Procesamiento en tiempo real de log de actividad en sistemas de Receta Electrónica y uso de técnicas avanzadas de clustering en entornos Hadoop

Traballo Fin de Grao en Enxeñaría Informática. Curso 2014-2015El objetivo de este proyecto es proporcionar una herramienta capaz de analizar los logs de actividad de un sistema de Receta Electrónica en tiempo real, en concreto, del sistema de Dispensación Electrónica implantado en el SERGAS, de tal forma que los usuarios puedan tener a su disposición una serie de informes que pueden actualizar utilizando los parámetros que deseen. Se busca una solución que ofrezca un sistema robusto y con unos tiempos de respuesta reducidos con el fin de poder ofrecer un servicio en tiempo real y de alta disponibilidad prestando especial atención a la posibilidad de que se produzca pérdida de datos. En este sentido, las etapas de análisis tecnológico y de diseño de la arquitectura del sistema cobrarán especial relevancia a la hora de poder confeccionar un sistema de estas características. Surgen un gran número de cuestiones inherentes al procesamiento en tiempo real que se deben solventar y que en el proyecto anterior no se tenían en cuenta. Por un lado, necesitamos establecer un mecanismo de comunicación robusto con el sistema de Receta Electrónica que no sea sensible al estado del sistema o de la red. Por otro lado, se imponen unas fuertes restricciones sobre los tiempos de procesamiento, en este sentido, cabe destacar que cuando nos referimos a procesamiento en “tiempo real”, realmente, nos estamos refiriendo a sistemas de procesamiento NRT (“near real time”), se trata de sistemas capaces de procesar y analizar los datos a medida que son recibidos ofreciendo unos tiempos de respuesta muy bajos pero que, realmente, tienen un pequeño retardo con respecto a los datos originales

Short-term anchor linking and long-term self-guided attention for video object detection

We present a new network architecture able to take advantage of spatio-temporal information available in videos to boost object detection precision. First, box features are associated and aggregated by linking proposals that come from the same anchor box in the nearby frames. Then, we design a new attention module that aggregates short-term enhanced box features to exploit long-term spatio-temporal information. This module takes advantage of geometrical features in the long-term for the first time in the video object detection domain. Finally, a spatio-temporal double head is fed with both spatial information from the reference frame and the aggregated information that takes into account the short- and long-term temporal context. We have tested our proposal in five video object detection datasets with very different characteristics, in order to prove its robustness in a wide number of scenarios. Non-parametric statistical tests show that our approach outperforms the state-of-the-art. Our code is available at https://github.com/daniel-cores/SLTnetThis research was partially funded by the Spanish Ministry of Science, Innovation and Universities under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grants ED431C 2018/29, ED431C 2017/69 and accreditation 2016-2019, ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Autonomous navigation for UAVs managing motion and sensing uncertainty

We present a motion planner for the autonomous navigation of UAVs that manages motion and sensing uncertainty at planning time. By doing so, optimal paths in terms of probability of collision, traversal time and uncertainty are obtained. Moreover, our approach takes into account the real dimensions of the UAV in order to reliably estimate the probability of collision from the predicted uncertainty. The motion planner relies on a graduated fidelity state lattice and a novel multi-resolution heuristic which adapt to the obstacles in the map. This allows managing the uncertainty at planning time and yet obtaining solutions fast enough to control the UAV in real time. Experimental results show the reliability and the efficiency of our approach in different real environments and with different motion models. Finally, we also report planning results for the reconstruction of 3D scenarios, showing that with our approach the UAV can obtain a precise 3D model autonomouslyThis research was funded by the Spanish Ministry for Science, Innovation, Spain and Universities (grant TIN2017-84796-C2-1-R) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C 2018/29 and “accreditation 2016–2019, ED431G/08”). These grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S

A full data augmentation pipeline for small object detection based on generative adversarial networks

