944 resultados para purpose trusts
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Mode of access: Internet.
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Paged continuously.
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Mode of access: Internet.
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Reproduction of original from Yale Law School Library.
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Photocopy.
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Ellison D. Smith, chairman.
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Mode of access: Internet.
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Includes index.
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Includes index.
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Mode of access: Internet.
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It is the present practice that asset revaluation reserve distributions by trustees of discretionary trusts are not taxed in Australia. Are such distributions not meant to be taxed, or have relevant sections in the Income Tax Assessment Acts been overlooked? This article will review how trustees of discretionary trusts perform asset revaluation reserve distributions. It then challenges the current accepted view that they can be distributed tax-free to discretionary beneficiaries by analysing relevant CGT events, which the authors regard as forgotten events. It will be submitted that a discretionary beneficiary in receipt of an asset revaluation reserve distribution may have a capital gain which is required to be included in its assessable income. This liability for tax is regardless of the government's recent introduction of s 109XA to address the practice of asset revaluation reserve distributions bypassing the operation of Div 7A of the ITAA 1936 with such distributions.
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The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.