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There are many multiple-stage racing competitions in various sports such as swimming, running, or cycling. The wide availability of affordable tracking devices facilitates monitoring the position along with the race of all participants, even for non-professional contests. Getting real-time information of contenders is useful but also unleashes the possibility of creating more complex visualization systems that ease the understanding of the behavior of all participants during a simple stage or throughout the whole competition. In this paper we focus on bicycle races, which are highly popular, especially in Europe, being the Tour de France its greatest exponent. Current visualizations from TV broadcasting or real-time tracking websites are useful to understand the current stage status, up to a certain extent. Unfortunately, still no current system exists that visualizes a whole multi-stage contest in such a way that users can interactively explore the relevant events of a single stage (e.g. breakaways, groups, virtual leadership...), as well as the full competition. In this paper, we present an interactive system that is useful both for aficionados and professionals to visually analyze the development of multi-stage cycling competitions. This work has been supported in part by project TIN2017-88515-C2-1-R(GEN3DLIVE), from the Spanish Ministerio de Economía y Competitividad, by 839 FEDER (EU) funds and by the Grant No. PID2019-106426RB-C31 / AEI / 10.13039/501100011033 from the Spanish Government.

Computerized Tomography (CT) and, more recently, Magnetic Resonance Imaging (MRI) have become the state-of-the art techniques for morpho-volumetric analysis of abdominal cavities. Due to its constant motility, the colon is an organ difficult to analyze. Unfortunately, CT’s radiative nature makes it only indicated for patients with important disorders. Lately, acquisition techniques that rely on the use of MRI have matured enough to enable the analysis of colon data. This allows gathering data of patients with- out preparation (i.e. administration of drugs or contrast agents), and incorporating data of patients with non life-threatening diseases and healthy subjects to databases. In this paper we present an end-to-end framework that comprises all the steps to extract colon content and morphology data coupled with a web-based visualization tool that facilitates the visual exploration of such data. We also introduce the set of tools for the extraction of morphological data, and a detailed description of a specifically-designed interactive tool that facilitates a visual comparison of numerical variables within a set of patients, as well as a detailed inspection of an individual. Our prototype was evaluated by domain experts, which showed that our visual approach may reduce the costly process of colon data analysis. As a result, physicians have been able to get new insights on the effects of diets, and also to obtain a better understanding on the motility of the colon. This project has been supported by the project TIN2017-88515-C2-1-R (GEN3DLIVE), from the Spanish Ministerio de Economía y Competitividad with FEDER (EU) funds.

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under grant TIN2017-88515-C2-1-R.

Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpointqualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack ofclosed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to sepa-rate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the in¿uence of the meshquality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approachinsensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise inthis context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the labeldecision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint pre-dictions for models from different object categories and for different viewpoint qualities. Additionally, we show that predictiontimes are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality eval-uation. Code and training data is available at https://github.com/schellmi42/viewpoint_learning, whichis to our knowledge the biggest viewpoint quality dataset available. This work was supported in part by project TIN2017-88515-C2-1-R(GEN3DLIVE), from the Spanish Ministerio de Economía yCompetitividad, by 839 FEDER (EU) funds.

Path finding for autonomous agents has been traditionally driven by finding optimal paths, typically by using A* search or any of its variants. When it comes to simulating virtual humanoids, traditional approaches rarely consider aspects of human memory or orientation. In this work, we propose a new path finding algorithm, inspired by current research regarding how the brain learns and builds cognitive maps. Our method represents the space as a hexagonal grid with counters, based on brain research that has investigated how memory cells are fired. Our path finder then combines a method for exploring unknown environments while building such a cognitive map, with an A* search using a modified heuristic that takes into account the cognitive map. The resulting paths show how as the agent learns the environment, the paths become shorter and more consistent with the optimal A* search. Moreover, we run a perceptual study to demonstrate that the viewers could successfully identify the intended level of knowledge of the simulated agents. This line of research could enhance the believability of autonomous agents’ path finding in video games and other VR applications. This work was partly funded by the Spanish Ministry of Economy, Industry and Competitiveness under Grant No. TIN2017- 88515-C2-1-R.

