Multivariate Density Estimation: An SVM Approach

We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. The algorithm is based upon a Support Vector Machine (SVM) approach to solving inverse operator problems. The algorithm is implemented and tested on simulated data from different distributions and different dimensionalities, gaussians and laplacians in $R^2$ and $R^{12}$. A comparison in performance is made with Gaussian Mixture Models (GMMs). Our algorithm does as well or better than the GMMs for the simulations tested and has the added advantage of being automated with respect to parameters.

Stimulus Simplification and Object Representation: A Modeling Study

Tsunoda et al. (2001) recently studied the nature of object representation in monkey inferotemporal cortex using a combination of optical imaging and extracellular recordings. In particular, they examined IT neuron responses to complex natural objects and "simplified" versions thereof. In that study, in 42% of the cases, optical imaging revealed a decrease in the number of activation patches in IT as stimuli were "simplified". However, in 58% of the cases, "simplification" of the stimuli actually led to the appearance of additional activation patches in IT. Based on these results, the authors propose a scheme in which an object is represented by combinations of active and inactive columns coding for individual features. We examine the patterns of activation caused by the same stimuli as used by Tsunoda et al. in our model of object recognition in cortex (Riesenhuber 99). We find that object-tuned units can show a pattern of appearance and disappearance of features identical to the experiment. Thus, the data of Tsunoda et al. appear to be in quantitative agreement with a simple object-based representation in which an object's identity is coded by its similarities to reference objects. Moreover, the agreement of simulations and experiment suggests that the simplification procedure used by Tsunoda (2001) is not necessarily an accurate method to determine neuronal tuning.

Modeling Stock Order Flows and Learning Market-Making from Data

Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling the market as a dynamic system and a reinforcement learning algorithm that learns profitable market-making strategies when run on this model. The sequence of buys and sells for a particular stock, the order flow, we model as an Input-Output Hidden Markov Model fit to historical data. When combined with the dynamics of the order book, this creates a highly non-linear and difficult dynamic system. Our reinforcement learning algorithm, based on likelihood ratios, is run on this partially-observable environment. We demonstrate learning results for two separate real stocks.

Uncertainty Propagation in Model-Based Recognition

Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three- dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three- dimensional objects, robust implementations of alignmentt interpretation- tree search, and ransformation clustering.

Towards Grid-Wide Modeling and Simulation

Modeling and simulation permeate all areas of business, science and engineering. With the increase in the scale and complexity of simulations, large amounts of computational resources are required, and collaborative model development is needed, as multiple parties could be involved in the development process. The Grid provides a platform for coordinated resource sharing and application development and execution. In this paper, we survey existing technologies in modeling and simulation, and we focus on interoperability and composability of simulation components for both simulation development and execution. We also present our recent work on an HLA-based simulation framework on the Grid, and discuss the issues to achieve composability.

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Experimental Modeling and Control Strategies on an Open Mobile Robot Platform PRIM

This research work deals with the problem of modeling and design of low level speed controller for the mobile robot PRIM. The main objective is to develop an effective educational tool. On one hand, the interests in using the open mobile platform PRIM consist in integrating several highly related subjects to the automatic control theory in an educational context, by embracing the subjects of communications, signal processing, sensor fusion and hardware design, amongst others. On the other hand, the idea is to implement useful navigation strategies such that the robot can be served as a mobile multimedia information point. It is in this context, when navigation strategies are oriented to goal achievement, that a local model predictive control is attained. Hence, such studies are presented as a very interesting control strategy in order to develop the future capabilities of the system

Availability analysis of GMPLS connections based on physical network topology

This paper presents a study of connection availability in GMPLS over optical transport networks (OTN) taking into account different network topologies. Two basic path protection schemes are considered and compared with the no protection case. The selected topologies are heterogeneous in geographic coverage, network diameter, link lengths, and average node degree. Connection availability is also computed considering the reliability data of physical components and a well-known network availability model. Results show several correspondences between suitable path protection algorithms and several network topology characteristics

Modeling of hybrid systems by piecewise decomposition

Piecewise linear models systems arise as mathematical models of systems in many practical applications, often from linearization for nonlinear systems. There are two main approaches of dealing with these systems according to their continuous or discrete-time aspects. We propose an approach which is based on the state transformation, more particularly the partition of the phase portrait in different regions where each subregion is modeled as a two-dimensional linear time invariant system. Then the Takagi-Sugeno model, which is a combination of local model is calculated. The simulation results show that the Alpha partition is well-suited for dealing with such a system

Evaluation of the effectiveness of an organization in the field of brand perception using a computer agent-based simulation system

Este proyecto de investigación busca usar un sistema de cómputo basado en modelación por agentes para medir la percepción de marca de una organización en una población heterogénea. Se espera proporcionar información que permita dar soluciones a una organización acerca del comportamiento de sus consumidores y la asociada percepción de marca. El propósito de este sistema es el de modelar el proceso de percepción-razonamiento-acción para simular un proceso de razonamiento como el resultado de una acumulación de percepciones que resultan en las acciones del consumidor. Este resultado definirá la aceptación de marca o el rechazo del consumidor hacia la empresa. Se realizó un proceso de recolección información acerca de una organización específica en el campo de marketing. Después de compilar y procesar la información obtenida de la empresa, el análisis de la percepción de marca es aplicado mediante procesos de simulación. Los resultados del experimento son emitidos a la organización mediante un informe basado en conclusiones y recomendaciones a nivel de marketing para mejorar la percepción de marca por parte de los consumidores.

