Cross-situational and supervised learning in the emergence of communication

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Transcriptional responses of host peripheral blood cells to tuberculosis infection

Host responses following exposure to Mycobacterium tuberculosis (TB) are complex and can significantly affect clinical outcome. These responses, which are largely mediated by complex immune mechanisms involving peripheral blood cells (PBCs) such as T-lymphocytes, NK cells and monocyte-derived macrophages, have not been fully characterized. We hypothesize that different clinical outcome following TB exposure will be uniquely reflected in host gene expression profiles, and expression profiling of PBCs can be used to discriminate between different TB infectious outcomes. In this study, microarray analysis was performed on PBCs from three TB groups (BCG-vaccinated, latent TB infection, and active TB infection) and a control healthy group. Supervised learning algorithms were used to identify signature genomic responses that differentiate among group samples. Gene Set Enrichment Analysis was used to determine sets of genes that were co-regulated. Multivariate permutation analysis (p < 0.01) gave 645 genes differentially expressed among the four groups, with both distinct and common patterns of gene expression observed for each group. A 127-probeset, representing 77 known genes, capable of accurately classifying samples into their respective groups was identified. In addition, 13 insulin-sensitive genes were found to be differentially regulated in all three TB infected groups, underscoring the functional association between insulin signaling pathway and TB infection. Published by Elsevier Ltd.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Análise de um ciclone semi-estacionário na costa sul do Brasil associado a bloqueio atmosférico

Na primeira semana de maio de 2008, durante quatro dias, um ciclone em superfície permaneceu semi-estacionário na costa da região sul do Brasil. Este sistema foi responsável por chuvas e ventos fortes no Rio Grande do Sul e Santa Catarina, os quais causaram muitos danos (queda de árvores, enchentes e desabamentos). O objetivo deste trabalho é avaliar o processo de formação e entender os mecanismos responsáveis pelo lento deslocamento do ciclone, já que a maioria dos ciclones nesta região possui deslocamento mais rápido. A equação de desenvolvimento de Sutcliffe mostrou que a advecção de vorticidade absoluta ciclônica na média troposfera e a advecção de ar quente na camada entre 1000-500 hPa foram mecanismos importantes para a ciclogênese. Neste período, o intenso aquecimento diabático também contribuiu para a ciclogênese, à medida que se contrapôs ao intenso resfriamento adiabático devido aos movimentos verticais ascendentes. A advecção de vorticidade absoluta ciclônica que favoreceu a ciclogênese esteve associada a um Vórtice Ciclônico em Altos Níveis (VCAN), que se formou numa região de anomalia de vorticidade potencial. O VCAN se manteve semi-estacionário e compôs o setor norte de um bloqueio do tipo dipolo. Tal bloqueio intensificou um anticiclone em superfície, situado a sul/leste do ciclone, o que contribuiu para o ciclone se manter semi-estacionário. O movimento atípico e lento do ciclone para sul, e em alguns períodos para sudoeste, esteve associado com advecções de vorticidade absoluta ciclônica na média troposfera e de ar quente no seu setor sul. Somente quando o bloqueio em níveis médios e a anomalia de vorticidade potencial em níveis médios/altos se enfraqueceram, o ciclone em superfície se afastou da costa sul do Brasil.

Convecção linearmente organizada na área de Petrolina, semi-árido do Nordeste do Brasil: aspectos em meso e grande escala

É apresentado um estudo sobre sistemas convectivos linearmente organizados e observados por um radar meteorológico banda-C na região semi-árida do Nordeste do Brasil. São analisados três dias (27 a 29) de março de 1985, com ênfase na investigação do papel desempenhado por fatores locais e de grande escala no desenvolvimento dos sistemas. No cenário de grande escala, a área de cobertura do radar foi influenciada por um cavado de ar superior austral no dia 27 e por um vórtice ciclônico de altos níveis no dia 29. A convergência de umidade próxima à superfície favoreceu a atividade convectiva nos dias 27 e 29, enquanto que divergência de umidade próxima à superfície inibiu a atividade convectiva no dia 28. No cenário de mesoescala, foi observado que o aquecimento diurno é um fator importante para a formação de células convectivas, somando-se a ele o papel determinante da orografia na localização dos ecos. De maneira geral, as imagens de radar mostram os sistemas convectivos linearmente organizados em áreas elevadas e núcleos convectivos intensos envolvidos por uma área de precipitação estratiforme. Os resultados indicam que convergência do fluxo de umidade em grande escala e aquecimento radiativo, são fatores determinantes na evolução e desenvolvimento dos ecos na área de estudo.

Learning processes and the neural analysis of conditioning

Classical and operant conditioning principles, such as the behavioral discrepancy-derived assumption that reinforcement always selects antecedent stimulus and response relations, have been studied at the neural level, mainly by observing the strengthening of neuronal responses or synaptic connections. A review of the literature on the neural basis of behavior provided extensive scientific data that indicate a synthesis between the two conditioning processes based mainly on stimulus control in learning tasks. The resulting analysis revealed the following aspects. Dopamine acts as a behavioral discrepancy signal in the midbrain pathway of positive reinforcement, leading toward the nucleus accumbens. Dopamine modulates both types of conditioning in the Aplysia mollusk and in mammals. In vivo and in vitro mollusk preparations show convergence of both types of conditioning in the same motor neuron. Frontal cortical neurons are involved in behavioral discrimination in reversal and extinction procedures, and these neurons preferentially deliver glutamate through conditioned stimulus or discriminative stimulus pathways. Discriminative neural responses can reliably precede operant movements and can also be common to stimuli that share complex symbolic relations. The present article discusses convergent and divergent points between conditioning paradigms at the neural level of analysis to advance our knowledge on reinforcement.

