We here devise a new method for detecting and assessing RNA secondary structure by using multiple sequence alignment. The central idea of the method is to first detect conserved stems in the alignment using a special ...
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ISBN:
(纸本)1595934804;9781595934802
We here devise a new method for detecting and assessing RNA secondary structure by using multiple sequence alignment. The central idea of the method is to first detect conserved stems in the alignment using a special matrix and then assess them by evaluating the ratio of the signal to the noise. We tested the method on data sets composed of pairwise and three-way alignments of known ncRNAs. For the pairwise tests, our method has sensitivity 61.42% and specificity 97.05% for structural alignments, and sensitivity 42.05% and specificity 98.15% for BLAST alignments. For the three-way tests, our method has sensitivity 65.17% and specificity 97.96% for structural alignments, and sensitivity 40.70% and specificity 97.87% for CLUSTALW alignments. Our method can detect conserved secondary structures in gapped or ungapped RNA alignments. Copyright 2007 ACM.
Demographic data regarding users and items exist in most available recommender systems data sets. Still, there has been limited research involving such data. This work sets the foundations for a novel filtering techni...
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Approximate string matching problem is a common and often repeated task in information retrieval and bioinformatics. This paper proposes a generic design of a programmable array processor architecture for a wide varie...
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The work we are going to present concerns an extensible system that has been developed in order to recognize Greek sign language modules. By saying Greek sign language modules we mean either signs or finger-spelled wo...
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The work we are going to present concerns an extensible system that has been developed in order to recognize Greek sign language modules. By saying Greek sign language modules we mean either signs or finger-spelled words, formed in isolation or combined into sentences. Greek sign language (GSL) is the natural way of communication between deaf people in Greece. The system we have developed can be used in public services, where it could play the role of an interpreter from GSL to spoken Greek, permitting deaf people to communicate using their natural way of "speaking". Having contacted a great number of deaf people, we found out that most of them face a great difficulty in spelling Greek spoken words. Since the system is capable of recognizing finger-spelled words too, it could also be used as a trainer for deaf students during their attempt to learn Greek spoken words
Electropalatography is a well established technique for recording information on the patterns of contact between the tongue and the hard palate during speech, leading to a stream of binary vectors called electropalato...
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Electropalatography is a well established technique for recording information on the patterns of contact between the tongue and the hard palate during speech, leading to a stream of binary vectors called electropalatograms, consisting of elecropalatographic events - contacts or non-contacts between the tongue and the palate. A data-driven approach to mapping the speech signal onto electropalatographic information is presented. A combination of principal component analysis and support vector regression is used, yielding classification scores of more than 93% on individual electropalatographic events, for a single speaker. This may be viewed as a special case of the, well-known in the speech community, speech inversion problem which refers to inferring production parameters from the speech signal
We report work on mapping the acoustic speech signal, parametrized using Mel Frequency Cepstral Analysis, onto electromagnetic articulography trajectories from the MOCHA database. We employ the machine learning techni...
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We report work on mapping the acoustic speech signal, parametrized using Mel Frequency Cepstral Analysis, onto electromagnetic articulography trajectories from the MOCHA database. We employ the machine learning technique of Support Vector Regression, contrasting previous works that applied Neural Networks to the same task. Our results are comparable to those older attempts, even though, due to training time considerations, we use a much smaller training set, derived by means of clustering the acoustic data.
This paper presents the basic parallel implementation and a variation for matrix - vector multiplication. We evaluated and compared the performance of the two implementations on a cluster of workstations using Message...
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This paper proposes a generic programmable array processor architecture for a wide variety of approximate string matching algorithms. Further, we describe the architecture of the array and the architecture of the cell...
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This paper proposes a generic programmable array processor architecture for a wide variety of approximate string matching algorithms. Further, we describe the architecture of the array and the architecture of the cell in detail in order to efficiently implement for both the preprocessing and searching phases of most string matching algorithms. Further, the architecture performs approximate string matching for complex patterns that contain don't care, complement and classes symbols. Our architecture maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. It may be adopted as a basic structure for a universal flexible string matcher engine.
In this paper we examine the use of a matrix factorization technique called singular value decomposition (SVD) in item-based collaborative filtering. After a brief introduction to SVD and some of its previous applicat...
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In this paper we examine the use of a matrix factorization technique called singular value decomposition (SVD) in item-based collaborative filtering. After a brief introduction to SVD and some of its previous applications in recommender systems, we proceed with a full description of our algorithm, which uses SVD in order to reduce the dimension of the active item's neighborhood. The experimental part of this work first locates the ideal parameter settings for the algorithm, and concludes by contrasting it with plain item-based filtering which utilizes the original, high dimensional neighborhood. The results show that a reduction in the dimension of the item neighborhood is promising, since it does not only tackle some of the recorded problems of recommender systems, but also assists in increasing the accuracy of systems employing it.
In this paper we present the Unison-CF algorithm, which provides an efficient way to combine multiple collaborative filtering approaches, drawing advantages from each one of them. Each collaborative filtering approach...
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