It is known that an often used implementation method for regular expressions that uses a combination of counters and nondeterministic finite automatons is incorrect for certain regular expressions. Determining which e...
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It is known that an often used implementation method for regular expressions that uses a combination of counters and nondeterministic finite automatons is incorrect for certain regular expressions. Determining which expressions can be correctly implemented with this method has proven nontrivial and has previously been done without proof. Presented is the first automatic method to prove the correctness of the implementation method for specific expressions and to detect which expressions should be implemented differently. The use (in previous work) of this implementation method in network intrusion detection systems without proof of correctness for every regular expression constitutes a security risk to the network it is supposed to protect. Presented is a solution for this issue.
Motivation: Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the corre...
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Motivation: Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered. Results: In this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data.
We investigate a method for automatic extraction of inter-song similarity for songs selected from several genres of Western popular music. The specific purpose of this approach is to evaluate the predictive power of d...
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We investigate a method for automatic extraction of inter-song similarity for songs selected from several genres of Western popular music. The specific purpose of this approach is to evaluate the predictive power of different feature extraction sets based on human perception of music similarity and to develop an algorithm able to reproduce and predict human ratings. The algorithm is a linear model that was trained and tested using perceptual data. We use publicly available algorithms to extract acoustic feature values from 78 songs used in a previous perceptual experiment. Feature value differences between songs are used in a multivariate linear regression calculation to find the optimal weighting coefficients for the feature values to best approximate the human similarity perception data. We use two evaluation methods: metrical and ordinal. We use a bootstrapping approach by randomly separating the experimental data into training and testing sets. We compare the performance of this model against the G1C model by Pampalk, winner of the MIREX 2006 competition on music similarity prediction. Both models produce a rather low performance on the metrical evaluation. However, on the ordinal evaluation, the linear regression model shows encouraging results (significantly outperforming the G1C algorithm): in the triadic comparison task, it can correctly predict 52.3 +/- 0.5% of the most similar pairs, while the estimated theoretical maximum, based on participant consistency on the most similar pair rankings is 78 +/- 8%. In a comparison of feature sets, we found the MIR toolbox to produce the best performance.
A GT-suite commercial code was used to develop a fully integrated model of a light duty commercial vehicle with a V6 diesel engine, to study the use of a BorgWarner dual mode coolant pump (DMCP) in active thermal mana...
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A GT-suite commercial code was used to develop a fully integrated model of a light duty commercial vehicle with a V6 diesel engine, to study the use of a BorgWarner dual mode coolant pump (DMCP) in active thermal management of the vehicle. An Urban Dynamometer Driving Schedule (UDDS) was used to validate the simulation results with the experimental data. The conventional mechanical pump from the validated model was then replaced with the dual mode coolant pump. The control algorithm for the pump was based on controlling the coolant temperature with pump speed. Maximum electrical speed of the pump and the efficiency of the pump were used to determine whether the pump should run in mechanical or electrical mode. The model with the dual mode coolant pump was simulated for the UDDS cycle to demonstrate the effectiveness of control strategy. Benefits of using the dual mode coolant pump in reducing auxiliary power, reducing friction losses and increasing fuel economy of the vehicle was investigated in this study. Lastly, a hot start UDDS cycle was used to demonstrate changes in the operating mode of the pump. The results of this study confirm the capability of the DMCP in effective thermal management of the system with an increased fuel economy benefit of more than 2%.
We provide a new gossip algorithm to investigate the problem of opinion consensus with the time-varying influence factors and weakly connected graph among multiple agents. What is more, we discuss not only the effect ...
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We provide a new gossip algorithm to investigate the problem of opinion consensus with the time-varying influence factors and weakly connected graph among multiple agents. What is more, we discuss not only the effect of the time-varying factors and the randomized topological structure but also the spread of misinformation and communication constrains described by probabilistic quantized communication in the social network. Under the underlying weakly connected graph, we first denote that all opinion states converge to a stochastic consensus almost surely;that is, our algorithm indeed achieves the consensus with probability one. Furthermore, our results show that the mean of all the opinion states converges to the average of the initial states when time-varying influence factors satisfy some conditions. Finally, we give a result about the square mean error between the dynamic opinion states and the benchmark without quantized communication.
