Instantâneos disruptivos: uma experiência de crítica sociopolítica com arte

A partir do final da década de 1960, no mundo e no Brasil, o fenômeno de crítica sociopolítica com arte tem se desenvolvido expressivamente e apresentado um considerável conjunto em termos de quantidade, mas também de qualidade de produções. De meados da década de 1990 até a presente data, esse fenômeno se apresenta de maneira mais acelerada, e em torno de coletivizações artísticas. Tais coletivizações caracterizam-se por desenvolver ainda mais as aproximações entre a arte, o sociopolítico e o cultural, e também por revelar lugares outros para o desenvolvimento de expressões artísticas e de formas variadas de atuação artística no e sobre o espaço público no sentido de subversão e de crítica sobre os sistemas sociais. A presente dissertação observa o desenvolvimento do fenômeno em suas diferentes nuances ao longo do tempo, bem como faz uma análise apreciativa e investigativa sobre algumas obras individuais e de coletivos. Visa apontar e percorrer as diferentes produções artísticas presente e passado aqui elencadas, dado que incorporam uma visada de crítica sociopolítica com arte, o que nos possibilita observar a potência da arte para tratar questões que emergem dos ambientes sociopolítico e cultural; sua reincidência, variedade, conexões e diferenças

A criação da Associação Brasileira de Obstetrizes e Enfermeiros Obstetras (ABENFO) e sua participação no Movimento de Humanização do Parto e Nascimento (1989-2002)

Este estudo, de natureza histórico-social, tem como objeto a criação da Associação Brasileira de Obstetrizes e Enfermeiros Obstetras (ABENFO) e suas estratégias no Movimento de Humanização do Parto e Nascimento Brasileiro (1989-2002). A delimitação temporal do estudo abrange o período de 1989 a 2002. Os objetivos da pesquisa são: analisar a transição da Associação Brasileira de Obstetrizes (ABO) para Associação Brasileira de Obstetrizes e Enfermeiros Obstetras (ABENFO); analisar as estratégias elaboradas pela ABENFO para a atualização do habitus das agentes; analisar o fortalecimento do Movimento de Humanização do Parto e Nascimento empreendido pela ABENFO. O estudo apoia-se teoricamente nos conceitos desenvolvidos pelo sociólogo Pierre Bourdieu e utilizou o método da história oral temática. Na análise, houve a articulação de documentos escritos e depoimentos orais à luz do referencial teórico. Os resultados da pesquisa evidenciam que, no processo de surgimento da ABENFO, houve um período de aproximações de agentes que durou aproximadamente 15 anos. A primeira aproximação foi entre parteiras/obstetrizes e as enfermeiras no campo sindical; a segunda aproximação de agentes, desta vez pelo habitus profissional, foi de enfermeiras de saúde pública e enfermeiras obstétricas no campo hospitalar e científico; e a terceira aproximação foi entre as parteiras/obstetrizes com as enfermeiras obstétricas. Após essas aproximações, a enfermeira obstétrica assumiu a diretoria provisória da ABO, realizando, em seguida, a transição para a ABENFO. Após a transição, a ABENFO nacional consolidou-se como representante das enfermeiras obstétricas e obstetrizes. Em seguida, foi necessário criar estratégias para atualizar o habitus das agentes, tais como: Estratégias de fortalecimento da Associação no campo político da Enfermagem e da Saúde da Mulher; Estratégias de ampliação da sua representação nacional entre enfermeiras obstétricas; Estratégias para divulgação do capital social da ABENFO. Dentre as estratégias de divulgação, aconteceram três Congressos Brasileiros de Enfermagem Obstétrica e Neonatal (COBEONS) que fortaleceram o Movimento de Humanização do Parto e Nascimento, pois neste espaço circulou o capital sociocultural do movimento social entre as associadas, levando aos mesmos uma atualização do seu habitus, e, por outro lado, fortalecendo o Movimento por meio do reconhecimento. Portanto, o fortalecimento do processo de humanização do parto e nascimento brasileiro confirmou a hipótese de que a criação da ABENFO possibilitou a elaboração de estratégias que impulsionaram a atualização do habitus das agentes. Este estudo foi esclarecedor, na medida em que favoreceu a compreensão das circunstâncias de criação da ABENFO e sua participação como a única representante das enfermeiras obstétricas e obstetrizes no Movimento de Humanização do Parto e Nascimento, além de demonstrar o quanto estas agentes contribuíram para a sua consolidação.

Aproximação fisionômica pericial através de função de base radial hermitiana

A aproximação fisionômica é o método que busca, a partir do crânio, simular a fotografia de um indivíduo quando em vida. Deve ser empregada como último recurso, na busca de desaparecidos, quando não houver possibilidade de aplicação de um método válido de identificação. O objetivo deste estudo foi obter a aproximação fisionômica, a partir de um crânio seco e de tomografia computadorizada multislice de indivíduos vivos, através da função de base radial hermitiana (FBRH). Constituiu-se também em avaliar o resultado da mesma quanto ao reconhecimento. Na primeira etapa do estudo, foi utilizada a imagem escaneada de um crânio seco, de origem desconhecida, com o intuito de avaliar se a quantidade de pontos obtidos seria suficiente para aplicação da FBRH e consequente reconstrução da superfície facial. Na segunda fase, foram utilizadas três tomografias de indivíduos vivos, para análise da semelhança alcançada entre a face escaneada e as aproximações faciais. Nesta etapa, foi aplicada uma associação de diferentes metodologias já publicadas, para reconstrução de uma mesma região da face, a partir de um mesmo crânio. Na última etapa, foram simuladas situações de reconhecimento com familiares e amigos dos indivíduos doadores das tomografias. Observou-se que a metodologia de FBRH pode ser empregada em aproximação fisionômica. Houve reconhecimento positivo nos três sujeitos estudados, sendo que, em dois deles, os resultados foram ainda mais significativos. Desta forma, conclui-se que a metodologia é rápida, objetiva e proporciona o reconhecimento. Esta permite a criação de múltiplas versões de aproximações fisionômicas a partir do mesmo crânio, o que amplia as possibilidades de reconhecimento. Observou-se ainda que a técnica não exige habilidade artística do profissional.

Deriving acceptable biological catch from the overfishing limit: implications for assessment models

The recently revised Magnuson–Stevens Fishery Conservation and Management Act requires that U.S. fishery management councils avoid overfishing by setting annual catch limits (ACLs) not exceeding recommendations of the councils’ scientific advisers. To meet that requirement, the scientific advisers will need to know the overfishing limit (OFL) estimated in each stock assessment, with OFL being the catch available from applying the limit fishing mortality rate to current or projected stock biomass. The advisers then will derive ‘‘acceptable biological catch’’ (ABC) from OFL by reducing OFL to allow for scientific uncertainty, and ABC becomes their recommendation to the council. We suggest methodology based on simple probability theory by which scientific advisers can compute ABC from OFL and the statistical distribution of OFL as estimated by a stock assessment. Our method includes approximations to the distribution of OFL if it is not known from the assessment; however, we find it preferable to have the assessment model estimate the distribution of OFL directly. Probability-based methods such as this one provide well-defined approaches to setting ABC and may be helpful to scientific advisers as they translate the new legal requirement into concrete advice.

Bayesian adaptation and adaptively trained systems

As the use of found data increases, more systems are being built using adaptive training. Here transforms are used to represent unwanted acoustic variability, e.g. speaker and acoustic environment changes, allowing a canonical model that models only the "pure" variability of speech to be trained. Adaptive training may be described within a Bayesian framework. By using complexity control approaches to ensure robust parameter estimates, the standard point estimate adaptive training can be justified within this Bayesian framework. However during recognition there is usually no control over the amount of data available. It is therefore preferable to be able to use a full Bayesian approach to applying transforms during recognition rather than the standard point estimates. This paper discusses various approximations to Bayesian approaches including a new variational Bayes approximation. The application of these approaches to state-of-the-art adaptively trained systems using both CAT and MLLR transforms is then described and evaluated on a large vocabulary speech recognition task. © 2005 IEEE.

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods. © 2009 IFAC.

Enhanced poisson sum representation for alpha-stable processes

In this paper we present Poisson sum series representations for α-stable (αS) random variables and a-stable processes, in particular concentrating on continuous-time autoregressive (CAR) models driven by α-stable Lévy processes. Our representations aim to provide a conditionally Gaussian framework, which will allow parameter estimation using Rao-Blackwellised versions of state of the art Bayesian computational methods such as particle filters and Markov chain Monte Carlo (MCMC). To overcome the issues due to truncation of the series, novel residual approximations are developed. Simulations demonstrate the potential of these Poisson sum representations for inference in otherwise intractable α-stable models. © 2011 IEEE.

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.

Scalar and its dissipation in the near field of turbulent lifted jet flame

In this study various scalar dissipation rates and their modelling in the context of partially premixed flame are investigated. A DNS dataset of the near field of a turbulent hydrogen lifted jet flame is processed to analyse the mixture fraction and progress variable dissipation rates and their cross dissipation rate at several axial positions. It is found that the classical model for the passive scalar dissipation rate ε{lunate}̃ZZ gives good agreement with the DNS, while models developed based on premixed flames for the reactive scalar dissipation rate ε{lunate}̃cc only qualitatively capture the correct trend. The cross dissipation rate ε{lunate}̃cZ is mostly negative and can be reasonably approximated at downstream positions once ε{lunate}̃ZZ and ε{lunate}̃cc are known, although the sign cannot be determined. This approach gives better results than one employing a constant ratio of turbulent timescale and the scalar covariance c'Z'̃. The statistics of scalar gradients are further examined and lognormal distributions are shown to be very good approximations for the passive scalar and acceptable for the reactive scalar. The correlation between the two gradients increases downstream as the partially premixed flame in the near field evolves ultimately to a diffusion flame in the far field. A bivariate lognormal distribution is tested and found to be a reasonable approximation for the joint PDF of the two scalar gradients. © 2011 The Combustion Institute.

A variational perspective on noise-robust speech recognition

Model compensation methods for noise-robust speech recognition have shown good performance. Predictive linear transformations can approximate these methods to balance computational complexity and compensation accuracy. This paper examines both of these approaches from a variational perspective. Using a matched-pair approximation at the component level yields a number of standard forms of model compensation and predictive linear transformations. However, a tighter bound can be obtained by using variational approximations at the state level. Both model-based and predictive linear transform schemes can be implemented in this framework. Preliminary results show that the tighter bound obtained from the state-level variational approach can yield improved performance over standard schemes. © 2011 IEEE.

Surface tension-induced global instability of planar jets and wakes

The effect of surface tension on global stability of co-flow jets and wakes at a moderate Reynolds number is studied. The linear temporal two-dimensional global modes are computed without approximations. All but one of the flow cases under study are globally stable without surface tension. It is found that surface tension can cause the flow to be globally unstable if the inlet shear (or equivalently, the inlet velocity ratio) is strong enough. For even stronger surface tension, the flow is re-stabilized. As long as there is no change of the most unstable mode, increasing surface tension decreases the oscillation frequency. Short waves appear in the high-shear region close to the nozzle, and their wavelength increases with increasing surface tension. The critical shear (the weakest inlet shear at which a global instability is found) gives rise to antisymmetric disturbances for the wakes and symmetric disturbances for the jets. However, at stronger shear, the opposite symmetry can be the most unstable one, in particular for wakes at high surface tension. The results show strong effects of surface tension that should be possible to reproduce experimentally as well as numerically.

Occlusive Components Analysis

We study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learnt from an unlabelled set of images in which objects occlude one another. Exact maximum-likelihood learning is intractable. However, we show that tractable approximations to Expectation Maximization (EM) can be found if the training images each contain only a small number of objects on average. In numerical experiments it is shown that these approximations recover the correct set of object parameters. Experiments on a novel version of the bars test using colored bars, and experiments on more realistic data, show that the algorithm performs well in extracting the generating causes. Experiments based on the standard bars benchmark test for object learning show that the algorithm performs well in comparison to other recent component extraction approaches. The model and the learning algorithm thus connect research on occlusion with the research field of multiple-causes component extraction methods.

Two problems with variational expectation maximisation for time-series models

Variational methods are a key component of the approximate inference and learning toolbox. These methods fill an important middle ground, retaining distributional information about uncertainty in latent variables, unlike maximum a posteriori methods (MAP), and yet generally requiring less computational time than Monte Carlo Markov Chain methods. In particular the variational Expectation Maximisation (vEM) and variational Bayes algorithms, both involving variational optimisation of a free-energy, are widely used in time-series modelling. Here, we investigate the success of vEM in simple probabilistic time-series models. First we consider the inference step of vEM, and show that a consequence of the well-known compactness property of variational inference is a failure to propagate uncertainty in time, thus limiting the usefulness of the retained distributional information. In particular, the uncertainty may appear to be smallest precisely when the approximation is poorest. Second, we consider parameter learning and analytically reveal systematic biases in the parameters found by vEM. Surprisingly, simpler variational approximations (such a mean-field) can lead to less bias than more complicated structured approximations.

First-principles energetics of water: a many-body analysis

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for solid, liquid and cluster forms of water. We use a many-body separation of the total energy into its 1-body, 2-body (2B) and beyond-2-body (B2B) components to analyze the deficiencies of two popular DFT approximations. We show how machine-learning methods make this analysis possible for ice structures as well as for water clusters. We find that the crucial energy balance between compact and extended geometries can be distorted by 2B and B2B errors, and that both types of first-principles error are important.

Balanced realisations of all-pass systems using exact arithmetic with application to the approximation of delay systems

Numerically well-conditioned state-space realisations for all-pass systems, such as Padé approximations to exp(-s), are derived that can be computed using exact integer arithmetic. This is then applied to the a series of functions of exp(-s). It is also shown that the H-infinity norm of the transfer function from the input to the state of a balanced realisation of the Padé approximation of exp(-s) is unity. © 2012 IEEE.