Results 271 to 280 of about 866,116 (323)
Atomistic Mechanisms Triggered by Joule Heating Effects in Metallic Cu‐Bi Nanowires for Spintronics
Bi doped metallic Cu nanowires are promising for spintronics thanks to the stabilization of a giant spin Hall effect. However, heat resulting from current injection forces Bi to leave solution, forcing segregation into monoatomic decorations which evolve into coherent crystalline aggregates.
Alejandra Guedeja‐Marrón +6 more
wiley +1 more source
Bayesian neural networks and Gaussian processes in identification of concrete properties
Marek Słoński
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The transient interactions between TFSI− anions and a diluent within an anion‐dictated electrolyte are revealed, which can reduce interfacial reorganization energy, thereby accelerating ion kinetics and markedly facilitating sustainable anion storage in high‐voltage graphite cathodes for dual‐ion batteries at fast charge and wide temperature range ...
Sungho Kim +9 more
wiley +1 more source
In Situ Amine Formation to Modulate MOF‐Derived PdIn N‐Doped Carbon Catalysts
An amine‐assisted approach converts PdIn‐MOF into PdIn intermetallic nanoparticles embedded in N‐doped carbon. In situ‐generated amines trigger early Pd nucleation, producing smaller PdIn domains than direct pyrolysis. Amine sterics and basicity tune composition and particle size, while solvent and amine co‐determine textural features.
Gonzalo Egea +9 more
wiley +1 more source
Polymorph‐Specific Electronic Transduction in WO3 during Molecular Sensing
Metal‐oxide polymorphs with similar surface chemistry can nevertheless exhibit distinct sensing properties. In γ‐ and ε‐WO3, analyte adsorption appears comparable; yet, only ε‐WO3 induces a pronounced lattice electronic perturbation that accommodates charge in sub‐conduction band minimum states.
Matteo D'Andria +6 more
wiley +1 more source
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IEEE Transactions on Neural Networks, 2011
Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of ...
Demiris, Yiannis, Chatzis, Sotirios P.
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Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of ...
Demiris, Yiannis, Chatzis, Sotirios P.
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Acta Mathematicae Applicatae Sinica, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2005
Abstract We return in this chapter to the general Bayesian formalism for a single model. So far we have worked out everything in terms of the posterior distribution p(w D) of the model parameters w, given the data D; to get predictions, we need to integrate over this distribution.
A C C Coolen, R Kühn, P Sollich
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Abstract We return in this chapter to the general Bayesian formalism for a single model. So far we have worked out everything in terms of the posterior distribution p(w D) of the model parameters w, given the data D; to get predictions, we need to integrate over this distribution.
A C C Coolen, R Kühn, P Sollich
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1999
Abstract This chapter studies some examples of processes that are Gaussian or conditionally Gaussian.
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Abstract This chapter studies some examples of processes that are Gaussian or conditionally Gaussian.
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