Technical efficiency in primary health care: does quality matter?

The accuracy required in the measurement of output is an issue that has as yet still not been satisfactorily addressed in empirical research on efficiency in primary health care. We exploit information retrieved from a newly constructed database (APEX06) for the Spanish region of Extremadura. The richness of our dataset allows us to consider original synthetic measures of output that take into account both the quantity and the quality of services provided by 85 primary care centres (PCCs) in 2006. We provide evidence that neglecting the issue of properly accounting for the quality of health services can lead to misleading results. Our main finding is that adjusting output for quality influences efficiency analysis in three senses. First, inefficiency now explains relatively more of the deviation from the potential output. Second, the average technical efficiency in the sector is lower, while its dispersion among PCCs is significantly higher. And third, the efficiency ranking of the PCCs is also affected.Primary Health Care; Stochastic Frontier Analysis; Technical Efficiency; Quality

Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions

LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems during their decision making processes. Deep neural networks are achieving state-of-the-art results on large public benchmarks on this task. Unfortunately, finding models that generalize well or adapt to additional domains, where data distribution is different, remains a major challenge. This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. Our approach combines novel ideas on top of the current state-of-the-art approaches and yields new state-of-the-art results. We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space. Besides, we propose a learning-based approach that aligns the distribution of the semantic classes of the target domain to the source domain. The presented ablation study shows how each part contributes to the final performance. Our strategy is shown to outperform previous approaches for domain adaptation with comparisons run on three different domains.Comment: 7 pages, 3 figure

MiniNet: An Efficient Semantic Segmentation ConvNet for Real-Time Robotic Applications

Efficient models for semantic segmentation, in terms of memory, speed, and computation, could boost many robotic applications with strong computational and temporal restrictions. This article presents a detailed analysis of different techniques for efficient semantic segmentation. Following this analysis, we have developed a novel architecture, MiniNet-v2, an enhanced version of MiniNet. MiniNet-v2 is built considering the best option depending on CPU or GPU availability. It reaches comparable accuracy to the state-of-the-art models but uses less memory and computational resources. We validate and analyze the details of our architecture through a comprehensive set of experiments on public benchmarks (Cityscapes, Camvid, and COCO-Text datasets), showing its benefits over relevant prior work. Our experiments include a sample application where these models can boost existing robotic applications

Performance of object recognition in wearable videos

Wearable technologies are enabling plenty of new applications of computer vision, from life logging to health assistance. Many of them are required to recognize the elements of interest in the scene captured by the camera. This work studies the problem of object detection and localization on videos captured by this type of camera. Wearable videos are a much more challenging scenario for object detection than standard images or even another type of videos, due to lower quality images (e.g. poor focus) or high clutter and occlusion common in wearable recordings. Existing work typically focuses on detecting the objects of focus or those being manipulated by the user wearing the camera. We perform a more general evaluation of the task of object detection in this type of video, because numerous applications, such as marketing studies, also need detecting objects which are not in focus by the user. This work presents a thorough study of the well known YOLO architecture, that offers an excellent trade-off between accuracy and speed, for the particular case of object detection in wearable video. We focus our study on the public ADL Dataset, but we also use additional public data for complementary evaluations. We run an exhaustive set of experiments with different variations of the original architecture and its training strategy. Our experiments drive to several conclusions about the most promising directions for our goal and point us to further research steps to improve detection in wearable videos.Comment: Emerging Technologies and Factory Automation, ETFA, 201

Event Transformer+. A multi-purpose solution for efficient event data processing

Event cameras record sparse illumination changes with high temporal resolution and high dynamic range. Thanks to their sparse recording and low consumption, they are increasingly used in applications such as AR/VR and autonomous driving. Current top-performing methods often ignore specific event-data properties, leading to the development of generic but computationally expensive algorithms, while event-aware methods do not perform as well. We propose Event Transformer+, that improves our seminal work evtprev EvT with a refined patch-based event representation and a more robust backbone to achieve more accurate results, while still benefiting from event-data sparsity to increase its efficiency. Additionally, we show how our system can work with different data modalities and propose specific output heads, for event-stream predictions (i.e. action recognition) and per-pixel predictions (dense depth estimation). Evaluation results show better performance to the state-of-the-art while requiring minimal computation resources, both on GPU and CPU

Evaluación del servicio de odontología del Centro de Salud de Torrijos Carter en el distrito de San Miguelito, 1986.

OBJETIVO GENERAL. Evaluar el funcionamiento de los servicios odontológicos en e1 Centro de Salud de Torrijos Carter, para retroalimentar el proceso de atención odontológica y proponer ajustes y modificaciones al modelo de atención vigente, y al modelo de evaluación utilizado. OBJETIVOS ESPECIFICOS. Recopilar y analizar la informaci6n referente a accesibilidad, cobertura, impacto de los servicios, así como producción y productividad de los recursos. Evaluación del subsistema de información de Salud bucal como tal. Medir el alcance y la magnitud de los indicadores más importantes de la salud bucal que nos permitan efectuar evaluaciones eficaces y efectivas del programa a nivel nacional. Formular recomendaciones para mejorar los servicios odontológicos en esta zona