Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

CoDa-dendrogram: A new exploratory too

The use of orthonormal coordinates in the simplex and, particularly, balance coordinates, has suggested the use of a dendrogram for the exploratory analysis of compositional data. The dendrogram is based on a sequential binary partition of a compositional vector into groups of parts. At each step of a partition, one group of parts is divided into two new groups, and a balancing axis in the simplex between both groups is defined. The set of balancing axes constitutes an orthonormal basis, and the projections of the sample on them are orthogonal coordinates. They can be represented in a dendrogram-like graph showing: (a) the way of grouping parts of the compositional vector; (b) the explanatory role of each subcomposition generated in the partition process; (c) the decomposition of the total variance into balance components associated with each binary partition; (d) a box-plot of each balance. This representation is useful to help the interpretation of balance coordinates; to identify which are the most explanatory coordinates; and to describe the whole sample in a single diagram independently of the number of parts of the sample

A Factor analysis of hidrochemical composition of Llobregat river basin

Hydrogeological research usually includes some statistical studies devised to elucidate mean background state, characterise relationships among different hydrochemical parameters, and show the influence of human activities. These goals are achieved either by means of a statistical approach or by mixing models between end-members. Compositional data analysis has proved to be effective with the first approach, but there is no commonly accepted solution to the end-member problem in a compositional framework. We present here a possible solution based on factor analysis of compositions illustrated with a case study. We find two factors on the compositional bi-plot fitting two non-centered orthogonal axes to the most representative variables. Each one of these axes defines a subcomposition, grouping those variables that lay nearest to it. With each subcomposition a log-contrast is computed and rewritten as an equilibrium equation. These two factors can be interpreted as the isometric log-ratio coordinates (ilr) of three hidden components, that can be plotted in a ternary diagram. These hidden components might be interpreted as end-members. We have analysed 14 molarities in 31 sampling stations all along the Llobregat River and its tributaries, with a monthly measure during two years. We have obtained a bi-plot with a 57% of explained total variance, from which we have extracted two factors: factor G, reflecting geological background enhanced by potash mining; and factor A, essentially controlled by urban and/or farming wastewater. Graphical representation of these two factors allows us to identify three extreme samples, corresponding to pristine waters, potash mining influence and urban sewage influence. To confirm this, we have available analysis of diffused and widespread point sources identified in the area: springs, potash mining lixiviates, sewage, and fertilisers. Each one of these sources shows a clear link with one of the extreme samples, except fertilisers due to the heterogeneity of their composition. This approach is a useful tool to distinguish end-members, and characterise them, an issue generally difficult to solve. It is worth note that the end-member composition cannot be fully estimated but only characterised through log-ratio relationships among components. Moreover, the influence of each endmember in a given sample must be evaluated in relative terms of the other samples. These limitations are intrinsic to the relative nature of compositional data

On the use of principal components in contemporany population genetics: a case study

In human Population Genetics, routine applications of principal component techniques are often required. Population biologists make widespread use of certain discrete classifications of human samples into haplotypes, the monophyletic units of phylogenetic trees constructed from several single nucleotide bimorphisms hierarchically ordered. Compositional frequencies of the haplotypes are recorded within the different samples. Principal component techniques are then required as a dimension-reducing strategy to bring the dimension of the problem to a manageable level, say two, to allow for graphical analysis. Population biologists at large are not aware of the special features of compositional data and normally make use of the crude covariance of compositional relative frequencies to construct principal components. In this short note we present our experience with using traditional linear principal components or compositional principal components based on logratios, with reference to a specific dataset

Interpretation of wind components as compositional variables

The classical statistical study of the wind speed in the atmospheric surface layer is made generally from the analysis of the three habitual components that perform the wind data, that is, the component W-E, the component S-N and the vertical component, considering these components independent. When the goal of the study of these data is the Aeolian energy, so is when wind is studied from an energetic point of view and the squares of wind components can be considered as compositional variables. To do so, each component has to be divided by the module of the corresponding vector. In this work the theoretical analysis of the components of the wind as compositional data is presented and also the conclusions that can be obtained from the point of view of the practical applications as well as those that can be derived from the application of this technique in different conditions of weather

Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

A new distribution on the simplex containing the Dirichlet family

The Dirichlet family owes its privileged status within simplex distributions to easyness of interpretation and good mathematical properties. In particular, we recall fundamental properties for the analysis of compositional data such as closure under amalgamation and subcomposition. From a probabilistic point of view, it is characterised (uniquely) by a variety of independence relationships which makes it indisputably the reference model for expressing the non trivial idea of substantial independence for compositions. Indeed, its well known inadequacy as a general model for compositional data stems from such an independence structure together with the poorness of its parametrisation. In this paper a new class of distributions (called Flexible Dirichlet) capable of handling various dependence structures and containing the Dirichlet as a special case is presented. The new model exhibits a considerably richer parametrisation which, for example, allows to model the means and (part of) the variance-covariance matrix separately. Moreover, such a model preserves some good mathematical properties of the Dirichlet, i.e. closure under amalgamation and subcomposition with new parameters simply related to the parent composition parameters. Furthermore, the joint and conditional distributions of subcompositions and relative totals can be expressed as simple mixtures of two Flexible Dirichlet distributions. The basis generating the Flexible Dirichlet, though keeping compositional invariance, shows a dependence structure which allows various forms of partitional dependence to be contemplated by the model (e.g. non-neutrality, subcompositional dependence and subcompositional non-invariance), independence cases being identified by suitable parameter configurations. In particular, within this model substantial independence among subsets of components of the composition naturally occurs when the subsets have a Dirichlet distribution

