4,073 research outputs found

    Holographic particle localization under multiple scattering

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    We introduce a novel framework that incorporates multiple scattering for large-scale 3D particle-localization using single-shot in-line holography. Traditional holographic techniques rely on single-scattering models which become inaccurate under high particle-density. We demonstrate that by exploiting multiple-scattering, localization is significantly improved. Both forward and back-scattering are computed by our method under a tractable recursive framework, in which each recursion estimates the next higher-order field within the volume. The inverse scattering is presented as a nonlinear optimization that promotes sparsity, and can be implemented efficiently. We experimentally reconstruct 100 million object voxels from a single 1-megapixel hologram. Our work promises utilization of multiple scattering for versatile large-scale applications

    Generalized Stochastic Gradient Learning

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    We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity.adaptive learning, E-stability, recursive least squares, robust estimation

    Generalized Stochastic Gradient Learning

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    We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity.

    Soft Computing Techniques for Stock Market Prediction: A Literature Survey

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    Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

    Implementing the New Structural Model of the Czech National Bank

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    The purpose of the paper is to introduce the new “g3†structural model of the Czech National Bank and illustrate how it is used for forecasting and policy analysis. As from January 2007 the model was regularly used for shadowing official forecasts, and in July 2008 it became the core model of the CNB. In the paper we highlight the most important and unusual features of the model and discuss tools and procedures that help us in forecasting and assessing the economy with the model. The paper is not meant to provide a full derivation of the model or the complete characteristics of its behavior and should not be regarded as model documentation. Rather, the paper demonstrates how the model is used and how it contributes to policy analysis.DSGE, filtering, forecasting, general equilibrium, monetary policy.
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