Generative models based on eigendecomposition for dense ray tracing.

In this work, we present an algorithm capable of emulating ray trajectories that obey the least action principle. The method is based on spectral decomposition of geometric shapes taken from a set of raypaths. As the proposed work relies on shape analysis, it is agnostic on the underlying physics of raypath generation. As such, it is independent of the ray tracing method used to generate the training paths. In cases of mildly heterogeneous media or scenarios with a limited number of geometrical scatters, we show that the algorithm is capable of efficiently populating a given scenario with a dense array of emulated rays whose trajectories are in close agreement with actual rays. We argue that the algorithm also serves as an effective method capable of detecting regions where ray variation is high, such as when possible shadow zones are present.

On ray tracing for sharp changing media.

Ray tracing is an integral part of modern imaging and inversion techniques. Several methods have been proposed depending on the requirements of the application. Some algorithms are best applied to fast changing material properties, like an interface between two differing media, while others are well suited to media with gradually changing properties, like composite materials. In this paper, an enhanced numerical algorithm for ray tracing is presented. Focus is given to solutions involving ordinary differential equations with initial-value conditions. The proposed algorithm is the result of a combination of two classical implementations and the authors show that it is well suited for media with both sharp and gradual changes in the index of refraction. Additionally, the authors present an application of ray path computation by using the technique known as the shooting method.

Vortex-like vs. turbulent mixing of a Viscum album preparation affects crystalline structures formed in dried droplets.

Various types of motion introduced into a solution can affect, among other factors, the alignment and positioning of molecules, the agglomeration of large molecules, oxidation processes, and the production of microparticles and microbubbles. We employed turbulent mixing vs. laminar flow induced by a vortex vs. diffusion-based mixing during the production of Viscum album Quercus L. 10-3 following the guidelines for manufacturing homeopathic preparations. The differently mixed preparation variants were analyzed using the droplet evaporation method. The crystalline structures formed in dried droplets were photographed and analyzed using computer-supported image analysis and deep learning. Computer-supported evaluation and deep learning revealed that the patterns of the variant succussed under turbulence are characterized by lower complexity, whereas those obtained from the vortex-mixed variant are characterized by greater complexity compared to the diffusion-based mixed control variant. The droplet evaporation method could provide a relatively inexpensive means of testing the effects of liquid flow and serve as an alternative to currently used methods.

On reliability of annotations in contextual emotion imagery.

We documented the relabeling process for a subset of a renowned database for emotion-in-context recognition, with the aim of promoting reliability in final labels. To this end, emotion categories were organized into eight groups, while a large number of participants was requested for tagging. A strict control strategy was performed along the experiments, whose duration was 13.45 minutes average per day. Annotators were free to participate in any of the daily experiments (the average number of participants was 28), and a Z-Score filtering technique was implemented to keep trustworthiness of annotations. As a result, the value of the agreement parameter Fleiss' Kapa increasingly varied from slight to almost perfect, revealing a coherent diversity of the experiments. Our results support the hypothesis that a small number of categories and a large number of voters benefit reliability of annotations in contextual emotion imagery.

Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts.

This paper introduces a deep learning based methodology for analyzing the self-assembled, fractal-like structures formed in evaporated droplets. To this end, an extensive image database of such structures of the plant extract Viscum album Quercus [Formula: see text] was used, prepared by three different mixing procedures (turbulent, laminar, and diffusion based). The proposed pattern analysis approach is based on two stages: (1) automatic selection of patches that exhibit rich texture along the database; and (2) clustering of patches in accordance with prevalent texture by means of a Dense Convolutional Neural Network. The fractality of the patterns in each cluster is verified through Local Connected Fractal Dimension histograms. Experiments with Gray-Level Co-Occurrence matrices are performed to determine the benefit of the proposed approach in comparison with well established image analysis techniques. For the investigated plant extract, significant differences were found between the production modalities; whereas the patterns obtained by laminar flow showed the highest fractal structure, the patterns obtained by the application of turbulent mixture exhibited the lowest fractality. Our approach is the first to analyze, at the pure image level, the clustering properties of regions of interest within a database of evaporated droplets. This allows a greater description and differentiation of the patterns formed through different mixing procedures.

A coupled statistical model for face shape recovery from brightness images.

We focus on the problem of developing a coupled statistical model that can be used to recover facial shape from brightness images of faces. We study three alternative representations for facial shape. These are the surface height function, the surface gradient, and a Fourier basis representation. We jointly capture variations in intensity and the surface shape representations using a coupled statistical model. The model is constructed by performing principal components analysis on sets of parameters describing the contents of the intensity images and the facial shape representations. By fitting the coupled model to intensity data, facial shape is implicitly recovered from the shape parameters. Experiments show that the coupled model is able to generate accurate shape from out-of-training-sample intensity images.

Formation of Self-organized Structures in Drying Droplets: a Promising Analytical Tool for Homeopathy Basic Research

The droplet evaporation method (DEM) is based on the evaporation-induced pattern formation in droplets and is applied mainly for medical diagnosis[1].Here, we present aseries of experiments performed by our team showing DEMs potential also forhomeopathy basic research, in particular, for the investigation of(i) low potencies, (ii) low potency complexes (physical model), and (iii) the action of high potencies (plant-based model).Methods:(i) DEM differentiated significantly between Luffa, Baptisia, Echinacea, and Spongiauntil 4x[2]. Furthermore, the patterns varied in function of the numberof succussion strokes (0, 10, or 100) applied during potentization[3]. The performance of chaotic succussions vs. laminar flow vs. slight mixing during the potentization of Viscum album quercus3x influenced the DEM patterns; the chaotic succussions reduced, whereas laminar flow enhanced the patterns complexity vs. the unsuccussed control.(ii) The addition of Mercurius bijodatus9x to Luffa4x changed significantly the DEM patterns, even if the material quantity present in the 9x potency lied far beyond that of ultrapure water.(iii) Leakages obtained by placing healthy or arsenic-damaged wheat-seeds into Arsenicum album45x orheat-damaged intoZincum metallicum30c vs. water created significantly different DEM structures [4, 5]. Results:The damaged seeds put into the potency created structures characterized by a higher complexity than those obtained from damaged seeds put into control water. Furthermore, the potency action seemed to increase with rising numbers ofsuccussion strokes applied during potentization,ascould be shown by means of DEM patterns and germination rate using the same wheat-seed model[6].In all our studies, the pattern evaluation was computerized (texture and fractal analysis performed by means of ImageJ) or based on deep-learning algorithms and the robustness of the experimental system was checked by means of systematic control experiments.Conclusion:DEM together with other similarmethods has also been reviewed by our team for what concerns theapplication in homeopathy basic research[7].