Hard-Loop Dynamics of Non-Abelian Plasma Instabilities

Non-Abelian plasma instabilities may be responsible for the fast apparent quark-gluon thermalization in relativistic heavy-ion collisions if their exponential growth is not hindered by nonlinearities. We study the real-time evolution of instabilities in an anisotropic non-Abelian plasma with an SU(2) gauge group in the hard-loop approximation. We find exponential growth of non-Abelian plasma instabilities both in the linear and in the strongly nonlinear regime, with only a brief phase of subexponential behavior in between.Comment: 4 pages REVTEX4, 3 figures; updated to match version published in Phys. Rev. Lett. (shorter introduction, added details on quality of numerical simulation

Hadronic vacuum polarization contribution to the muon g−2g-2 in holographic QCD

We evaluate the leading-order hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon with two light flavors in minimal hard-wall and soft-wall holographic QCD models, as well as in simple generalizations thereof, and compare with the rather precise results available from dispersive and lattice approaches. While holographic QCD cannot be expected to shed light on the existing small discrepancies between the latter, this comparison in turn provides useful information on the holographic models, which have been used to evaluate hadronic light-by-light contributions where errors in data-driven and lattice approaches are more sizable. In particular, in the hard-wall model that has recently been used to implement the Melnikov-Vainshtein short-distance constraint on hadronic light-by-light contributions, a matching of the hadronic vacuum polarization to the data-driven approach points to the same correction of parameters that has been proposed recently in order to account for next-to-leading order effects.Comment: REVTEX, 18 pages, 3 figures, 3 tables; v2: references adde

Analyzing Patient Trajectories With Artificial Intelligence

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery

Hard-loop dynamics of non-abelian plasma instabilities

I discuss recent advances in the understanding of non-equilibrium gauge field dynamics in plasmas which have particle distributions which are locally anisotropic in momentum space. In contrast to locally isotropic plasmas such anisotropic plasmas have a spectrum of soft unstable modes which are characterized by exponential growth of transverse (chromo)-magnetic fields at short times. The long-time behavior of such instabilities depends on whether or not the gauge group is abelian or non-abelian. Here I will report on recent numerical simulations which attempt to determine the long-time behavior of an anisotropic non-abelian plasma within hard-loop effective theory.Comment: 4 pages, 1 figure; Contribution to proceedings of Quark Matter 2005, Budapest, Hungary, Aug 4-9 200

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