Topic
Stack (abstract data type)
About: Stack (abstract data type) is a research topic. Over the lifetime, 23541 publications have been published within this topic receiving 202128 citations.
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Book•
01 Jan 2003
TL;DR: In this article, the authors present a history of SOFCs, including the development of cell-and-stack designs, cell and stack modelling, and cell and Stack testing.
Abstract: Introduction History of SOFCs Thermodynamics Electrolyte Cathode Anode Interconnect (ceramic, metallic) Electrode Polarizations Fuels and Fuel Processing Cell and Stack Designs Cell and Stack Modelling Cell and Stack Testing Applications and Demonstrations
1,731 citations
TL;DR: A comprehensive review of the flow-field layouts developed by different companies and research groups and the pros and cons associated with these designs is presented in this article, where the authors also present a comprehensive analysis of the performance and economic advantages of these layouts.
816 citations
Patent•
04 Jun 1979
TL;DR: A coherent surgical-staple stack comprising a plurality of staples, bonded together in a parallel contiguous relationship by a biodegradable, absorbable plastic, was constructed in this paper.
Abstract: A coherent surgical-staple stack comprising a plurality of staples, bonded together in a parallel contiguous relationship by a biodegradable, absorbable plastic.
700 citations
TL;DR: A new sequential decoding algorithm is introduced that uses stack storage at the receiver that is much simpler to describe and analyze than the Fano algorithm, and is about six times faster than the latter at transmission rates equal to Rcomp.
Abstract: In this paper a new sequential decoding algorithm is introduced that uses stack storage at the receiver It is much simpler to describe and analyze than the Fano algorithm, and is about six times faster than the latter at transmission rates equal to Rcomp the rate below which the average number of decoding steps is bounded by a constant Practical problems connected with implementing the stack algorithm are discussed and a scheme is described that facilitates satisfactory performance even with limited stack storage capacity Preliminary simulation results estimating the decoding effort and the needed stack siazree presented
635 citations
Posted Content•
TL;DR: The authors propose a stack LSTM to learn representations of parser states in transition-based dependency parsers, which can be used to learn a parser's state, including the buffer of incoming words, the history of actions taken by the parser, and the complete contents of the stack of partially built tree fragments.
Abstract: We propose a technique for learning representations of parser states in transition-based dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks---the stack LSTM. Like the conventional stack data structures used in transition-based parsing, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. This lets us formulate an efficient parsing model that captures three facets of a parser's state: (i) unbounded look-ahead into the buffer of incoming words, (ii) the complete history of actions taken by the parser, and (iii) the complete contents of the stack of partially built tree fragments, including their internal structures. Standard backpropagation techniques are used for training and yield state-of-the-art parsing performance.
630 citations