Using non-homogeneous Poisson models with multiple change-points to estimate the number of ozone exceedances in Mexico City

In this paper, we consider some non-homogeneous Poisson models to estimate the probability that an air quality standard is exceeded a given number of times in a time interval of interest. We assume that the number of exceedances occurs according to a non-homogeneous Poisson process (NHPP). This Poisson process has rate function lambda(t), t >= 0, which depends on some parameters that must be estimated. We take into account two cases of rate functions: the Weibull and the Goel-Okumoto. We consider models with and without change-points. When the presence of change-points is assumed, we may have the presence of either one, two or three change-points, depending of the data set. The parameters of the rate functions are estimated using a Gibbs sampling algorithm. Results are applied to ozone data provided by the Mexico City monitoring network. In a first instance, we assume that there are no change-points present. Depending on the adjustment of the model, we assume the presence of either one, two or three change-points. Copyright (C) 2009 John Wiley & Sons, Ltd.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Price discovery in dual-class shares across multiple markets

We extend the standard price discovery analysis to estimate the information share of dual-class shares across domestic and foreign markets. By examining both common and preferred shares, we aim to extract information not only about the fundamental value of the rm, but also about the dual-class premium. In particular, our interest lies on the price discovery mechanism regulating the prices of common and preferred shares in the BM&FBovespa as well as the prices of their ADR counterparts in the NYSE and in the Arca platform. However, in the presence of contemporaneous correlation between the innovations, the standard information share measure depends heavily on the ordering we attribute to prices in the system. To remain agnostic about which are the leading share class and market, one could for instance compute some weighted average information share across all possible orderings. This is extremely inconvenient given that we are dealing with 2 share prices in Brazil, 4 share prices in the US, plus the exchange rate (and hence over 5,000 permutations!). We thus develop a novel methodology to carry out price discovery analyses that does not impose any ex-ante assumption about which share class or trading platform conveys more information about shocks in the fundamental price. As such, our procedure yields a single measure of information share, which is invariant to the ordering of the variables in the system. Simulations of a simple market microstructure model show that our information share estimator works pretty well in practice. We then employ transactions data to study price discovery in two dual-class Brazilian stocks and their ADRs. We uncover two interesting ndings. First, the foreign market is at least as informative as the home market. Second, shocks in the dual-class premium entail a permanent e ect in normal times, but transitory in periods of nancial distress. We argue that the latter is consistent with the expropriation of preferred shareholders as a class.

Experience-dependent upregulation of multiple plasticity factors in the hippocampus during early REM sleep

Sleep is beneficial to learning, but the underlying mechanisms remain controversial. The synaptic homeostasis hypothesis (SHY) proposes that the cognitive function of sleep is related to a generalized rescaling of synaptic weights to intermediate levels, due to a passive downregulation of plasticity mechanisms. A competing hypothesis proposes that the active upscaling and downscaling of synaptic weights during sleep embosses memories in circuits respectively activated or deactivated during prior waking experience, leading to memory changes beyond rescaling. Both theories have empirical support but the experimental designs underlying the conflicting studies are not congruent, therefore a consensus is yet to be reached. To advance this issue, we used real-time PCR and electrophysiological recordings to assess gene expression related to synaptic plasticity in the hippocampus and primary somatosensory cortex of rats exposed to novel objects, then kept awake (WK) for 60 min and finally killed after a 30 min period rich in WK, slow-wave sleep (SWS) or rapid-eye-movement sleep (REM). Animals similarly treated but not exposed to novel objects were used as controls. We found that the mRNA levels of Arc, Egr1, Fos, Ppp2ca and Ppp2r2d were significantly increased in the hippocampus of exposed animals allowed to enter REM, in comparison with control animals. Experience-dependent changes during sleep were not significant in the hippocampus for Bdnf, Camk4, Creb1, and Nr4a1, and no differences were detected between exposed and control SWS groups for any of the genes tested. No significant changes in gene expression were detected in the primary somatosensory cortex during sleep, in contrast with previous studies using longer post-stimulation intervals (>180 min). The experience-dependent induction of multiple plasticity-related genes in the hippocampus during early REM adds experimental support to the synaptic embossing theory.

Calculating the Best Dual Bound for Problems with Multiple Lagrangian Relaxations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A new implementation of population based incremental learning method for optimizations in electromagnetics

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

The swing-by effect in the Vesta-Magnya case. Single and multiple encounters.

It was verified that the asteroid Magnya has some physical and chemical characteristics similar to the bodies from Vesta family. However, astronomical observations revealed that Magnya is distant from these bodies. In the present work, we assumed that Magnya originated from Vesta and we try to justify its current distant orbital location taking into account the effects of close encounters between Magnya and Vesta. The methodology adopted involved an analytical approach considering the technique of the gravity assisted maneuver, also known as swing-by. We found that the energy variation achieved through a single swing-by between Vesta and Magnya are very small when compared to the variation that would be required to change the orbit of Magnya. The effects of multiple close encounters were also considered and discussed. We concluded that the possibility of multiple encounters is limited, and therefore, that Magnya should suffer other perturbations (such as resonances, collisions or close encounters with other bodies, for instance) that would provide the supposed change in its orbit.

