1444557
Conditional entropy coding of VQ indexes for image compression
Block sizes of practical vector quantization (VQ) image coders are not large enough to exploit all high-order statistical dependencies among pixels. Therefore, adaptive entropy coding of VQ indexes via statistical context modeling can significantly reduce the bit rate of VQ coders for given distortion. Address VQ was a pioneer work in this direction. In this paper we develop a framework of conditional entropy coding of VQ indexes (CECOVI) based on a simple Bayesian-type method of estimating probabilities conditioned on causal contexts, CECOVI is conceptually cleaner and algorithmically more efficient than address VQ, with address-VQ technique being its special case. It reduces the bit rate of address VQ by more than 20% for the same distortion, and does so at only a tiny fraction of address VQ's computational cost.
14960694
Comparative analysis of gene sets in the gene ontology space under the multiple hypothesis testing framework
The gene ontology (GO) resource can be used as a powerful tool to uncover the properties shared among, and specific to, a list of genes produced by high-throughput functional genomics studies, such as microarray studies. In the comparative analysis of several gene lists, researchers maybe interested in knowing which GO terms are enriched in one list of genes but relatively depleted in another. Statistical tests such as Fisher's exact test or Chi-square test can be performed to search for such GO terms. However, because multiple GO terms are tested simultaneously, individual p-values from individual tests do not serve as good indicators for picking GO terms. Furthermore, these multiple tests are highly correlated, usual multiple testing procedures that work under an independence assumption are not applicable. In this paper we introduce a procedure, based on false discovery rate (FDR), to treat this correlated multiple testing problem. This procedure calculates a moderately conserved estimator of q-value for every GO term. We identify the GO terms with q-values that satisfy a desired level as the significant GO terms. This procedure has been implemented into the GoSurfer software. GoSurfer is a windows based graphical data mining tool. It is freely available at http://www.gosurfer.org.
4523050
Determination of Local Statistical Significance of Patterns in Markov Sequences with Application to Promoter Element Identification
High-level eukaryotic genomes present a particular challenge to the computational identification of transcription factor binding sites (TFBSs) because of their long noncoding regions and large numbers of repeat elements. This is evidenced by the noisy results generated by most current methods. In this paper, we present a p-value-based scoring scheme using probability generating functions to evaluate the statistical significance of potential TFBSs. Furthermore, we introduce the local genomic context into the model so that candidate sites are evaluated based both on their similarities to known binding sites and on their contrasts against their respective local genomic contexts. We demonstrate that our approach is advantageous in the prediction of myogenin and MEF2 binding sites in the human genome. We also apply LMM to large-scale human binding site sequences in situ and found that, compared to current popular methods, LMM analysis can reduce false positive errors by more than 50% without compromising sensitivity. This improvement will be of importance to any subsequent algorithm that aims to detect regulatory modules based on known PSSMs.
40827612
Conditional entropy coding of VQ indexes for image compression
Vector quantization (VQ) is a source coding methodology with provable rate-distortion optimality. However, despite more than two decades of intensive research, VQ theoretical promise is yet to be fully realized in image compression practice. Restricted by high VQ complexity in dimensions and due to high-order sample correlations in images, block sizes of practical VQ image coders are hardly large enough to achieve the rate-distortion optimality. Among the large number of VQ variants in the literature, a technique called address VQ (A-VQ) by Nasrabadi and Feng (1990) achieved the best rate-distortion performance so far to the best of our knowledge. The essence of A-VQ is to effectively increase VQ dimensions by a lossless coding of a group of 16-dimensional VQ codewords that are spatially adjacent. From a different perspective, we can consider a signal source that is coded by memoryless basic VQ to be just another signal source whose samples are the indices of the memoryless VQ codewords, and then induce the problem of lossless compression of the VQ-coded source. If the memoryless VQ is not rate-distortion optimal (often the case in practice), then there must exist hidden structures between the samples of VQ-coded source (VQ codewords). Therefore, an alternative way of approaching the rate-distortion optimality is to model and utilize these inter-codewords structures or correlations by context modeling and conditional entropy coding of VQ indexes.