Virtual Reality (VR) is being integrated into many different areas of our lives, from industrial engineering to video-games, and also including teaching and education. We have several examples where VR has been used to engage students and facilitate their 3D spatial understanding, but can VR help also teachers? What are the benefits teachers can obtain on using VR applications? In this paper we present an application (VR4Health) designed to allow students to directly inspect 3D models of several human organs by using Virtual Reality systems. The application is designed to be used in an HMD device autonomously as a self-learning tool and also reports information to teachers in order that he/she becomes aware of what the students do and can redirect his/her work to the concrete necessities of the student. We evaluate both the students’ and the teachers’ perception by doing an experiment and asking them to fill-in a questionnaire at the end of the experiment. This study was partially funded by the Spanish Ministry of Science and Innovation (grant number TIN2017-88515-C2-1-R).

Though variable selection is one of the most relevant tasks in microbiome analysis, e.g. for the identification of microbial signatures, many studies still rely on methods that ignore the compositional nature of microbiome data. The applicability of compositional data analysis methods has been hampered by the availability of software and the difficulty in interpreting their results. This work is focused on three methods for variable selection that acknowledge the compositional structure of microbiome data: selbal, a forward selection approach for the identification of compositional balances, and clr-lasso and coda-lasso, two penalized regression models for compositional data analysis. This study highlights the link between these methods and brings out some limitations of the centered log-ratio transformation for variable selection. In particular, the fact that it is not subcompositionally consistent makes the microbial signatures obtained from clr-lasso not readily transferable. Coda-lasso is computationally efficient and suitable when the focus is the identification of the most associated microbial taxa. Selbal stands out when the goal is to obtain a parsimonious model with optimal prediction performance, but it is computationally greedy. We provide a reproducible vignette for the application of these methods that will enable researchers to fully leverage their potential in microbiome studies.

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Modern computer graphics courses require students to complete assignments involving computer programming. The evaluation of student programs, either by the student (self-assessment) or by the instructors (grading) can take a considerable amount of time and does not scale well with large groups. Interactive judges giving a pass/fail verdict do constitute a scalable solution, but they only provide feedback on output correctness. In this article, we present a tool to provide extensive feedback on student submissions. The feedback is based both on checking the output against test sets, as well as on syntactic and semantic analysis of the code. These analyses are performed through a set of code features and instructor-defined rubrics. The tool is built with Python and supports shader programs written in GLSL. Our experiments demonstrate that the tool provides extensive feedback that can be useful to support self-assessment, facilitate grading, and identify frequent programming mistakes. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER Grant TIN2017-88515-C2-1-R.

We present an automatic animatable 3D character reconstruction method from frontal and lateral RGB pictures of the person to reconstruct. We propose a method where, after obtaining a 3D reconstruction from a frontal view, we adapt the template of the Skinned Multi-Person Linear Model (SMPL) to it. This frontal modified SMPL is then used in a second step as an input to reconstruct the character from the lateral view, thus obtaining a correct person reconstruction in all dimensions. Our method enables the creation of 3D animatable characters focusing on two main aspects: shape and texture. The obtained results show that the proposed method achieves an accurate automatic 3D character reconstruction from only two views that enhances current methods while offering a reproducible work-flow. This work is funded by the European Research Council (ERC) Advanced Grant Moments in Time in Immersive Virtual Environments (MoTIVE) number 742989.

