34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
16162037
Ranking-Based Statistical Localization for Wireless Sensor Networks
This paper studies Ranking-Based localization method, which, as a special case of common Range-Based localization methods, statistically estimates node positions using the ranking-order constraints rather than the actual values of network measurements. Through extensive empirical evaluation, the advantages of Ranking-Based approach over traditional Range-Based approaches under extreme network conditions are verified. Ranking-Based approach turns out to be more robust to measurement insufficiency and more immune to measurement noise.
39739560
Optimal Motion Generation of Flexible Macro-micro Manipulator Systems Using Estimation of Distribution Algorithm
In this paper, a new approach for motion generation and optimization of the flexible macro-micro manipulator system is proposed based on Estimation of Distribution Algorithm (EDA). The macro-micro manipulator system is a redundant system, of which inverse kinematics remains challenging, with no generic solution to date. Here, the manipulator system configurations, or the optimal joint motions, are generated using the EDA algorithm base on Gaussian probability model. Compared with simple genetic algorithms (SGA), this approach uses fewer parameters and the time for motion optimization is remarkably reduced. The proposed approach shows excellent performance on motion generation and optimization of a flexible macro-micro manipulator system, as demonstrated by the simulation results.
18862565
Inferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM
Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.
40155382
Efficient Solution of Bidomain Equations in Simulation of Cardiac Excitation Anisotropic Propagation
Bidomain equations are used to characterize myocardial physiological activation and propagation. Their numerical solution is costly computation because of the higher temporal and spatial discretization requirements, especially in three dimensions. In most previous studies, the heart was supposed to be homogeneous isotropic medium, and thus can use the mondomain equation in stead of the bidomain equations to simulate the heart excitation propagation. Simulation of heart excitation anisotropic propagation in three-dimensional large tissue size by solving bidomain equations has not been implemented yet. In this paper, we present an efficient solution of bidomain equations in simulation of heart excitation anisotropic propagation by combining some numerical techniques such as non-standard finite difference (NSFD), domain decomposition and multigrid methods. The results show that the proposed method can successfully be used to simulate heart excitation anisotropic propagation in three-dimensional large tissue size, and it suggests that such method may provide a good basis for heart simulation research in a more physiologically way.
2385031
Semi-Supervised Multi-Task Regression
Labeled data are needed for many machine learning applications but the amount available in some applications is scarce. Semi-supervised learning and multi-task learning are two of the approaches that have been proposed to alleviate this problem. In this paper, we seek to integrate these two approaches for regression applications. We first propose a new supervised multi-task regression method called SMTR, which is based on Gaussian processes (GP) with the assumption that the kernel parameters for all tasks share a common prior. We then incorporate unlabeled data into SMTR by changing the kernel function of the GP prior to a data-dependent kernel function, resulting in a semi-supervised extension of SMTR, called SSMTR. Moreover, we incorporate pairwise information into SSMTR to further boost the learning performance for applications in which such information is available. Experiments conducted on two commonly used data sets for multi-task regression demonstrate the effectiveness of our methods.
16272826
Analyzing the impact of on-chip network traffic on program phases for CMPs
It is known that the execution of programs exhibits repetitive phases; in other words, the execution of programs can be partitioned into segments of execution, during which the application exhibits unique architectural properties. This property has been used for various optimization goals. In addition, phase information is utilized to reduce the run time of the architectural simulation. Conventionally, an application is examined in an architecture-independent manner (such as the number of times a basic block is executed) to extract information about the phases and then only the representative execution intervals are executed to analyze architectural choices. We claim that such approaches are becoming inadequate in the many-core era as application execution is not dominated by the instructions only, but instead the communication structure of the application is becoming as important as the instruction behavior. Hence, we propose to utilize communication behavior to determine the phases of an application. Our results reveal that the inclusion of the communication information can increase the accuracy of the phase detection significantly. Specifically, for SPLASH2 and MineBench applications, the average (geometric mean) CPI error rate with the instruction-based phase detection is 11.01%, while our phase detection scheme has an average error rate of 3.41% when compared to the simulations that run the applications to completion.
17572677
Decision-Feedback Receiver for Quasi-Orthogonal Space-Time Coded OFDM Using Correlative Coding Over Fast Fading Channels
Orthogonal frequency division multiplexing (OFDM) is robust against frequency selective fading, but it is very vulnerable to time selective fading. In quasi-orthogonal space-time coded OFDM (ST-OFDM) systems, channel variations cause not only inter-carrier interference among different subcarriers in one OFDM block, but also inter-transmit-antenna interference (ITAI). When applied in fast fading channels, common ST-ODFM receivers usually suffer from an irreducible error floor. In this letter, we apply frequency-domain correlative coding combined with a modified decision-feedback detection scheme to effectively suppress the error floor of quasi-orthogonal ST-OFDM over fast fading channels. The effectiveness of the proposed scheme in mitigating the effects of channel time selectivity is demonstrated through comparison with existing schemes such as zero-forcing, two-stage zero-forcing, and sequential decision feedback estimation
4846997
Semi-supervised Discriminant Analysis Via CCCP
Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often available in large quantities. We propose a novel semi-supervised discriminant analysis algorithm called SSDA$_{\mathit{CCCP}}$. We utilize unlabeled data to maximize an optimality criterion of LDA and use the constrained concave-convex procedure to solve the optimization problem. The optimization procedure leads to estimation of the class labels for the unlabeled data. We propose a novel confidence measure for selecting those unlabeled data points with high confidence. The selected unlabeled data can then be used to augment the original labeled data set for performing LDA. We also propose a variant of SSDA$_{\mathit{CCCP}}$, called M-SSDA$_{\mathit{CCCP}}$, which adopts the manifold assumption to utilize the unlabeled data. Extensive experiments on many benchmark data sets demonstrate the effectiveness of our proposed methods.