Object detection accuracy on small objects, i.e., objects under 32 32 pixels, lags behind that of large ones. To address this issue, innovative architectures have been designed and new datasets have been released. Still, the number of small objects in many datasets does not suffice for training. The advent of the generative adversarial networks (GANs) opens up a new data augmentation possibility for training architectures without the costly task of annotating huge datasets for small objects. In this paper, we propose a full pipeline for data augmentation for small object detection which combines a GAN-based object generator with techniques of object segmentation, image inpainting, and image blending to achieve high-quality synthetic data. The main component of our pipeline is DS-GAN, a novel GAN-based architecture that generates realistic small objects from larger ones. Experimental results show that our overall data augmentation method improves the performance of state-of-the-art models up to 11.9% AP on UAVDT and by 4.7% AP on iSAID, both for the small objects subset and for a scenario where the number of training instances is limited.This research was partially funded by the Spanish Ministerio de Ciencia e Innovación [grant numbers PID2020-112623GB-I00, RTI2018-097088-B-C32], and the Galician Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/29, ED431C 2021/048, ED431G 2019/04]. These grants are co-funded by the European Regional Development Fund (ERDF). This paper was supported by European Union’s Horizon 2020 research and innovation programme under grant numberS

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Multiancestry analysis of the HLA locus in Alzheimer’s and Parkinson’s diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues

Reconstrucción 3D densa de escenas utilizando una cámara monocular

Traballo Fin de Máster en Tecnoloxías de Análisis de Datos Masivos: Big Data. Curso 2016-2017La reconstrucción 3D densa de escenas es de gran interés tanto para la navegación de robots como para el modelado 3D de objetos o la realidad aumentada. En este artículo se describe la arquitectura de un sistema capaz de generar una reconstrucción 3D densa del entorno utilizando una cámara monocular. Para ello se ha implementado un algoritmo de estéreo basado en movimiento capaz de calcular un mapa de profundidad en cada imagen para su posterior integración en un mapa denso. La utilización de una cámara monocular permite evitar las desventajas en cuanto al rango y las condiciones de funcionamiento de otros tipos de sensores como las cámaras RGB-D o los pares estéreo. El sistema propuesto ha sido validado tanto en conjuntos de datos sintéticos en escenas interiores como en entornos reales exteriores

Spatio-temporal convolutional neural networks for video object detection

The object detection problem is composed of two main tasks, object localization and object classification. The detection precision in images has greatly improved with the use of Deep Learning techniques, especially with the adoption of Convolutional Neural Networks. However, object detection in videos presents new challenges such as motion blur, out-of-focus or object occlusions that deteriorate object features in some specific frames. Moreover, traditional object detectors do not exploit spatio-temporal information that can be crucial to address these new challenges, boosting the detection precision. Hence, new object detection frameworks specifically designed for videos are needed to replicate the same success achieved in the single image domain. The availability of spatio-temporal information unlocks the possibility of analyzing long- and short-term relations among detections at different time steps. This highly improves the object classification precision in deteriorated frames in which a single image object detector would not be able to provide the correct object category. We propose new methods to establish these relations and aggregate information from different frames, proving through experimentation that they improve single image baseline and previous video object detectors. In addition, we also explore the utility of spatio-temporal information to reduce the number of training examples, keeping a competitive detection precision. Thus, this approach makes it possible to apply our proposal in domains in which training data is scarce and, also, it generally reduces the annotation costs.2023-11-2

Spatiotemporal tubelet feature aggregation and object linking for small object detection in videos

This paper addresses the problem of exploiting spatiotemporal information to improve small object detection precision in video. We propose a two-stage object detector called FANet based on short-term spatiotemporal feature aggregation and long-term object linking to refine object detections. First, we generate a set of short tubelet proposals. Then, we aggregate RoI pooled deep features throughout the tubelet using a new temporal pooling operator that summarizes the information with a fixed output size independent of the tubelet length. In addition, we define a double head implementation that we feed with spatiotemporal information for spatiotemporal classification and with spatial information for object localization and spatial classification. Finally, a long-term linking method builds long tubes with the previously calculated short tubelets to overcome detection errors. The association strategy addresses the generally low overlap between instances of small objects in consecutive frames by reducing the influence of the overlap in the final linking score. We evaluated our model in three different datasets with small objects, outperforming previous state-of-the-art spatiotemporal object detectors and our spatial baselineOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureS

New insights into the genetic etiology of Alzheimer’s disease and related dementias

Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele