Fingerprint Pore Detection: A Survey

This work presents the first survey on fingerprint pore detection. The survey provides a general overview of the field and discusses methods, datasets, and evaluation protocols. We also present a baseline method inspired on the state-of-the-art that implements a customizable Fully Convolutional Network, whose hyperparameters were tuned to achieve optimal pore detection rates. Finally, we also reimplementated three other approaches proposed in the literature for evaluation purposes. We have made the source code of (1) the baseline method, (2) the reimplemented approaches, and (3) the training and evaluation processes for two different datasets available to the public to attract more researchers to the field and to facilitate future comparisons under the same conditions. The code is available in the following repository: https://github.com/azimIbragimov/Fingerprint-Pore-Detection-A-Surve

La dimensión política de la fe propuesta pedagógica para el fortalecimiento de la conciencia política en la comunidad de la Arquidiócesis de Tunja

El año 2015 en el territorio colombiano se ve enmarcado dentro del contexto político por las jornadas electorales programadas para el mes de Octubre, las cuales tienen como objetivo la elección de diputados, gobernadores, alcaldes y concejales para el período administrativo 2016-2019. De cara a este panorama electoral existen críticas al sistema político en cuanto estructura, visibles en temas como la corrupción y la burocracia, que llevan a una manipulación de conciencias mediante mecanismos de polización y carnización . Esto a su vez evidencia una notable carencia educativa orientada a formar la conciencia política de los ciudadanos, muchos de los cuales optan por no involucrarse con las maquinarias establecidas y por tanto renuncian al ejercicio democrático.Licenciado (a) en TeologíaPregrad

Reducing Training Demands for 3D Gait Recognition with Deep Koopman Operator Constraints

Deep learning research has made many biometric recognition solution viable, but it requires vast training data to achieve real-world generalization. Unlike other biometric traits, such as face and ear, gait samples cannot be easily crawled from the web to form massive unconstrained datasets. As the human body has been extensively studied for different digital applications, one can rely on prior shape knowledge to overcome data scarcity. This work follows the recent trend of fitting a 3D deformable body model into gait videos using deep neural networks to obtain disentangled shape and pose representations for each frame. To enforce temporal consistency in the network, we introduce a new Linear Dynamical Systems (LDS) module and loss based on Koopman operator theory, which provides an unsupervised motion regularization for the periodic nature of gait, as well as a predictive capacity for extending gait sequences. We compare LDS to the traditional adversarial training approach and use the USF HumanID and CASIA-B datasets to show that LDS can obtain better accuracy with less training data. Finally, we also show that our 3D modeling approach is much better than other 3D gait approaches in overcoming viewpoint variation under normal, bag-carrying and clothing change conditions

Typical and atypical imaging characteristics

Funding Information: No funds, grants, or other support was received. Publisher Copyright: © 2022 The Authors. Journal of Neuroimaging published by Wiley Periodicals LLC on behalf of American Society of Neuroimaging.Cavernous malformations (CMs) are benign vascular malformations that maybe seen anywhere in the central nervous system. They are dynamic lesions, growing or shrinking over time and only rarely remaining stable. Size varies from a few millimeters to a few centimeters. CMs can be sporadic or familial, and while most of them are congenital, de novo and acquired lesions may also be seen. Etiology is still unknown. A genetic molecular mechanism has been proposed since a cerebral cavernous malformation gene loss of function was found in both familial and sporadic lesions. Additionally, recent studies suggest that formation of CMs in humans may be associated with a distinctive bacterial gut composition (microbioma). Imaging is fairly typical but may vary according to age, location, and etiology. Follow-up is not well established because CMs patients have a highly unpredictable clinical course. Angiogenic and inflammatory mechanisms have been implicated in disease activity, as well as lesional hyperpermeability and iron deposition. Imaging and serum biomarkers of these mechanisms are under current investigation. Treatment options, including surgery or radiosurgery, are not well defined and are dependent upon multiple factors, including clinical presentation, lesion location, number of hemorrhagic events, and medical comorbidities. Our purpose is to review the imaging features of CMs based on their size, location, and etiology, as well as their differential diagnosis and best imaging approach. New insights in etiology will be briefly considered. Follow-up strategies, including serum and imaging biomarkers, and treatment options will also be discussed.publishersversionepub_ahead_of_prin

Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis

This paper focuses on the statistical analysis of shapes of data objects called shape graphs, a set of nodes connected by articulated curves with arbitrary shapes. A critical need here is a constrained registration of points (nodes to nodes, edges to edges) across objects. This, in turn, requires optimization over the permutation group, made challenging by differences in nodes (in terms of numbers, locations) and edges (in terms of shapes, placements, and sizes) across objects. This paper tackles this registration problem using a novel neural-network architecture and involves an unsupervised loss function developed using the elastic shape metric for curves. This architecture results in (1) state-of-the-art matching performance and (2) an order of magnitude reduction in the computational cost relative to baseline approaches. We demonstrate the effectiveness of the proposed approach using both simulated data and real-world 2D and 3D shape graphs. Code and data will be made publicly available after review to foster research

Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study

Background: Although European guidelines recommend vascular ultrasound for the assessment of cardiovascular risk in low-to-moderate risk individuals, no algorithm properly identifies patients who could benefit from it. The aim of this study is to develop a sex-specific algorithm to identify those patients, especially women who are usually underdiagnosed. Methods: Clinical, anthropometrical, and biochemical data were combined with a 12-territory vascular ultrasound to predict severe atheromatosis (SA: ≥ 3 territories with plaque). A Personalized Algorithm for Severe Atheromatosis Prediction (PASAP-ILERVAS) was obtained by machine learning. Models were trained in the ILERVAS cohort (n = 8,330; 51% women) and validated in the control subpopulation of the NEFRONA cohort (n = 559; 47% women). Performance was compared to the Systematic COronary Risk Evaluation (SCORE) model. Results: The PASAP-ILERVAS is a sex-specific, easy-to-interpret predictive model that stratifies individuals according to their risk of SA in low, intermediate, or high risk. New clinical predictors beyond traditional factors were uncovered. In low- and high-risk (L&H-risk) men, the net reclassification index (NRI) was 0.044 (95% CI: 0.020-0.068), and the integrated discrimination index (IDI) was 0.038 (95% CI: 0.029-0.048) compared to the SCORE. In L&H-risk women, PASAP-ILERVAS showed a significant increase in the area under the curve (AUC, 0.074 (95% CI: 0.062-0.087), p-value: < 0.001), an NRI of 0.193 (95% CI: 0.162-0.224), and an IDI of 0.119 (95% CI: 0.109-0.129). Conclusion: The PASAP-ILERVAS improves SA prediction, especially in women. Thus, it could reduce the number of unnecessary complementary explorations selecting patients for a further imaging study within the intermediate risk group, increasing cost-effectiveness and optimizing health resources.This work was supported by grants from the Diputació de Lleida, Instituto de Salud Carlos III (RETIC RD16/0009/0011) and Ministerio de Ciencia, Innovación y Universidades (IJC2018-037792-I