Strings represents

Lecture Introduction to C programming language - Lesson 12: String. This lecture provides students with content including: store in the memory; representing a string; string declaration; input/output a string; character in string; program to calculating the number of character;... Please refer to the detailed content of the lecture!

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Ebook "Physics of asymmetric continuum: Extreme and fracture processes - Earthquake rotation and soliton waves" include relations not only for the displacement velocities but also for a spin motion and basic point deformations as well. We include here the axial point - formation and twist point deformation represented by the string-string and string-membrane motions. A twist vector is defined here as a vector p- pendicular to the string-string plane and representing its magnitude. It - comes an important counterpart to spin and a key to the presented theory.

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Identifying repeated factors that occur in a string of letters or common factors that occur in a set of strings represents an important task in computer science and biology. Such patterns are called motifs, and the process of identifying them is called motif extraction.

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Molecular structures can be represented as strings of special characters using SMILES. Since each molecule is represented as a string, the similarity between compounds can be computed using SMILES-based string similarity functions. Most previous studies on drug-target interaction prediction use 2D-based compound similarity kernels such as SIMCOMP.

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Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions.

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We study parsing of tree adjoining grammars with particular emphasis on the use of shared forests to represent all the parse trees deriving a well-formed string. We show that there are two distinct ways of representing the parse forest one of which involves the use of linear indexed grammars and the other the use of context-free grammars. The work presented in this paper is intended to give a general framework for studying tag parsing.

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This set of candidate surface strings, represented as a word lattice, is then rescored by a wordbigram language model, to produce the bestranked output sentence. FERGUS (Bangalore and Rambow, 2000), on the other hand, employs a model of syntactic structure during sentence realization. In simple terms, it adds a tree-based stochastic model to the approach taken by the Nitrogen system. This tree-based model chooses a best-ranked XTAG representation for a given dependency structure.

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Using this approach, an SRN. trained to the phonotactics of a Dutch monosyllabic corpus containing 4500 words, was reported to distinguish words from non-words with 7 % error, Since the phonotactics of a given language is represented by the constraints allowing a given sequence to be a word or not, and the SRN managed to distinguish words from random strings with tolerable error, the authors claim that SRNs are able to learn the phonotactics of Dutch language.

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We propose a new language learning model that learns a syntactic-semantic grammar from a small number of natural language strings annotated with their semantics, along with basic assumptions about natural language syntax. We show that the search space for grammar induction is a complete grammar lattice, which guarantees the uniqueness of the learned grammar.

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Efficient processing of tera-scale text data is an important research topic. This paper proposes lossless compression of N gram language models based on LOUDS, a succinct data structure. LOUDS succinctly represents a trie with M nodes as a 2M + 1 bit string. We compress it further for the N -gram language model structure. We also use ‘variable length coding’ and ‘block-wise compression’ to compress values associated with nodes. Experimental results for three large-scale N -gram compression tasks achieved a significant compression rate without any loss. ...

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Social networking and micro-blogging sites are stores of opinion-bearing content created by human users. We describe C-Feel-It, a system which can tap opinion content in posts (called tweets) from the micro-blogging website, Twitter. This web-based system categorizes tweets pertaining to a search string as positive, negative or objective and gives an aggregate sentiment score that represents a sentiment snapshot for a search string.

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In this paper I present a Master’s thesis proposal in syntax-based Statistical Machine Translation. I propose to build discriminative SMT models using both tree-to-string and tree-to-tree approaches. Translation and language models will be represented mainly through the use of Tree Automata and Tree Transducers. These formalisms have important representational properties that makes them well-suited for syntax modeling. nce it’s u

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Learning for sentence re-writing is a fundamental task in natural language processing and information retrieval. In this paper, we propose a new class of kernel functions, referred to as string re-writing kernel, to address the problem. A string re-writing kernel measures the similarity between two pairs of strings, each pair representing re-writing of a string. It can capture the lexical and structural similarity between two pairs of sentences without the need of constructing syntactic trees.

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ProDom(Corpet et al., 2000) is one of the earliest clustered protein family databases and continually updates its methods and services. Currently, it coordinates some of its larger entries with Pfam-A and uses PSI-BLAST to cluster the remaining sequences in SWISS-PROT and TrEMBL. While only large entries have been scrutinized manually, the consistency of all families is assessed by computing a series of numerical measurements. The resulting families are represented as consensus sequences and gapped multiple alignments.

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Any message written over a fixed set of symbols can be represented as a binary string (a sequence of 0's and 1's) Binary digits 0 and 1 are called bits To reduce computation overhead, encryption algorithms should only use operations that are easy to implement For a binary string X: The length of X, denoted by |X|, is the number of bits in X If |X| = l, X is an l-bit binary string Let a be a binary bit and k a non-negative integer. Denote by ak a binary string consisting of k copies of a Denote the concatenation of X and Y by XY or...

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During August/September 2001, a group of 75 physicists from 51 laboratories in 15 countries met in Erice to participate in the 39th Course of the International School of Subnuclear Physics. The countries represented by the participants were: Argentina, Austria, Canada, China, Denmark, France, Germany, Greece, Hungary, Israel, Italy, Japan, the Netherlands, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, Ukraine and the United States of America.

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TDictionary represents a generic collection of key-value pairs. This class provides a mapping from a collection of keys to a collection of values. When you create a TDictionary object, you can specify various combinations of initial capacity, equality operation, and initial content. You can add a key that is associated with a corresponding value with the Add or AddOrSetValue methods. You can remove entries with Remove or Clear, which removes all key-value pairs. Adding or removing a key-value pair and looking up a key are efficient, close to O(1), because keys are hashed.

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Zend's new PHP 5 Certification Exam represent an excellent tool for professional PHP developers who want to distinguish themselves in their field. php|architect's Zend PHP 5 Certification Study Guide, edited and produced by the publishers of php|architect magazine, provides the most comprehensive and thorough preparation tool for developers who wish to take the exam.

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Example 8-3. The Food class (continued) public function getName ( ):String { return name; } public function setName (newName:String):void { name = newName; } } } Example 8-4 shows the code for the Apple class, which represents a specific type of food that pets eat. Example 8-4. The Apple class package zoo { public class Apple extends Food { private static var DEFAULT_CALORIES:int = 100; private var wormInApple:Boolean; public function Apple (initialCalories:int = 0) { if (initialCalories = .

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A string is a sequence of characters enclosed by double quotes and all contained on a single line. Verilog treats strings used as operands in expressions and assignments as a sequence of eight-bit ASCII values, with one eight-bit ASCII value representing one character.

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