Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Modelo regional da ionosfera (MOD_ION): implementação em tempo real

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Discursos presentes em um processo de reestruturação curricular de um curso de licenciatura em física: o legal, o real, e o possível

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

As (des)ilusões do (ir)real na ficção de Murilo Rubião

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Comparative Analysis of In-Medium Spectral Functions for N(940) and N (au)(1535) in Real-Time Thermal Field Theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks—the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity—generating novel, testable predictions—and generality—spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.

Disentangling social networks from spatiotemporal dynamics: the temporal structure of a dolphin society

Social networks are static illustrations of dynamic societies, within which social interactions are constantly changing. Fundamental sources of variation include ranging behaviour and temporal demographic changes. Spatiotemporal dynamics can favour or limit opportunities for individuals to interact, and then a network may not essentially represent social processes. We examined whether a social network can embed such nonsocial effects in its topology, whereby emerging modules depict spatially or temporally segregated individuals. To this end, we applied a combination of spatial, temporal and demographic analyses to a long-term study of the association patterns of Guiana dolphins, Sotalia guianensis. We found that association patterns are organized into a modular social network. Space use was unlikely to reflect these modules, since dolphins' ranging behaviour clearly overlapped. However, a temporal demographic turnover, caused by the exit/entrance of individuals (most likely emigration/immigration), defined three modules of associations occurring at different times. Although this factor could mask real social processes, we identified the temporal scale that allowed us to account for these demographic effects. By looking within this turnover period (32 months), we assessed fission-fusion dynamics of the poorly known social organization of Guiana dolphins. We highlight that spatiotemporal dynamics can strongly influence the structure of social networks. Our findings show that hypothetical social units can emerge due to the temporal opportunities for individuals to interact. Therefore, a thorough search for a satisfactory spatiotemporal scale that removes such nonsocial noise is critical when analysing a social system. (C) 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Structure of trajectories of complex-matrix eigenvalues in the Hermitian-non-Hermitian transition

The statistical properties of trajectories of eigenvalues of Gaussian complex matrices whose Hermitian condition is progressively broken are investigated. It is shown how the ordering on the real axis of the real eigenvalues is reflected in the structure of the trajectories and also in the final distribution of the eigenvalues in the complex plane.

Temporal changes of CB1 cannabinoid receptor in the basal ganglia as a possible structure-specific plasticity process in 6-OHDA lesioned rats

The endocannabinoid system has been implicated in several neurobiological processes, including neurodegeneration, neuroprotection and neuronal plasticity. The CB1 cannabinoid receptors are abundantly expressed in the basal ganglia, the circuitry that is mostly affected in Parkinson’s Disease (PD). Some studies show variation of CB1 expression in basal ganglia in different animal models of PD, however the results are quite controversial, due to the differences in the procedures employed to induce the parkinsonism and the periods analyzed after the lesion. The present study evaluated the CB1 expression in four basal ganglia structures, namely striatum, external globus pallidus (EGP), internal globus pallidus (IGP) and substantia nigra pars reticulata (SNpr) of rats 1, 5, 10, 20, and 60 days after unilateral intrastriatal 6-hydroxydopamine injections, that causes retrograde dopaminergic degeneration. We also investigated tyrosine hydroxylase (TH), parvalbumin, calbindin and glutamic acid decarboxylase (GAD) expression to verify the status of dopaminergic and GABAergic systems. We observed a structure-specific modulation of CB1 expression at different periods after lesions. In general, there were no changes in the striatum, decreased CB1 in IGP and SNpr and increased CB1 in EGP, but this increase was not sustained over time. No changes in GAD and parvalbumin expression were observed in basal ganglia, whereas TH levels were decreased and the calbindin increased in striatum in short periods after lesion. We believe that the structure-specific variation of CB1 in basal ganglia in the 6-hydroxydopamine PD model could be related to a compensatory process involving the GABAergic transmission, which is impaired due to the lack of dopamine. Our data, therefore, suggest that the changes of CB1 and calbindin expression may represent a plasticity process in this PD model

La Real Hacienda y el control del gasto. La Tesorería General en la España del siglo XVIII

[ES] La evolución historiográfica ocurrida en los últimos años en relación con el mundo de las finanzas en general y las Haciendas reales en particular, ha generado grandes cambios en la percepción del interés por el gasto de los Estados. La potenciación del deseo de observar cómo las Monarquías del Antiguo Régimen gestionan la atención a sus necesidades, conlleva aparejada una creciente atención por el estudio de los organismos encargados de dicha actuación. Entre estas instituciones destaca en la España del siglo XVIII la Tesorería Mayor o Tesorería General. Su estudio permite conocer la evolución política de la Monarquía y de su relación con las finanzas privadas desde una perspectiva diferente. Los estudios centrados en la actuación de este organismo, su composición interna, su estructura territorial, los mecanismos de su ordenación contable y, en definitiva, cualquier tipo de actuación ocurrida dentro del ámbito de su gestión son el objeto del presente estudio.

