939 resultados para Marginal territories


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The theme of this work is the study of marginal territories of the Cidade do Príncipe, sketch in time than today is constituted as the city of Caicó/RN, with the time frame the years 1880 to 1900. Our goal is to investigate the construction of these territories from multiple speech subjects practitioners of the city. Thus, we propose to discover the mapping of the deviation in the city in question, as they were drawn, those who experienced and through which practices. Investigate the construction of these spaces from the speeches, and fill a historiographical gap, allows to find stories sharp, silenced or ignored, abortions and madness in prison, repressed passions and avenged. To meet these spaces the sources are diverse, such as the newspaper O Povo, the Codes of posture, the Reports of Presidents and Heads of Provincial Police and process-crime. Regarding the methodological aspects of research, we decided by discourse analysis, discourse here conceived not only as what is said about someone or something, but as a set of statements circulating at any given time in society, articulating speech, intentions, actions and thought. The city is then considered from their territories, imagined as a space configured in/by the relations power that the dispute, but also as a space for experiences multiple, different feelings, place and non-place, the discipline and mockery, of power and resistance.

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This investigation aims to gain a better understanding of the glacial history of the Pine Point Mining district, Northwest Territories, by examining the sedimentological properties of the glacial sediments including, geochemical analysis, heavy mineral concentrate analysis, clast macro-­‐fabrics, pebble lithologies, and micromorphological investigation. Four till units were identified, and three were associated with identified erosional bedrock features and streamlined landforms in the area, indicating a minimum of three ice flow directions. Sedimentological properties suggest that these units were all Type-­B tectomict/mélange till, emplaced as part of a soft subglacial deformable bed. The lack of ice-­‐marginal advance and retreat sequences within the studied till, suggests the Middle Wisconsinan Laurentide Ice margin was likely north and west of the Pine Point area, as opposed to along the margin of the Canadian Shield and Western Sedimentary Basin where it has been suggested to have existed.

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Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

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Vulnerable and marginalised populations are not only over-represented in the criminal justice system, but also in civil jurisdictions like the coronial system. Moreover, many of the personnel who deal with criminal matters, especially in rural and regional areas, are also those who manage the coronial death investigation. This movement back and forth between civil and criminal jurisdictions is difficult for the both professional personnel and the families, but especially for those families who may also have had dealings with these personnel in the criminal justice system, or who present as suspicious due to larger historical and global issues. While coronial legislation now allows families to raise cultural and religious concerns about the process, particularly to do with the autopsy of their loved one, this also requires them to identify themselves to police at the initial stage of the death investigation. This paper, part of a larger body of work on autopsy decision making, discusses the ways in which this information is gathered by police, how it is communicated through the system, the ways in which families are supported through the process, and the difficulties that ensue.

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The emergence of pseudo-marginal algorithms has led to improved computational efficiency for dealing with complex Bayesian models with latent variables. Here an unbiased estimator of the likelihood replaces the true likelihood in order to produce a Bayesian algorithm that remains on the marginal space of the model parameter (with latent variables integrated out), with a target distribution that is still the correct posterior distribution. Very efficient proposal distributions can be developed on the marginal space relative to the joint space of model parameter and latent variables. Thus psuedo-marginal algorithms tend to have substantially better mixing properties. However, for pseudo-marginal approaches to perform well, the likelihood has to be estimated rather precisely. This can be difficult to achieve in complex applications. In this paper we propose to take advantage of multiple central processing units (CPUs), that are readily available on most standard desktop computers. Here the likelihood is estimated independently on the multiple CPUs, with the ultimate estimate of the likelihood being the average of the estimates obtained from the multiple CPUs. The estimate remains unbiased, but the variability is reduced. We compare and contrast two different technologies that allow the implementation of this idea, both of which require a negligible amount of extra programming effort. The superior performance of this idea over the standard approach is demonstrated on simulated data from a stochastic volatility model.

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This paper focuses on a case study of local postcard production in a rural community in Western Australia. Drawing on in-depth interviews with key producers of these postcards, the analysis presented explores perceptions of and contexts for the emergence of this production, in turn examining the notion of ‘creativity’ articulated and privileged by this cultural work. Connections are identified between the making of postcards, the broader historical field of local cultural work and the construction of community. This, in turn, forms the basis for consideration of the role and relativity of ‘marginality’.

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This article considers the rules relating to the use of marginal notes and headings in interpreting Queensland legislation, both in its original form and in reprinted legislation. It also examines the application of the principles of construction as to the use of section headings in the interpretation of provisions of the Criminal Code 1899 (Qld). Finally, it suggests that amendments should be made to the Acts Interpretation Act 1954 (Qld) in order to clarify the position as to the status of marginal notes and headings in statutory interpretation.

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We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the (equivalent) algorithms known as "biased sampling" or "reverse logistic regression" in the statistics literature and "the density of states" in physics. Through a pair of numerical examples (including mixture modeling of the well-known galaxy dataset) we highlight the remarkable diversity of sampling schemes amenable to such recursive normalization, as well as the notable efficiency of the resulting pseudo-mixture distributions for gauging prior-sensitivity in the Bayesian model selection context. Our key theoretical contributions are to introduce a novel heuristic ("thermodynamic integration via importance sampling") for qualifying the role of the bridging sequence in this procedure, and to reveal various connections between these recursive estimators and the nested sampling technique.

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Several analytical methods for Dynamic System Optimum (DSO) assignment have been proposed but they are basically classified into two kinds. This chapter attempts to establish DSO by equilbrating the path dynamic marginal time (DMT). The authors analyze the path DMT for a single path with tandem bottlenecks and showed that the path DMT is not the simple summation of DMT associated with each bottleneck along the path. Next, the authors examined the DMT of several paths passing through a common bottleneck. It is shown that the externality at the bottleneck is shared by the paths in proportion to their demand from the current time until the queue vanishes. This share of the externality is caused by the departure rate shift under first in first out (FIFO) and the externality propagates to the downstream bottlenecks. However, the externalities propagates to the downstream are calculated out if downstream bottlenecks exist. Therefore, the authors concluded that the path DMT can be evaluated without considering the propagation of the externalities, but just as in the evaluation of the path DMT for a single path passing through a series of bottlenecks between the origin and destination. Based on the DMT analysis, the authors finally proposed a heuristic solution algorithm and verified it by comparing the numerical solution with the analytical one.

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A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.