The study of the colonic volume is a procedure with strong relevance to gastroenterologists. Depending on the clinical protocols, the volume analysis has to be performed on MRI of the unprepared colon without contrast administration. In such circumstances, existing measurement procedures are cumbersome and time-consuming for the specialists. The algorithm presented in this paper permits a quasi-automatic segmentation of the unprepared colon on T2-weighted MRI scans. The segmentation algorithm is organized as a three-stage pipeline. In the first stage, a custom tubularity filter is run to detect colon candidate areas. The specialists provide a list of points along the colon trajectory, which are combined with tubularity information to calculate an estimation of the colon medial path. In the second stage, we delimit the region of interest by applying custom segmentation algorithms to detect colon neighboring regions and the fat capsule containing abdominal organs. Finally, within the reduced search space, segmentation is performed via 3D graph-cuts in a three-stage multigrid approach. Our algorithm was tested on MRI abdominal scans, including different acquisition resolutions, and its results were compared to the colon ground truth segmentations provided by the specialists. The experiments proved the accuracy, efficiency, and usability of the algorithm, while the variability of the scan resolutions contributed to demonstrate the computational scalability of the multigrid architecture. The system is fully applicable to the colon measurement clinical routine, being a substantial step towards a fully automated segmentation.

The popularization of inexpensive 3D scanning, 3D printing, 3D publishing and AR/VR display technologies have renewed the interest in open-source tools providing the geometry processing algorithms required to clean, repair, enrich, optimize and modify point-based and polygonal-based models. Nowadays, there is a large variety of such open-source tools whose user community includes 3D experts but also 3D enthusiasts and professionals from other disciplines. In this paper we present a Python-based tool that addresses two major caveats of current solutions: the lack of easy-to-use methods for the creation of custom geometry processing pipelines (automation), and the lack of a suitable visual interface for quickly testing, comparing and sharing different pipelines, supporting rapid iterations and providing dynamic feedback to the user (demonstration). From the user's point of view, the tool is a 3D viewer with an integrated Python console from which internal or external Python code can be executed. We provide an easy-to-use but powerful API for element selection and geometry processing. Key algorithms are provided by a high-level C library exposed to the viewer via Python-C bindings. Unlike competing open-source alternatives, our tool has a minimal learning curve and typical pipelines can be written in a few lines of Python code.

Background Some patients complain that eating lettuce, gives them gas and abdominal distention. Our aim was to determine to what extent the patients' assertion is sustained by evidence. Methods An in vitro study measured the amount of gas produced during the process of fermentation by a preparation of human colonic microbiota (n = 3) of predigested lettuce, as compared to beans, a high gas‐releasing substrate, to meat, a low gas‐releasing substrate, and to a nutrient‐free negative control. A clinical study in patients complaining of abdominal distention after eating lettuce (n = 12) measured the amount of intestinal gas and the morphometric configuration of the abdominal cavity in abdominal CT scans during an episode of lettuce‐induced distension as compared to basal conditions. Key Results Gas production by microbiota fermentation of lettuce in vitro was similar to that of meat (P = .44), lower than that of beans (by 78 ± 15%; P, In some patients lettuce produces abdominal distension, which is not related to gas, but rather to an uncontrolled reaction of the abdominal muscles and the diaphragm. Hence, learning to control the abdominal muscles may prevent distension with no need of dietary restrictions.

We present an end-to-end learned system for fusing multiple misaligned photographs of the same scene into a chosen target view. We demonstrate three use cases: 1) color transfer for inferring color for a monochrome view, 2) HDR fusion for merging misaligned bracketed exposures, and 3) detail transfer for reprojecting a high definition image to the point of view of an affordable VR180-camera. While the system can be trained end-to-end, it consists of three distinct steps: feature extraction, image warping and fusion. We present a novel cascaded feature extraction method that enables us to synergetically learn optical flow at different resolution levels. We show that this significantly improves the network’s ability to learn large disparities. Finally, we demonstrate that our alignment architecture outperforms a state-of-the art optical flow network on the image warping task when both systems are trained in an identical manner.

State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.

This paper presents a real-time animation system for fully embodied virtual humans that satisfies accurate foot placement constraints for different human walking and running styles. Our method offers a fine balance between motion fidelity and character control, and can efficiently animate over sixty agents in real time (25 FPS) and over a hundred characters at 13 FPS. Given a point cloud of reachable support foot configurations extracted from the set of available animation clips, we compute the Delaunay triangulation. At runtime, the triangulation is queried to obtain the simplex containing the next footstep, which is used to compute the barycentric blending weights of the animation clips. Our method synthesizes animations to accurately follow footsteps, and a simple IK solver adjusts small offsets, foot orientation, and handles uneven terrain. To incorporate root velocity fidelity, the method is further extended to include the parametric space of root movement and combine it with footstep based interpolation. The presented method is evaluated on a variety of test cases and error measurements are calculated to offer a quantitative analysis of the results achieved.

