Patents as indicators of the technological position of countries on a global level?

Scientometrics - The technological capabilities of a country play a key role in identifying paths to economic growth and development. Policymakers have a special interest in understanding the...     

Persistence Analysis of Multi-scale Planar Structure Graph in Point Clouds

Modern acquisition techniques generate detailed point clouds that sample complex geometries. For instance, we are able to produce millimeter-scale acquisition of whole buildings. Processing and exploring geometrical information within such point clouds requires scalability, robustness to acquisition defects and the ability to model shapes at different scales. In this work, we propose a new representation that enriches point clouds with a multi-scale planar structure graph. We define the graph nodes as regions computed with planar segmentations at increasing scales and the graph edges connect regions that are similar across scales. Connected components of the graph define the planar structures present in the point cloud within a scale interval. For instance, with this information, any point is associated to one or several planar structures existing at different scales. We then use topological data analysis to filter the graph and provide the most prominent planar structures. Our representation naturally encodes a large range of information. We show how to efficiently extract geometrical details (e.g. tiles of a roof), arrangements of simple shapes (e.g. steps and mean ramp of a staircase), and large-scale planar proxies (e.g. walls of a building) and present several interactive tools to visualize, select and reconstruct planar primitives directly from raw point clouds. The effectiveness of our approach is demonstrated by an extensive evaluation on a variety of input data, as well as by comparing against state-of-the-art techniques and by showing applications to polygonal mesh reconstruction.     

Temporal relation algebra for audiovisual content analysis

Multimedia Tools and Applications - The context of this work is to characterize the content and the structure of audiovisual documents by analysing the temporal relationships between basic events...     

Induced continuous Choquet integral operators and their application to group decision making

With respect to multi-attribute group decision making, in this study two induced continuous Choquet integral operators named as the induced continuous Choquet weighted averaging (ICCWA) operator and the induced continuous Choquet geometric mean (ICCGM) operator are defined, which reflect the interactive characteristics between elements. Meantime, some associated desirable properties are studied to provide assurance in applications. In order to globally reflect the interactions between elements, we further define the probabilistic generalized semivalue ICCWA (PGS-ICCWA) operator and the probabilistic generalized semivalue ICCGM (PGS-ICCGM) operator. If the information about the weights of experts and attributes is incompletely known, the models for the optimal fuzzy measures on experts set and on attribute set based on consistency principle and TOPSIS method are respectively established. Moreover, an approach to uncertain multi-attribute group decision making with incomplete weight information and interactive conditions is developed. Finally, a numerical example is provided to illustrate the practicality and feasibility of the developed procedure.     

Experimenting with the partnership ability φ-index on a million computer scientists

Schubert introduced the partnership ability φ-index relying on a researcher’s number of co-authors and collaboration rate. As a Hirsch-type index, φ was expected to be consistent with Schubert–Glänzel’s model of h-index. Schubert demonstrated this relationship with the 34 awardees of the Hevesy medal in the field of nuclear and radiochemistry (r ² = 0.8484). In this paper, we upscale this study by testing the φ-index on a million researchers in computer science. We found that the Schubert–Glänzel’s model correlates with the million empirical φ values (r ² = 0.8695). In addition, machine learning through symbolic regression produces models whose accuracy does not exceed a 6.1 % gain (r ² = 0.9227). These results suggest that the Schubert–Glänzel’s model of φ-index is accurate and robust on the domain-wide bibliographic dataset of computer science.     

Decidability of Equivalence of Symbolic Derivations

We give in this paper an alternative, and we believe simpler, proof of a deep result by Mathieu Baudet, namely that the equivalence of symbolic constraints is decidable for deduction systems on a finite signature modulo a subterm convergent equational theory.     

Implicit Decals: Interactive Editing of Repetitive Patterns on Surfaces

Texture mapping is an essential component for creating 3D models and is widely used in both the game and the movie industries. Creating texture maps has always been a complex task and existing methods carefully balance flexibility with ease of use. One difficulty in using texturing is the repeated placement of individual textures over larger areas. In this paper, we propose a method which uses decals to place images onto a model. Our method allows the decals to compete for space and to deform as they are being pushed by other decals. A spherical field function is used to determine the position and the size of each decal and the deformation applied to fit the decals. The decals may span multiple objects with heterogeneous representations. Our method does not require an explicit parametrization of the model. As such, varieties of patterns, including repeated patterns like rocks, tiles and scales can be mapped. We have implemented the method using the GPU where placement, size and orientation of thousands of decals are manipulated in real time.