El modelo Avilés para la implantación del tratamiento asertivo comunitario en España

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Psiquiatría. Fecha de lectura: 4-09-201

Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information

Applying people detectors to unseen data is challenging since patterns distributions, such as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt frame by frame people detectors during runtime classification, without requiring any additional manually labeled ground truth apart from the offline training of the detection model. Such adaptation make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation discriminates between relevant instants in a video sequence, i.e., identifies the representative frames for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration (i.e., detection threshold) of each detector under analysis, maximizing the mutual information to obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not require training the detectors for each new scenario and uses standard people detector outputs, i.e., bounding boxes. The experimental results demonstrate that the proposed approach outperforms state-of-the-art detectors whose optimal threshold configurations are previously determined and fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R (HAVideo

Robust Design of Artificial Neural Networks Methodology in Neutron Spectrometry

Applications of artificial neural networks (ANNs) have been reported in literature in various areas. [1–5] The wide use of ANNs is due to their robustness, fault tolerant and the ability to learn and generalize, through training process, from examples, complex nonlinear and multi input/output relationships between process parameters using the process data. [6–10] The ANNs have many other advantageous characteristics, which include: generalization, adaptation, universal function approximation, parallel data processing, robustness, etc. Multilayer perceptron (MLP) trained with backpropagation (BP) algorithm is the most used ANN in modeling, optimization classification and prediction processes. [11, 12] Although BP algorithm has proved to be efficient, its convergence tends to be very slow, and there is a possibility to get trapped in some undesired local minimum. [4, 10, 11, 13] Most literature related to ANNs focused on specific applications and their results rather than the methodology of developing and training the networks. In general, the quality of the developed ANN is highly dependable not only on ANN training algorithm and its parameters but also on many ANN architectural parameters such as the number of hidden layers and nodes per layer which have to be set during training process and these settings are very crucial to the accuracy of ANN model. [8, 14–19

Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization

Finding optimal parametrizations for people detectors is a complicated task due to the large number of parameters and the high variability of application scenarios. In this paper, we propose a framework to adapt and improve any detector automatically in multi-camera scenarios where people are observed from various viewpoints. By accurately transferring detector results between camera viewpoints and by self-correlating these transferred results, the best configuration (in this paper, the detection threshold) for each detector-viewpoint pair is identified online without requiring any additional manually-labeled ground truth apart from the offline training of the detection model. Such a configuration consists of establishing the confidence detection threshold present in every people detector, which is a critical parameter affecting detection performance. The experimental results demonstrate that the proposed framework improves the performance of four different state-of-the-art detectors (DPM , ACF, faster R-CNN, and YOLO9000) whose Optimal Fixed Thresholds (OFTs) have been determined and fixed during training time using standard datasets. Keywords: self-correlationmaximization;multi-camera; people detection; automaticThis work has been partially supported by the Spanish government under the project TEC2014-53176-

The value capture potential of the Lisbon subway

JTLU vol 5, no 1, pp 65-82 (2012)This paper tries to build on traditional value capture measures, to estimate the potential of some of these mechanisms for the Lisbon subway by examining their ability to mitigate the system’s operation and development costs. The study focus is on the municipality of Lisbon where this system mainly operates. This research uses spatial hedonic pricing models of the real estate of the region, calibrated on previous stages of the study, to assess the extent to which transportation infrastructure is currently capitalized into the real estate market. The paper uses a Monte Carlo simulation procedure to estimate a synthetic population of residential and non-residential properties that matches the census blocks statistics, measuring the subway valuation for each synthetic property and aggregating the results for the whole municipality. This potential value capture estimate is then used to estimate an annual tax that could be charged under different value capture measure configurations (i.e., land value tax, special assessment). The results suggest that there is significant potential for the use of this instrument to finance the subway infrastructure