Measuring and testing for the systemically important financial institutions

This paper analyzes the measure of systemic importance ∆CoV aR proposed by Adrian and Brunnermeier (2009, 2010) within the context of a similar class of risk measures used in the risk management literature. In addition, we develop a series of testing procedures, based on ∆CoV aR, to identify and rank the systemically important institutions. We stress the importance of statistical testing in interpreting the measure of systemic importance. An empirical application illustrates the testing procedures, using equity data for three European banks.

Shape complexity based on mutual information

Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others

Integrated management of urban wastewater systems: a model-based approach

The main objective pursued in this thesis targets the development and systematization of a methodology that allows addressing management problems in the dynamic operation of Urban Wastewater Systems. The proposed methodology will suggest operational strategies that can improve the overall performance of the system under certain problematic situations through a model-based approach. The proposed methodology has three main steps: The first step includes the characterization and modeling of the case-study, the definition of scenarios, the evaluation criteria and the operational settings that can be manipulated to improve the system’s performance. In the second step, Monte Carlo simulations are launched to evaluate how the system performs for a wide range of operational settings combinations, and a global sensitivity analysis is conducted to rank the most influential operational settings. Finally, the third step consists on a screening methodology applying a multi-criteria analysis to select the best combinations of operational settings.

Atmospheric downwelling longwave radiation at the surface during cloudless and overcast conditions. Measurements and modeling

Atmospheric downwelling longwave radiation is an important component of the terrestrial energy budget; since it is strongly related with the greenhouse effect, it remarkably affects the climate. In this study, I evaluate the estimation of the downwelling longwave irradiance at the terrestrial surface for cloudless and overcast conditions using a one-dimensional radiative transfer model (RTM), specifically the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART). The calculations performed by using this model were compared with pyrgeometer measurements at three different European places: Girona (NE of the Iberian Peninsula), Payerne (in the East of Switzerland), and Heselbach (in the Black Forest, Germany). Several studies of sensitivity based on the radiative transfer model have shown that special attention on the input of temperature and water content profiles must be held for cloudless sky conditions; for overcast conditions, similar sensitivity studies have shown that, besides the atmospheric profiles, the cloud base height is very relevant, at least for optically thick clouds. Also, the estimation of DLR in places where radiosoundings are not available is explored, either by using the atmospheric profiles spatially interpolated from the gridded analysis data provided by European Centre of Medium-Range Weather Forecast (ECMWF), or by applying a real radiosounding of a nearby site. Calculations have been compared with measurements at all sites. During cloudless sky conditions, when radiosoundings were available, calculations show differences with measurements of -2.7 ± 3.4 Wm-2 (Payerne). While no in situ radiosoundings are available, differences between modeling and measurements were about 0.3 ± 9.4 Wm-2 (Girona). During overcast sky conditions, when in situ radiosoundings and cloud properties (derived from an algorithm that uses spectral infrared and microwave ground based measurements) were available (Black Forest), calculations show differences with measurements of -0.28 ± 2.52 Wm2. When using atmospheric profiles from the ECMWF and fixed values of liquid water path and droplet effective radius (Girona) calculations show differences with measurements of 4.0 ± 2.5 Wm2. For all analyzed sky conditions, it has been confirmed that estimations from radiative transfer modeling are remarkably better than those obtained by simple parameterizations of atmospheric emissivity.

Improvements in the ray tracing of implicit surfaces based on interval arithmetic

Las superfícies implícitas son útiles en muchas áreasde los gráficos por ordenador. Una de sus principales ventajas es que pueden ser fácilmente usadas como primitivas para modelado. Aun asi, no son muy usadas porque su visualización toma bastante tiempo. Cuando se necesita una visualización precisa, la mejor opción es usar trazado de rayos. Sin embargo, pequeñas partes de las superficies desaparecen durante la visualización. Esto ocurre por la truncación que se presenta en la representación en punto flotante de los ordenadores; algunos bits se puerden durante las operaciones matemáticas en los algoritmos de intersección. En este tesis se presentan algoritmos para solucionar esos problemas. La investigación se basa en el uso del Análisis Intervalar Modal el cual incluye herramientas para resolver problemas con incertidumbe cuantificada. En esta tesis se proporcionan los fundamentos matemáticos necesarios para el desarrollo de estos algoritmos.