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  1. Free, publicly-accessible full text available December 10, 2024
  2. Abstract

    Revealing the formation, dynamics, and contribution to plasma heating of magnetic field fluctuations in the solar wind is an important task for heliospheric physics and for a general plasma turbulence theory. Spacecraft observations in the solar wind are limited to spatially localized measurements, so that the evolution of fluctuation properties with solar wind propagation is mostly studied via statistical analyses of data sets collected by different spacecraft at various radial distances from the Sun. In this study we investigate the evolution of turbulence in the Earth’s magnetosheath, a plasma system sharing many properties with the solar wind. The near-Earth space environment is being explored by multiple spacecraft missions, which may allow us to trace the evolution of magnetosheath fluctuations with simultaneous measurements at different distances from their origin, the Earth’s bow shock. We compare ARTEMIS and Magnetospheric Multiscale (MMS) Mission measurements in the Earth magnetosheath and Parker Solar Probe measurements of the solar wind at different radial distances. The comparison is supported by three numerical simulations of the magnetosheath magnetic and plasma fluctuations: global hybrid simulation resolving ion kinetic and including effects of Earth’s dipole field and realistic bow shock, hybrid and Hall-MHD simulations in expanding boxes that mimic the magnetosheath volume expansion with the radial distance from the dayside bow shock. The comparison shows that the magnetosheath can be considered as a miniaturized version of the solar wind system with much stronger plasma thermal anisotropy and an almost equal amount of forward and backward propagating Alfvén waves. Thus, many processes, such as turbulence development and kinetic instability contributions to plasma heating, occurring on slow timescales and over large distances in the solar wind, occur more rapidly in the magnetosheath and can be investigated in detail by multiple near-Earth spacecraft.

     
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  3. null (Ed.)
    Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications in cognitive radio and recommender systems, and enjoys features that are analogous to FL. This paper proposes a general framework of FMAB and then studies two specific federated bandit models. We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution. This model introduces a new uncertainty of client sampling, as the global model may not be reliably learned even if the finite local models are perfectly known. Furthermore, this uncertainty cannot be quantified a priori without knowledge of the suboptimality gap. We solve the approximate model by proposing Federated Double UCB (Fed2-UCB), which constructs a novel “double UCB” principle accounting for uncertainties from both arm and client sampling. We show that gradually admitting new clients is critical in achieving an O(log(T)) regret while explicitly considering the communication loss. The exact model, where the global bandit model is the exact average of heterogeneous local models, is then studied as a special case. We show that, somewhat surprisingly, the order-optimal regret can be achieved independent of the number of clients with a careful choice of the update periodicity. Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and demonstrate the effectiveness and efficiency of the proposed algorithms. 
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