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  1. Abstract

    Listening to music is an enjoyable behaviour that engages multiple networks of brain regions. As such, the act of music listening may offer a way to interrogate network activity, and to examine the reconfigurations of brain networks that have been observed in healthy aging. The present study is an exploratory examination of brain network dynamics during music listening in healthy older and younger adults. Network measures were extracted and analyzed together with behavioural data using a combination of hidden Markov modelling and partial least squares. We found age- and preference-related differences in fMRI data collected during music listening in healthy younger and older adults. Both age groups showed higher occupancy (the proportion of time a network was active) in a temporal-mesolimbic network while listening to self-selected music. Activity in this network was strongly positively correlated with liking and familiarity ratings in younger adults, but less so in older adults. Additionally, older adults showed a higher degree of correlation between liking and familiarity ratings consistent with past behavioural work on age-related dedifferentiation. We conclude that, while older adults do show network and behaviour patterns consistent with dedifferentiation, activity in the temporal-mesolimbic network is relatively robust to dedifferentiation. These findings may help explain how music listening remains meaningful and rewarding in old age.

     
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  2. Abstract

    The first significant sunquake event of Solar Cycle 25 was observed during the X1.5 flare of 2022 May 10, by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory. We perform a detailed spectro-polarimetric analysis of the sunquake photospheric sources, using the Stokes profiles of the Fei6173 Å line, reconstructed from the HMI linear and circular polarized filtergrams. The results show fast variations of the continuum emission with rapid growth and slower decay lasting 3–4 minutes, coinciding in time with the hard X-ray impulses observed by the Konus instrument on board the Wind spacecraft. The variations in the line core appeared slightly ahead of the variations in the line wings, showing that the heating started in the higher atmospheric layers and propagated downward. The most significant feature of the line profile variations is the transient emission in the line core in three of the four sources, indicating intense, impulsive heating in the lower chromosphere and photosphere. In addition, the observed variations of the Stokes profiles reflect transient and permanent changes in the magnetic field strength and geometry in the sunquake sources. Comparison with the radiative hydrodynamics models shows that the physical processes in the impulsive flare phase are substantially more complex than those predicted by proton and electron beam flare models currently presented in the literature.

     
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  3. Free, publicly-accessible full text available October 1, 2024
  4. Abstract

    Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) observations reveal a class of solar flares with substantial energy and momentum impacts in the photosphere, concurrent with white-light emission and helioseismic responses, known as sunquakes. Previous radiative hydrodynamic modeling has demonstrated the challenges of explaining sunquakes in the framework of the standard flare model of “electron beam” heating. One of the possibilities to explain the sunquakes and other signatures of the photospheric impact is to consider additional heating mechanisms involved in solar flares, for example via flare-accelerated protons. In this work, we analyze a set of single-loop Fokker–Planck and radiative hydrodynamics RADYN+FP simulations where the atmosphere is heated by nonthermal power-law-distributed proton beams which can penetrate deeper than the electron beams into the low atmospheric layers. Using the output of the RADYN models, we calculate synthetic Fei6173 Å line Stokes profiles and from those the line-of-sight observables of the SDO/HMI instrument, as well as the 3D helioseismic response, and compare them with the corresponding observational characteristics. These initial results show that the models with proton beam heating can produce the enhancement of the HMI continuum observable and explain qualitatively the generation of sunquakes. The continuum observable enhancement is evident in all models but is more prominent in ones withEc≥ 500 keV. In contrast, the models withEc≤ 100 keV provide a stronger sunquake-like helioseismic impact according to the 3D acoustic modeling, suggesting that low-energy (deka- and hecto-keV) protons have an important role in the generation of sunquakes.

     
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  5. Abstract

    Ecological analyses typically involve many interacting variables. Ecologists often specify lagged interactions in community dynamics (i.e. vector‐autoregressive models) or simultaneous interactions (e.g. structural equation models), but there is less familiarity with dynamic structural equation models (DSEM) that can include any simultaneous or lagged effect in multivariate time‐series analysis.

    We propose a novel approach to parameter estimation for DSEM, which involves constructing a Gaussian Markov random field (GMRF) representing simultaneous and lagged path coefficients, and then fitting this as a generalized linear mixed model to missing and/or non‐normal data. We provide a new R‐packagedsem, which extends the ‘arrow interface’ from path analysis to represent user‐specified lags when constructing the GMRF. We also outline how the resulting nonseparable precision matrix can generalize existing separable models, for example, for time‐series and species interactions in a vector‐autoregressive model.

    We first demonstratedsemby simulating a two‐species vector‐autoregressive model based on wolf–moose interactions on Isle Royale. We show that DSEM has improved precision when data are missing relative to a conventional dynamic linear model. We then demonstrate DSEM via two contrasting case studies. The first identifies a trophic cascade where decreased sunflower starfish has increased urchin and decreased kelp densities, while sea otters have a simultaneous positive effect on kelp in the California Current from 1999 to 2018. The second estimates how declining sea ice has decreased cold‐water habitats, driving a decreased density for fall copepod predation and inhibiting early‐life survival for Alaska pollock from 1963 to 2023.

    We conclude that DSEM can be fitted efficiently as a GLMM involving missing data, while allowing users to specify both simultaneous and lagged effects in a time‐series structural model. DSEM then allows conceptual models (developed with stakeholder input or from ecological expertise) to be fitted to incomplete time series and provides a simple interface for granular control over the number of estimated time‐series parameters. Finally, computational methods are sufficiently simple that DSEM can be embedded as component within larger (e.g. integrated population) models. We therefore recommend greater exploration and performance testing for DSEM relative to familiar time‐series forecasting methods.

     
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  6. Abstract

    Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations. Assessing the viability of sampling algorithms for lattice field theory at scale has traditionally been accomplished using simple cost scaling laws, but as we discuss in this work, their utility is limited for flow-based approaches. We conclude that flow-based approaches to sampling are better thought of as a broad family of algorithms with different scaling properties, and that scalability must be assessed experimentally.

     
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  7. We investigate the suitability of using GeV laser wakefield accelerated electron beams to measure strong, B > 0.1 MT, magnetic fields. This method is explored as an alternative to proton deflectometry, which cannot be used for quantitative measurement using conventional analysis techniques at these extreme field strengths. Using such energetic electrons as a probe brings about several additional aspects for consideration, including beam divergence, detectors, and radiation reaction, which are considered here. Quantum radiation reaction on the probe is found to provide an additional measurement of the strength and length of fields, extending the standard deflectometry measurement that can only measure the path integrated fields. An experimental setup is proposed and measurement error is considered under near-term experimental conditions.

     
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    Free, publicly-accessible full text available September 1, 2024
  8. Free, publicly-accessible full text available March 31, 2024
  9. Free, publicly-accessible full text available May 1, 2024
  10. The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans through the systematic investigation of a visual-motor task that has been well-explored—that of intercepting a target moving over a ground plane. Previous research demonstrated that humans performing this task resorted to anticipatory changes in speed intended to compensate for semi-predictable changes in target speed later in the approach. To capture this behavior, our proposed “neural” AIF agent uses artificial neural networks to select actions on the basis of a very short term prediction of the information about the task environment that these actions would reveal along with a long-term estimate of the resulting cumulative expected free energy. Systematic variation revealed that anticipatory behavior emerged only when required by limitations on the agent's movement capabilities, and only when the agent was able to estimate accumulated free energy over sufficiently long durations into the future. In addition, we present a novel formulation of the prior mapping function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy/reward. Together, these results demonstrate the use of AIF as a plausible model of anticipatory visually guided behavior in humans. 
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