Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Non-native freshwater fish from drainages of Rio Grande do Sul State, Brazil

The present study catalogues exotic and allochthonous fish species found in the three main freshwater river drainages of Rio Grande do Sul State using records of scientific collections and literature, and discusses the main impacts caused by their introduction in natural environments. Ten exotic species are found in the area, i.e., Clarias gariepinus, Coptodon rendalli, Ctenopharyngodon idella, Cyprinus carpio, Hypophthalmichthys molitrix, Hypophthalmichthys nobilis, Ictalurus punctatus, Micropterus salmoides, Oncorhynchus mykiss and Oreochromis niloticus, belonging to five orders, nine genera and seven families. These fishes are native from African, Asian, European and North American countries. The eight allochthonous species, i.e., Acestrorhynchus pantaneiro, Hoplerythrinus unitaeniatus, Hoplias lacerdae, Megaleporinus macrocephalus, Piaractus mesopotamicus, Pachyurus bonariensis, Serrasalmus maculatus, and Trachelyopterus lucenai, belong to three orders, eight genera, and six families, are native from the Río La Plata basin, that includes the Río Uruguay, and have been all registered in the Laguna dos Patos. Two of these species are further recorded in the Rio Tramandaí system (A. pantaneiro and T. lucenai). The study also presentes a brief history of the first records of exotic species in the state and in the country, and their main vectors of introduction. According to the records of exotic species in scientific collections, the two exotic species with the highest number of records in the country are tilapias Coptodon rendalii (508 records) and Oreochromis niloticus (376 records), and most records occurred in the last two decades. The two carps Cyprinus carpio and Ctenopharyngodon idella are the only exotic species recorded in the three main drainage basins of the state. In addition, we warn about the importance of studies about the biology and negative impacts of exotic species over native species on the understanding of management in wild environments

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019), additional materials: http://www.byungsoo.me/project/deep-fluids

Information retrieval in institutional repositories using the summarization technique derived from the selection of Cassiopeia attributes/ Recuperação de informação em repositórios institucionais utilizando a técnica de sumarização a partir da seleção de atributos do Cassiopeia

The large volume of available text documents arising from the increase in scientific output creates a need for researching and implementing methods that facilitate information search and retrieval in academic text bases, such as institutional repositories. This study’s objective is thus to analyze whether the application of the summarization technique, based on the method of selecting attributes (words) of the Cassiopeia model (implemented in the PragmaSUM summarizer), in academic texts, is helpful for retrieving information by reducing information overload and improving the accuracy of user search results. The research was developed in steps: elaboration of the reference collection; implementation of a search engine; execution of standard information retrieval; evaluation of information retrieval using the precision metric; and data analysis from Friedman ANOVA and Kendall’s Coefficient of Concordance statistical tests. Results revealed that summarization, mainly performed with high compression rates (80% and 90%), reduced information overload and increased the accuracy of the results presented to the user, allowing quality information retrieval in academic texts. Furthermore, it simplified the indexing process, attenuated high dimensionality and promoted faster information retrieval

As manifestações brasileiras de junho de 2013 e suas implicações jurídico-políticas

