Data-driven Modelling of Shape Structure

In recent years, the study of shape structure has shown great promise, by taking steps towards exposing shape semantics and functionality to algorithms spanning a wide range of areas in computer graphics and vision. By shape structure, we refer to the set of parts that make a shape, the relations between these parts, and the ways in which they correspond and vary between shapes of the same family. These developments have been largely driven by the abundance of 3D data, with collections of 3D models becoming increasingly prominent and websites such as Trimble 3D Warehouse offering millions of free 3D models to the public. The ability to use large amounts of data inside these shape collections for discovering shape structure has made novel approaches to acquisition, modelling, fabrication, and recognition of 3D objects possible. Discovering and modelling the structure of shapes using such data is therefore of great importance. In this thesis we address the problem of discovering and modelling shape structure from large, diverse and unorganized shape collections. Our hypothesis is that by using the large amounts of data inside such shape collections we can discover and model shape structure, and thus use such information to enable structure-aware tools for 3D modelling, including shape exploration, synthesis and editing. We make three key contributions. First, we propose an efficient algorithm for co-aligning large and diverse collections of shapes, to tackle the first challenge in detecting shape structure, which is to place shapes in a common coordinate frame. Then, we introduce a method to parameterize shapes in terms of locations and sizes of their parts, and we demonstrate its application to concurrently exploring a shape collection and synthesizing new shapes. Finally, we define a meta-representation for a shape family, which models the relations of shape parts to capture the main geometric characteristics of the family, and we demonstrate how it can be used to explore shape collections and intelligently edit shapes

Absorbing boundary conditions for the Westervelt equation

The focus of this work is on the construction of a family of nonlinear absorbing boundary conditions for the Westervelt equation in one and two space dimensions. The principal ingredient used in the design of such conditions is pseudo-differential calculus. This approach enables to develop high order boundary conditions in a consistent way which are typically more accurate than their low order analogs. Under the hypothesis of small initial data, we establish local well-posedness for the Westervelt equation with the absorbing boundary conditions. The performed numerical experiments illustrate the efficiency of the proposed boundary conditions for different regimes of wave propagation

ENDOCRINOLOGIC PROFILE OF OLIGOMENORRHEIC STRENUOUSLY EXERCISING ADOLESCENTS

The endocrinological profile of 20 strenuously exercising oligomenorrheic adolescents divided into 2 groups (groups A and B), was correlated with that of 10 athletes (group C) with normal menstrual cycles and without strenuous exercise. Group A LH serum baseline values were found to be statistically significantly lower than those of group C (P < 0.001). FSH/LH basic values were 1.9- and 2.9-times higher in group A athletes than those of group B or C (P < 0.05 and P < 0.001, respectively). 17-beta-estradiol (E2) and prolactin serum levels were found lower in group A and B athletes than those o group C (P < 0.01-0.05). Dehydroepiandrosterone sulfate and DELTA-4 androstenedione serum levels were found lower in group A athletes than those of group C (P < 0.001). The low LH and E2 values indicate the anovulatory status of group A and B cases which were also confirmed by ultrasound. It is concluded that no severe endocrinological changes exist in strenuously exercising oligomenorrheic athletes in relation to menarche

Effects of perfusion and vascular architecture on contrast dispersion: validation in ex-vivo porcine liver under machine perfusion

Dynamic contrast enhanced ultrasound (DCE-US) enables imaging of cancer angiogenesis by quantification of perfusion and dispersion. Although increased perfusion may be found in areas of active angiogenesis due to increased demands for blood supply, decreased perfusion may be caused by the decreased efficiency and functionality, typical of cancer angiogenic microvasculature. Contrast dispersion, mainly determined by the flow profile in large vessels and by the multipath trajectories in the microvasculature, may thus represent a suitable alternative to characterize cancer angiogenesis. Based on a model of the contrast transport kinetics as a convective-dispersion process, several DCE-US methods have been proposed estimating dispersion for characterization of cancer angiogenic vasculature. Although dispersion imaging has shown promising in a clinical context, its physical link with variations in flow and vascular architecture has never been shown. The objective of this work is thus to investigate the influence of flow and underlying vascular architecture on the estimation of dispersion in an ex-vivo machine-perfused pig liver