This is the peer reviewed version of the following article: Bendezú, Á., Mego, M., Monclús, E., Merino, X., Accarino, A., Malagelada, J., Navazo, I., Azpiroz, F. Colonic content: effect of diet, meals, and defecation. "Neurogastroenterology and motility", Febrer 2017, vol. 29, núm. 2, which has been published in final form at [http://onlinelibrary.wiley.com.recursos.biblioteca.upc.edu/doi/10.1111/nmo.12930/full]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. The metabolic activity of colonic microbiota is influenced by diet; however, the relationship between metabolism and colonic content is not known. Our aim was to determine the effect of meals, defecation, and diet on colonic content. Methods: In 10 healthy subjects, two abdominal MRI scans were acquired during fasting, 1 week apart, and after 3 days on low- and high-residue diets, respectively. With each diet, daily fecal output and the number of daytime anal gas evacuations were measured. On the first study day, a second scan was acquired 4 hours after a test meal (n=6) or after 4 hours with nil ingestion (n=4). On the second study day, a scan was also acquired after a spontaneous bowel movement. Results: On the low-residue diet, daily fecal volume averaged 145 ± 15 mL; subjects passed 10.6 ± 1.6 daytime anal gas evacuations and, by the third day, non-gaseous colonic content was 479 ± 36 mL. The high-residue diet increased the three parameters to 16.5 ± 2.9 anal gas evacuations, 223 ± 19 mL fecal output, and 616 ± 55 mL non-gaseous colonic content (P

Objective: To investigate the behavior of the sciatic nerve during hip rotation at subgluteal space. Materials and methods: Sonographic examination (high-resolution ultrasound machine at 5.0–14 MHZ) of the gemelli-obturator internus complex following two approaches: (1) a study on cadavers and (2) a study on healthy volunteers. The cadavers were examined in pronation, pelvis-fixed position by forcing internal and external rotations of the hip with the knee in 90° flexion. Healthy volunteers were examined during passive internal and external hip rotation (prone position; lumbar and pelvic regions fixed). Subjects with a history of major trauma, surgery or pathologies affecting the examined regions were excluded. Results: The analysis included eight hemipelvis from six fresh cadavers and 31 healthy volunteers. The anatomical study revealed the presence of connective tissue attaching the sciatic nerve to the structures of the gemellus-obturator system at deep subgluteal space. The amplitude of the nerve curvature during rotating position was significantly greater than during resting position. During passive internal rotation, the sciatic nerve of both cadavers and healthy volunteers transformed from a straight structure to a curved structure tethered at two points as the tendon of the obturator internus contracted downwards. Conversely, external hip rotation caused the nerve to relax. Conclusion: Anatomically, the sciatic nerve is closely related to the gemelli-obturator internus complex. This relationship results in a reproducible dynamic behavior of the sciatic nerve during passive hip rotation, which may contribute to explain the pathological mechanisms of the obturator internal gemellus syndrome.

The investigation attempts to adapt a be am finite element procedure based on the Generalised Beam Theory (GBT) to the analysis of perforated columns. The presence of perforations is taken into account through the use of two beam elements with different properties, for the non- perforated and perforated parts of the member. Each part is meshed with its corresponding finite element and, afterwards, they are linked by means of constraint equations . Linear buckling analyses on steel storage rack columns are carried out to demonstrate how the proposed procedure should be applied. Some practical issues are discussed, such as the GBT deformation modes to be included in the analyses, or the optimum finite element discretiz ation. The resulting buckling loads are validated by comparison with the values obtained in anal yses performed using shell finite element models. Finally, it is verified that the buck ling loads produced with the propo sed method are rather accurate

