In this paper we are investigating a method for the design and integration of 3D external compression inlet geometries on parametric geometries of air-breathing hypersonic aircraft. We view the geometries as the first stage of a mixed compression inlet. The investigations are based on waverider geometries generated with the osculating cones waverider forebody design method. The osculating cones...

In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samp...

Fisher information and natural gradient provided deep insights and powerful tools to artificial neural networks. However related analysis becomes more and more difficult as the learner’s structure turns large and complex. This paper makes a preliminary step towards a new direction. We extract a local component from a large neural system, and define its relative Fisher information metric that de...

We present a new approach to reflectance estimation for dynamic scenes. Non-parametric image statistics are used to transfer reflectance properties from a static example set to a dynamic image sequence. The approach allows reflectance estimation for surface materials with inhomogeneous appearance, such as those which commonly occur with patterned or textured clothing. Material reflectance prope...

In this work we address the challenges in the realization of free-form architecture and complex shapes in general with the technical advantages of ruled surfaces. We propose a geometry processing framework to approximate (rationalize) a given shape by one or multiple strips of ruled surfaces. We discuss techniques to achieve an overall smooth surface and develop a parametric model for the gener...

Divergence functions are the non-symmetric “distance” on the manifold,Mθ, of parametric probability density functions over a measure space, (X,μ). Classical information geometry prescribes, on Mθ: (i) a Riemannian metric given by the Fisher information; (ii) a pair of dual connections (giving rise to the family of α-connections) that preserve the metric under parallel transport by their joint a...

Multidisciplinary optimization systems rely increasingly on parametric CAD engines to supply the geometries required by their analysis components. Such parametric geometry models usually result from an uneasy compromise between high flexibility, that is, the ability to morph into a wide variety of topologies and shapes, and robustness, the ability to produce feasible, sensible topologies and sh...

Algorithms are given for determining weighted L∞ isotonic regressions satisfying order constraints given by a directed acyclic graph (dag) with n vertices andm edges. An algorithm is given takingΘ(m log n) time for the general case. However, it relies on parametric search, so a practical approach is introduced, based on calculating prefix solutions. While not as fast in the general case, for li...

Suppose we are given n moving postmen described by their motion equations pi(t) = si + vit; i = 1; : : : ; n, where si 2 IR 2 is the position of the i'th postman at time t = 0, and vi 2 IR 2 is his velocity. The problem we address is how to preprocess the postmen data so as to be able to e ciently answer two types of nearest neighbor queries. The rst one asks \who is the nearest postman at time...

We present linear-time algorithms for partitioning a path or a tree with weights on the vertices by removing k edges to maximize the minimum-weight component. We also use the same framework to partition a path with weight on the vertices, removing k edges to minimize the maximum-weight component. The algorithms use the parametric search paradigm, testing candidate values until an optimum is fou...