Caracterización y estudio de los sistemas operativos de tiempo real para sistemas embebidos FreeRTOS y ChibiOS/RT sobre un procesador SAM3X8E

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Dispersion approach to real and virtual Compton scattering

In recent years, new precision experiments have become possible withthe high luminosity accelerator facilities at MAMIand JLab, supplyingphysicists with precision data sets for different hadronic reactions inthe intermediate energy region, such as pion photo- andelectroproduction and real and virtual Compton scattering.By means of the low energy theorem (LET), the global properties of thenucleon (its mass, charge, and magnetic moment) can be separated fromthe effects of the internal structure of the nucleon, which areeffectively described by polarizabilities. Thepolarizabilities quantify the deformation of the charge andmagnetization densities inside the nucleon in an applied quasistaticelectromagnetic field. The present work is dedicated to develop atool for theextraction of the polarizabilities from these precise Compton data withminimum model dependence, making use of the detailed knowledge of pionphotoproduction by means of dispersion relations (DR). Due to thepresence of t-channel poles, the dispersion integrals for two ofthe six Compton amplitudes diverge. Therefore, we have suggested to subtract the s-channel dispersion integrals at zero photon energy($nu=0$). The subtraction functions at $nu=0$ are calculated through DRin the momentum transfer t at fixed $nu=0$, subtracted at t=0. For this calculation, we use the information about the t-channel process, $gammagammatopipito Nbar{N}$. In this way, four of thepolarizabilities can be predicted using the unsubtracted DR in the $s$-channel. The other two, $alpha-beta$ and $gamma_pi$, are free parameters in ourformalism and can be obtained from a fit to the Compton data.We present the results for unpolarized and polarized RCS observables,%in the kinematics of the most recent experiments, and indicate anenhanced sensitivity to the nucleon polarizabilities in theenergy range between pion production threshold and the $Delta(1232)$-resonance.newlineindentFurthermore,we extend the DR formalism to virtual Compton scattering (radiativeelectron scattering off the nucleon), in which the concept of thepolarizabilities is generalized to the case of avirtual initial photon by introducing six generalizedpolarizabilities (GPs). Our formalism provides predictions for the fourspin GPs, while the two scalar GPs $alpha(Q^2)$ and $beta(Q^2)$ have to befitted to the experimental data at each value of $Q^2$.We show that at energies betweenpion threshold and the $Delta(1232)$-resonance position, thesensitivity to the GPs can be increased significantly, as compared tolow energies, where the LEX is applicable. Our DR formalism can be used for analysing VCS experiments over a widerange of energy and virtuality $Q^2$, which allows one to extract theGPs from VCS data in different kinematics with a minimum of model dependence.

Sviluppo di un sistema miniaturizzato per il controllo real-time di assetto di nano e microsatelliti

Microsatelliti e nanosatelliti, come ad esempio i Cubesat, sono carenti di sistemi integrati di controllo d’assetto e di manovra orbitale. Lo scopo di questa tesi è stato quello di realizzare un sistema compatibile con Cubesat di una unità, completo di attuatori magnetici e attuatori meccanici, comprendente tutti i sensori e l’elettronica necessaria per il suo funzionamento, creando un dispositivo totalmente indipendente dal veicolo su cui è installato, capace di funzionare sia autonomamente che ricevendo comandi da terra. Nella tesi sono descritte le campagne di simulazioni numeriche effettuate per validare le scelte tecnologiche effettuate, le fasi di sviluppo dell’elettronica e della meccanica, i test sui prototipi realizzati e il funzionamento del sistema finale. Una integrazione così estrema dei componenti può implicare delle interferenze tra un dispositivo e l’altro, come nel caso dei magnetotorquer e dei magnetometri. Sono stati quindi studiati e valutati gli effetti della loro interazione, verificandone l’entità e la validità del progetto. Poiché i componenti utilizzati sono tutti di basso costo e di derivazione terrestre, è stata effettuata una breve introduzione teorica agli effetti dell’ambiente spaziale sull’elettronica, per poi descrivere un sistema fault-tolerant basato su nuove teorie costruttive. Questo sistema è stato realizzato e testato, verificando così la possibilità di realizzare un controller affidabile e resistente all’ambiente spaziale per il sistema di controllo d’assetto. Sono state infine analizzate alcune possibili versioni avanzate del sistema, delineandone i principali aspetti progettuali, come ad esempio l’integrazione di GPS e l’implementazione di funzioni di determinazione d’assetto sfruttando i sensori presenti a bordo.