287 research outputs found
VoroCrust: Voronoi Meshing Without Clipping
Polyhedral meshes are increasingly becoming an attractive option with
particular advantages over traditional meshes for certain applications. What
has been missing is a robust polyhedral meshing algorithm that can handle broad
classes of domains exhibiting arbitrarily curved boundaries and sharp features.
In addition, the power of primal-dual mesh pairs, exemplified by
Voronoi-Delaunay meshes, has been recognized as an important ingredient in
numerous formulations. The VoroCrust algorithm is the first provably-correct
algorithm for conforming polyhedral Voronoi meshing for non-convex and
non-manifold domains with guarantees on the quality of both surface and volume
elements. A robust refinement process estimates a suitable sizing field that
enables the careful placement of Voronoi seeds across the surface circumventing
the need for clipping and avoiding its many drawbacks. The algorithm has the
flexibility of filling the interior by either structured or random samples,
while preserving all sharp features in the output mesh. We demonstrate the
capabilities of the algorithm on a variety of models and compare against
state-of-the-art polyhedral meshing methods based on clipped Voronoi cells
establishing the clear advantage of VoroCrust output.Comment: 18 pages (including appendix), 18 figures. Version without compressed
images available on https://www.dropbox.com/s/qc6sot1gaujundy/VoroCrust.pdf.
Supplemental materials available on
https://www.dropbox.com/s/6p72h1e2ivw6kj3/VoroCrust_supplemental_materials.pd
Deep Point Correlation Design
Designing point patterns with desired properties can require substantial
effort, both in hand-crafting coding and mathematical derivation. Retaining
these properties in multiple dimensions or for a substantial number of points
can be challenging and computationally expensive. Tackling those two issues,
we suggest to automatically generate scalable point patterns from design
goals using deep learning. We phrase pattern generation as a deep composition of weighted distance-based unstructured filters. Deep point pattern
design means to optimize over the space of all such compositions according to
a user-provided point correlation loss, a small program which measures a pattern’s fidelity in respect to its spatial or spectral statistics, linear or non-linear
(e. g., radial) projections, or any arbitrary combination thereof. Our analysis
shows that we can emulate a large set of existing patterns (blue, green, step,
projective, stair, etc.-noise), generalize them to countless new combinations
in a systematic way and leverage existing error estimation formulations to
generate novel point patterns for a user-provided class of integrand functions.
Our point patterns scale favorably to multiple dimensions and numbers of
points: we demonstrate nearly 10 k points in 10-D produced in one second
on one GPU. All the resources (source code and the pre-trained networks)
can be found at https://sampling.mpi-inf.mpg.de/deepsampling.html
DTI Economics Paper No. 2: A comparative study of the British and Italian Textile and Clothing Industries.
Commissioned by: Association of Suppliers to the British Clothing Industry Conference, Hucknell, Nottingham, February 2004
During the 1990s the Italian clothing and textiles industry grew while the British, French and German textile and clothing industries declined by 40%. In 2001 the Italian textiles & clothing sector was three times larger than the British, accounting for 11.7% of Italian manufacturing output but only 3.3% in Britain. In 2000 Italian fabric exports were 15 times that of the UK.
The study was conducted in response to a recommendation by the Textiles and Clothing Strategy Group (TCSG), comprising UK industry, trade unions, Higher Education and the DTI.