Lluís Oliver i els components fonamentals de la matèria

Entrevista en Lluís Oliver, químic gironí

Adding new components to the knowledge plane GMPLS over WDM networks

A recent study defines a new network plane: the knowledge plane. The incorporation of the knowledge plane over the network allows having more accurate information of the current and future network states. In this paper, the introduction and management of the network reliability information in the knowledge plane is proposed in order to improve the quality of service with protection routing algorithms in GMPLS over WDM networks. Different experiments prove the efficiency and scalability of the proposed scheme in terms of the percentage of resources used to protect the network

I-SS: integrated supervision systems approach based on interactive components

Process supervision is the activity focused on monitoring the process operation in order to deduce conditions to maintain the normality including when faults are present Depending on the number/distribution/heterogeneity of variables, behaviour situations, sub-processes, etc. from processes, human operators and engineers do not easily manipulate the information. This leads to the necessity of automation of supervision activities. Nevertheless, the difficulty to deal with the information complicates the design and development of software applications. We present an approach called "integrated supervision systems". It proposes multiple supervisors coordination to supervise multiple sub-processes whose interactions permit one to supervise the global process

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Transfer of knowledge of the structural components of the model Q400 and Global Express: the case of Aerospace Bombardier, Querétaro

The aim of this paper is to analyze the knowledge transfer in the production of structural components of two aircraft:Q400 and Global Express of Bombardier Aerospace Company, Querétaro. Bombardier Aerospace is a pioneer company in the aviation sector in Mexico, and the third largest civil aircraft manufacturer. In 2005, Bombardier decided to invest in Mexico, creating Bombardier Aerospace de Mexico S. A. C. V. and transferring production lines from Japan and Toronto to Queretaro. The relocation strategy of both plants aims to reduce modular and general production costs facing other competitors. The relocation has been supported by the State Government funds, through a trust and the creation of Queretaro aerospace cluster. Among various benefits, the State of Queretaro donated seventy-eight acres of land where the Queretaro International Airport (QIA) and a training centre will be built to promote the development of this sector. The interest in this research is to analyze and describe the transfer of knowledge to the production of structural components of both aircraft models, thanks to the results of productivity and internal and external factors which have contributed along with this transfer

Conceptuals Mentefactos as a Didactic-Pedagogical Strategy of the Basic Concepts in the Sampling Theory Applyed to the Health Research

In populational sampling it is vitally important to clarify and discern: first, the design or sampling method used to solve the research problem; second, the sampling size, taking into account different components (precision, reliability, variance); third, random selection and fourth, the precision estimate (sampling errors), so as to determine if it is possible to infer the obtained estimates from the target population. The existing difficulty to use concepts from the sampling theory is to understand them with absolute clarity and, to achieve it, the help from didactic-pedagogical strategies arranged as conceptual “mentefactos” (simple hierarchic diagrams organized from propositions) may prove useful. This paper presents the conceptual definition, through conceptual “mentefactos”, of the most important populational probabilistic sampling concepts, in order to obtain representative samples from populations in health research.

salud financiera y riesgo en los estados financieros de la empresa avianca del sector aeronautico

La globalización y la competitividad como realidad de las empresas, implica que los gerentes preparen a sus empresas de la mejor manera para sobrevivir en este mundo tan inestable y cambiante. El primer paso consta de investigar y medir como se encuentra la empresa en cada uno de sus componentes, tales como recurso humano, mercadeo, logística, operación y por último y más importante las finanzas. El conocimiento de salud financiera y de los riesgos asociados a la actividad de las empresas, les permitirá a los gerentes tomar las decisiones correctas para ser rentables y perdurables en el mundo de los negocios inmerso en la globalización y competitividad. Esta apreciación es pertinente en Avianca S.A. esto teniendo en cuenta su progreso y evolución desde su primer vuelo el 5 de diciembre de 1919 comercial, hasta hoy cuando cotiza en la bolsa de Nueva York. Se realizó un análisis de tipo descriptivo, acompañado de la aplicación de ratios y nomenclaturas, dando lugar a establecer la salud financiera y los riesgos, no solo de Avianca sino también del sector aeronáutico. Como resultado se obtuvo que el sector aeronáutico sea financieramente saludable en el corto plazo, pero en el largo plazo su salud financiera se ve comprometida por los riegos asociados al sector y a la actividad desarrollada.