On the Construction of Regional IO Tables

Regional policy makers need regional economical data in order to define and backup their economical policy decisions. Regional input-output (IO) tables have proved to be useful in the policy making process, since the economic effects of policy decisions can be analysed in these models for the region as a whole. Unfortunately, the construction of regional IO tables on the basis of survey methods and other primary data collection methods is very costly and often incomplete. In this paper, we will discuss two techniques which can be applied to derive regional IO tables from national IO tables. In both methods, sectoral production specialization at the regional level is accounted for and affects the interindustrial structure of the region. The IO tables are constructed for 29 industrial sectors and 12 regions in the Netherlands. Policy makers, however, are not interested in the construction of regional IO tables themselves, but more in the economic indicators derived from them. Therefore, we present simple output- and employment-multipliers and employment-transformators derived from the IO tables and discuss some of the differences between them. A description of the economic performance of the Dutch regions is made by looking at the development of the economic indicators over a period of 12 years (1980-1992).Economics ;

Catching Up At The Regional Level

A convenient way to find out how things are going is by taking a look at your neighbours. In this paper we develop a method for the evaluation of regional economic performance based on an input-output (IO) framework. Once we defined economic criteria for measuring this performance, such as real GDP per worker or employment per output unit, we pick out the ''best'' performing region per sector. Taken together, they describe an optimal regional industrial structure for all sectors, a so called ''optimal'' input coefficients table. On the basis of this table, we will investigate the causes of regional convergence. Furthermore, this table will be used as a point of reference for economic policy makers at the regional level. Structural deviations from the ''optimal'' industrial structure may be reasons for policy action, so that the industrial structure can be evaluated in a normative way. In this paper, we investigate those deviations for 11 regions and 29 sectors in the Netherlands for the 1980-1992 period. The central focus is on the question how regional policy makers can improve regional economic performance by adjusting the regional industrial structure.Economics ;

Differentiable Surface Triangulation

Triangle meshes remain the most popular data representation for surface geometry. This ubiquitous representation is essentially a hybrid one that decouples continuous vertex locations from the discrete topological triangulation. Unfortunately, the combinatorial nature of the triangulation prevents taking derivatives over the space of possible meshings of any given surface. As a result, to date, mesh processing and optimization techniques have been unable to truly take advantage of modular gradient descent components of modern optimization frameworks. In this work, we present a differentiable surface triangulation that enables optimization for any per-vertex or per-face differentiable objective function over the space of underlying surface triangulations. Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation. We translate this result into a computational algorithm by proposing a soft relaxation of the classical weighted Delaunay triangulation and optimizing over vertex weights and vertex locations. We extend the algorithm to 3D by decomposing shapes into developable sets and differentiably meshing each set with suitable boundary constraints. We demonstrate the efficacy of our method on various planar and surface meshes on a range of difficult-to-optimize objective functions. Our code can be found online: https://github.com/mrakotosaon/diff-surface-triangulation

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local surface fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data-driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely-sampled point clouds. In our extensive evaluation, both on synthesic and real data, we show an increased robustness to strong noise levels compared to various state-of-the-art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline

Voronoi-Based Curvature and Feature Estimation from Point Clouds

International audienceWe present an efficient and robust method for extracting curvature information, sharp features, and normal directions of a piecewise smooth surface from its point cloud sampling in a unified framework. Our method is integral in nature and uses convolved covariance matrices of Voronoi cells of the point cloud which makes it provably robust in the presence of noise. We show that these matrices contain information related to curvature in the smooth parts of the surface, and information about the directions and angles of sharp edges around the features of a piecewise-smooth surface. Our method is applicable in both two and three dimensions, and can be easily parallelized, making it possible to process arbitrarily large point clouds, which was a challenge for Voronoi-based methods. In addition, we describe a Monte-Carlo version of our method, which is applicable in any dimension. We illustrate the correctness of both principal curvature information and feature extraction in the presence of varying levels of noise and sampling density on a variety of models. As a sample application, we use our feature detection method to segment point cloud samplings of piecewise-smooth surfaces

Optimal face-to-face coupling for fast self-folding kirigami

Kirigami-inspired designs can enable self-folding three-dimensional materials from flat, two-dimensional sheets. Hierarchical designs of connected levels increase the diversity of possible target structures, yet they can lead to longer folding times in the presence of fluctuations. Here, we study the effect of rotational coupling between levels on the self-folding of two-level kirigami designs driven by thermal noise in a fluid. Naturally present due to hydrodynamic resistance, we find that optimization of this coupling as control parameter can significantly improve a structure's self-folding rate and yield