3D human pose estimation from depth maps using a deep combination of poses

Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding. In recent years, depth sensors have become a popular approach to obtain three-dimensional information. The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images. This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach. We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person. In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input. We have thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them.Comment: Accepted for publication at "Journal of Visual Communication and Image Representation

Displaced genital arch in a Drosophila melanogaster male

Drosophila melanogaster mutant ebony (e) is characterized by its pigmentation defects in the adult cuticle (Bridges and Morgan, 1923); eyegone (eyg) has been described as having head and eyes much smaller than normal (Ives, 1942); and the vestigial (vg) locus seems to be only involved in wing development (Bridges and Morgan, 1919). While analyzing the F1 offspring from the parental cross between the D. melanogaster strains e eyg and vg, a particular fly was observed. It was a male, with no extended wings and normal color (although slightly darker because it was heterozygote for e). Interestingly, its genital arch was displaced from its normal position. It was not located in the ventral tip of the abdomen, instead it was displaced almost 90 degrees towards the end of the abdomen (Figures 1 and 2). The abdominal area where the genital arch should be was covered with a thin tegument (Figures 3 and 4). Sex combs were properly located. The animal died by accident nine days after emerging and left no progeny (he was caught in the culture medium) [...]

Fast ultrasound-assisted synthesis of highly crystalline MIL-88A particles and their application as ethylene adsorbents

Highly crystalline MIL-88A particles have been successfully synthesized via fast ultrasound-assisted processes. The influence of the sonication generator and synthesis time on the structure, crystallinity, morphology and surface area of the materials were studied in detail. Under this modified ultrasonic method, X-ray diffraction patterns of MIL-88A particles showed highly crystalline structures in contrast to those reported in literature. Significant differences on surface areas and microporosity were appreciated under ultrasound conditions employed. Specific surface areas in the range between 179 and 359 m2 g−1 were obtained. That material synthesized under ultrasound batch conditions during 1 h had the highest surface area and microporous character. Different particle sizes and morphologies were obtained depending on the synthesis procedure. In general, probe sonicators led to smaller particle sizes. Moreover, a comparative study of the ethylene adsorption of the MIL-88A particles and several common MOFs in the ethylene adsorption was investigated. The results suggest that the modified ultrasound-assisted procedure for the synthesis of MIL-88A is effective to obtain highly crystalline particles, which are very efficient to adsorb ethylene molecules

Global Effects of Focal Brain Tumors on Functional Complexity and Network Robustness: A Prospective Cohort Study.

BACKGROUND: Neurosurgical management of brain tumors has entered a paradigm of supramarginal resections that demands thorough understanding of peritumoral functional effects. Historically, the effects of tumors have been believed to be local, and long-range effects have not been considered. OBJECTIVE: To test the hypothesis that tumors affect the brain globally, producing long-range gradients in cortical function. METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from 11 participants with glioblastoma and split into discovery and validation datasets in a single-center prospective cohort study. Fractal complexity was computed with a wavelet-based estimator of the Hurst exponent. Distance-related effects of the tumors were tested with a tumor mask-dilation technique and parcellation of the underlying Hurst maps. RESULTS: Fractal complexity demonstrates a penumbra of suppression in the peritumoral region. At a global level, as distance from the tumor increases, this initial suppression is balanced by a subsequent overactivity before finally normalizing. These effects were best fit by a quadratic model and were consistent across different network construction pipelines. The Hurst exponent was correlated with graph theory measures of centrality including network robustness, but graph theory measures did not demonstrate distance-dependent effects. CONCLUSION: This work provides evidence supporting the theory that focal brain tumors produce long-range gradients in function. Consequently, the effects of focal lesions need to be interpreted in terms of the global changes on functional complexity and network architecture rather than purely in terms of functional localization. Determining whether peritumoral changes represent potential plasticity may facilitate extended resection of tumors without functional cost.MGH is funded by the Wellcome Trust Neuroscience in Psychiatry Network with additional support from the National Institute for Health Research Cambridge Biomedical Research Centre. The imaging studies were funded by an NIHR Clinician Scientist Fellowship for SJP (NIHR/CS/009/011)

