A Herbivore Tag-and-Trace System Reveals Contact- and Density-Dependent Repellence of a Root Toxin

Foraging behavior of root feeding organisms strongly affects plant-environment-interactions and ecosystem processes. However, the impact of plant chemistry on root herbivore movement in the soil is poorly understood. Here, we apply a simple technique to trace the movement of soil-dwelling insects in their habitats without disturbing or restricting their interactions with host plants. We tagged the root feeding larvae of Melolontha melolontha with a copper ring and repeatedly located their position in relation to their preferred host plant, Taraxacum officinale, using a commercial metal detector. This method was validated and used to study the influence of the sesquiterpene lactone taraxinic acid β-D-glucopyranosyl ester (TA-G) on the foraging of M. melolontha. TA-G is stored in the latex of T. officinale and protects the roots from herbivory. Using behavioral arenas with TA-G deficient and control plants, we tested the impact of physical root access and plant distance on the effect of TA-G on M. melolontha. The larvae preferred TA-G deficient plants to control plants, but only when physical root contact was possible and the plants were separated by 5 cm. Melolontha melolontha showed no preference for TA-G deficient plants when the plants were grown 15 cm apart, which may indicate a trade-off between the cost of movement and the benefit of consuming less toxic food. We demonstrate that M. melolontha integrates host plant quality and distance into its foraging patterns and suggest that plant chemistry affects root herbivore behavior in a plant-density dependent manner.     

Electrical characterization of polyaniline‐based adhesive blends

Preparation and processing of conductive blends based on doped polyaniline (c‐PANI) or tetra‐aniline (c‐TANI) with epoxy resins is described. The dedoping of c‐PANI by the epoxy hardener, in the process of the blend curing, has been investigated by UV–vis–NIR spectroscopy. Classical amine hardeners lead to a quick increase of the blend resistivity during its processing, which can be correlated with the observed spectral features, characteristic of the deprotonation of c‐PANI. For these reasons, for further investigations, BF₃‐amine complexes have been selected as curing agents. Using these hardeners and tuning the curing conditions (temperature and time), it is possible to obtain blends with resistivities down to 10² Ω·cm, depending on the type of the epoxy resin used. In general, resins with higher epoxy network densities give c‐PANI‐based blends of lower percolation thresholds. The effect of the c‐PANI processing solvent on the resistivity of the resulting blend is even more pronounced than the epoxy network density. In particular, blends processed from toluene show much higher resistivities than those processed from tetrahydrofuran (THF) of ethylacetate (EA). Above the percolation threshold, c‐TANI‐based epoxy blends show at least three orders of magnitude higher resistivities than their c‐PANI analogues. They are however technologically interesting, because they are not very sensitive to the processing/curing conditions and show lower percolation thresholds. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Freeze-substitution of plant tissues with a new medium containing dimethoxypropane*

Meristematic cells from root tips of Dahlia variabilis (L.) and highly vacuolated storage parenchyma cells of Helianthus tuberosus (L.) were fixed by the high-pressure freezing technique and freeze-substituted. A new water-free medium for freeze-substitution was developed to obtain better preservation of the ultrastructure of plant tissues. Adding dimethoxypropane (DMP) to the substitution medium gave two positive results: (1) water could be withdrawn from the samples without an exchange of the medium and without adding drying agents or working in a nitrogen atmosphere, and (2) the ultrastructural preservation of the substituted tissues was better than in a methanol medium and at least as good as in tissues fixed chemically at room temperature.     

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.     

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.