Decoding structures

Xem 1-19 trên 19 kết quả Decoding structures
  • This part of the book is concerned with the inter-related issues of how BP and signals intelligence were made over the course of WW2. The focus is primarily upon various forms of organizational structuring, broadly conceived, and the emphasis on ‘making’ indicates that, in line with the general approach outlined in the introduction to the book, I will seek to explore some of the processes of ‘organizing’ which lie beneath the production of ‘organization’.

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  • The task of aligning corresponding phrases across two related sentences is an important component of approaches for natural language problems such as textual inference, paraphrase detection and text-to-text generation. In this work, we examine a state-of-the-art structured prediction model for the alignment task which uses a phrase-based representation and is forced to decode alignments using an approximate search approach.

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  • Syntactic knowledge is important for pronoun resolution. Traditionally, the syntactic information for pronoun resolution is represented in terms of features that have to be selected and defined heuristically. In the paper, we propose a kernel-based method that can automatically mine the syntactic information from the parse trees for pronoun resolution. Specifically, we utilize the parse trees directly as a structured feature and apply kernel functions to this feature, as well as other normal features, to learn the resolution classifier.

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  • ∗ Microsoft Research One Microsoft Way Redmond, WA, 98052 colinc@microsoft.com Abstract Phrase-based decoding produces state-of-theart translations with no regard for syntax. We add syntax to this process with a cohesion constraint based on a dependency tree for the source sentence. The constraint allows the decoder to employ arbitrary, non-syntactic phrases, but ensures that those phrases are translated in an order that respects the source tree’s structure. In this way, we target the phrasal decoder’s weakness in order modeling, without affecting its strengths.

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  • Today, we are going to talk about: Another class of linear codes, known as Convolutional codes. Structures of the encoder and different ways for representing it.: state diagram and trellis representation of the code. What is a Maximum likelihood decoder? How the decoding is performed for Convolutional codes (the Viterbi algorithm) ?

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  • IN 1995, WHEN I WAS IN MY SECOND YEAR IN COLLEGE, I was introduced to UNIX network programming. In C, you could create sockets to open TCP connections to servers and code the servers that accepted these connections. I remember the excitement I felt the fi rst time I created a TCP server: I could accept connections and receive and send messages on them. If I wanted my server to accept many concurrent connections, the common solution was to use threads, and soon I had created my fi rst multi-threaded TCP server.

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  • Let’s consider a simple base 10 number, 1585. The number is structured in a very orderly way. To begin with, note that the digits all come from the decimal symbol set (0, 1, 2, 3, 4, 5, 6, 7, 8, and 9) and that the symbol 5 appears twice in the number.

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  • A number of antibiotics have been reported to disturb the decoding process in prokaryotic translation and to inhibit the function of various natural ribozymes. We investigated the e€ect of several antibiotics on in vitro splicing of a eukaryotic nuclear pre-mRNA (b-globin). Of the eight antibiotics studied, erythromycin, Cl-tetracycline and streptomycinwere identi®ed as splicing inhibitors in nuclear HeLa cell extract. TheKi values were 160, 180 and 230lM, respectively.

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  • A 2-to-1 multiplexer – WITH-SELECT-WHEN statement A 2-to-1 multiplexer – WHEN-ELSE statement A 2-to-1 multiplexer – IF statement 4 Bit Ripple Carry Model using For Statement4 Bit Ripple Carry Model Want to write a VHDL model for a 4 bit ripple carry adder. Logic equation for each full adder is: sum

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  • The two fundamental building blocks of a digital communication system are modulation and channel coding. They enable reliable communication by providing signaling schemes and receiver structures that utilize the available spectrum and power efficiently.

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  • RBF Turbo Equalization This chapter presents novel turbo equalization scheme, a which employs a RBF equaliser instead of the conventional trellis-based equaliserDouillard et al. [ 1.531. The basic principles of of turbo equalization will be highlighted. Structural, computational cost and performance comparisons of the RBF-based and trellis-based turbo equalisers areprovided. A novel element of our design isthat in order to reduce the computational complexity the RBF turbo of equaliser (TEQ), we propose invoking further iterations only, if the decoded symbol has a high error probability....

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  • We propose a novel technique of learning how to transform the source parse trees to improve the translation qualities of syntax-based translation models using synchronous context-free grammars. We transform the source tree phrasal structure into a set of simpler structures, expose such decisions to the decoding process, and find the least expensive transformation operation to better model word reordering.

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  • This chapter presents novel turbo equalization scheme, a which employs a RBF equaliser instead of the conventional trellis-based equaliserDouillard et al. [ 1.531. The basic principles of of turbo equalization will be highlighted. Structural, computational cost and performance comparisons of the RBF-based and trellis-based turbo equalisers areprovided. A novel element of our design isthat in order to reduce the computational complexity the RBF turbo of equaliser (TEQ), we propose invoking further iterations only, if the decoded symbol has a high error probability.

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  • Another class of linear codes, known as Convolutional codes. We studied the structure of the encoder and different ways for representing it.What are the state diagram and trellis representation of the code? How the decoding is performed for Convolutional codes? What is a Maximum likelihood decoder? What are the soft decisions and hard decisions? How does the Viterbi algorithm work?

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  • EURASIP Journal on Applied Signal Processing 2003:13, 1328–1334 c 2003 Hindawi Publishing Corporation Interleaved Convolutional Code and Its Viterbi Decoder Architecture Jun Jin Kong Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street, Minneapolis, MN 55455, USA Email: jjkong@ece.umn.edu Keshab K. Parhi Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street, Minneapolis, MN 55455, USA Email: parhi@ece.umn.

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  • In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations. We detail the computational complexity and average retrieval times for looking up phrase translations in our suffix array-based data structure. We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality. ...

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  • Tree-based statistical machine translation models have made significant progress in recent years, especially when replacing 1-best trees with packed forests. However, as the parsing accuracy usually goes down dramatically with the increase of sentence length, translating long sentences often takes long time and only produces degenerate translations. We propose a new method named subsentence division that reduces the decoding time and improves the translation quality for tree-based translation. Our approach divides long sentences into several sub-sentences by exploiting tree structures.

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  • We present a hierarchical phrase-based statistical machine translation in which a target sentence is efficiently generated in left-to-right order. The model is a class of synchronous-CFG with a Greibach Normal Form-like structure for the projected production rule: The paired target-side of a production rule takes a phrase prefixed form. The decoder for the targetnormalized form is based on an Earlystyle top down parser on the source side.

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  • Despite much recent progress on accurate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label sequence models via Viterbi decoding. This stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependencies between arguments. We show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative loglinear models. ...

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