The purpose of the study was to account for these differences, assess relative merits against value for money and identify best practice in the Italian industry. The methodology comprised comparative analysis and case studies of British and Italian textile mills and tailoring manufacturers, based on my initial recommendations. We visited 5 textile mills in Yorkshire and 15 in Italy plus 3 factories in each country. I conducted a detailed comparative technical analysis of the construction of suit jackets against 13 devised criteria, a number of interviews,compared technologies, equipment and manufacturing methods across all factories, against 8 criteria, drawing on my specialist knowledge and experience as a menswear clothing technologist. The technical reports I compiled formed a section of the final report. Findings were presented to the Clothing Strategy Group and published by the DTI as their Economic Paper No 2 . I made further presentations to industry and academic groups including ASBCI, FCDE, The Textile Society, Savile Row Tailors Association, and LSE. Other outcomes were a publication in the Journal of the Textile Society Text, an article in Selvedge magazine and contributions to the Encyclopaedia of Clothing by Thomson Gale. As a result of this research further consultancy projects have been conducted with the Industry Forum and ASBCI
Um ambiente para desevonvoimento de algoritmos de amostragem e remoção de ruĂdo
In the context of Monte Carlo rendering, although many sampling and denoising techniques have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique has required selecting a particular rendering system, which makes the technique tightly coupled to the chosen renderer and limits the amount of scenes it can be tested on. In this work, we propose a renderer-agnostic framework for developing and benchmarking sampling and denoising techniques for Monte Carlo rendering. It decouples techniques from rendering systems by hiding the renderer details behind a general API. This improves productivity and allows for direct comparisons among techniques using scenes from different rendering systems. The proposed framework contains two main parts: a software development kit that helps users to develop and and test their techniques locally, and an online system that allows users to submit their techniques and have them automatically benchmarked on our servers. We demonstrate its effectiveness by using our API to instrument four rendering systems and a variety of Monte Carlo denoising techniques — including recent learning-based ones — and performing a benchmark across different rendering systems.No contexto de Monte Carlo rendering, apesar de diversas tĂ©cnicas de amostragem e remoção de ruĂdo tenham sido propostas nos Ăşltimos anos, aportar qual tĂ©cnica deve ser usada para uma cena especĂfica ainda Ă© uma tarefa difĂcil. AlĂ©m disso, desenvolver uma nova tĂ©cnica requer escolher um renderizador em particular, o que torna a tĂ©cnica dependente do renderizador escolhido e limita a quantidade de cenas disponĂveis para testar a tĂ©cnica. Neste trabalho, um framework para desenvolvimento e avaliação de tĂ©cnicas de amostragem e remoção de ruĂdo para Monte Carlo rendering Ă© proposto. Ele permite desacoplar as tĂ©cnicas dos renderizadores por meio de uma API genĂ©rica, promovendo a reprodutibilidade e permitindo comparações entre tĂ©cnicas utilizando-se cenas de diferentes renderizadores. O sistema proposto contĂ©m duas partes principais: um kit de desenvolvimento de software que ajuda os usuários a desenvolver e testar suas tĂ©cnicas localmente, e um sistema online que permite que usuários submetam tĂ©cnicas para que as mesmas sejam automaticamente avaliadas no nosso servidor. Para demonstramos a efetividade do ambiante proposto, modificamos quatro renderizadores e várias tĂ©cnicas de remoção de ruĂdo — incluindo tĂ©cnicas recentes baseadas em aprendizado de máquina — e efetuamos uma avaliação utilizando cenas de diferentes renderizadores
Proceedings of Mathsport international 2017 conference
Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017.
MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet.
Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports
Adaptive Sampling for Geometric Approximation
Geometric approximation of multi-dimensional data sets is an essential algorithmic component for applications in machine learning, computer graphics, and scientific computing. This dissertation promotes an algorithmic sampling methodology for a number of fundamental approximation problems in computational geometry. For each problem, the proposed sampling technique is carefully adapted to the geometry of the input data and the functions to be approximated. In particular, we study proximity queries in spaces of constant dimension and mesh generation in 3D.
We start with polytope membership queries, where query points are tested for inclusion in a convex polytope. Trading-off accuracy for efficiency, we tolerate one-sided errors for points within an epsilon-expansion of the polytope. We propose a sampling strategy for the placement of covering ellipsoids sensitive to the local shape of the polytope. The key insight is to realize the samples as Delone sets in the intrinsic Hilbert metric. Using this intrinsic formulation, we considerably simplify state-of-the-art techniques yielding an intuitive and optimal data structure.
Next, we study nearest-neighbor queries which retrieve the most similar data point to a given query point. To accommodate more general measures of similarity, we consider non-Euclidean distances including convex distance functions and Bregman divergences. Again, we tolerate multiplicative errors retrieving any point no farther than (1+epsilon) times the distance to the nearest neighbor. We propose a sampling strategy sensitive to the local distribution of points and the gradient of the distance functions. Combined with a careful regularization of the distance minimizers, we obtain a generalized data structure that essentially matches state-of-the-art results specific to the Euclidean distance.
