45019016
A Simplified Algorithm for Testing Microprocessors
Abstract In this paper, we present a number of algorithms to test the instruction decoding function of microprocessors. The algorithms are based on the knowledge of some timing and control information available to users through microprocessor manuals and data sheets. The tests are functional in nature. We also establish the order of complexity of the algorithms presented in this paper. As an example, the test complexity for a microprocessor is computed and the results are compared with a known algorithm.
62074016
Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data
The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.
13746374
A Generalized Output-Coding Scheme with SVM for Multiclass Microarray Classification
Multiclass cancer classification based on microarray data is described. A generalized output-coding scheme combined with binary classifiers is used. Different coding strategies, decoding functions and feature selection methods are combined and validated on two cancer datasets: GCM and ALL. The effects of these different methods and their combinations are then discussed. The highest testing accuracies achieved are 78% and 100% for the two datasets respectively. The results are considered to be very good when compared with the other researchers’ work.
7641777
GBNet: Deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach
BackgroundCombinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.ResultsWe present here a Bayesian network approach called GBNet to search for DNA motifs that may be cooperative in transcriptional regulation and the sequence constraints that these motifs may satisfy. We showed that GBNet outperformed the other available methods in the simulated and the yeast data. We also demonstrated the usefulness of GBNet on learning regulatory rules between YY1, a human TF, and its co-factors. Most of the rules learned by GBNet on YY1 and co-factors were supported by literature. In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.ConclusionWe thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.
31075540
Reducing multiclass cancer classification to binary by output coding and SVM
Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.
31019926
A Functional Testing Method for Microprocessors
A method is presented for functional testing of microprocessors. First, a control fault model at the RTL (register transfer language) level is developed. Based on this model, the authors establish testing requirements for control faults. They present two test procedures to verify the write and read sequences, and use the write and read sequences to test each instruction in the microprocessor. By utilizing k-out-of-m codes, they use fewer tests to cover more faults, thereby reducing the test generation time. >
11412243
Customizing computation accelerators for extensible multi-issue processors with effective optimization techniques
Compared with single-issue general purpose processors (GPPs), extensible multi-issue/VLIW processors can exploit instruction - level parallelism, which are more suitable for computation intensive tasks. Moreover, they offer the ability of customizing computation accelerators for an application domain. In this paper, we present an automated methodology that customizes computation accelerators for the multi-issue/VLIW extensible processors, where several techniques are also proposed to optimize the design of an accelerator.
15158980
Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics
We present an approach for large-scale modeling of parametric surfaces using spherical harmonics (SHs). A standard least square fitting (LSF) method for SH expansion is not scalable and cannot accurately model large 3D surfaces. We propose an iterative residual fitting (IRF) algorithm, and demonstrate its effectiveness and scalability in creating accurate SH models for large 3D surfaces. These large-scale and accurate parametric models can be used in many applications in computer vision, graphics, and biomedical imaging. As a simple extension of LSF, IRF is very easy to implement and requires few machine resources.
21499977
Predicate Analysis Based on Path Information
Predicated execution leads new challenges to traditional optimizers. Limited by predicate representations, current predicate analysis methods could only perform locally or have low efficiency. A new representation based on path information is proposed in this paper, based on which a global predicate relation query system is constructed. Experiment results indicate that precise and efficient global predicate analysis could be achieved with this system and the performance of optimized codes can also be improved.
22846762
SCENS: a system for the mediated sharing of sensitive data
We introduce SCENS, a secure content exchange negotiation system suitable for the exchange of private digital data that reside in distributed digital repositories. SCENS is an open negotiation system with flexibility, security and scalability. SCENS is currently being designed to support data sharing in scientific research, by providing incentives and goals specific to a research community. However, it can easily be extended to apply to other communities, such as government, commercial and other types of exchanges. It is a trusted third party software infrastructure enabling independent entities to interact and conduct multiple forms of negotiation.
23994030
Fast Association Discovery in Derivative Transaction Collections
Abstract. Association discovery from a transaction collection is an important data-mining task. We study a new problem in this area whose solution can provide users with valuable association rules in some relevant collections: association discovery in derivative transaction collections. In this problem, we are given association rules in two transaction collections D1 and D2, and aim to find new association rules in derivative transaction collections D1∖D2, D1∩D2, D2∖D1 and D1∪D2. Direct application of existing algorithms can solve this problem, but in an expensive way. We propose an efficient solution through making full use of already discovered information, taking advantage of the relationships existing among relevant collections, and avoiding unnecessary but expensive support-counting operations by scanning databases. Experiments on well-known synthetic data show that our solution consistently outperforms the naive solution by factors from 2 to 3 in most cases. We also propose an efficient parallelization of our approach, as parallel algorithms are often interesting and necessary in the area of data mining.