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. >
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.
3239171
Nonlinear Kernel MSE Methods for Cancer Classification
Combination of kernel PLS (KPLS) and kernel SVD (KSVD) with minimum-squared-error (MSE) criteria has created new machine learning methods for cancer classification and has been successfully applied to seven publicly available cancer datasets. Besides the high accuracy of the new methods, very fast training speed is also obtained because the matrix inversion in the original MSE procedure is avoided. Although the KPLS-MSE and the KSVD-MSE methods have equivalent accuracies, the KPLS achieves the same results using significantly less but more qualitative components.
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.
14020196
Spherical mapping for processing of 3D closed surfaces
Abstract The spherical harmonic (SPHARM) description is a powerful surface modeling technique used by many applications in biomedical imaging. However, earlier SPHARM studies employ a spherical parameterization procedure that can be applied only to voxel surfaces. This paper presents CALD, a new spherical parameterization algorithm that makes the SPHARM model applicable to general triangle meshes. The CALD algorithm starts from an initial mapping and performs novel local and global smoothing methods alternately until a solution is approached. This new algorithm can also be used for processing of 3D closed surfaces in many different areas, including medical image analysis, computer vision, and computer graphics.
17173257
DBTIM: An Advanced Hardware Assisted Full Virtualization Architecture
Previous full virtualization techniques are implemented in software without any hardware assist. In this paper, for the first time, we propose an advanced hardware assisted full virtualization architecture-Dynamic Binary Translation in DIMM (DBTIM). We integrate a reconfigurable dynamic binary translation chip into a DIMM. DBTIM could be inserted to the mainboard as a normal DIMM, hence easily making the host CPU virtualizable. We also implement a DBTIM prototype on FPGA. Furthermore, we propose an EDBTIM technique-Enhanced Dynamic Binary Translator in Memory, which integrates dynamic binary translator and memory on one SDRAM chip. We analyze our new approaches and compare them with other modern virtualization techniques. We demonstrate that the new techniques in this paper could provide full virtualization to all kinds of CPU architectures, including x86, with better virtualization capability, better software and hardware compatibility, better performance and lower overheads.
14826038
A Spatio-Temporal Modeling Method for Shape Representation
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected three dimensional (3D) objects. Because SPHARM based 3D models can derive functional information analysis and classify different pathological symptoms, it has been used in many applications in biomedical image computing. There is an urgent requirement for efficient spatio-temporal shape modeling to represent the dynamic anatomical structures in many applications (e.g., medical image analysis, geospatial information systems). In this paper we propose a novel real spherical harmonics based spatio-temporal shape modeling method to efficiently and flexibly represent the shapes sequence of anatomical structures in medical images. Our method works well on the simply connected 3D objects and the effectiveness of our approach is demonstrated through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for spatio-temporal shape modeling efficiency is proposed and the comparison results showed the good performance of our method.
14116656
The design and implementation of a program development system based on rewriting method
In this paper, we present a program development system based on rewriting method with the goal of providing an powerful tool for automatic software development and rapid prototyping. New mechanisms for defining constrained types and optimal rules are introduced to functional programming languages to achieve strong expressiveness. The enhanced functional programming language is combined with an algebraic specification language. Thus, the design from specification to program can be supported, and the efficiency and flexibility of programming can also be improved. In this system, both static and dynamic techniques are used to deal with constrained type check. All function definitions, computation constraints and optimal rules are regarded as rewriting rules. In order to provide a strong support for a large group of TRSs which may be neither terminating nor orthogonal, we propose a method using structure measure to decide TRSs' confluences. Based on this method, we present a partial completion algorithm to generate a rewriting model from a specification. Depending on term rewriting system, the mixed language system computes with parallel outermost and needed reduction. We explain the principles and implementation techniques in detail, some examples are provided.
15513401
A Novel Hardware Assisted Full Virtualization Technique
Virtual machine technology is rapidly emerging as an important component of future computer systems. Full virtualization with dynamic binary translation outperforms paravirtualization and hardware assisted virtualization in x86 CPU virtualization. Previous full virtualization techniques are implemented in software without any hardware assist. In this paper, for the first time, we propose a novel hardware assisted full virtualization technique-dynamic binary translation in DIMM (DBTIM). We integrate a reconfigurable dynamic binary translation chip into a DIMM. DBTIM could be inserted to the mainboard as a normal DIMM, hence easily making the host CPU virtualizable. We also implement a DBTIM prototype on FPGA. We analyze our new approach and compare it with other modern virtualization techniques. We demonstrate that the new technique in this paper could provide full virtualization to all kinds of CPU architectures, including x86, with better virtualization capability, better software and hardware compatibility, better performance and lower overheads.