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Perturbations by analytic discs along generating CR-submanifolds of C^{n}are considered. In the case all partial indices of a closed path$p$in a generating CR-fibration {$M$(ξ)}_{ξ∈∂$D$}are greater or equal to −1 we can completely parametrize all small holomorphic perturbations of the path$p$along the fibration {$M$(ξ)}_{ξ∈∂$D$}. In this case we also study the geometry of perturbations by analytic discs and their relation to the conormal bundle of the fibration.

In virtual reality/augmented reality (VR/AR) applications, especially medical image visualization, it is highly desirable to magnify region of interests. In this work, we propose a novel method for virtual magnifying glass for visualizing volumetric data based on Optimal Mass Transportation. An optimal mass transportation map deforms a volume to itself, transforms the source measure (volumetric element) to the target measure with the minimal transportation cost. Solving the optimal mass transportation problem is equivalent to a convex optimization, and can be converted to computing power Voronoi diagrams in classical computational geometry. The proposed method allows the user to accurately control the target measure, and select multiple regions of interests with irregular shapes. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications.

Mean field games (MFG) and mean field control (MFC) are critical classes of multi-agent models for efficient analysis of massive populations of interacting agents. Their areas of application span topics in economics, finance, game theory, industrial engineering, crowd motion, and more. In this paper, we provide a flexible machine learning framework for the numerical solution of potential MFG and MFC models. State of-the-art numerical methods for solving such problems utilize spatial discretization that leads to a curse-of-dimensionality. We approximately solve high-dimensional problems by combining Lagrangian and Eulerian viewpoints and leveraging recent advances from machine learning. More precisely, we work with a Lagrangian formulation of the problem and enforce the underlying Hamilton-Jacobi-Bellman (HJB) equation that is derived from the Eulerian formulation. Finally, a tailored neural network parameterization of the MFG/MFC solution helps us avoid any spatial discretization. Our numerical results include the approximate solution of 100-dimensional instances of optimal transport and crowd motion problems on a standard work station and a validation using a Eulerian solver in two dimensions. These results open the door to much-anticipated applications of MFG and MFC models that were beyond reach with existing numerical methods.