String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Computer implemented methods for constructing a compressed data structure from a data string and for using the data structure to find data patterns in the data string

Abstract: A method for constructing a data structure for a data string of characters includes producing a matrix of sorted rotations of the data string. This matrix defines an A array which is a sorted list of the characters in the data string, a B array which is a permutation of the data string, and a correspondence array C which contains correspondence entries linking the characters in the A array to the same characters in the B array. A reduced A' array is computed to identify each unique character in the A array and a reduced C' array is computed to contain every sth entry of the C array. The B array is segmented into blocks of size s. During a search, the A' and C' arrays are used to index the B array to reconstruct any desired row from the matrix of rotations. Through this representation, the matrix of rotations can thus be used as a conventional sorted list for pattern matching or information retrieval applications. A data structure containing only the A', B, and C' has very little memory overhead. The B array contains the same number of characters as the original data string, and can be compressed in a block wise manner to reduce its size. The A' array is a fixed size equal to the size of the alphabet used to construct the data string, and the C' array is variable size according to the relationship n/s, where n is the number of characters in the data string and s is the size of the blocks of the B array. Accordingly, the data structure enables a tradeoff between access speed and memory overhead, the product of which is constant with respect to block size s.

A neural syntactic language model

Abstract: This paper presents a study of using neural probabilistic models in a syntactic based language model. The neural probabilistic model makes use of a distributed representation of the items in the conditioning history, and is powerful in capturing long dependencies. Employing neural network based models in the syntactic based language model enables it to use efficiently the large amount of information available in a syntactic parse in estimating the next word in a string. Several scenarios of integrating neural networks in the syntactic based language model are presented, accompanied by the derivation of the training procedures involved. Experiments on the UPenn Treebank and the Wall Street Journal corpus show significant improvements in perplexity and word error rate over the baseline SLM. Furthermore, comparisons with the standard and neural net based N-gram models with arbitrarily long contexts show that the syntactic information is in fact very helpful in estimating the word string probability. Overall, our neural syntactic based model achieves the best published results in perplexity and WER for the given data sets.

Data compression method and apparatus

Abstract: A data compressor scans one or more data files to locate variable-length strings of characters (data) that can be more efficiently compressed by delta compression (i.e. by referring to a string in an earlier version of the data file) than by traditional compression (i.e. by referring to a string of characters that occurs earlier in the data file). The compressor employs a criterion to select the strings that are likely to be most amenable to delta compression, i.e. to provide the highest overall compression ratio. The compressor creates a dictionary that describes the selected strings, which can then be advantageously used to delta-compress edited (changed) versions of the data files for transmission to another computer. In addition, a "dual-mode" data compressor employs a combination of delta and traditional compression to efficiently compress data and send one or more files from a sending computer to a receiving computer, where a decompressor reconstitutes a copy of the files. As the dual-mode compressor processes the files, it creates a dictionary.

Vibration Control of a Flexible String With Both Boundary Input and Output Constraints

Abstract: This paper investigates control design for a flexible string system with both boundary input and output constraints. Dynamics of the string system are represented as a homogeneous partial differential equation describing the vibrations of the system. First, model-based boundary control is proposed by employing an auxiliary system for handling the input saturation and an integral barrier Lyapunov function for eliminating the effect of output constraint. The exponential stability is achieved through rigorous analysis without any simplification of the dynamics. Subsequently, because the system parameters are unknown, adaptive boundary feedback control is proposed and uniform ultimate boundedness is guaranteed. Finally, the simulation results are provided for demonstrating the effectiveness of the proposed control, where all the signals in the control inputs can be measured using the most common sensors.