999 resultados para compression parallel


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This case study deals with the role of time series analysis in sociology, and its relationship with the wider literature and methodology of comparative case study research. Time series analysis is now well-represented in top-ranked sociology journals, often in the form of ‘pooled time series’ research designs. These studies typically pool multiple countries together into a pooled time series cross-section panel, in order to provide a larger sample for more robust and comprehensive analysis. This approach is well suited to exploring trans-national phenomena, and for elaborating useful macro-level theories specific to social structures, national policies, and long-term historical processes. It is less suited however, to understanding how these global social processes work in different countries. As such, the complexities of individual countries - which often display very different or contradictory dynamics than those suggested in pooled studies – are subsumed. Meanwhile, a robust literature on comparative case-based methods exists in the social sciences, where researchers focus on differences between cases, and the complex ways in which they co-evolve or diverge over time. A good example of this is the inequality literature, where although panel studies suggest a general trend of rising inequality driven by the weakening power of labour, marketisation of welfare, and the rising power of capital, some countries have still managed to remain resilient. This case study takes a closer look at what can be learned by applying the insights of case-based comparative research to the method of time series analysis. Taking international income inequality as its point of departure, it argues that we have much to learn about the viability of different combinations of policy options by examining how they work in different countries over time. By taking representative cases from different welfare systems (liberal, social democratic, corporatist, or antipodean), we can better sharpen our theories of how policies can be more specifically engineered to offset rising inequality. This involves a fundamental realignment of the strategy of time series analysis, grounding it instead in a qualitative appreciation of the historical context of cases, as a basis for comparing effects between different countries.

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The duration compression effect is a phenomenon in which prior adaptation to a spatially circumscribed dynamic stimulus results in the duration of subsequent subsecond stimuli presented in the adapted region being underestimated. There is disagreement over the frame of reference within which the duration compression phenomenon occurs. One view holds that the effect is driven by retinotopic-tuned mechanisms located at early stages of visual processing, and an alternate position is that the mechanisms are spatiotopic and occur at later stages of visual processing (MT+). We addressed the retinotopic-spatiotopic question by using adapting stimuli – drifting plaids - that are known to activate global-motion mechanisms in area MT. If spatiotopic mechanisms contribute to the duration compression effect, drifting plaid adaptors should be well suited to revealing them. Following adaptation participants were tasked with estimating the duration of a 600ms random dot stimulus, whose direction was identical to the pattern direction of the adapting plaid, presented at either the same retinotopic or the same spatiotopic location as the adaptor. Our results reveal significant duration compression in both conditions, pointing to the involvement of both retinotopic-tuned and spatiotopic-tuned mechanisms in the duration compression effect.

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This paper builds on previous work to show how using holistic and iterative design optimisation tools can be used to produce a commercially viable product that reduces a costly assembly into a single moulded structure. An assembly consisting of a structural metallic support and compression moulding outer shell undergo design optimisation and analysis to remove the support from the assembly process in favour of a structural moulding. The support is analysed and a sheet moulded compound (SMC) alternative is presented, this is then combined into a manufacturable shell design which is then assessed on viability as an alternative to the original.
Alongside this a robust material selection system is implemented that removes user bias towards materials for designs. This system builds on work set out by the Cambridge Material Selector and Boothroyd and Dewhurst, while using a selection of applicable materials currently available for the compression moulding process. This material selection process has been linked into the design and analysis stage, via scripts for use in the finite element environment. This builds towards an analysis toolkit that is suggested to develop and enhance manufacturability of design studies.

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Energy consumption is an important concern in modern multicore processors. The energy consumed by a multicore processor during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy minimization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the time and the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical energy-performance model for parallel workloads that accounts for the time and the energy consumed by the CPU chip on memory accesses in addition to the time and energy consumed by the CPU on CPU instructions. In addition, the model we present also accounts for the dynamic energy consumed by the idle cores. The existing work on global DVFS for parallel workloads shows that using a single frequency for the entire duration of a parallel application is not energy optimal and that varying the frequency according to the changes in the parallelism of the workload can save energy. We present an analytical framework around our energy-performance model to predict the operating frequencies (that depend upon the amount of parallelism) for global DVFS that minimize the overall CPU energy consumption. We show how the optimal frequencies in our model differ from the optimal frequencies in a model that does not account for memory accesses. We further show how the memory intensity of an application affects the optimal frequencies.

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In this paper a parallel implementation of an Adaprtive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

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In this paper a parallel implementation of an Adaprtive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

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The Adaptive Generalized Predictive Control (AGPC) algorithm can be speeded up using parallel processing. Since the AGPC algorithm needs to be fed with the knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

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Discrete optimization problems are very difficult to solve, even if the dimantion is small. For most of them the problem of finding an ε-approximate solution is already NP-hard.

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The Adaptive Generalized Predictive Control (GPC) algorithm can be speeded up using parallel processing. Since the GPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here.

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Least squares solutions are a very important problem, which appear in a broad range of disciplines (for instance, control systems, statistics, signal processing). Our interest in this kind of problems lies in their use of training neural network controllers.

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Least squares solutions are a very important problem, which appear in a broad range of disciplines (for instance, control systems, statistics, signal processing). Our interest in this kind of problems lies in their use of training neural network controllers.

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In this paper the parallelization of a new learning algorithm for multilayer perceptrons, specifically targeted for nonlinear function approximation purposes, is discussed. Each major step of the algorithm is parallelized, a special emphasis being put in the most computationally intensive task, a least-squares solution of linear systems of equations.

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A área de Extração da Informação tem como objetivo essencial investigar métodos e técnicas para transformar a informação não estruturada presente em textos de língua natural em dados estruturados. Um importante passo deste processo é a resolução de correferência, tarefa que identifica diferentes sintagmas nominais que se referem a mesma entidade no discurso. A área de estudos sobre resolução de correferência tem sido extensivamente pesquisada para a Língua Inglesa (Ng, 2010) lista uma série de estudos da área, entretanto tem recebido menos atenção em outras línguas. Isso se deve ao fato de que a grande maioria das abordagens utilizadas nessas pesquisas são baseadas em aprendizado de máquina e, portanto, requerem uma extensa quantidade de dados anotados.

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The work described here is part of a research program aiming to increase the sensitivity to disease detection using Doppler ultrasound by reducing the effects to the measurement procedure on the estimation of blood velocity and detection of flow disturbance.