The paper deals with a choice of the rational trajectory of motion of a landing module designed for the Moon landing, from the moment of its de-orbiting from the near-lunar orbit up to landing. An integrated conceptua...
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The paper deals with a choice of the rational trajectory of motion of a landing module designed for the Moon landing, from the moment of its de-orbiting from the near-lunar orbit up to landing. An integrated conceptual basis is used to develop multistep terminal algorithms for guidance for the three segments of the descent.
Motivation: Pairwise alignment of protein structures is a fundamental task in structural bioinformatics. There are numerous computer programs in the public domain that produce alignments for a given pair of protein st...
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Motivation: Pairwise alignment of protein structures is a fundamental task in structural bioinformatics. There are numerous computer programs in the public domain that produce alignments for a given pair of protein structures, but the results obtained by the various programs generally differ substantially. Hence, in the application of such programs the question arises which of the alignment programs are the most trustworthy in the sense of overall performance, and which programs provide the best result for a given pair of proteins. The major problem in comparing, evaluating and judging alignment results is that there is no clear notion of the optimality of an alignment. As a consequence, the numeric criteria and scores reported by the individual structure alignment programs are largely incomparable. Results: Here we report on the development and application of a new approach for the evaluation of structure alignment results. The method uses the translation vector and rotation matrix to generate the superposition of two structures but discards the alignment reported by the individual programs. The optimal alignment is then generated in standardized form based on a suitably implemented dynamic programming algorithm where the length of the alignment is the single most informative parameter. We demonstrate that some of the most popular programs in protein structure research differ considerably in their overall performance. In particular, each of the programs investigated here produced in at least in one case the best and the worst alignment compared with all others. Hence, at the current state of development of structure comparison techniques, it is advisable to use several programs in parallel and to choose the optimal alignment in the way reported here.
Multiple parameter optimisation in drug discovery is difficult, but Matched Molecular Pair Analysis (MMPA) can help. computer algorithms can process data in an unbiased way to yield design rules and suggest better mol...
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Multiple parameter optimisation in drug discovery is difficult, but Matched Molecular Pair Analysis (MMPA) can help. computer algorithms can process data in an unbiased way to yield design rules and suggest better molecules, cutting the number of design cycles. The approach often makes more suggestions than can be processed manually and methods to deal with this are proposed. However, there is a paucity of contextually specific design rules, which would truly make the technique powerful. By combining extracted information from multiple sources there is an opportunity to solve this problem and advance medicinal chemistry in a matter of months rather than years.
An analytical model is proposed for a low-output fractionation column of an air-separation unit operating on a high-pressure cycle. The algorithm is developed for solution of a system of equations, which makes it poss...
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An analytical model is proposed for a low-output fractionation column of an air-separation unit operating on a high-pressure cycle. The algorithm is developed for solution of a system of equations, which makes it possible to predict variation in the degree of cleaning and the amount of product obtained as a function of variation in intake thermodynamic parameters, and calculate the optimal operating regime.
In computational approaches to the study of variation among folk song melodies from oral culture, both global and local features of melodies have been used. From a computational point of view, the representation of a ...
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In computational approaches to the study of variation among folk song melodies from oral culture, both global and local features of melodies have been used. From a computational point of view, the representation of a melody as a vector of global feature values, each summarizing an aspect of the entire melody, is attractive. However, from an annotation study on perceived melodic similarity and human categorization in music it followed that local features of melodies are most important to classify and recognize melodies. We compare both approaches in a computational classification task. In both cases, the discriminative power of features is assessed. We use a feature evaluation criterion that is based on the performance of a nearest-neighbour classifier. As distance measure for vectors of global features, we use the Euclidian distance. For the sequences of local features, we use the score of the NeedlemanWunsch alignment algorithm. In each of our comparisons, the local features correspond to the global features. In all cases, it appears that the local approach outperforms the global approach in a classification task for melodies, which indicates that local features carry more information about the identity of melodies. Therefore, locality is a crucial factor in modelling melodic similarity among folk song melodies.
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