Refining predictions of iberian plant distribution: lessons from p. nigra and p. sylvestris palaeoecological-based habitat suitability models

In recent years, challenged by the climate scenarios put forward by the IPCC and its potential impact on plant distribution, numerous predictive techniques -including the so called habitat suitability models (HSM)- have been developed. Yet, as the output of the different methods produces different distribution areas, developing validation tools are strong needs to reduce uncertainties. Focused in the Iberian Peninsula, we propose a palaeo-based method to increase the robustness of the HSM, by developing an ecological approach to understand the mismatches between the palaeoecological information and the projections of the HSMs. Here, we present the result of (1) investigating causal relationships between environmental variables and presence of Pinus sylvestris L. and P. nigra Arn. available from the 3rd Spanish Forest Inventory, (2) developing present and past presence-predictions through the MaxEnt model for 6 and 21 kyr BP, and (3) assessing these models through comparisons with biomized palaeoecological data available from the European Pollen Database for the Iberian Peninsula.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.

Models for Internal Quality Fruit Sorting, Based on Time- Domain Laser Reflectance Spectroscopy (TDRS)

Time domain laser reflectance spectroscopy (TDRS) was applied for the first time to evaluate internal fruit quality. This technique, known in medicine-related knowledge areas, has not been used before in agricultural or food research. It allows the simultaneous non-destructive measuring of two optical characteristics of the tissues: light scattering and absorption. Models to measure firmness, sugar & acid contents in kiwifruit, tomato, apple, peach, nectarine and other fruits were built using sequential statistical techniques: principal component analysis, multiple stepwise linear regression, clustering and discriminant analysis. Consistent correlations were established between the two parameters measured with TDRS, i.e. absorption & transport scattering coefficients, with chemical constituents (sugars and acids) and firmness, respectively. Classification models were built to sort fruits into three quality grades, according to their firmness, soluble solids and acidity.

Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models

Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models.

Automatic generation of models for abdominal aortic aneurysms and intraluminal thrombus based on hexahedral meshes

To propose an automated patient-specific algorithm for the creation of accurate and smooth meshes of the aortic anatomy, to be used for evaluating rupture risk factors of abdominal aortic aneurysms (AAA). Finite element (FE) analyses and simulations require meshes to be smooth and anatomically accurate, capturing both the artery wall and the intraluminal thrombus (ILT). The two main difficulties are the modeling of the arterial bifurcations, and of the ILT, which has an arbitrary shape that is conforming to the aortic wall.

Analysis of the Technical Efficiency of Urban Bus Services in Spain Based on SBM Models

During the first decade of the new millennium, fueled by the economic development in Spain, urban bus services were extended. Since the years 2008 and 2009, the root of the economic crisis, the improvement of these services is at risk due to economic problems. In this paper, the technical efficiency of the main urban bus companies in Spain during the 2004–2009 period are studied using SBM (slack-based measures) models and by establishing the slacks in the services' production inputs. The influence of a series of exogenous variables on the operation of the different services is also analyzed. It is concluded that only the 24% of the case studies are efficient, and some urban form variables can explain part of the inefficiency. The methodology used allows studying the inefficiency in a disaggregated way that other DEA (data envelopment analysis) models do not.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks.

Automatic segmentation of abdominal aortic aneurysm from medical images based on active shape models and texture models

A semi-automatic segmentation algorithm for abdominal aortic aneurysms (AAA), and based on Active Shape Models (ASM) and texture models, is presented in this work. The texture information is provided by a set of four 3D magnetic resonance (MR) images, composed of axial slices of the abdomen, where lumen, wall and intraluminal thrombus (ILT) are visible. Due to the reduced number of images in the MRI training set, an ASM and a custom texture model based on border intensity statistics are constructed. For the same reason the shape is characterized from 35-computed tomography angiography (CTA) images set so the shape variations are better represented. For the evaluation, leave-one-out experiments have been held over the four MRI set.

Models for internal quality sorting of fruit, based on time-domain laser reflectance spectroscopy (TDRS).

Non-destructive measurement of fruit quality has been an important objective through recent years (Abbott, 1999). Near infrared spectroscopy (NIR) is applicable to the cuantification of chemicals in foods and NIK "laser spectroscopy" can be used to estimate the firmness of fruits. However, die main limitation of current optical techniques that measure light transmission is that they do not account for the coupling between absorption and scattering inside the tissue, when quantifying the intensity o f reemitted light. The solution o f this l i m i t a t i o n was the goal o f the present work.

Characterization of ICT-based business models of social enterprises

In recent years new models for organizations working on overty alleviation have emerged. One of them, the social enterprise, has attracted the attention of both academics and practitioners all over the world. Even if defined in different ways depending on the context, social enterprise has an enormous potential to generate social benefits and to promote local agency and private initiative in poverty alleviation. In this sense, it is fitting to highlight the importance of identifying the main standards that permit the characterization of diverse social enterprises, in order to understand their main specificities and guarantee value generation for low-income populations. Another crucial factor is understanding innovation as a critical factor in promoting social enterprises. A powerful tool to enhance the impact and application of this model is Information and Communication Technologies. In the 21st century,these tools allow users to find new ways of collaboration, new sustainable business models and a cost-effective way of scaling-up initiatives. This paper, a product of the collaborative research between the Universidad Politécnica de Madrid and the Universidade Federal Fluminense, examines different business models for social enterprises and the role that ICT can play in scale and impact of these initiatives

Brain injury MRI simulator based on theoretical models of neuroanatomic damage

In order to improve the body of knowledge about brain injury impairment is essential to develop image database with different types of injuries. This paper proposes a new methodology to model three types of brain injury: stroke, tumor and traumatic brain injury; and implements a system to navigate among simulated MRI studies. These studies can be used on research studies, to validate new processing methods and as an educational tool, to show different types of brain injury and how they affect to neuroanatomic structures.

Interpretation of the pressuremeter test using numerical models based on deformation tensor equations

The pressuremeter test in boreholes has proven itself as a useful tool in geotechnical explorations, especially comparing its results with those obtained from a mathematical model ruled by a soil representative constitutive equation. The numerical model shown in this paper is aimed to be the reference framework for the interpretation of this test. The model analyses variables such as: the type of response, the initial state, the drainage regime and the constitutive equations. It is a model of finite elements able to work with a mesh without deformation or one adapted to it.