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South Andean Loasas (Blumenbachia, Caiophora, Loasa, Scyphanthus) are a monophyletic group of taxa within Loasaceae subfam. Loasoideae, comprising some 100 species, 49 of which are investigated here. They retain a many-layered tes...
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South Andean Loasas (Blumenbachia, Caiophora, Loasa, Scyphanthus) are a monophyletic group of taxa within Loasaceae subfam. Loasoideae, comprising some 100 species, 49 of which are investigated here. They retain a many-layered testa in the mature seeds and usually have passive transfer testas with complex, spongiose wall outgrowths. Additional modifications concern the undulations of the testa epidermis, presence or absence of the outer periclinal wall, secondary sculpturing, the presence or absence of spines, warts and finally spongiose structures on the anticlinal walls of the testa epidermis and the inner periclinal wall. Seeds of the widespread "deeply pitted" type are plesiomorphic, while various subclades of South Andean Loasas have derivations underscoring their relationships and confirming the relationships found with molecular markers and other morphological characters. The genus Blumenbachia has either seeds with a many-layered testa forming longitudinal lamellae (sect. Angulatae), or balloon seeds with a loose outer testa layer and spongiose wall outgrowths on the inner periclinal walls (sect. Blumenbachia and sect. Gripidea) and is clearly monophyletic. Loasa s.str. (ser. Loasa, ser. Macrospermae, ser. Floribundae, ser. Deserticolae) is characterized by the presence of a subterminal hilum or hilar scar and one subgroup (ser. Loasa, ser. Macrospermae) by very large and heavy seeds with a collapsed testa. L. ser. Pinnatae, ser. Acaules, ser. Volubiles, Scyphanthus and Caiophora share more or less one seed types with minor modifications. Within Caiophora various derivations are observed, of which the gradual loss of the secondary sculpture of the inner periclinal wall is the most striking one. Anemochoria is the most widespread dispersal mechanism in South Andean Loasas and is achieved in at least five structurally different ways. (c) 2005 Elsevier GmbH. All rights reserved.
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Deployment of high-speed network enables high quality multimedia communication; however, bottlenecks can occur at senders' access links in many-to-many multimedia communication. To improve quality of many-to-many multimedia commun...
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Deployment of high-speed network enables high quality multimedia communication; however, bottlenecks can occur at senders' access links in many-to-many multimedia communication. To improve quality of many-to-many multimedia communication, we proposed an iterative tree construction method for Application-Layer Multicast (ALM). After constructing a simple initial ALM tree, our scheme iteratively and locally reconstructs the tree in order to improve bandwidth allocation and to meet delay tolerance. The details of this iterative algorithm for the proposed method are discussed in this paper.
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As systems-on-chip increase in complexity, the underlying technology presents us with significant challenges due to increased power consumption as well as decreased reliability. Today, designers must consider building systems that...
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As systems-on-chip increase in complexity, the underlying technology presents us with significant challenges due to increased power consumption as well as decreased reliability. Today, designers must consider building systems that achieve the requisite functionality and performance using components that may be unreliable. In order to do so, it is crucial to understand the close interplay between the different layers of a system: technology, platform, and application. This will enable the most general tradeoff exploration, reaping the most benefits in power, performance and reliability. This paper surveys various cross layer techniques and approaches for power, performance, and reliability tradeoffs are technology, circuit, architecture and application layers.
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Abstract In this paper, we propose a CVAM (continuous-valued associative memory for one-to-many associations) with back-propagation learning and analyze the performance in detail. Conventional associative memories often deal with ...
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Abstract In this paper, we propose a CVAM (continuous-valued associative memory for one-to-many associations) with back-propagation learning and analyze the performance in detail. Conventional associative memories often deal with binary patterns, however, most of the data handled today are continuous-valued data. The basic architecture of the proposed CVAM is a three-layer perceptron with multiple sub-layers in the hidden layer. The multiple sub-layers enable one-to-many associations using back-propagation (BP) learning algorithm; each sub-layer memorizes single one-to-one association and the multiple sub-layers enables one-to-many associations. We carried out experiments to analyze the important properties such as memory capacity and noise tolerance performance using continuous-valued data. In addition, we conducted a demonstrative experiment to visually confirm the behavior of the proposed CVAM as an associative memory model using the CIFAR-10 image data set.
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An advanced numerical method of parallel shooting is proposed for solving steady state boundary problems in membrane electrochemistry. The advanced algorithm is described. The possibilities of the method are discussed. The possibl...