Multiple higgs and vector boson production beyond the standard model

If the electroweak symmetry breaking is originated from a strongly coupled sector, as for instance in composite Higgs models, the Higgs boson couplings can deviate from their Standard Model values. In such cases, at sufficiently high energies there could occur an onset of multiple Higgs boson and longitudinally polarised electroweak gauge boson (V L ) production. We study the sensitivity to anomalous Higgs couplings in inelastic processes with 3 and 4 particles (either Higgs bosons or V L 's) in the final state. We show that, due to the more severe cancellations in the corresponding amplitudes as compared to the usual 2 → 2 processes, large enhancements with respect to the Standard Model can arise even for small modifications of the Higgs couplings. In particular, we find that triple Higgs production provides the best multiparticle channel to look for these deviations. We briefly explore the consequences of multiparticle production at the LHC. © 2013 SISSA.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Cultura, linguagem e ensino da língua estrangeira: um estudo acerca desta inter-relação

Pós-graduação em Estudos Linguísticos - IBILCE

Desafios da pós-graduação em Psicologia no Brasil

Com menos de cinco décadas de regulamentação, o sistema de pós-graduação no Brasil pode ser considerado bem sucedido do ponto de vista de suas realizações,embora inacabado quanto a algumas de suas feições fundamentais. Na área de Psicologia, o sistema compreende 64 Programas, 42 deles com o nível de Doutorado. Para responder de modo eficiente às demandas dirigidas à pós-graduação no Brasil,a área de Psicologia precisará enfrentar um conjunto de desafios, dentre os quais destacamos: a expansão da abrangência geográfica e temática, de modo a vencer assimetrias regionais e desequilíbrios na cobertura das subáreas e temas de pesquisa em Psicologia; o aperfeiçoamento do sistema de avaliação, a fim de contemplar a diversidade das estratégias de formação e da produção de conhecimento nas subáreas da Psicologia; a articulação dos diferentes resultados possíveis da pós-graduação em Psicologia, a partir do reconhecimento de que as bases para a produção de conhecimento na área variam quanto à imposição de demandas adicionais e à possibilidade de associar produção de conhecimento ao desenvolvimento de tecnologias de intervenção; e a formulação de políticas voltadas à qualificação do sistema, por exemplo, por meio do incremento das redes de pesquisa, da internacionalização dos grupos, da divulgação da produção científica e da formação metodológica mais sólida e ampla. Um diagnóstico mais cuidadoso desses e de outros desafios, em suas múltiplas dimensões, poderá conduzir a um aproveitamento mais eficiente das potencialidades dos grupos brasileiros de pesquisa em Psicologia, na formação de novos pesquisadores e na produção de conhecimento.

A multiple intelligences theory-based 3D virtual lab environment for digital systems teaching.

This paper describes a 3D virtual lab environment that was developed using OpenSim software integrated into Moodle. Virtuald software tool was used to provide pedagogical support to the lab by enabling to create online texts and delivering them to the students. The courses taught in this virtual lab are methodologically in conformity to theory of multiple intelligences. Some results are presented.

The Dorsolateral Periaqueductal Gray and Its Role in Mediating Fear Learning to Life Threatening Events

The dorsolateral column of the periaqueductal gray (dlPAG) integrates aversive emotional experiences and represents an important site responding to life threatening situations, such as hypoxia, cardiac pain and predator threats. Previous studies have shown that the dorsal PAG also supports fear learning; and we have currently explored how the dlPAG influences associative learning. We have first shown that N-methyl-D-aspartate (NMDA) 100 pmol injection in the dlPAG works as a valuable unconditioned stimulus (US) for the acquisition of olfactory fear conditioning (OFC) using amyl acetate odor as conditioned stimulus (CS). Next, we revisited the ascending projections of the dlPAG to the thalamus and hypothalamus to reveal potential paths that could mediate associative learning during OFC. Accordingly, the most important ascending target of the dlPAG is the hypothalamic defensive circuit, and we were able to show that pharmacological inactivation using beta-adrenoceptor blockade of the dorsal premammillary nucleus, the main exit way for the hypothalamic defensive circuit to thalamo-cortical circuits involved in fear learning, impaired the acquisition of the OFC promoted by NMDA stimulation of the dlPAG. Moreover, our tracing study revealed multiple parallel paths from the dlPAG to several thalamic targets linked to cortical-hippocampal-amygdalar circuits involved in fear learning. Overall, the results point to a major role of the dlPAG in the mediation of aversive associative learning via ascending projections to the medial hypothalamic defensive circuit, and perhaps, to other thalamic targets, as well. These results provide interesting perspectives to understand how life threatening events impact on fear learning, and should be useful to understand pathological fear memory encoding in anxiety disorders.

Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

Abstract Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.