Molecular simulations are used in many areas of biotechnology, such as drug design and enzyme engineering. Despite the development of automatic computational protocols, analysis of molecular interactions is still a major aspect where human comprehension and intuition are key to accelerate, analyze, and propose modifications to the molecule of interest. Most visualization algorithms help the users by providing an accurate depiction of the spatial arrangement: the atoms involved in inter-molecular contacts. There are few tools that provide visual information on the forces governing molecular docking. However, these tools, commonly restricted to close interaction between atoms, do not consider whole simulation paths, long-range distances and, importantly, do not provide visual cues for a quick and intuitive comprehension of the energy functions (modeling intermolecular interactions) involved. In this paper, we propose visualizations designed to enable the characterization of interaction forces by taking into account several relevant variables such as molecule-ligand distance and the energy function, which is essential to understand binding affinities. We put emphasis on mapping molecular docking paths obtained from Molecular Dynamics or Monte Carlo simulations, and provide time-dependent visualizations for different energy components and particle resolutions: atoms, groups or residues. The presented visualizations have the potential to support domain experts in a more efficient drug or enzyme design process.

Throughout the years, many shading techniques have been developed to improve the conveying of information in volume visualization. Some of these methods, usually referred to as realistic, are supposed to provide better cues for the understanding of volume data sets. While shading approaches are heavily exploited in traditional monoscopic setups, no previous study has analyzed the effect of these techniques in virtual reality. To further explore the influence of shading on the understanding of volume data in such environments, we carried out a user study in a desktop-based stereoscopic setup. The goals of the study were to investigate the impact of well-known shading approaches and the influence of real illumination on depth perception. Participants had to perform three different perceptual tasks when exposed to static visual stimuli. 45 participants took part in the study, giving us 1152 trials for each task. Results show that advanced shading techniques improve depth perception in stereoscopic volume visualization. As well, external lighting does not affect depth perception when these shading methods are applied. As a result, we derive some guidelines that may help the researchers when selecting illumination models for stereoscopic rendering.

International audience; A navigation mesh is a representation of a 2D or 3D virtual environment that enables path planning and crowd simulation for walking characters. Various state-of-the-art navigation meshes exist, but there is no standardized way of evaluating or comparing them. Each implementation is in a different state of maturity, has been tested on different hardware, uses different example environments, and may have been designed with a different application in mind. In this paper, we conduct the first comparative study of navigation meshes. First, we give general definitions of 2D and 3D environments and navigation meshes. Second, we propose theoretical properties by which navigation meshes can be classified. Third, we introduce metrics by which the quality of a navigation mesh implementation can be measured objectively. Finally, we use these metrics to compare various state-of-the-art navigation meshes in a range of 2D and 3D environments. We expect that this work will set a new standard for the evaluation of navigation meshes, that it will help developers choose an appropriate navigation mesh for their application, and that it will steer future research on navigation meshes in interesting directions.

An error-aware robust estimation of the normal vectors for noisy point clouds.A panorama-based 3D representation for gigantic point clouds.An efficient algorithm for interactive navigation of gigantic point clouds. Obtaining 3D realistic models of urban scenes from accurate range data is nowadays an important research topic, with applications in a variety of fields ranging from Cultural Heritage and digital 3D archiving to monitoring of public works. Processing massive point clouds acquired from laser scanners involves a number of challenges, from data management to noise removal, model compression and interactive visualization and inspection. In this paper, we present a new methodology for the reconstruction of 3D scenes from massive point clouds coming from range lidar sensors. Our proposal includes a panorama-based compact reconstruction where colors and normals are estimated robustly through an error-aware algorithm that takes into account the variance of expected errors in depth measurements. Our representation supports efficient, GPU-based visualization with advanced lighting effects. We discuss the proposed algorithms in a practical application on urban and historical preservation, described by a massive point cloud of 3.5 billion points. We show that we can achieve compression rates higher than 97% with good visual quality during interactive inspections.