Este artigo realiza um estudo das manifestações ocorridas no Brasil, em junho de 2013. O objetivo é explorar relações entre movimentos sociais e direitos, e consequentemente investigar implicações causadas pelas manifestações nos sistemas jurídico e político brasileiros. Parte-se da hipótese de que movimentos sociais, no contexto de complexidade da sociedade contemporânea, provocam irritações no ambiente do direito e da política, que processam ou não as expectativas dos manifestantes. Se não indiferentes, os sistemas podem reagir nas formas de conservação ou mudança. Para ilustrar essa hipótese, três eventos destacados das jornadas de junho são sequencialmente analisados: (i) demanda pelo direito de transporte; (ii) repressão ao direito de manifestação; (iii) discussão sobre projetos de alteração legislativa PEC n. 37/2011 e PDC n. 234/2011. Os resultados desta investigação sugerem que as manifestações, a princípio rejeitadas, produziram mudanças jurídicas e políticas, evidenciadas pela revogação do aumento nas tarifas do transporte e pelo arquivamento dos projetos legislativos.This research performs a study of the protests in June 2013 in Brazil. The aim is to explore the relations between social movements, law and politics and, consequently, investigate the possible implications caused by June 2013 protests in the Brazilian legal and political systems. In the context of complexity and contingency of contemporary society, the initial hypothesis is that social movements produce annoyance in the environment of functional systems, which process or not the protestors’ expectations. If not indifferent, the systems can react to claims through conservation (rejection) or change (acceptance). In order to empirically test the hypothesis raised, three socio-legal events highlighted during the protests in June are sequentially analyzed: (i) the demand for the right to public transportation; (ii) the repression to the right to protests and (iii) public discussion around the constitutional amendment projects (37/2011) and Legislative Decree (234/2011). The results of this investigation suggest that the demonstrations in June 2013, initially repressed by functional systems, were able to produce practical effects on law and politics, which were evidenced by the revocation of the increase in public transport fares and the archiving of projects able to change rules within the constitutional framework

Spatially Adaptive Cloth Regression with Implicit Neural Representations

The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics. The inherently non-uniform structure of cloth wrinkles mandates the employment of intricate discretization strategies, which are frequently characterized by high computational demands and complex methodologies. Addressing this, the research introduced in this paper elucidates a novel anisotropic cloth regression technique that capitalizes on the potential of implicit neural representations of surfaces. Our first core contribution is an innovative mesh-free sampling approach, crafted to reduce the reliance on traditional mesh structures, thereby offering greater flexibility and accuracy in capturing fine cloth details. Our second contribution is a novel adversarial training scheme, which is designed meticulously to strike a harmonious balance between the sampling and simulation objectives. The adversarial approach ensures that the wrinkles are represented with high fidelity, while also maintaining computational efficiency. Our results showcase through various cloth-object interaction scenarios that our method, given the same memory constraints, consistently surpasses traditional discrete representations, particularly when modelling highly-detailed localized wrinkles.Comment: 16 pages, 13 figure

Curl-Flow: Boundary-Respecting Pointwise Incompressible Velocity Interpolation for Grid-Based Fluids

We propose to augment standard grid-based fluid solvers with pointwise divergence-free velocity interpolation, thereby ensuring exact incompressibility down to the sub-cell level. Our method takes as input a discretely divergence-free velocity field generated by a staggered grid pressure projection, and first recovers a corresponding discrete vector potential. Instead of solving a costly vector Poisson problem for the potential, we develop a fast parallel sweeping strategy to find a candidate potential and apply a gauge transformation to enforce the Coulomb gauge condition and thereby make it numerically smooth. Interpolating this discrete potential generates a pointwise vector potential whose analytical curl is a pointwise incompressible velocity field. Our method further supports irregular solid geometry through the use of level set-based cut-cells and a novel Curl-Noise-inspired potential ramping procedure that simultaneously offers strictly non-penetrating velocities and incompressibility. Experimental comparisons demonstrate that the vector potential reconstruction procedure at the heart of our approach is consistently faster than prior such reconstruction schemes, especially those that solve vector Poisson problems. Moreover, in exchange for its modest extra cost, our overall Curl-Flow framework produces significantly improved particle trajectories that closely respect irregular obstacles, do not suffer from spurious sources or sinks, and yield superior particle distributions over time

Neural Smoke Stylization with Color Transfer

Artistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies workflows as it transfers the style of arbitrary input images onto 3D smoke simulations. However, the method only modifies the shape of the fluid but omits color information. In this work, we therefore extend the previous approach to obtain a complete pipeline for transferring shape and color information onto 2D and 3D smoke simulations with neural networks. Our results demonstrate that our method successfully transfers colored style features consistently in space and time to smoke data for different input textures.Comment: Submitted to Eurographics202