A Drift-Kinetic Analytical Model for SOL Plasma Dynamics at Arbitrary Collisionality

A drift-kinetic model to describe the plasma dynamics in the scrape-off layer region of tokamak devices at arbitrary collisionality is derived. Our formulation is based on a gyroaveraged Lagrangian description of the charged particle motion, and the corresponding drift-kinetic Boltzmann equation that includes a full Coulomb collision operator. Using a Hermite-Laguerre velocity space decomposition of the gyroaveraged distribution function, a set of equations to evolve the coefficients of the expansion is presented. By evaluating explicitly the moments of the Coulomb collision operator, distribution functions arbitrarily far from equilibrium can be studied at arbitrary collisionalities. A fluid closure in the high-collisionality limit is presented, and the corresponding fluid equations are compared with previously-derived fluid models

Risk Assessment for Alzheimer Patients, using GPS and Accelerometers with a Machine Learning Approach

Alzheimer is a pathology with an increasing incidence as people age. The epidemiology of the Alzheimer’s disease crosses every country at later stages in life and, for that motive, has become a major concern as expectancy of life is raising in developed world. The problem, as the disease progresses, becomes, the continuity of the person’s life as normal as possible and the insurance of safety and security for that person. It is known that Alzheimer patients tend to forget about important things such as their identity and location and for that it is important to provide that they become aware of such relevant information for them and for those who could provide them support. It is also known that people with Alzheimer tend to wander and, when that happens, they can get lost and become exposed to danger. The aim of this work is to assess if the person is getting away from usual paths and to monitor if the person falls which becomes riskier while wandering out of usual paths. The usage of GPS makes it possible to keep track of routes and, with the detection of possible deviations, it becomes possible to act accordingly, either issuing warnings for that person and later to carers and family. On the other hand, using machine learning to evaluate usual movements, it is possible to determine if a person falls or endures an excessively quiet position. With those strategies working together it is aimed to ensure safety of a person and request for assistance when high risk is assessed by the technological setup. To address these cases, the proposded setup will be based on a smartphone, together with a smartwatch, both carried by that person, as such devices already provide some needed sensors and GPS receiver while providing processing capabilities

Theory of the Drift-Wave Instability at Arbitrary Collisionality

A numerically efficient framework that takes into account the effect of the Coulomb collision operator at arbitrary collisionalities is introduced. Such model is based on the expansion of the distribution function on a Hermite-Laguerre polynomial basis, to study the effects of collisions on magnetized plasma instabilities at arbitrary mean-free path. Focusing on the drift-wave instability, we show that our framework allows retrieving established collisional and collisionless limits. At the intermediate collisionalities relevant for present and future magnetic nuclear fusion devices, deviations with respect to collision operators used in state-of-the-art turbulence simulation codes show the need for retaining the full Coulomb operator in order to obtain both the correct instability growth rate and eigenmode spectrum, which, for example, may significantly impact quantitative predictions of transport. The exponential convergence of the spectral representation that we propose makes the representation of the velocity space dependence, including the full collision operator, more efficient than standard finite difference methods.Comment: 7 pages, 3 figures, accepted for publication on Physical Review Letter

LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond

Distributional semantics based on neural approaches is a cornerstone of Natural Language Processing, with surprising connections to human meaning representation as well. Recent Transformer-based Language Models have proven capable of producing contextual word representations that reliably convey sense-specific information, simply as a product of self-supervision. Prior work has shown that these contextual representations can be used to accurately represent large sense inventories as sense embeddings, to the extent that a distance-based solution to Word Sense Disambiguation (WSD) tasks outperforms models trained specifically for the task. Still, there remains much to understand on how to use these Neural Language Models (NLMs) to produce sense embeddings that can better harness each NLM's meaning representation abilities. In this work we introduce a more principled approach to leverage information from all layers of NLMs, informed by a probing analysis on 14 NLM variants. We also emphasize the versatility of these sense embeddings in contrast to task-specific models, applying them on several sense-related tasks, besides WSD, while demonstrating improved performance using our proposed approach over prior work focused on sense embeddings. Finally, we discuss unexpected findings regarding layer and model performance variations, and potential applications for downstream tasks.Comment: Accepted to Artificial Intelligence Journal (AIJ

Linear Theory of Electron-Plasma Waves at Arbitrary Collisionality

The dynamics of electron-plasma waves are described at arbitrary collisionality by considering the full Coulomb collision operator. The description is based on a Hermite-Laguerre decomposition of the velocity dependence of the electron distribution function. The damping rate, frequency, and eigenmode spectrum of electron-plasma waves are found as functions of the collision frequency and wavelength. A comparison is made between the collisionless Landau damping limit, the Lenard-Bernstein and Dougherty collision operators, and the electron-ion collision operator, finding large deviations in the damping rates and eigenmode spectra. A purely damped entropy mode, characteristic of a plasma where pitch-angle scattering effects are dominant with respect to collisionless effects, is shown to emerge numerically, and its dispersion relation is analytically derived. It is shown that such a mode is absent when simplified collision operators are used, and that like-particle collisions strongly influence the damping rate of the entropy mode.Comment: 23 pages, 10 figures, accepted for publication on Journal of Plasma Physic