This is the peer reviewed version of the following article: Bendezú, R. A., Barba, E., Burri, E., Cisternas, D., Accarino, A., Quiroga, S., Monclus, E., Navazo, I., Malagelada, J.-R. and Azpiroz, F. (2016), Colonic content in health and its relation to functional gut symptoms. Neurogastroenterol. Motil., 28: 849–854, which has been published in final form at [doi:10.1111/nmo.12782]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving Gut content may be determinant in the generation of digestive symptoms, particularly in patients with impaired gut function and hypersensitivity. Since the relation of intraluminal gas to symptoms is only partial, we hypothesized that non-gaseous component may play a decisive role. Methods: Abdominal computed tomography scans were evaluated in healthy subjects during fasting and after a meal (n = 15) and in patients with functional gut disorders during basal conditions (when they were feeling well) and during an episode of abdominal distension (n = 15). Colonic content and distribution were measured by an original analysis program. Key results: In healthy subjects both gaseous (87 ± 24 mL) and non-gaseous colonic content (714 ± 34 mL) were uniformly distributed along the colon. In the early postprandial period gas volume increased (by 46 ± 23 mL), but non-gaseous content did not, although a partial caudad displacement from the descending to the pelvic colon was observed. No differences in colonic content were detected between patients and healthy subjects. Symptoms were associated with discrete increments in gas volume. However, no consistent differences in non-gaseous content were detected in patients between asymptomatic periods and during episodes of abdominal distension. Conclusions & inferences: In patients with functional gut disorders, abdominal distension is not related to changes in non-gaseous colonic content. Hence, other factors, such as intestinal hypersensitivity and poor tolerance of small increases in luminal gas may be involved.

All-atom simulations are crucial in biotechnology. In Pharmacology, for example, molecular knowledge of protein-drug interactions is essential in the understanding of certain pathologies and in the development of improved drugs. To achieve this detailed information, fast and enhanced molecular visualization is critical. Moreover, hardware and software developments quickly deliver extensive data, providing intermediate results that can be analyzed by scientists in order to interact with the simulation process and direct it to a more promising configuration. In this paper we present a GPU-friendly data structure for real-time illustrative visualization of all-atom simulations. Our system generates both ambient occlusion and halos using an occupancy pyramid that needs no precalculation and that is updated on the fly during simulation, allowing the real time rendering of simulation results at sustained high framerates. Graphical abstractDisplay Omitted HighlightsAn algorithm for rendering complex, dynamic molecules at interactive frame rates.Renders high and low frequency ambient occlusion shading.Our algorithms can be applied to Space-filling and Ball and Stick models.

Today molecular simulations produce complex data sets capturing the interactions of molecules in detail. Due to the complexity of this time-varying data, advanced visualization techniques are required to support its visual analysis. Current molecular visualization techniques utilize ambient occlusion as a global illumination approximation to improve spatial comprehension. Besides these shadow-like effects, interreflections are also known to improve the spatial comprehension of complex geometric structures. Unfortunately, the inherent computational complexity of interreflections would forbid interactive exploration, which is mandatory in many scenarios dealing with static and time-varying data. In this paper, we introduce a novel analytic approach for capturing interreflections of molecular structures in real-time. By exploiting the knowledge of the underlying space filling representations, we are able to reduce the required parameters and can thus apply symbolic regression to obtain an analytic expression for interreflections. We show how to obtain the data required for the symbolic regression analysis, and how to exploit our analytic solution to enhance interactive molecular visualizations. All static molecules have been obtained from the RCSB Protein Data Bank (http://www.rcsb.org), while the dynamic bind-ing studies have been computed using the PELE Monte Carlo solver (http://pele.bsc.es ) in cooperation with the pharmaceutical company AstraZeneca. This research is partially supported by TIN2014-52211-C2-1-R from the Spanish Government.