sSLAM: Speeded-Up Visual SLAM Mixing Artificial Markers and Temporary Keypoints

Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g., shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking. This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers, allowing for a substantial reduction in the computing time and memory required without noticeably degrading the tracking accuracy. In the first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just in the time of tracking, which are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposed sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed, without statistically significant differences in accuracy.This research was funded by the project PID2019-103871GB-I00 of the Spanish Ministry of Economy, Industry and Competitiveness, FEDER, Project 1380047-F UCOFEDER-2021 of Andalusia and by the European Union–NextGeneration EU for requalification of Spanish University System 2021–2023

sSLAM: Speeded-Up Visual SLAM Mixing Artificial Markers and Temporary Keypoints

Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g., shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking. This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers, allowing for a substantial reduction in the computing time and memory required without noticeably degrading the tracking accuracy. In the first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just in the time of tracking, which are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposed sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed, without statistically significant differences in accuracy

Vesicle formation induced by thermal fluctuations

The process of fission and vesicle formation depends on the geometry of the membrane that will split. For instance, a flat surface finds it difficult to form vesicles because of the lack of curved regions where to start the process. Here we show that vesicle formation can be promoted by temperature, by using a membrane phase field model with Gaussian curvature. We find a phase transition between fluctuating and vesiculation phases that depends on temperature, spontaneous curvature, and the ratio between bending and Gaussian moduli. We analysed the energy dynamical behaviour of these processes and found that the main driving ingredient is the Gaussian energy term, although the curvature energy term usually helps with the process as well. We also found that the chemical potential can be used to investigate the temperature of the system. Finally we address how temperature changes the condition for spontaneous vesiculation for all geometries, making it happen in a wider range of values of the Gaussian modulus.Comment: 31 pages, 10 figure

Improving adaptive generalized polynomial chaos method to solve nonlinear random differential equations by the random variable transformation technique

[EN] Generalized polynomial chaos (gPC) is a spectral technique in random space to represent random variables and stochastic processes in terms of orthogonal polynomials of the Askey scheme. One of its most fruitful applications consists of solving random differential equations. With gPC, stochastic solutions are expressed as orthogonal polynomials of the input random parameters. Different types of orthogonal polynomials can be chosen to achieve better convergence. This choice is dictated by the key correspondence between the weight function associated to orthogonal polynomials in the Askey scheme and the probability density functions of standard random variables. Otherwise, adaptive gPC constitutes a complementary spectral method to deal with arbitrary random variables in random differential equations. In its original formulation, adaptive gPC requires that both the unknowns and input random parameters enter polynomially in random differential equations. Regarding the inputs, if they appear as non-polynomial mappings of themselves, polynomial approximations are required and, as a consequence, loss of accuracy will be carried out in computations. In this paper an extended version of adaptive gPC is developed to circumvent these limitations of adaptive gPC by taking advantage of the random variable transformation method. A number of illustrative examples show the superiority of the extended adaptive gPC for solving nonlinear random differential equations. In addition, for the sake of completeness, in all examples randomness is tackled by nonlinear expressions.This work has been partially supported by the Ministerio de Economia y Competitividad grants MTM2013-41765-P.Cortés, J.; Romero, J.; Roselló, M.; Villanueva Micó, RJ. (2017). Improving adaptive generalized polynomial chaos method to solve nonlinear random differential equations by the random variable transformation technique. Communications in Nonlinear Science and Numerical Simulation. 50:1-15. https://doi.org/10.1016/j.cnsns.2017.02.011S1155

Stenosis detection in coronary angiography images using deep learning models

The emergence of deep learning has caused its massive application to different fields in industry and research, among which is the clinical field, especially in those where the data is structured in the form of images or video. The present proposal intends to develop a coronary angiography image analysis system based on artificial intelligence. These images are radiocontrast X-ray images of the coronary arteries. The proposed system will be able to analyze these coronary angiography images of patients with no obstructive coronary lesions to detect and characterize smooth and irregular coronary arteries and predict the presence of cardiovascular events during follow-up. Deep learning convolutional artificial neural networks will support the algorithmic basis of the proposed system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech