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1. A proof of the instability of AdS for the Einstein-null dust system with an inner mirror

   https://doi.org/10.2140/apde.2020.13.1671  

   Moschidis, Georgios ( January 2020 , Analysis & PDE)

   null (Ed.)
2. Triggering tearing in a forming current sheet with the mirror instability

   https://doi.org/10.1017/S0022377822000150  

   Winarto, Himawan W. ; Kunz, Matthew W. ( April 2022 , Journal of Plasma Physics)

   We study the time-dependent formation and evolution of a current sheet (CS) in a magnetised, collisionless, high-beta plasma using hybrid-kinetic particle-in-cell simulations. An initially tearing-stable Harris sheet is frozen into a persistently driven incompressible flow so that its characteristic thickness gradually decreases in time. As the CS thins, the strength of the reconnecting field increases, and adiabatic invariance in the inflowing fluid elements produces a field-biased pressure anisotropy with excess perpendicular pressure. At large plasma beta, this anisotropy excites the mirror instability, which deforms the reconnecting field on ion-Larmor scales and dramatically reduces the effective thickness of the CS. Tearing modes whose wavelengths are comparable to that of the mirrors then become unstable, triggering reconnection on smaller scales and at earlier times than would have occurred if the thinning CS were to have retained its Harris profile. A novel method for identifying and tracking X-points is introduced, yielding X-point separations that are initially intermediate between the perpendicular and parallel mirror wavelengths in the upstream plasma. These mirror-stimulated tearing modes ultimately grow and merge to produce island widths comparable to the CS thickness, an outcome we verify across a range of CS formation timescales and initial CS widths. Our results may find their most immediate application in the tearing disruption of magnetic folds generated by turbulent dynamo in weakly collisional, high-beta, astrophysical plasmas. 

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3. ”Mirror, Mirror, on the Wall” - Promoting Self-Regulated Learning using Affective States Recognition via Facial Movements

   https://doi.org/10.1145/3532106.3533500  

   Chen, Si ; Liu, Yixin ; Lu, Risheng ; Zhou, Yuqian ; Lee, Yi-Chieh ; Huang, Yun ( June 2022 , DIS '22: Designing Interactive Systems Conference)
4. Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent

   Gunasekar, Suriya ; Woodworth, Blake ; Srebro, Nathan ( January 2021 , Proceedings of Machine Learning Research)

   null (Ed.)

   We present a direct (primal only) derivation of Mirror Descent as a “partial” discretization of gradient flow on a Riemannian manifold where the metric tensor is the Hessian of the Mirror Descent potential function. We contrast this discretization to Natural Gradient Descent, which is obtained by a “full” forward Euler discretization. This view helps shed light on the relationship between the methods and allows generalizing Mirror Descent to any Riemannian geometry in Rd, even when the metric tensor is not a Hessian, and thus there is no “dual.” 

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5. Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent

   Gunasekar, S ; Woodworth, B ; Srebro, N ( April 2021 , Proceedings of the 24th International Conference on Artificial Intelligence and Statistics)

   null (Ed.)