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.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.
15225252
Morphometric Analysis of Hippocampal Shape in Mild Cognitive Impairment: An Imaging Genetics Study
A computational framework is presented for surface based morphometry to localize shape changes between groups of 3D objects. It employs the spherical harmonic (SPHARM) method for surface modeling and random field theory (RFT) for statistical inference. Several new components are introduced to overcome previous limitations: (1) a general linear model is used to facilitate controlling for covariates; (2) a new SPHARM registration method SHREC is proposed to better align SPHARM models; and (3) an estimated smoothness is used in RFT-based analysis to obtain more accurate results. This framework is applied in a mild cognitive impairment (MCI) study to examine hippocampal shape changes related to diagnostic and genetic conditions. Several interesting findings from our analyses suggest combining imaging phenotypes and genetic profiles has the potential to elucidate biological pathways for better understanding MCI and Alzheimer's disease.
33719729
New Algorithms for Efficient Mining of Association Rules
Abstract Discovery of association rules is an important data mining task. Several algorithms have been proposed to solve this problem. Most of them require repeated passes over the database, which incurs huge I/O overhead and high synchronization expense in parallel cases. There are a few algorithms trying to reduce these costs. But they contain weaknesses such as often requiring high pre-processing cost to get a vertical database layout, containing much redundant computation in parallel cases, and so on. We propose new association mining algorithms to overcome the above drawbacks, through minimizing the I/O cost and effectively controlling the computation cost. Experiments on well-known synthetic data show that our algorithms consistently outperform a priori, one of the best algorithms for association mining, by factors ranging from 2 to 4 in most cases. Also, our algorithms are very easy to be parallelized, and we present a parallelization for them based on a shared-nothing architecture. We observe that our parallelization develops the parallelism more sufficiently than two of the best existing parallel algorithms.
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.
2480556
Data Brokers: Building Collections through Automated Negotiation
Collecting digital materials is time-consuming and can gain from automation. Since each source – and even each acquisition – may involve a separate negotiation of terms, a collector may prefer to use a broker to represent his interests with owners. This paper describes the Data Broker Framework (DBF), which is designed to automate the process of digital object acquisition. For each acquisition, a negotiation agent is assigned to negotiate on the collector’s behalf, choosing from strategies in a strategy pool to automatically handle most bargaining cases and decide what to accept and what counteroffers to propose. We introduce NOODLE (Negotiation OntOlogy Description LanguagE) to formally specify terms in the negotiation domain.
8607029
Illumination Brush: Interactive Design of All-Frequency Lighting
We present an appearance-based user interface for artists to efficiently design customized image-based lighting environments. 1 Our approach avoids typical iterations of parameter editing, rendering, and confirmation by providing a set of intuitive user interfaces for directly specifying the desired appearance of the model in the scene. Then the system automatically creates the lighting environment by solving the inverse shading problem. To obtain a realistic image, all-frequency lighting is used with a spherical radial basis function (SRBF) representation. Rendering is performed using precomputed radiance transfer (PRT) to achieve a responsive speed. User experiments demonstrated the effectiveness of the proposed system compared to a previous approach.
60639655
Parallel simulation of chip-multiprocessor by using multi-threading
Multi-core on one chip becomes the current main trend of micro processors' architecture, and simulation plays an important role in evaluating new processor architectures. Traditional ways of sequential simulation for chip-multiprocessor (CMP) require lots of simulation time which can't be afford during processor design phase. Therefore, exploiting parallelism is necessary for speeding up the multi-core processor simulation. 
 
In this paper, we present a parallel CMP simulator, which is implemented by using POSIX threads, and also present the programming methodology for converting a sequential single core simulator to a parallel CMP simulator. Furthermore, the tradeoffs in parallel simulator implementation are also discussed. The experiments are carried out on a four-CPU dual core Xeon server. Under weak consistence order model, the parallel simulator achieves a speedup of 7.2X comparing with the sequential one.
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.
17498280
A Hardware Approach for Reducing Interpretation Overhead
Abstract—An interpreter reads instructions of the source executable and perform each corresponding operation in turn on a software-maintained version of the old ISA’s state. Interpretation has emerged as an important tool with real world applications. Interpretation is always adopted to support instruction set emulation and handle the non-hotspot code in a two-stage dynamic binary translation strategy. An important consideration of an interpreter is the runtime overhead. We investigate that repeated re-decode operations are the bottleneck of interpretation overhead. We propose interpreted code cache (Pcache), a hardware assist, to save the information of the decoded instruction for reuse. We analyze and model Pcache performance via a real interpretation system. Results from SPEC2000 integer benchmarks show that Pcache could significantly reduce re-decode operations and the overhead of interpretation. We demonstrate that the hardware Pcache is a highly effective approach, and the extra overhead is neglectable compared to the performance gains.
14935665
Optimal Matrix Computing Using Vector Division with Sub-word Parallel
Available computing is expected to be effective and efficient in ubiquitous multimedia services. And, matrix multiplication is particular to ubiquitous computing such as image transformation so that it is urgent to exploit potential parallelism as well as data reuse in matrix multiplication. Therefore, based on synchronized data triggered architecture (SDTA), we propose a method of parallel vector division (PVD) as well as sub-word parallel (SWP) supported on SDTA to optimize the matrix computation on specified architecture. Experimental results show that PVD with SWP is suitable for matrix multiplication on SDTA instruction set architecture. Besides, we could extend the proposed model into other data-intensive computing on SDTA.