Finally, we investigate the generation of Voronoi meshes, where a given domain is decomposed into Voronoi cells as desired for a number of important solvers in computational fluid dynamics. The challenge is to arrange the cells near the boundary to yield an accurate surface approximation without sacrificing quality. We propose a sampling algorithm for the placement of seeds to induce a boundary-conforming Voronoi mesh of the correct topology, with a careful treatment of sharp and non-manifold features. The proposed algorithm achieves significant quality improvements over state-of-the-art polyhedral meshing based on clipped Voronoi cells
Using The Old To Speak To The New: An Appropriative Studio Approach
This thesis is an A/R/Tographically-based investigation of my appropriative studio approach, resulting in a series of multi-media collage works entitled Tonight’s Programming, dealing with issues of militarism and commercialism in our everyday lives. Through research regarding appropriation in art history, examination of personal artistic influences, and regarding the work through the lenses of Artist, Researcher, and Teacher, I gained a deeper insight into not only my appropriative practices, but how these practices could be applied in the high school art classroom
FullExpression - Emotion Recognition Software
During human evolution emotion expression became an important social tool that contributed to the complexification of societies. Human-computer interaction is commonly present in our daily life, and the industry is struggling for solutions that can analyze human emotions, in an attempt to provide better experiences. The purpose of this study was to understand if a software built using the transfer-learning technique on a deep learning model was capable of classifying human emotions, through facial expression analysis. A Convolutional Neuronal Network model was trained and used in a web application, which is available online. Several tools were created to facilitate the software development process, including the training and validation processes, and these are also available online. The data was collected after the combination of several facial expression emotion databases, such as KDEF_AKDEF, TFEID, Face_Place and jaffe. Software evaluation reveled an accuracy in identifying the correct emotions close to 80%. In addition, a comparison between the software and preliminary data from human’s performance, on recognizing facial expressed emotions, suggested that the software performed better. This work can be useful in many different domains such as marketing (to understand the effect of marketing campaigns on people’s emotional states), health (to help mental diseases diagnosis) and industry 4.0 (to create a better collaborating environment between humans and machines).Durante a evolução da espĂ©cie humana, a expressões de emoções tornou-se uma ferramenta social importante, que permitiu a criação de sociedades cada vez mais complexas. A interação entre humanos e máquinas acontece regularmente, evidenciando a necessidade da indĂşstria desenvolver soluções que possam analisar emoções, de modo a proporcionar melhores experiĂŞncias aos utilizadores. O propĂłsito deste trabalho foi perceber se soluções de software desenvolvidas a partir da tĂ©cnica de transfer-learning sĂŁo capazes de classificar emoções humanas, a partir da análise de expressões faciais. Um modelo que implementa a arquitetura Convolutional Neuronal Network foi escolhido para ser treinado e utilizado na aplicação web desenvolvida neste trabalho, que está disponĂvel online. A par da aplicação web, diferentes ferramentas foram criadas de forma a facilitar o processo de criação e avaliação de modelos Deep Learning, e estas tambĂ©m estĂŁo disponĂveis online. Os dados foram recolhidos apĂłs a combinação de várias bases de dados de expressões de emoções (KDEF_AKDEF, TFEID, Face_Place and jaffe). A avaliação do software demostrou uma precisĂŁo na classificação de emoções prĂłxima dos 80%. Para alĂ©m disso, uma comparação entre o software e dados preliminares relativos ao reconhecimento de emoções por pessoas sugere que o software Ă© melhor a classificar emoções. Os resultados deste trabalho podem aplicados em diversas áreas, como a publicidade (de forma a perceber os efeitos das campanhas no estado emocional das pessoas), a saĂşde (para um melhor diagnĂłstico de doenças mentais) e na indĂşstria 4.0 (de forma a criar um melhor ambiente de colaboração entre humanos e máquinas)
Recommended from our members
Ultrasonic mid-air haptic technology in context of science communication
This dissertation charts opportunities and challenges of engaging people with learning about science through the use of mid-air haptic technology. To that end, research has been carried out at the intersection of three multidisciplinary fields: Science Communication, Haptics, and Human-Computer Interaction.
For science communication to be effective, different tools are required for different audiences. For example, when a child pours cold milk into hot tea and sips a warm beverage, we can raise awareness of the haptic experience; triggering interest and facilitating learning about thermal equilibrium. Not every scientific concept may be explained through changing temperature, and not everybody likes tea, but the principle of haptic experience facilitated public engagement with science remains a valid basis to examine.
Science communicators seek new technological solutions and innovative modalities of communication, some of which include haptic technology and touch interaction. Ultrasonic mid-air haptic technology is a novel tool, which enables the creation of programable, invisible, cutaneous tactile sensations on an airborne interface between humans and the digital world. Mid-air haptic sensations may bring many benefits when used in science communication, but these have not yet been systematically studied
- …