (National Institute of Astrophysics) Astronomical Observatories of Napoli and the California Institute of Technology, aims at creating a single, sustainable, distributed e-infrastructure for data mining and exploration in massive data sets, to be offered to the astronomical (but not only) community as a web application. The framework makes use of distributed computing environments (e.g. S.Co.P.E.) and matches the international IVOA standards and requirements. The integration process is technically challenging due to the need of achieving a specific quality of service when running on top of different native platforms. In these terms, the result of the DAME/VO-Neural project effort will be a service-oriented architecture, obtained by using appropriate standards and incorporating Grid paradigms and restful Web services frameworks where needed, that will have as main target the integration of interdisciplinary distributed systems within and across organizational domains.
The dynamics of two counter-streaming electron-positron-ion unmagnetized plasma shells with zero net charge is analyzed in the context of magnetic field generation in GRB internal shocks due to the Weibel instability. The effects of large thermal motion of plasma particles, arbitrary mixture of plasma species and space charge effects are taken into account. We show that, although thermal effects slow down the instability, baryon loading leads to a non-negligible growth rate even for large temperatures and different shell velocities, thus guaranteeing the robustness and the occurrence of the Weibel instability for a wide range of scenarios.
The results from 2.5-dimensional Particle-in-Cell simulations for the interaction of a picosecond-long ignition laser pulse with a plasma pellet of 50-$μm$ diameter and 40 critical density are presented. The high density pellet is surrounded by an underdense corona and is isolated by a vacuum region from the simulation box boundary. The laser pulse is shown to filament and create density channels on the laser-plasma interface. The density channels increase the laser absorption efficiency and help generate an energetic electron distribution with a large angular spread. The combined distribution of the forward-going energetic electrons and the induced return electrons is marginally unstable to the current filament instability. The ions play an important role in neutralizing the space charges induced by the the temperature disparity between different electron groups. No global coalescing of the current filaments resulted from the instability is observed, consistent with the observed large angular spread of the energetic electrons.