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An advanced numerical method of parallel shooting is proposed for solving steady state boundary problems in membrane electrochemistry. The advanced algorithm is described. The possibilities of the method are discussed. The possible applications to solving some boundary tasks of ordinary and singular perturbated differential equations in many-layers membrane systems are indicated. The transport task of two ions in three-layers systems at over-limiting current is solved.
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Feed-forward networks are generally trained to represent functions or many-to-one (m-o) mappings. In this paper, however, a feed-forward network with modified training algorithm is trained to represent multi-valued or one-to-many ...
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Feed-forward networks are generally trained to represent functions or many-to-one (m-o) mappings. In this paper, however, a feed-forward network with modified training algorithm is trained to represent multi-valued or one-to-many (o-m) mappings. The o-m mapping is replaced by a m-o mapping with the values corresponding to a value of the independent variable constituting a set. Thus the problem of representing a o-m mapping has been converted into a problem of training a network to return sets rather than vectors. The o-m mapping may have variable multiplicity leading to sets of variable cardinality. The crisp sets of variable cardinality in turn are replaced by fuzzy sets of fixed cardinality by adding elements, called "do not care" which have membership values of zero. Since the target outputs of the feed-forward network are now sets of fixed cardinality and the actual output of a feed-forward network is a vector the training algorithm is modified to take into account the fact that order should be removed as a constraint when the error vector is calculated. Results of simulations show that the method proposed is very effective.
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It is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization problems ...
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It is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization problems (MaOPs), which have more than three objectives. Therefore, a more valid selection method is needed to balance convergence and distribution. This paper presents a many objective evolutionary algorithm, called Adaptive Neighborhood Selection for Many-objective evolutionary algorithm(ANS-MOEA), to deal with MaOPs. This method defines the performance of each individual by two types of information, convergence information (CI) and distribution information (DI). In the critical layer, a well-converged individual is selected first from the population, and its neighbors, calculated by DI, are pushed into neighbor collection (NC) soon afterwards. Then, the proper distribution of the population is ensured by competition individuals with large DI go back to the population and individuals with small DI remain in the collection. Four state-of-the-art MaOEAs are selected as the competitive algorithms to validate ANS-MOEA. The experimental results show that ANS-MOEA can solve a MaOP and generate a set of remarkable solutions to balance convergence and distribution. (C) 2017 Elsevier B.V. All rights reserved.
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The energy and gradient expressions for the many-body dispersion scheme (MBD@rsSCS) of Ambrosetti et al (2014 J. Chem. Phys. 140 18A508) needed for an efficient implementation of the method for systems under periodic boundary cond...
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The energy and gradient expressions for the many-body dispersion scheme (MBD@rsSCS) of Ambrosetti et al (2014 J. Chem. Phys. 140 18A508) needed for an efficient implementation of the method for systems under periodic boundary conditions are reported. The energy is expressed as a sum of contributions from points sampled in the first Brillouin zone, in close analogy with planewave implementations of the RPA method for electrons in the dielectric matrix formulation. By avoiding the handling of large supercells, considerable computational savings can be achieved for materials with small and medium sized unit cells. The new implementation has been tested and used for geometry optimization and energy calculations of inorganic and molecular crystals, and layered materials.
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Many problems in real world have not only one objective to be met. In the majority of cases, a set of trade-off solutions which spread evenly along the entire Pareto optimal front are generated by multi-objective evolutionary algo...
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Many problems in real world have not only one objective to be met. In the majority of cases, a set of trade-off solutions which spread evenly along the entire Pareto optimal front are generated by multi-objective evolutionary algorithms (MOEAs). Taking the preference of decision maker (DM) into consideration, some specified solutions can be obtained, which is of great interest in practical applications. In this paper, a novel multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition (denoted as MLIP-MOEA/D) is proposed. In MLIP-MOEA/D, a multi-layer interactive strategy is developed during evolution, in the first-layer interaction, the DM will provide a reference vector and an initial radius to determine a preference range, then all solutions in this range will be updated. The algorithm will stop if the DM is satisfied with the first output result, otherwise it will go on to the second-layer interaction. In this step, the most preferred solution generated from the first-layer interaction will be chosen as the new preference direction, and the weight vector is redefined by the angle-based method, and the range of preferred region is reduced gradually, until the closest solution that meet the DM's need is found. The algorithm is tested on a set of benchmark problems including DTLZ problems with more than three objectives, the experimental studies show that the proposed algorithm can effectively search the preferred solutions with the preference information and successfully deal with many-objective optimization problems. (C) 2018 Elsevier Inc. All rights reserved.
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