Recent advances in 3D scanning technologies have opened new possibilities in a broad range of applications including cultural heritage, medicine, civil engineering, and urban planning. Virtual Reality systems can provide new tools to professionals that want to understand acquired 3D models. In this review paper, we analyze the concept of data comprehension with an emphasis on visualization and inspection tools on immersive setups. We claim that in most application fields, data comprehension requires model measurements, which in turn should be based on the explicit visualization of uncertainty. As 3D digital representations are not faithful, information on their fidelity at local level should be included in the model itself as uncertainty bounds. We propose the concept of Measurable 3D Models as digital models that explicitly encode such local uncertainty bounds. We claim that professionals and experts can strongly benefit from immersive interaction through new specific, fidelity-aware measurement tools, which can facilitate 3D data comprehension. Since noise and processing errors are ubiquitous in acquired datasets, we discuss the estimation, representation, and visualization of data uncertainty. We show that, based on typical user requirements in Cultural Heritage and other domains, application-oriented measuring tools in 3D models must consider uncertainty and local error bounds. We also discuss the requirements of immersive interaction tools for the comprehension of huge 3D and nD datasets acquired from real objects.

Background: The precise relation of intestinal gas to symptoms, particularly abdominal bloating and distension remains incompletely elucidated. Our aim was to define the normal values of intestinal gas volume and distribution and to identify abnormalities in relation to functional-type symptoms. Methods:Abdominal computed tomography scans were evaluated in healthy subjects (n = 37) and in patients in three conditions: basal (when they were feeling well; n = 88), during an episode of abdominal distension (n = 82) and after a challenge diet (n = 24). Intestinal gas content and distribution were measured by an original analysis program. Identification of patients outside the normal range was performed by machine learning techniques (one-class classifier). Results are expressed as median (IQR) or mean ± SE, as appropriate. Key Results: In healthy subjects the gut contained 95 (71, 141) mL gas distributed along the entire lumen. No differences were detected between patients studied under asymptomatic basal conditions and healthy subjects. However, either during a spontaneous bloating episode or once challenged with a flatulogenic diet, luminal gas was found to be increased and/or abnormally distributed in about one-fourth of the patients. These patients detected outside the normal range by the classifier exhibited a significantly greater number of abnormal features than those within the normal range (3.7 ± 0.4 vs 0.4 ± 0.1; p < 0.001). Conclusions & Inferences: The analysis of a large cohort of subjects using original techniques provides unique and heretofore unavailable information on the volume and distribution of intestinal gas in normal conditions and in relation to functional gastrointestinal symptoms.

BACKGROUND & AIMS: In patients with functional gut disorders, abdominal distension has been associated with descent of the diaphragm and protrusion of the anterior abdominal wall. We investigated mechanisms of abdominal distension in these patients. METHODS: We performed a prospective study of 45 patients (42 women, 24-71 years old) with functional intestinal disorders (27 with irritable bowel syndrome with constipation, 15 with functional bloating, and 3 with irritable bowel syndrome with alternating bowel habits) and discrete episodes of visible abdominal distension. Subjects were assessed by abdominothoracic computed tomography (n = 39) and electromyography (EMG) of the abdominothoracic wall (n = 32) during basal conditions (without abdominal distension) and during episodes of severe abdominal distension. Fifteen patients received a median of 2 sessions (range, 1-3 sessions) of EMG-guided, respiratory-targeted biofeedback treatment; 11 received 1 control session before treatment. RESULTS: Episodes of abdominal distension were associated with diaphragm contraction (19% +/- 3% increase in EMG score and 12 +/- 2 mm descent; P < .001 vs basal values) and intercostal contraction (14% +/- 3% increase in EMG scores and 6 +/- 1 mm increase in thoracic antero-posterior diameter; P < .001 vs basal values). They were also associated with increases in lung volume (501 +/- 93 mL; P < .001 vs basal value) and anterior abdominal wall protrusion (32 +/- 3 mm increase in girth; P < .001 vs basal). Biofeedback treatment, but not control sessions, reduced the activity of the intercostal muscles (by 19% +/- 2%) and the diaphragm (by 18% +/- 4%), activated the internal oblique muscles (by 52% +/- 13%), and reduced girth (by 25 +/- 3 mm) (P

