Can water level stations be used for thermal assessment in aquatic ecosystem?

Many studies focus on stream water temperature (WT) because it is considered a key ecological factor. However, few of them have investigated the use of WT data from water level monitoring networks, which often measure WT as ancillary data. Our study was conducted in southern Belgium at a high temporal resolution with continuous data recorded at intervals of 10 min between 2012 and 2016 and large spatial scale greater than 16,000 km². This study aimed to assess whether a regional water level network (140 stations) is reliable for continuous WT monitoring based on a Bland–Altman analysis with WT collected through a European monitoring network (Water Framework Directive). This study also investigates whether WT data acquired by water level stations can be used to perform both state‐of‐the‐art visualization of thermal regimes and spatio‐temporal queries for specific ecological monitoring. We found that the water level stations were reliable tools in recording continuous WT in the streams of the study area. The temperature difference between the two WT monitoring networks was ?0.57°C on average. Our positive results promote the use of WT from water level stations in order to globally characterize the thermal regime of streams as well as to provide spatial or temporal information on this regime at high frequencies. As an example, our data showed the effectiveness for brown trout (Salmo trutta fario L.) in spatializing thermal risk areas related to the thermal requirement of this fish species; in 2015, 19% of stations located in brown trout fish zone recorded temperatures above 25°C.     

Stability properties of the Discontinuous Galerkin Material Point Method for hyperbolic problems in one and two space dimensions

In this paper, stability conditions are derived for the Discontinuous Galerkin Material Point Method (DGMPM) on the scalar linear advection equation for the sake of simplicity and without loss of generality for linear problems. The discrete systems resulting from the application of the DGMPM discretization in one and two space dimensions are first written. For these problems, a second-order Runge-Kutta and the forward Euler time discretizations are respectively considered. Moreover, the numerical fluxes are computed at cell faces by means of either the Donor-Cell Upwind or the Corner Transport Upwind methods for multidimensional problems. Second, the discrete scheme equations are derived assuming that all cells of a background grid contain at least one particle. Although a Cartesian grid is considered in two space dimensions, the results can be extended to regular grids. The von Neumann linear stability analysis then allows the computation of the critical Courant number for a given space discretization. Although the DGMPM is equivalent to the first-order finite volume method if one particle lies in each element, so that the Courant number can be set to unity, other distributions of particles may restrict the stability region of the scheme. The study of several configurations is then proposed.     

Influence of lignin's pH on polyurethane flexible foam formation and how to control it

Low pH of commercial lignins has a catastrophic impact on the polyurethane foaming reactions. Experiments were performed with 10 wt% of lignin with various pHs in polyols. Virgin lignin (pH 2.5, 35% moisture) has the most negative impact as it reduces the initial foam rising rate by 85% and the foam's final height by 35% as compared to the reference foam, lignin free. Drying of this lignin at 80°C for 12 h can reduce this impact while alkaline treatment to bring the lignin's pH to 6.6 almost cancel it. As revealed by in situ dielectric constant measurements, both reactions, gelling via polymerization and blowing via CO₂ degassing, are impacted. In situ Fourier transform infrared analysis of the foaming process demonstrated that blowing reaction is the most pH sensitive. Two methods to counter the pH influence by pH modification were tested and provide interesting results but also significant drawbacks limiting their applicability.     

Stratified Markov Chain Monte Carlo Light Transport

Markov chain Monte Carlo (MCMC) sampling is a powerful approach to generate samples from an arbitrary distribution. The application to light transport simulation allows us to efficiently handle complex light transport such as highly occluded scenes. Since light transport paths in MCMC methods are sampled according to the path contributions over the sampling domain covering the whole image, bright pixels receive more samples than dark pixels to represent differences in the brightness. This variation in the number of samples per pixel is a fundamental property of MCMC methods. This property often leads to uneven convergence over the image, which is a notorious and fundamental issue of any MCMC method to date. We present a novel stratification method of MCMC light transport methods. Our stratification method, for the first time, breaks the fundamental limitation that the number of samples per pixel is uncontrollable. Our method guarantees that every pixel receives a specified number of samples by running a single Markov chain per pixel. We rely on the fact that different MCMC processes should converge to the same result when the sampling domain and the integrand are the same. We thus subdivide an image into multiple overlapping tiles associated with each pixel, run an independent MCMC process in each of them, and then align all of the tiles such that overlapping regions match. This can be formulated as an optimization problem similar to the reconstruction step for gradient-domain rendering. Further, our method can exploit the coherency of integrands among neighboring pixels via coherent Markov chains and replica exchange. Images rendered with our method exhibit much more predictable convergence compared to existing MCMC methods.     

Segment Tracing Using Local Lipschitz Bounds

We introduce Segment Tracing, a new algorithm that accelerates the classical Sphere Tracing method for computing the intersection between a ray and an implicit surface. Our approach consists in computing the Lipschitz bound locally over a segment to improve the marching step computation and accelerate the overall process. We describe the computation of the Lipschitz bound for different operators and primitives. We demonstrate that our algorithm significantly reduces the number of field function queries compared to previous methods, without the need for additional accelerating data-structures. Our method can be applied to a vast variety of implicit models ranging from hierarchical procedural objects built from complex primitives, to simulation-generated implicit surfaces created from many particles.     

A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data

The amount of captured 3D data is continuously increasing, with the democratization of consumer depth cameras, the development of modern multi‐view stereo capture setups and the rise of single‐view 3D capture based on machine learning. The analysis and representation of this ever growing volume of 3D data, often corrupted with acquisition noise and reconstruction artefacts, is a serious challenge at the frontier between computer graphics and computer vision. To that end, segmentation and optimization are crucial analysis components of the shape abstraction process, which can themselves be greatly simplified when performed on lightened geometric formats. In this survey, we review the algorithms which extract simple geometric primitives from raw dense 3D data. After giving an introduction to these techniques, from the acquisition modality to the underlying theoretical concepts, we propose an application‐oriented characterization, designed to help select an appropriate method based on one's application needs and compare recent approaches. We conclude by giving hints for how to evaluate these methods and a set of research challenges to be explored.     

A Survey on Gradient‐Domain Rendering

Monte Carlo methods for physically‐based light transport simulation are broadly adopted in the feature film production, animation and visual effects industries. These methods, however, often result in noisy images and have slow convergence. As such, improving the convergence of Monte Carlo rendering remains an important open problem. Gradient‐domain light transport is a recent family of techniques that can accelerate Monte Carlo rendering by up to an order of magnitude, leveraging a gradient‐based estimation and a reformulation of the rendering problem as an image reconstruction. This state of the art report comprehensively frames the fundamentals of gradient‐domain rendering, as well as the pragmatic details behind practical gradient‐domain uniand bidirectional path tracing and photon density estimation algorithms. Moreover, we discuss the various image reconstruction schemes that are crucial to accurate and stable gradient‐domain rendering. Finally, we benchmark various gradient‐domain techniques against the state‐of‐the‐art in denoising methods before discussing open problems.     

Procedural Modeling of a Building from a Single Image

Creating a virtual city is demanded for computer games, movies, and urban planning, but it takes a lot of time to create numerous 3D building models. Procedural modeling has become popular in recent years to overcome this issue, but creating a grammar to get a desired output is difficult and time consuming even for expert users. In this paper, we present an interactive tool that allows users to automatically generate such a grammar from a single image of a building. The user selects a photograph and highlights the silhouette of the target building as input to our method. Our pipeline automatically generates the building components, from large‐scale building mass to fine‐scale windows and doors geometry. Each stage of our pipeline combines convolutional neural networks (CNNs) and optimization to select and parameterize procedural grammars that reproduce the building elements of the picture. In the first stage, our method jointly estimates camera parameters and building mass shape. Once known, the building mass enables the rectification of the façades, which are given as input to the second stage that recovers the façade layout. This layout allows us to extract individual windows and doors that are subsequently fed to the last stage of the pipeline that selects procedural grammars for windows and doors. Finally, the grammars are combined to generate a complete procedural building as output. We devise a common methodology to make each stage of this pipeline tractable. This methodology consists in simplifying the input image to match the visual appearance of synthetic training data, and in using optimization to refine the parameters estimated by CNNs. We used our method to generate a variety of procedural models of buildings from existing photographs.     

Contextual text/non-text stroke classification in online handwritten notes with conditional random fields

Analysing online handwritten notes is a challenging problem because of the content heterogeneity and the lack of prior knowledge, as users are free to compose documents that mix text, drawings, tables or diagrams. The task of separating text from non-text strokes is of crucial importance towards automated interpretation and indexing of these documents, but solving this problem requires a careful modelling of contextual information, such as the spatial and temporal relationships between strokes. In this work, we present a comprehensive study of contextual information modelling for text/non-text stroke classification in online handwritten documents. Formulating the problem with a conditional random field permits to integrate and combine multiple sources of context, such as several types of spatial and temporal interactions. Experimental results on a publicly available database of freely hand-drawn documents demonstrate the superiority of our approach and the benefit of contextual information combination for solving text/non-text classification.