5640149
A Constraint Solver for Code-based Test Data Generation
13223545
Quantifying the closeness between program components and features
Abstract One of the most important steps towards effective software maintenance of a large complicated system is to understand how program features are spread over the entire system and their interactions with the program components. However, we must first be able to represent an abstract feature in terms of some concrete program components. In this paper, we use an execution slice-based technique to identify the basic blocks which are used to implement a program feature. Three metrics are then defined, based on this identification, to determine quantitatively , the disparity between a program component and a feature, the concentration of a feature in a program component, and the dedication of a program component to a feature. The computations of these metrics are automated by incorporating them in a tool ( χ Suds), which makes the use of our metrics immediately applicable in real-life contexts. We demonstrate the effectiveness of our technique by experimenting with a reliability and performance evaluator. Results of our study suggest that these metrics can provide an indication of the closeness between a feature and a program component which is very useful for software programmers and maintainers to better understand the system at hand.
1401424
An analytical approach to architecture-based software performance and reliability prediction
Conventional approaches to analyze the behavior of software applications are black box based, that is, the software application is treated as a whole and only its interactions with the outside world are modeled. The black box approaches ignore information about the internal structure of the application and the behavior of the individual parts. Hence, they are inadequate to model the behavior of a realistic software application, which is likely to be made up of several interacting parts. Architecture-based analysis, which seeks to assess the behavior of a software application taking into consideration the behavior of its parts and the interactions among the parts is thus essential. Most of the research in the area of architecture-based analysis has been devoted to developing analytical models, with very little, if any effort being devoted to how these models might be applied to real software applications. In order to apply these models to software applications, methods must be developed to extract the parameters of the analytical models from information collected during the execution of the application. In this paper, we present an experimental approach to extract the parameters of architecture-based models from code coverage measurements obtained during the execution of the application. To facilitate this, we use a coverage analysis tool called automatic test analyzer in C (ATAC), which is a part of Telcordia Software Visualization and Analysis Toolsuite (TSVAT) developed at Telcordia Technologies. We demonstrate the approach by predicting the performance and reliability of an application called Symbolic Hierarchical Automated Reliability Predictor (SHARPE), which has been widely used to solve stochastic models of reliability, performance and performability.
38720222
Evaluation of the continuation bit in the Cyclic Pipeline Computer
Abstract The Cyclic Pipeline Computer (CPC) is a shared resource computer which shares its pipeline among multiple instruction streams to create distinct virtual processors. This effectively removes data dependencies which reduce the performance of highly pipelined computers. However, when executing unbalanced parallel programs, the CPC suffers from performance degradation due to synchronization overhead. In this paper we introduce a hardware extension called the continuation bit . The continuation bit controls the instruction issue and determines whether an instruction is issued from the same stream or the next stream. Simulations of various programs indicate that the continuation bit is able to balance the execution of parallel programs by exploiting instruction level parallelism.
21945290
Statistical cerebrovascular segmentation in three-dimensional rotational angiography based on maximum intensity projections
Segmentation of three-dimensional rotational angiography (3D-RA) can provide quantitative 3D morphological information of vasculature. The expectation maximization-(EM-) based segmentation techniques have been widely used in the medical image processing community, because of the implementation simplicity, and computational efficiency of the approach. In a brain 3D-RA, vascular regions usually occupy a very small proportion (around 1%) inside an entire image volume. This severe imbalance between the intensity distributions of vessels and background can lead to inaccurate statistical modeling in the EM-based segmentation methods, and thus adversely affect the segmentation quality for 3D-RA. In this paper we present a new method for the extraction of vasculature in 3D-RA images. The new method is fully automatic and computationally efficient. As compared with the original 3D-RA volume, there is a larger proportion (around 20%) of vessels in its corresponding maximum intensity projection (MIP) image. The proposed method exploits this property to increase the accuracy of statistical modeling with the EM algorithm. The algorithm takes an iterative approach to compiling the 3D vascular segmentation progressively with the segmentation of MIP images along the three principal axes, and use a winner-takes-all strategy to combine the results obtained along individual axes. Experimental results on 12 3D-RA clinical datasets indicate that the segmentations obtained by the new method exhibit a high degree of agreement to the ground truth segmentations and are comparable to those produced by the manual optimal global thresholding method.