The Experts below are selected from a list of 4051803 Experts worldwide ranked by ideXlab platform
Julie Grollier - One of the best experts on this subject based on the ideXlab platform.
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Spin-torque building blocks
Nature Materials, 2014Co-Authors: Nicolas Locatelli, Vincent Cros, Julie GrollierAbstract:The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate — precisely, rapidly and at low energy cost — the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks.
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Spin-torque building blocks
Nature Materials, 2014Co-Authors: Nicolas Locatelli, Vincent Cros, Julie GrollierAbstract:The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate — precisely, rapidly and at low energy cost — the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks. Memory devices based on the spin-transfer-torque effect offer a range of attractive properties, such as speed of operation and low energy cost. This Progress Article outlines a strategy for assembling different nanodevices based on the spin-torque effect to achieve qualitatively different computing architectures.
Prabhas Chongstitvatana - One of the best experts on this subject based on the ideXlab platform.
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The use of explicit building blocks in evolutionary computation
International Journal of Systems Science, 2014Co-Authors: Chalermsub Sangkavichitr, Prabhas ChongstitvatanaAbstract:This paper proposes a new algorithm to identify and compose building blocks. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks from a population explicitly. Explicit building blocks are identified from shared alleles among multiple chromosomes. These building blocks are stored in an archive. They are recombined to generate offspring. The additively decomposable problems and hierarchical decomposable problems are used to validate the algorithm. The results are compared with the Bayesian optimisation algorithm, the hierarchical Bayesian optimisation algorithm, and the chi-square matrix. This proposed algorithm is simple, effective, and fast. The experimental results confirm that building block identification is an important process that guides the recombination procedure to improve the solutions. In addition, the method efficiently solves hard problems.
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Solving multi-objective problems with Building Blocks Identification
2007 International Symposium on Communications and Information Technologies, 2007Co-Authors: J. Ponsawat, W. Punyaporn, Nachol Chaiyaratana, Prabhas ChongstitvatanaAbstract:Multiple-objective problems are a challenge for evolutionary algorithms. The requirement to improve the quality of the solution and at the same time maintain good candidates which may have different and conflicting objectives is a difficult one. This work proposes to apply the concept of building blocks to improve evolutionary algorithms to tackle such problems. Building block identification algorithm is used to guide the crossover operator in order to maintain good building blocks and mix them effectively. The proposed method is evaluated by using building block identification guided crossover in a well-known genetic algorithm to solve multiple-objective problems. The result shows that the proposed method is effective. Moreover, it obtains a good spread of solutions even when the building blocks are loosely encoded.
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Congress on Evolutionary Computation - A quantitative approach for validating the building-block hypothesis
2005 IEEE Congress on Evolutionary Computation, 1Co-Authors: Chatchawit Aporntewan, Prabhas ChongstitvatanaAbstract:The building blocks are common structures of high-quality solutions. Genetic algorithms often assume the building-block hypothesis. It is hypothesized that the high-quality solutions are composed of building blocks and the solution quality can be improved by composing building blocks. The studies of building blocks are limited to some artificial optimization functions in which it is obvious that the building blocks exist. A large number of successful applications have been reported without a strong evidence that proves the hypothesis. This paper proposes a quantitative approach for validating the building-block hypothesis. We define the quantity of building blocks and the degree of discontinuity by using the chi-square matrix. We test the building-block hypothesis with 15-bit onemax, 5/spl times/3-trap, parabola 1 -(x/sup 2//10/sup 10/), and two-dimensional Euclidian traveling salesman problem (TSP). The building-block hypothesis holds for onemax, 5/spl times/3-trap, and parabola. In the case of parabola, Gray coding gives a higher quantity of building blocks than that of binary coding. The hypothesis is accepted for random instances of TSP with a low confidence.
Nicolas Locatelli - One of the best experts on this subject based on the ideXlab platform.
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Spin-torque building blocks
Nature Materials, 2014Co-Authors: Nicolas Locatelli, Vincent Cros, Julie GrollierAbstract:The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate — precisely, rapidly and at low energy cost — the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks.
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Spin-torque building blocks
Nature Materials, 2014Co-Authors: Nicolas Locatelli, Vincent Cros, Julie GrollierAbstract:The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate — precisely, rapidly and at low energy cost — the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks. Memory devices based on the spin-transfer-torque effect offer a range of attractive properties, such as speed of operation and low energy cost. This Progress Article outlines a strategy for assembling different nanodevices based on the spin-torque effect to achieve qualitatively different computing architectures.
Yitzhak Tor - One of the best experts on this subject based on the ideXlab platform.
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Fluorescent Analogs of Biomolecular Building Blocks: Design and Applications - Fluorescent Analogs of Biomolecular Building Blocks: Design and Applications
2016Co-Authors: Marcus Wilhelmsson, Yitzhak TorAbstract:© 2016 by John Wiley & Sons, Inc. All rights reserved. Fluorescent Biomolecules and their Building Blocks focuses on the design of fluorescent probes for the four major families of macromolecular building blocks. Compiling the expertise of multiple authors, this book moves from introductory chapters to an exploration of the design, synthesis, and implementation of new fluorescent analogues of biomolecular building blocks, including examples of small-molecule fluorophores and sensors that are part of biomolecular assemblies.
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Coordination compounds as synthetic building blocks
Synlett, 2002Co-Authors: Yitzhak TorAbstract:Transition metal complexes encompass a unique pool of building blocks with diverse stereochemical, electrochemical and photophysical features. Despite the current interest in employing polypyridine-containing coordinationcompounds for the fabrication of functional assemblies, their full potential as synthetic building blocks remains under-utilized. The account discusses the inspiration and rationale for advancing the synthetic chemistry of coordination compounds and presents recent developments where the complexity of these intriguing 'inorganic' building blocks is increased via 'organic' transformations.
Vincent Cros - One of the best experts on this subject based on the ideXlab platform.
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Spin-torque building blocks
Nature Materials, 2014Co-Authors: Nicolas Locatelli, Vincent Cros, Julie GrollierAbstract:The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate — precisely, rapidly and at low energy cost — the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks.
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Spin-torque building blocks
Nature Materials, 2014Co-Authors: Nicolas Locatelli, Vincent Cros, Julie GrollierAbstract:The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates for active elements of memory devices and applications. Magnetoresistive effects allow the read-out of increasingly small magnetic bits, and the spin torque provides an efficient tool to manipulate — precisely, rapidly and at low energy cost — the magnetic state, which is in turn the central information medium of spintronic devices. By keeping the same magnetic stack, but by tuning a device's shape and bias conditions, the spin torque can be engineered to build a variety of advanced magnetic nanodevices. Here we show that by assembling these nanodevices as building blocks with different functionalities, novel types of computing architecture can be envisaged. We focus in particular on recent concepts such as magnonics and spintronic neural networks. Memory devices based on the spin-transfer-torque effect offer a range of attractive properties, such as speed of operation and low energy cost. This Progress Article outlines a strategy for assembling different nanodevices based on the spin-torque effect to achieve qualitatively different computing architectures.