Bar charts are among the most commonly used visualization graphs. Their main goal is to communicate quantities that can be visually compared. Since they are easy to produce and interpret, they are found in any situation where quantitative data needs to be conveyed (websites, newspapers, etc.). However, depending on the layout, the perceived values can vary substantially. For instance, previous research has shown that the positioning of bars (e.g. stacked vs separate) may influence the accuracy in bar ratio length estimation. Other works have studied the effects of embellishments on the perception of encoded quantities. However, to the best of the authors’ knowledge, the effect of perceptual elements used to reinforce the quantity depicted within the bars, such as contrast and inner lines, has not been studied in depth. In this research we present a study that analyzes the effect of several internal contrast and framing enhancements with respect to the use of basic solid bars. Our results show that the addition of minimal visual elements that are easy to implement with current technology can help users to better recognize the amounts depicted by the bar charts.

Several visual representations have been developed over the years to visualize molecular structures, and to enable a better understanding of their underlying chemical processes. Today, the most frequently used atom-based representations are the Space-filling, the Solvent Excluded Surface, the Balls-and-Sticks, and the Licorice models. While each of these representations has its individual benefits, when applied to large-scale models spatial arrangements can be difficult to interpret when employing current visualization techniques. In the past it has been shown that global illumination techniques improve the perception of molecular visualizations; unfortunately existing approaches are tailored towards a single visual representation. We propose a general illumination model for molecular visualization that is valid for different representations. With our illumination model, it becomes possible, for the first time, to achieve consistent illumination among all atom-based molecular representations. The proposed model can be further evaluated in real-time, as it employs an analytical solution to simulate diffuse light interactions between objects. To be able to derive such a solution for the rather complicated and diverse visual representations, we propose the use of regression analysis together with adapted parameter sampling strategies as well as shape parametrization guided sampling, which are applied to the geometric building blocks of the targeted visual representations. We will discuss the proposed sampling strategies, the derived illumination model, and demonstrate its capabilities when visualizing several dynamic molecules.

The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.

The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette-defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.

Achieving realistic virtual humans is crucial in virtual reality applications and video games. Nowadays there are software and game development tools, that are of great help to generate and simulate characters. They offer easy to use GUIs to create characters by dragging and drooping features, and making small modifications. Similarly, there are tools to create animation graphs and setting blending parameters among others. Unfortunately, even though these tools are relatively user friendly, achieving natural animation transitions is not straight forward and thus non-expert users tend to spend a large amount of time to generate animations that are not completely free of artefacts. In this paper we present a method to automatically generate animation blend spaces in Unreal engine, which offers two advantages: the first one is that it provides a tool to evaluate the quality of an animation set, and the second one is that the resulting graph does not depend on user skills and it is thus not prone to user errors.

Deep learning systems extensively use convolution operations to process input data. Though convolution is clearly defined for structured data such as 2D images or 3D volumes, this is not true for other data types such as sparse point clouds. Previous techniques have developed approximations to convolutions for restricted conditions. Unfortunately, their applicability is limited and cannot be used for general point clouds. We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques. Learning is enabled by four key novelties: first, representing the convolution kernel itself as a multilayer perceptron; second, phrasing convolution as a Monte Carlo integration problem, third, using this notion to combine information from multiple samplings at different levels; and fourth using Poisson disk sampling as a scalable means of hierarchical point cloud learning. The key idea across all these contributions is to guarantee adequate consideration of the underlying non-uniform sample distribution function from a Monte Carlo perspective. To make the proposed concepts applicable to real-world tasks, we furthermore propose an efficient implementation which significantly reduces the GPU memory required during the training process. By employing our method in hierarchical network architectures we can outperform most of the state-of-the-art networks on established point cloud segmentation, classification and normal estimation benchmarks. Furthermore, in contrast to most existing approaches, we also demonstrate the robustness of our method with respect to sampling variations, even when training with uniformly sampled data only. To support the direct application of these concepts, we provide a ready-to-use TensorFlow implementation of these layers at https://github.com/viscom-ulm/MCCNN, Comment: ACM Transactions on Graphics (Proocedings of SIGGRAPH Asia 2018)