Estudo da mobilização de metais e elementos traços em ambientes aquáticos do semi-árido brasileiro aplicando análises de componentes principais

The spatial and temporal retention of metals has been studied in water and sediments of the Gavião River, Anagé and Tremedal Reservoirs, located in the semi-arid region, Bahia - Brazil, in order to identify trends in the fluxes of metals from the sediments to the water column. The determination of metals was made by ICP OES and ET AAS. The application of statistical methods showed that this aquatic system presents suitable conditions to move Cd2+ and Pb2+ from the water column to the sediment.

Diferenças na adesão ao tratamento da tuberculose em relação ao sexo no distrito de saúde da Freguesia do Ó/Brasilândia - São Paulo

Os motivos para as diferenças epidemiológicas e para a adesão ao tratamento da tuberculose em relação a homens e mulheres são desconhecidos. Este trabalho tem como objetivo verificar diferenças na adesão ao tratamento da tuberculose em relação ao sexo; identificar aspectos facilitadores e dificultadores para a adesão ao tratamento da tuberculose em relação ao sexo; analisar as crenças consideradas importantes para a adesão ao tratamento da tuberculose. Foi utilizado o referencial teórico do Modelo de Crenças em Saúde de Rosenstock e a técnica da Análise de Conteúdos de Bardin. Foram realizadas 28 entrevistas semiestruturadas com homens e mulheres em tratamento supervisionado de tuberculose do Distrito de Saúde da Freguesia do Ó/Brasilândia. Os resultados mostraram que o perfil daqueles que falharam na terapêutica da tuberculose em relação ao sexo foi: mulher - solteira e separada, com atividade remunerada não comprovada, nível de escolaridade entre fundamental I completo e ensino médio completo; homem - casado, com atividade remunerada comprovada, nível de escolaridade entre ensino fundamental II completo e ensino médio completo. Os aspectos facilitadores encontrados para a boa adesão residem no bom atendimento dos profissionais de saúde e na percepção, por parte do paciente, da sua melhora de saúde. As crenças para a boa adesão ao tratamento no sexo masculino e feminino foram: bom atendimento do serviço de saúde e bom tratamento (em relação aos medicamentos).

A percepção do usuário do Programa Saúde da Família sobre a privacidade e a confidencialidade de suas informações

Trata-se de estudo que procurou conhecer como o usuário do Programa Saúde da Família (PSF) percebe o direito à privacidade e à confidencialidade de suas informações reveladas ao agente comunitário de saúde (ACS) e como relaciona a visita domiciliar ao seu direito à privacidade. Estudo qualitativo, de natureza exploratória e como instrumento de investigação elaborou-se um roteiro de entrevistas semiestruturadas, com questões abertas, realizadas com usuários de uma Unidade do PSF do município de São Paulo. Os resultados mostraram que os usuários não consideram a entrada do ACS em suas residências como uma invasão à sua privacidade e que esse profissional é visto, muitas vezes, apenas como um facilitador do acesso ao serviço de saúde. Constatou-se tendência em se admitir que as informações dadas em sigilo podem ser reveladas pelo ACS. Notou-se a importância das relações de gênero e do cuidado quando da revelação de determinadas condições de saúde. Enfermidades como AIDS, tuberculose, câncer, doenças da próstata e o diabetes apareceram como doenças que podem causar preconceito e, nesse sentido, não deveriam ser reveladas ao ACS, a não ser pela necessidade do acesso mais rápido às consultas médicas. Pareceu, ainda, haver certa passividade do usuário em relação à percepção da falta de respeito ao sigilo das suas informações.

Rapid detection of bovine coronavirus by a semi-nested RT-PCR

Bovine coronavirus (BCoV) is a member of the group 2 of the Coronavirus (Nidovirales: Coronaviridae) and the causative agent of enteritis in both calves and adult bovine, as well as respiratory disease in calves. The present study aimed to develop a semi-nested RT-PCR for the detection of BCoV based on representative up-to-date sequences of the nucleocapsid gene, a conserved region of coronavirus genome. Three primers were designed, the first round with a 463bp and the second (semi-nested) with a 306bp predicted fragment. The analytical sensitivity was determined by 10-fold serial dilutions of the BCoV Kakegawa strain (HA titre: 256) in DEPC treated ultra-pure water, in fetal bovine serum (FBS) and in a BCoV-free fecal suspension, when positive results were found up to the 10-2, 10-3 and 10-7 dilutions, respectively, which suggests that the total amount of RNA in the sample influence the precipitation of pellets by the method of extraction used. When fecal samples was used, a large quantity of total RNA serves as carrier of BCoV RNA, demonstrating a high analytical sensitivity and lack of possible substances inhibiting the PCR. The final semi-nested RT-PCR protocol was applied to 25 fecal samples from adult cows, previously tested by a nested RT-PCR RdRp used as a reference test, resulting in 20 and 17 positives for the first and second tests, respectively, and a substantial agreement was found by kappa statistics (0.694). The high sensitivity and specificity of the new proposed method and the fact that primers were designed based on current BCoV sequences give basis to a more accurate diagnosis of BCoV-caused diseases, as well as to further insights on protocols for the detection of other Coronavirus representatives of both Animal and Public Health importance.