In this p a p e r we relate a number of parsing algorithms which have been developed in very different areas of parsing theory, and which include deterministic algorithms, tabular algorithms, and a parallel algorithm. We show that these algorithms are based on the same underlying ideas. By relating existing ideas, we hope to provide an opportunity to improve some algorithms based on features of others. A second purpose of this paper is to answer a question which has come up in the area of tabular parsing, namely how to obtain a parsing algorithm with the property that...
This book deals with the acceleration of EDA algorithms using hardware platforms such as FPGAs and GPUs. Widely applied CAD algorithms are evaluated and compared for potential acceleration on FPGAs and GPUs. Coverage includes discussion of conditions under which it is preferable to use one platform over another, e.g., when an EDA problem has a high degree of data parallelism, the GPU is typically the preferred platform, whereas when the problem has more control, an FPGA may be preferred.
Parallel and distributed computing has offered the opportunity of solving a wide range
of computationally intensive problems by increasing the computing power of sequential
computers. Although important improvements have been achieved in this field in the last
30 years, there are still many unresolved issues. These issues arise from several broad areas,
such as the design of parallel systems and scalable interconnects, the efficient distribution of
processing tasks, or the development of parallel algorithms....
JR is a language for concurrent programming. It is an imperative language
that provides explicit mechanisms for concurrency, communication, and synchronization.
JR is an extension of the Java programming language with additional
concurrency mechanisms based on those in the SR (Synchronizing
Resources) programming language. It is suitable for writing programs for both
shared- and distributed-memory applications and machines; it is, of course, also
suitable for writing sequential programs.
In recent years, the credit derivatives market has become extremely active. Especially
credit default swaps (CDSs) and collateralized debt obligations (CDOs) have contributed
to what has been an amazing development.
The most important benefit of credit derivatives is their ability to transfer the credit
risk of an arbitrary number of obligors in a simple, efficient, and standardized way, giving
rise to a liquid market for credit risk that can be easily accessed by many market
Báo cáo khoa học để tài "Thuật toán luyện kim song song (Parallel Simulated Annealing Algorithms) giải quyết bài toán Max sat" được nghiên cứu với các nội dung: Tổng quan thuật toán mô phỏng luyện kim (Simulated Annealing = SA), xây dựng khung thuật toán SA, ứng dụng của thuật toán SA. Để nắm vững hơn nội dung kiến thức bài báo cáo mời các bạn cùng tham khảo tài liệu.
Numerous cross-lingual applications, including state-of-the-art machine translation systems, require parallel texts aligned at the sentence level. However, collections of such texts are often polluted by pairs of texts that are comparable but not parallel. Bitext maps can help to discriminate between parallel and comparable texts. Bitext mapping algorithms use a larger set of document features than competing approaches to this task, resulting in higher accuracy. In addition, good bitext mapping algorithms are not limited to documents with structural mark-up such as web pages. ...
Traditional accounts of quantifier scope employ qualitative constraints or rules to account for scoping preferences. This paper outlines a feature-based parsing algorithm for a grammar with multiple simultaneous levels of representation, one of which corresponds to a partial ordering among quantifiers according to scope. The optimal such ordering (as well as the ranking of other orderings) is determined in this grammar not by absolute constraints, but by stochastic heuristics based on the degree of alignment among the representational levels. ...
We estimate the parameters of a phrasebased statistical machine translation system from monolingual corpora instead of a bilingual parallel corpus. We extend existing research on bilingual lexicon induction to estimate both lexical and phrasal translation probabilities for MT-scale phrasetables. We propose a novel algorithm to estimate reordering probabilities from monolingual data. We report translation results for an end-to-end translation system using these monolingual features alone.
In this paper, we report our parallel implementations of the Lanczos sparse linear system solving algorithm over large prime ﬁelds, on a multi-core platform. We employ several load-balancing methods suited to these platforms.
Special issue paper PAR-3D-BLAST: A parallel tool for searching and aligning protein structures present a parallel tool, parallel 3D-BLAST (PAR- 3D-BLAST), which lists the similar structures to the query protein. Each protein in the result list has a structural similarity score and an alignment to the query structure. The presented tool is implemented to ﬁt both the standalone multi-core computers and clusters of multi-core nodes. The achieved speedup is linear and scalable.
Parallel Implementation of MAFFT on CUDA-Enabled Graphics Hardware present a new approach to accelerat- ing MAFFT on Graphics Processing Units (GPUs) using the Compute Uniﬁed Device Architecture (CUDA) programming model. Compared with the implementations of other MSA algorithms on GPUs, parallelization of MAFFT is more challenging since the space complexity.
We propose a language-independent method for the automatic extraction of transliteration pairs from parallel corpora. In contrast to previous work, our method uses no form of supervision, and does not require linguistically informed preprocessing. We conduct experiments on data sets from the NEWS 2010 shared task on transliteration mining and achieve an F-measure of up to 92%, outperforming most of the semi-supervised systems that were submitted.
While speech recognition systems have come a long way in the last thirty years, there is still room for improvement. Although readily available, these systems are sometimes inaccurate and insufficient. The research presented here outlines a technique called Distributed Listening which demonstrates noticeable improvements to existing speech recognition methods. The Distributed Listening architecture introduces the idea of multiple, parallel, yet physically separate automatic speech recognizers called listeners. Distributed Listening also uses a piece of middleware called an interpreter.
This paper extends previous work on extracting parallel sentence pairs from comparable data (Munteanu and Marcu, 2005). For a given source sentence S, a maximum entropy (ME) classiﬁer is applied to a large set of candidate target translations . A beam-search algorithm is used to abandon target sentences as non-parallel early on during classiﬁcation if they fall outside the beam. This way, our novel algorithm avoids any document-level preﬁltering step.
Word alignment using recency-vector based approach has recently become popular. One major advantage of these techniques is that unlike other approaches they perform well even if the size of the parallel corpora is small. This makes these algorithms worth-studying for languages where resources are scarce. In this work we studied the performance of two very popular recency-vector based approaches, proposed in (Fung and McKeown, 1994) and (Somers, 1998), respectively, for word alignment in English-Hindi parallel corpus.
Example-based parsing has already been proposed in literature. In particular, attempts are being made to develop techniques for language pairs where the source and target languages are different, e.g. Direct Projection Algorithm (Hwa et al., 2005). This enables one to develop parsed corpus for target languages having fewer linguistic tools with the help of a resourcerich source language.
This paper discusses the use of statistical word alignment over multiple parallel texts for the identiﬁcation of string spans that cannot be constituents in one of the languages. This information is exploited in monolingual PCFG grammar induction for that language, within an augmented version of the inside-outside algorithm. Besides the aligned corpus, no other resources are required. We discuss an implemented system and present experimental results with an evaluation against the Penn Treebank. ...
Among the various approaches to WSD, the supervised learning approach is the most successful to date. In this approach, we first collect a corpus in which each occurrence of an ambiguous word w has been manually annotated with the correct sense, according to some existing sense inventory in a dictionary. This annotated corpus then serves as the training material for a learning algorithm. After training, a model is automatically learned and it is used to assign the correct sense to any previously unseen occurrence of w in a new context. ...