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This paper presents a series of further modifications to the parallel algorithm used for finding digraphs realisations of the characteristic polynomial. What distinguishes the mentioned algorithm from other state-of-the-art solutions is the ability to find a complete set of existing solutions, not just a few of them. Moreover, solutions found tend to be minimal in terms of a rank of matrices created...
Laplacian Eigenmaps (LE) is a typical nonlinear graph-based (manifold) dimensionality reduction (DR) method, applied to many practical problems such as pattern recognition and spectral clustering. It is generally difficult to assign appropriate values for the neighborhood size and heat kernel parameter for LE graph construction. In this paper, we modify graph construction by learning a graph in the...
A little over a decade ago, Goto and van de Geijn wrote about the importance of the treatment of the translation lookaside buffer (TLB) on the performance of matrix multiplication. Crucially, they did not say how important, nor did they provide results that would allow the reader to make his own judgement. In this paper, we revisit their work and look at the effect on the performance of their algorithm...
With the development of Graphics Processing Unit (GPU) and the Compute Unified Device Architecture (CUDA) platform, researchers shift their attentions to general-purpose computing applications with GPU. In this paper, we present a novel parallel approach to run artificial fish swarm algorithm (AFSA) on GPU. Experiments are conducted by running AFSA both on GPU and CPU respectively to optimize four...
A great number of dimensionality reduction methods are finally reduced to solving generalized eigenvector problems. Optimization techniques are promising ways to solve the parameter selection problems in these dimensionality reduction methods. The most important step in these optimization methods is to compute the objective function with respect to the parameter, which depends on computing the gradient...
Current programming models and compiler technologies for multi-core processors do not exploit well the performance benefits obtainable by applying algorithm-specific, i.e., semantic-specific optimizations to a particular application. In this work, we propose a pattern-making methodology that allows algorithm-specific optimizations to be encapsulated into “optimization patterns” that are expressed...
State-of-the-art Complex Event Processing technology (CEP), while effective for pattern matching on event streams, is limited in its capability of reacting in real-time to opportunities and risks detected when monitoring the physical or virtual world. We propose to tackle this problem by embedding active rule support within the CEP engine, henceforth called Active Complex Event Processing technology,...
This paper proposes a sparsity driven shape registration method for occluded facial feature localization. Most current shape registration methods search landmark locations which comply both shape model and local image appearances. However, if the shape is partially occluded, the above goal is inappropriate and often leads to distorted shape results. In this paper, we introduce an error term to rectify...
Branch and Bound (B&B) algorithms are highly parallelizable but they are irregular and dynamic load balancing techniques have been used to avoid idle processors. In previous work, authors use a dynamic number of threads at run time, which depends on the measured performance of the application for just one interval B&B algorithm running on the system. In this way, load balancing is achieved...
Checkpointing is an important method for providing fault tolerance, load balancing, process migration, periodic backup, and many other functions. It is also the basic tool used in CAPE, a paradigm which aims at distributing the execution of a program on a distributed-memory environment. This paper presents a new approach to checkpoint and an original optimization on the checkpoint structure that we...
This paper proposes a parallelization scheme for parameter sweep (PS) applications using the compute unified device architecture (CUDA). Our scheme focuses on PS applications with irregular access patterns, which usually result in lower performance on the GPU. The key idea to resolve this irregularity is to exploit the similarity of data accesses between different parameters. That is, the scheme simultaneously...
Fast Fourier Transform (FFT) is a useful tool for applications requiring signal analysis and processing. However, its high computational cost requires efficient implementations, specially if real time applications are used, where response time is a decisive factor. Thus, the computational cost and wide application range that requires FFT transforms has motivated the research of efficient implementations...
This work presents a SystemC-based simulation approach for fast performance analysis of parallel software components, using source code annotated with low-level timing properties. In contrast to other source-level approaches for performance analysis, timing attributes obtained from binary code can be annotated even if compiler optimizations are used without requiring changes in the compiler. To consider...
The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov complexity), transductive inference and hypothesis testing. While the formulation of the framework guarantees validity, the efficiency of the framework...
STRIKE was introduced and implemented to predict protein-protein interactions where proteins interact if they contain similar substrings of amino acids. On the yeast protein interaction literature, STRIKE was shown to improve upon the existing state-of-the-art methods for protein-protein interaction prediction. Herein, we describe the parallelization of STRIKE and its multithreaded implementation...
Algorithm parameters' influence on performance of ACOR (extension of ant colony optimization) is analyzed in this paper. Parameter establishment in ACOR is a multi-factor and multi-level optimization problem. And uniform design is introduced for solutions of high quantity to this problem through fewer experiments. This method is proved to be feasible and valid by simulation analysis in this paper.
Support vector machine (SV machine, SVM) is a genius invention with many merits, such as the non-existence of local minima, the largest separating margins of different clusters, as well as the solid theoretical foundation. However, it is also well-noted that SVMs are frequently with a large number of SVs. In this paper, we investigate the number of SVs in a benchmark problem, the parity problem experimentally...
This paper studies the identification algorithm of parameters self adaptive SMO based on linear kernel function, and analyses its performance and advantages. For ARX model and long-term prediction model, the method is used to identify the model of main steam pressure of thermal system and dual-lane gas turbine engine of aero system. The simulation results show that the algorithm can effectively identify...
This paper focuses on the identification of nonlinear hybrid systems involving unknown nonlinear dynamics. The proposed method extends the framework of by introducing nonparametric models based on kernel functions in order to estimate arbitrary nonlinearities without prior knowledge. In comparison to the previous work of, which also dealt with unknown nonlinearities, the new algorithm assumes the...
A multi-layer adaptive optimizing parameters algorithm is developed for improving least squares support vector machines (LS-SVM), and a military equipment intelligent cost estimation model is proposed based on the optimized LS-SVM. The intelligent cost estimation process is divided into three steps in the model. In the first step, cost-drive-factor is needed to be selected, which is significant for...
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